The triglyceride-glucose index trajectories are associated with cardiovascular diseases in people living with HIV: evidence from a prospective cohort study in China, 2005-2022

The triglyceride-glucose index trajectories are associated with cardiovascular diseases in people living with HIV: evidence from a prospective cohort study in China, 2005-2022 | 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 The triglyceride-glucose index trajectories are associated with cardiovascular diseases in people living with HIV: evidence from a prospective cohort study in China, 2005-2022 Yinsong Luo, Liqin Sun, Yun He, Fang Zhao, Duo Shan, Fan Bu, Lingyun Ge, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5352658/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background The triglyceride-glucose (TyG) index has been validated as a novel biomarker for cardiovascular disease (CVD) risk. However, the prospective relationship between baseline and long-term trajectories of the TyG index and CVD risk in people living with HIV (PLWH) remains unexplored. Methods This cohort study included 16,122 treatment-naive PLWH who initiated antiretroviral therapy (ART) at the Third People's Hospital of Shenzhen from 2005 to 2022. The TyG index was calculated as Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. Group-based trajectory modeling (GBTM) was used to identify distinct TyG index trajectories over the follow-up period. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariate Cox proportional hazards models to assess the association between TyG index trajectories and CVD risk. Nonlinear relationships were investigated using a restricted cubic spline plot. Subgroup analysis was conducted to examine whether the association between the TyG index and CVD risk differed across various subgroups. Results During a median follow-up of 70 months, 214 PLWH developed CVD. Each 1-standard deviation (SD) increase in the baseline TyG index was associated with a 33% higher risk of CVD (HR = 1.33, 95% CI 1.18–1.51) after adjusting for traditional CVD risk factors. Participants were categorized into four distinct TyG trajectory groups: low-stable, low-moderate-stable, high-moderate-stable, and high-increasing. After multivariate adjustment, the high-increasing trajectory group had a 2.72-fold (95% CI 1.58–4.69) increased risk of CVD compared to the low-stable group. The restriction cubic spline plot showed an upward trend between the baseline TyG index and the CVD occurrence ( P < 0.001), with the threshold at 8.479. Significantly positive correlations between the TyG index and CVD were observed both below the TyG threshold with HR 3.71 (95% CI 1.18, 11.68) and above the threshold with HR 1.39 (95% CI 1.03, 1.87) (both P < 0.05). Conclusions Higher baseline TyG index levels and the high-increasing trajectory were significantly associated with increased CVD risk in PLWH. Long-term trajectories of the TyG index could aid in identifying individuals at higher risk of CVD, highlighting the importance of incorporating TyG index monitoring into routine clinical assessments for tailored prevention strategies. Triglyceride-glucose index Cohort study Cardiovascular disease HIV Incidence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Globally, there were 1.3 million new HIV infections in 2022, and 29.8 million of the 39 million people living with HIV (PLWH) were on antiretroviral therapy (ART) by 2023 [ 1 ]. While ART has transformed HIV from a fatal disease to a chronic, manageable condition, it has also introduced new health challenges, particularly a heightened risk of cardiovascular disease (CVD) [ 2 , 3 ]. This risk is notably higher in PLWH compared to that in uninfected population [ 4 , 5 ], posing a major concern for the long-term management of HIV. HIV-infected individuals face a complex range of CVD risk factors, including virological factors, ART-related factors, and traditional risk factors such as smoking, hypertension, diabetes, dyslipidemia, and obesity [ 6 ]. The D:A:D model, developed specifically for HIV-infected individuals, offers a 5-year CVD risk assessment tool by integrating HIV-specific and traditional CVD risk factors [ 7 ]. However, its external applicability is limited, performing adequately only in HIV populations with characteristics similar to its initial European cohort [ 8 ]. Furthermore, the model's validity may have diminished over time due to advances in ART, making it less reflective of current HIV management. General population models, such as the Framingham Risk Score (FRS) and Pooled Cohort Equations, also prove inadequate for assessing CVD risk in HIV-infected populations [ 9 ]. Therefore, exploring new, accurate, and specific markers is essential for comprehensive risk assessment in this population. The triglyceride-glucose (TyG) index is a composite marker that integrates measurements of triglycerides and glucose, providing a straightforward and effective way to assess insulin resistance and CVD risk [ 10 , 11 ]. Unlike traditional indices such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), which relies on stable insulin levels, the TyG index circumvents this limitation, offering a more adaptable and reliable measure for populations with fluctuating insulin levels [ 12 ]. This makes the TyG index a promising marker for comprehensive CVD risk assessment in diverse populations. However, most previous studies in the general population have primarily focused on elevated baseline levels of the TyG index rather than dynamic changes over time [ 13 – 15 ]. Exploring the relationship between the trajectory of TyG index fluctuates over time and CVD risk could help identify disease risks in HIV-infected individuals, thereby informing tailored prevention strategies. Therefore, we conducted this large cohort study to evaluate the predictive value of the TyG index for CVD in PLWH, exploring the associations between baseline levels and the trajectory of the TyG index with CVD risk. Methods Study design and participants We conducted a prospective cohort study of treatment-naive PLWH who sought care at the Third People’s Hospital of Shenzhen during the period 2005–2022. This hospital is the only designated facility for HIV treatment and management in Shenzhen. Detailed information about this cohort is provided in the supplementary material (Brief introduction of the cohort). After excluding participants with missing TyG index data (n = 163), those diagnosed with prevalent CVD prior to, at, or within three months of study enrollment (n = 139), and those followed for less than three months (n = 1824), a total of 16,122 individuals remained for the final analysis. After further excluding