{"paper_id":"33c10357-95ca-49e8-a74d-cd744cafda1c","body_text":"Low-level Viremia Increases the Risk of Diabetes Mellitus in People with HIV in China: A 7-Year Retrospective Longitudinal Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Low-level Viremia Increases the Risk of Diabetes Mellitus in People with HIV in China: A 7-Year Retrospective Longitudinal Cohort Study Chunxing Tao, Aidan Nong, Minn Thit Aung, Longyu Liao, Liangjia Wei, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5380470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract Background It is unclear whether low-level viremia (LV) during antiretroviral therapy (ART), increase the incidence of diabetes mellitus (DM). This study aims to assess the association between HIV viremia exposure during ART and DM using retrospective cohort data. Methods People with HIV (PWH) who started ART in 2003 or later were identified from the China’s National Free ART Program database. Participants on ART ≥ 6 months without DM at enrolment were included in this study. According to the two consecutive viral load (VL) measurements after 6 months of ART, participants categorized into three groups: viral suppression (VS), transient episode low-level viremia (Blips), and persistent low-level viremia (LLV). Blips and LLV collectively classified as LV group. We analyzed the incidence of DM depending on viremia exposure using Cox proportional hazard models adjusted for age, sex, baseline VL, CD4 count, ART initial regimen, and WHO HIV stage. Heterogeneous linear mixed models identified fast blood glucose (FBG) trajectory patterns during the follow-up. Results During 26,097 person-years of follow-up, we observed 1297 cases of DM in 8731 participants, with median follow-up: 2.4 years [IQR:1.2, 4.5]. Two distinct FBG trajectories, labeled as “Stable” and “Rapid increase”, were identified. The LLV group had a significantly higher proportion of FBG in “Rapid increase” trajectory (OR: 2.53, P < 0.001). Both the Blips (cHR: 1.40, P < 0.001) and LLV (cHR: 1.74, P < 0.001) groups increased the incidence of DM than VS group. After propensity score matching, the LV group showed a higher DM risk (HR: 1.27, P = 0.011). When restricted to the 35–49 age group, the risk of DM was even higher in both the LLV (cHR: 2.24, p = 0.006) and Blips (cHR: 1.43, p = 0.011) groups than the VS group. Conclusions Low-level viremia (LV) substantially increased the risk of diabetes mellitus (DM), particularly in middle-aged individuals. Monitoring VL and FBG is crucial to prevent the development of DM and to improve life expectancy among ART patients. cohort study HIV low-level viremia diabetes mellitus China Figures Figure 1 Figure 2 Figure 3 Background With the widespread use of antiretroviral therapy (ART), the life expectancy of people with HIV (PWH) has increased significantly. However, as life expectancy increases, the risk of chronic diseases has risen significantly among PWH, such as diabetes, hypertension, and dyslipidemia. Diabetes is a common comorbidity in PWH and leads to serious adverse health outcomes[ 1 ]. PWH have been shown to have a higher prevalence of diabetes mellitus (DM) compared to those without HIV-infection[ 2 ]. Specifically, the prevalence of DM among HIV-infected adults was reported to be 10.3%, which is 3.8% higher than in those without HIV[ 3 ]. Furthermore, PWH on ART have been reported to have a fourfold higher risk of developing DM compared to the general population[ 4 ]. Therefore, it is crucial to identify the factors contributing to the high prevalence of DM in this population, in order to develop effective strategies for early diagnosis, prevention, and treatment of DM in PWH. The primary goal of ART is to achieve viral suppression, where HIV replication is sustained below the detectable threshold. However, some individuals experienced low-level viremia, characterized by a detectable, albeit low, viral load (VL) even while on ART. Low-level viremia refers to episodes of detectable HIV viremia (ie, > 50 copies/mL) that do not meet the criteria for virologic failure (VF) or blips[ 5 ]. VL cutoffs used to define low-level viremia vary based on the organization making the recommendation[ 5 , 6 ]. Current US guidelines define low-level viremia as VLs that are detectable but are less than 200 copies/mL, whereas the World Health Organization (WHO) uses a threshold of less than 1000 copies/mL to define this condition[ 5 , 6 ]. Studies found that low-level viremia increases the risk of non-AIDS related events, such as cardiovascular disease, chronic kidney disease, and decompensated liver disease[ 7 – 9 ]. Persistent low-level viremia has also been linked to elevated levels of inflammatory markers such as IL-6 and TNF-α, contributing to chronic inflammation, which may play a key role in the development of metabolic syndrome (MetS)[ 10 , 11 ]. MetS is a cluster of conditions that collectively increase the risk of heart disease, stroke, and diabetes. Research has shown that persistent viremia is a significant predictor of MetS development[ 12 ]. Although numerous studies have established an association between viremia and MetS in PWH, the specific impact of low-level viremia on the risk of developing DM remains uncertain. A Zambia Study indicated that insulin resistance, a precursor to DM, was linked to higher plasma VLs in PWH on long-term ART. Notably, a VL below 1000 copies/mL was associated with a lower likelihood of insulin resistance[ 13 ]. Another African Cohort study across four African countries found that participants with persistent low-level viremia (pLLV) had a statistically significant increased risk of developing hyperglycaemia[ 14 ]. Also a French Cohort Study: In contrast, the French cohort revealed a higher prevalence of DM among virologically suppressed individuals (10%) compared to those with detectable viral loads on ART(5.8%)[ 15 ]. These findings underscore the ambiguity surrounding the influence of low-level viremia on the development of DM in PWH. Moreover, many previous studies have been limited by small sample sizes, inconsistent definitions of low-level viremia, or cross-sectional designs that fail to establish causal relationships. Given the limited evidence on the relationship between low-level viremia (LV) and diabetes mellitus (DM) in PWH, particularly in China, it is crucial to clarify this association to better inform clinical management strategies and optimize treatment regimens. In this study, we explore the impact of low-level viremia (LV), including persistent LV (LLV) and transient episode (Blips), on the development of diabetes mellitus (DM) among PWH using a retrospective longitudinal cohort design in Guangxi, China. Methods Study Design and Participants This is a retrospective cohort study based on data extracted from China's National Free ART Program (CNFAP) in Guangxi, a southwest province with a severe HIV epidemic, ranking among the highest in China. PWH who started ART on or after January 1, 2003, were included. The inclusion criteria were: (1) age ≥ 18 years at the time of ART initiation; (2) having received ART for more than 6 months; (3) having at least 2 viral load (VL) measurements. The exclusion criteria were: (1) virological failure; (2) having abnormal baseline fasting blood glucose (FBG) levels (≥ 7.0mmol/L or ≤ 2.8 mmol/L); (3) having fewer than two records of FBG. Eligible participants were followed every 3 months until the incidence of DM, loss to follow-up (> 180 days between FBG measurements), or administrative censoring, with a maximum observation of 7 years or until the cohort-wide deadline of October 30, 2023. Definitions of Exposure The primary exposure of interest was the occurrence of low-level viremia after 6 months of ART in the next two VL measurements. Participants were enrolled into 3 groups: 1- Virologic Suppression (VS): All VLs were below 50 copies/mL. Transient Episode Low-Level Viremia (Blips): Defined by one VL measurement between 51–999 copies/mL, with VLs before and after at or below 50 copies/mL. Persistent Low-Level Viremia (LLV): Characterized by at least two consecutive VLs between 51–199 copies/mL, spaced at least 30 days apart, and not meeting the criteria for virologic failure (VF), which is defined as either two consecutive VLs of ≥ 200 copies/mL or a single VL of ≥ 1000 copies/mL Both LLV and Blips were classified under low-level viremia (LV). Clinical Follow-up Variables For the clinical follow-up variables, the baseline CD4 count or baseline viral load was defined as the first CD4 or viral load measurement taken at the time of HIV diagnosis. The recent CD4 count was defined as the last CD4 measurement before the end of follow-up and the recent viral load was defined as the last viral load measurement before the end of follow-up. Co-trimoxazole use history was recorded based on whether the individual had used co-trimoxazole to prevent opportunistic infections prior to starting ART. The initial ART regimen referred to the specific ART regimen used when the individual initiated ART. Specifically, if the regimen contained efavirenz (EFV), nevirapine (NVP), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTs), the ART regimen was classified as EFV-based, NVP-based, PIs-based, and INSTs-based respectively. Regimens that did not fall into these categories were classified as “Other regimens”. Definition of Outcome Variable Definition of Outcome Variable Diabetes mellitus (DM), the outcome variable, was defined as two consecutive FBG measurements of ≥ 7 mmol/L during the follow-up period, with at least 30 days between the tests and both measurements occurring within 180 days. Since hemoglobin A1c is known to be less accurate for PWH and is therefore not the preferred diagnostic method, the American Diabetes Association (ADA) recommends using plasma glucose-based criteria rather than hemoglobin A1c for diagnosing diabetes in this population[ 16 ]. We used two consecutive measurements to ensure the accuracy of the diagnosis. Statistical Methods We used Pearson’s χ2 tests to compare baseline characteristics across the different enrollment VL groups. We performed 1:1 nearest neighbor propensity score matching (PSM) with a caliper of 0.001 to adjust for sex and age between the VS and LV groups (R 4.3.1 MatchIt package). Sensitivity analyses were also conducted, stratifying by age and sex. A trajectory analysis using heterogeneous linear mixed models, also known as growth mixture models (R 4.3.1. lcmm package) was conducted to assess changes in FBG over time and to identify the different trajectories within each enrollment VL group. The FBG data over time were fitted using a maximum likelihood method as a mixture of multiple latent trajectories in a censored normal model with a polynomial function of time. The optimal number of groups was determined by synthesizing the Bayes information criterion (BIC) and group size. (Supplementary table 1 ) We fitted Cox regression models to assess the risk of DM by enrollment VL groups. Univariate Cox analysis was performed to identify significant variables first, and variables with a p-value less than 0.1 were included in the multivariate analysis. Then, using the backward stepwise regression method (R 4.3.1. autoReg package), variables with no significant effect (p < 0.10) on the model were gradually eliminated until only variables with significant contributions to the model remained. The final significance level was set at p < 0.05. Results Socio-demographic and Clinical Characteristics Of the 42,196 patients, 14,445 (34.2%) were aged over 18, had received ART for more than 6 months, and had normal baseline FBG levels. Of these, 5,724 (39.6%) were excluded due to having fewer than two follow-up VL measurements ( Supplementary Fig. 1) . Among the remaining 8,731 participants, the majority (7,423, 85.0%) were classified into the VS group, while 1,308 (15.0%) were classified into the LV group. Of those in the LV group, 1,125 (86.0%) were in the Blips group, and 183 (14.0%) were in the LLV group. Most participants were male (5,949, 68.1%), married or had a steady partner (5,528, 63.3%), and started ART with an EFV-based ART regimen (6,172, 70.7%). The majority of participants acquired HIV through heterosexual contact (7,289, 83.6%) and had achieved viral loads ≤ 50 copies/mL based on their recent VL measurement (8,162, 93.5%). ( Table 1 ) Table 1 Socio-demographic and clinical characteristics of all the participants Vars Total VS(N = 7423) Blips (N = 1125) LLV (N = 183) Follow time (Mean ± SD) 35.9 ± 25.3 36.3 ± 25.4 33.9 ± 24.7 30.1 ± 22.2 ART initial age (years) 18–34 2686 (30.8%) 2361 (31.8%) 298 (26.5%) 27 (14.8%) 35–49 2696 (30.9%) 2344 (31.6%) 308 (27.4%) 44 (24%) >=50 3349 (38.4%) 2718 (36.6%) 519 (46.1%) 112 (61.2%) Sex Male 5949 (68.1%) 4998 (67.3%) 807 (71.7%) 144 (78.7%) Female 2782 (31.9%) 2425 (32.7%) 318 (28.3%) 39 (21.3%) Marital status Married/Partnered 5528 (63.3%) 4684 (63.1%) 728 (64.7%) 116 (63.4%) Divorced/Widowed 1299 (14.9%) 1071 (14.4%) 194 (17.2%) 34 (18.6%) Single 1904 (21.8%) 1668 (22.5%) 203 (18%) 33 (18%) Ethnic Han 4050 (46.4%) 3461 (46.6%) 504 (44.8%) 85 (46.4%) Minority 4681 (53.6%) 3962 (53.4%) 621 (55.2%) 98 (53.6%) Education Primary school and below 2996 (34.3%) 2484 (33.5%) 432 (38.4%) 80 (43.7%) Junior school 3680 (42.1%) 3139 (42.3%) 470 (41.8%) 71 (38.8%) High school and above 2055 (23.5%) 1800 (24.2%) 223 (19.8%) 32 (17.5%) Occupation Farmer 4872 (55.8%) 4038 (54.4%) 719 (63.9%) 115 (62.8%) Others 3859 (44.2%) 3385 (45.6%) 406 (36.1%) 68 (37.2%) Transmission rout Heterosexual contact 7298 (83.6%) 6168 (83.1%) 973 (86.5%) 157 (85.8%) Other or unkonwn 1433 (16.4%) 1255 (16.9%) 152 (13.5%) 26 (14.2%) Co-trimoxazole use history at baseline No 5758 (65.9%) 4980 (67.1%) 665 (59.1%) 113 (61.7%) Yes 2973 (34.1%) 2443 (32.9%) 460 (40.9%) 70 (38.3%) Baseline WHO HIV stage Stage I 4270 (48.9%) 3707 (49.9%) 499 (44.4%) 64 (35%) Stage II 973 (11.1%) 825 (11.1%) 125 (11.1%) 23 (12.6%) Stage III 1400 (16.0%) 1146 (15.4%) 209 (18.6%) 45 (24.6%) Stage IV 2088 (23.9%) 1745 (23.5%) 292 (26%) 51 (27.9%) ART initial regimen EFV-based 6172 (70.7%) 5325 (71.7%) 734 (65.2%) 113 (61.7%) NVP-based 1477 (16.9%) 1237 (16.7%) 217 (19.3%) 23 (12.6%) PIs-based 993 (11.4%) 782 (10.5%) 167 (14.8%) 44 (24%) INSTIs-based 89 (1.0%) 79 (1.1%) 7 (0.6%) 3 (1.6%) Baseline CD4 + T cell count (cells/µL) < 200 4571 (52.4%) 3750 (50.5%) 704 (62.6%) 117 (63.9%) 200–350 2368 (27.1%) 2073 (27.9%) 260 (23.1%) 35 (19.1%) 350–500 1233 (14.1%) 1098 (14.8%) 114 (10.1%) 21 (11.5%) >=500 559 (6.4%) 502 (6.8%) 47 (4.2%) 10 (5.5%) Recent CD4 + T cell count (cells/µL) < 200 838 (9.6%) 690 (9.3%) 130 (11.6%) 18 (9.8%) 200–350 2068 (23.7%) 1715 (23.1%) 308 (27.4%) 45 (24.6%) 350–500 2178 (24.9%) 1844 (24.8%) 286 (25.4%) 48 (26.2%) >=500 3647 (41.8%) 3174 (42.8%) 401 (35.6%) 72 (39.3%) Baseline viral load (copies/mL) < 50 5804 (66.5%) 5386 (72.6%) 396 (35.2%) 22 (12%) 50-1000 1407 (16.1%) 762 (10.3%) 522 (46.4%) 123 (67.2%) >=1000 1520 (17.4%) 1275 (17.2%) 207 (18.4%) 38 (20.8%) Recent viral load (copies/mL) < 50 8162 (93.5%) 7043 (94.9%) 972 (86.4%) 147 (80.3%) 50-1000 407 (4.7%) 261 (3.5%) 115 (10.2%) 31 (16.9%) >=1000 162 (1.9%) 119 (1.6%) 38 (3.4%) 5 (2.7%) Table 2 Socio-demographic and clinical characteristics of the participants after matching for sex and age Vars VS(N = 1308) LV(N = 1308) Follow time (Mean ± SD) 34.7 ± 24.9 33.4 ± 24.4 ART initial age (years) 18–34 325 (24.8%) 325 (24.8%) 35–49 352 (26.9%) 352 (26.9%) >=50 631 (48.2%) 631 (48.2%) Sex Male 951 (72.7%) 951 (72.7%) Female 357 (27.3%) 357 (27.3%) Marital status Married/Partnered 843 (64.4%) 844 (64.5%) Divorced/Widowed 204 (15.6%) 228 (17.4%) Single 261 (20%) 236 (18%) Ethnic Han 629 (48.1%) 589 (45%) Minority 679 (51.9%) 719 (55%) Educational attainment Primary school and below 505 (38.6%) 512 (39.1%) Junior school 