Author: admin

Introduction

Diabetes is a major chronic disease threatening global health and a leading cause of blindness, kidney failure, heart attacks, stroke, lower limb amputation and metabolic disorders. In China, 90% of diabetes cases are Type 2 Diabetes Mellitus (T2DM), and over 20% of people with diabetes are aged 60 years or older.¹ As the population ages, this proportion will continue to rise. T2DM is characterized by hyperglycemia, insulin resistance, and pancreatic β-cell decompensation. Glucose, lipid, amino acid and their metabolites contribute to T2DM pathogenesis through distinct metabolic and immunologic pathways. Gut microbiota dysbiosis has been implicated in adverse metabolic profiles and diabetes development.

Recent studies indicate that gut microbiota is a key factor in developing IR through the production of metabolites and interactions with the host’s intestinal cells. A reduction in gut bacterial diversity has been associated with IR, obesity and increased inflammation. Gut microbiota also produces metabolites such as short-chain fatty acids (SCFAs), bile acids (BAs), and trimethylamine N-oxide (TMAO). One crucial metabolic product is TMAO, primarily produced by the bacterial metabolism of substrates such as phosphatidylcholine, choline and betaine in the colon. Elevated levels of choline metabolites—derived from red meat, eggs, and fish—were found to be associated with increased T2DM risk.² Choline in the bloodstream can be oxidized to betaine, which is an important osmolyte that provides a methyl group in the liver. Intestinal bacteria convert unabsorbed choline into trimethylamine (TMA), which is oxidized in the liver by flavin monooxygenase 3 (FMO3) and transformed into trimethylamine-N-oxide (TMAO). The original compounds are represented by choline, betaine, phosphatidylcholine and carnitine. TMAO has an important role in the onset and progression of T2DM and is correlated with a significant risk of other metabolic disorders, including cardiovascular diseases, hypertension, renal dysfunction.^3–6 Elevated levels of TMAO have been shown to impair glucose-stimulated insulin secretion, reduce β-cell mass, and worsen glucose tolerance, all of which can contribute to the progression of diabetes. Furthermore, choline, as a essential nutrient needed for lipid metabolism and hepatic production of very-low-density lipoproteins (VLDLs), influences glucose and lipid homeostasis through insulin resistance (IR) and the production of TMAO, accelerating prediabetes and diabetes progression.

Currently, there are conflicting views on the correlation between choline and TMAO with T2DM. A 19.3 years prospective Finnish cohort study reported a 5.0% lower incidence of T2DM with the highest choline intake,⁷ whereas two Puerto Rican Cohorts studies showed neither choline nor TMAO were associated with incidence of T2DM.⁸ Conversely, a cohort study involving 13,440 participants from the Atherosclerosis Risk in Communities (ARIC) study in the United States, the incidence rate of T2DM of highest quartile of dietary choline intake was 1.54 times that of the lowest one among women.⁹ Plasma TMAO also yields inconsistent results. There have been cross-sectional studies investigating the association between plasma choline, TMAO and T2DM, but few studies are prospective, especially among north rural China population. In addition, some studies were conducted among patients with specific diseases, which calls into question whether such associations can be generalized to a healthy population. This study utilizes data from the Handan Eye Study (HES),¹⁰ a population-based cohort study to determine the prevalence and impact of visual impairment and major ocular diseases in Chinese adults living in a rural region north China. Our prior findings on amino acids with T2DM risk in our population¹¹ in this cohort align with other studies.^12–14 In this study, we extend this work to investigate associations between gut microbiota-related metabolites (choline, betaine, TMAO) with dyslipidemia, adiposity and the risk of T2DM in healthy China population.

Materials and Methods

Study Participants

Participants were recruited from the Handan Eye Study (HES), a population-based cohort study investigating the prevalence of eye diseases and chronic conditions among adults aged ≥30 years in Yongnian County, Handan City, Hebei Province, China. The study design and baseline data collection methods for HES have been previously described.¹⁰ The baseline survey was conducted from 2006 to 2007, with a follow-up survey from 2012 to 2013. Participants were eligible if they met the following inclusion criteria: (1) subjects of 30 years or above. (2) the household registration was in the local area. (3) the household registration was not in the local area but subjects had lived in the local area for more than half a year. (4) voluntarily participate in the study. A stratified cluster sampling method was employed. From 453 villages: 1) 13 villages were randomly selected, comprising 5,111 residents aged ≥50 years. 2) 6 villages were randomly selected from these 13, yielding 3,532 residents aged 30–49 years. A total of 8,643 subjects were initially selected and 7557 subjects met the criteria. After excluding 727 declined participation and 1318 with incomplete data (missing blood glucose tests, physical examinations, or questionnaires, 5,512 participants remained at baseline. During the 2012–2013 follow-up, 981 were lost to follow-up and left 4531 participants for follow-up. Individuals were further excluded due to the following conditions: 1) 366 with baseline diabetes; 2) 33 missing follow-up glucose tests. Following these exclusions, 4,132 participants remained. Among 4,132 participants: 218 developed T2DM during follow-up. Each case was matched with two controls (1:2 ratio) using propensity score matching (SAS 9.2), balanced for age and sex. After excluding 57 participants with insufficient samples, the final analysis included 209 T2DM cases and 394 matched controls.

Data Collection

Demographic data: At enrollment, demographic data were collected through face-to-face interviews conducted by professionally trained interviewers. The content of standardized survey questionnaire covered demographic information, occupation, educational background, lifestyle variables, medication use and family history of disease, and so on. The physical examination included blood pressure, height, weight, waist circumference, and hip circumference. Systolic and diastolic blood pressures are the average of two measurements.

Assessment of Plasma Metabolites

Blood tests include fasting plasma glucose (FPG), lipid profile, and liver and kidney function tests. Blood samples were collected at baseline and follow-up survey, respectively. Blood samples collection requires empty stomach for more than 8 hours. Fasting glucose, glycated hemoglobin and insulin were determined both at baseline and follow-up. Glycated hemoglobin was performed by cation exchange high-performance liquid chromatography (Bio-Rad D10, USA). Fasting plasma glucose (FPG) was measured by the hexokinase method (Olympus AU2700, Japan). Insulin was detected by chemiluminescent immunoassay (Siemens AdviaCP, Germany). Aliquots were coded and stored at −80°C. In the present study, we performed additional metabolomic measurements including plasma choline, betaine and TMAO. These were performed by ultra-performance liquid chromatography (Ultra Performance Liquid Chromatography, UPLC) (Waters, UPLC I-Class, USA).

Diagnostic Criteria for T2DM

Diagnostic criteria for T2DM in follow-up were based on the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2013 edition) and the recommendations of the American Diabetes Association,^1,15 which is Fasting Plasma Glucose (FPG)≥7.0 mmol/L or glycated hemoglobin ≥6.5%.

Statistical Analysis

We use logistic regression analysis to calculate odds ratios of plasma choline, betaine, TMAO level and T2DM. Further, we divided plasma choline, betaine, TMAO into four group by quartiles and calculate odds ratios and 95% Cis for T2DM. We use linear regression analysis to calculate the correlation between choline, betaine, TMAO and insulin resistance parameter, such as HOMA-IR, HOMA-β and TyG. Spearman correlation coefficient was calculated between choline, betaine, TMAO level and obesity, lipid and insulin resistance parameters. We use ANVOA to analyze the difference between fruit intake frequencies, exercise intensity levels with choline, betaine and TMAO.

