Category: 8. Health

Background

Serum uric acid (SUA), which results from purine metabolism, is normally filtered by the kidneys and excreted in urine.¹ However, hyperuricemia (HUA) occurs when SUA production exceeds renal excretion capacity or when impaired kidney clearance reduces uric acid output. These mechanisms elevate serum SUA concentrations.² HUA is closely related to gout, and asymptomatic HUA can lead to the deposition of urate in the joints of patients and even bone erosion. Studies had found that asymptomatic HUA leading to gout was a continuous pathological process.³ Epidemiological evidence has demonstrated that HUA is both a critical contributor to gout pathogenesis and significantly associated with malignancies. According to a survey conducted in Chinese population, the overall prevalence of HUA among adults was 11.1% during 2015–2019, while the prevalence rate had risen to 14% by 2019.⁴ Additionally, a national health and nutrition survey conducted in the United States showed that the risk of death from HUA was similar to that of diabetes.⁵ HUA had become a serious risk factor for public health that could not be ignored.

The Framingham Heart Study indicated that SUA levels in men remain stable after puberty, whereas the SUA levels gradually increase after middle age in women.⁶ During perimenopause, declining ovarian function reduces estrogen levels. This impairs uric acid excretion, consequently elevating SUA concentrations.⁷ According to epidemiological studies, the prevalence of HUA increases with age. One study found that the prevalence of HUA among women was around 11%.⁸ A Meta-analysis indicated that the prevalence of HUA among Chinese perimenopausal women reached 13%, and it continued to rise as women enter menopause.⁹ This could potentially lead to various health issues in this demographic. Additionally, the hormonal changes make women more prone to obesity and metabolic syndrome during perimenopause, which could further exacerbate elevated SUA levels and consequently contribute to HUA.¹⁰

Previous studies showed that HUA was closely linked to the development and progression of cardiovascular disease, diabetes, and other diseases.¹¹ Therefore, perimenopausal women should pay more attention to the changes in serum uric acid levels and take some measures to prevent HUA. According to one study, 88% of women experience menopause at a mean age of 51.4 years, with over 800 million women worldwide currently in this life stage.¹² Therefore, predicting the prevalence of HUA in this group is crucial for their sustainable health management.

Currently, there were relatively few reports on the risk factors and predictive models for HUA in perimenopausal women. Eljaaly et al found that HUA was linked to SCR, high-density lipoprotein, triglycerides, hip circumference, total cholesterol.¹³ Zhang et al explored the risk factors for elevated serum uric acid (SUA) levels in elderly individuals and constructed a predictive model, which identified a SUA level ≥ 360 μmol/L as a common risk factor for both men and women.¹⁴ Therefore to advance clinical practice for perimenopausal women’s health promotion, we aimed to identify risk factors for HUA in perimenopausal women and to construct and validate a nomogram model for clinical risk prediction.

Methods

Study Population

The study was conducted in accordance with the provisions of the Declaration of Helsinki, and approval was granted by the First Affiliated Hospital of the University of Science and Technology of China (2025-RE-191). Informed consent was also obtained from all patients prior to the study. We collected clinical data from perimenopausal women who had completed standardized health evaluations at USTC First Affiliated Hospital’s Health Management Center.

The determination of HUA was based on a reasonable standard: the SUA level was equal to more than 360 mmol/L.¹⁵ The exclusion criteria included: (1) younger than 45 years old or older than 55 years old; (2) duplicated clinical data; (3) Severe hepatic and renal dysfunction; (4) currently suffering from malignant tumors.

We collected information from 6225 physical examinees. After excluding 512 records with missing medical data and 28 duplicate records, we retained records for 5685 examinees. Further excluding 46 individuals with abnormal liver and kidney function and 2 patients with malignant tumors, a total of 5637 examinees met the inclusion criteria. In this study, we collected a total of 28 different variables, according to the 10 EPV (Events Per Variable) rule,¹⁶ the number of positive samples should exceed 270. With a total of 733 positive samples among all study subjects, the sample size meets the requirement for model development. These were randomly divided into a model training set of 3945 individuals and a validation set of 1692 individuals. The flowchart is shown in Figure 1.

Figure 1 The process of determining research subjects.

Data Collection

The data for this study were sourced from the participants’ medical records, including the following information: (1) Demographic characteristics – Age; Gender; Body Mass Index (BMI), (2) Relevant medical history – HUA, fatty liver, (3) Renal function tests: Blood Urea Nitrogen (BUN); Serum Creatinine (SCR), (4) Liver function tests: Alanine Aminotransferase (ALT); Aspartate Aminotransferase (AST); Alkaline Phosphatase (ALP); Total Protein (TP); Albumin (ALB); Globulin (GLB), (5) Random blood glucose (GLU), (6) Blood lipid tests: High-density lipoprotein (HDL); Low-density lipoprotein (LDL); Very Low-Density Lipoprotein (VLDL); Total Cholesterol (TC); Triglyceride (TG), (7) Blood cell tests: White Blood Cell Count (WBC); Red Blood Cell Count (RBC); Hemoglobin (HGB); Platelet count (PLT); Eosinophils percentage (Eos%); Basophils percentage (Baso%); Lymphocyte Percentage (LY%); Neutrophil percentage (NEUT%), (8) Routine Urine index: Urine pH (UPH); Urine Specific Gravity (SG).

Statistical Analysis

Statistical analysis was performed via R 4.4.2 and SPSS 26.0. For Gaussian distribution was used to represent the data, for non-normally distributed data, the median and interquartile range (IQR) were used for representation; For categorical variables, we reported frequencies and proportions. The entire study population was random divided into a model training set and a model validation set at a ratio of 7:3. Continuous variables were analyzed using Student’s t-test (for normally distributed data) or the Mann–Whitney U-test (for nonparametric data), while categorical variables were assessed with the chi-square test. Since the study mainly selected hematological examination results as independent variables, which had strong collinearity, we used the least absolute shrinkage and selection operator (Lasso)¹⁷ and binary logistic regression were employed for feature variable screening, and employed Decision Curve Analysis (DCA) to evaluate the maximum net benefit of the prediction model. To visualize the prediction model, we presented it using a nomogram and evaluated its performance through Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). All analyses used two-tailed tests, with statistical significance set at P < 0.05. We utilized R packages “glmnet” and “rms” to perform Lasso regression and construct a nomogram model.

Result

Baseline Characteristics

In the group of 5637 perimenopausal women who met the research criteria, a total of 733 individuals suffered from HUA, the prevalence rate of HUA among all participants was 13%. There were statistically significant differences between non-HUA group and HUA group in terms of AGE, BMI, renal function (BUN, SCR), liver function (ALT, AST, ALP, TP, ALB, GLB), blood lipid (HDL, TC, VLDL, LDL, TG), blood cell (WBC, RBC, HGB, PLT, Eos), fasting blood glucose, and medical history (hypertension, fatty liver) (P< 0.05) (Table 1).

Table 1 Baseline Characteristics of HUA Group and Non-HUA Group

The training set comprised 3945 participants, while the validation set included 1692 participants. No statistically significant differences were observed in baseline characteristics or clinical features between the two groups (Table 2).

Table 2 Baseline Characteristics of Training Set and Validation Set

The Related Risk Factors of HUA

Variable selection was performed on the training data through Lasso regression coupled with tenfold cross-validation (Figure 2), where the final model was chosen based on the one standard error criterion, the λ was taken. Finally, thirteen indicators were screened, including BMI, UPH, ALP, WBC, SCR, HDL, LDL, HGB, ALT, TP, TG, hypertension, fatty liver. Subsequently, the above indicators were included in the binary logistic regression analysis, and the results were shown in Table 3. Finally, we found BMI (P<0.001), UPH (P=0.002), ALP (P<0.001), WBC (P=0.011), HDL (P<0.001), LDL (P<0.001), SCR (P<0.001), ALT (P=0.003), TP (P=0.005) and fatty liver (P<0.001) were recognized as independent risk factors for HUA in perimenopausal women.

Table 3 Binary Logistic Regression Analysis of the Risk Factors for HUA in Perimenopausal Women

Figure 2 Lasso regression results. (A) Lasso coefficient profiles of the variables. (B) Demonstrates the process of selecting the optimal parameter (lambda) in Lasso.

Constructing and Evaluating a Prediction Model for HUA in Perimenopausal Women

From the analysis of Lasso-logistic regression, we created a nomogram prediction model (Figure 3). The risk level of the results could be calibrated, and we could obtain the total score for the probability of a certain outcome event by combining them. An increased total score was associated with a higher risk of HUA. The Bootstrap method was used to validate the nomogram by resampling 1000 times for internal validation of the model, and calibration curves were plotted for both the training and validation sets (Figure 4). The Lasso-logistic method had good performance in predicting HUA in perimenopausal women.

Figure 3 Nomogram for predicting the risk of HUA in perimenopausal women.

Figure 4 The calibration plot for nomogram. (A) Calibration plot for the accuracy of the training set model. (B) Calibration plot for the accuracy of the validation set model.

The AUC of the prediction model in training set was 0.819 (95% CI: 0.801–0.838), and the AUC of the prediction model in validation set was 0.787 (95% CI: 0.756–0.818) (Figure 5A). As shown in Figure 5B, the DCA depicted was utilized to ascertain the maximum net benefit of the predictive model.

Figure 5 (A) ROC curve of the risk factor for predicting HUA in perimenopausal women. (B) DCA curve of the predictive model.

Stratified Analysis

In this study, we further performed a stratified analysis by BMI. The perimenopausal women were categorized into two groups according to their BMI: the women with BMI ≥ 25 and the women with BMI < 25.¹⁸ Then Lasso regression was used to screen the significant variables, and binary logistic regression analysis was performed in the two groups, and nomogram models were respectively established.

In the population with BMI < 25.0, the prevalence of HUA was 9.4%, while in the population with BMI ≥ 25.0, the prevalence of HUA was 24.3%. In the population with BMI < 25.0, the occurrence of HUA was associated with the following factors: BMI (P<0.001), SCR (P<0.001), HDL (P=0.001), LDL (P<0.001), ALT (P<0.001), TP (P=0.006), TG (P<0.001), Fatty liver (P<0.001) (Table 4 and Figure 6A). In the population with BMI ≥ 25.0, the development of HUA was influenced by the following factors: BMI (P=0.004), ALP (P=0.004), WBC (P=0.006), SCR (P<0.001), HDL (P=0.001), ALT (P=0.023), Hypertension (P=0.034), Fatty liver (P<0.001) (Table 5 and Figure 6B). As presented in Figure 7, the predictive model had an AUC of 0.765 in the population with BMI ≥ 25.0 and an AUC of 0.793 in the population with BMI < 25.0.

Table 4 Binary Logistic Regression Analysis of the Risk Factors for HUA in Perimenopausal Women with BMI < 25.0

Table 5 Binary Logistic Regression Analysis of the Risk Factors for HUA in Perimenopausal Women with BMI ≥ 25.0

Figure 6 (A) Nomogram model for predicting the risk of HUA with BMI < 25. (B) Nomogram model for predicting the risk of HUA with BMI ≥ 25.

Figure 7 ROC curve to evaluate two models.

Discussion

At present, HUA has become a major threat to public health, so it is of great significance to investigate the influencing factors of HUA and then construct a prediction model. In this study, we found that the independent risk factors for HUA were BMI, UPH, ALP, WBC, HDL, LDL, SCR, ALT, TP, and history of fatty liver disease in perimenopausal women through Lasso-logistic regression analysis. We further developed HUA prediction model using these factors in perimenopausal women, achieving an AUC of 0.787. Subsequently, we constructed BMI-stratified prediction models: for individuals with BMI < 25, the model AUC was 0.793; for those with BMI ≥ 25, AUC reached 0.765.

Previous studies have found that estrogen contributes to promote the excretion of SUA in the body.¹⁹ For perimenopausal women, the decline in ovarian function leads to a reduction in estrogen secretion, affecting their SUA levels, thereby increasing the prevalence of HUA in this population. In additional, epidemiological research indicates that the abnormal changes of blood lipid levels, especially triglyceride and HDL, are independent risk factors for HUA. Moreover, insulin resistance caused by obesity and the inhibition of uric acid excretion mediated by adipokines can increase serum uric acid levels.²⁰ Hence, HUA is more prevalent in overweight or obese individuals, which is consistent with the findings of our study.^21,22 In our study, the increased levels of ALP, ALT, and HDL were related to the increased risk of HUA, which is similar to the previous findings.²³

Based on the results of binary logistic regression, HDL and UPH were identified as protective factors against HUA in perimenopausal women. Previous studies suggest that fluctuations in HDL levels can affect the kidneys, thereby influencing the excretion of uric acid.^24,25 Acidic urine might aggravate insulin resistance, leading to increased serum uric acid levels, and increased insulin resistance could further reduce UPH. In the preset study, fatty liver was a significant risk factor for HUA in perimenopausal women. However, the exact mechanism is not yet clear. It is speculated that insulin resistance increases hepatic fat synthesis, promoting the development of fatty liver, which finally leads to disordered purine metabolism and elevated serum uric acid levels.²⁶ We also found that SCR was closely related to HUA, which is contrary to a previous study conducted on the general population.²⁷ This discrepancy might be attributed to the specific physiological stage of the study subjects, and the all subjects were perimenopausal women. Specifically, the estrogen levels in women are relatively stable, therefore having a smaller impact on uric acid or SCR levels, before entering perimenopause. However, a significant increase in SCR levels typically requires a longer period of estrogen deficiency, a pathophysiological process that often occurs after women have fully entered perimenopause. Notably, all participants were already in perimenopause in this study, which might influence the interpretation of these results.

After stratifying by different BMI, we also found significant differences in the risk factors for HUA among perimenopausal women with different BMI. Compared to the group with BMI ≥ 25.0, LDL, TP, and TG were identified as different predictors in the group with BMI < 25.0. The reason for this situation might be that visceral fat accumulation leads to increased release of free fatty acids and disordered secretion of adipokines, resulting in increased hepatic lipoprotein synthesis and reduced uric acid excretion. High LDL levels might reflect a disturbance in reverse cholesterol transport, elevated TP suggests hepatic protein metabolism disorder, and abnormal TG directly participates in the formation of insulin resistance. Together, these three factors constitute the core characteristics of “metabolic obesity”.²⁸ In the group with BMI ≥ 25.0, ALP, WBC, and Hypertension were identified as different predictors, and the combination of these risk factors exhibits a stronger characteristic of systemic inflammation. An elevated ALP level might reflect the progression of non-alcoholic fatty liver disease, an increased WBC count suggests a state of chronic low-grade inflammation, and the synergistic effect between hypertension and HUA might originate from reduced renal blood flow and inhibited uric acid excretion caused by the activation of the renin-angiotensin system.²⁹

Previous studies have identified PPARγ gene, BMI, and gender as significant predictors of hyperuricemia (HUA) when developing prediction models.³⁰ However, applying this model requires individuals to possess a high level of professional knowledge and necessitates consideration of other factors, which presents significant challenges for its promotion and practical use. Furthermore, a Japanese study utilized gut microbiota to predict hyperuricemia.³¹ Although the model demonstrated strong predictive ability, its requirement for stool sample collection makes it difficult to promote clinically.

However, this study still has several limitations. First, as a single-center study, we lacked external data to validate our model, which might affect the generalizability of our findings. Second, the definition of perimenopausal women did not include hormone level testing for all participants. Finally, the collected information lacked data on subjects’ daily lifestyle factors.

In Summary, our study developed a nomogram model for predicting HUA risk in perimenopausal women using ten distinct clinical indicators (BMI, UPH, ALP, WBC, HDL, LDL, SCR, ALT, TP, and history of fatty liver disease). Furthermore, the performance of this model were proven to be quite effective. These findings provide important clues for enhancing the health management of perimenopausal women.

Data Sharing Statement

The data of this study are available from Dr. Tian-Ping Zhang upon reasonable request.

Ethics Statement

This study was approved by the Ethical Committee of the First Affiliated Hospital of USTC (2025-RE-191).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

Yu-Fei Liu and Xiao-Jing Li are co-first authors for this study. The authors have no conflicts of interest in this work.

References

1. Fathallah-Shaykh SA, Cramer MT. Uric acid and the kidney. Pediatr Nephrol. 2014;29(6):999–1008. doi:10.1007/s00467-013-2549-x

2. Yanai H, Adachi H, Hakoshima M, Katsuyama H. Molecular biological and clinical understanding of the pathophysiology and treatments of hyperuricemia and its association with metabolic syndrome, cardiovascular diseases and chronic kidney disease. Int J Mol Sci. 2021;22(17):9221. doi:10.3390/ijms22179221

3. Dalbeth N, House ME, Aati O, et al. Urate crystal deposition in asymptomatic hyperuricaemia and symptomatic gout: a dual energy CT study. Ann Rheum Dis. 2015;74(5):908–911. doi:10.1136/annrheumdis-2014-206397

4. Zhang M, Zhu X, Wu J, et al. Prevalence of hyperuricemia among chinese adults: findings from two nationally representative cross-sectional surveys in 2015–16 and 2018–19. Front Immunol. 2022;12:791983. doi:10.3389/fimmu.2021.791983

5. Chen PH, Chen YW, Liu WJ, Hsu SW, Chen CH, Lee CL. Approximate mortality risks between hyperuricemia and diabetes in the United States. J Clin Med. 2019;8(12):2127. doi:10.3390/jcm8122127

6. Culleton BF, Larson MG, Kannel WB, Levy D. Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study. Ann Intern Med. 1999;131(1):7–13. doi:10.7326/0003-4819-131-1-199907060-00003

7. Lega IC, Jacobson M. Perimenopause. CMAJ. 2024;196(34):E1169. doi:10.1503/cmaj.240337

8. Huang J, Ma ZF, Zhang Y, et al. Geographical distribution of hyperuricemia in mainland China: a comprehensive systematic review and meta-analysis. Glob Health Res Policy. 2020;5(1):52. doi:10.1186/s41256-020-00178-9

9. Li Y, Shen Z, Zhu B, Zhang H, Zhang X, Ding X. Demographic, regional and temporal trends of hyperuricemia epidemics in mainland China from 2000 to 2019: a systematic review and meta-analysis. Glob Health Action. 2021;14(1):1874652. doi:10.1080/16549716.2021.1874652

10. Brinton RD, Yao J, Yin F, Mack WJ, Cadenas E. Perimenopause as a neurological transition state. Nat Rev Endocrinol. 2015;11(7):393–405. doi:10.1038/nrendo.2015.82

11. Li W, Wang Y, Ouyang S, et al. Association between serum uric acid level and carotid atherosclerosis and metabolic syndrome in patients with type 2 diabetes mellitus. Front Endocrinol. 2022;13:890305. doi:10.3389/fendo.2022.890305

12. Harlow SD, Gass M, Hall JE, et al. Executive summary of the stages of reproductive aging workshop + 10: addressing the unfinished agenda of staging reproductive aging. J Clin Endocrinol Metab. 2012;97(4):1159–1168. doi:10.1210/jc.2011-3362

13. Eljaaly Z, Mujammami M, Nawaz SS, Rafiullah M, Siddiqui K. Risk predictors of high uric acid levels among patients with type-2 diabetes. Diabetes Metab Syndr Obes. 2021;14:4911–4920. doi:10.2147/DMSO.S344894

14. Zhang D, Xu X, Ye Z, Zhang Z, Xiao J. One-year risk prediction of elevated serum uric acid levels in older adults: a longitudinal cohort study. Clin Interv Aging. 2024;19:1951–1964. doi:10.2147/CIA.S476806

15. Mei CL, Ge JB, Zou HJ, et al. Multi-disciplinary expert task force on hyperuricemia and its related diseases. Zhonghua Nei Ke Za Zhi. 2017;56(3):235–248. doi:10.3760/cma.j.issn.0578-1426.2017.03.021

16. Dhiman P, Ma J, Qi C, et al. Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review. BMC Med Res Methodol. 2023;23(1):188. doi:10.1186/s12874-023-02008-1

17. Xie QY, Wang MW, Hu ZY, et al. Screening the influence of biomarkers for metabolic syndrome in occupational population based on the Lasso algorithm. Front Public Health. 2021;9:743731. doi:10.3389/fpubh.2021.743731

18. Wu Y, Li D, Vermund SH. Advantages and limitations of the Body Mass Index (BMI) to assess adult obesity. Int J Environ Res Public Health. 2024;21(6):757. doi:10.3390/ijerph21060757

19. JJung JH, Song GG, Lee YH, Kim JH, Hyun MH, Choi SJ. Serum uric acid levels and hormone therapy type: a retrospective cohort study of postmenopausal women. Menopause. 2018;25(1):77–81. doi:10.1097/GME.0000000000000953

20. Chen Q, Xiao J, Zhang P, Chen L, Chen X, Wang S. Lower serum levels of uric acid in uterine fibroids and fibrocystic breast disease patients in Dongying City, China. Iran J Public Health. 2016;45(5):596–605.