522 (39.9%) 541 (41.4%) High school and above 281 (21.5%) 255 (19.5%) Occupation Farmer 758 (58%) 834 (63.8%) Others 550 (42%) 474 (36.2%) Transmission rout Heterosexual contact 1107 (84.6%) 1130 (86.4%) Other or unkonwn 201 (15.4%) 178 (13.6%) Co-trimoxazole use history at baseline No 856 (65.4%) 778 (59.5%) Yes 452 (34.6%) 530 (40.5%) Baseline WHO HIV stage Stage I 635 (48.5%) 563 (43%) Stage II 160 (12.2%) 148 (11.3%) Stage III 212 (16.2%) 254 (19.4%) Stage IV 301 (23%) 343 (26.2%) ART initial regimen EFV-based 919 (70.3%) 847 (64.8%) NVP-based 204 (15.6%) 240 (18.3%) PIs-based 170 (13%) 211 (16.1%) INSTIs-based 15 (1.1%) 10 (0.8%) Baseline CD4 + T cell count (cells/µL) < 200 690 (52.8%) 821 (62.8%) 200–350 361 (27.6%) 295 (22.6%) 350–500 178 (13.6%) 135 (10.3%) >=500 79 (6%) 57 (4.4%) Recent CD4 + T cell count (cells/µL) < 200 139 (10.6%) 148 (11.3%) 200–350 316 (24.2%) 353 (27%) 350–500 329 (25.2%) 334 (25.5%) >=500 524 (40.1%) 473 (36.2%) Baseline viral load (copies/mL) < 50 952 (72.8%) 418 (32%) 50-1000 154 (11.8%) 645 (49.3%) >=1000 202 (15.4%) 245 (18.7%) Recent viral load (copies/mL) < 50 1233 (94.3%) 1119 (85.6%) 50-1000 52 (4%) 146 (11.2%) >=1000 23 (1.8%) 43 (3.3%) Table 3 Cox regression model for factors associated with diabetes mellitu (DM) Vars N(n%) HR (univariable) HR (multivariable) HR (final) Enrollment VL groups VS 7423 (85.0%) Blips 1125 (12.9%) 1.40 (1.21–1.63, p < .001) 1.21 (1.03–1.42, p = .022) 1.25 (1.08–1.45, p = .004) LLV 183 (2.1%) 1.74 (1.26–2.41, p < .001) 1.27 (0.90–1.78, p = .170) 1.33 (0.96–1.84, p = .088) ART initial age (years) 18–34 2686 (30.8%) 35–49 2696 (30.9%) 2.36 (1.98–2.81, p < .001) 2.18 (1.82–2.61, p < .001) 2.20 (1.84–2.63, p < .001) >=50 3349 (38.4%) 3.86 (3.28–4.54, p < .001) 3.40 (2.83–4.08, p < .001) 3.42 (2.88–4.07, p < .001) Sex Male 5949 (68.1%) Female 2782 (31.9%) 0.73 (0.65–0.83, p < .001) 0.72 (0.63–0.82, p < .001) 0.72 (0.63–0.81, p < .001) Ethnic Han 4050 (46.4%) Minority 4681 (53.6%) 0.82 (0.74–0.92, p < .001) 0.79 (0.71–0.89, p < .001) 0.80 (0.71–0.89, p < .001) Educational attainment Primary school and below 2996 (34.3%) Junior school 3680 (42.1%) 0.73 (0.65–0.82, p < .001) 0.94 (0.83–1.07, p = .328) 0.93 (0.82–1.06, p = .266) High school and above 2055 (23.5%) 0.49 (0.42–0.58, p < .001) 0.74 (0.61–0.90, p = .002) 0.73 (0.61–0.87, p < .001) Occupation Farmer 4872 (55.8%) Others 3859 (44.2%) 0.69 (0.62–0.77, p < .001) 0.98 (0.86–1.12, p = .780) Transmission rout Heterosexual contact 7298 (83.6%) Other or unkonwn 1433 (16.4%) 0.65 (0.54–0.78, p < .001) 0.97 (0.80–1.18, p = .758) Co-trimoxazole use history at baseline No 5758 (65.9%) Yes 2973 (34.1%) 1.21 (1.09–1.36, p < .001) 1.09 (0.94–1.28, p = .249) Baseline WHO HIV stage Stage I 4270 (48.9%) Stage II 973 (11.1%) 1.12 (0.94–1.35, p = .206) 0.95 (0.79–1.14, p = .555) Stage III 1400 (16.0%) 1.24 (1.07–1.45, p = .005) 0.96 (0.82–1.13, p = .647) Stage IV 2088 (23.9%) 1.14 (1.00-1.31, p = .052) 1.00 (0.86–1.17, p = .965) ART initial regimen EFV-based 6172 (70.7%) NVP-based 1477 (16.9%) 1.01 (0.88–1.16, p = .904) 1.05 (0.90–1.21, p = .545) PIs-based 993 (11.4%) 1.18 (1.00-1.39, p = .051) 0.96 (0.81–1.14, p = .661) INSTIs-based 89 (1.0%) 0.93 (0.35–2.48, p = .882) 1.11 (0.41–2.97, p = .841) Baseline CD4 + T cell count (cells/µL) < 200 4571 (52.4%) 200–350 2368 (27.1%) 0.92 (0.81–1.05, p = .212) 1.06 (0.90–1.26, p = .473) 350–500 1233 (14.1%) 0.82 (0.69–0.98, p = .025) 1.01 (0.81–1.26, p = .941) >=500 559 (6.4%) 0.92 (0.72–1.18, p = .496) 1.11 (0.83–1.48, p = .469) Recent CD4 + T cell count (cells/µL) < 200 838 (9.6%) 200–350 2068 (23.7%) 0.75 (0.62–0.92, p = .005) 0.85 (0.69–1.04, p = .110) 350–500 2178 (24.9%) 0.76 (0.63–0.93, p = .007) 0.98 (0.80–1.20, p = .869) >=500 3647 (41.8%) 0.61 (0.51–0.74, p < .001) 0.92 (0.75–1.13, p = .444) Baseline viral load (copies/mL) < 50 5804 (66.5%) 50-1000 1407 (16.1%) 1.37 (1.19–1.57, p < .001) 1.10 (0.94–1.28, p = .231) >=1000 1520 (17.4%) 0.87 (0.74–1.02, p = .084) 0.98 (0.83–1.16, p = .819) Recent viral load (copies/mL) < 50 8162 (93.5%) 50-1000 407 (4.7%) 1.64 (1.32–2.04, p < .001) 1.24 (0.99–1.56, p = .057) 1.26 (1.01–1.58, p = .043) >=1000 162 (1.9%) 1.54 (1.07–2.21, p = .020) 1.23 (0.85–1.78, p = .272) 1.24 (0.86–1.78, p = .251) n = 8731, events = 1297, Likelihood ratio test = 406.89 on 27 df(p < .001) Incidence of DM The overall median follow-up was 2.4 [IQR: 1.2, 4.5] years. During 26,097 person-years of follow-up, 1297 (14.9%) participants developed DM with an incidence rate [IR] of 49 per 1,000 person-years (95%CI: 46–52). In the LLV, Blips, and VS groups, the incidence of DM was 38 (20.8%), 209 (18.6%), and 1,050 (14.1%), respectively, with incidence rates of 82, 65, and 46 per 1,000 person-years (95% CI: 56–109, 56–74, and 43–49, respectively) ( Fig. 3 ) . After matching for sex and age, in the subset of 2,616 participants, 451 (17.5%) developed DM over 7,415 person-years (IR: 60 per 1,000 person-years, 95% CI: 62–73). The incidence was 204 (15.6%) in the VS group (IR: 54 per 1,000 person-years, 95% CI: 46–61), while a significantly higher incidence of 247 (18.9%) was observed in the LV group (IR: 67 per 1,000 person-years, 95% CI: 59–76). FBG Trajectory Group Analysis Two distinct FBG trajectories were identified within each group (VS, Blips, and LLV). Individuals with a stable FBG trajectory were classified as the “Stable group” (n = 7,134 [96.11%] in the VS group, n = 1,082 [96.18%] in the Blips group, and n = 166 [90.71%] in the LLV group). The “Stable group” was used as the reference for comparison. Participants who experienced a marked increase in FBG during the follow-up period were classified as the “Rapid Increase” group. A significantly higher proportion of participants in the LLV group followed the “Rapid Increase” FBG trajectory (OR [95%CI]: 2.53 [1.53–4.16], P < 0.001) compared to the VS group (289 [3.89%] in the VS group vs. 17 [9.29%] in the LLV group). ( Fig. 1 , Fig. 3 ) Association Between LV and the Risk of DM Compared to the VS group, the incidence of DM was significantly higher in both the Blips group (cHR [95%CI]: 1.40 [1.21–1.63], p < 0.001) and the LLV group (cHR [95%CI]: 1.74[1.26–2.41], p < 0.001), with the LLV group presenting a greater risk than the Blips group (Fig. 2 A). Although the association between DM and the LLV group lost significance after adjustment, the Blips group remained significantly associated with DM (aHR [95%CI]: 1.25 [1.08–1.45], p < 0.004) (Table 4 ). After matching for age and sex between VS and LV groups, the risk of developing DM remained elevated in the LV group (aHR [95%CI]: 1.27 [1.06–1.53], p = 0.011) compared to the VS group (Table 5). In a stratified analysis by age, we found that Blips and LLV groups were significantly associated with DM development in individuals aged 35–49 years, with cHRs of 1.43 (95% CI: 1.08–1.88, p = 0.011) and 2.24 (95% CI: 1.26–3.98, p = 0.006), respectively ( Fig. 2 E ) . After matching for age and sex between VS and LV groups, the association remained significant, with a cHR of 1.72 (95% CI: 1.18–2.51, p = 0.005) in the LV group. (Supplementary table 2 , Supplementary table 3) Table 4 Cox regression model for factors associated with diabetes mellitus (DM) after matching for age and sex Vars N(n%) HR (univariable) HR (multivariable) HR (final) Enrollment VL groups VS 1308 (50.0%) LV 1308 (50.0%) 1.26 (1.05–1.52, p = .015) 1.24 (1.03–1.49, p = .026) 1.27 (1.06–1.53, p = .011) ART initial age (years) 18–34 650 (24.8%) 35–49 704 (26.9%) 2.33 (1.66–3.26, p < .001) 2.18 (1.54–3.08, p < .001) 2.30 (1.64–3.22, p < .001) >=50 1262 (48.2%) 3.76 (2.77–5.10, p < .001) 3.54 (2.54–4.94, p < .001) 3.77 (2.78–5.12, p < .001) Sex Male 1902 (72.7%) Female 714 (27.3%) 0.79 (0.64–0.98, p = .031) 0.79 (0.63–0.99, p = .042) 0.78 (0.63–0.97, p = .023) Ethnic Han 1218 (46.6%) Minority 1398 (53.4%) 0.85 (0.71–1.03, p = .092) 0.81 (0.67–0.97, p = .026) 0.84 (0.70–1.01, p = .060) Educational attainment Primary school and below 1017 (38.9%) Junior school 1063 (40.6%) 0.74 (0.61–0.91, p = .004) 0.97 (0.78–1.19, p = .743) High school and above 536 (20.5%) 0.59 (0.44–0.78, p < .001) 0.87 (0.62–1.20, p = .386) Occupation Farmer 1592 (60.9%) Others 1024 (39.1%) 0.69 (0.57–0.84, p < .001) 0.92 (0.73–1.16, p = .483) Transmission rout Heterosexual contact 2237 (85.5%) Other or unkonwn 379 (14.5%) 0.75 (0.55–1.02, p = .066) 1.15 (0.82–1.60, p = .421) Co-trimoxazole use history at baseline No 1634 (62.5%) Yes 982 (37.5%) 1.16 (0.96–1.39, p = .125) Baseline WHO HIV stage Stage I 1198 (45.8%) Stage II 308 (11.8%) 0.98 (0.72–1.35, p = .914) 0.87 (0.63–1.19, p = .372) Stage III 466 (17.8%) 1.25 (0.98–1.60, p = .078) 1.02 (0.79–1.31, p = .884) Stage IV 644 (24.6%) 1.18 (0.93–1.48, p = .167) 1.08 (0.85–1.37, p = .528) ART initial regimen EFV-based 1766 (67.5%) NVP-based 444 (17.0%) 0.84 (0.65–1.08, p = .176) PIs-based 381 (14.6%) 1.06 (0.82–1.37, p = .645) INSTIs-based 25 (1.0%) 0.77 (0.11–5.48, p = .792) Baseline CD4 + T cell count (cells/µL) < 200 1511 (57.8%) 200–350 656 (25.1%) 0.90 (0.72–1.12, p = .328) 350–500 313 (12.0%) 0.79 (0.58–1.08, p = .142) >=500 136 (5.2%) 0.74 (0.45–1.20, p = .223) Recent CD4 + T cell count (cells/µL) < 200 287 (11.0%) 200–350 669 (25.6%) 0.77 (0.55–1.06, p = .111) 0.85 (0.61–1.18, p = .325) 350–500 663 (25.3%) 0.84 (0.61–1.16, p = .289) 1.05 (0.76–1.46, p = .766) >=500 997 (38.1%) 0.58 (0.43–0.80, p < .001) 0.83 (0.60–1.15, p = .272) Recent viral load (copies/mL) < 50 2352 (89.9%) 50-1000 198 (7.6%) 1.51 (1.10–2.07, p = .010) 1.22 (0.89–1.69, p = .220) >=1000 66 (2.5%) 1.01 (0.54–1.88, p = .987) 0.83 (0.44–1.58, p = .578) n = 2616, events = 451, Likelihood ratio test = 123.57 on 17 df(p < .001) The risk of DM increased significantly in both the 35–49 age group (aHR [95%CI]: 2.20[1.84–2.63], p < 0.001) and the ≥ 50 age group (aHR [95%CI]: 3.42 [2.88–4.07], p < 0.001), compared to the 18–34 age group. Females (aHR [95%CI]: 0.72 [0.63–0.81], p < 0.001) were less likely to develop DM compared to males (Table 4 ). After matching for sex and age between VS and LV groups, these associations remained significant, with aHRs of 2.30 (95%CI: 1.64–3.22, p < 0.001) and 3.77 (95%CI: 2.78–5.12, p < 0.001) in the 35–49 years and ≥ 50 years age groups, respectively, and an aHR of 0.78 (95% CI: 0.63–0.97, p = 0.023) for females compared to males (Table 5). Compared to participants with a recent viral load < 50 copies/ml, those with a viral load of 50-1000 copies/ml had a higher risk of developing DM (aHR [95%CI] : 1.26 (1.01–1.58, P = 0.043) (Table 4 ). After matching for age and sex, a baseline viral load of 50-1000 copies/ml was also associated with a higher risk of developing DM (cHR [95%CI] : 1.51 [1.10–2.07], p = 0.010). Additionally, a baseline viral load of 50-1000 copies/ml (cHR [95%CI] : 1.37 [1.19–1.57], p < 0.001) demonstrated a greater propensity for developing DM compared to those with a baseline viral load of ≤ 50 copies/ml (Table 5) Discussion This study is the first cohort study in China to examine the effect of low-level viremia (LV) on the incidence of diabetes mellitus (DM), utilizing a large sample size and a long-term follow-up period of 7 years. Two distinct FBG trajectories, “Stable” and “Rapid increase”, were identified within the VS, Blips, and LLV groups. A significantly larger proportion of participants in the LLV group followed the “Rapid Increase” FBG trajectory compared to the VS group. Additionally, the LV group had an elevated risk of developing DM compared to the VS group, with both LLV and Blips groups significantly associated with an increased risk of developing DM in individuals aged 35–49. Proactive management of LV could have a positive impact on reducing the risk of DM in PWH. During the follow-up period, 14.9% of PHW developed DM with an incidence rate of 49 per 1,000 person-years. This incidence was higher than that observed in a French cohort, where the prevalence of DM was 8.5%, with an incidence rate of 9.6 per 1,000 person-year[ 15 ]. Similarly, the RESPOND cohort reported a prevalence of DM at 3.7%, with an incidence rate of 7.3 per 1,000 person-year[ 17 ]. However, these previous studies included all HIV-infected individuals, while our study specifically focused on those who had received ART for more than 6 months. We hypothesize that DM may develop earlier in individuals with LV, potentially contributing to the higher incidence density. In contrast, the incidence of DM was lower than that reported in an African cohort, which included individuals with pLLV (persistent low-level viremia), where 21.26% of them developed hyperglycemia[ 14 ]. Nonetheless, our findings are consistent with a meta-analysis showing that the prevalence of DM among PWH in low- and middle-income countries (LMIC) ranges from 1.3–18%[ 18 ]. This wide variation might be due to differences in the definitions of diabetes and in the populations studied[ 16 ]. Persistent viremia in HIV-infected individuals has been identified as a predictor of metabolic syndrome (MetS), a cluster of conditions that collectively increase the risk of heart disease, stroke, and type 2 diabetes[ 19 , 20 ]. Besides, low-level viremia has been linked to dysfunctional immunometabolism in HIV-infected individuals[ 21 ]. We observed that a significantly higher proportion of individuals in the “Rapid increase” fasting blood glucose trajectory in the LLV group compared to the VS group. This suggested a stronger association between LLV and rapid increases in blood glucose, indicating that individuals with LLV may be at higher risk of metabolic dysregulation than those with VS. This finding highlights a potential metabolic vulnerability in individuals with LLV. Therefore, individuals with LLV should be monitored more frequently for metabolic markers, such as FBG and insulin levels, to reduce the incidence of DM and other metabolic dysregulation. This need for enhanced monitoring is reinforced by our findings, which revealed that the incidence of DM was significantly higher in both the Blips group and the LLV group compared to the VS group. Although the association between DM and the LLV group did not show significant differences after adjusting for other variables, the Blips group remained significantly associated with the incidence of DM. This finding may be attributed to the small sample size of the LLV group. However, the trajectory analysis, which utilized all the records of FBG across the follow-up period, verified the association between LLV and a rapid increase in FBG, implicitly supporting its impact on the development of DM. When restricting the analysis to participants aged 35–49, both LLV and Blips were significantly linked to an increased risk of developing DM. Sensitivity analysis reinforced these findings, confirming that LV was significantly linked to a higher risk of DM onset in this subgroup. These findings underscore the importance of prioritizing PHW with LV, especially middle-aged individuals, as they may exhibit a heightened vulnerability to metabolic disorders such as DM. Consequently, targeted monitoring and tailored preventive strategies are imperative for this demographic to mitigate the risk of DM. Furthermore, individuals with the recent VL of 50-1000 copies/ml exhibited an increased risk of developing DM, while those with a VL greater than 1000 copies/ml showed no significant difference compared to those with a VL of less than 50 copies/ml. This clinically counter-intuitive and unexpected finding suggests that moderate levels of viremia may have a more detrimental impact on metabolic processes than very high levels, potentially due to the chronic immune activation associated with lower, persistent viral replication. It further supported our other finding that LV is more strongly linked to the development of DM. A study from South Carolina similarly found that a higher percentage of days with VS was