The obesity, lipid and insulin resistance parameters calculation formulas were as follows: homeostatic model assessment for insulin resistance, HOMA-IR= FINS (fasting insulin) ×FPG/22.5; homeostatic model assessment for insulin, HOMA-β=20×FINS/[FPG-3.5]; triglyceride-glucose index, TyG= ln(TG*88.6×FPG×18.02/2); visceral adiposity index (VAI), VAIm=[WC/(39.68+1.88×BMI)]×(TG/1.03)×(1.31/HDL-C), VAIf=[WC/(36.58+1.89×BMI)]×(TG/0.81)×(1.52/HDL-C); Chinese visceral adiposity index(CVAI), CVAIm=−267.93+0.68×age+0.03×BMI+4.00×WC+22.00×LgTG-16.32×HDL-C, CVAIf=−187.32+1.71×age+4.32×BMI+1.12×WC+39.76×LgTG-11.66×HDL-C; Lipid accumulation product, LAP, LAPm=WC-65)×TG; LAPf= (WC-58)×TG.

Data were analyzed using SAS 9.2 (SAS Institute Inc., Cary, NC, USA) and GraphPad 8.0. All statistical tests were two-sided at alpha = 0.05.

Results

Participant Characteristics

Table 1 presents the baseline characteristics of participants. Case group had significantly higher levels of blood lipids, FPG, FINS, insulin resistance (HOMA-IR) and hs-CRP compared to controls. Betaine is lower in the case group, but there are no statistical differences between two groups. No differences were observed in fruit intake frequencies and exercise intensity (p > 0.05).

Association of Plasma Metabolites with T2DM and IR Risk

Adjusted for age, gender, BMI, WHR, and family history of diabetes, the odds ratios ORs for T2DM associated with choline, betaine, TMAO of T2DM was 0.99, 1.00 and 1.00 (p > 0.05). Subsequently, Choline, betaine, and TMAO were divided into four groups by quartiles as Q1 to Q4 from low to high, as shown in Table 2. Compared with Q1, none of the metabolites showed a significant association with T2DM risk. However, a trend toward reduced risk was observed for betaine: the ORs for decreased from 0.89,0.79 to 0.59, with the Q4 group showing a 41% lower risk of T2DM compared to Q1 (p = 0.06).

Table 2 Logistic Regression Analysis of Choline, Betaine and TMAO with T2DM by Quartiles

Linear regression analysis revealed that betaine was significantly inversely correlated with HOMA-IR (β = −0.16), HOMA-β (β = −0.13), and TyG index (β = −0.21) (p < 0.05). Choline and TMAO showed no significant correlation with insulin resistance.

The Correlation of Plasma Metabolites with Obesity, Lipids and IR

As shown in Figure 1, Betaine was negatively correlated with obesity indices, blood lipids and IR markers. The Spearman correlation coefficients for LAP, waist circumference, BMI, VAI, CVAI and TyG were −0.22, −0.21, −0.19, −0.16 and −0.12, respectively. The correlation coefficients for TC, LDLC and TG were −0.19, −0.16 and-0.16. HOMA-IR (r = −0.15), INS (r = −0.14), HOMA-β (r = −0.11)) (p < 0.05). Choline showed a weak positive correlation with the obesity indicator CVAI (r = 0.11). TMAO was inversely correlated with blood lipids (TG (r = −0.08), TyG (r = −0.09)) (p < 0.05).

Figure 1 Correlation coefficients between plasma metabolites and metabolic parameters.

The Association of Plasma Metabolites with Diet and Exercise

As shown in Table 3, Plasma choline level is associated with fruit intake frequently, participants consuming fruit less than once per month had lower plasma choline levels than those with higher intake frequencies (p < 0.05); and plasma TMAO is associated with exercise intensity, participants reporting high exercise levels had lower TMAO levels (p < 0.05).

Table 3 Comparison of Plasma Choline Levels with Different Fruit Intake Frequencies and Exercise Levels

Discussion

Diabetes is a metabolic disease characterized by hyperglycemia, accompanied by dysregulation of lipid and amino acid metabolism. In this study, baseline levels of FPG, FIN, HOMA-IR, TyG index and lipid profile (TC, TG, LDL) were significantly elevated in the case group. The TyG index, which comprehensively reflects glucose and lipids metabolism, is considered to have relationship with T2DM onset, retinal arteriosclerosis and other metabolic syndrome.^16,17 Central obesity (defined as waist circumference of ≥90 cm in males and ≥85 cm in females) and other adiposity indices such as BMI, WC, WHR, VAI, CVAI and LAP, were also higher in the case group at the baseline. These findings indicated abnormalities in glucose, lipid and amino acids metabolism at least 5 years before diagnosis. No differences were observed in liver and kidney function between the two groups, but hs-CRP was significantly higher in the case group at baseline. Collectively, these results suggested that in the early stage of diabetes, IR, lipid metabolites and inflammatory factors all contribute to the process of disease development.

Beyond conventional metabolites, we examined gut microbiota-derived TMAO and its precursors choline and betaine. Choline and betaine are important methyl donors in the body, mainly participating in the methionine cycle and re-methylation of homocysteine (Hcy). They played important roles against metabolic diseases through reducing Hcy levels in the body, alleviating oxidative stress or stabilizing DNA methylation. Previous studies reported that higher serum betaine correlates with lean body tissue in middle-aged and elderly individuals and reduced risk of diabetic complications. A dietary survey questionnaire study in Newfoundland found inverse associations between betaine and choline intake and IR.¹⁸ In cohort studies, plasma betaine levels were negatively correlated with blood glucose, insulin, HbA1c and IR and betaine was also associated with a 22% lower incidence of T2DM; however, choline, and TMAO showed no significant associations.^8,19 The “REACTION” cohort study that conducted in Lanzhou China in 2011 found dietary choline intake was below the recommended amount in local residents and there was no change in the incidence of T2DM as choline intake increased, though non-diabetic females exhibited increased risk with higher intake.²⁰ In our cohort, no difference was observed in plasma choline and betaine levels between the two groups at baseline. When analyzed by quartiles, decreased risk was observed as betaine level increased, while choline exhibited no association. Choline positively correlated with obesity index CVAI (r = 0.11), potentially reflecting dietary intake from animal sources, since plasma choline mainly comes from animal-derived foods. Additionally, choline levels were found to be related to the frequency of fruit intake. Lower plasma choline was observed in participants consuming fruit <1/month. It is speculated to be related to gut microbiota metabolism of less conversion to TMAO. Betaine demonstrated consistent inverse correlations with lipid profiles, obesity indices and IR. Betaine was negatively correlated with HOMA-IR (β = −0.16), HOMA-β (β = −0.13) and TyG (β = −0.21). This supports that betaine, as a methyl donor, plays a protective role in T2DM development through reduced lipid accumulation.

