Diabetes Risk in HIV Infection and Antiretroviral Therapy: A Systematic Review and Meta-Analysis of Prospective Cohort Evidence

Diabetes Risk in HIV Infection and Antiretroviral Therapy: A Systematic Review and Meta-Analysis of Prospective Cohort Evidence | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 September 2025 V1 Latest version Share on Diabetes Risk in HIV Infection and Antiretroviral Therapy: A Systematic Review and Meta-Analysis of Prospective Cohort Evidence Authors : Xuanlan LI 0009-0004-0887-3516 , Kang LI , Yuxuan LI , Yue ZhAO , Yingqiong HUANG , Jun LUO , Li JIANG , Sisi LI , Juan JING , and Chuanyi Ning 0000-0002-8748-2780 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175751655.50893002/v1 Published HIV Medicine Version of record Peer review timeline 200 views 109 downloads Contents Abstract INTRODUCTION Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Introduction: People living with HIV (PLWH) are at increased risk of metabolic disorders, including diabetes, due to both HIV infection itself and the effects of antiretroviral therapy (ART). However, the magnitude of this risk and its variation across different populations and treatment exposures remain unclear. Methods: We searched PubMed up to July 20, 2025, for prospective cohort studies including PLWH aged ≥18 with ≥6 months of follow-up and reported diabetes incidence. Two reviewers independently assessed study quality using the Newcastle-Ottawa Scale and extracted data. A random-effects model estimated pooled incidence rates and subgroup differences. Meta-regression evaluated associations with HIV infection and ART exposure. Results: Thirty-one studies contributed 1,230,314 person-years (PY) of follow-up. The diabetes incidence rate (IR) in PLWH was 12.99 cases per 1,000 PY (95% CI: 10.94–15.04). No significant difference was observed between PLWH (13.04 cases per 1,000 PY) and HIV-negative individuals (12.58 cases per 1,000 PY; p = 0.946). However, incidence among PLWH on ART (13.78 cases per 1,000 PY) was significantly higher than in those not on ART (7.42 cases per 1,000 PY; p = 0.022). Each year of ART exposure was associated with an increase of 0.354 cases per 1,000 PY (p = 0.008). Conclusion: HIV infection and ART exposure are associated with increased diabetes risk. Routine screening and tailored prevention strategies should be incorporated into long-term HIV care. INTRODUCTION The widespread use of ART has significantly improved survival in PLWH, with many now approaching the general population’s life expectancy [1] . Consequently, chronic conditions such as diabetes are increasingly recognized as critical determinants of long-term health outcomes in this population [2] . The International Diabetes Federation (IDF) predicts that 463 million adults globally, aged 20 to 79, had diabetes in 2019, with projections indicating an increase to 700 million by 2045 [3] . For PLWH, the onset of diabetes not only diminishes their quality of life but also markedly increases the risk of cardiovascular illnesses and other serious comorbidities, negatively affecting their overall health outlook [4] . While ART is essential for managing HIV infection, prolonged usage may lead to many severe metabolic consequences, such as mitochondrial toxicity, dyslipidemia, hyperlactatemia, lactic acidosis, pancreatitis, lipodystrophy, and glucose metabolism abnormalities [5-14] . These ART-related metabolic disturbances collectively increase diabetes risk among PLWH. Furthermore, HIV itself may affect glucose metabolism through mechanisms such as inflammatory activation, immune dysfunction, and hormonal dysregulation [15] . Consequently, diabetes, in conjunction with other non-communicable chronic illnesses, presents a substantial risk to the longevity and standard of living of PLWH, thereby undermining the long-term health advantages of ART. Numerous research papers have examined the correlation of HIV infection with diabetes, although the findings have been incongruous. A multifactorial analysis based on an Italian cohort revealed that individuals diagnosed with HIV had a significantly higher likelihood of developing diabetes (OR = 1.70), with a prevalence of 4.1% compared to 2.5% in their HIV-negative counterparts [16] . Furthermore, research has shown that the incidence of diabetes among PLWH is higher than that observed in the general population, regardless of gender [17, 18] . However, some studies have suggested the opposite. A cohort study of 13,632 participants found a higher diabetes rate among HIV-negative participants than among those living with HIV (13.60 vs. 11.35 per 1,000 PY). Findings from a multivariate model revealed that PLWH undergoing antiretroviral treatment exhibited a markedly lower likelihood of diabetes onset (aHR = 0.55) [19] . In addition, Guo et al. [20] evaluated 1,406 HIV/AIDS patients and determined that the incidence of diabetes was about 1.7%, suggesting that HIV itself did not appear to independently contribute to diabetes onset. Although numerous studies have examined the correlation between HIV infection and diabetes, current