associated with the incidence of DM[ 22 ], aligning with our observation that the presence, duration, and consistency of viremia play a crucial role in driving metabolic complications. Mechanistically, LV keeps the immune system in a prolonged state of activation, which can lead to chronic inflammation. This inflammation is an established contributor to insulin resistance, a key mechanism in the development of DM[ 23 , 24 ]. Therefore, these findings underscore the importance of not only achieving VS but also maintaining it consistently over time to mitigate the risk of DM among PWH. This study had several limitations, and the results did not suggest a causal relationship. First, although overweight and obesity are are generally linked to an increased risk of DM, we were unable to incorporate BMI data in our analysis. It's important to note that HIV-specific factors may predispose individuals to diabetes at lower levels of adiposity compared to the general population[ 25 ]. Research suggest that HIV-infected individuals often experience insulin resistance (IR) at lower BMI levels, indicating that factors beyond BMI may significantly influence the pathogenesis of DM[ 4 ]. Second, FBG levels were used to diagnose diabetes, though it was not guaranteed that all patients were fasting at the time of testing. Yet, to enhance the reliability of our diagnosis, we used two consecutive records of FBG (taken at intervals of more than 30 days and less than 180 days) that exceeded the normal threshold of 7 mmol/L, and we did FBG trajectory analysis using all the records of FBG throughout the follow-up period. Third, the limited number of participants with LLV prevented us from performing matching for this group, which could have affected the results. Nevertheless, we matched the Blips, LLV, and VS groups for analysis, and the findings still suggested that LV was linked to an increased risk of developing DM. Conclusions In summary, this study underscores that low-level viremia (LV) is significantly associated with the development of diabetes mellitus (DM) among PWH, particularly in middle-aged individuals. Proactive monitoring of both viral load (VL) and fasting blood glucose (FBG) is essential to prevent the development of DM at both the individual and population levels, and to contribute to extending the life expectancy of patients undergoing ART. Abbreviations PWH People with HIV ART Antiretroviral therapy DM Diabetes mellitus FBG Fasting blood glucose LV Low-level viremia LLV Persistent low-level viremia Blips Transient episode low-level viremia VS Viral suppression LV Viral load EFV Efavirenz NVP Nevirapine PIs Protease inhibitors INSTs Integrase strand transfer inhibitors Declarations Ethics approval and consent to participate This study was conducted following the Helsinki Declaration and was approved by the Human Research Ethics Committee of Guangxi Medical University (No. KY0294). Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during this study are not publicly accessible due to ethical and legal considerations. However, they are available from the corresponding author, Bingyu Liang, upon reasonable request. Competing interests The authors report no conflicts of interests. Funding This study was supported by the Scientific Research and Technology Development Program of Guangxi Zhuang Autonomous Region (Grant No. 2022AC23005, 2022JJA141110), the National Natural Science Foundation of China (Grant No. 82103899), National Key R&D Program of China (Grant No. 2022YFC2305001), the China Scholarship Council (To Bingyu Liang), the Thousands of Young and Middle Aged Key Teachers Training Program in Guangxi Colleges and Universities (To Bingyu Liang), and the Guangxi Bagui Young Top Scholar (To Bingyu Liang). Authors ’ Contributions LBY, BLJ, and WLJ conceptualized and designed the study. LLY, YYB and NAD contributed to literature review, HLJ, HXH and WSX handled the collection and testing of blood samples, HL and HRY assisted with administering the questionnaire survey and inputting the data, TCX performed data analysis and interpretation, revised the manuscript critically for important intellectual content, MTA and SG provided critical feedback on data interpretation and reviewed the manuscript. NCY, LH, and YL oversaw the data analysis. All authors participated in critically revising the manuscript and approved the final version for publication. Acknowledgements Not applicable. References Liu Z, Zhang J, Yang X, Gao H, Chen S, Weissman S, et al. The dynamic risk factors of cardiovascular disease among people living with HIV: a real-world data study. BMC Public Health. 2024;24:1162. Njoroge A, Augusto O, Page ST, Kigondu C, Oluka M, Puttkammer N, et al. Increased risk of prediabetes among virally suppressed adults with HIV in Central Kenya detected using glycated haemoglobin and fasting blood glucose. Endocrinol Diabetes Metabolism. 2021;4:e00292. Hernandez-Romieu AC, Garg S, Rosenberg ES, Thompson-Paul AM, Skarbinski J. Is diabetes prevalence higher among HIV-infected individuals compared with the general population? Evidence from MMP and NHANES 2009–2010. BMJ Open Diabetes Res Care. 2017;5:e000304. Chebrolu P, Sangle S, Nimkar S, Salvi S, Chavan A, Kulkarni V, et al. Inflammatory profile associated with insulin resistance in non-overweight versus overweight people living with HIV in Pune, Western India. Diabetes metabolic syndrome. 2022;16:102551. Guidelines for the use of antiretroviral agents in adults and adolescents with HIV. 2024. https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/whats-new . Accessed 8 Sep 2024. Consolidated guidelines on the use of antiretroviral drugs for treating. and preventing HIV infection: recommendations for a public health approach, 2nd ed. https://www.who.int/publications/i/item/9789241549684 . Accessed 8 Sep 2024. Ganesan A, Hsieh H-C, Chu X, Colombo RE, Berjohn C, Lalani T, et al. Low Level Viremia Is Associated With Serious non-AIDS Events in People With HIV. Open Forum Infect Dis. 2024;11:ofae147. Elvstam O, Marrone G, Medstrand P, Treutiger CJ, Sönnerborg A, Gisslén M et al. All-Cause Mortality and Serious Non-AIDS Events in Adults With Low-level Human Immunodeficiency Virus Viremia During Combination Antiretroviral Therapy: Results From a Swedish Nationwide Observational Study. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. 2020;72:2079–86. Ding H, Xu J, Liu J, Wang Q, Kang J, Li X, et al. Outcomes of persistent low-level viremia among HIV patients on antiretroviral therapy: A prospective cohort study. HIV Med. 2022;23(Suppl 1):64–71. Mazzuti L, Turriziani O, Mezzaroma I. The Many Faces of Immune Activation in HIV-1 Infection: A Multifactorial Interconnection. Biomedicines. 2023;11:159. Zicari S, Sessa L, Cotugno N, Ruggiero A, Morrocchi E, Concato C, et al. Immune Activation, Inflammation, and Non-AIDS Co-Morbidities in HIV-Infected Patients under Long-Term ART. Viruses. 2019;11:200. Squillace N, Zona S, Stentarelli C, Orlando G, Beghetto B, Nardini G, et al. Detectable HIV Viral Load Is Associated With Metabolic Syndrome. JAIDS J Acquir Immune Defic Syndr. 2009;52:459. Mulenga L, Musonda P, Chirwa L, Siwingwa M, Mweemba A, Suwilanji S, et al. Insulin Resistance is Associated with Higher Plasma Viral Load Among HIV-Positive Adults Receiving Longer-Term (1 Year) Combination Antiretroviral Therapy (ART). J Infect disease therapy. 2019;7:406. Esber AL, Colt S, Jian N, Dear N, Slike B, Sing’oei V, et al. Persistent low-level viraemia is associated with non‐infectious comorbidities in an observational cohort in four African countries. J Int AIDS Soc. 2024;27:e26316. Nacher M, Rabier S, Lucarelli A, Hureau L, Adenis A, Hafsi N, et al. Diabetes in a hospital cohort of persons living with HIV: a descriptive and comparative study in French Guiana. BMC Infect Dis. 2023;23:470. Sarkar S, Brown TT. Diabetes in People with HIV. Curr Diab Rep. 2021;21:13. Rupasinghe D, Bansi-Matharu L, Law M, Zangerle R, Rauch A, Tarr PE, et al. Integrase strand transfer inhibitor (INSTI) related changes in BMI and risk of diabetes: a prospective study from the RESPOND cohort consortium. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America; 2024. p. ciae406. Patel P, Rose CE, Collins PY, Nuche-Berenguer B, Sahasrabuddhe VV, Peprah E, et al. Noncommunicable diseases among HIV-infected persons in low-income and middle-income countries: a systematic review and meta-analysis. AIDS. 2018;32(Suppl 1):S5–20. Alencastro PR, Fuchs SC, Wolff FH, Ikeda ML, Brandão AB, Barcellos NT. Independent Predictors of Metabolic Syndrome in HIV-Infected Patients. Aids Patient Care Stds. 2011;25:627–34. Collins LF, Adekunle RO, Cartwright EJ. Metabolic Syndrome in HIV/HCV Co-infected Patients. Curr Treat Options Infect Dis. 2019;11:351–71. Butterfield TR, Landay AL, Anzinger JJ. Dysfunctional Immunometabolism in HIV Infection: Contributing Factors and Implications for Age-Related Comorbid Diseases. Curr Hiv/aids Rep. 2020;17:125–37. Mohammad Pritom GS, Yang X, Gao H, Chen S, Zhang J, Olatosi B, et al. Examining incidence of diabetes in people with HIV: tracking the shift in traditional and HIV-related risk factors. AIDS. 2024;38:1057–65. Mu W, Patankar V, Kitchen S, Zhen A. Examining Chronic Inflammation, Immune Metabolism, and T Cell Dysfunction in HIV Infection. Viruses. 2024;16:219. Bourgi K, Wanjalla C, Koethe JR. Inflammation and Metabolic Complications in HIV. Curr Hiv/aids Rep. 2018;15:371–81. Levitt NS, Peer N, Steyn K, Lombard C, Maartens G, Lambert EV, et al. Increased risk of dysglycaemia in South Africans with HIV; especially those on protease inhibitors. Diabetes Res Clin Pract. 2016;119:41–7. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medicine → Version 1 posted Editorial decision: Revision requested 25 Feb, 2025 Reviews received at journal 24 Feb, 2025 Reviewers agreed at journal 14 Jan, 2025 Reviews received at journal 28 Nov, 2024 Reviewers agreed at journal 06 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor assigned by journal 04 Nov, 2024 Submission checks completed at journal 04 Nov, 2024 First submitted to journal 03 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5380470\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":378957929,\"identity\":\"d7d586cb-0ef3-45f7-a67d-aa0d0e65983a\",\"order_by\":0,\"name\":\"Chunxing Tao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical 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University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hao\",\"middleName\":\"\",\"lastName\":\"Liang\",\"suffix\":\"\"},{\"id\":378957940,\"identity\":\"4d299cb0-5478-44f9-b90c-89c0927a84e5\",\"order_by\":11,\"name\":\"Chuanyi Ning\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chuanyi\",\"middleName\":\"\",\"lastName\":\"Ning\",\"suffix\":\"\"},{\"id\":378957941,\"identity\":\"37383e7d-d6e4-4ac9-b00c-7412b66861c5\",\"order_by\":12,\"name\":\"Salma Gayed\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Infectious Diseases, University of North Carolina, Chapel Hill\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Salma\",\"middleName\":\"\",\"lastName\":\"Gayed\",\"suffix\":\"\"},{\"id\":378957942,\"identity\":\"6ebf94e4-3bb8-4b89-a9e1-27dafa17b8e7\",\"order_by\":13,\"name\":\"Lijuan Bao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chongzuo Center for Disease Control and Prevention\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lijuan\",\"middleName\":\"\",\"lastName\":\"Bao\",\"suffix\":\"\"},{\"id\":378957943,\"identity\":\"7c32ada9-e531-4e14-9cb4-d1617cfcb76e\",\"order_by\":14,\"name\":\"Bingyu Liang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACPmYgwdggAWIzPoAKGuDVwoakhRmmlIAWsPENELYEcVrYmZ89/LrDIk8+Ises8mvbtsQG9uZtEgw1d/A4jM3cWPaMRLHhjRyz2zJnbic28Bwrk2A49gyfX8ykJdskEjfOAGqRqABqkcgxk2BsOIxHC/s3uJZiCQOgFvk3hLTwmEl+BGqZDzSc8QPYFh6CWsqkGc9IJG7geVYszXDmtnEbT1qxRcIx3Fr4+Y9vk/y5oy5xfnvyxo8/227L9rMf3njjQw1uLSDAzAMkDC4kQBjgmErAqwEYkz+AhHz/AQhjFIyCUTAKRgE6AABb0lFOeSmpAwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Bingyu\",\"middleName\":\"\",\"lastName\":\"Liang\",\"suffix\":\"\"},{\"id\":378957944,\"identity\":\"cb5bb2fc-72a6-402a-bff6-e892665ede77\",\"order_by\":15,\"name\":\"Xiaohuan Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Infectious Diseases, Lingshan County People’s Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaohuan\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":378957945,\"identity\":\"d722d9f8-78ab-452f-be56-dc0d2352c4eb\",\"order_by\":16,\"name\":\"Yanbing Yao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yanbing\",\"middleName\":\"\",\"lastName\":\"Yao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-11-03 04:23:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5380470/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5380470/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12916-025-04166-z\",\"type\":\"published\",\"date\":\"2025-07-01T15:57:16+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":70508776,\"identity\":\"4b7de4e4-5282-4946-9d66-1160628132c3\",\"added_by\":\"auto\",\"created_at\":\"2024-12-04 00:08:53\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":126927,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMean fasting blood glucose (FBG) trajectories during the follow-up period across VL groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/a6a0e151233aded7e658f75b.jpg\"},{\"id\":70508050,\"identity\":\"3edca066-2464-4914-a998-28c891787f11\",\"added_by\":\"auto\",\"created_at\":\"2024-12-04 00:00:53\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4296481,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCumulative risk of DM by VL groups, including all participants and stratified by sex and age. A.Cumulative risk of DM among VS, Blips and LLV groups; B.Cumulative risk of DM among VS, Blips and LLV groups in males; C.Cumulative risk of DM among VS, Blips and LLV groups in females; D.Cumulative risk of DM among VS, Blips and LLV groups in participants aged 18-34; E.Cumulative risk of DM among VS, Blips and LLV groups in participants aged 35-50; F.Cumulative risk of DM among VS, Blips and LLV groups in participants in aged \\u0026gt;=50.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/9547b13cafe355f930fd0525.jpg\"},{\"id\":70508046,\"identity\":\"04307cd4-2974-4713-a89c-ed8f47129da8\",\"added_by\":\"auto\",\"created_at\":\"2024-12-04 00:00:53\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":67633,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIncidence of rapid increase FBG trajectory changes and diabetes mellitus across the three VL groups. A. Incidence of rapid increase FBG trajectory in the VS, Blips, and LLV Groups. B. Incidence of diabetes mellitus in the VS, Blips, and LLV Groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/610b91877d119b3b50624c12.jpg\"},{\"id\":86179170,\"identity\":\"093c56a3-f6b2-42ee-99e8-e70e1e56be08\",\"added_by\":\"auto\",\"created_at\":\"2025-07-07 16:16:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6371960,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/1f858882-d98f-4525-85f6-92e9aa5b52d5.pdf\"},{\"id\":70508048,\"identity\":\"5a4c2428-bc14-4afa-aceb-4a5baae73279\",\"added_by\":\"auto\",\"created_at\":\"2024-12-04 00:00:53\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":169966,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/ba60c6d86dc83ec1c6cc9b31.docx\"},{\"id\":70508777,\"identity\":\"6474b4e7-1f95-4456-b4cc-7b7fdfba1315\",\"added_by\":\"auto\",\"created_at\":\"2024-12-04 00:08:53\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":201888,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5380470/v1/ced7af6005f9fbf7a785c426.