21. Ni Q, Lu X, Chen C, Du H, Zhang R. Risk factors for the development of hyperuricemia: a STROBE-compliant cross-sectional and longitudinal study. Medicine. 2019;98(42):e17597. doi:10.1097/MD.0000000000017597

22. Qi D, Liu J, Wang C, et al. Sex-specific differences in the prevalence of and risk factors for hyperuricemia among a low-income population in China: a cross-sectional study. Postgrad Med. 2020;132(6):559–567. doi:10.1080/00325481.2020.1761133

23. Redon P, Maloberti A, Facchetti R, et al. Gender-related differences in serum uric acid in treated hypertensive patients from central and east European countries: findings from the Blood Pressure control rate and CArdiovascular Risk profilE study. J Hypertens. 2019;37(2):380–388. doi:10.1097/HJH.0000000000001908

24. Almuqrin A, Alshuweishi YA, Alfaifi M, Daghistani H, Al-Sheikh YA, Alfhili MA. Prevalence and association of hyperuricemia with liver function in Saudi Arabia: a large cross-sectional study. Ann Saudi Med. 2024;44(1):18–25. doi:10.5144/0256-4947.2024.18

25. Lee MJ, Khang AR, Kang YH, Yun MS, Yi D. Synergistic interaction between hyperuricemia and abdominal obesity as a risk factor for metabolic syndrome components in Korean population. Diabetes Metab J. 2022;46(5):756–766. doi:10.4093/dmj.2021.0166

26. Kawachi K, Kataoka H, Manabe S, Mochizuki T, Nitta K. Low HDL cholesterol as a predictor of chronic kidney disease progression: a cross-classification approach and matched cohort analysis. Heart Vessels. 2019;34(9):1440–1455. doi:10.1007/s00380-019-01375-4

27. Abudureyimu P, Pang Y, Huang L, et al. A predictive model for hyperuricemia among type 2 diabetes mellitus patients in Urumqi, China. BMC Public Health. 2023;23(1):1740. doi:10.1186/s12889-023-16669-6

28. Chihara Y, Wakabayashi I, Kataoka Y, Yamamoto T. Serum creatinine is more strongly associated with hyperuricemia than eGFR in males but not in females. Mod Rheumatol. 2025;35(2):378–385. doi:10.1093/mr/roae083

29. Shirasawa T, Ochiai H, Yoshimoto T, et al. Cross-sectional study of associations between normal body weight with central obesity and hyperuricemia in Japan. BMC Endocr Disord. 2020;20(1):2. doi:10.1186/s12902-019-0481-1

30. Lee MF, Liou TH, Wang W, et al. Gender, body mass index, and PPARγ polymorphism are good indicators in hyperuricemia prediction for Han Chinese. Genet Test Mol Biomarkers. 2013;17(1):40–46. doi:10.1089/gtmb.2012.0231

31. Miyajima Y, Karashima S, Mizoguchi R, et al. Prediction and causal inference of hyperuricemia using gut microbiota. Sci Rep. 2024;14(1):9901. doi:10.1038/s41598-024-60427-6

Introduction

Endometriosis, a chronic inflammatory disorder characterized by the presence of endometrial-like tissue ectopic to the uterus, is linked to pelvic pain and infertility.^1,2 It is estrogen-dependent, prevalent during the reproductive years, and affects 5–15% of women globally.^3,4 This disorder poses a serious public health problem and economic strain.⁵ According to Ballweg et al, the average wait time for a final diagnosis of endometriosis is nine years.⁶ There is growing evidence that endometriosis raises the risk of several pregnancy-related complications, including premature placental abruption, retained placenta, premature rupture of membranes, pre-eclampsia, pregnancy-induced hypertension, gestational diabetes mellitus, gestational cholestasis, antepartum and postpartum hemorrhages, labor dystocia, stillbirth, neonatal deaths, and uterine congenital abnormalities.⁷ In a few cases, endometriosis can undergo malignant transformation, with ovarian cancer being the most frequent malignancy associated with the disease.^8–10

Deep endometriotic lesions have the potential to infiltrate nerves¹¹ and lymph nodes,¹² causing heightened negative effects on the body. However, research on the severity of endometriosis is currently limited. Existing studies have identified advancing age,¹³ concomitant autoimmune diseases, and the frequency of laparoscopic operations as predictors of endometriosis severity.¹⁴ To date, a few studies have explored the correlation between patients’ metabolic profiles and the severity of their endometriosis. The liver plays a central role in regulating systemic metabolism, including glucose homeostasis. Dysregulation in liver function can lead to imbalances in metabolic pathways that influence inflammation and immune responses, both of which are implicated in the development and progression of endometriosis.¹⁵ Additionally, altered glucose metabolism can affect energy availability and cellular function in endometrial tissue, potentially contributing to the survival and growth of ectopic endometrial implants.¹⁶ Thus, understanding these metabolic connections may provide insights into the underlying mechanisms of endometriosis.

In this study, we aimed to investigate the correlation between metabolic indicators and the severity of endometriosis using both univariate and multivariate logistic regression analyses. Additionally, restricted cubic spline modeling was applied to examine nonlinear relationships. This research may provide valuable early diagnostic markers and therapeutic strategies for severe endometriosis.

Materials and Methods

Research Cohort and Profile

This study retrospectively collected patients diagnosed with endometriosis by laparoscopy or laparotomy based on histological confirmation in Zhongshan Hospital (Xiamen), Fudan University from January 2018 to August 2022. Patients were excluded from the study if they presented with abnormal metabolic markers, hypertension, diabetes, hyperlipidemia, liver or gallbladder diseases, autoimmune diseases, a history of uterine surgery or pregnancy, hormone therapy, or if there was any missing information. The collected variables included covariates and indicators reflecting patient lipid metabolism, hepatobiliary metabolism, renal metabolism, and electrolyte metabolism. Covariates included age, body mass index (BMI), carbohydrate antigen 125 (CA-125), human epididymis protein 4 (HE4). Metabolic indicators included apolipoprotein A, apolipoprotein B, fasting blood glucose, serum albumin, serum total protein, direct bilirubin, total bilirubin, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, γ-glutamyl transferase, lactate dehydrogenase, prealbumin, urea, creatinine, glomerular filtration rate, uric acid, sodium, potassium, chloride, CO2, total cholesterol, triglycerides, HDL, and LDL. All laboratory data were collected within 3 days of the end of the patient’s menstrual period. ASRM staging data for endometriosis were collected from patients, with all diagnoses confirmed through pathological examination. This study was performed in accordance with the declaration of Helsinki and was approved by the ethics committee of Xiamen Hospital, Zhongshan Hospital, Fudan University.

Statistical Analysis

Categorical variables were described using frequency and percentage (%), with the chi-square test was used to compare the differences between the two groups. Continuous variables were tested for normality. Continuous variables with normal distribution were described using mean and standard deviation (Mean (SD)), and group differences were compared using a t-test. However, non-normally distributed continuous variables were described using medians and quartiles (Median [IQR]), and the differences between the two groups were compared using the rank sum test. Independent factors influencing endometriosis severity were ascertained by univariate logistic regression. Notably, according to the results of univariate regression and stepwise regression combined with factors that were known or suspected to be related to endometriosis severity, we finally determined the variable selection in multivariate models. Moreover, restricted cubic spline models were developed to analyze the nonlinear relationship between metabolic indicators and outcomes. A nomograph was drawn to visualize the independent influencing factors, and the ROC curve was used to verify the discriminative ability of the independent influencing factors. All statistical analyzes were performed using R 4.2.1 (https://www.r-project.org), and a double trailed P value < 0.05 was considered statistically significant.

Results

Patient Characteristics

In accordance with the patient inclusion criteria, this study included a total of 94 endometriosis patients, 32 of whom were diagnosed with ASRM stage IV. The mean age of all patients was 34.85 years old, with the ASRM stage IV patients having a mean age of 36.81 years. Table 1 offers a comprehensive summary of the demographic and clinical characteristics of the patients.

Table 1 The Characteristics of All Patients

Influence of Metabolic Indicators on the Severity of Endometriosis

To analyze the effect of different levels of metabolic indicators on outcome indicators, we categorized metabolic indicators according to their quartiles and included them in logistic regression for analysis in the form of both continuous and categorical variables. The results of the univariate logistic regression showed that FBG (OR [95% CI]: Q4: 3.5[1.093, 11.974], continuous: 3.422[1.116, 11.539]), total protein (OR [95% CI]: continuous: 1.094[1.012, 1.198]), direct bilirubin (OR [95% CI]: Q4: 0.176[0.035, 0.683], continuous: 0.645[0.402, 0.972]), TBil (OR [95% CI]: Q4: 0.278[0.073, 0.933]) and ALT (OR [95% CI]: Q4: 0.239[0.049, 0.888]) were statistically significant in relation to the severity of endometriosis (Table 2).

Table 2 The Results of the Univariate Analysis

For the above variables, we included covariates for adjustment (Model 1: unadjusted; Model 2: adjusted for age, BMI; Model 3: adjusted for age, BMI, CA125, HE4). The results showed that FBG and total protein were not statistically significant associated with endometriosis severity after adjustment for age and BMI. However, TBil (OR [95% CI]: 0.28[0.073, 0.957], P: 0.0499) and direct bilirubin (OR [95% CI]: 0.18[0.035, 0.702], P: 0.0209) remained significantly associated with endometriosis severity after adjustment for age and BMI. Additionally, ALT (Model 2: OR [95% CI]: 0.194[0.037, 0.768], P: 0.03, Model 3: OR [95% CI]: 0.138[0.019, 0.67], P: 0.0247) remained significantly associated with endometriosis severity after adjustment for age, BMI, CA125, and HE4 (Table 3).

Table 3 Impact of Metabolic Indicators on Outcome

Nonlinear Relationship Between Metabolic and Outcome Indicators

Restricted cubic spline models were constructed to analyze the potential nonlinear relationship between metabolic indicators and endometriosis severity. The results showed that, with the exception of FBG which showed a significant nonlinear relationship (P-nonlinear: 0.0362), the remaining metabolic markers did not exhibit a significant nonlinear association with the outcome measures (P-nonlinear > 0.05) (Figures 1 and 2).

Figure 1 RCS cubic spline plots of FBG (A–C), TP (D–F), and DBIL (G–I) in different models. The vertical dotted line indicates the value of the metabolic indicator when the OR is equal to 1. Abbreviations: TP, total protein; DBIL, direct bilirubin.

Figure 2 RCS cubic spline plots of TBil (A–C) and ALT (D–F) in different models. The vertical dotted line indicates the value of the metabolic indicator when the OR is equal to 1.

Predictive Power of Metabolic Indicators

We conducted ROC curve analysis for metabolic indicators that were statistically significant in univariate analyses, including TBil, direct bilirubin, FBG, total protein, and ALT, and computed the AUC values (Figure 3). The AUC ranges from 0 to 1, with 0.5 indicating a random classifier and 1 representing a perfect classifier. The AUC results were as follows: TBil (continuous: 0.645 [0.529, 0.76]; categorical: 0.664 [0.552, 0.776]), direct bilirubin (continuous: 0.644 [0.528, 0.76]; categorical: 0.651 [0.539, 0.762]), FBG (continuous: 0.604 [0.474, 0.733]; categorical: 0.631 [0.512, 0.75]), ALT (continuous: 0.624 [0.506, 0.742]; categorical: 0.652 [0.543, 0.761]). All AUC values were above 0.6, suggesting these indicators possess a high level of predictive capability.

Figure 3 Results of ROC analysis of metabolic indicators. (A) TBil; (B) Direct bilirubin; (C) FBG; (D) Total protein; (E) ALT.

Discussion

This study explored the relationship between standard metabolic indices and the severity of endometriosis. Findings indicated that ALT exhibited a negative association with endometriosis severity. The RCS analysis showed that the majority of these metabolic indicators bore a substantially nonlinear relationship with outcomes.

The present study showed that CA-125 was positively correlated with the severity of endometriosis. Izabela Kokot et al compared serum inflammatory markers between patients with endometriosis and those without and found that CA-125 concentration was significantly elevated in individuals with endometriosis when compared to the non-endometriosis group (p < 0.001). Another study showed that the AUC for the diagnostic ability of serum IL-32 for endometriosis was 0.638; however, when the serum IL-32 level was combined with the serum CA-125 level, the AUC increased to 0.749, suggesting that CA-125 may improve the accuracy of diagnosing endometriosis.¹⁷ It has also been shown that increased CA-125 is a marker of severe and deep infiltrating endometriosis. In addition, many studies reported similar results to our research outcomes.^18–21 In addition, HE4 (continuous) was not significantly correlated with endometriosis severity. However, HE4 within the Q2 (25%–50% quantile) range was associated with lower severity compared with Q1 (0%–25% quantile). Previous studies have reported that HE4 alone has limited correlation with endometriosis severity, future studies with larger sample sizes are needed to further explore this relationship.

In terms of hepatobiliary metabolic indicators, our results showed that direct bilirubin, TBil and ALT were significantly associated with endometriosis severity. Bilirubin is a breakdown product of heme, released from the lysis of red blood cells. Slightly elevated plasma bilirubin levels have been associated with protective effects against a range of pathologies, and slight decreases in serum bilirubin concentrations have been linked to an increased risk of cardiovascular and metabolic diseases.^22,23 However, few studies have directly elucidated the association between direct bilirubin and endometriosis severity. Shogo Imanaka et al reported higher blood bilirubin levels in endometriotic patients compared to non-endometriotic individuals,²⁴ but the study did not correlate bilirubin levels with the severity of endometriosis. Concurrently, a study by the same agency investigated the effects of iron-related compounds and bilirubin on redox homeostasis in endometriosis and its potential for malignant transformation, revealing higher levels of total iron, heme iron, free iron, and bilirubin in endometriosis patients compared to those with endometriosis-associated ovarian cancer.²⁵ This suggests that low bilirubin may indicate disease progression and malignancy in endometriosis, although further high-quality research is needed to confirm this association. In addition to CA-125 and bilirubin, ALT is also an independent factor. ALT is regarded as a marker of liver injury, and its decreased concentration is generally considered to be of no clinical significance. However, no study has analyzed the relationship between ALT and the severity of endometriosis; therefore, further studies are needed.

Analyzing the correlation between metabolic indices and endometriosis severity could provide clinicians with non-invasive biomarkers for early detection and more accurate monitoring of disease progression. This would enable more timely identification of patients at risk for severe manifestations. Additionally, understanding these metabolic associations may facilitate the development of targeted therapies tailored to specific metabolic profiles, enhancing treatment efficacy and personalization. Ultimately, such insights could improve diagnostic accuracy and guide more effective management strategies, offering significant benefits in the clinical care of endometriosis patients.

The present study explored the relationship between standard metabolic indices and the severity of endometriosis. It was found that ALT was statistically associated with endometriosis severity. The ROC curve analysis showed that these indicators have a robust discriminatory capacity. Nevertheless, this study has some limitations. First, the retrospective design limited the types of data that could be collected, and certain elusive endogenous metabolites were beyond the scope of this study. Second, the inability to capture data across all menstrual cycle phases (proliferative, secretory, and menstrual) is a constraint of this retrospective approach. Third, the sample size of this study is relatively small. Future studies with larger sample sizes are needed to further validate these findings. These limitations are exactly what our next research intends to remedy.

Conclusion

CA-125 and HE4 were identified as significant independent factors affecting the severity of endometriosis. ALT demonstrated a negative correlation with endometriosis severity and emerged as an independent factor with statistical significance. In contrast, FBG, total protein, direct bilirubin and TBil were not found to be independent factors influencing the severity of endometriosis. The logistic regression model incorporating the aforementioned indicators exhibited strong discriminatory power. Future prospective studies with larger samples and more refined designs are needed to further validate these findings.

Data Sharing Statement

The data that support the findings of this study are available from either corresponding author, Hongyang Xiao or Ruiqin Tu, upon reasonable request.

Ethics Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Zhongshan Hospital (Xiamen), Fudan University (No. B2022-046).Written informed consent was obtained from the participants.

Consent to Participate

Written informed consent was obtained from the participants.

Funding

No funding was received for this research.

Disclosure

The authors report there are no competing interests to declare for this work.