evidence is predominantly derived from retrospective analyses or limited-size cohorts, resulting in inconsistent findings and methodological uncertainties. This study aims to systematically review and meta-analyze prospective cohort studies to quantify the incidence of diabetes in PLWH. In addition, it compares diabetes incidence between PLWH and HIV-negative populations and explores potential sources of heterogeneity using meta-regression. An exploratory assessment of prediabetes incidence was also conducted to provide supplementary insights into early metabolic disturbances in PLWH. Retrieval Strategies This review followed PRISMA guidelines and was registered with PROSPERO (CRD420251036186). A search was performed on the PubMed database spanning from January 1981 to July 20, 2025, to identify prospective cohort studies evaluating the incidence of diabetes in PLWH aged 18 years and older. No linguistic constraints were imposed. Two reviewers, Xuanlan Li (XL) and Yuxuan Li (YX), independently evaluated titles, abstracts, and complete texts based on established eligibility criteria. Duplicate entries were eliminated with EndNote 21. Disputes were settled through dialogue or, if necessary, by consulting a third reviewer (Chuanyi Ning [CY], the corresponding author). No automated techniques were employed except for deduplication. The search strategy is thoroughly outlined in the supplementary material. The criteria for inclusion were as follows: a. Study design: Prospective cohort study. b. Population: PLWH aged over 18 years. c. Outcomes: Incident cases of diabetes or prediabetes during follow-up. d. Outcome measurement: Data on incident cases and follow-up duration, or sufficient information to calculate these values. Exclusion Criteria: a. Follow-up duration < six months. b. Duplicate reports (only the most comprehensive and updated version was included). Diagnostic Criteria in Included Studies A standardized diagnostic definition of diabetes was not applied across the included studies. Instead, we extracted and synthesized the diagnostic criteria used in each prospective cohort study to capture methodological heterogeneity and minimize potential misclassification bias. The diagnostic criteria extracted from the studies were classified into three overarching definitions to reflect variation across studies: Definition I: Diabetes was identified when any of the following conditions applied: (i) documented use of anti-diabetic medication; (ii) a fasting plasma glucose (FPG) concentration of 126 mg/dL (7.0 mmol/L) or higher; (iii) random plasma glucose≥ 200 mg/dL (iv) self-reported physician diagnosis of diabetes. Definition II: Criteria consistent with Definition I, with the additional requirement that elevated FPG values (≥ 126 mg/dL) be confirmed on at least two occasions. Definition III: Diabetes was identified when any of the following conditions applied: (i) use of anti-diabetic medication; (ii) FPG≥126 mg/dL; (iii) random plasma glucose≥ 200 mg/dL (iv) glycated hemoglobin (HbA1c) level≥6.5%; (v) self-reported diagnosis of diabetes. Where available, we recorded the specific diagnostic criteria adopted by each study. Most studies (n = 16) applied criteria consistent with Definition I, while fewer used Definition II or III. This variability in case definitions was considered during the risk of bias assessment and subgroup analyses. Data extraction and quality assessment Data were independently extracted by two reviewers (XL and YX) from eligible studies, covering details such as the first author’s name, publication year, geographic region, median follow-up time, number of diabetes cases, total person-years, baseline age, sex distribution, diagnostic criteria, and covariates considered in the analysis. Any disagreements were addressed through consensus or resolved by a third reviewer (CY). A Study quality assessment was performed according to the Newcastle-Ottawa Scale (NOS), where a maximum of nine stars could be awarded. Studies were then classified as poor (0-3), moderate (4-6), or high quality (7-9) [21] . To assess the consistency between reviewers in study selection and data extraction, Cohen’s kappa (κ) coefficient [22] was applied. Data analysis All analyses were conducted with STATA 17.0. Each outcome was analyzed independently. The effect measure for all outcomes was the IR per 1000 person-years. A Freeman-Tukey double arcsine transformation was applied to stabilize variance [23] , and the pooled incidence was calculated by synthesizing individual study estimates through a random-effects meta-analytic approach [24] . Confidence intervals (CIs) at a 95% level were computed utilizing the Wilson Score technique [25] . The I 2 statistic was utilized to evaluate statistical heterogeneity, with an I 2 value below 25% indicating a low degree of heterogeneity, 25%-50% moderate, and above 75% high [26] . To elucidate the underlying sources of pronounced heterogeneity, stratified analyses and meta-regression were utilized. Statistical significance for subgroup differences was defined as p < 0.05. Funnel plots and Egger’s regression test