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Low-level Viremia Increases the Risk of Diabetes Mellitus in People with HIV in China: A 7-Year Retrospective Longitudinal Cohort Study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eWith the widespread use of antiretroviral therapy (ART), the life expectancy of people with HIV (PWH) has increased significantly. However, as life expectancy increases, the risk of chronic diseases has risen significantly among PWH, such as diabetes, hypertension, and dyslipidemia. Diabetes is a common comorbidity in PWH and leads to serious adverse health outcomes[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. PWH have been shown to have a higher prevalence of diabetes mellitus (DM) compared to those without HIV-infection[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Specifically, the prevalence of DM among HIV-infected adults was reported to be 10.3%, which is 3.8% higher than in those without HIV[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Furthermore, PWH on ART have been reported to have a fourfold higher risk of developing DM compared to the general population[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Therefore, it is crucial to identify the factors contributing to the high prevalence of DM in this population, in order to develop effective strategies for early diagnosis, prevention, and treatment of DM in PWH.\\u003c/p\\u003e \\u003cp\\u003eThe primary goal of ART is to achieve viral suppression, where HIV replication is sustained below the detectable threshold. However, some individuals experienced low-level viremia, characterized by a detectable, albeit low, viral load (VL) even while on ART. Low-level viremia refers to episodes of detectable HIV viremia (ie, \\u0026gt;\\u0026thinsp;50 copies/mL) that do not meet the criteria for virologic failure (VF) or blips[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. VL cutoffs used to define low-level viremia vary based on the organization making the recommendation[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Current US guidelines define low-level viremia as VLs that are detectable but are less than 200 copies/mL, whereas the World Health Organization (WHO) uses a threshold of less than 1000 copies/mL to define this condition[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Studies found that low-level viremia increases the risk of non-AIDS related events, such as cardiovascular disease, chronic kidney disease, and decompensated liver disease[\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Persistent low-level viremia has also been linked to elevated levels of inflammatory markers such as IL-6 and TNF-α, contributing to chronic inflammation, which may play a key role in the development of metabolic syndrome (MetS)[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. MetS is a cluster of conditions that collectively increase the risk of heart disease, stroke, and diabetes. Research has shown that persistent viremia is a significant predictor of MetS development[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAlthough numerous studies have established an association between viremia and MetS in PWH, the specific impact of low-level viremia on the risk of developing DM remains uncertain. A Zambia Study indicated that insulin resistance, a precursor to DM, was linked to higher plasma VLs in PWH on long-term ART. Notably, a VL below 1000 copies/mL was associated with a lower likelihood of insulin resistance[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Another African Cohort study across four African countries found that participants with persistent low-level viremia (pLLV) had a statistically significant increased risk of developing hyperglycaemia[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Also a French Cohort Study: In contrast, the French cohort revealed a higher prevalence of DM among virologically suppressed individuals (10%) compared to those with detectable viral loads on ART(5.8%)[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. These findings underscore the ambiguity surrounding the influence of low-level viremia on the development of DM in PWH. Moreover, many previous studies have been limited by small sample sizes, inconsistent definitions of low-level viremia, or cross-sectional designs that fail to establish causal relationships.\\u003c/p\\u003e \\u003cp\\u003eGiven the limited evidence on the relationship between low-level viremia (LV) and diabetes mellitus (DM) in PWH, particularly in China, it is crucial to clarify this association to better inform clinical management strategies and optimize treatment regimens. In this study, we explore the impact of low-level viremia (LV), including persistent LV (LLV) and transient episode (Blips), on the development of diabetes mellitus (DM) among PWH using a retrospective longitudinal cohort design in Guangxi, China.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Design and Participants\\u003c/h2\\u003e \\u003cp\\u003eThis is a retrospective cohort study based on data extracted from China's National Free ART Program (CNFAP) in Guangxi, a southwest province with a severe HIV epidemic, ranking among the highest in China. PWH who started ART on or after January 1, 2003, were included. The inclusion criteria were: (1) age\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years at the time of ART initiation; (2) having received ART for more than 6 months; (3) having at least 2 viral load (VL) measurements. The exclusion criteria were: (1) virological failure; (2) having abnormal baseline fasting blood glucose (FBG) levels (\\u0026ge;\\u0026thinsp;7.0mmol/L or \\u0026le;\\u0026thinsp;2.8 mmol/L); (3) having fewer than two records of FBG. Eligible participants were followed every 3 months until the incidence of DM, loss to follow-up (\\u0026gt;\\u0026thinsp;180 days between FBG measurements), or administrative censoring, with a maximum observation of 7 years or until the cohort-wide deadline of October 30, 2023.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eDefinitions of Exposure\\u003c/h3\\u003e\\n\\u003cp\\u003eThe primary exposure of interest was the occurrence of low-level viremia after 6 months of ART in the next two VL measurements. Participants were enrolled into 3 groups:\\u003c/p\\u003e \\u003cp\\u003e1- Virologic Suppression (VS): All VLs were below 50 copies/mL.\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eTransient Episode Low-Level Viremia (Blips): Defined by one VL measurement between 51\\u0026ndash;999 copies/mL, with VLs before and after at or below 50 copies/mL.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003ePersistent Low-Level Viremia (LLV): Characterized by at least two consecutive VLs between 51\\u0026ndash;199 copies/mL, spaced at least 30 days apart, and not meeting the criteria for virologic failure (VF), which is defined as either two consecutive VLs of \\u0026ge;\\u0026thinsp;200 copies/mL or a single VL of \\u0026ge;\\u0026thinsp;1000 copies/mL\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eBoth LLV and Blips were classified under low-level viremia (LV).\\u003c/p\\u003e\\n\\u003ch3\\u003eClinical Follow-up Variables\\u003c/h3\\u003e\\n\\u003cp\\u003eFor the clinical follow-up variables, the baseline CD4 count or baseline viral load was defined as the first CD4 or viral load measurement taken at the time of HIV diagnosis. The recent CD4 count was defined as the last CD4 measurement before the end of follow-up and the recent viral load was defined as the last viral load measurement before the end of follow-up. Co-trimoxazole use history was recorded based on whether the individual had used co-trimoxazole to prevent opportunistic infections prior to starting ART. The initial ART regimen referred to the specific ART regimen used when the individual initiated ART. Specifically, if the regimen contained efavirenz (EFV), nevirapine (NVP), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTs), the ART regimen was classified as EFV-based, NVP-based, PIs-based, and INSTs-based respectively. Regimens that did not fall into these categories were classified as \\u0026ldquo;Other regimens\\u0026rdquo;.\\u003c/p\\u003e\\n\\u003ch3\\u003eDefinition of Outcome Variable\\u003c/h3\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003eDefinition of Outcome Variable\\u003c/div\\u003e \\u003cp\\u003eDiabetes mellitus (DM), the outcome variable, was defined as two consecutive FBG measurements of \\u0026ge;\\u0026thinsp;7 mmol/L during the follow-up period, with at least 30 days between the tests and both measurements occurring within 180 days. Since hemoglobin A1c is known to be less accurate for PWH and is therefore not the preferred diagnostic method, the American Diabetes Association (ADA) recommends using plasma glucose-based criteria rather than hemoglobin A1c for diagnosing diabetes in this population[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. We used two consecutive measurements to ensure the accuracy of the diagnosis.\\u003c/p\\u003e\\n\\u003ch3\\u003eStatistical Methods\\u003c/h3\\u003e\\n\\u003cp\\u003eWe used Pearson\\u0026rsquo;s χ2 tests to compare baseline characteristics across the different enrollment VL groups. We performed 1:1 nearest neighbor propensity score matching (PSM) with a caliper of 0.001 to adjust for sex and age between the VS and LV groups (R 4.3.1 MatchIt package). Sensitivity analyses were also conducted, stratifying by age and sex.\\u003c/p\\u003e \\u003cp\\u003eA trajectory analysis using heterogeneous linear mixed models, also known as growth mixture models (R 4.3.1. lcmm package) was conducted to assess changes in FBG over time and to identify the different trajectories within each enrollment VL group. The FBG data over time were fitted using a maximum likelihood method as a mixture of multiple latent trajectories in a censored normal model with a polynomial function of time. The optimal number of groups was determined by synthesizing the Bayes information criterion (BIC) and group size. \\u003cb\\u003e(Supplementary table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eWe fitted Cox regression models to assess the risk of DM by enrollment VL groups. Univariate Cox analysis was performed to identify significant variables first, and variables with a p-value less than 0.1 were included in the multivariate analysis. Then, using the backward stepwise regression method (R 4.3.1. autoReg package), variables with no significant effect (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.10) on the model were gradually eliminated until only variables with significant contributions to the model remained. The final significance level was set at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocio-demographic and Clinical Characteristics\\u003c/h2\\u003e \\u003cp\\u003eOf the 42,196 patients, 14,445 (34.2%) were aged over 18, had received ART for more than 6 months, and had normal baseline FBG levels. Of these, 5,724 (39.6%) were excluded due to having fewer than two follow-up VL measurements (\\u003cb\\u003eSupplementary Fig.\\u0026nbsp;1)\\u003c/b\\u003e. Among the remaining 8,731 participants, the majority (7,423, 85.0%) were classified into the VS group, while 1,308 (15.0%) were classified into the LV group. Of those in the LV group, 1,125 (86.0%) were in the Blips group, and 183 (14.0%) were in the LLV group. Most participants were male (5,949, 68.1%), married or had a steady partner (5,528, 63.3%), and started ART with an EFV-based ART regimen (6,172, 70.7%). The majority of participants acquired HIV through heterosexual contact (7,289, 83.6%) and had achieved viral loads\\u0026thinsp;\\u0026le;\\u0026thinsp;50 copies/mL based on their recent VL measurement (8,162, 93.5%). \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSocio-demographic and clinical characteristics of all the participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVars\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVS(N\\u0026thinsp;=\\u0026thinsp;7423)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBlips (N\\u0026thinsp;=\\u0026thinsp;1125)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eLLV (N\\u0026thinsp;=\\u0026thinsp;183)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFollow time (Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;25.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;25.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;24.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;22.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial age (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2686 (30.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2361 (31.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e298 (26.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27 (14.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2696 (30.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2344 (31.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e308 (27.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e44 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3349 (38.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2718 (36.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e519 (46.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e112 (61.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5949 (68.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4998 (67.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e807 (71.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e144 (78.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2782 (31.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2425 (32.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e318 (28.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39 (21.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/Partnered\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5528 (63.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4684 (63.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e728 (64.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e116 (63.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDivorced/Widowed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1299 (14.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1071 (14.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e194 (17.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34 (18.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1904 (21.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1668 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e203 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnic\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4050 (46.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3461 (46.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e504 (44.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e85 (46.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinority\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4681 (53.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3962 (53.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e621 (55.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98 (53.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary school and below\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2996 (34.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2484 (33.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e432 (38.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80 (43.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3680 (42.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3139 (42.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e470 (41.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e71 (38.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh school and above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2055 (23.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1800 (24.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e223 (19.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32 (17.