TMAO, derived from dietary phosphatidylcholine, choline, betaine, and carnitine (abundant in seafood, eggs, and meat), exhibits complex metabolic effects. Animal studies showed high TMAO levels have been associated with impaired glucose tolerance, disordered insulin signaling, and inflammation in adipose tissue, via TMAO-mediated oxidative stress and inflammatory responses. Cross-sectional studies showed association of plasma TMAO with T2DM, especially gestational diabetes mellitus (GDM).^4,21,22 One meta-analysis on the association between TMAO and obesity reported a dose–response relationship between TMAO and BMI (with a 0.58 kg/m² increase per unit in TMAO),²³ consistent with our finding that higher exercise intensity correlated with lower TMAO. Another meta-analysis based on cohort studies suggested increased diabetes risk with elevated TMAO (OR = 1.71).²⁴ However, a 2-year ORIGINS cohort study in the United States²⁵ found no association with TMAO and fasting blood glucose, HbA1c, or HOMA-IR. While a strong inverse prospective association was documented between plasma TMAO concentrations with T2DM risk in Spanish elderly Mediterranean individuals.²⁶ In our study, no significant association was found between baseline TMAO concentration and the incident of diabetes. Divided by quartiles, ORs from the lowest to the highest quartiles of TMAO with T2DM were 1.00, 0.68 (95% CI: 0.42~1.09), 0.84 (95% CI: 0.52~1.35), 0.74 (95% CI: 0.45~1.21). Baseline TMAO levels exhibited a nonlinear relationship with the risk of T2DM and the second quartile showed negative association compared with the lowest group (OR = 0.68). Several factors may account for these discrepancies: 1) this is a prospective study instead of cross-sectional with average follow-up of 6.5 years, the participants were healthy adults aged 30 and above. In cross-sectional study with high-risk population or specific metabolic diseases TMAO seem to be high a risk factor; 2) TMAO levels fluctuate based on diet and an individual’s gut microbiome composition, as well as FMO3 expression. Population of different races and dietary habits are of great influence. Early-stage TMAO may primarily reflect dietary intake rather than pathological processes. Thus, TMAO’s role in metabolic regulation is multifaceted, with its production being influenced by diet, microbiome composition, and liver function. Given that the potential value of TMAO in early T2DM interventions still needs to be explored.

Limitations of this study: The cohort population was from north rural areas of China and could not represent whole population, for a variety of dietary and environment differences in China. The sample size was limited, which may introduce selection bias. There was a lack of an oral glucose tolerance test and may result in underdiagnosis of T2DM, leading to potential biases in the results. Third, we only tested baseline plasma metabolites and may not fully reflect changes during the disease development process. The narrow scope of analyzed metabolites limits the ability to fully capture the complex interactions within gut microbiota-derived metabolic networks, while the absence of dietary assessment data in the study cohort further restricts the interpretation of potential nutritional influences on these metabolic pathways.

Conclusion

This study indicates that betaine plays a positive regulatory role in resisting obesity and early metabolic disorders of diabetes. While dietary betaine supplementation and microbiota modulation may represent promising strategies for early diabetes intervention, further research is needed to define optimal dosing, mechanistic pathways linking dietary betaine, host metabolism, and gut microbiota.

Ethics Approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Beijing Tongren Hospital (TREC2006-22), and all subjects have written informed consent.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Chinese Society of Diabetes. Chinese guidelines for the prevention and treatment of type 2 diabetes (2020 edition). Chin J Diabetes. 2021;13(4):317–411.

2. Xu M, Zheng J, Hou T, et al. SGLT2 inhibition, choline metabolites, and cardiometabolic diseases: a mediation Mendelian randomization study. Diabetes Care. 2022;45(11):2718–2728. doi:10.2337/dc22-0323

3. Heianza Y, Ma W, DiDonato JA, et al. Long-term changes in gut microbial metabolite trimethylamine N-oxide and coronary heart disease risk. J Am Coll Cardiol. 2020;75(7):763–772. doi:10.1016/j.jacc.2019.11.060

4. Shan Z, Sun T, Huang H, et al. Association between microbiota-dependent metabolite trimethylamine-N-oxide and type 2 diabetes. Am J Clin Nutr. 2017;106(3):888–894. doi:10.3945/ajcn.117.157107

5. Zhang W-Q, Wang Y-J, Zhang A, et al. TMA/TMAO in hypertension: novel horizons and potential therapies. J Cardiovasc Transl Res. 2021;14(6):1117–1124. doi:10.1007/s12265-021-10115-x

6. Flores-Guerrero JL, Osté MCJ, Baraldi PB, et al. Association of circulating trimethylamine N-oxide and its dietary determinants with the risk of kidney graft failure: results of the transplantlines cohort study. Nutrients. 2021;13(1):262. doi:10.3390/nu13010262

7. Virtanen JK, Tuomainen TP, Voutilainen S. Dietary intake of choline and phosphatidylcholine and risk of type 2 diabetes in men: the Kuopio Ischaemic Heart Disease Risk Factor Study. Eur J Nutr. 2020;59(8):3857–3861. doi:10.1007/s00394-020-02223-2

8. Sawicki CM, Pacheco LS, Rivas-Tumanyan S, et al. Association of gut microbiota-related metabolites and type 2 diabetes in two Puerto Rican cohorts. Nutrients. 2024;16(7):959. doi:10.3390/nu16070959

9. Dibaba DT, Johnson KC, Kucharska-Newton AM, et al. The association of dietary choline and betaine with the risk of type 2 diabetes: the Atherosclerosis Risk In Communities (ARIC) study. Diabetes Care. 2020;43(11):2840–2846. doi:10.2337/dc20-0733

10. Liang YB, Friedman DS, Wong TY, et al. Handan Eye Study Group. Rationale, design, methodology, and baseline data of a population-based study in rural China: the Handan Eye Study. Ophthalmic Epidemiol. 2009;16(2):115–127. doi:10.1080/09286580902738159

11. Wen JP, Hao J, Tao LX, et al. Nested case-control study on the association between plasma amino acid levels and the incidence of type 2 diabetes. Chin J Diabetes. 2017;9(12):764–769.

12. Savolainen O, Fagerberg B, Vendelbo Lind M, Sandberg A-S, Ross AB, Bergström G. Biomarkers for predicting type 2 diabetes development—can metabolomics improve on existing biomarkers? PLoS One. 2017;12(7):e0177738. doi:10.1371/journal.pone.0177738

13. Tillin T, Hughes AD, Wang Q, et al. Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) study. Diabetologia. 2015;58(5):968–979. doi:10.1007/s00125-015-3517-8

14. Ramzan I, Ardavani A, Vanweert F, Mellett A, Atherton PJ, Idris I. The association between circulating branched chain amino acids and the temporal risk of developing type 2 diabetes mellitus: a systematic review & meta-analysis. Nutrients. 2022;14(20):4411. doi:10.3390/nu14204411

15. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care. 2016;39(Suppl 1):S13–22. doi:10.2337/dc16-S005

16. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, et al. Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: a systematic review. Int J Endocrinol. 2020;2020:4678526. doi:10.1155/2020/4678526

17. Srinivasan S, Singh P, Kulothungan V, Sharma T, Raman R. Relationship between triglyceride glucose index, retinopathy and nephropathy in Type 2 diabetes. Endocrinol Diabetes Metab. 2020;4(1):e00151. doi:10.1002/edm2.151

18. Gao X, Wang Y, Sun G. High dietary choline and betaine intake is associated with low insulin resistance in the Newfoundland population. Nutrition. 2017;33:28–34. doi:10.1016/j.nut.2016.08.005

19. Heianza Y, Ma W, Manson JE, Rexrode KM, Qi L. Gut microbiota metabolites and risk of major adverse cardiovascular disease events and death: a systematic review and meta-analysis of prospective studies. J Am Heart Assoc. 2017;6(7):e004947. doi:10.1161/JAHA.116.004947

20. Wang Y. Cohort Study on Dietary Choline Levels and the Risk of Type 2 Diabetes in Middle-Aged and Elderly People. Lanzhou University; 2023.