were employed to assess potential publication bias. All statistical tests were two-tailed, and a p-value below 0.05 was considered indicative of significance. RESULTS Literature search and study characteristics Supplementary Table 1 presents the detailed search strategies used in this review, including the databases, keywords, and search combinations applied. Figure 1 illustrates the flowchart of the literature search, including the research selection procedure. A total of 212 records were discovered, including 200 from database searches and 12 from other sources. Following the elimination of duplicate articles and the evaluation of titles and abstracts, the remaining 56 papers were examined for eligibility. A total of 33 articles were incorporated for data synthesis [17, 27-58] . Two of the studies [17, 50] were not included in the calculation of the incidence rate of diabetes and were only included in the subgroup analysis of whether ART was used. The total follow-up person-years involved in the following discussion only include 31 studies. Table 1 delineates the fundamental features of the research covered. 33 studies were published from 2003 to 2024. The investigations encompassed 13,963 diabetes cases and a cumulative follow-up of 1,230,314 PY. Two studies were limited to males [17, 33] , and one study was limited to females [51] . The 33 prospective cohort studies included in the article were categorized as high-quality studies according to the Newcastle-Ottawa scale criteria (Supplementary Table 2). Incidence of diabetes in PLWH The pooled incidence rate was 12.99 cases per 1,000 person-years (PY) (95% CI: 10.94-15.04; I 2 = 99%; 31 studies) among PLWH. Supplementary Figure 1 illustrates the continent-specific incidence of diabetes among PLWH, demonstrating significant variability across regions. The Oceania studies reported the highest incidence rate of 18.85 cases per 1,000 PY (95% CI: 6.41-31.29), whereas the European studies showed the lowest incidence rate of 6.21 cases per 1,000 PY (95% CI: 4.80-7.61). Continental analysis demonstrated notable disparities in incidence rates among the continents (p<0.01). CD4 count, female proportion, and smoking did not explain heterogeneity (Supplementary Table 3). The funnel plot in Supplementary Figure 2 assesses potential publication bias for diabetes incidence. The formal Egger test corroborates this assertion with a p-value of 0.221 (Supplementary Table 4). To determine whether the findings were influenced by any single study, a leave-one-out sensitivity procedure was applied. After each of the 31 studies was removed in turn, the summary incidence was recalculated. All resulting incidence estimates ranged narrowly between 12.99 and 14.85 cases per 1,000 PY, remaining inside the bounds of the original 95% CI. This indicates that no one research study disproportionately affected the total pooled estimate, hence affirming the stability and dependability of the meta-analytic results (Supplementary Table 5). Comparison of the incidence of diabetes in PLWH and HIV-negative individuals We conducted subgroup analyses to compare the incidence of diabetes between PLWH and HIV-negative individuals according to whether they were infected with HIV. Four of the included articles were analyzed as subgroups [31, 37, 44, 51] . Figure 2 compares the diabetes incidence rates between PLWH and HIV-negative individuals using a random-effects model, showing that the incidence of diabetes in the PLWH was 13.04 cases per 1,000 PY (95% CI: 5.20-20.87), whereas in the HIV-negative individuals, it was 12.58 cases per 1,000 PY (95% CI: 2.14-23.03). The pooled incidence rate was 12.80 cases per 1,000 PY (95% CI: 7.20-18.39). Analysis of subgroups showed no significant difference in diabetes incidence between PLWH and HIV-negative individuals (p = 0.946). Comparison of the incidence of diabetes in PLWH with and without ART use To further explore potential contributors to diabetes incidence in PLWH, we examined whether ART exposure affected the risk. Of these, 5 of 34 studies documented the incidence of diabetes with and without ART [27, 44, 47, 50, 51] . Figure 3 illustrates the IR of diabetes in PLWH, differentiated by ART exposure, utilizing a random-effects model. The incidence rate in the ART-exposed group was 13.78 cases per 1,000 PY (95% CI: 9.64-17.93), whereas the incidence in the ART-unexposed group was 7.42 cases per 1,000 PY (95% CI: 3.91-10.94). The pooled incidence rate was 10.55 cases per 1,000 PY (95% CI: 7.82-13.28). Comparative analysis revealed a statistically significant disparity in diabetes incidence between the ART-exposed and unexposed cohorts (p=0.022). Effect of ART duration on the incidence of diabetes Seventeen studies documented ART duration and associated diabetes incidence (per 1000 PY) during the study period. Figure 4 illustrates the dose-response relationships between ART duration and diabetes incidence, derived from meta-regression analysis, where scattered dots represent each study and the curves are fitted regression lines. Regression analysis indicated that an extended duration