References

1. Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021;397(10276):839–852. doi:10.1016/S0140-6736(21)00389-5

2. Koninckx PR, Fernandes R, Ussia A, et al. Pathogenesis Based Diagnosis and Treatment of Endometriosis. Front Endocrinol. 2021;12:745548. doi:10.3389/fendo.2021.745548

3. Dunselman GA, Vermeulen N, Becker C, et al. ESHRE guideline: management of women with endometriosis. Hum Reprod. 2014;29(3):400–412. doi:10.1093/humrep/det457

4. Falcone T, Flyckt R. Clinical Management of Endometriosis. Obstet Gynecol. 2018;131(3):557–571. doi:10.1097/AOG.0000000000002469

5. Chapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15(11):666–682. doi:10.1038/s41574-019-0245-z

6. Ballweg ML. Treating endometriosis in adolescents: does it matter? J Pediatr Adolesc Gynecol. 2011;24(5 Suppl):S2–6. doi:10.1016/j.jpag.2011.07.003

7. Kobayashi H, Kawahara N, Ogawa K, Yoshimoto C. A relationship between endometriosis and obstetric complications. Reprod Sci. 2020;27(3):771–778. doi:10.1007/s43032-019-00118-0

8. Guidozzi F. Endometriosis-associated cancer. Climacteric. 2021;24(6):587–592. doi:10.1080/13697137.2021.1948994

9. Bulun SE, Wan Y, Matei D. Epithelial Mutations in Endometriosis: link to Ovarian Cancer. Endocrinology. 2019;160(3):626–638. doi:10.1210/en.2018-00794

10. Hermens M, van Altena AM, Bulten J, van Vliet H, Siebers AG, Bekkers RLM. Increased incidence of ovarian cancer in both endometriosis and adenomyosis. Gynecol Oncol. 2021;162(3):735–740. doi:10.1016/j.ygyno.2021.07.006

11. Ac SDS, Capek S, Amrami KK, Spinner RJ. Neural involvement in endometriosis: review of anatomic distribution and mechanisms. Clin Anat. 2015;28(8):1029–1038. doi:10.1002/ca.22617

12. Jerman LF, Hey-Cunningham AJ. The role of the lymphatic system in endometriosis: a comprehensive review of the literature. Biol Reprod. 2015;92(3):64. doi:10.1095/biolreprod.114.124313

13. Conroy I, Mooney SS, Kavanagh S, et al. Pelvic pain: what are the symptoms and predictors for surgery, endometriosis and endometriosis severity. Aust N Z J Obstet Gynaecol. 2021;61(5):765–772. doi:10.1111/ajo.13379

14. Vanni VS, Villanacci R, Salmeri N, et al. Publisher Correction: concomitant autoimmunity may be a predictor of more severe stages of endometriosis. Sci Rep. 2021;11(1):17715. doi:10.1038/s41598-021-97506-x

15. Kim B-S, Kim B, Yoon S, et al. Warburg-like Metabolic Reprogramming in Endometriosis: from Molecular Mechanisms to Therapeutic Approaches. Pharmaceuticals. 2025;18(6):813. doi:10.3390/ph18060813

16. Datkhayeva Z, Iskakova A, Mireeva A, et al. The Multifactorial Pathogenesis of Endometriosis: a Narrative Review Integrating Hormonal, Immune, and Microbiome Aspects. Medicina. 2025;61(5):811. doi:10.3390/medicina61050811

17. Choi YS, Kim S, Ys O, Cho S, Hoon KS. Elevated serum interleukin-32 levels in patients with endometriosis: a cross-sectional study. Am J Reprod Immunol. 2019;82(2):e13149. doi:10.1111/aji.13149

18. Fiala L, Bob P, Raboch J. Oncological markers CA-125, CA 19-9 and endometriosis. Medicine. 2018;97(51):e13759. doi:10.1097/MD.0000000000013759

19. Oliveira MAP, Raymundo TS, Soares LC, Pereira TRD, Demoro AVE. How to Use CA-125 More Effectively in the Diagnosis of Deep Endometriosis. Biomed Res Int. 2017;2017:9857196. doi:10.1155/2017/9857196

20. Shin KH, Kim HH, Kwon BS, Suh DS, Joo JK, Kim KH. Clinical Usefulness of Cancer Antigen (CA) 125, Human Epididymis 4, and CA72-4 Levels and Risk of Ovarian Malignancy Algorithm Values for Diagnosing Ovarian Tumors in Korean Patients With and Without Endometriosis. Ann Lab Med. 2020;40(1):40–47. doi:10.3343/alm.2020.40.1.40

21. Dochez V, Caillon H, Vaucel E, Dimet J, Winer N, Ducarme G. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res. 2019;12(1):28. doi:10.1186/s13048-019-0503-7

22. Hinds Jr TD, Stec DE. Bilirubin Safeguards Cardiorenal and Metabolic Diseases: a Protective Role in Health. Curr Hypertens Rep. 2019;21(11):87. doi:10.1007/s11906-019-0994-z

23. Hinds Jr TD, Stec DE. Bilirubin, a Cardiometabolic Signaling Molecule. Hypertension. 2018;72(4):788–795. doi:10.1161/HYPERTENSIONAHA.118.11130

24. Imanaka S, Yamada Y, Kawahara N, Kobayashi H. A delicate redox balance between iron and heme oxygenase-1 as an essential biological feature of endometriosis. Arch Med Res. 2021;52(6):641–647. doi:10.1016/j.arcmed.2021.03.006

25. Shigetomi H, Imanaka S, Kobayashi H. Effects of iron-related compounds and bilirubin on redox homeostasis in endometriosis and its malignant transformations. Horm Mol Biol Clin Investig. 2021;43(2):187–192. doi:10.1515/hmbci-2021-0065

Background

International education enables students to expand their knowledge, experience foreign cultures, and broaden their horizons.^1,2 According to China’s Ministry of Education (2022),³ over 80% of Chinese students studying abroad return to China after completing their education. A significant number of students studying abroad will have a direct impact on the growth of many parts of the industry once they return home. Most health professionals work in medical universities or affiliated hospitals after returning to China.

Studying abroad provides teachers with opportunities to develop a deeper appreciation for diverse cultures, adapt to social changes, and gain insights into varied health practices and disease profiles. Research has highlighted that physicians who studied abroad often reported enhanced motivation, broadened perspectives, increased confidence, refined clinical skills, and a more informed approach to choosing their medical specialties.⁴

The 2022 work plan of the China’s Ministry of Education emphasizes that teachers are crucial for educational development and efforts should be made to build a highly qualified and innovative teaching faculty.⁵ Faculty development is closely tied to international exchange and global engagement. Programs such as overseas visiting scholar initiatives and international study opportunities expose educators to innovative teaching methods and advanced pedagogical practices. Initiatives like visiting scholar programs enhance educators’ pedagogical expertise and prepare them for interconnected academic environments.⁶ Our focus is to examine whether tutors’ study abroad experiences influence their students’ research achievements and perceived scientific skill development. Government-sponsored overseas education is a high-return investment and a strategic initiative essential for the country’s long-term development. Optimal allocation of resources and an efficient study abroad program layout are crucial for achieving the best results. Recent studies in China’s medical education system demonstrate that faculty members’ international training experiences significantly enhance both their professional competencies and student development outcomes.^7,8 Therefore, it is essential for medical colleges to promote the internationalization of medical education and enhance the quality and efficiency of study abroad selection.

China’s medical education system employs a dual-track approach encompassing both basic medical sciences and clinical medicine, training professionals through integrated undergraduate-to-doctoral programs. Basic medical sciences focus on training researchers in fundamental medical studies, while clinical medicine develops clinician-scientists who integrate patient care with research. At the doctoral level, strict disciplinary boundaries are maintained, with faculty tutors guiding candidates exclusively within their fields. These tutors play a crucial role in doctoral training by overseeing research, providing academic guidance, and collaborating on projects. Nevertheless, the differential impact of tutors’ international exposure on these two distinct training tracks remains underexplored. To investigate this gap, we implemented a cross-sectional study assessing: (1) student academic achievements across basic/clinical medicine programs, and (2) self-reported competency improvements associated with tutors’ overseas experience. We specifically hypothesized that basic medicine students would demonstrate greater enhancement in high-impact publications (as measured by IF), attributable to their exclusive research focus, whereas clinical students’ gains might be moderated by their dual clinical-academic commitments.

Methods

Harbin Medical University (HMU), which has nine affiliated hospitals, is a higher medical education research base established by the People’s Government of Heilongjiang Province. It also implements the national high-level university public graduate program. As of May 2024, HMU has established academic partnerships with over 200 universities and research institutions across more than 20 countries, including Russia, the United States, Australia, Japan, and Canada. These collaborations create greater opportunities for faculty to engage in international study. Consequently, the number of tutors pursuing overseas education continues to rise each year.

The study was conducted from January 2015 to December 2023 and focused on doctoral graduates (PhD and MD degrees, which are unified as PhD in China) from the 2012 to 2019 cohorts whose tutors had overseas experience. In China, student cohorts are classified by their year of enrollment, with doctoral programs typically lasting 3–4 years. By 2023, students from the 2019 cohort had recently graduated, and to ensure a sufficient sample size, the study included cohorts dating back to 2012. Previous research indicates that tutors generally require 3–4 months to adapt to a new academic environment abroad.^7,8 Consequently, study abroad periods of less than 6 months are insufficient to fully prepare an independent researcher. To maintain consistency, all tutors in this study had a minimum of 6 months of overseas study experience. To exclude the influence of students’ ages on this study, we rigorously matched 263 students from HMU basic medicine (Group A), who specialize in basic research, with 263 students from the affiliated hospitals (Group B), who specialize in clinical medicine, based on their graduation ages. In order to exclude the influence of students’ abroad experience on this study, all students had no overseas study experience.

The Science Citation Index (SCI) is widely recognized as the most authoritative tool for retrieving scientific publications and evaluating scientific research. Its IF serves as a key metric for assessing the significance of SCI-indexed papers. For instance, prior studies have noted that the Journal Impact Factor is frequently used in academic evaluations, including reviews, promotions, and tenure decisions, particularly at research-intensive institutions.⁹ While recognizing IF’s limitations in assessing teaching quality,¹⁰ we employed it as a validated proxy for research mentorship effectiveness in academic evaluation. High-IF journals typically demand rigorous methodology and novelty—qualities that tutors must impart to their trainees. The number of SCI papers and their impact factors serve as quantitative indicators of an author’s research capability and academic standing. Because authors’ contributions to research vary, there is no international system that can eliminate these discrepancies; therefore, we ranked authors using the “HMU Promotion System (2013)” to reflect this distinction. The paper’s first and corresponding author receives 100% of the IF, the second author receives 50% of the IF, and the third or subsequent author receives 25% of the IF.¹¹

To collect information effectively, we constructed a questionnaire presented in Additional Material 1. The questionnaire is divided into two phases: (a) Basic information and close-ended questions, including age of graduation, number of published SCI papers and total impact factor during their PhD study, which has been used in our previous research;^7,8 (b) Five open-ended questions about the experience and benefits of overseas study, covering the degree of improvement in foreign language skills, learning ability, idea renewal, research ability, and international academic communication skills. Students’ perceived attainment of the program outcomes was assessed using a 5-point Likert-type scale (1 = To a Minimal Extent; 5 = To a Very Large Extent). The questionnaire was filled out when the students graduated. Ethical approval was obtained from the Harbin Medical University Research Centre ethics committee (Approval Number: 2024376).

The questionnaires were self‐administered, completely anonymized, and collected immediately after completion. All students received written and verbal information about the research project before signing a consent form to participate. Response rates were 100%. All data were anonymized to protect the respondents’ privacy. Data analysis for the quantitative measures was performed using SPSS for Windows, version 24 (SPSS Inc., Chicago, IL, USA). Continuous variables with normal distribution were presented as mean±standard deviation (SD) and compared by t-test. Non-normally distributed variables were reported as median (interquartile range) and analyzed using the rank sum test. Statistical significance was determined using P < 0.05.

Results

In this study, students of the two groups have the same age distribution, which ranged from 26 to 39 years (mean = 31.403 years). In both group A (specialized in basic research, n=263) and group B (specialized in clinical medicine, n=263), most students believed that their tutors’ overseas study experience had a very large extent impact on their foreign language skills (Group A: 104/263, 39.5%; Group B: 114/263, 43.3%), learning ability (Group A: 91/263, 34.6%; Group B: 115/263, 43.7%), idea renewal (Group A: 67/263, 25.5%; Group B: 91/263, 34.6%) and research ability (Group A: 92/263, 35.0%; Group B: 114/263, 43.3%). A majority of students thought that the influence of their tutors’ overseas study experience on their international academic communication skills had been achieved a considerable extent (Group A: 75/263, 28.5%; Group B: 83/263, 31.6%) (Table 1). Among the various effects of tutors’ overseas study experiences on students, the significant impact on foreign language skills was the most commonly identified by participants in both groups (Group A: 104/263, 39.5%; Group B: 114/263, 43.3%).

Table 1 Extent to Which the Number of Students Believed That Their Tutors’ Overseas Study Experience Delivered a Range of Desirable Outcomes

The number of SCI papers published by individual students from HMU basic medicine during their PhD study (Table 2, Group A) ranged from 0 to 10 (mean = 1.140), with a total IF during their PhD study ranging from 0.00 to 58.23 (mean = 4.639). In contrast, the number of SCI papers published by students from the affiliated hospitals during their PhD study (Table 2, Group B) ranged from 0 to 3 (mean = 1.060), and the total IF for papers published during their PhD study ranged from 0.00 to 34.23 (mean = 3.791). We found statistically significant differences between Group A and Group B for total IF during their PhD study (P = 0.024, Cohen’s d=0.07), but the difference between the two groups for number of SCI papers during their PhD study (Group A mean=1.140; Group B mean=1.060; P = 0.220) was not statistically significant (Figure 1a).

Table 2 Comparison of Research Output (SCI Publications and Impact Factors) and Students’ Perceived Competency Improvements Between Group A and Group B During Their PhD Study

Figure 1 Comparison of research output (SCI publications and impact factors) and students’ perceived competency improvements between group A and group B during their PhD study. (a) The number of SCI papers and total IF of papers between the two groups during their PhD study. (b) The total score of the questionnaire and each item score between the two groups. Group A: Students from HMU basic medicine, specialized in basic research; Group B: Students from the affiliated hospitals, specialized in clinical medicine; *P < 0.05. **P < 0.01.

The influence of tutors’ study abroad experience on students is often multifaceted, and is not limited to the improvement of students’ unilateral ability. Therefore, we use the total score of the questionnaire to reflect the level of influence on students in all aspects. The total questionnaire scores for Group A (Table 2) ranged from 5 to 25 (mean = 17.52), while the total questionnaire scores for Group B (Table 2) ranged from 5 to 25 (mean = 18.97). While statistically significant differences were observed in total questionnaire scores between Group A (basic medicine, M=17.52) and Group B (clinical medicine, M=18.97), P = 0.005, the effect size was modest (Cohen’s d=0.25). This suggests that although tutors’ overseas experience was consistently associated with positive outcomes across groups, the absolute differences in perceived benefits were relatively small. A 5-point scale was used to assess and quantify the students’ subjective evaluation of their tutors. The single score of each item in the questionnaire ranged from 1 (lowest) to 5 (highest). We performed a Cronbach’s alpha analysis on the 5-point Likert scale items (526 responses in total). The results indicated high internal consistency (α= 0.959 for the overall scale), exceeding the threshold of 0.7 and meeting acceptable reliability standards. There were significant differences between Group A and Group B in mean scores for the degree of improvement in learning ability (Group A mean=3.630; Group B mean=3.910; P = 0.013, Cohen’s d=0.81), idea renewal (Group A mean=3.370; Group B mean=3.640; P = 0.018, Cohen’s d=0.75), research ability (Group A mean=3.700; Group B mean=3.950; P = 0.021, Cohen’s d=0.83) and international academic communication skills (Group A mean=3.080; Group B mean=3.430; P = 0.002, Cohen’s d=0.69). The difference in foreign language skills improvement scores between the two groups was not statistically significant (Group A mean=3.750; Group B mean=3.950; P = 0.061) (Figure 1b).

Discussion

Studying abroad is an essential component of medical training at many institutions and universities, and it is highly valued by medical students. When choosing study abroad destinations, students are often motivated by a desire to enhance their clinical or research ability and by the opportunity to travel, experience diverse cultures, and learn about various healthcare environments.^12,13 The overall ability of medical students in postgraduate or doctoral studies depends on many factors. Among the most important are the educational background and qualifications of their tutors, and the tutors’ ability to convey knowledge and scientific research skills. The questionnaire results showed that the tutors’ overseas study experience had a significant positive impact on most basic and clinical medical students across all five measured dimensions. These findings support the beneficial role of international exposure inmedical education.

Our prior research has demonstrated that professionals’ overseas study experiences not only enhance their own competencies,⁷ but also positively influence the ability development of their students – particularly in medical education.⁸ However, it remains unclear which type of students – those majoring in basic medicine or clinical medicine – benefit more from their tutors’ international experience. To address this gap, our current study further investigates the differential impacts of tutors’ overseas study experiences on these two student groups, evaluating outcomes across five key measurement dimensions. After returning to China, tutors from basic medical schools can continue to engage in foundational research, which should maintain the positive influence of their overseas study experience. However, tutors from clinical medicine need to balance clinical works and scientific research. Will the positive impact of a clinical tutor’s overseas study experience be diminished by the heavy clinical workload after returning to China? Will students majoring in basic medicine benefit more from their tutors’ overseas study experience? Should we encourage and provide fundings for more tutors in basic medicine to study abroad? Should the study abroad funding policy be biased towards foundation tutors or clinical tutors? This study aims to answer the above questions by comparing the influence of tutors’ overseas study experience on their students in clinical medicine versus those in basic medicine.

In this study, there is no difference in the number of SCI papers between the two groups during their PhD study. However, there is a significant difference in the total IF of the papers during their PhD study. This indicate that the IF of individual articles published by students from HMU basic medicine who are scientific researchers is higher. This result suggests basic researchers prioritize quality publications in high-IF journals, reflecting fundamental science’s emphasis on novelty and methodological rigor.¹⁴ By contrast, clinical medicine’s focus on translational competencies and patient-centered skills – while equally vital for professional development – is less captured by traditional impact metrics. Besides, students of HMU basic medicine are primarily engaged in scientific research, whereas students at affiliated hospitals spend the majority of their time in clinical work, with limited time for scientific research. We suggest that this result may be also related to factors such as the longer time that students from HMU basic medicine devote to scientific research.

Our comparative analysis reveals that clinical students from affiliated hospitals perceived significantly greater improvement in learning ability, idea renewal, research ability and international academic communication skills than their basic science counterparts at HMU. This divergence stems from distinctive features of clinical training: (1) its applied nature demands rapid knowledge translation into patient care, with overseas-trained tutors introducing practical techniques, updated protocols, and multidisciplinary approaches that immediately enhance clinical competencies; (2) exposure to patient-centered pedagogies (eg, Anglo-American bedside teaching models) fosters more interactive, feedback-intensive supervision styles among clinical tutors; and (3) international experience cultivates cross-cultural communication skills and global health awareness¹⁵ – competencies particularly salient during clinical rotations with diverse patient populations. These discipline-specific mechanisms elucidate why clinical students reported stronger perceived benefits despite lower SCI impact factors (Table 2). Consequently, policy formulations for international exchange programs should judiciously weigh both bibliometric indicators and learner-reported outcomes.