21. Obeid R, Awwad HM, Rabagny Y, Graeber S, Herrmann W, Geisel J. Plasma trimethylamine N-oxide concentration is associated with choline, phospholipids, and methyl metabolism. Am J Clin Nutr. 2016;103(3):703–711. doi:10.3945/ajcn.115.121269

22. Lemaitre RN, Jensen PN, Wang Z, et al. Association of trimethylamine N-oxide and related metabolites in plasma and incident type 2 diabetes: the cardiovascular health study. JAMA Network Open. 2021;4(8):e2122844. doi:10.1001/jamanetworkopen.2021.22844

23. Dehghan P, Farhangi MA, Nikniaz L, Nikniaz Z, Asghari-Jafarabadi M. Gut microbiota-derived metabolite trimethylamine N-oxide (TMAO) potentially increases the risk of obesity in adults: an exploratory systematic review and dose-response meta- analysis. Obes Rev. 2020;21(5):e12993. doi:10.1111/obr.12993

24. Li D, Lu Y, Yuan S, et al. Gut microbiota-derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis. Am J Clin Nutr. 2022;116(1):230–243. doi:10.1093/ajcn/nqac074

25. Roy S, Yuzefpolskaya M, Nandakumar R, Colombo PC, Demmer RT. Plasma trimethylamine-N-oxide and impaired glucose regulation: results from the Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS). PLoS One. 2020;15(1):e0227482. doi:10.1371/journal.pone.0227482

26. Papandreou C, Bulló M, Zheng Y, et al. Plasma trimethylamine-N-oxide and related metabolites are associated with type 2 diabetes risk in the Prevención con Dieta Mediterránea (PREDIMED) trial. Am J Clin Nutr. 2018;108(1):163–173. doi:10.1093/ajcn/nqy058

A significant outbreak of chikungunya, a mosquito-borne infection, is sweeping through southern China, with thousands of cases now confirmed.

The city of Foshan in Guangdong province has been particularly hard-hit, with reported cases of chikungunya fever soaring to 4,014 by Friday.

This marks a rapid escalation since authorities began tracking the infection just two weeks prior, according to public records from local health departments.

Sun Yang, deputy director of the National Center for Disease Control and Prevention, described the outbreak as “quite severe” during a news conference in Foshan on Wednesday.

The World Health Organisation states that chikungunya, transmitted by infected mosquito bites, typically causes fever and severe joint pain, though fatalities are uncommon.

In response, the Chinese Center for Disease Control and Prevention has issued advisories for preventing both chikungunya and dengue fever, another mosquito-borne illness.

open image in gallery

Recommendations include using physical barriers like screen doors and mosquito nets, alongside applying repellent to exposed skin. The CDC also noted the epidemic was “imported,” without disclosing its origin.

The Chinese agency also called for people who have symptoms like fever, rash and joint pain to see a doctor.

Shunde district in Foshan, where 90 per cent of the cases are located, is famous for its Cantonese food, and sees many visitors each year.

Patients who tested positive for chikungunya fever stayed in hospital beds covered by mosquito nets, according to photos shown on state-run broadcaster CCTV.

Local media reports said on Thursday that local authorities had almost doubled the number of mosquito-proof isolation beds to 7,220 to meet the growing demand.

Authorities in Guangdong are urging residents to make sure there’s no standing water in their homes, such as in flowerpots, coffee machines or spare bottles.

The Health Commission in Foshan stated on Thursday that a fine of up to 10,000 yuan ($1,400) could be applied if violations are found.

open image in gallery

The Beijing CDC said on Tuesday that the city occasionally experiences imported cases of chikungunya fever.

There are two chikungunya vaccines that have received regulatory approvals in several countries and/or have been recommended for use in populations at risk, but the vaccines are neither widely available nor in widespread use, according to WHO.

China had its first chikungunya spike in 2010 with 253 cases in Dongguan, a nearby city in the same province, according to the Guangdong provincial CDC. Several cases were found in years since then, but they weren’t widespread.

The country’s first case was imported in 1987, according to research papers and media reports.

The European Medicines Agency (EMA) has initiated a review of Tecovirimat SIGA, also called TPOXX, a medicine used to treat mpox. This follows emerging clinical trial data suggesting a lack of effectiveness. 

The mpox virus is an orthopoxvirus, a class that includes the smallpox virus. 

Tecovirimat SIGA was originally approved in the US to treat smallpox. The EMA and the Medicines and Healthcare products Regulatory Agency in the UK authorized its use in January 2022, at the start of an mpox outbreak in Europe, for the treatment of smallpox, mpox, cowpox, and vaccinia complications following smallpox vaccination, in adults and children weighing at least 13 kg.

The drug is an oral medicine that interferes with a protein called VP37 found on the surface of orthopoxviruses, preventing them from reproducing normally and thus slowing the spread of infection. No other treatments are authorized for mpox or cowpox infections, which may in rare cases be fatal.

European Cases Linked With Risky Sexual Behaviors

Mpox is a zoonotic infection occurring mainly in West and Central Africa, with most cases in Europe before 2022 either imported from countries where mpox is endemic or from contacts with documented epidemiological links to imported cases. The outbreak in the EU and UK has been transmitted between humans mainly through sexual contact. It primarily affects gay, bisexual, or other men who have sex with men and who have multiple sexual partners, participate in group sex, or attend sex-on-premises venues. Transmission occurs primarily within interconnected sexual networks.

Symptoms of mpox typically appear 1-3 weeks after infection and include fever, headache, chills, physical weakness, lymph node swelling, back pain, and muscle aches, along with a distinct, fast-spreading papular rash on the skin and mucosal sores in the mouth, nose, throat, or digestive tract that then turn into fluid-filled vesicles. Mild-to-moderate symptoms usually last 2-4 weeks and are followed by full recovery, though some people develop permanent scars. During outbreaks, the case fatality of mpox ranges from 0% to 11%. People who are immunocompromised, including those with HIV infection or AIDS, are at a higher risk for severe disease. 

Exceptional Circumstances Approval During Outbreak

Approval for Tecovirimat SIGA was granted under “exceptional circumstances” provisions, based only on pharmacodynamic and pharmacokinetic studies, because the disease is rare and sporadic, so human studies were not available. A condition of such authorization is that the company marketing Tecovirimat SIGA is required to provide an annual update on benefits and risks.

The EMA’s review is a postauthorization procedure that involves a scientific assessment by the agency on behalf of the EU aimed at resolving issues such as concerns over the safety or benefit-risk balance of a medicine or a class of medicines. In the case of Tecovirimat SIGA (tecovirimat), the review was initiated at the request of the European Commission.

The review follows publication of preliminary results from two clinical trials. In the randomized, placebo-controlled PALM007 trial involving 597 children and adults with laboratory-confirmed clade I mpox in the Democratic Republic of the Congo (DRC), results reported in April in The New England Journal of Medicine showed that the drug did not reduce the duration of mpox lesions (median time to resolution, 7 vs 8 days). The overall mortality among enrollees, regardless of whether or not they received the drug, was 1.6% — much lower than that generally reported in the DRC, but this was attributed to hospitalization and high-quality supportive care within the trial.

The other trial, STOMP, involved people from multiple countries with mild-to-moderate laboratory-confirmed or presumptive clade II mpox. Again, the active drug did not demonstrate efficacy in time to skin and mucosal lesion resolution compared with placebo.

The EMA said that similar results had recently been obtained from another study, UNITY, which also did not indicate faster skin lesion resolution on tecovirimat compared with placebo. Further analyses from ongoing or recently completed studies are still awaited and will also inform the EMA’s final assessment.

Dr Sheena Meredith is an established medical writer, editor, and consultant in healthcare communications, with extensive experience writing for medical professionals and the general public. She is qualified in medicine and in law and medical ethics.

Introduction

Obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) are respiratory diseases with a high clinical prevalence. OSA is characterized by repeated upper airway collapse during sleep, leading to upper airway stenosis or complete obstruction, followed by intermittent decreases in blood oxygen saturation.¹ COPD is characterized by chronic respiratory symptoms and irreversible persistent airflow limitation, with or without structural lung abnormalities.² Both have similar characteristics and interact with each other, and when they simultaneously coexist in a patient, the condition is called COPD-OSA overlap, or overlap syndrome (OS). David C. Flenley³ first proposed this concept in 1985.