of ART was strongly correlated with an increased incidence of diabetes (p<0.01). The regression equation indicates that each extra year of ART usage correlates with an average increase of 0.354 cases per 1,000 PY. Prediabetes Incidence Among PLWH: Transition from Normoglycemia We extracted data from four eligible studies that reported the incidence of prediabetes among PLWH transitioning from normoglycemia [32, 33, 38, 41] . Supplementary Figure 3 displays a forest plot generated using a random-effects model to estimate the pooled incidence rate. The overall pooled incidence of prediabetes was 84.60 cases per 1,000 PY (95% CI: 50.95-118.24). These findings suggest that PLWH may face a notably elevated risk of progressing to prediabetes, which calls for early screening and intervention strategies.The markedly higher IR of prediabetes may reflect earlier detection of glucose dysregulation and highlights the need for timely interventions. Diabetes Incidence Stratified by Diagnostic Criteria Diabetes incidence varied significantly by diagnostic criteria (p < 0.01). As shown in Supplementary Figure 4, pooled incidence rates were 14.68 cases per 1,000 PY (95% CI: 11.56-17.80) for Definition I, 5.05 cases per 1,000 PY (95% CI: 3.77–6.33) for Definition II, and 15.39 cases per 1,000 PY (95% CI: 11.93–18.85) for Definition III. Definition II yielded the lowest incidence rate, while Definition III showed the highest. These findings suggest that diagnostic variability substantially impacts reported diabetes rates in PLWH. DISCUSSION This meta-analysis is the first to systematically synthesize prospective cohort data to estimate the incidence of diabetes and prediabetes among people living with HIV (PLWH) and compare it with HIV-negative populations. Drawing on 31 prospective studies encompassing a total of 1,230,314 PY of observation, this meta-analysis estimated that diabetes occurred among PLWH at a rate of 12.99 cases per 1,000 PY (95% CI: 10.94-15.04). While the incidence of diabetes among PLWH (13.04 cases per 1,000 PY) compared to HIV-negative individuals (12.58 cases per 1,000 PY) lacked statistical significance (p=0.946), the incidence among PLWH on ART (13.78 cases per 1,000 PY) was substantially greater than that in those not receiving ART (7.42 cases per 1,000 PY) (p=0.022). Traditional risk factors for diabetes—aging, obesity, dyslipidemia—remain relevant in PLWH, with even greater impact among those on ART. The extensive application and effectiveness of diverse ART regimens have markedly prolonged the life expectancy of PLWH, therefore subjecting them to identical risk variables as the general populace, including age, obesity/overweight, glucose, HDL, LDL, total cholesterol, as highlighted by our findings. Furthermore, antiretroviral regimens might impair glucose metabolism, potentially contributing to dysglycemia in treated individuals. Mitochondrial dysfunction-linked insulin resistance, stemming from the influence of protease inhibitors and NRTIs, along with persistent inflammation driven by HIV, underlies these metabolic disturbances [6, 10, 12] . The analysis did not reveal a notable disparity in diabetes incidence when comparing PLWH to HIV-negative persons. Within the PLWH cohort, meta-regression analysis revealed that extended ART duration was strongly correlated with a heightened prevalence of diabetes. Multiple variables may account for the absence of distinction found between PLWH and HIV-negative persons, including discrepancies in sample size, geographic distribution, ethnic background [59] , and the choice and use of antiviral regimens across the included studies [17, 60-62] . Numerous mechanistic investigations substantiate the correlation between cumulative ART exposure and the risk of diabetes. We noted an increase of 0.354 cases per 1,000 PY for each additional year of ART therapy, indicating a trend of escalating risk with prolonged treatment duration. This finding aligns with results from the NA-ACCORD cohort [55] , which reported a ~3% risk increase per year of ART exposure. Mechanistically, several ART drug classes have been implicated in insulin resistance through distinct pathways. First-generation protease inhibitors (PIs), such as indinavir and ritonavir, directly impair glucose metabolism by inhibiting glucose transporter type 4 (GLUT4) and contributing to mitochondrial dysfunction, while non-nucleoside reverse transcriptase inhibitors (NNRTIs), such as efavirenz, are also associated with increased risk of hyperglycemia [17, 63] .In contrast, integrase strand transfer inhibitors (INSTIs) like dolutegravir and raltegravir generally exhibit lower mitochondrial and metabolic toxicity [64] . However, recent studies have suggested that INSTIs may lead to significant weight gain, potentially offsetting their presumed metabolic neutrality [65, 66] . These class-specific effects may help explain the heterogeneity in diabetes incidence observed across studies. Therefore, careful consideration of ART composition is essential when interpreting pooled estimates, and long-term metabolic consequences of distinct ART