We contend that IF, as publication metrics, inadequately reflect mentoring quality. Clinical mentors often incorporate internationally acquired case-based pedagogies that develop practical competencies, independent of IF improvements. Basic science mentors typically emphasize high-impact research, enhancing technical skills but offering less immediate improvement in broader competencies. Consequently, while basic medicine’s higher IF indicates scholarly standing, it remains compatible with clinical students’ greater perceived gains. Basic science students’ higher IF publications indicate potential research quality improvements from tutors’ international exposure. Yet given clinical students’ stronger perceived benefits, educational impact assessments should combine IF with learner-reported outcomes.

There was no significant difference in students’ perceptions of how their tutors’ overseas study experience had enhanced their foreign language skills between the two groups. English proficiency is a key criterion for admission to medical school, both groups of students have gone through the same training process. And their reading amount and frequency are basically similar. Additionally, tutors with overseas study experience typically possess high-level English skills and can provide a consistent, high-quality English learning environment upon their return. Consequently, both groups benefit equally in this aspect.

Early interventions promoting international study programs (through information campaigns and language preparation) could improve participation, particularly when faculty exemplify the benefits through their own experiences.¹⁶ We propose that governments and educational institutions could increase funding opportunities for medical tutors to study abroad, ensuring that more tutors can gain international education and research experience. Our findings regarding the distinct requirements of basic versus clinical medicine can guide the development of differentiated funding strategies. For instance, basic medical tutors can be offered more research training opportunities, while clinical medical tutors can receive increased opportunities for clinical skills developments and academic exchanges. Subsequent research should investigate customized funding mechanisms tailored to the differing internationalization requirements between basic and clinical medicine. Meanwhile, the government may encourage and support medical schools to establish long-term cooperative relationships with internationally renowned universities and research institutions, regularly sending tutors and students for exchange and study to form stable international cooperation mechanisms.

Limitation

It is important to acknowledge several limitations in this study that should be considered when interpreting the findings. First, our research focused on students from HMU and its affiliated hospitals, which may not fully represent the experiences of all Chinese medical students. This limits the ability to generalize the findings to other institutions or international contexts. Future studies should include data from a wider range of medical schools across China and beyond to improve the applicability of the results. Second, we relied on the “HMU Promotion System (2013)” to evaluate contributions, a system that may not be relevant to all SCI paper publications worldwide. This may restrict the comparison of our findings to broader international research standards. Additionally, data collection was based on self-reported questionnaires, which depend on students’ self-assessment and may introduce bias or affect response accuracy.¹¹ Future research could incorporate more objective methods, such as interviews or focus group discussions, to gather deeper insights. A further limitation involves our measurement scope – while age-matching controlled for core demographic factors, we did not systematically assess other potential confounders like institutional resources, pre-existing student abilities, or curriculum delivery variations. Although participants shared standardized institutional frameworks, residual confounding may persist. For example, clinical students’ equipment access likely differs from basic researchers’ laboratory resources; Not consider tutors’ personal characteristics, such as teaching skills, research expertise, and individual charisma. Dot account for the specifics of tutors’ overseas experiences, such as the country of study, research projects, or cultural adaptation processes. These unmeasured variables may contribute to intergroup outcome differences, indicating our findings reflect combined effects of tutors’ international experience and institutional contexts rather than isolated causation. These factors can have varying effects on both tutors and students. Future research should analyze these details to offer a more nuanced perspective on the influence of overseas study experiences.

Conclusions

This study, based on HMU, found that tutors’ overseas study experience significantly benefits the majority of medical students in both groups, highlighting the positive influence of studying abroad on medical professionals. Although promising, these single-institution findings necessitate validation across diverse settings. Both basic and clinical students should value overseas study experience when choosing a tutor. A disciplinary divergence was observed: basic science students with internationally trained mentors achieved greater research productivity, whereas clinical students reported more comprehensive perceived gains. Our institutional findings advocate for balanced international study policies serving both basic and clinical medicine, pending validation across multiple institutions.

Data Sharing Statement

The datasets used and analyzed for the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

Ethical approval was obtained from the Harbin Medical University Research Centre ethics committee (Approval Number: 2024376). This work was carried out in accordance with the Declaration of Helsinki, including but not limited to the anonymity of participants being guaranteed and the informed consent of participants being obtained. All participants received written and verbal information about the research project before providing written consent to participate.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded by the education science “14th Five-Year” plan 2023 key topics of the China Heilongjiang Province (GJB1423211) and Youth Medical Talent Training Funding Project of the First Affiliated Hospital of Harbin Medical University (2024YQ11).

Disclosure

The authors declare that they have no competing interests in this work.

References

1. Merklen E, Wolfe KL. Assessing Cultural intelligence and study abroad experiences of dietetics students and professionals. J Nutr Educ Behav. 2020;52(10):964–969. doi:10.1016/j.jneb.2020.07.003

2. Oka H, Ishida Y, Hong G. Study of factors related to the attitudes toward studying abroad among preclinical/clinical undergraduate dental students at three dental schools in Japan. Clin Exp Dent Res. 2018;4(4):119–124. doi:10.1002/cre2.114

3. Jian C. Title of subordinate document. In: more than 80% of those who study abroad choose to return to China after completing their studies. Ministry of education of people’s republic of China. 2022. Available from: http://www.moe.gov.cn/fbh/live/2022/54849/mtbd/202209/t20220920_663340.html. Accessed September 20, 2022.

4. Fox TA, Byrne G, Byrne-Davis LM. The educational impact of experience overseas. Clin Teach. 2018;15(4):298–303. doi:10.1111/tct.12670

5. Work highlights of the ministry of education in 2022. China government network. Available from: www.gov.cn. Accessed 9, Feb 2022.

6. Standley HJ, Bowater L. International mobility placements enable students and staff in higher education to enhance transversal and employability-related skills. FEMS Microbiol Lett. 2015;362(19):fnv157. doi:10.1093/femsle/fnv157

7. Liu T, Zhang L, Sun L, Wang X. Impact of international experience on research capacity of Chinese health professionals. Global Health. 2015;11(1):1. doi:10.1186/s12992-014-0086-4

8. Liu T, Zhang L, Zhao T, Chen N. The association between health professionals’ international experience and the academic output of their students in Harbin. China BMC Med Educ. 2019;19(1):428. doi:10.1186/s12909-019-1853-y

9. McKiernan EC, Schimanski LA, Muñoz Nieves C, Matthias L, Niles MT, Alperin JP. Use of the journal impact factor in academic review, promotion, and tenure evaluations. Elife. 2019;8.

10. Hicks D, Wouters P, Waltman L, de Rijcke S, Rafols I. Bibliometrics: the Leiden Manifesto for research metrics. Nature. 2015;520(7548):429–431. doi:10.1038/520429a

11. Personnel department and research department of Harbin medical university. Prom Syst HMU. 2013;20130106–102.

12. Kumwenda B, Dowell J, Daniels K, Merrylees N. Medical electives in sub-Saharan Africa: a host perspective. Med Educ. 2015;49(6):623–633. doi:10.1111/medu.12727

13. Brown M, Boateng EA, Evans C. Should I stay or should I go? A systematic review of factors that influence healthcare students’ decisions around study abroad programmes. Nurse Educ Today. 2016;39:63–71. doi:10.1016/j.nedt.2015.12.024

14. Nosek BA, Spies JR, Motyl M. Scientific Utopia: II. Restructuring incentives and practices to promote truth over publishability. Perspect Psychol Sci. 2012;7(6):615–631. doi:10.1177/1745691612459058

15. Hon JJ. Embracing global health in medical education: a necessity for modern doctors. JACC Case Rep. 2024;29(17):102498. doi:10.1016/j.jaccas.2024.102498

16. Trapani J, Cassar M. Intended and actual outcomes of study abroad programs: nursing students’. Experiences J Nurs Educ. 2020;59(9):501–505. doi:10.3928/01484834-20200817-04

HaDEA opens 2025 EU4Health proposals focused on CBRN threats and vector-borne diseases and crisis preparedness 

The European Health and Digital Executive Agency (HaDEA) has announced new funding opportunities under the 2025 EU4Health Work Programme. This new funding will improve the European Union’s crisis preparedness and response to future health emergencies.

These calls for proposals are focused on the development of innovative medical countermeasures for chemical, biological, radiological, and nuclear (CBRN) threats, as well as advanced diagnostics for vector-borne diseases.

Applications are open until 4 December 2025 at 17:00 CEST, through the EU Funding and Tenders Portal.

Focusing on CBRN medical countermeasures

The first central funding line, EU4H-2025-HERA-PJ-1, invites proposals to support the development of cutting-edge medical tools to counter CBRN threats. This call, along with the Health Emergency Preparedness and Response Authority (HERA), is divided into three subtopics:

• Medicinal Products (EU4H-2025-HERA-PJ-1-a): 
  • Funding will support the development of innovative medicinal products to prevent or treat illnesses resulting from CBRN exposure.
• Reusable Respiratory PPE and Protection Suits (EU4H-2025-HERA-PJ-1-b):
  • This subtopic targets innovation in high-quality, reusable personal protective equipment designed for use in CBRN environments.
• Detection and Diagnosis (EU4H-2025-HERA-PJ-1-c):
  • This part of the call supports the development of technologies for rapid detection and accurate diagnosis of CBRN-related threats.

These initiatives aim to enhance the EU’s crisis preparedness for health emergencies, enhance civilian protection, and ensure the availability of effective countermeasures during crises.

Diagnostics for vector-borne diseases

The second major funding call, EU4H-2025-HERA-PJ-2, addresses the growing concern of vector-borne diseases (VBDs), which are transmitted by insects such as mosquitoes and ticks. The call focuses on the development of innovative diagnostic tests that can help health systems detect and manage these diseases more effectively.

With climate change and increased global travel contributing to the spread of VBDs like dengue, chikungunya, and Lyme disease, the EU aims to strengthen health surveillance and early detection capabilities. Improved diagnostics are expected to play a key role in reducing the burden of these diseases and enhancing public health resilience.

Information session on 15 September

To support potential applicants, HaDEA and HERA will host an Info Session on Monday, 15 September 2025, from 14:30 to 17:00 CEST. The session will provide an overview of the calls, including their policy background, objectives, expected outcomes, and application procedures. Interested participants are encouraged to register in advance to secure their attendance.

These calls form part of the EU4Health programme, the EU’s largest health funding initiative to date. Launched in response to the COVID-19 pandemic, EU4Health aims to build more resilient healthcare systems across Europe. By supporting innovation in medical technologies, public health infrastructure, and cross-border cooperation, the programme contributes to a healthier and more secure Europe.

From 2021 to 2027, HaDEA is responsible for implementing most of the EU4Health budget, managing grants and tenders to support a wide range of health-related projects.

Scientists are hailing promising breakthroughs in the fight against cancer, with one new therapy appearing to kill tumors without damaging healthy flesh.

Novartis’s radioligand therapy targets mutations in tumors, delivering radiation only where it is needed, unlike ordinary radiotherapy which kills non-cancerous cells as well as cancerous ones. In a trial, the Novartis treatment removed all disease from 21% of patients whose cancers had spread around the body, which an oncologist told The New York Times was “never seen before.”

In other progress in the fight against cancer, The Economist reported that scientists are attempting to prevent the disease by boosting the metabolism of non-cancerous cells so they grow faster, “levelling the arms race between unhealthy and healthy cells.”

Introduction

Community-acquired pneumonia (CAP), a significant health concern around the globe, represents a leading cause of hospitalization in children.^1,2 The overall incidence rate of CAP was 15.97 per 1000 person-years in children below 5 years old in southeastern China from January 1, 2015 to December 31, 2020.³ Pediatric CAP is characterized by heterogeneous clinical presentations, ranging from mild respiratory or systemic symptoms (eg, fever, cough, and wheezing) to severe complications, such as acute renal injury, sepsis, and multiorgan failure.⁴ Intricate molecular mechanisms including inflammatory responses, oxidative reactions, and cellular apoptosis play pivotal roles in the progression of childhood CAP.⁵ The pediatric critical illness score (PCIS) is summed based on 10 indicators from laboratory tests and physical examination.⁶ The clinical pulmonary infection score (CPIS) is calculated at a basis of clinical, analytical, imaging and microbiological data.⁷ Both PICS and CPIS are conventionally used to evaluate CAP severity in children.^8,9 Complicated CAP, a severe form manifested by local or systemic complications, signifies disease progression and necessitates aggressive treatments.^10–12 Accordingly, early identification of complicated CAP may be of utmost significance in the clinical practice of CAP treatment in children. However, complexities of PICS and CPIS calculations may limit their clinical feasibility in clinical work, necessitating continued search for blood biomarkers owning to easy obtainability of blood samples in terms of discrimination of complicated CAP in children.

Inflammasomes have been implicated in a spectrum of pathophysiological processes, including the occurrence and development of pulmonary infections.^13,14 Absent in melanoma 2 (AIM2), a key component of the inflammasome complex, is a critical mediator of the inflammatory responses in various inflammation-related diseases.^15,16 AIM2 expression in the lung tissues was substantially elevated.¹⁷ In addition, lung injury was attenuated, and survival was significantly improved in AIM2-deficient mice with influenza-induced lung injury.¹⁸ Similarly, AIM2-driven alveolar macrophage pyroptosis markedly aggravated experimental lung injury, whereas genetic silencing of AIM2 notably diminished inflammation.¹⁹ Moreover, higher AIM2 levels in the bronchoalveolar lavage fluid were associated with pulmonary fibrosis.²⁰ Intriguingly, increased serum AIM2 levels were independently associated with stroke-associated pneumonia in adults with acute intracerebral hemorrhage.²¹ These data suggest that AIM2 could be specifically derived from lung injury, therefore leading to the conception that serum AIM2 may be a potential biomarker of lung injury. Here, serum AIM2 levels were measured in a group of children with CAP to investigate serum AIM2 as a biomarker for assessing severity and identifying complicated CAP in children.

Materials and Methods

Study Design and Subject Selection

This prospective cohort study was done at the Hangzhou Children’s Hospital between January 2022 and June 2023. All children with CAP were enrolled consecutively. The inclusion criteria were as follows: (1) newly diagnosed CAP, (2) 3 months < age <14 years in consideration of blood-sampling obtainability and physiological traits of children, and (3) admission of children with CAP to the hospital. The exclusion criteria were (1) other respiratory diseases, such as allergic pneumonia, asthma, or tuberculosis; (2) use of immunosuppressive drugs, underlying immune system disorders, congenital illnesses, and severe illness in other organs; and (3) other specific conditions, such as reluctance to participate, loss to follow-up, incomplete information, and unqualified blood samples. Children who underwent routine examinations at Hangzhou Children’s Hospital were recruited as controls. This study was conducted in accordance with the principles of the Declaration of Helsinki, and the research protocol was approved by the Ethics Committee of the Hangzhou Children’s Hospital (Ethics Approval Number: 2021–47) and written informed consent was obtained from the children’s guardians.

Data Collection

Some basic information, including age, sex, weight, height, preterm birth, family smoking status, vaccination, preadmission antibiotic use, preadmission fever and cough durations, were registered. Disease severity was assessed using the PCIS⁶ and the CPIS.⁷ Pathogens were classified into bacteria, virus, mycoplasma pneumoniae and mixed type. Complicated CAP was considered when any local or systemic complication was identified.^10–12 Local complications included parapneumonic effusion, empyema, necrotizing pneumonia, and lung abscess, and systemic complications included bacteremia, metastatic infection, multiorgan failure, acute respiratory distress syndrome, and disseminated intravascular coagulation and so forth.^10–12

Immune Analysis

Peripheral venous blood samples were collected at admission from children with CAP and at the entrance of the study from the control children. The blood samples were centrifuged to separate the serum for storage at −80 °C until subsequent testing. Serum AIM2 levels were measured using enzyme-linked immunosorbent assay (Catalog No. ZY-E6125H; Shanghai Zeye Biotechnology Co. Ltd., Shanghai, China). The detection range of this kit was 0.156–10 ng/mL with a sensitivity of 0.094 ng/mL, and both intra- and inter-assay coefficients of variation were less than 10%. All samples were tested in duplicate by identical proficient technicians, who were inaccessible to clinical details. The two measurements were averaged for subsequent analyses.

Statistical Analysis

Statistical analyses were completed applying SPSS 25.0 (IBM Corporation, Armonk, NY, USA), GraphPad Prism 9.0 (GraphPad Software, La Jolla, CA, USA), R 4.2.2 (https://www.r-project.org), and MedCalc 20.305 (MedCalc Software, Mariakerke, Belgium). The Kolmogorov–Smirnov test was used to determine the distribution normality of the quantitative variables. Normally distributed variables are presented as mean±standard deviation, whereas non-normally distributed variables are presented as median (25th-75th percentiles). Qualitative data are reported as counts (proportions). Based on the different data types, the χ2 test, Fisher’s exact test, Mann–Whitney U-test, or t-test was employed for intergroup comparisons, as applicable. Bivariate correlation analysis was performed using the Spearman correlation test. A multivariate linear regression model was used to identify variables that were independently associated with serum AIM2 levels. Serum AIM2 levels were dichotomized according to their median values as high and low levels. The relevant variables were compared between the two groups to determine substantially different variables. These markedly different factors were included in the binary logistic regression model to reveal the independently associated parameters. In order to ascertain whether linear model was appropriate for statistical analysis, the restricted cubic spline was drawn to discern the possible linear correlation between serum AIM2 levels and risk of complicated CAP; and if P value was above 0.05 for nonlinear assumption, the linear model should be adopted for data analysis. To compare the differences of data between children with and without complicated CAP, a binary logistic regression model was used to investigate independently associated variables. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were calculated to show associations. Subgroup analyses were performed to investigate whether the association was moderated by other variables, such as age, sex, weight, height and so forth. E-value, a component of sensitivity analysis, was computed based on OR value in regression analysis for reflecting the robustness of the association, with higher value signifying more strong result association.²² A variance inflation factor (VIF) was generated to evaluate multicollinearity in the regression model; a VIF value < 10 indicates the absence of multicollinearity.²³ Receiver operating characteristic (ROC) curves were constructed to explore the discrimination efficiency. Z-test was used to compare the area under the curve. The independent predictors of complicated CAP were consolidated to develop the model. The model was pictorially represented by the nomogram, so as to predict CAP risk, in which each independent predictor corresponded to the respective point and all points were aggregated to mirror risk. A calibration curve was plotted to demonstrate the stability of the model and a decision curve was drawn to assess the clinical applicability of the model. Meanwhile, the Hosmer-Lemeshow test was done and brier score was computed in order to unveil whether the model was performed stably. Net reclassification improvement and integrated discrimination improvement indices were calculated to determine the improvement rate of the model. Here, the sample size was estimated at a type 1 error value (alpha) of 0.05, test power (1-beta) of 0.95, and Cohen’s d of at least 0.8 for effect size in comparison of serum AIM2 levels across complicated CAP. A priori power analysis was performed to validate the adequate sample size by employing the G*Power 3.1.9.4 (Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, Düsseldorf, Germany). Differences were considered statistically significant at a two-sided P-value of <0.05.

