Category: 8. Health

Addis Ababa, Ethiopia | 4 September 2025 — The Emergency Consultative Group (ECG), which advises the Director General of the Africa Centres for Disease Control and Prevention (Africa CDC) on mpox, has urged that the Public Health Emergency of Continental Security (PHECS) remain in place.

Meeting on 2 September 2025 to review the outbreak and assess whether the emergency status should be lifted, the Group concluded that maintaining the declaration is essential to preserve political will, mobilise resources, and keepcountries on high alert. Members warned that lifting the status prematurely could trigger complacency, reduce funding, and increase the risk of resurgence.

The Group’s recommendation followed a detailed review of the mpox situation. Weekly confirmed cases declined by 52 per cent between weeks 17–22 and weeks 27–32 of 2025. Yet, surges emerged in Ghana, Liberia, Kenya, Zambia, and Tanzania, with fresh introductions of the virus reported in Malawi, Ethiopia, Senegal, Togo, The Gambia, and Mozambique.

Ethiopia and the Central African Republic reported infant deaths, while several countries — including Sierra Leone, Congo, Malawi, Zambia, Kenya, CAR, Ethiopia, South Africa, and Cameroon — continued to register case fatality rates above one per cent. The overall continental case fatality rate stood at 0.5 per cent.

Testing coverage improved significantly, rising from 30 per cent in late 2024 to 59 per cent by mid-2025. Burundi was highlighted as a success story for decentralising diagnostics. More than 1.01 million vaccine doses reached 921,000 people across 12 countries, though regulatory approval for vaccinating children under 12 remains absent in many high-burden settings despite paediatric cases.

The ECG also expressed concern over reduced international support, including the withdrawal of programmes such as PEPFAR. This shift leaves people living with HIV — among the most vulnerable to severe illness and death — at increased risk. Weaknesses in surveillance persist, as poor sample collection, preservation, and transport undermineeffective response. In the Democratic Republic of the Congo, transport system collapse has prevented timely testing and follow-up.

To address these challenges, the ECG recommended strengthening sample collection and referral systems, including the use of community-based surveillance and wastewater monitoring as early warning tools. The Group also called for more rigorous investigation of mpox-related deaths, particularly among children, and for expanding vaccine access to under-12s in high-risk countries.

It further advised maintaining strong continental coordination, integrating the mpox response with other ongoing health emergencies such as cholera and circulating vaccine-derived polioviruses, and developing a scalable, tiered alert system to sustain vigilance without relying solely on a binary emergency declaration.

The PHECS was first declared on 13 August 2024, and the ECG has met regularly since then to provide technical advice on the evolving outbreak. While acknowledging progress in surveillance, laboratory capacity, and vaccination, the Group stressed that the current downward trends are not yet stable enough to justify lifting the emergency.

The unanimous decision underscores both the fragility of recent gains and the need to sustain Africa’s collective momentum until mpox is under durable control.

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NOTE TO EDITORS

About Africa Centres for Disease Control and Prevention (Africa CDC)

The Africa Centres for Disease Control and Prevention (Africa CDC) is a public health agency of the African Union. It is autonomous and supports member states in strengthening health systems. It also helps improve disease surveillance, emergency response, and disease control. Learn more at: http://www.africacdc.org and connect with us on LinkedIn, Twitter, Facebook and YouTube

For more information and media inquiries :

Margaret Edwin | Director of Communication and Public Information | Africa CDC EdwinM@africacdc.org

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.”