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOccupation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFarmer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4872 (55.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4038 (54.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e719 (63.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e115 (62.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3859 (44.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3385 (45.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e406 (36.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e68 (37.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTransmission rout\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeterosexual contact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7298 (83.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6168 (83.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e973 (86.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e157 (85.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther or unkonwn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1433 (16.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1255 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e152 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26 (14.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCo-trimoxazole use history at baseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5758 (65.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4980 (67.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e665 (59.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e113 (61.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2973 (34.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2443 (32.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e460 (40.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70 (38.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline WHO HIV stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4270 (48.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3707 (49.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e499 (44.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e64 (35%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e973 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e825 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23 (12.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1400 (16.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1146 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e209 (18.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45 (24.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2088 (23.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1745 (23.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e292 (26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e51 (27.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial regimen\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEFV-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6172 (70.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5325 (71.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e734 (65.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e113 (61.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNVP-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1477 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1237 (16.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e217 (19.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23 (12.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e993 (11.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e782 (10.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e167 (14.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e44 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINSTIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e89 (1.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79 (1.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (0.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (1.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4571 (52.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3750 (50.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e704 (62.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e117 (63.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2368 (27.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2073 (27.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e260 (23.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35 (19.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1233 (14.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1098 (14.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e114 (10.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21 (11.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e559 (6.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e502 (6.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47 (4.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10 (5.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e838 (9.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e690 (9.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e130 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18 (9.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2068 (23.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1715 (23.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e308 (27.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45 (24.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2178 (24.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1844 (24.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e286 (25.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48 (26.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3647 (41.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3174 (42.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e401 (35.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e72 (39.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5804 (66.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5386 (72.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e396 (35.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1407 (16.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e762 (10.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e522 (46.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e123 (67.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1520 (17.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1275 (17.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e207 (18.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38 (20.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8162 (93.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7043 (94.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e972 (86.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e147 (80.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e407 (4.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e261 (3.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115 (10.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e119 (1.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38 (3.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 (2.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSocio-demographic and clinical characteristics of the participants after matching for sex and age\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVars\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVS(N\\u0026thinsp;=\\u0026thinsp;1308)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLV(N\\u0026thinsp;=\\u0026thinsp;1308)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFollow time (Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;24.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;24.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial age (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e325 (24.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e325 (24.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e352 (26.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e352 (26.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e631 (48.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e631 (48.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e951 (72.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e951 (72.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e357 (27.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e357 (27.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/Partnered\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e843 (64.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e844 (64.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDivorced/Widowed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e204 (15.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e228 (17.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e261 (20%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e236 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnic\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e629 (48.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e589 (45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinority\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e679 (51.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e719 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducational attainment\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary school and below\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e505 (38.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e512 (39.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e522 (39.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e541 (41.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh school and above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e281 (21.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e255 (19.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOccupation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFarmer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e758 (58%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e834 (63.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e550 (42%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e474 (36.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTransmission rout\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeterosexual contact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1107 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1130 (86.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther or unkonwn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e201 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e178 (13.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCo-trimoxazole use history at baseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e856 (65.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e778 (59.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e452 (34.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e530 (40.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline WHO HIV stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e635 (48.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e563 (43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e160 (12.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148 (11.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e212 (16.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e254 (19.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e301 (23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e343 (26.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial regimen\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEFV-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e919 (70.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e847 (64.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNVP-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e204 (15.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e240 (18.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e170 (13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e211 (16.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINSTIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15 (1.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (0.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e690 (52.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e821 (62.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e361 (27.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e295 (22.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e178 (13.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e135 (10.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79 (6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57 (4.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139 (10.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148 (11.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e316 (24.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e353 (27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e329 (25.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e334 (25.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e524 (40.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e473 (36.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e952 (72.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e418 (32%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e154 (11.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e645 (49.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e202 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e245 (18.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1233 (94.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1119 (85.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52 (4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e146 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23 (1.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e43 (3.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCox regression model for factors associated with diabetes mellitu (DM)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVars\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN(n%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHR (univariable)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHR (multivariable)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHR (final)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEnrollment VL groups\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7423 (85.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlips\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1125 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.40 (1.21\\u0026ndash;1.63, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.21 (1.03\\u0026ndash;1.42, p\\u0026thinsp;=\\u0026thinsp;.022)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.25 (1.08\\u0026ndash;1.45, p\\u0026thinsp;=\\u0026thinsp;.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e183 (2.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.74 (1.26\\u0026ndash;2.41, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.27 (0.90\\u0026ndash;1.78, p\\u0026thinsp;=\\u0026thinsp;.170)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.33 (0.96\\u0026ndash;1.84, p\\u0026thinsp;=\\u0026thinsp;.088)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial age (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2686 (30.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2696 (30.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.36 (1.98\\u0026ndash;2.81, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.18 (1.82\\u0026ndash;2.61, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.20 (1.84\\u0026ndash;2.63, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3349 (38.