Results

Subject Selection and Features

An initial assessment was performed on 362 children with CAP who met pre-established inclusion criteria. In accordance with the prespecified exclusion criteria, fifty-seven children were excluded from this study because of other respiratory diseases (17 cases), use of immunosuppressive drugs (6 cases), underlying immune system disorders (7 cases), congenital illnesses (8 cases), severe sickness in other organs (8 cases), reluctance to participate in this study (3 cases), missed visits (2 cases), incomplete information (2 cases), and unqualified blood samples (4 cases). Ultimately, 305 children were included in the epidemiological survey. Baseline patient characteristics are outlined in Table 1. A group of 100 healthy children was used as a control. This group of controls consisted of 54 boys and 46 girls, encompassed 24 children experiencing family smoking, included 10 suffering from preterm birth, were aged at mean value of 43.4 months (standard deviation, 33.4 months), had mean weight of 16.8 kg (standard deviation, 7.8 kg) and showed mean height of 101.8 cm (standard deviation, 25.3 cm). The above six variables did not differ significantly between the diseased children and the controls (all P>0.05).

Table 1 Baseline Characteristics of Diseased Children and Factors in Correlation with Serum Absent in Melanoma 2 Levels of Children with Community-Acquired Pneumonia

Serum AIM2 Levels and Disease Severity

Serum-based AIM2 levels were markedly higher in children with CAP than in the controls (P<0.001; Figure 1). Serum AIM2 levels were significantly negatively correlated with the PCIS (P<0.001; Figure 2) and were substantially positively related to CPIS (P< 0.001; Figure 3). In addition to the PCIS and CPIS, body temperature, blood procalcitonin levels, white blood cell counts, and blood C-reactive protein levels were closely related to serum AIM2 levels (all P<0.05; Table 1). By incorporating the six aforementioned factors, that is the PCIS, CPIS, body temperature, blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels, into the multivariable linear regression model, the PCIS (beta, −0.020; 95% CI, −0.025–0.015; VIF, 1.408; P=0.001) and CPIS (beta, 0.092; 95% CI, 0.069–0.115; VIF, 1.553; P=0.002) were independently correlated with serum AIM2 levels. Next, diseased children were divided into two groups according to the median serum AIM2 level, that is the levels ≥ 1.45 ng/mL and < 1.45 ng/mL. As compared to children with serum AIM2 levels < 1.45 ng/mL, those with the levels ≥ 1.45 ng/mL displayed substantially elevated PCIS, CPIS, body temperature, blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels (all P<0.05; Table 2). Subsequently, those significant variables, encompassing PCIS, CPIS, body temperature, blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels, were included in the binary logistic regression model, and then PCIS (OR, 0.864; 95% CI, 0.824–0.907; VIF, 1.982; P=0.002) and CPIS (OR, 1.924; 95% CI, 1.531–2.417; VIF, 2.103; P=0.003) were independently associated with serum AIM2 levels ≥ 1.45 ng/mL.

Table 2 Baseline Features Between Community-Acquired Pneumonia Children with High and Low Serum Absent in Melanoma 2 Levels

Figure 1 Differences in serum levels of absent in melanoma 2 between healthy controls and children with community-acquired pneumonia. Serum absent in melanoma 2 levels are expressed as the median (upper quartile-lower quartile). Using the Mann–Whitney U-test, serum absent in melanoma 2 levels in children with community-acquired pneumonia were significantly higher than those in healthy controls (P<0.001). AIM2 indicates absent in melanoma 2.

Figure 2 Relationship between serum absent in melanoma 2 levels and pediatric critical illness score after community-acquired pneumonia in children. Using Spearman correlation coefficient, serum absent in melanoma 2 levels were strongly inversely correlated with the pediatric critical illness score after childhood community-acquired pneumonia (P<0.001). AIM2 means absent in melanoma 2. Abbreviation: PCIS, pediatric critical illness score.

Figure 3 Relationship between serum absent in melanoma 2 levels and clinical pulmonary infection score after pediatric community-acquired pneumonia. Using Spearman correlation coefficient, serum absent in melanoma 2 levels were intimately positively correlated with the clinical pulmonary infection score of children with community-acquired pneumonia (P<0.001). AIM2 denotes absent in melanoma 2. Abbreviation: CPIS, clinical pulmonary infection score.

Serum AIM2 Levels and Complicated CAP

In contrast to children without complicated CAP, those with the adverse event had notably increased serum AIM2 levels (P<0.001; Figure 4). Alternatively, serum-based AIM2 levels effectively anticipated complicated CAP, and its threshold was selected at 1.58 ng/mL using the Youden approach, generating the maximum Youden index of 0.535 for outcome prediction (Figure 5). In the context of the restricted cubic spline analysis, serum AIM2 levels were linearly related to the probability of complicated CAP (P for nonlinearity > 0.05; Figure 6), signifying suitability of linear model in the next statistical analysis. As shown in Table 3, children presenting with complicated CAP, relative to those without such an event, had obviously decreased age and height, as well as held apparently increased serum AIM2 levels, PCIS, CPIS, blood procalcitonin levels, white blood cell counts, and blood C-reactive protein levels (all P<0.05). When all eight significantly different parameters, encompassing age, height, serum AIM2 levels, PCIS, CPIS, blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels, were integrated into the binary logistic regression module, we found that serum AIM2 levels (OR, 6.162; 95% CI, 1.752–21.670; VIF, 2.312; P=0.005), PCIS (OR, 0.907; 95% CI, 0.867–0.949; VIF, 2.419; P=0.001), and CPIS (OR, 1.391; 95% CI, 1.114–1.738; VIF, 2.375; P=0.004) independently predicted complicated CAP. In the subgroup analysis framework, the association between serum AIM2 levels and complicated CAP was not moderated by certain factors, such as age, sex, weight, height, family smoking, preadmission fever duration, and cough duration (all P interaction > 0.05; Figure 7). As for the sensitivity analysis in Figure 8, the E-value was 11.8 (95% CI, 2.90, 42.83), denoting enough high E-value versus OR value. In the next step, we modelled a prediction system by integrating the three independent predictors of complicated CAP, namely, serum AIM2, PCIS, and CPIS. The model was pictorially exhibited via the nomogram to instruct clinicians to prognosticate complicated CAP, with higher total scores corresponding to higher risk (Figure 9). In the milieu of the calibration curve analysis, the model had satisfactory goodness of fit, as confirmed by a small mean absolute error at 0.025 (Figure 10). Using the Hosmer-Lemeshow test, P value equaled to 0.235. And, brier score was 0.258. Based on the background of the decision curve analysis, the model presented good clinical validity, as opposed to serum AIM2, PCIS, CPIS, and PCIS combined with CPIS (Figure 11). Under the ROC curve (Figure 12 and Table 4), predictive ability of serum AIM2 resembled those of PCIS and CPIS (both P>0.05); combination of CPIS and PCIS significantly outperformed serum AIM2, PCIS and CPIS (all P<0.05); as well as predictive capability of the model, in which three predictors were integrated, substantially surpassed those of serum AIM2, PCIS, CPIS, and PCIS combined with CPIS (all P<0.05). Also, the conventional biomarkers, that is blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels, were not in possession of obvious advantages in identifying childhood complicated CAP (all P<0.001; Table 4). Next, the model improvement rate was estimated. As shown in Figure 13, the net reclassification improvement was 0.126 (95% CI, 0.011–0.242) (P=0.032) and the integrated discrimination improvement was 0.066 (95% CI, 0.018–0.114) (P=0.007).

Table 3 Factors Associated with Complicated Community-Acquired Pneumonia

Table 4 Areas Under Receiver Operating Characteristic Curve for Identifying Complicated Community-Acquired Pneumonia in Children

Figure 4 Differences in serum absent in melanoma 2 levels between children with complicated community-acquired pneumonia and those without such an adverse event. Using the Mann–Whitney U-test, serum absent in melanoma 2 levels were substantially higher in children with complicated community-acquired pneumonia than in those not presenting with such an affair (P<0.001). AIM2 signifies absent in melanoma 2. Abbreviation: CAP, community-acquired pneumonia.

Figure 5 Receiver operating characteristic curve evaluating discrimination efficiency of serum absent in melanoma 2 levels on complicated community-acquired pneumonia in children. Complicated community-acquired pneumonia was effectively anticipated due to the absence of serum absent in melanoma 2 levels in children. The Youden approach was applied to determine the threshold value of serum absent in melanoma 2 levels to make predictions with medium-to-high sensitivity and specificity. Circle refers to the cutoff value of serum absent in melanoma 2 levels. Abbreviation: CAP, indicates community-acquired pneumonia; AUC, area under the curve; 95% CI, 95% confidence interval.

Figure 6 Restricted cubic spline assessing linear relationship between serum absent in melanoma 2 levels and risk of complicated community-acquired pneumonia in children. Serum absent in melanoma 2 levels were linearly correlated with the likelihood of pediatric complicated community-acquired pneumonia (P for nonlinear > 0.05), indicating that result association could be verified in regression model. AIM2 is indicative of absent in melanoma 2. Abbreviation: CAP, community-acquired pneumonia.

Figure 7 Subgroup analyses examining interactional effects of some conventional variables on association of serum absent in melanoma 2 levels with childhood complicated community-acquired pneumonia. Age, sex, weight, height, family smoking, pre-admission fever duration, and pre-admission cough duration did not show a markedly moderate relationship between serum absent in melanoma 2 levels and pediatric complicated community-acquired pneumonia (all P interaction > 0.05). OR stands for odds ratio; 95% CI, 95% confidence interval.

Figure 8 Diagrammatic sketch showing E-value for expressing robustness of association between serum absent in melanoma 2 levels and childhood complicated community-acquired pneumonia. For sensitivity analysis, the E-value was 11.8 (95% confidence interval, 2.90–42.83) for displaying a robust association of serum absent in melanoma 2 levels with pediatric complicated community-acquired pneumonia.

Figure 9 Nomogram exhibiting model of complicated community-acquired pneumonia in children. The three predictors of complicated community-acquired pneumonia, that is, serum absent in melanoma 2, pediatric critical illness score, and clinical pulmonary infection score, were consolidated to develop a combined model for outcome anticipation in children. The model was visualized via the nomogram, with the summed scores reflecting risk. AIM2 denotes absent in melanoma 2. Abbreviations: PCIS, Pediatric Critical Illness Score; CPIS, Clinical Pulmonary Infection Score; CAP, community-acquired pneumonia.

Figure 10 Calibration curve determining stability of the merged model for forecasting complicated community-acquired pneumonia in children. A model containing serum absent in melanoma 2, pediatric critical illness score, and clinical pulmonary infection score was established to predict complicated pediatric community-acquired pneumonia. In accordance with low mean absolute error at 0.025, the model remained stable for outcome prediction. CAP is indicative of community-acquired pneumonia.

Figure 11 Decision curve observing validity of the combined model in prognosticating complicated community-acquired pneumonia in children. The model was composed of serum absent in melanoma 2, pediatric critical illness score, and clinical pulmonary infection score. In contrast to serum absent in melanoma 2, pediatric critical illness score, clinical pulmonary infection score, and combination of pediatric critical illness score with clinical pulmonary infection score, the model was demonstrated to benefit the clinical prediction of pediatric complicated community-acquired pneumonia on account of biggest area occupied by the model. AIM2 denotes absent in melanoma 2. Abbreviations: PCIS, Pediatric Critical Illness Score; CPIS, Clinical Pulmonary Infection Score.

Figure 12 Receiver operating characteristic curve investigating predictive strength of the model on pediatric complicated community-acquired pneumonia. The model was formed by combining the serum absent in melanoma 2, pediatric critical illness score, and clinical pulmonary infection score. In contrast to serum absent in melanoma 2, pediatric critical illness score, clinical pulmonary infection score, and the combination of pediatric critical illness score with clinical pulmonary infection score, the model was confirmed to possess significantly efficacious prediction ability in childhood complicated community-acquired pneumonia. AIM2 signifies absent in melanoma 2. Abbreviations: PCIS, Pediatric Critical Illness Score; CPIS, Clinical Pulmonary Infection Score.

Figure 13 Plot showing calculation of net reclassification improvement and integrated discrimination improvement. The standard model was composed of the pediatric critical illness and clinical pulmonary infection scores. The new model comprised serum absent in melanoma 2, pediatric critical illness score, and clinical pulmonary infection score. The net reclassification improvement was 0.126 (95% confidence interval, 0.011–0.242) and the integrated discrimination improvement was 0.066 (95% confidence interval, 0.018–0.114), meaning that the combined model may be in possession of markedly higher improvement rate.

Discussion

To the best of our knowledge, this may be the first study to explore the relationship between serum AIM2 levels, disease severity, and complicated CAP in children diagnosed of CAP. First, a profound increase in serum AIM2 levels after childhood CAP has been demonstrated in comparison to controls. Second, PCIS and CPIS were independent correlates of serum AIM2 levels, whether serum AIM2 was identified as a continuous variable or transformed into a binary variable. Third, serum AIM2, PCIS, and CPIS levels were independently predictive of complicated CAP in children. Finally, the model combining serum AIM2, PCIS, and CPIS showed a good performance in forecasting complicated CAP in children. Taken together, serum AIM2 levels may represent a promising biomarker for estimating CAP severity and predicting complicated CAP in children.

AIM2 functions as a cytosolic receptor for double-stranded DNA and is extensively involved in inflammasome activation.²⁴ It is widely expressed in epithelial and immune cells, particularly under infection and stress.²⁵ AIM2 is upregulated in lung tissues during infections such as tuberculosis and idiopathic pulmonary fibrosis.^26,27 Furthermore, increased expression of AIM2 has been documented in alveolar macrophages and lung epithelial cells in inflammatory and fibrotic lung diseases.^17–20 In adults with acute intracerebral hemorrhage, markedly enhanced admission serum AIM2 levels were strongly associated with a higher risk of stroke-associated pneumonia.²¹ Based on our finding that serum AIM2 levels are significantly higher following pediatric CAP, AIM2 may be actively involved in the host immune response to pulmonary tissue injury secondary to childhood CAP. Although it is unclear about detailed mechanisms of AIM2’ involvement in CAP or its complications, evidence about inflammasome signaling activation in other diseases implies that AIM2 activation may result in the synthesis of active interleukin-1beta and interleukin-18, thereby inducing pyroptosis, with subsequent participation in pathophysiological processes of pneumonia.^28–30 However, such a hypothesis needs to be demonstrated in future studies.

Compelling data suggest that AIM2 may be a deleterious factor in pulmonary infections,^18,19 and therefore AIM2 may be a potential therapeutic target of CAP and even its complications. On the other hand, it leads to the assumption that serum AIM2 levels may be positively related to CAP severity. CPIS and PCIS are two highly acknowledged severity assessment systems for childhood CAP.^8,9 In this cohort of children with CAP, serum AIM2 levels were strongly associated with CPIS and PCIS in univariate analysis. Using multivariate analysis, serum AIM2 was present in two forms: continuous and binary variables. Finally, it was affirmed that CPIS and PCIS were independently related to serum AIM2 levels in two multivariate modules, namely, the multivariate linear regression model and binary logistic regression model. These data strongly support the notion that serum AIM2 levels are highly correlated with CAP severity in children.

Complicated CAP encompasses one or more of the local or systemic complications of CAP.^10–12 Complicated CAP, which is marked by severe conditions, may massively protract from the disease course, thereby prolonging the length of hospitalization.^10–12 In this study, complicated CAP was identified as the outcome variable of interest. The two CAP severity scaling metrics, CPIS and PCIS, together with serum AIM2, were fully corroborated using multivariate analysis as the three associative factors of pediatric complicated CAP. A restricted cubic spline assessment was initiated in advance to verify the linear relationship between serum AIM2 levels and the possibility of complicated CAP in children. Moreover, the VIF for scaling multicollinearity was less than 10 in the current study, thereby avoiding multicollinearity.²³ Subgroup analysis was performed to investigate the moderating effect, and the association of serum AIM2 levels with complicated CAP was not affected by age, sex, weight, height, or other factors. E-value calculation is a sensitivity analysis modality.²² The E-value, relative to the OR value, was within the rational range in this cohort of subjects with childhood CAP. This series of statistical measurements ensured the validity and reliability of the results. Therefore, serum AIM2 may be an encouraging biomarker for identifying the risk of childhood complicated CAP.

Early and accurate recognition of the likelihood of pediatric complicated CAP is of the utmost importance in clinical practice.^10–12 Serum AIM2, PCIS, and CPIS levels are three determinants of childhood complicated CAP here. Serum AIM2 levels had a predictive ability comparable to that of PCIS and CPIS. Also, serum AIM2 levels transcended the conventional biomarkers, that is blood procalcitonin levels, white blood cell counts and blood C-reactive protein levels, in terms of identification ability of childhood complicated CAP. The prediction model was composed of independent predictors. As demonstrated by the ROC curve, calibration curve and decision curve, the model was clinically efficient, steady, and beneficial for prognosticating complicated CAP in children. Addition of the Hosmer-Lemeshow test and brier score calculation to statistical analysis further supports the steadiness of the model. Moreover, by estimating the net reclassification improvement and integrated discrimination improvement, the model, as opposed to PCIS combined with CPIS, achieved a significantly elevated improvement rate. Overall, accumulating statistical analyses showed that, from the perspective of additive effects possessed by serum AIM2, serum AIM2 may be an effective predictor of complicated CAP in children.

Several strengths and weaknesses should be mentioned. The strengths are shown below. First, the novelty of our study is pointed out here. To the best of our knowledge, this may be a first series of investigating serum AIM2 in children diseased of CAP and therefore finding that serum AIM2 may be a potential biomarker in relation to severity and complicated CAP in childhood. Second, the clinical values of our study should be elucidated here. In accordance with the cutoff value of serum AIM2 levels, a risk stratification could be done for children with CAP. If serum AIM2 levels are greater than the cutoff value, these diseased children may be at high risk of complicated CAP; so, this group of children should be actively monitored and even admitted into intensive care unit, followed by an aggressive treatment. And, based on numerous statistical methods, the integrated model containing serum AIM2 may be effective in clinical practice of pediatric complicated CAP because the model is able to facilitate risk stratification of complicated CAP in children and assists with aggressive intervention of childhood complicated CAP. The weaknesses are displayed in the following. First, because the risk of overfitting may be existent in model construction in a single-center design lacking external validation, and there are different populations or settings in clinical applications, particularly potential ethnic and environmental differences; these unstable factors possibly lead to difficulty in generalization of model in clinical use. And accordingly, a larger cohort study is warranted to validate effectiveness and stability of the model before the model is applied in prediction of pediatric complicated CAP. Second, even if serum AIM2 alone or the combined model integrating serum AIM2 is demonstrated to be a potential tool for discriminating children at risk of complicated CAP and subsequently instructing clinical treatments, its clinical practicability should be validated in future interventional study.