International epidemiological studies have shown that OSA prevalence in the general population is approximately 9%–38%, and is closely related to obesity, age, and gender, and affects approximately 200 million individuals worldwide.⁴ As a common disease, COPD has a global prevalence of approximately 7.6%–10%.² In terms of common overlap syndrome (OS) prevalence, and due to differences in study populations, prevalence rates can vary greatly. Shawon et al⁵ in their 2017 systematic review, reported that overall OS prevalence in the general population was low, ranging from 1%–3.6%, but that this prevalence was significantly increased in individuals with OSA or COPD alone. In patients with OSA, the OSA-COPD overlap prevalence ranges from 7.6%–55.7%, and among patients with COPD, the COPD-OSA overlap prevalence ranges from 2.9%–65.9%. Although ranges are wide, they represent a significantly higher prevalence compared to that observed in the general population.⁶ In a study involving 355 patients, patients with OS for COPD and moderate to severe OSA had the highest death risks, and their all-cause mortality was significantly higher than that for patients with OSA or COPD alone.⁷

Genetic studies have shown that OSA has significant family aggregation and polygenic inheritance tendencies, and its onset is closely related to genetic factors such as craniofacial structure, fat distribution, and neural regulation.⁸ COPD also has a clear genetic basis.⁹ Genome-wide association studies (GWAS) have identified susceptible gene loci including CHRNA3/5 and HHIP, among which the population attributable risk of the rs8034191 allele reaches 12.2%.¹⁰ The latest research indicates that OSA and COPD may interact through inflammatory pathways (such as systemic inflammation caused by intermittent hypoxia) and genetic mechanisms.¹¹ However, systematic studies on their common genetic basis are still lacking, and further research is needed to reveal their shared genetic structure and molecular mechanisms.

OS is becoming more common in clinical practice and is usually accompanied by cardiovascular diseases, such as systemic and pulmonary hypertension.¹² When compared with patients with either disease alone, OS has a poor prognosis and a heavier disease burden on a patient’s body. Currently, the molecular mechanisms underpinning OS are unclear, but with rapid microarray and high-throughput sequencing technology development, bioinformatics has been widely used to study the mechanisms underpinning various comorbidities, and can effectively mine biologically significant genes from data. Previous studies have identified novel biomarkers for OSA and established a reliable diagnostic model through bioinformatics. The identified transcriptional changes may contribute to revealing the pathogenesis, mechanisms, and sequelae of OSA.¹³ Other studies have screened for biomarkers related to COPD based on bioinformatics and machine learning, providing new insights into the early diagnosis, prevention, and treatment of COPD.¹⁴ However, there are currently no bioinformatics studies on COPD complicated with OSA. All existing studies focus on either COPD or OSA alone, and there is still a lack of research on the molecular mechanisms of OS. This study is the first to conduct a preliminary exploration of the molecular mechanisms of OS through bioinformatics and experimental verification, providing a new direction for the pathophysiological research of OSA complicated with COPD. In this study, we used bioinformatics to explore common differentially expressed genes (DEGs) between OSA and COPD, which could help clarify potential OS molecular mechanisms. Our study provides guidance on potential OS gene targets and a theoretical basis for the early clinical diagnosis and treatment of OS.

Materials and Methods

Data Downloads

OSA and COPD gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The OSA (GSE135917) gene expression dataset contained two study groups. To prevent possible effects of positive pressure ventilation therapy on outcomes, we selected the first study arm in the dataset. The experimental design involved total RNA isolation from the subcutaneous fat of patients and controls, including 10 subjects with OSA and eight healthy controls. The COPD (GSE38974) gene expression dataset included two study groups, smokers without any evidence of COPD and smokers with COPD; mRNA expression in lung tissue was detected in 59 samples, including 17 normal controls and 42 smokers with COPD. Also, to evaluate diagnostic efficiency and perform external dataset validation, we downloaded a COPD dataset (GSE106986), and selected mRNA expression data for five non-smoker tumor-free tissue samples (control group) and 14 COPD tumor-free lung tissue samples.

DEG Identification

The original gene expression matrix was normalized using R (4.0.4) software. Then, principal component analysis (PCA) and cluster analysis were used to assess sample validity in datasets. The `limma” R package was then used to screen for DEGs in GSE135917 and GSE38974 datasets. GSE135917 DEGs were screened and P values adjusted to <0.05 and |logFC| ≥ 1 values. GSE38974 DEGs were screened and P values adjusted to <0.05 and |logFC| ≥ 1 values. DEG PCA and cluster analysis graphs, heatmaps, and volcano graphs were drawn in R software. We used the online Venn diagram tool to intersect GSE135917 and GSE38974 DEGs and identify common DEGs. R software was used to draw Venn diagrams.

Weighted Gene Co-Expression Network Analysis (WGCNA)

The WGCNA R package was used to construct a weighted co-expression network. First, an appropriate soft threshold power was selected based on approximate scale-free topology criteria. The pickSoftThreshold function was used to analyze network topology and calculate soft threshold power, and a network was constructed using the automatic network construction function. Secondly, a hierarchical clustering tree was established based on similarity and difference coefficients between genes for module detection. Gene significance and module membership were defined to quantify any correlations between modules and clinical features. Modules were ranked according to the absolute value of module importance, and any modules that were strongly associated with specific cell subtypes were selected as hub modules.

Functional Enrichment Analysis

To explore Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) was used to explore functions between genes of interest, with an adjusted P<0.05 value set as the standard cutoff. GO annotations contained three sub-ontology categories (biological process (BP), cellular component (CC), and molecular function (MF)), which were used to identify biological characteristics of genes and gene sets in organisms. KEGG provided high-level gene functions and utilities in biological systems. All results were visualized in R.

Core Gene Selection

Receiver operating characteristic (ROC) curves and box plots were used to verify gene intersections and preliminarily screen out core genes.

Clinical Samples

This study has been reviewed and approved by the Scientific Research Ethics Committee of Wuxi People’s Hospital. All patients were from Wuxi People’s Hospital in Wuxi City, Jiangsu Province, China. Sleep monitoring was conducted on the general population and patients with COPD. The analysis results of patients’ nighttime sleep of ≥7 h monitored by a sleep breathing monitor were collected. The diagnosis and severity assessment of OSA were carried out according to the “Primary Diagnosis and Treatment Guidelines for Adult Obstructive Sleep Apnea (2018)”.Inclusion criteria: (1) Aged 18–80 years, regardless of gender; (2) Normal healthy individuals with a monitored AHI < 5 events/h were included in the healthy control group, and patients previously diagnosed with COPD at Wuxi People’s Hospital in Jiangsu Province were included in the COPD group; (3) Among patients with a monitored AHI ≥ 5 events/h, those with only OSA were included in the simple OSA group, and patients previously diagnosed with COPD at Wuxi People’s Hospital in Jiangsu Province were included in the OS group. Exclusion criteria: (1) Patients with acute asthma disease, pulmonary tuberculosis, respiratory failure, or in the acute exacerbation period of COPD; (2) Patients who were bed – ridden for a long time and had concurrent severe liver, kidney, or cardiovascular and cerebrovascular diseases; (3) Patients with mental or nervous system diseases who could not cooperate; (4) Patients who could not participate in sleep breathing monitoring for other reasons.