classes warrant further investigation in both clinical practice and research. Research indicates that [67] extended HIV infection is associated with an increased likelihood of developing glucose metabolism disorders, as opposed to shorter-term infections (20.8% vs 8.8%). Some of the accessory proteins of HIV (e.g., Tat and Vpr) may directly contribute to the onset of insulin resistance [68, 69] . Tat protein activates nuclear factor κB (NF-κB) and stimulates TNF-α release, which obstructs the absorption of free fatty acids by adipocytes, disrupts insulin receptor signaling, inhibits GLUT4 translocation, and induces IRS-1 phosphorylation. Vpr proteins, on the other hand, contribute to the onset and development of insulin resistance by interfering with the function of peroxisome proliferator-activated receptor γ (PPAR-γ), affecting the gene expression of insulin pathways [69] . Research suggests that the sources of diabetes risk may change with the progression of infection and treatment. In the early stages of infection, the risk may be primarily driven by chronic inflammation caused by HIV itself [70] ; however, in the later stages of infection or treatment (>10 years), the risk is more likely to stem from the cumulative toxicity of antiretroviral therapy and age-related metabolic dysfunction [67] . Therefore, these temporal differences in mechanisms suggest that diabetes prevention strategies should adopt stage-specific and individualized intervention measures. Our study found that the incidence varied considerably between the different diagnostic criteria for diabetes. Of these, definition II had the most stringent diagnostic criteria, requiring a mandatory FPG ≥ 126 mg/dL and at least 2 abnormal tests on top of definition I. Therefore, the incidence of diabetes was lower compared with the other two definitions, being 5.05 per 1,000 PY (95% CI: 3.77-6.33). Definition III added the condition of glycated hemoglobin (A1C) ≥ 6.5%. Among the three definitions, Definition III had the highest incidence of diabetes at 15.39 per 1,000 PY (95% CI: 11.93-18.85). This funding suggests that we have a higher rate of underdiagnosis of diabetes in PLWH. Failure to diagnose diabetes in PLWH in a timely manner can lead to serious clinical outcomes, notably increasing the likelihood of both small and large vessel complications, including ocular, renal, neural, and cardiac impairments. Furthermore, an uncontrolled state of diabetes may exacerbate HIV-related complications and affect the efficacy of ART, increasing the risk of hospitalization and death. Prediabetes represents a critical intermediate state of dysglycemia and a window of opportunity for intervention. Given its markedly high incidence among PLWH, early detection and management of prediabetes may help prevent the progression to overt diabetes and associated complications [71, 72] . Some scholars recommend regular monitoring of fasting blood glucose [73] if the presence of increased fasting glucose in the patient is recommended for prompt medical attention to clarify the diagnosis of diabetes. Our study has several strengths, the foremost being its focus on prospective cohort studies evaluating diabetes incidence in HIV individuals who had normoglycemia at baseline. Secondly, the study compared diabetes rates in PLWH versus HIV-negative individuals and assessed morbidity risk in those receiving or not receiving antiretroviral treatment. The study independently assessed how ART duration and diabetes frequency correlate in PLWH. Third, we performed subgroup analyses to summarize the incidence of antecedent diabetes among PLWH. Importantly, statistical testing revealed no significant publication bias, and the inclusion of recently published studies enhances the relevance and generalizability of our findings to real-world settings. However, it is crucial to acknowledge the subsequent limitations when analyzing the results of this study. Initially, study-level variables could not fully explain the significant variation in incidence estimates among studies. Consequently, we performed subgroup analyses and meta-regressions for different continents, diabetes diagnostic criteria, proportion of women, and median follow-up time to explore potential influences. Secondly, we could not comprehensively evaluate other factors that could influence heterogeneity due to insufficient methodological details reported in certain studies. For example, the adjustment of an individual’s antiretroviral regimen during follow-up and the specific type of medication used may have influenced outcomes, but this study was unable to explore those factors in depth. Last but not least, restricting the inclusion to English-language publications may have introduced selection bias. Therefore, future studies should consider including them in more languages to improve the representativeness and robustness of the results. CONCLUSION This study demonstrates that regional variation and differences in diagnostic criteria influence the observed association between HIV infection and diabetes incidence. Routine diabetes screening and early