Conclusions

In children with CAP, significantly elevated serum AIM2 levels are independently correlated with PCIS and CPIS. Serum AIM2 levels are independent predictors of complicated pediatric CAP. The integrated model containing serum AIM2, PCIS, and CPIS has high clinical effectiveness in forecasting childhood complicated CAP. In summary, serum AIM2 level may be a potential biochemical indicator for pediatric CAP severity appraisal and anticipation of complicated CAP in children; and the combined model incorporating serum AIM2 may be a good tool for risk stratification of pediatric complicated CAP.

Abbreviations

AIM2, absent in melanoma 2; CAP, community-acquired pneumonia; PCIS, pediatric critical illness score; CPIS, clinical pulmonary infection score; ROC, receiver operating characteristic; AUC, area under the curve; OR, odds ratio; 95% CI, 95% confidence interval.

Data Sharing Statement

The raw data supporting the conclusions of this study will be provided by the authors without undue retention.

Funding

This study was financially supported by Zhejiang Provincial Medical and Health Science and Technology Plan (No. 2023RC248).

Disclosure

The authors declare that they have no competing interests in this work.

References

1. Walker CLF, Rudan I, Liu L, et al. Global burden of childhood pneumonia and diarrhoea. Lancet. 2013;381(9875):1405–1416. doi:10.1016/S0140-6736(13)60222-6

2. le Roux DM, Zar HJ. Community-acquired pneumonia in children-a changing spectrum of disease. Pediatr Radiol. 2017;47(11):1392–1398. doi:10.1007/s00247-017-3827-8

3. Jiang N, Li R, Bao J, et al. Incidence and disease burden of community-acquired pneumonia in southeastern China: data from integrated medical resources. Hum Vaccin Immunother. 2021;17(12):5638–5645. doi:10.1080/21645515.2021.1996151

4. Jain S, Williams DJ, Arnold SR, et al. Community-acquired pneumonia requiring hospitalization among U.S. children. N Engl J Med. 2015;372(9):835–845. doi:10.1056/NEJMoa1405870

5. Leung AKC, Wong AHC, Hon KL. Community-acquired pneumonia in children. Recent Pat Inflamm Allergy Drug Discov. 2018;12(2):136–144. doi:10.2174/1872213X12666180621163821

6. Fang C, Mao Y, Jiang M, Yin W. Pediatric critical illness score, clinical characteristics and comprehensive treatment of children with severe mycoplasma pneumoniae pneumonia. Front Surg. 2022;9:897550. doi:10.3389/fsurg.2022.897550

7. Becerra-Hervás J, Guitart C, Covas A, et al. The clinical pulmonary infection score combined with procalcitonin and lung ultrasound (CPIS-PLUS), a good tool for ventilator associated pneumonia early diagnosis in pediatrics. Children. 2024;11(5):592. doi:10.3390/children11050592

8. Liu QZ, Feng ZQ, Huang KW, Yang ZJ, Xu LQ, Shen YY. Diagnostic value of ultrasound for community-acquired pneumonia in children and its correlation with serum PCT level and PCIS. Medicine. 2024;103(43):e39590. doi:10.1097/MD.0000000000039590

9. Xie S, Wang J, Tuo W, et al. Serum level of S100A8/A9 as a biomarker for establishing the diagnosis and severity of community-acquired pneumonia in children. Front Cell Infect Microbiol. 2023;13:1139556. doi:10.3389/fcimb.2023.1139556

10. de Benedictis FM, Kerem E, Chang AB, Colin AA, Zar HJ, Bush A. Complicated pneumonia in children. Lancet. 2020;396(10253):786–798. doi:10.1016/S0140-6736(20)31550-6

11. Tuğcu GD, Özsezen B, Türkyılmaz İ, et al. Risk factors for complicated community-acquired pneumonia in children. Pediatr Int. 2022;64(1):e15386. doi:10.1111/ped.15386

12. Erlichman I, Breuer O, Shoseyov D, et al. Complicated community acquired pneumonia in childhood: different types, clinical course, and outcome. Pediatr Pulmonol. 2017;52(2):247–254. doi:10.1002/ppul.23523

13. De Nardo D, De Nardo CM, Latz E. New insights into mechanisms controlling the NLRP3 inflammasome and its role in lung disease. Am J Pathol. 2014;184(1):42–54. doi:10.1016/j.ajpath.2013.09.007

14. Tseng YH, Chen IC, Li WC, Hsu JH. Regulatory cues in pulmonary fibrosis-with emphasis on the AIM2 inflammasome. Int J Mol Sci. 2023;24(13):10876. doi:10.3390/ijms241310876

15. Hornung V, Ablasser A, Charrel-Dennis M, et al. AIM2 recognizes cytosolic dsDNA and forms a caspase-1-activating inflammasome with ASC. Nature. 2009;458(7237):514–518. doi:10.1038/nature07725

16. Man SM, Karki R, Kanneganti TD. AIM2 inflammasome in infection, cancer, and autoimmunity: role in DNA sensing, inflammation, and innate immunity. Eur J Immunol. 2016;46(2):269–280. doi:10.1002/eji.201545839

17. Zhang Q, Hu Q, Chu Y, Xu B, Song Q. The Influence of radiotherapy on AIM2 inflammasome in radiation pneumonitis. Inflammation. 2016;39(5):1827–1834. doi:10.1007/s10753-016-0419-y

18. Zhang H, Luo J, Alcorn JF, et al. AIM2 inflammasome is critical for influenza-induced lung injury and mortality. J Immunol. 2017;198(11):4383–4393. doi:10.4049/jimmunol.1600714

19. Li H, Li Y, Song C, et al. Neutrophil extracellular traps augmented alveolar macrophage pyroptosis via AIM2 inflammasome activation in LPS-induced ALI/ARDS. J Inflamm Res. 2021;14:4839–4858. doi:10.2147/JIR.S321513

20. Trachalaki A, Tsitoura E, Mastrodimou S, et al. Enhanced IL-1β release following NLRP3 and AIM2 inflammasome stimulation is linked to mtROS in airway macrophages in pulmonary fibrosis. Front Immunol. 2021;12:661811. doi:10.3389/fimmu.2021.661811

21. Zhang C, Wang C, Yang M, Wen H, Li P. Usability of serum AIM2 as a predictive biomarker of stroke-associated pneumonia and poor prognosis after acute supratentorial intracerebral hemorrhage: a prospective longitudinal cohort study. Heliyon. 2024;10(10):e31007. doi:10.1016/j.heliyon.2024.e31007

22. Vale CCR, Almeida NKO, Almeida RMVR. On the use of the E-value for sensitivity analysis in epidemiologic studies. Cad Saude Publica. 2021;37(6):e00294720. doi:10.1590/0102-311X00294720

23. Kim JH. Multicollinearity and misleading statistical results. Korean J Anesthesiol. 2019;72(6):558–569. doi:10.4097/kja.19087

24. Hu B, Jin C, Li HB, et al. The DNA-sensing AIM2 inflammasome controls radiation-induced cell death and tissue injury. Science. 2016;354(6313):765–768. doi:10.1126/science.aaf7532

25. Sharma BR, Karki R, Kanneganti TD. Role of AIM2 inflammasome in inflammatory diseases, cancer and infection. Eur J Immunol. 2019;49(11):1998–2011. doi:10.1002/eji.201848070

26. Saiga H, Kitada S, Shimada Y, et al. Critical role of AIM2 in Mycobacterium tuberculosis infection. Int Immunol. 2012;24(10):637–644. doi:10.1093/intimm/dxs062

27. Cho SJ, Moon JS, Nikahira K, et al. GLUT1-dependent glycolysis regulates exacerbation of fibrosis via AIM2 inflammasome activation. Thorax. 2020;75(3):227–236. doi:10.1136/thoraxjnl-2019-213571

28. Wang L, Ren W, Wu Q, et al. NLRP3 inflammasome activation: a therapeutic target for cerebral ischemia-reperfusion injury. Front Mol Neurosci. 2022;15:847440. doi:10.3389/fnmol.2022.847440

29. Danielski LG, Giustina AD, Bonfante S, Barichello T, Petronilho F. The NLRP3 inflammasome and its role in sepsis development. Inflammation. 2020;43(1):24–31. doi:10.1007/s10753-019-01124-9

30. Du L, Wang X, Chen S, Guo X. The AIM2 inflammasome: a novel biomarker and target in cardiovascular disease. Pharmacol Res. 2022;186:106533. doi:10.1016/j.phrs.2022

Introduction

Acute ischemic stroke is one of the most prevalent diseases in the world, affecting 77 million people worldwide with a mortality rate of 3.3 million each year, becoming the second leading cause of death after heart disease.¹ The disease affected 8.3% of the population, causing a death toll of up to 192 deaths per 100,000 people and costs up to 2.57 trillion rupiahs.² The burden of stroke extends beyond death tolls because it is a leading cause of long-term disability. Around five million of stroke survivors suffer from permanent disabilities, such as vision loss, speech loss, paralysis, confusion, and thus lowers productivity and quality of life.³ These debilitating effects of stroke can be mitigated by early and effective interventions for acute ischemic stroke.⁴

Alteplase is the standard therapy for acute stroke. As a recombinant tissue plasminogen activator (rt-PA), alteplase must be administered within less than 4.5 hours of onset in stroke patients with no contraindications. Delayed treatment with alteplase will only diminish the effects or even worsen, and it will increase the risk of intracranial hemorrhage.⁵ This poses challenges for developing countries with their problems in recognition, admission, and diagnosis of stroke patients. Knowledge regarding stroke symptoms, awareness of the window period, and decision-making continue to contribute to delays.^6,7 Other than that, inaccessible areas and low-income status have restricted stroke patients from accessing costly thrombolytic agents.⁷ These problems are aggravated by the limited availability of hospitals performing thrombolysis.^2,8 As the gold standard therapy for acute ischemic stroke, alteplase still has its own limitations, especially when applied in developing countries. Therefore, alternative and adjuvant therapies have been recommended.

Lumbrokinase, a fibrinolytic agent, has been deemed a potential adjuvant therapy for ischemic stroke. This fibrinolytic enzyme, specifically Lumbricus sp., has inhibitory effects on platelet aggregation and is currently being rigorously studied for the treatment of various diseases, including cardiovascular and cerebrovascular diseases. It has the ability to hydrolyze both fibrin and fibrinogen which will prevent the formation of blood clots.⁹ Lumbrokinase has qualities superior to other similar fibrinolytic agents, such as urokinase and streptokinase, due to its higher thermal stability, alkali resistance, no conversion of plasminogen into plasmin, and highly specific to fibrin. Due to that, lumbrokinase can be administered orally, can reduce the risk of hemorrhage, and does not induce hyperfibrinolysis.^10,11 Other than that, due to its natural availability and non-invasive nature, lumbrokinase cuts off unnecessary expenses and does not pose various risks.¹² The overall ability and characteristics of lumbrokinase exhibit valuable potential as a therapeutic agent in treating stroke.

Lumbrokinase is typically administered orally in capsule form, which makes it more practical and non-invasive for stroke patients, particularly in low-resource settings. Studies have also explored intravenous and intramuscular routes, but oral administration remains the most common and accessible approach. Compared to alteplase – and even the newer drug, tenecteplase – lumbrokinase is considered less expensive, particularly in developing countries where healthcare costs are a major barrier to standard thrombolytic therapy. However, robust evidence for lumbrokinase as an adjunctive therapy for ischemic stroke is scarce. This increases the urgency for the systemic evaluation of lumbrokinase to treat ischemic stroke patients, including its efficacy and safety, when compared with standard supportive management. This paper unveils the efficacy and safety of lumbrokinase as an adjunctive therapy combined with supportive management for acute stroke when compared with standard management alone.

Methods

Study Design

A systematic review and meta-analysis was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020.^13,14

Data Sources and Search Strategy

A literature search was conducted independently and comprehensively up to July 2024 throughout several databases, including PubMed, Science Direct, EMBASE, EBSCO, Clinical key, Scopus, Proquest, MedRxiv, BioRxiv, SSRN, ClinicalTrials, PsycInfo, PsycNet, Web of Science, Google Scholar, and Cochrane. The keywords that were used are “lumbrokinase”, “acute ischemic stroke”, and “randomized controlled trials”. Any discrepancies were further discussed by the authors. Subsequently, the results were scrutinized for duplication and screened using the predetermined eligibility criteria.

Study Selection

All articles were independently reviewed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. A screening process was started through title and abstract and continued with full-text screening of selected studies to exclude studies that met the exclusion criteria. We included RCTs comparing lumbrokinase with supportive management to supportive management alone and excluded any other study designs such as non-RCTs and observational studies. All the selected studies were validated to ensure their eligibility for the next step.

Although older and smaller RCTs were included, they were retained based on predefined eligibility criteria and the limited availability of large, high-quality trials evaluating lumbrokinase in acute ischemic stroke. These studies contribute valuable data to a relatively under-researched area, and their inclusion allows for a more comprehensive understanding of the intervention’s efficacy and safety.

Outcomes Measured

This study measured both the primary and secondary outcomes to evaluate the effectiveness and safety of the intervention. The primary outcomes included the Barthel Index and NIHSS score, which assess functional independence and neurological deficits, respectively. Secondary outcomes included adverse events, such as gastrointestinal discomfort and bleeding, along with laboratory parameters, such as activated partial thromboplastin time (aPTT) and D-dimer levels.

Data Analysis

To ensure the reliability of the findings, the risk of bias in each included study was assessed using the Cochrane Risk of Bias 2.0 (RoB 2.0) tool.^15,16 Statistical analysis was conducted using a random effects model to account for potential variability among studies. The results were reported as odds ratios (OR) for dichotomous outcomes and mean differences (MD) for continuous outcomes, each with corresponding 95% confidence intervals (CI).

To evaluate publication bias, funnel plots were visually inspected for asymmetry, which may have indicated selective reporting. Heterogeneity among the included studies was assessed using the I² statistic, which quantifies the proportion of total variation in the effect estimates owing to heterogeneity rather than chance. An I² value of 0% indicated no observed heterogeneity, values above 50% indicated substantial heterogeneity, and values above 75% indicated considerable heterogeneity. The presence of significant heterogeneity may reflect clinical, methodological, or statistical differences between studies, which informs the interpretation of the pooled estimates in the meta-analysis.

Results

A total of 64 studies^17–80 were included in the analysis as illustrated in the PRISMA flowchart (Figure 1). These studies were selected after a comprehensive screening process that involved the identification of 1836 records through database searches and manual reviews, followed by the exclusion of 1773 duplicates and irrelevant studies. The final dataset comprised randomized controlled trials evaluating the efficacy and safety of the intervention across various clinical and laboratory outcomes, which are summarized in Table 1. The risk of bias is shown in Figure 2.

Table 1 Summary of Key Outcomes from the Meta-Analysis on Lumbrokinase’s Effectiveness and Safety in Treating Acute Ischemic Stroke

Figure 1 PRISMA 2020 flowchart of the included studies.

Figure 2 Risk of bias of the included studies.

Adverse events were grouped into five groups; three studies^60,69,79 reported GI discomfort, and the results shown in Figure 3 showed that there was no significant difference between the experimental and control groups, with an OR of 1.00 [95% CI 0.32; 3.16]. Seven studies^{36,38,47,56,58,69,79} reported vomiting, and the results shown in Supplementary Figure 1 showed that the experimental group was favorable with an OR of 2.00 [95% CI 0.74; 5.39], rash with an OR of 1.64 [95% CI 0.38; 7.01],^47,56,69 and GI tract bleeding with an OR of 1.42 [95% CI 0.55; 3.67].^19,65,79 Heterogeneity was absent across groups, and the funnel plot showed no evidence of heterogeneity.

Figure 3 Forest plot of adverse events.

Eight studies^{26,38,57,66,69,73,77,80} reported aPTT, and the results as seen in Supplementary Figure 2 showed that the control group is favorable with MD of 1.93 [95% CI 1.58; 2.28]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Nine studies^{19,27,38,51,63,64,69,79,80} reported the Barthel Index, and the results as seen in Supplementary Figure 3 showed that the control group is favorable with MD of 15.17 [95% CI 14.60; 15.74]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Three studies^20,75,77 reported carotid artery intima media thickness, and the results as seen in Supplementary Figure 4 showed that the experimental group is favorable with MD of −0.27 [95% CI −0.36; −0.17]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity.

Four studies^47,48,53,63 reported cell aggregation rate, and the results as seen in Supplementary Figure 5 showed that the control group is favorable with MD of 0.31 [95% CI 0.26; 0.36]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Two studies^54,55 reported coagulation factor, and the results as seen in Supplementary Figure 6 showed that the experimental group is favorable with MD of −0.62 [95% CI −0.88; −0.35]. Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity. Five studies^{17,18,21,65,77} reported CRP, and the results as seen in Supplementary Figure 7 showed that the experimental group is favorable with MD of −1.40 [95% CI −1.47; −1.34]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Thirty five^{19,33–42,44–46,48,51,52,54–62,64–66,69–74} studies reported curative effect, and the results as seen in Supplementary Figure 8 showed that experimental group is favorable with odds ratio of 2.77 [95% CI 2.33; 3.29]. Heterogeneity was moderate, and the funnel plot showed few outliers, indicating true heterogeneity.

Six studies^{38,66,69,74,77,78} reported D-dimer, and the results shown in Supplementary Figure 9 showed that the experimental group was favorable, with an MD of −0.04 [95% CI −0.05; −0.03]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Three studies^48,55,62 reported erythrocyte sedimentation rate, and the results as seen in Supplementary Figure 10 showed that the experimental group is favorable with MD of −0.58 [95% CI −2.55; −1.40]. Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity.

Eight studies^{33,36,43,51,54,55,62,73} reported hematocrit, and the results as seen in Supplementary Figure 11 showed that the control group is favorable with MD of 0.07 [95% CI 0.06; 0.08]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Three studies^19,77,80 reported INR, and the results as seen in Supplementary Figure 12 showed that the control group is favorable with MD of 0.05 [95% CI 0.02; 0.09]. Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity. Three studies^65,66,79 reported mRS score, and the results as seen in Supplementary Figure 13 showed that the experimental group is favorable with MD of −1.28 [95% CI −1.54; −1.05]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity.

Twenty four studies^{19,25,28,32,37,38,42,47,48,50,55,56,59,60,64–69,71,76–79} reported NIHSS Score, and the results as seen in Supplementary Figure 14 showed that the experimental group is favorable with MD of −2.01 [95% CI −2.06; −1.97]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Two studies^20,77 reported the number of carotid plaques, and the results shown in Supplementary Figure 15 showed that there was no difference between the experimental and control groups (MD −0.00 [95% CI −0.13; 0.12]). Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity. Thirty three^{18–21,25,36,37,39,40,43,45,47–50,52,53,56,57,59,62,63,66–69,71–74,76,77} studies reported plasma fibrinogen, and the results as seen in Supplementary Figure 16 showed that the experimental group is favorable with MD of −1.00 [95% CI −1.03; −0.96]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Twenty two studies^{29,33,35,36,39,40,45,47,48,51–55,57,59,62,63,66,68,73,77} reported plasma specific viscosity, and the results as seen in Supplementary Figure 17 showed that the experimental group is favorable with MD of −0.16 [95% CI −0.17; −0.14]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity.