RNA Extraction and RT-qPCR

The Novozymes VeZol-Pure Total RNA Isolation Kit/RNA Extraction Kit (Vazyme, Nanjing, Jiangsu Province, China) was used to extract RNA from peripheral blood samples, after which reverse transcription reactions were conducted using the Vazyme HiScript II RT SuperMix for qPCR kit (Vazyme, Nanjing, Jiangsu Province, China). Parameters included: incubation at 25°C for 5 min, heating at 50°C for 15 min, heating at 85°C for 2 min, and incubation at 4°C. First strand cDNA was stored at −40°C. qPCR was performed on cDNAs from reverse transcription reactions using the 2×Q3 SYBR qPCR Master mix (Universal) reagent kit (Tolo Biotech, Shanghai, China). RT-qPCR primers were designed using PrimerPremier5.0 software (Table 1).

Statistical Methods

Experimental RT-qPCR data are presented as the mean ± standard error of the mean (SEM). SPSS 27.0 was used to analyze the data, and the differences between different experimental groups were determined using one-way analysis of variance, with P-values corrected using Bonferroni method. All other analyses were performed using R.

Results

DEG Identification

Based on PCA (Figures 1A and 2A) and sample clustering results (Figures 1B and 2B), no abnormal samples were detected in GSE135917 and GSE38974 datasets. Using established criteria, we determined DEGs. The OSA groups had 26 and 206 up- and down-regulated genes, respectively, while the COPD group had 205 and 229 up- and down-regulated genes, respectively. Then, heatmaps (Figures 1C and 2C) and volcano maps (Figures 1D and 2D) were separately constructed. Intersections in Venn diagrams yielded nine common DEGs for OSA and COPD (Figure 3).

Figure 1 Differentially expressed gene identification in GSE135917. (A) Principal Component Analysis results, with red C1 representing the normal control group and green C2 representing the OSA group. (B) Sample clustering results. (C) Heatmap showing expression levels gradually increasing from blue to red. (D) Volcano plot, with blue on the left indicating down-regulated genes and red on the right indicating up-regulated genes. The dashed line above the vertical axis represents P<0.05, and the two dashed lines on the horizontal axis represent |log FC| ≥ 1.

Figure 2 Differentially expressed gene identification in GSE38974. (A) Principal component analysis results, red C1 represents the normal control group, and green C2 represents the COPD group. (B) Sample clustering results. (C) Heatmap showing expression levels gradually increasing from blue to red. (D) Volcano plot, with blue on the left indicating down-regulated genes and red on the right indicating up-regulated genes. The dashed line above the vertical axis represents P<0.05 and the two dashed lines on the horizontal axis represent |logFC| ≥ 1.

Figure 3 Venn diagram showing differentially expressed genes between OSA and COPD groups. Different colors represent up- and down-regulated differentially expressed genes in OSA and COPD groups, respectively, and the numbers represent intersecting genes.

Identifying Common Key Module Genes Using WGCNA

We identified four and seven key modules from GSE135917 and GSE38974 datasets, respectively. After separately calculating correlations between modules and OSA and COPD, we plotted corresponding module trait relationship heatmaps (Figures 4A and 5A), with each module containing correlation coefficients and corresponding P-values. The turquoise module was related to OSA and the red module to COPD, and were considered the most relevant key modules related to diseases, respectively. Dendrogram of all differentially expressed genes of OSA and COPD was clustered based on the measurement of dissimilarity. The color band showed the results obtained from the automatic single-block analysis (Figures 4B and 5B). Using the pickSoftThreshold function in WGCNA, the optimal soft threshold power for the GSE135917 sample was 16, while for GSE38974, it was 10 (Figures 4C and 5C). By overlapping key OSA and COPD modules, 128 common key module genes were obtained (Figure 6).

Figure 4 Weighted gene co-expression network analysis of GSE135917. (A) Module trait relationship heatmap. (B) Hierarchical clustering map. (C) The left panel shows the determination of the optimal soft threshold, and the right panel shows the network connections under various soft thresholds.

Figure 5 Weighted gene co-expression network analysis of GSE38974. (A) Module trait relationship heatmap. (B) Hierarchical clustering map. (C) The left panel shows the determination of the optimal soft threshold, and the right panel shows the network connections under various soft thresholds.

Figure 6 Venn diagram showing weighted gene co-expression network analysis intersection results for GSE135917 and GSE38974 datasets. The green circle (left) represents OSA dataset results and the blue circle (right) represents COPD dataset results.

Functional Enrichment Analysis

We conducted functional enrichment analysis on these 128 common key module genes, which were shared by OSA and COPD groups from WGCNA results. The bubble plot of GO and KEGG were drawn (Figure 7). See Tables 2 and 3 for detailed results.

Table 2 Results of GO Enrichment Analysis

Table 3 Results of KEGG Pathway Enrichment Analysis

Figure 7 Functional enrichment analysis. (A) Gene Ontology pathway enrichment analysis of key module genes shared by OSA and COPD. The color changes in the bubble chart correspond to different P-values, with different shapes representing biological processes, cellular components, and molecular functions. Bubble size represents the number of genes. (B) The bubble plot shows the Kyoto Encyclopedia of Genes and Genomes pathways involved in OS, with bubble size indicating gene numbers.

Preliminary Core Gene Identification

By intersecting the nine DEGs and 128 common genes, five common genes (MYH11, BCHE, SOSTDC1, GRM8, OGN) were identified (Figure 8). We then used an external COPD dataset (GSE106986) to perform ROC curve and box plot validation analyses (Figure 9). In the GSE106986 sample, glutamate metabotropic receptor 8 (GRM8) showed significant differences between control and COPD groups. ROC curve results showed that the area under the curve was 0.857 and the 95% confidence interval was 0.614–1.000, which indicated that GRM8 had a certain diagnostic value for COPD. Therefore, we preliminarily considered GRM8 as a core OS gene.

Figure 8 Venn diagram showing common key module gene and common DEGs intersection in WGCNA. The green circle (left) represents WGCNA intersection genes and the blue circle (right) represents DEG intersections.

Figure 9 Verification of the external COPD dataset (GSE106986). (A) Box plot. Blue represents COPD group, red represents normal control group. (B) ROC curve of the hub diagnostic genes.

Core Gene Expression Comparisons in Peripheral Blood Samples

Due to a lack of a suitable external OSA dataset, we validated OSA using 30 clinical samples, including eight (healthy control group), eight (simple OSA group), six (COPD group), and eight (OS group). The clinical characteristics of 30 participants are shown in Table 4. RT-qPCR results (Figure 10) showed significant differences (P<0.05) in GRM8 levels between healthy control and simple OSA groups. We also conducted clinical sample validation on OS, with RT-qPCR results (Figure 10) showing significant differences (P<0.05) in GRM8 levels between healthy control and OS groups. Therefore, we identified GRM8 as a core gene in OS.

Table 4 The Clinical Characteristics of 30 Participants

Figure 10 Clinical sample validation results. GRM8 expression differences between healthy control, OSA, COPD, and OS groups. Vertical axis: Relative mRNA expression of GRM8.

Discussion

COPD is a serious lung disease characterized by persistent and progressive airflow obstruction due to airway and alveoli abnormalities. OSA is a sleep-related respiratory disorder characterized by obstructive sleep apnea and hypopnea and is a complication in patients with COPD. The coexistence of these two diseases can lead to increased nocturnal oxygen desaturation, which is the most significant sleep abnormality in both diseases,¹⁵ and is due to their combined effects and mutually reinforces the influence of both diseases. Upper respiratory tract stenosis in patients with OSA can exacerbate existing hypoxia and ventilation dysfunction in patients with COPD, leading to further hypoxic burden.¹⁶ Although research and understanding of pure COPD or OSA alone have grown, the pathogenesis of OS remains complex and understudied. Therefore, identifying common DEGs between COPD and OSA, exploring the molecular mechanisms underpinning OS, and improving early diagnosis and treatment interventions for OS are imperative.