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.86 (3.28\\u0026ndash;4.54, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.40 (2.83\\u0026ndash;4.08, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.42 (2.88\\u0026ndash;4.07, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5949 (68.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2782 (31.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.73 (0.65\\u0026ndash;0.83, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.72 (0.63\\u0026ndash;0.82, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.72 (0.63\\u0026ndash;0.81, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnic\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4050 (46.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinority\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4681 (53.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.82 (0.74\\u0026ndash;0.92, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.79 (0.71\\u0026ndash;0.89, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.80 (0.71\\u0026ndash;0.89, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducational attainment\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary school and below\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2996 (34.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3680 (42.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.73 (0.65\\u0026ndash;0.82, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.94 (0.83\\u0026ndash;1.07, p\\u0026thinsp;=\\u0026thinsp;.328)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.93 (0.82\\u0026ndash;1.06, p\\u0026thinsp;=\\u0026thinsp;.266)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh school and above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2055 (23.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.49 (0.42\\u0026ndash;0.58, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.74 (0.61\\u0026ndash;0.90, p\\u0026thinsp;=\\u0026thinsp;.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.73 (0.61\\u0026ndash;0.87, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOccupation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFarmer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4872 (55.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3859 (44.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.69 (0.62\\u0026ndash;0.77, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.86\\u0026ndash;1.12, p\\u0026thinsp;=\\u0026thinsp;.780)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTransmission rout\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeterosexual contact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7298 (83.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther or unkonwn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1433 (16.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.65 (0.54\\u0026ndash;0.78, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97 (0.80\\u0026ndash;1.18, p\\u0026thinsp;=\\u0026thinsp;.758)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCo-trimoxazole use history at baseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5758 (65.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2973 (34.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.21 (1.09\\u0026ndash;1.36, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.09 (0.94\\u0026ndash;1.28, p\\u0026thinsp;=\\u0026thinsp;.249)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline WHO HIV stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4270 (48.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e973 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.12 (0.94\\u0026ndash;1.35, p\\u0026thinsp;=\\u0026thinsp;.206)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.95 (0.79\\u0026ndash;1.14, p\\u0026thinsp;=\\u0026thinsp;.555)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1400 (16.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.24 (1.07\\u0026ndash;1.45, p\\u0026thinsp;=\\u0026thinsp;.005)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96 (0.82\\u0026ndash;1.13, p\\u0026thinsp;=\\u0026thinsp;.647)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2088 (23.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.14 (1.00-1.31, p\\u0026thinsp;=\\u0026thinsp;.052)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00 (0.86\\u0026ndash;1.17, p\\u0026thinsp;=\\u0026thinsp;.965)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial regimen\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEFV-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6172 (70.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNVP-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1477 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.01 (0.88\\u0026ndash;1.16, p\\u0026thinsp;=\\u0026thinsp;.904)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05 (0.90\\u0026ndash;1.21, p\\u0026thinsp;=\\u0026thinsp;.545)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e993 (11.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.18 (1.00-1.39, p\\u0026thinsp;=\\u0026thinsp;.051)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96 (0.81\\u0026ndash;1.14, p\\u0026thinsp;=\\u0026thinsp;.661)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINSTIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e89 (1.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.93 (0.35\\u0026ndash;2.48, p\\u0026thinsp;=\\u0026thinsp;.882)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.11 (0.41\\u0026ndash;2.97, p\\u0026thinsp;=\\u0026thinsp;.841)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4571 (52.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2368 (27.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92 (0.81\\u0026ndash;1.05, p\\u0026thinsp;=\\u0026thinsp;.212)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.06 (0.90\\u0026ndash;1.26, p\\u0026thinsp;=\\u0026thinsp;.473)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1233 (14.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.82 (0.69\\u0026ndash;0.98, p\\u0026thinsp;=\\u0026thinsp;.025)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01 (0.81\\u0026ndash;1.26, p\\u0026thinsp;=\\u0026thinsp;.941)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e559 (6.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92 (0.72\\u0026ndash;1.18, p\\u0026thinsp;=\\u0026thinsp;.496)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.11 (0.83\\u0026ndash;1.48, p\\u0026thinsp;=\\u0026thinsp;.469)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e838 (9.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2068 (23.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.75 (0.62\\u0026ndash;0.92, p\\u0026thinsp;=\\u0026thinsp;.005)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.85 (0.69\\u0026ndash;1.04, p\\u0026thinsp;=\\u0026thinsp;.110)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2178 (24.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.76 (0.63\\u0026ndash;0.93, p\\u0026thinsp;=\\u0026thinsp;.007)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.80\\u0026ndash;1.20, p\\u0026thinsp;=\\u0026thinsp;.869)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3647 (41.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.61 (0.51\\u0026ndash;0.74, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.92 (0.75\\u0026ndash;1.13, p\\u0026thinsp;=\\u0026thinsp;.444)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5804 (66.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1407 (16.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.37 (1.19\\u0026ndash;1.57, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.10 (0.94\\u0026ndash;1.28, p\\u0026thinsp;=\\u0026thinsp;.231)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1520 (17.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.87 (0.74\\u0026ndash;1.02, p\\u0026thinsp;=\\u0026thinsp;.084)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.83\\u0026ndash;1.16, p\\u0026thinsp;=\\u0026thinsp;.819)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8162 (93.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e407 (4.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.64 (1.32\\u0026ndash;2.04, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.24 (0.99\\u0026ndash;1.56, p\\u0026thinsp;=\\u0026thinsp;.057)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.26 (1.01\\u0026ndash;1.58, p\\u0026thinsp;=\\u0026thinsp;.043)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.54 (1.07\\u0026ndash;2.21, p\\u0026thinsp;=\\u0026thinsp;.020)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.23 (0.85\\u0026ndash;1.78, p\\u0026thinsp;=\\u0026thinsp;.272)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.24 (0.86\\u0026ndash;1.78, p\\u0026thinsp;=\\u0026thinsp;.251)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;8731, events\\u0026thinsp;=\\u0026thinsp;1297, Likelihood ratio test\\u0026thinsp;=\\u0026thinsp;406.89 on 27 df(p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eIncidence of DM\\u003c/h3\\u003e\\n\\u003cp\\u003eThe overall median follow-up was 2.4 [IQR: 1.2, 4.5] years. During 26,097 person-years of follow-up, 1297 (14.9%) participants developed DM with an incidence rate [IR] of 49 per 1,000 person-years (95%CI: 46\\u0026ndash;52). In the LLV, Blips, and VS groups, the incidence of DM was 38 (20.8%), 209 (18.6%), and 1,050 (14.1%), respectively, with incidence rates of 82, 65, and 46 per 1,000 person-years (95% CI: 56\\u0026ndash;109, 56\\u0026ndash;74, and 43\\u0026ndash;49, respectively) \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. After matching for sex and age, in the subset of 2,616 participants, 451 (17.5%) developed DM over 7,415 person-years (IR: 60 per 1,000 person-years, 95% CI: 62\\u0026ndash;73). The incidence was 204 (15.6%) in the VS group (IR: 54 per 1,000 person-years, 95% CI: 46\\u0026ndash;61), while a significantly higher incidence of 247 (18.9%) was observed in the LV group (IR: 67 per 1,000 person-years, 95% CI: 59\\u0026ndash;76).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFBG Trajectory Group Analysis\\u003c/h2\\u003e \\u003cp\\u003eTwo distinct FBG trajectories were identified within each group (VS, Blips, and LLV). Individuals with a stable FBG trajectory were classified as the \\u0026ldquo;Stable group\\u0026rdquo; (n\\u0026thinsp;=\\u0026thinsp;7,134 [96.11%] in the VS group, n\\u0026thinsp;=\\u0026thinsp;1,082 [96.18%] in the Blips group, and n\\u0026thinsp;=\\u0026thinsp;166 [90.71%] in the LLV group). The \\u0026ldquo;Stable group\\u0026rdquo; was used as the reference for comparison. Participants who experienced a marked increase in FBG during the follow-up period were classified as the \\u0026ldquo;Rapid Increase\\u0026rdquo; group. A significantly higher proportion of participants in the LLV group followed the \\u0026ldquo;Rapid Increase\\u0026rdquo; FBG trajectory (OR [95%CI]: 2.53 [1.53\\u0026ndash;4.16], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) compared to the VS group (289 [3.89%] in the VS group vs. 17 [9.29%] in the LLV group). \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation Between LV and the Risk of DM\\u003c/h2\\u003e \\u003cp\\u003eCompared to the VS group, the incidence of DM was significantly higher in both the Blips group (cHR [95%CI]: 1.40 [1.21\\u0026ndash;1.63], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and the LLV group (cHR [95%CI]: 1.74[1.26\\u0026ndash;2.41], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with the LLV group presenting a greater risk than the Blips group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Although the association between DM and the LLV group lost significance after adjustment, the Blips group remained significantly associated with DM (aHR [95%CI]: 1.25 [1.08\\u0026ndash;1.45], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.004) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). After matching for age and sex between VS and LV groups, the risk of developing DM remained elevated in the LV group (aHR [95%CI]: 1.27 [1.06\\u0026ndash;1.53], p\\u0026thinsp;=\\u0026thinsp;0.011) compared to the VS group (Table\\u0026nbsp;5). In a stratified analysis by age, we found that Blips and LLV groups were significantly associated with DM development in individuals aged 35\\u0026ndash;49 years, with cHRs of 1.43 (95% CI: 1.08\\u0026ndash;1.88, p\\u0026thinsp;=\\u0026thinsp;0.011) and 2.24 (95% CI: 1.26\\u0026ndash;3.98, p\\u0026thinsp;=\\u0026thinsp;0.006), respectively \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE\\u003cb\\u003e)\\u003c/b\\u003e. After matching for age and sex between VS and LV groups, the association remained significant, with a cHR of 1.72 (95% CI: 1.18\\u0026ndash;2.51, p\\u0026thinsp;=\\u0026thinsp;0.005) in the LV group. \\u003cb\\u003e(Supplementary table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Supplementary table 3)\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCox regression model for factors associated with diabetes mellitus (DM) after matching for age and sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVars\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN(n%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHR (univariable)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHR (multivariable)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHR (final)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEnrollment VL groups\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1308 (50.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1308 (50.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.26 (1.05\\u0026ndash;1.52, p\\u0026thinsp;=\\u0026thinsp;.015)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.24 (1.03\\u0026ndash;1.49, p\\u0026thinsp;=\\u0026thinsp;.026)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.27 (1.06\\u0026ndash;1.53, p\\u0026thinsp;=\\u0026thinsp;.011)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial age (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e650 (24.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e704 (26.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.33 (1.66\\u0026ndash;3.26, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.18 (1.54\\u0026ndash;3.08, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.30 (1.64\\u0026ndash;3.22, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1262 (48.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.76 (2.77\\u0026ndash;5.10, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.54 (2.54\\u0026ndash;4.94, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.77 (2.78\\u0026ndash;5.12, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1902 (72.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e714 (27.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.79 (0.64\\u0026ndash;0.98, p\\u0026thinsp;=\\u0026thinsp;.031)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.79 (0.63\\u0026ndash;0.99, p\\u0026thinsp;=\\u0026thinsp;.042)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.78 (0.63\\u0026ndash;0.97, p\\u0026thinsp;=\\u0026thinsp;.023)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnic\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1218 (46.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinority\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1398 (53.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.85 (0.71\\u0026ndash;1.03, p\\u0026thinsp;=\\u0026thinsp;.092)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.81 (0.67\\u0026ndash;0.97, p\\u0026thinsp;=\\u0026thinsp;.026)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.84 (0.70\\u0026ndash;1.01, p\\u0026thinsp;=\\u0026thinsp;.060)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducational attainment\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary school and below\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1017 (38.