Five studies^{27,38,66,77,78} reported plasminogen activator inhibitor, and the results as seen in Supplementary Figure 18 showed that the experimental group is favorable with MD of −0.77 [95% CI −0.85; −0.69]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Three studies^53,54,62 reported platelet aggregation rate (0.5 min), and the results as seen in Supplementary Figure 19 showed that the experimental group is favorable with MD of −205.86 [95% CI −206.77; −204.96]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity. Nine studies^{36,38,42,53,63,66,67,73,74} reported platelet aggregation rate (1 min), and the results as seen in Supplementary Figure 20 showed that the experimental group is favorable with MD of −7.25 [95% CI −8.61; −5.90]. Heterogeneity was high, and the funnel plot showed few outliers, indicating evidence of true heterogeneity.

Two studies^18,72 reported platelet aggregation rate, and the results as seen in Supplementary Figure 21 showed that the control group is favorable with MD of 6.59 [95% CI 3.22; 9.96]. Heterogeneity was high, and the funnel plot indicated evidence of true heterogeneity. Seven studies^{19,26,28,35,37,43,71} reported platelet count, and the results as seen in Supplementary Figure 22 showed that the experimental group is favorable with MD of −8.78 [95% CI −13.49; −4.07]. The heterogeneity was moderate, and the funnel plot indicated evidence of true heterogeneity. Seventeen studies^{21,26,28,31,35,38–40,57,63,66,69,71,77,80} reported PT (second), and the results as seen in Supplementary Figure 23 showed that the experimental group is favorable with MD of −0.17 [95% CI −0.22; 0.12]. Heterogeneity was high, and the funnel plot indicated evidence of true heterogeneity.

Three studies^47,55,62 reported red blood cell deformation coefficient, and the results as seen in Supplementary Figure 24 showed that the experimental group is favorable with MD of −0.33 [95% CI −0.39; −0.27]. Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity. Two studies^18,21 reported thrombosis precursor protein, and the results as seen in Supplementary Figure 25 showed that the experimental group is favorable with MD of −3.10 [95% CI −3.99; −2.21]. Heterogeneity was absent, and the funnel plot showed no evidence of true heterogeneity. Four studies^26,38,66,77 reported tissue plasminogen activator, and the results shown in Supplementary Figure 26 showed that the control group was favorable (MD 0.22 [95% CI 0.19; 0.24]). The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity.

Two studies^20,54 reported total cholesterol, and the results as seen in Supplementary Figure 27 showed that the experimental group is favorable with MD of −0.92 [95% CI −1.20; −0.65]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity. Two studies^20,54 reported triacylglycerol, and the results as seen in Supplementary Figure 28 showed that the experimental group is favorable with MD of −0.46 [95% CI −0.66; −0.25]. Heterogeneity was low, and the funnel plot showed no evidence of true heterogeneity. Two studies^38,66 reported TT (second), and the results shown in Supplementary Figure 29 showed that the experimental group was favorable, with an MD of −0.08 [95% CI −0.48; 0.32]. Heterogeneity was absent, and the funnel plot showed no evidence of true heterogeneity.

Seventeen studies^{23,29,33,35,36,38,48,51–55,57,59,69,73,77} reported whole blood high viscosity shear, and the results as seen in Supplementary Figure 30 showed that the experimental group is favorable with MD of −0.14 [95% CI −0.16; −0.12]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity. Fifteen studies^{23,29,33,35,36,48,52–55,57,59,69,73,77} reported whole blood low viscosity shear, and the results as seen in Supplementary Figure 31 showed that the experimental group is favorable with MD of −1.29 [95% CI −1.40; −1.17]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity. Six studies^{48,52,55,62,66,68} reported whole blood reduced viscosity, and the results as seen in Supplementary Figure 32 showed that the control group is favorable with MD of 0.03 [95% CI 0.01; 0.05]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity. Five studies^{18,21,28,66,68} reported whole blood specific viscosity, and the results as seen in Supplementary Figure 33 showed that the experimental group is favorable with MD of −0.67 [95% CI −0.78; −0.57]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity. Four studies^36,38,53,77 reported whole blood viscosity mid-cut, and the results as seen in Supplementary Figure 34 showed that the experimental group is favorable with MD of −0.42 [95% CI −0.53; −0.30]. The heterogeneity was high, and the funnel plot showed evidence of true heterogeneity.

Clinical efficacy was divided into seven groups, as shown in Supplementary Figure 35, and eight groups showed that the results favored the experimental group. Heterogeneity was high across the groups, and the funnel plot showed true evidence of heterogeneity.

Discussion

Lumbrokinase has been known to have anti-ischemic properties through inhibition of platelet aggregation and promotion of platelet disintegration.⁸¹ Based on the result of this meta-analysis, it is shown that lumbrokinase can improve functional outcomes in acute ischemic stroke patients compared to those with standard therapy. These were concluded by significant improvements in the functional outcome components, such as improvements in the Barthel Index and lowered NIHSS and mRS scores. These results suggest a positive efficacy of lumbrokinase in the treatment of acute ischemic stroke.

In addition to its fibrinolytic properties, lumbrokinase can lower stress in the endoplasmic reticulum. This meta-analysis found improvements in the Barthel Index in the lumbrokinase group compared to that in the control group, suggesting higher independence in the lumbrokinase group. The Barthel Index, which is commonly used to observe improvements during or after rehabilitation, can depict a patient’s dependency in daily life. A study in mice showed that stroke-induced endoplasmic reticulum stress was reduced by decreasing the phosphorylation of inositol-requiring enzyme-1 (IRE1) and attenuating autophagy and inflammation. Lower IRE1 levels are associated with lower activity of apoptosis-promoting pathways.⁸² Our study is in agreement with previous studies. Thus, lumbrokinase exhibits neuroprotective functions, prevents neuronal death, and further halts the worsening of Barthel Index score. Therefore, it can be inferred that lumbrokinase can increase independence in daily life in patients with acute ischemic stroke compared to those treated with standard therapy. In addition, the NIHSS and mRS scores, which reflect neurological deficits, were lower in the lumbrokinase group. Despite its clear effect favoring the lumbrokinase group, we also found high heterogeneity, which indicated variability in the study results. Differences in the study design, follow-up duration, and baseline data could potentially affect the results. More studies are needed to explore the neuroprotective and neurorehabilitative effects of lumbrokinase in patients with acute stroke.

Current standard therapies for acute ischemic stroke, such as antiplatelet and thrombolytic agents, have shown significant clinical benefits in daily usage. However, adverse effects such as an increased risk of bleeding complications remain a major clinical concern. Alternatives with similar efficacies have been rigorously investigated to minimize complications. In this meta-analysis, we found that there was no significant increase in adverse events in the lumbrokinase group compared with the control group. This suggests that despite its high fibrinolytic activity, lumbrokinase does not cause a higher adverse effect than the standard therapy. Previous studies have shown that the fibrinolytic properties of lumbrokinase are highly specific for fibrin; plasminogens are not activated into plasmin with lumbrokinase. Therefore, they are only active in the presence of fibrin.⁸³ Meanwhile, other thrombolytics, such as tPA, are not specific for fibrin. Thus, adverse effects, especially bleeding effects, are minimized in the lumbrokinase group by this mechanism.⁸⁴ We also did not find any significant differences in other adverse effects, such as gastrointestinal discomfort, vomiting, and rash between lumbrokinase therapy and standard therapy.

Various laboratory test results were compared between the two groups. One such test is aPTT, which reflects the ability of platelets to form blood clots and stop bleeding. Our study found that aPTT was significantly longer in the lumbrokinase group. Lower platelet aggregation rates, whether at 0.5 second or 1 second, were observed in the lumbrokinase group. Moreover, D-dimer levels were lower in the kinase group. This supports previous studies that stated its antithrombotic potential.⁸¹ Whilst laboratory results might have statistically significant differences between groups, several precautions should be accounted for. A previous study stated that only aPTT is likely to develop moderate to severe bleeding.⁸⁵ Our study shows that the mean time for aPTT lies between 22 and 39.9 seconds in the lumbrokinase group, while in the standard therapy group it ranged from 24 to 37.72 seconds. Another study also found that shortened aPTT, defined as shorter than 28.4 seconds, is an independent factor for ischemic stroke, stroke severity, and neurological decline.⁸⁶ Previous study conducted by Nurindar et al found that there was only a weak positive correlation between lower platelet aggregation rate and the degree of neurological deficit, and no statistically significant result was observed in the study.⁸⁷ A study conducted by Zi et al found that the range of D-dimer in acute ischemic stroke was 0.28 to 2.11 mg/L,⁸⁸ while our study reports a range from 0.007 to 0.91 mg/L in the lumbrokinase group and 0.07 to 0.65 mg/L in the control group. Thus, even when lumbrokinase is found to statistically improve laboratory findings, it should not be interpreted alone, and the clinical context should be considered. Although statistically significant, the three indicators also had high heterogeneity, which might be affected by different baseline characteristics, differences in measurement techniques, and study designs.

As previously described, we can infer that lumbrokinase holds potential as an adjuvant or alternative therapy for acute ischemic stroke compared to the standard therapy currently available, especially when resources are limited and access to alteplase might be minimal. Lumbrokinase was isolated and extracted from the earthworm, Lumbricus rubellus. These earthworms can be found in several places with limited resources.⁸⁹ have favorable outcomes in improving neurological status and laboratory findings, while having no statistical difference in adverse effects when compared to standard therapy demonstrated its potential. However, the clinical findings should be considered when implementing these findings. Our findings should not be overstated because of our moderate quality of evidence, and alteplase remains the gold-standard therapy based on international guidelines. Further robust research for lumbrokinase in treating acute ischemic stroke with less heterogeneity should be conducted, for our findings of high heterogeneity remain our limitations. We also found that there was a lack of direct comparison between lumbrokinase and alteplase and the moderate-to-high risk of bias studies used in our study, which might have affected our study.

Several limitations must be acknowledged. First, although a large number of RCTs were included, many of them presented a moderate to high risk of bias, which may impact the reliability of pooled estimates. Second, the lack of direct comparisons between lumbrokinase and alteplase limits our ability to determine its effectiveness relative to standard thrombolytic therapy. Third, heterogeneity in study designs, follow-up durations, and baseline characteristics was substantial in several outcomes. Finally, as most included studies were conducted in specific regions, external validity and generalizability remain limited. Future high-quality, multicenter studies are needed to address these concerns.

The findings of this study have notable implications for clinical practice, particularly in low- and middle-income countries where access to intravenous thrombolytics is limited. Lumbrokinase, as an orally administered agent with a favorable safety profile, presents a promising adjunctive or alternative therapy. Its affordability, non-invasive administration, and comparable efficacy in improving outcomes could help expand stroke care beyond major urban centers. However, clinicians must still interpret laboratory improvements cautiously and consider patient-level characteristics, comorbidities, and timing of therapy.

Lumbrokinase has the potential to improve outcomes and reduce stroke severity, particularly when used in combination with standard supportive care. Further high-quality RCTs with direct comparisons to alteplase, exploration of long-term outcomes, and broader safety profiles may be beneficial for further validation of lumbrokinase use. Although lumbrokinase shows promise in improving outcomes in acute ischemic stroke when combined with supportive management, the results must be interpreted with caution due to low-to-moderate evidence quality, and it should not be considered as a replacement for established thrombolytic agents, such as alteplase, but rather as a supplementary treatment.

Conclusion

Lumbrokinase, a fibrinolytic enzyme derived from earthworms, appears to improve neurological function and reduce laboratory markers of thrombosis in patients with acute ischemic stroke when used alongside supportive care. It offers a safe and potentially cost-effective alternative in settings with limited access to conventional thrombolytic agents. However, high heterogeneity, risk of bias, and lack of direct comparisons with current standard therapies limit definitive conclusions. Future research should focus on large-scale randomized trials comparing lumbrokinase directly with established treatments and evaluating its long-term safety and clinical benefits.

Data Sharing Statement

All data generated or analyzed during the study are included in this published article.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

There is no funding to report.

Disclosure

The authors declare no conflicts of interest in this work.

References

1. Feigin VL, Brainin M, Norrving B, et al. World Stroke Organization (WSO): global stroke fact sheet 2022. Int J Stroke. 2022;17(1):18–29. doi:10.1177/17474930211065917

2. Venketasubramanian N, Yudiarto FL, Tugasworo D. Stroke burden and stroke services in Indonesia. Cerebrovasc Dis Extra. 2022;12(1):53–57. doi:10.1159/000524161

3. WHO Regional Office for Eastern Mediterranean. Stroke, Cerebrovascular accident [Internet]. [cited July 20, 2025]. Available from: https://www.emro.who.int/health-topics/stroke-cerebrovascular-accident/index.html. Accessed August 18, 2025.

4. Jauch EC, Saver JL, Adams HP, et al. Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2013;44(3):870–947. doi:10.1161/STR.0b013e318284056a

5. Lees KR, Bluhmki E, von Kummer R, et al. Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials. Lancet. 2010;375(9727):1695–1703. doi:10.1016/S0140-6736(10)60491-6

6. Ferris A, Robertson RM, Fabunmi R, Mosca L. American Heart Association and American Stroke Association national survey of stroke risk awareness among women. Circulation. 2005;111(10):1321–1326. doi:10.1161/01.CIR.0000157745.46344.A1

7. Ghandehari K. Barriers of thrombolysis therapy in developing countries. Stroke Res Treat. 2011;2011:686797. doi:10.4061/2011/686797

8. Hidayat R, Rima SPP, Pangeran D, et al. Optimizing stroke care in Indonesia: a policy brief on expanding access to thrombolysis for improved outcomes. Acta Neurol Indones. 2023;1(01). doi:10.69868/ani.v1i01.12

9. Kumar Verma M, Pulicherla KK. Lumbrokinase-a potent and stable fibrin-specific plasminogen activator. Int J Bio-Sci Bio-Technol. 2011;3(2):57–69.

10. Fu T, Yang F, Zhu H, Zhu H, Guo L. Rapid extraction and purification of lumbrokinase from Lumbricus rubellus using a hollow fiber membrane and size exclusion chromatography. Biotechnol Lett. 2015;38:251–258. doi:10.1007/s10529-015-1979-x

11. Nguyen QTT, Rhee H, Kim M, Lee MY, Lee EJ. Lumbrokinase, a fibrinolytic enzyme, prevents intra-abdominal adhesion by inhibiting the migrative and adhesive activities of fibroblast via attenuation of the AP-1/ICAM-1 signaling pathway. Biomed Res Int. 2023;2023:4050730. doi:10.1155/2023/4050730

12. Costs of Caring | AHA [Internet]. [cited July 20, 2025]. Available from: https://www.aha.org/costsofcaring. Accessed August 18, 2025.

13. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372.

14. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62(10):e1–34. doi:10.1016/j.jclinepi.2009.06.006

15. Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366.

16. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M. Cochrane Handbook for Systematic Reviews of Interventions Version 6.5. Welch V, editor. Cochrane; 2024.

17. Huang X. Clinical analysis of urokinase combined with aspirin and clopidogrel sulfate in the treatment of acute cerebral infarction. Syst Med. 2019;2019:135–137.

18. Lin Y. Analysis of the efficacy of lumbrokinase combined with clopidogrel in the treatment of transient ischemic attack and diabetes mellitus. Chin J Pract Nerv Dis. 2013;2013:53–54.

19. Chen H, He Y, Mo M. Comparative observation of lumbrokinase alone and combined with low-dose urokinase in the treatment of acute progressive cerebral infarction. Med Innov China. 2017;14:17–20.

20. Liu W, Yang C, Liu Y, Wang L, Liu F. Effect of lumbrokinase and atorvastatin on the carotid plaque in patients with acute cerebral infarction. Intern Med China. 2013;8:347–349.

21. Jiang B, Yang Q. Evaluation of the effect and safety of combination of lumbrokinase and clopidogrel hydrogen for patients with TIA and diabetes mellitus. Chin J Pract Nerv Dis. 2013;16(7):16–18.

22. Baolin X, Gao H. Comparative study on the clinical efficacy of lumbrokinase enteric-coated tablets in the treatment of progressive cerebral infarction. Chin Mod Doc. 2012;32:82–84.

23. Wei, Du Y, Liu Y, Fang Z. Quantitative evaluation of the efficacy of lumbrokinase on the recovery of upper limb motor function in patients with acute cerebral infarction. Capital Med. 2010;2010:44–45.

24. Junqing. Observation on the efficacy of lumbrokinase enteric-coated capsules combined with sodium ozagrel in the treatment of acute cerebral infarction. Chin Mod Dr. 2010;48(4):73–74.

25. Xu H, Wang X, Hua H. Observation on the efficacy of lumbrokinase in the treatment of acute cerebral infarction. Ind Enterp Med J. 2011;5:11–13.

26. Peixiang Z, Guanglian S. Observation on the efficacy of lumbrokinase combined with sodium ozagrel in the treatment of acute cerebral infarction. Chin J Clin Ration Drug Use. 2012;5(7B):63.

27. Xiaoxing W, Xixian W, Chunhua G. Analysis of the efficacy of lumbrokinase in the treatment of acute cerebral infarction. Chin J Pract Nerv Dis. 2011;14(9):47–48.

28. Yuan Y, Xu Y. Observation on the treatment of acute cerebral infarction with lumbrokinase combined with sodium ozagrel Pizhou People’s Hospital, Jiangsu Province (221300) Yuan Yuan, Xu Yuqiu. Cap Med. 2010;2010:38–39.

29. Wei Z, Linhong Z, Wuping X. Effects of lumbrokinase on blood lipids and blood rheology in patients with ischemic cerebrovascular disease in the recovery period combined with hyperlipidemia. Cap Med. 2010;2010:51–52.

30. Hongbin L, Liyan J, Guangna Y. Lumbrokinase combined with atorvastatin calcium in the treatment of transient ischemic attack. China Prac Med. 2010;5(20):172.

31. Huiyu Z, Wei T, Weidong Z. Observation on the efficacy of Bio-lumbrokinase combined with ligustrazine in the treatment of 38 cases of acute cerebral infarction. Zhou Huiyu. 2008;141:101–102.

32. Liqin Z, Chunhua Q. Clinical observation of lumbrokinase capsule and compound danshen injection in preventing recurrence of cerebral infarction. J Changchin Univ Tradit Chin Med. 2008;24:290.

33. Xinhong L. Comparative study on the efficacy of aspirin alone and in combination with lumbrokinase enteric-coated capsules in the treatment of cerebral infarction. J Clin Exp Med. 2008;7(2):68–69.

34. Hui X. Lumbrokinase capsule combined with probucol tablets in the treatment of patients with cerebral infarction: observation on the efficacy of unstable atherosclerotic plaques in the carotid artery. Treat Obs. 2016;10(11):82–83.

35. Qiangfeng L, Hong X, Shuizhong J. Observation on the clinical effect of lumbrokinase enteric-coated capsules in preventing recurrence of cerebral infarction in the elderly. Chin J Clin Ration Drug Use. 2013;6(5A):94.