metabolic intervention should be prioritized in PLWH, with strategies adapted to local epidemiological contexts. Diabetes remains a concern across different ART statuses and tends to increase with longer ART duration, emphasizing the importance of individualized prevention and management strategies. Moreover, the immunometabolic mechanisms underlying the interactions between HIV infection, ART exposure, and glucose dysregulation remain incompletely understood and warrant deeper investigation. Additional mechanistic and prospective cohort studies, including bigger, varied populations and prolonged follow-up, are crucial to elucidate these associations and guide targeted therapies aimed at enhancing long-term outcomes in PLWH. Contributors Xuanlan Li and Kang Li contributed equally to the conception, design, and methodology of the meta-analysis and should be considered joint first authors. Yuxuan Li and Yue Zhao assisted with the systematic review, data extraction, and analysis. Yingqiong Huang participated in data interpretation and critically revised the manuscript. Jun Luo and Li Jiang contributed to interpreting the findings and refining the manuscript. Chuanyi Ning, Juan Jing and SiSi Li supervised the overall study and approved the final version of the manuscript; they should be considered joint corresponding authors. All authors reviewed and approved the final manuscript. Funding This study was financed by the Natural Science Foundation of Guangxi Zhuang Autonomous Region (2023GXNSFFA026007) and the Guangxi Key Research and Development Program (AB24010175), provided by the Guangxi Science and Technology Department. The prepublication history and supplementary materials for this work are accessible online. To access these materials, kindly visit the journal online. Provenance and peer review Not commissioned; subjected to external peer assessment. Patient consent for publication Not applicable. Ethics approval Not applicable. Competing interests The authors disclose no conflicting interests. Data availability statement Additional information can be acquired from the relevant author. Patient and public involvement Neither patients nor the public participated in the design, execution, reporting, or dissemination strategies of this research. Data availability statement All data pertinent to the study are incorporated in the paper or provided as extra material. REFERENCES 1. Wang J, Yuan T, Ding H, Xu J, Keusters WR, Ling X, et al. Development and external validation of a prognostic model for survival of people living with HIV/AIDS initiating antiretroviral therapy . 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Clinical Infectious Diseases 2015; 60(3):453-462. FIGURE LEGENDS Figure 1. PRISMA flow diagram illustrating the study selection process for the meta-analysis. The diagram presents the number of records identified, screened, assessed for eligibility, and included in the final analysis, along with reasons for exclusion at each stage. Figure 2. Forest plot comparing diabetes incidence between PLWH and HIV-negative individuals. Data from four studies were pooled, revealing no statistically significant difference in incidence rates between the two groups (p = 0.946). Figure 3. Forest plot comparing diabetes incidence between PLWH exposed to ART and those unexposed to ART. A statistically significant difference was observed, with higher incidence among those exposed to ART (p = 0.022). Figure 4. Meta-regression analysis examining the association between ART duration and diabetes incidence among PLWH. The analysis revealed a significant positive association between longer ART duration and increased diabetes incidence (p = 0.008). Supplementary Material File (table 1.docx.docx) Download 34.86 KB Information & Authors Information Version history V1 Version 1 10 September 2025 Peer review timeline Published HIV Medicine Version of Record 10 May 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords antiviral agents epidemiology human immunodeficiency virus virus classification Authors Affiliations Xuanlan LI 0009-0004-0887-3516 Guangxi Medical University Nursing College View all articles by this author Kang LI Guangxi Medical University View all articles by this author Yuxuan LI Guangxi Medical University Nursing College View all articles by this author Yue ZhAO Guangxi Medical University View all articles by this author Yingqiong HUANG Guangxi Medical University Nursing College View all articles by this author Jun LUO Guangxi Medical University Nursing College View all articles by this author Li JIANG Guangxi Medical University Nursing College View all articles by this author Sisi LI Guangxi Medical University View all articles by this author Juan JING Guangxi Medical University View all articles by this author Chuanyi Ning 0000-0002-8748-2780 [email protected] Guangxi Medical University Nursing College View all articles by this author Metrics & Citations Metrics Article Usage 200 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xuanlan LI, Kang LI, Yuxuan LI, et al. 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