For the first time, we used WGCNA analysis and differential expression analysis to identify the common key module genes of COPD and OSA. Through WGCNA analysis, we identified 128 common key module genes of COPD and OSA. By performing functional enrichment analysis on these common key module genes, we found that there were overlapping parts in the molecular functions, biological processes, and cellular components of the genes related to COPD and OSA, indicating that the two diseases may share some common mechanisms during their occurrence and development. Through KEGG pathway analysis, we found that COPD and OSA shared some common pathways: chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, and proteoglycans in cancer. Consistent with previous studies, the results of a clinical study by Monika et al evaluating the impact of certain comorbidities on a panel of 45 chemokines in OSA patients found that in OSA patients with COPD, elevated levels of certain pro – inflammatory cytokines such as chemokine CCL11 may contribute to the persistence of the chronic inflammatory state and lead to further complications.¹⁷ Our research results revealed a close association between the occurrence and development of OS and the chemokine signaling pathway. Currently, there is a lack of research on the two pathways of viral protein interaction with cytokine and cytokine receptor and proteoglycans in cancer, which may be potential research directions for OS in the future.

To further screen central genes, we used differential expression analysis technology to screen out 9 differentially expressed genes shared by COPD and OSA. These genes were intersected with the 128 common key module genes obtained by WGCNA. Finally, 5 key genes (MYH11, BCHE, SOSTDC1, GRM8, OGN) were obtained. Through research and verification, it was confirmed that GRM8 is closely related to OS.

GRM8 is a G protein-coupled glutamate receptor that affects the inhibition of cyclic AMP (Adenosine monophosphate) cascade and regulates presynaptic glutamate release. In our study, GRM8 was significantly down-regulated in OSA, COPD, and OS groups. Previous reports suggested that smoking and obesity were the most common factors in COPD patients with OSA. Bauer, Ph.D et al¹⁸ conducted a simple exercise inhibition study on 122 European and American adults and found that the GRM8 locus was associated with substance dependence risks, and that secondary allele deletion at candidate loci was associated with substance dependence diagnoses (eg, alcohol dependance, cocaine dependence). In a previous genome-wide association study, glutamatergic neurotransmission, involved in most aspects of normal brain function, was affected in many neuropathological conditions, and showed a significant correlation between GRM8 and nicotine dependence and addiction susceptibility.¹⁹ Glutamate signaling may have important roles in smoking behavior,¹⁹ while GRM8 down-regulation or deletion may increase nicotine addiction, thereby increasing COPD risks. Previous studies also reported that GRM8 was associated with an increased risk of alcohol abuse, and that mechanisms affecting alcohol and other substance dependence may overlap with food appetite regulatory processes,²⁰ indicating a potential relationship between GRM8 and feeding behavior. In an animal study by Oka et al,²¹ it was suggested that GRM8 may have a role in feeding behavior and metabolism via the hypothalamic pathway. A clinical study by Marcel S. Woo et al²² reported that the major G allele rs2237781 in GRM8 was significantly associated with increased feeding behavior inhibition scores. Therefore, low GRM8 expression levels in patients with OS may reduce feeding restraints, affect metabolism, increase obesity risks, and increase OSA incidence rates. In our study, we found that GRM8 tended to be further downregulated in OS patients compared with OSA alone, although this was not significant. Therefore, down-regulated GRM8 may increase concurrent COPD risks in patients with OSA, thus providing a possible OS biomarker for early prevention and diagnosis in patients with OSA.

However, we also observed that GRM8 levels in patients with OSA were significantly lower than in the control group, and that GRM8 levels in patients with COPD and OS were more significantly decreased than those in the control group. A previous study by Woo et al²³ reported that GRM8-deficient neurons were more prone to glutamate excitotoxicity, leading to inflammation-driven neurodegeneration in related neurological diseases. This may be an indirect cause of more widespread and severe cognitive impairment in OS.²³ GRM8 activation can counteract the excitotoxicity induced by glutamate,²⁴ indicating that this activation may be a valuable therapeutic approach and provide new directions and perspectives for GRM8 as a novel therapeutic target for OS.

Our research elucidated OSA pathogenesis when combined with COPD. However, our study had some limitations. First, although genes shared by OSA and COPD were identified, their biological function and associated pathways in OS were not fully delineated. Therefore, the comprehensive molecular pathogenesis underlying these two comorbidities requires more study. Additionally, study sample size was too small, and there was some gender imbalance in case selection. OSA and COPD conditions are more common in males,⁶ which is a potential study limitation. Our sample was entirely male; therefore, our data may not fully represent female patients, thereby limiting the generalizability of our findings. However, our research still provides valuable references for clinical practice. In future research studies, it is necessary to validate through more clinical cases and more comprehensive inclusion of female patients, and to confirm through deeper functional and pathway analysis of the GRM8 gene.

Conclusions

This study is the first to use bioinformatics methods to study the hub gene of comorbid OSA and COPD. Finally, it was found that GRM8 is a hub gene closely related to OS. This provides a new direction for GRM8 as a preliminary screening and diagnosis for OS in the general population. GRM8 may provide a new perspective for exploring biomarkers of OS and potential OS mechanisms. The downregulation of GRM8 in the diseased population also provides a possible new therapeutic target for OS.

Abbreviations

OSA, Obstructive sleep apnea; COPD, Chronic obstructive pulmonary disease; OS, Overlap syndrome; GEO, Gene Expression Omnibus; DEGs, Differentially expressed genes; DAVID, Database for Annotation, Visualization, and Integrated Discovery; WGCNA, Weighted Gene Co-expression Network Analysis; ROC, Receiver Operating Characteristic; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, Molecular Function; BP, Biological Process; CC, Cellular Component; AHI, Apnea-Hypopnea Index.

Data Sharing Statement

The data used to support these findings are included in the manuscript.

Ethics Approval and Consent to Participate

This study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Institutional Review Board of the Wuxi People’s Hospital. Ethical Number: KY24042.

Consent for Publication

Informed consent was obtained from all participants.

Acknowledgments

We thank the Sleep Center of Wuxi People’s Hospital for providing data and information.

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 International Journal of Chronic Obstructive Pulmonary Disease has been submitted; and agree to be accountable for all aspects of the work.

Funding

No benefit of any form was received or will be received from a commercial party related directly or indirectly to the subject of this article.

Disclosure

The authors declare no conflicts of interest.

References

1. Lévy P, Kohler M, McNicholas WT, et al. Obstructive sleep apnoea syndrome. Nat Rev Dis Primers. 2015;1:15015. doi:10.1038/nrdp.2015.15

2. Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global Strategy for Prevention, Diagnosis and Management of COPD: 2025 Report. Bethesda: GOLD; 2025.