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1063 (40.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.74 (0.61\\u0026ndash;0.91, p\\u0026thinsp;=\\u0026thinsp;.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97 (0.78\\u0026ndash;1.19, p\\u0026thinsp;=\\u0026thinsp;.743)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh school and above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e536 (20.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.59 (0.44\\u0026ndash;0.78, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.87 (0.62\\u0026ndash;1.20, p\\u0026thinsp;=\\u0026thinsp;.386)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOccupation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFarmer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1592 (60.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1024 (39.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.69 (0.57\\u0026ndash;0.84, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.92 (0.73\\u0026ndash;1.16, p\\u0026thinsp;=\\u0026thinsp;.483)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTransmission rout\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeterosexual contact\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2237 (85.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther or unkonwn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e379 (14.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.75 (0.55\\u0026ndash;1.02, p\\u0026thinsp;=\\u0026thinsp;.066)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.15 (0.82\\u0026ndash;1.60, p\\u0026thinsp;=\\u0026thinsp;.421)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCo-trimoxazole use history at baseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1634 (62.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e982 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.16 (0.96\\u0026ndash;1.39, p\\u0026thinsp;=\\u0026thinsp;.125)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline WHO HIV stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1198 (45.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e308 (11.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.72\\u0026ndash;1.35, p\\u0026thinsp;=\\u0026thinsp;.914)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.87 (0.63\\u0026ndash;1.19, p\\u0026thinsp;=\\u0026thinsp;.372)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e466 (17.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.25 (0.98\\u0026ndash;1.60, p\\u0026thinsp;=\\u0026thinsp;.078)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02 (0.79\\u0026ndash;1.31, p\\u0026thinsp;=\\u0026thinsp;.884)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e644 (24.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.18 (0.93\\u0026ndash;1.48, p\\u0026thinsp;=\\u0026thinsp;.167)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.08 (0.85\\u0026ndash;1.37, p\\u0026thinsp;=\\u0026thinsp;.528)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART initial regimen\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEFV-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1766 (67.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNVP-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e444 (17.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.84 (0.65\\u0026ndash;1.08, p\\u0026thinsp;=\\u0026thinsp;.176)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e381 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.06 (0.82\\u0026ndash;1.37, p\\u0026thinsp;=\\u0026thinsp;.645)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINSTIs-based\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25 (1.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.77 (0.11\\u0026ndash;5.48, p\\u0026thinsp;=\\u0026thinsp;.792)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1511 (57.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e656 (25.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90 (0.72\\u0026ndash;1.12, p\\u0026thinsp;=\\u0026thinsp;.328)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e313 (12.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.79 (0.58\\u0026ndash;1.08, p\\u0026thinsp;=\\u0026thinsp;.142)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e136 (5.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.74 (0.45\\u0026ndash;1.20, p\\u0026thinsp;=\\u0026thinsp;.223)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent CD4\\u0026thinsp;+\\u0026thinsp;T cell count (cells/\\u0026micro;L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e287 (11.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e200\\u0026ndash;350\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e669 (25.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.77 (0.55\\u0026ndash;1.06, p\\u0026thinsp;=\\u0026thinsp;.111)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.85 (0.61\\u0026ndash;1.18, p\\u0026thinsp;=\\u0026thinsp;.325)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e350\\u0026ndash;500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e663 (25.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.84 (0.61\\u0026ndash;1.16, p\\u0026thinsp;=\\u0026thinsp;.289)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05 (0.76\\u0026ndash;1.46, p\\u0026thinsp;=\\u0026thinsp;.766)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e997 (38.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.58 (0.43\\u0026ndash;0.80, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.83 (0.60\\u0026ndash;1.15, p\\u0026thinsp;=\\u0026thinsp;.272)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRecent viral load (copies/mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2352 (89.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50-1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e198 (7.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.51 (1.10\\u0026ndash;2.07, p\\u0026thinsp;=\\u0026thinsp;.010)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.22 (0.89\\u0026ndash;1.69, p\\u0026thinsp;=\\u0026thinsp;.220)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e66 (2.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.01 (0.54\\u0026ndash;1.88, p\\u0026thinsp;=\\u0026thinsp;.987)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.83 (0.44\\u0026ndash;1.58, p\\u0026thinsp;=\\u0026thinsp;.578)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;2616, events\\u0026thinsp;=\\u0026thinsp;451, Likelihood ratio test\\u0026thinsp;=\\u0026thinsp;123.57 on 17 df(p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe risk of DM increased significantly in both the 35\\u0026ndash;49 age group (aHR [95%CI]: 2.20[1.84\\u0026ndash;2.63], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and the \\u0026ge;\\u0026thinsp;50 age group (aHR [95%CI]: 3.42 [2.88\\u0026ndash;4.07], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), compared to the 18\\u0026ndash;34 age group. Females (aHR [95%CI]: 0.72 [0.63\\u0026ndash;0.81], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were less likely to develop DM compared to males (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). After matching for sex and age between VS and LV groups, these associations remained significant, with aHRs of 2.30 (95%CI: 1.64\\u0026ndash;3.22, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 3.77 (95%CI: 2.78\\u0026ndash;5.12, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) in the 35\\u0026ndash;49 years and \\u0026ge;\\u0026thinsp;50 years age groups, respectively, and an aHR of 0.78 (95% CI: 0.63\\u0026ndash;0.97, p\\u0026thinsp;=\\u0026thinsp;0.023) for females compared to males (Table\\u0026nbsp;5).\\u003c/p\\u003e \\u003cp\\u003eCompared to participants with a recent viral load\\u0026thinsp;\\u0026lt;\\u0026thinsp;50 copies/ml, those with a viral load of 50-1000 copies/ml had a higher risk of developing DM (aHR [95%CI] : 1.26 (1.01\\u0026ndash;1.58, P\\u0026thinsp;=\\u0026thinsp;0.043) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). After matching for age and sex, a baseline viral load of 50-1000 copies/ml was also associated with a higher risk of developing DM (cHR [95%CI] : 1.51 [1.10\\u0026ndash;2.07], p\\u0026thinsp;=\\u0026thinsp;0.010). Additionally, a baseline viral load of 50-1000 copies/ml (cHR [95%CI] : 1.37 [1.19\\u0026ndash;1.57], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) demonstrated a greater propensity for developing DM compared to those with a baseline viral load of \\u0026le;\\u0026thinsp;50 copies/ml \\u003cb\\u003e(Table\\u0026nbsp;5)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study is the first cohort study in China to examine the effect of low-level viremia (LV) on the incidence of diabetes mellitus (DM), utilizing a large sample size and a long-term follow-up period of 7 years. Two distinct FBG trajectories, \\u0026ldquo;Stable\\u0026rdquo; and \\u0026ldquo;Rapid increase\\u0026rdquo;, were identified within the VS, Blips, and LLV groups. A significantly larger proportion of participants in the LLV group followed the \\u0026ldquo;Rapid Increase\\u0026rdquo; FBG trajectory compared to the VS group. Additionally, the LV group had an elevated risk of developing DM compared to the VS group, with both LLV and Blips groups significantly associated with an increased risk of developing DM in individuals aged 35\\u0026ndash;49. Proactive management of LV could have a positive impact on reducing the risk of DM in PWH.\\u003c/p\\u003e \\u003cp\\u003eDuring the follow-up period, 14.9% of PHW developed DM with an incidence rate of 49 per 1,000 person-years. This incidence was higher than that observed in a French cohort, where the prevalence of DM was 8.5%, with an incidence rate of 9.6 per 1,000 person-year[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Similarly, the RESPOND cohort reported a prevalence of DM at 3.7%, with an incidence rate of 7.3 per 1,000 person-year[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, these previous studies included all HIV-infected individuals, while our study specifically focused on those who had received ART for more than 6 months. We hypothesize that DM may develop earlier in individuals with LV, potentially contributing to the higher incidence density. In contrast, the incidence of DM was lower than that reported in an African cohort, which included individuals with pLLV (persistent low-level viremia), where 21.26% of them developed hyperglycemia[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Nonetheless, our findings are consistent with a meta-analysis showing that the prevalence of DM among PWH in low- and middle-income countries (LMIC) ranges from 1.3\\u0026ndash;18%[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. This wide variation might be due to differences in the definitions of diabetes and in the populations studied[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePersistent viremia in HIV-infected individuals has been identified as a predictor of metabolic syndrome (MetS), a cluster of conditions that collectively increase the risk of heart disease, stroke, and type 2 diabetes[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Besides, low-level viremia has been linked to dysfunctional immunometabolism in HIV-infected individuals[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. We observed that a significantly higher proportion of individuals in the \\u0026ldquo;Rapid increase\\u0026rdquo; fasting blood glucose trajectory in the LLV group compared to the VS group. This suggested a stronger association between LLV and rapid increases in blood glucose, indicating that individuals with LLV may be at higher risk of metabolic dysregulation than those with VS. This finding highlights a potential metabolic vulnerability in individuals with LLV. Therefore, individuals with LLV should be monitored more frequently for metabolic markers, such as FBG and insulin levels, to reduce the incidence of DM and other metabolic dysregulation.\\u003c/p\\u003e \\u003cp\\u003eThis need for enhanced monitoring is reinforced by our findings, which revealed that the incidence of DM was significantly higher in both the Blips group and the LLV group compared to the VS group. Although the association between DM and the LLV group did not show significant differences after adjusting for other variables, the Blips group remained significantly associated with the incidence of DM. This finding may be attributed to the small sample size of the LLV group. However, the trajectory analysis, which utilized all the records of FBG across the follow-up period, verified the association between LLV and a rapid increase in FBG, implicitly supporting its impact on the development of DM. When restricting the analysis to participants aged 35\\u0026ndash;49, both LLV and Blips were significantly linked to an increased risk of developing DM. Sensitivity analysis reinforced these findings, confirming that LV was significantly linked to a higher risk of DM onset in this subgroup. These findings underscore the importance of prioritizing PHW with LV, especially middle-aged individuals, as they may exhibit a heightened vulnerability to metabolic disorders such as DM. Consequently, targeted monitoring and tailored preventive strategies are imperative for this demographic to mitigate the risk of DM.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, individuals with the recent VL of 50-1000 copies/ml exhibited an increased risk of developing DM, while those with a VL greater than 1000 copies/ml showed no significant difference compared to those with a VL of less than 50 copies/ml. This clinically counter-intuitive and unexpected finding suggests that moderate levels of viremia may have a more detrimental impact on metabolic processes than very high levels, potentially due to the chronic immune activation associated with lower, persistent viral replication. It further supported our other finding that LV is more strongly linked to the development of DM. A study from South Carolina similarly found that a higher percentage of days with VS was associated with the incidence of DM[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e], aligning with our observation that the presence, duration, and consistency of viremia play a crucial role in driving metabolic complications. Mechanistically, LV keeps the immune system in a prolonged state of activation, which can lead to chronic inflammation. This inflammation is an established contributor to insulin resistance, a key mechanism in the development of DM[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Therefore, these findings underscore the importance of not only achieving VS but also maintaining it consistently over time to mitigate the risk of DM among PWH.\\u003c/p\\u003e \\u003cp\\u003eThis study had several limitations, and the results did not suggest a causal relationship. First, although overweight and obesity are are generally linked to an increased risk of DM, we were unable to incorporate BMI data in our analysis. It's important to note that HIV-specific factors may predispose individuals to diabetes at lower levels of adiposity compared to the general population[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Research suggest that HIV-infected individuals often experience insulin resistance (IR) at lower BMI levels, indicating that factors beyond BMI may significantly influence the pathogenesis of DM[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Second, FBG levels were used to diagnose diabetes, though it was not guaranteed that all patients were fasting at the time of testing. Yet, to enhance the reliability of our diagnosis, we used two consecutive records of FBG (taken at intervals of more than 30 days and less than 180 days) that exceeded the normal threshold of 7 mmol/L, and we did FBG trajectory analysis using all the records of FBG throughout the follow-up period. Third, the limited number of participants with LLV prevented us from performing matching for this group, which could have affected the results. Nevertheless, we matched the Blips, LLV, and VS groups for analysis, and the findings still suggested that LV was linked to an increased risk of developing DM.