36. Li W, Jianglong T, Yousheng X, Yunhua L. Observation on the efficacy of lumbrokinase combined with Ginkgo biloba in the treatment of 48 cases of cerebral infarction. New Med. 2007;38(8):550.

37. Jing L. Observation on the efficacy of lumbrokinase capsule in the treatment of acute cerebral infarction. Cap Med. 2011;2011:38–39.

38. Yongwei Z, Wen’an W, Zheng P, Wei C, Ge Y, Genfa W. Observation on the efficacy and safety of lumbrokinase capsule combined with aspirin in the treatment of patients with acute cerebral infarction. J Clin Intern Med. 2006;23(6):413–414.

39. Zihan C, Jaihui M. Clinical efficacy of lumbrokinase in the treatment of ischemic cerebrovascular disease. Contemp Med. 2016;22(27):143–144.

40. Jiajiao H, Yongqiu L. Observation on the efficacy of atorvastatin or lumbrokinase combined with aspirin in the treatment of ischemic cerebrovascular disease. Chin J Clin Ration Drug Use. 2015;8(7B):50.

41. Conghua L, Qiongxiao X. Clinical observation of lumbrokinase combined with ginkgo leaf injection in the treatment of acute ischemic cerebral infarction. Prac Chin W Med Clin. 2005;5(1):13–14.

42. Liang J, Yu W, Guofeng L, Yongmei K. Clinical observation of lumbrokinase capsule in the treatment of cerebral infarction. Heli Med J. 2015;37:246–247.

43. Sheng A, Wang X, Xu Z, et al. Study of the effects of lumbrokinase on fibrinogen, D-dimer & platelet aggregation on patients with ischemic cerebral vascular disease. Chin New Drugs J. 2002;11(1):82–84.

44. Chunxia W. Clinical observation of lumbrokinase in the treatment of cerebral infarction. Cap Med. 2008;8(10):49.

45. Zhenlei J, Miaofen L. Clinical analysis of 99 cases of acute cerebral infarction treated with Bio-lumbrokinase. China Pharm. 2012;21(12):17.

46. Qinfeng G. Clinical efficacy analysis of lumbrokinase combined with ozagrel sodium in the treatment of acute cerebral infarction. Strait Pharm. 2012;24(4):78–79.

47. Lijun L, Guohua C, Yulan J, Hanyun Y, Junhua M. Clinical observation of lumbrokinase in the treatment of acute cerebral infarction. Cap Med. 2011;7:28–29.

48. Baixue, Yundong J, Sijin Y. Clinical observation of lumbrokinase combined with xuesaitong in the treatment of acute cerebral infarction. Cap Med. 2010;1:46–47.

49. Zhai W, Song L, He X, et al. The effect of long-term fibrinogen-depleting on the carotid atherosclerosis and cerebral infarction. Chin Med Guid. 2011;9(20):221–222.

50. Xia Z, Xingchen W, Huikui Z. Clinical observation of sequential fibrinolytic enzyme lumbrokinase combined with aspirin in preventing recurrence of cerebral infarction. Cap Med. 2011;2:42.

51. Xu R, Tian M. Clinical observation of lumbrokinase combined with aspirin in secondary prevention of ischemic cerebrovascular disease. Cap Med. 2010;2010:39–40.

52. Min X, Yangbai F. Clinical efficacy and safety of lumbrokinase capsule combined with aspirin in the sequelae of ischemic stroke. Cap Med. 2010;2010:39–40.

53. Zhijie C, Lirong J, XInjian W, Hui J, Wei F, Guoping Z. Study on the effect of lumbrokinase on blood rheology in patients during stroke recovery period. J Chin Microcircul. 2011;5(4):288–289.

54. Huang Z, Li Z, Zhang W. Lumbrokinase in the treatment of cerebral infarction. Chin J New Drugs Clin Rem. 2000;19(6):453–455.

55. Liu J, Li L. Lumbrokinase in the treatment of acute ischemic cerebral infarction. Chin J New Drug Clin Rem. 1998;17(2):79–80.

56. Jianyong L. Clinical observation of lumbrokinase capsule in the treatment of acute cerebral infarction. Capital Med. 2004;2004:38–39.

57. Ma X, Zhan P, Xu J. Clinical observation of lumbrokinase combined with Ginkgo biloba extract in the treatment of acute cerebral infarction. Chin J Tradit Chin Emerg Med. 2007;16(2):269–270.

58. Zhang Z, Peng X. Clinical observation of Bio-lumbrokinase capsule combined with ozagrel sodium in the treatment of acute cerebral infarction. Capital Med. 2007;12:28–29.

59. Zhu X, Tan J, Zhu Y, et al. Clinical study on the treatment of acute cerebral infarction with Bio-lumbrokinase capsules. Capital Med. 2009;2:46–47.

60. Wang T, Yang S, Zhu B, Yuan Y; Chen Health Care. Clinical study on treatment of acute cerebral infarction with hydrochloric fasudil and lumbrokinase. Chin J Pract Nerv Dis. 2009;12(17):15–17.

61. Dongju Z, Zhiqiang Z, Hongyuan M. Observation on the efficacy of lumbrokinase, ticlopidine, and aspirin with alprostadil in preventing recurrence of acute cerebral infarction. Capital Med. 2004;11(8):43–44.

62. Jiping Z. Clinical observation of lumbrokinase in the treatment of cerebral infarction. Capital Med. 2004;11(10):29–30.

63. Ying D. Observation on the efficacy of batroxobin followed by lumbrokinase in the treatment of cerebral infarction. Hainan Med. 2007;18(1):101–103.

64. Mu Z, Kong F, Hou L. Clinical study of Shuxuening injection combined with lumbrokinase in the treatment of cerebral infarction. Drugs Clinic. 2018;33(2):238–241.

65. Tong J, Chen E, Yu Z, Jiang J. Observation on the efficacy of lumbrokinase in the treatment of acute cerebral infarction. Lingnan J Emerg Med. 2004;9(3):174–175.

66. Dong Q, Qiao J, Shi L, et al. Efficacy and safety of lumbrokinase capsules in patients with cerebral infarction. Chin J New Drugs. 2004;13(3):257–260.

67. Ding Y, Yin X. Small-dose aspirin plus lumbrukinase in improving neurological function of patients with acute cerebral infarction. Chin J Clin Rehabil. 2006;10:60.

68. Duan H, Liu H. Efficacy and safety observation of lumbrokinase combined with aspirin in secondary prevention of cerebral infarction. Capital Med. 2011;2011:43–44.

69. Qong L, Xian G. Effects of lumbrokinase enteric-coated capsules on coagulation indexes and neurological function in patients with acute cerebral infarction. Heilonghiang Med J. 2023;36(3):610–612.

70. Huawei F. Study and analyze the clinical value of lumbrokinase capsule in treating patients with cerebral infarction. China Rural Health. 2021;18:44–45.

71. Han Y, Wu X. Observation on the efficacy of ozagrel sodium combined with lumbrokinase in the treatment of progressive cerebral infarction. Chin J Pract Nerv Dis. 2012;15(14):46–47.

72. Feng S, Min T. Observation on the efficacy of lumbrokinase combined with ozagrel in the treatment of acute cerebral infarction. Capital Med. 2012;41:1.

73. Liu X, Zhang L. Evaluation of the efficacy and safety of lumbrokinase combined with clopidogrel in the treatment of diabetic acute cerebral infarction. Capital Med. 2012;2012:36–38.

74. Gao Z, Park Y, Kong Y, Zhu Z. Effects of clopidogrel combined with lumbrokinase on plasma D-dimer, platelet aggregation and fibrinogen in patients with acute cerebral infarction. Jiangsu Medicine. 2014;40(14):1707–1708.

75. Liu C, Chen J. The intervention and preventive effects of lumbrokinase capsule on patients with cerebral infarction. Shandong Med. 2011;51(33):85–86.

76. Sui X. Batroxobin combined with lumbrokinase: observation on the efficacy of treating acute cerebral infarction. Capital Med. 2006;2006:47–48.

77. Cao Y, Zhang X, Wang WH, et al. Oral fibrinogen-depleting agent lumbrokinase for secondary ischemic stroke prevention: results from a multicenter, randomized, parallel-group and controlled clinical trial. Chin Med J. 2013;126(21):4060. doi:10.3760/cma.j.issn.0366-6999.20131332

78. Changes in coagulation and tissue plasminogen activator after the treatment of cerebral infarction with lumbrokinase – PubMed [Internet]. [cited July 20, 2025]. Available from: https://pubmed.ncbi.nlm.nih.gov/11321442/. Accessed August 18, 2025.

79. Pinzon RT, Tjandrawinata RR, Wijaya VO, Veronica V. Effect of DLBS1033 on functional outcomes for patients with acute ischemic stroke: a randomized controlled trial. Stroke Res Treat. 2021;2021:5541616. doi:10.1155/2021/5541616

80. Setyopranoto I, Wibowo S, Tjandrawinata R. Hemostasis profile and clinical outcome of acute ischemic stroke patients treated with oral lumbrokinase DLBS1033: a comparative study versus aspirin and clopidogrel. Asian J Pharm Clin Res. 2016;9:171–177.

81. Wang YH, Chen KM, Chiu PS, et al. Lumbrokinase attenuates myocardial ischemia-reperfusion injury by inhibiting TLR4 signaling. J Mol Cell Cardiol. 2016;99:113–122. doi:10.1016/j.yjmcc.2016.08.004

82. Wang YH, Liao JM, Chen KM, et al. Lumbrokinase regulates endoplasmic reticulum stress to improve neurological deficits in ischemic stroke. Neuropharmacology. 2022;221:109277. doi:10.1016/j.neuropharm.2022.109277

83. Park YD, Kim JW, Min BG, Seo JW, Jeong JM. Rapid purification and biochemical characteristics of lumbrokinase III from earthworm for use as a fibrinolytic agent. Biotechnol Lett. 1998;20(2):169–172. doi:10.1023/A:1005384625974

84. Wang KY, Tull L, Cooper E, Wang N, Liu D. Recombinant protein production of earthworm lumbrokinase for potential antithrombotic application. Evid Based Complement Alternat Med. 2013;2013:783971. doi:10.1155/2013/783971

85. Del Zoppo GJ, Poeck K, Pessin MS, et al. Recombinant tissue plasminogen activator in acute thrombotic and embolic stroke. Ann Neurol. 1992;32(1):78–86. doi:10.1002/ana.410320113

86. Lin CH, Kuo YW, Kuo CY, et al. Shortened activated partial thromboplastin time is associated with acute ischemic stroke, stroke severity, and neurological worsening. J Stroke Cerebrovasc Dis. 2015;24(10):2270–2276. doi:10.1016/j.jstrokecerebrovasdis.2015.06.008

87. Nurindar M, Muhiddin RA, Muhadi D, Muis A, Nurulita A, Patellongi IJ. Correlation analysis between platelet aggregation and neurological outcomes in ischemic stroke patients. Indones J Clin Pathol Med Lab. 2024;31(1):12–16. doi:10.24293/ijcpml.v31i1.2336

88. Zi WJ, Shuai J. Plasma D-dimer levels are associated with stroke subtypes and infarction volume in patients with acute ischemic stroke. PLoS One. 2014;9(1):e86465. doi:10.1371/journal.pone.0086465

89. Stephani L, Rahayu P, Retnoningrum D, Suhartono MT, Rachmawati H, Tjandrawinata RR. Purification and proteomic analysis of potent fibrinolytic enzymes extracted from Lumbricus rubellus. Proteome Sci. 2023;21:8. doi:10.1186/s12953-023-00206-9

Central Africa  has approved 25 priority diseases for targeted prevention, detection, and response in a major step towards stronger epidemic preparedness.

Africa CDC in partnership with the European Centre for Disease Prevention and Control (ECDC) developed the list—covering threats such as viral hemorrhagic fevers, measles, dengue, cholera, yellow fever, mpox, and meningitis  through a rigorous risk-ranking exercise.

Experts from nine African Union Member States in Central Africa, together with regional and international partners, assessed diseases using epidemiological, socio-economic, and operational criteria.

The rating exercise carefully considered factors including frequency of outbreaks and cross-border spread, severity, case fatality rates and inclusion in the International Health Regulations (IHR, 2005) list of notifiable diseases.

 “This prioritization is a crucial step toward building a resilient health system that is ready to respond to emerging threats. It will enable more targeted planning, faster outbreak response, and more strategic resource allocation, while fostering regional coordination in the face of cross-border health risks,” said Dr. Brice Bicaba, Director of the Africa CDC Regional Coordinating Centre for Central Africa.

This risk-ranking exercise was first applied in 2024 by Africa CDC at continental level. Since then, it has shaped strategic public health preparedness and response initiatives. Building on this, Africa CDC is now focusing on regional-level risk assessments and prioritizations to ensure preparedness and response planning that is better adapted to the specific contexts of each region.

Given the evolving epidemiological landscape in Central Africa—characterized by a large population, the effects of climate change, and the persistent threat of emerging and re-emerging diseases—this regional prioritization exercise comes at a critical time in the region.

Beyond validation, representatives from the Ministries of Health strengthened their capacity to use the risk-ranking methodology and tool, developed through wide expert consultations across the continent. This tool has been applied to rank risks at both continental and regional levels.

“This workshop has helped enrich the evidence base on priority risks, capacity gaps, and the next steps needed to mitigate these risks. The ECDC looks forward to continuing its collaboration with Africa CDC in this area, which is a priority pillar of our partnership,” said Jonatan Suk, Head of the Health Security Projects Group on behalf of the European Centre for Disease Prevention and Control.

Added to this prioritization, a regional roadmap was developed to strengthen early detection, rapid response, and multisectoral coordination in the face of epidemics. It includes: enhancing epidemiological surveillance and health monitoring; establishing cross-border coordination mechanisms; improving national diagnostic and case management capacities; and developing multisectoral preparedness plans aligned with national and regional contexts.

The meeting helped consolidate an integrated regional approach by facilitating and strengthening exchanges between Central African countries, regional health institutions, and partners. The classification of epidemic-prone diseases will now serve as a foundation for planning, resource mobilization, capacity allocation, and community engagement in the region.

Through this initiative, Central Africa is reinforcing its collective ability to anticipate, prevent, and contain epidemics, aligning with the objectives of the International Health Regulations (IHR) and the Global Health Security Agenda.

Digestive issues such as bloating, gas, and indigestion are everyday concerns, and while medicines offer relief, natural remedies can often provide lasting support without side effects. Herbs and spices used for centuries in Indian kitchens are now being recognised by modern science for their gut-healing properties. Dr Saurabh Sethi, an AIIMS, Harvard, and Stanford-trained gastroenterologist, strongly believes that “real gut healing starts in your kitchen.” He recently shared eight herbs he personally relies on for better digestion. From turmeric to cumin, these simple additions to daily meals can help soothe discomfort and strengthen gut health naturally.

8 gut-healing herbs recommended by an AIIMSs doctor

On Instagram, AIIMS, Harvard, and Stanford-trained gastroenterologist Dr. Saurabh Sethi shared eight herbs he personally uses to improve gut health, reminding followers that “real gut healing starts in your kitchen.”

Turmeric supports digestion and reduces inflammation

Turmeric, a staple in Indian households, is well known for its anti-inflammatory compound curcumin. Dr Sethi suggests adding turmeric to warm milk or curries to soothe the gut, reduce inflammation, and support bile flow, which helps break down fats. This golden spice not only calms an irritated digestive tract but also promotes overall gut lining health. Regular use may reduce the risk of long-term inflammatory gut conditions. A human pilot study published in study found that supplementation with turmeric or curcumin significantly altered gut microbiota composition, including a notable increase in species diversity; curcumin increased detected species by 69% compared to a placebo, which saw a 15% decrease

Ginger relieves bloating and nausea

Ginger has long been used as a natural digestive aid. It stimulates gastric emptying, reduces bloating, and relieves nausea. Dr Sethi recommends steeping fresh ginger in hot water to make a soothing tea, especially after heavy meals. Its warming properties help settle the stomach, making digestion smoother and more comfortable. For people with sluggish digestion, ginger can act as a gentle stimulant.

Fennel seeds ease gas and bloating

Chewing fennel seeds after meals is a time-tested Indian practice, and science confirms its benefits. These seeds contain compounds that relax gut muscles, helping release trapped gas and easing bloating. Dr Sethi recommends chewing a teaspoon of fennel seeds after meals or making a calming tea. This simple habit can help reduce discomfort and improve digestion naturally.

Cumin improves bile flow and eases cramps

Cumin is another household spice with powerful gut benefits. It stimulates the release of bile, which aids digestion of fats. It is also useful for people with irritable bowel syndrome (IBS), as it helps relieve cramps. Dr Sethi suggests toasting cumin seeds and adding them to dals, curries, or vegetable stir-fries. Apart from improving flavour, this enhances nutrient absorption and digestive function.

Cinnamon regulates digestion and blood sugar

Cinnamon adds warmth and sweetness to foods, but it also has medicinal value. It helps regulate gut motility, making digestion smoother, and plays a role in stabilising blood sugar levels. Dr. Sethi advises sprinkling cinnamon on oats, kefir, or even coffee. Its ability to calm the gut makes it particularly helpful for people who experience erratic digestion.

Peppermint relaxes gut muscles

Peppermint has a cooling effect and works as a natural gut muscle relaxant. It helps reduce spasms and discomfort caused by digestive issues. Dr Sethi recommends drinking peppermint tea or using peppermint oil capsules to ease gut irritation. However, he cautions against using peppermint if you experience reflux, as it may worsen the symptoms by relaxing the lower oesophageal sphincter.

Garlic nourishes gut bacteria

Garlic is a natural prebiotic, meaning it feeds beneficial gut bacteria, helping them thrive. At the same time, it has antibacterial, antifungal, and antiparasitic properties that keep harmful microbes under control. Dr. Sethi advises lightly crushing garlic before cooking to activate its gut-boosting compounds. Regular consumption can improve microbial balance, which is essential for long-term gut and immune health.

Coriander reduces bloating and adds flavour

Coriander, also known as cilantro, is another herb that promotes gut comfort. It helps reduce gas, bloating, and indigestion while adding freshness to meals. Dr Sethi recommends adding coriander to curries, chutneys, and salads. Beyond its digestive benefits, coriander provides antioxidants that protect gut cells and support overall wellbeing.Gut health is deeply influenced by what we eat daily. Instead of relying solely on medicines, incorporating herbs like turmeric, ginger, fennel, cumin, cinnamon, peppermint, garlic, and coriander can make digestion smoother and more comfortable. These natural remedies work best when used consistently as part of a balanced diet.Dr Sethi’s advice is clear: healing starts in the kitchen. By rotating these herbs weekly and using them in everyday cooking, you can take simple, natural steps towards better gut health.Also Read: Don’t follow these 9 cooking habits that harm digestion and trigger Irritable Bowel Syndrome