3. Flenley DC. Sleep in chronic obstructive lung disease. Clin Chest Med. 1985;6(4):651–661. doi:10.1016/S0272-5231(21)00402-0

4. Senaratna CV, Perret JL, Lodge CJ, et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med Rev. 2017;34:70–81. doi:10.1016/j.smrv.2016.07.002

5. Shawon MS, Perret JL, Senaratna CV, Lodge C, Hamilton GS, Dharmage SC. Current evidence on prevalence and clinical outcomes of co-morbid obstructive sleep apnea and chronic obstructive pulmonary disease: a systematic review. Sleep Med Rev. 2017;32:58–68. doi:10.1016/j.smrv.2016.02.007

6. Brennan M, McDonnell MJ, Walsh SM, Gargoum F, Rutherford R. Review of the prevalence, pathogenesis and management of OSA-COPD overlap. Sleep Breath. 2022;26(4):1551–1560. doi:10.1007/s11325-021-02540-8

7. Bae E, Kwak N, Choi SM, et al. Mortality prediction in chronic obstructive pulmonary disease and obstructive sleep apnea. Sleep Med. 2021;87:143–150. doi:10.1016/j.sleep.2021.09.011

8. Xi-Long Z, Kai-Sheng Y. Genetic research of obstructive sleep apnea. Int J Genetics. 2006;02:152–154. doi:10.3760/cma.j.issn.1673-4386.2006.02.019

9. Silverman EK, Spira A, Paré PD. Genetics and genomics of chronic obstructive pulmonary disease. Proc Am Thorac Soc. 2009;6(6):539–542. doi:10.1513/pats.200904-021DS

10. Lee YJ, Choi S, Kwon SY, et al. A genome-wide association study in early COPD: identification of one major susceptibility Loci. Int J Chron Obstruct Pulmon Dis. 2020;15:2967–2975. doi:10.2147/COPD.S269263

11. Feng K, Yang J, Liu K. Shared genetic associations and etiology between obstructive sleep apnea and CVDs: a genome-wide cross-trait analysis and bidirectional Mendelian randomization analysis. Eur J Prev Cardiol. 2024;zwae347. doi:10.1093/eurjpc/zwae347

12. Banjade P, Kandel K, Itani A, et al. The Interplay between obstructive sleep apnea, chronic obstructive pulmonary disease, and congestive heart failure: time to collectively refer to them as triple overlap syndrome? Medicina. 2023;59(8):1374. doi:10.3390/medicina59081374

13. Zhang K, Wang C, Wu Y, Xu Z. Identification of novel biomarkers in obstructive sleep apnea via integrated bioinformatics analysis and experimental validation. PeerJ. 2023;11:e16608. doi:10.7717/peerj.16608

14. Cao Z, Zhao S, Hu S, Wu T, Sun F, Shi LI. Screening COPD-related biomarkers and traditional Chinese medicine prediction based on bioinformatics and machine learning. Int J Chron Obstruct Pulmon Dis. 2024;19:2073–2095. doi:10.2147/COPD.S476808

15. Adler D, Bailly S, Benmerad M, et al. Clinical presentation and comorbidities of obstructive sleep apnea-COPD overlap syndrome. PLoS One. 2020;15(7):e0235331. doi:10.1371/journal.pone.0235331

16. Biselli P, Grossman PR, Kirkness JP, et al. The effect of increased lung volume in chronic obstructive pulmonary disease on upper airway obstruction during sleep. J Appl Physiol. 2015;119(3):266–271. doi:10.1152/japplphysiol.00455.2014

17. Chaszczewska-Markowska M, Górna K, Bogunia-Kubik K, Brzecka A, Kosacka M. The Influence of comorbidities on chemokine and cytokine profile in obstructive sleep apnea patients: preliminary results. J Clin Med. 2023;12(3):801. doi:10.3390/jcm12030801

18. Bauer LO, Covault JM. GRM8 genotype is associated with externalizing disorders and greater inter-trial variability in brain activation during a response inhibition task. Clin Neurophysiol. 2020;131(6):1180–1186. doi:10.1016/j.clinph.2020.02.031

19. Vink JM, Smit AB, De Geus EJ, et al. Genome-wide association study of smoking initiation and current smoking. Am J Hum Genet. 2009;84(3):367–379. doi:10.1016/j.ajhg.2009.02.001

20. Volkow ND, Wang GJ, Tomasi D, Baler RD. Obesity and addiction: neurobiological overlaps. Obes Rev. 2013;14(1):2–18. doi:10.1111/j.1467-789X.2012.01031.x

21. Oka A, Hadano S, Ueda MT, Nakagawa S, Komaki G, Ando T. Rare CRHR2 and GRM8 variants identified as candidate factors associated with eating disorders in Japanese patients by whole exome sequencing. Heliyon. 2024;10(8):e28643. doi:10.1016/j.heliyon.2024.e28643

22. Gast MT, Tönjes A, Keller M, et al. The role of rs2237781 within GRM8 in eating behavior. Brain Behav. 2013;3(5):495–502. doi:10.1002/brb3.151

23. Wang Y, Li B, Li P, et al. Severe obstructive sleep apnea in patients with chronic obstructive pulmonary disease is associated with an increased prevalence of mild cognitive impairment. Sleep Med. 2020;75:522–530. doi:10.1016/j.sleep.2020.05.002

24. Woo MS, Ufer F, Rothammer N, et al. Neuronal metabotropic glutamate receptor 8 protects against neurodegeneration in CNS inflammation. J Exp Med. 2021;218(5):e20201290. doi:10.1084/jem.20201290

Thank you. Listen to this article using the player above. ✖

A potential target for experimental drugs that block PRMT5 — a naturally occurring enzyme some tumors rely more on for survival — has been identified by researchers with the Fralin Biomedical Research Institute’s Cancer Research Center in Washington, D.C.

In a study published this month in Cancer Research, Assistant Professor Kathleen Mulvaney of Virginia Tech’s Fralin Biomedical Research Institute shared research that could help guide development of new therapies for some treatment-resistant lung, brain, and pancreatic cancers.

“Using genetic screening, we found a new drug combination that seemingly works,” Mulvaney said.

New therapies are needed. Lung cancer is a leading cause of cancer-related death globally. The five-year survival rate is less than 15 percent for pancreatic cancer patients and even lower for glioblastoma.

“With one drug alone, tumors can become resistant really quickly,” Mulvaney said. Treatment often fails. The findings suggest the PRMT5 inhibitor could be a powerful new approach for certain hard-to-treat cancers. “In all cases, the combination is better at killing than the single agents.”

Many of these solid tumors share a genetic trait: They lack CDKN2A and MTAP, two genes that suppress tumors and help regulate cell growth. Without them, the cancers become dependent on PRMT5 and potentially vulnerable to drugs that block the enzyme.

Mulvaney and colleagues analyzed genetic data from thousands of cancer patients available through the cBioPortal.

They applied CRISPR editing tools to look at biological pathways across a range of samples to determine which genes make cancer cells more vulnerable to PRMT5 inhibitors and which combinations could improve response and long-term outcomes.

An estimated 5 percent of all cancer patients — about 80,000 to 100,000 per year in the U.S. — can benefit from the therapies identified, according to Mulvaney, who also holds an appointment in biomedical sciences and pathobiology in the Virginia-Maryland College of Veterinary Medicine.

Using PRMT5 inhibitors with drugs that block a communication system that tells cancer cells when to grow, divide, or shut down — known as the MAP kinase pathway — scientists identified potential treatments for clinical trials.

“We also discovered a number of genes that interact with PRMT5 signaling in cancer that were not previously known,” said Mulvaney, who is a member of the research institute’s Cancer Research Center in Washington, D.C.

In addition to lung, brain, and pancreatic cancers, the treatment shows promise for some types of melanoma and mesothelioma.

In both animal models and cell cultures derived from patient tissue, lab members saw success after testing potential therapies.

“In all cases, the combination is better at killing cancer cells than the single agents,” Mulvaney said. “Only the combinations led to complete regressions.”

Reference: Knoll N, Masser S, Bordas B, et al. CRISPR-drug combinatorial screening identifies effective combination treatments for MTAP-deleted cancer. Cancer Research. 2025. doi: 10.1158/0008-5472.CAN-25-1464

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.