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn summary, this study underscores that low-level viremia (LV) is significantly associated with the development of diabetes mellitus (DM) among PWH, particularly in middle-aged individuals. Proactive monitoring of both viral load (VL) and fasting blood glucose (FBG) is essential to prevent the development of DM at both the individual and population levels, and to contribute to extending the life expectancy of patients undergoing ART.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003ePWH \\u0026nbsp;People with HIV\\u003c/p\\u003e\\n\\u003cp\\u003eART \\u0026nbsp;Antiretroviral therapy\\u003c/p\\u003e\\n\\u003cp\\u003eDM \\u0026nbsp;Diabetes mellitus\\u003c/p\\u003e\\n\\u003cp\\u003eFBG \\u0026nbsp;Fasting blood glucose\\u003c/p\\u003e\\n\\u003cp\\u003eLV \\u0026nbsp; Low-level viremia\\u003c/p\\u003e\\n\\u003cp\\u003eLLV \\u0026nbsp;Persistent low-level viremia\\u003c/p\\u003e\\n\\u003cp\\u003eBlips \\u0026nbsp;Transient episode low-level viremia\\u003c/p\\u003e\\n\\u003cp\\u003eVS \\u0026nbsp; Viral suppression\\u003c/p\\u003e\\n\\u003cp\\u003eLV \\u0026nbsp; Viral load\\u003c/p\\u003e\\n\\u003cp\\u003eEFV \\u0026nbsp;Efavirenz\\u003c/p\\u003e\\n\\u003cp\\u003eNVP \\u0026nbsp;Nevirapine\\u003c/p\\u003e\\n\\u003cp\\u003ePIs \\u0026nbsp; Protease inhibitors\\u003c/p\\u003e\\n\\u003cp\\u003eINSTs \\u0026nbsp;Integrase strand transfer inhibitors\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted following the Helsinki Declaration and was approved by the Human Research Ethics Committee of Guangxi Medical University (No. KY0294).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analyzed during this study are not publicly accessible due to ethical and legal considerations. However, they are available from the corresponding author, Bingyu Liang, upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors report no conflicts of interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by the Scientific Research and Technology Development Program of Guangxi Zhuang Autonomous Region (Grant No. 2022AC23005, 2022JJA141110), the National Natural Science Foundation of China (Grant No. 82103899), National Key R\\u0026amp;D Program of China (Grant No. 2022YFC2305001), the China Scholarship Council (To Bingyu Liang), the Thousands of Young and Middle Aged Key Teachers Training Program in Guangxi Colleges and Universities (To Bingyu Liang), and the Guangxi Bagui Young Top Scholar (To Bingyu Liang).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u003c/strong\\u003e\\u003cstrong\\u003e’\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLBY, BLJ, and WLJ conceptualized and designed the study. LLY, YYB and NAD contributed to literature review, HLJ, HXH and WSX handled the collection and testing of blood samples, HL and HRY assisted with administering the questionnaire survey and inputting the data, TCX performed data analysis and interpretation, revised the manuscript critically for important intellectual content, MTA and SG provided critical feedback on data interpretation and reviewed the manuscript. NCY, LH, and YL oversaw the data analysis. All authors participated in critically revising the manuscript and approved the final version for publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eLiu Z, Zhang J, Yang X, Gao H, Chen S, Weissman S, et al. The dynamic risk factors of cardiovascular disease among people living with HIV: a real-world data study. BMC Public Health. 2024;24:1162.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNjoroge A, Augusto O, Page ST, Kigondu C, Oluka M, Puttkammer N, et al. Increased risk of prediabetes among virally suppressed adults with HIV in Central Kenya detected using glycated haemoglobin and fasting blood glucose. Endocrinol Diabetes Metabolism. 2021;4:e00292.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHernandez-Romieu AC, Garg S, Rosenberg ES, Thompson-Paul AM, Skarbinski J. Is diabetes prevalence higher among HIV-infected individuals compared with the general population? Evidence from MMP and NHANES 2009\\u0026ndash;2010. BMJ Open Diabetes Res Care. 2017;5:e000304.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChebrolu P, Sangle S, Nimkar S, Salvi S, Chavan A, Kulkarni V, et al. Inflammatory profile associated with insulin resistance in non-overweight versus overweight people living with HIV in Pune, Western India. Diabetes metabolic syndrome. 2022;16:102551.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGuidelines for the use of antiretroviral agents in adults and adolescents with HIV. 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/whats-new\\u003c/span\\u003e\\u003cspan address=\\\"https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/whats-new\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Accessed 8 Sep 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eConsolidated guidelines on the use of antiretroviral drugs for treating. and preventing HIV infection: recommendations for a public health approach, 2nd ed. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/publications/i/item/9789241549684\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/publications/i/item/9789241549684\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Accessed 8 Sep 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGanesan A, Hsieh H-C, Chu X, Colombo RE, Berjohn C, Lalani T, et al. Low Level Viremia Is Associated With Serious non-AIDS Events in People With HIV. Open Forum Infect Dis. 2024;11:ofae147.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eElvstam O, Marrone G, Medstrand P, Treutiger CJ, S\\u0026ouml;nnerborg A, Gissl\\u0026eacute;n M et al. All-Cause Mortality and Serious Non-AIDS Events in Adults With Low-level Human Immunodeficiency Virus Viremia During Combination Antiretroviral Therapy: Results From a Swedish Nationwide Observational Study. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. 2020;72:2079\\u0026ndash;86.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDing H, Xu J, Liu J, Wang Q, Kang J, Li X, et al. Outcomes of persistent low-level viremia among HIV patients on antiretroviral therapy: A prospective cohort study. HIV Med. 2022;23(Suppl 1):64\\u0026ndash;71.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMazzuti L, Turriziani O, Mezzaroma I. The Many Faces of Immune Activation in HIV-1 Infection: A Multifactorial Interconnection. Biomedicines. 2023;11:159.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZicari S, Sessa L, Cotugno N, Ruggiero A, Morrocchi E, Concato C, et al. Immune Activation, Inflammation, and Non-AIDS Co-Morbidities in HIV-Infected Patients under Long-Term ART. Viruses. 2019;11:200.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSquillace N, Zona S, Stentarelli C, Orlando G, Beghetto B, Nardini G, et al. Detectable HIV Viral Load Is Associated With Metabolic Syndrome. JAIDS J Acquir Immune Defic Syndr. 2009;52:459.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMulenga L, Musonda P, Chirwa L, Siwingwa M, Mweemba A, Suwilanji S, et al. Insulin Resistance is Associated with Higher Plasma Viral Load Among HIV-Positive Adults Receiving Longer-Term (1 Year) Combination Antiretroviral Therapy (ART). J Infect disease therapy. 2019;7:406.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEsber AL, Colt S, Jian N, Dear N, Slike B, Sing\\u0026rsquo;oei V, et al. Persistent low-level viraemia is associated with non‐infectious comorbidities in an observational cohort in four African countries. J Int AIDS Soc. 2024;27:e26316.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNacher M, Rabier S, Lucarelli A, Hureau L, Adenis A, Hafsi N, et al. Diabetes in a hospital cohort of persons living with HIV: a descriptive and comparative study in French Guiana. BMC Infect Dis. 2023;23:470.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSarkar S, Brown TT. Diabetes in People with HIV. Curr Diab Rep. 2021;21:13.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRupasinghe D, Bansi-Matharu L, Law M, Zangerle R, Rauch A, Tarr PE, et al. Integrase strand transfer inhibitor (INSTI) related changes in BMI and risk of diabetes: a prospective study from the RESPOND cohort consortium. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America; 2024. p. ciae406.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePatel P, Rose CE, Collins PY, Nuche-Berenguer B, Sahasrabuddhe VV, Peprah E, et al. Noncommunicable diseases among HIV-infected persons in low-income and middle-income countries: a systematic review and meta-analysis. AIDS. 2018;32(Suppl 1):S5\\u0026ndash;20.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlencastro PR, Fuchs SC, Wolff FH, Ikeda ML, Brand\\u0026atilde;o AB, Barcellos NT. Independent Predictors of Metabolic Syndrome in HIV-Infected Patients. Aids Patient Care Stds. 2011;25:627\\u0026ndash;34.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCollins LF, Adekunle RO, Cartwright EJ. Metabolic Syndrome in HIV/HCV Co-infected Patients. Curr Treat Options Infect Dis. 2019;11:351\\u0026ndash;71.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eButterfield TR, Landay AL, Anzinger JJ. Dysfunctional Immunometabolism in HIV Infection: Contributing Factors and Implications for Age-Related Comorbid Diseases. Curr Hiv/aids Rep. 2020;17:125\\u0026ndash;37.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMohammad Pritom GS, Yang X, Gao H, Chen S, Zhang J, Olatosi B, et al. Examining incidence of diabetes in people with HIV: tracking the shift in traditional and HIV-related risk factors. AIDS. 2024;38:1057\\u0026ndash;65.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMu W, Patankar V, Kitchen S, Zhen A. Examining Chronic Inflammation, Immune Metabolism, and T Cell Dysfunction in HIV Infection. Viruses. 2024;16:219.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBourgi K, Wanjalla C, Koethe JR. Inflammation and Metabolic Complications in HIV. Curr Hiv/aids Rep. 2018;15:371\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLevitt NS, Peer N, Steyn K, Lombard C, Maartens G, Lambert EV, et al. Increased risk of dysglycaemia in South Africans with HIV; especially those on protease inhibitors. Diabetes Res Clin Pract. 2016;119:41\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmed\",\"sideBox\":\"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)\",\"snPcode\":\"12916\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12916/3\",\"title\":\"BMC Medicine\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"cohort study, HIV, low-level viremia, diabetes mellitus, China\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5380470/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5380470/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eIt is unclear whether low-level viremia (LV) during antiretroviral therapy (ART), increase the incidence of diabetes mellitus (DM). This study aims to assess the association between HIV viremia exposure during ART and DM using retrospective cohort data.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003ePeople with HIV (PWH) who started ART in 2003 or later were identified from the China\\u0026rsquo;s National Free ART Program database. Participants on ART\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 months without DM at enrolment were included in this study. According to the two consecutive viral load (VL) measurements after 6 months of ART, participants categorized into three groups: viral suppression (VS), transient episode low-level viremia (Blips), and persistent low-level viremia (LLV). Blips and LLV collectively classified as LV group. We analyzed the incidence of DM depending on viremia exposure using Cox proportional hazard models adjusted for age, sex, baseline VL, CD4 count, ART initial regimen, and WHO HIV stage. Heterogeneous linear mixed models identified fast blood glucose (FBG) trajectory patterns during the follow-up.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eDuring 26,097 person-years of follow-up, we observed 1297 cases of DM in 8731 participants, with median follow-up: 2.4 years [IQR:1.2, 4.5]. Two distinct FBG trajectories, labeled as \\u0026ldquo;Stable\\u0026rdquo; and \\u0026ldquo;Rapid increase\\u0026rdquo;, were identified. The LLV group had a significantly higher proportion of FBG in \\u0026ldquo;Rapid increase\\u0026rdquo; trajectory (OR: 2.53, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Both the Blips (cHR: 1.40, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and LLV (cHR: 1.74, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) groups increased the incidence of DM than VS group. After propensity score matching, the LV group showed a higher DM risk (HR: 1.27, P\\u0026thinsp;=\\u0026thinsp;0.011). When restricted to the 35\\u0026ndash;49 age group, the risk of DM was even higher in both the LLV (cHR: 2.24, p\\u0026thinsp;=\\u0026thinsp;0.006) and Blips (cHR: 1.43, p\\u0026thinsp;=\\u0026thinsp;0.011) groups than the VS group.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eLow-level viremia (LV) substantially increased the risk of diabetes mellitus (DM), particularly in middle-aged individuals. Monitoring VL and FBG is crucial to prevent the development of DM and to improve life expectancy among ART patients.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Low-level Viremia Increases the Risk of Diabetes Mellitus in People with HIV in China: A 7-Year Retrospective Longitudinal Cohort Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-12-04 00:00:48\",\"doi\":\"10.21203/rs.3.rs-5380470/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-02-25T09:54:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-02-24T21:27:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"7190577215195236399707786773089131955\",\"date\":\"2025-01-14T12:14:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-11-28T10:14:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"165343131927148997196167301690608445652\",\"date\":\"2024-11-06T06:10:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-11-04T12:42:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-11-04T10:09:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-11-04T08:52:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Medicine\",\"date\":\"2024-11-03T04:19:58+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmed\",\"sideBox\":\"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)\",\"snPcode\":\"12916\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12916/3\",\"title\":\"BMC Medicine\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"f307bc97-1c7c-48b8-9d10-9445464d978e\",\"owner\":[],\"postedDate\":\"December 4th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-07T16:06:10+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5380470\",\"link\":\"https://doi.org/10.1186/s12916-025-04166-z\",\"journal\":{\"identity\":\"bmc-medicine\",\"isVorOnly\":false,\"title\":\"BMC Medicine\"},\"publishedOn\":\"2025-07-01 15:57:16\",\"publishedOnDateReadable\":\"July 1st, 2025\"},\"versionCreatedAt\":\"2024-12-04 00:00:48\",\"video\":\"\",\"vorDoi\":\"10.1186/s12916-025-04166-z\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12916-025-04166-z\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5380470\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5380470\",\"identity\":\"rs-5380470\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}