participants with data quality issues and outliers (n = 243), 15,879 individuals were included in the trajectory group analysis. We obtained baseline and follow-up data, including socio-demographic, clinical information (exposures and outcomes), and laboratory test results from survey questionnaires and the hospital’s routine diagnosis and treatment information systems. Patients were followed up every three months. The study protocol was compliant with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Shenzhen Third People’s Hospital (No. 2022 − 143). All participants provided written informed consent. Assessment of covariates Our study included the following categories of covariates from the hospital routine diagnosis and treatment information system: (1) Demographic characteristics included age, sex, marital status (married or cohabiting, divorced or separated, widowed, never married), body mass index (BMI), which is calculated by dividing weight in kilograms by height in meters squared, smoking (ever vs. never), and drinking (yes or no). (2) Laboratory parameters included glucose, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), white blood cells (WBC), and platelet count. (3) Comorbidities included hypertension (identified through clinical diagnostic records or prescriptions of antihypertensive drugs), diabetes (diagnosed through clinical records, prescriptions of diabetes medications, or a fasting blood glucose level ≥ 7mmol/L), and hypercholesterolemia (total cholesterol ≥ 5.2 mmol/L or use of lipid-modifying agents), hepatitis B virus (HBV) infection (positive HBV surface-antigen test or positive envelope-antigen test, or a detectable HBV DNA), and hepatitis C virus (HCV) infection (positive anti-HCV antibody test or detectable HCV RNA). (4) HIV-related factors included the route of HIV transmission (male-to-male sexual contact, heterosexual contact, injection drug use, or other), the CD4 count at initiation of ART, achievement of viral suppression within 3 months post-ART initiation, and the time interval between HIV diagnosis and ART initiation. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. The levels of triglycerides and glucose were measured every three months in patients who had fasted for at least eight hours but less than 24 hours. Follow-up and outcomes The outcome of this study was CVD, defined as myocardial infarction, coronary heart disease, stroke, peripheral arterial disease, heart failure, and transient ischemic attack. All these CVDs were comprehensively determined by inpatient and outpatient records, medical history, and relevant laboratory results, adhering to established criteria such as the World Health Organization guidelines. This ensures standardized diagnostic confirmation across the study. The baseline was established as the date PLWH first received ART at the participating hospital. We identified outcomes occurring from three months after PLWH entry into the cohort to death, loss to follow-up, or Sep 30, 2022, the final date of the follow-up, whichever came first. Patients were considered lost to follow-up if they failed to attend scheduled appointments for more than three months, based on standard clinical practice. Participants might develop two or more CVD outcomes during the study period, and the time of the earliest disease occurrence is considered the time of the outcome. Statistical analysis All statistical analyses were performed using R software (version 4.3.3) and SAS software (version 9.4). Participants were divided into four groups based on the quartiles of the TyG index. Continuous variables were described as mean and standard deviation (SD), while categorical variables were expressed as frequency and percentage. Statistical methods included one-way ANOVA for continuous variables and chi-square test for categorical variables. Group-based trajectory modeling (GBTM), implemented through SAS Proc Traj, was used to identify subgroups with similar TyG index trajectories over time, utilizing finite mixture modeling to group individuals with comparable variable trajectories [ 16 ]. Before fitting the model, likelihood ratio testing was conducted to determine the best-fit polynomial shape for each vital sign (i.e., linear, quadratic, cubic) [ 17 ]. The optimal GBTM model was selected by combining the following criteria: (1) the lowest BIC value; (2) higher average posterior probabilities of group membership (> 0.7); and (3) adequate patient representation (> 5% in proportion) in each group. Ultimately, four distinct groups were identified as the best-fit model, and details regarding model selection and strategies are provided in Supplemental Table 1 and Fig. 1 . A Kaplan-Meier cumulative incidence plot was used to evaluate the relationship between the TyG index and CVD incidence during the follow-up period, and a log-rank test was used for statistical evaluation. We developed a multivariate Cox proportional hazards model to examine the association between the TyG index and CVD risk, while also providing hazard ratios (HR) and 95% confidence intervals (CI) for each 1-SD increment in the log-transformed TyG index. We built three models to adjust for potential confounders in CVD: Model 1 was unadjusted, serving as a baseline for comparison. Model 2 was adjusted for age and sex, acknowledging basic demographic influences on CVD risk. Model 3 included further adjustments for age, sex, BMI, marital status, smoking, drinking, HDL-C, LDL-C, TC, hypertension, CD4 count, HIV transmission route, and ART treatment regimen. These variables were chosen based on their established or potential roles as confounders or modifiers in CVD pathogenesis. Increase in prediction risk upon addition of TyG index to the conventional risk model was examined by change in Net Reclassification Improvement (NRI)and Integrated Discrimination Improvement (IDI). To confirm and further explore the relationship between the TyG index and CVD incidence, we utilized a restricted cubic spline plot to assess the potential nonlinearity of this relationship. After identifying a nonlinear relationship, we applied a two-piecewise multivariate Cox proportional hazards model to both segments divided at the inflection point of the TyG index. The threshold was estimated using the maximum likelihood method. Subsequently, stratification and interaction analyses were performed by age, sex, marital status, HIV transmission route, BMI, smoking, drinking, hypertension, diabetes, hypercholesterolemia, and CD4 counts. In sensitivity analyses, we excluded individuals within the first year of follow-up, regardless of the outcome with or without CVD. Since our data were derived from hospital electronic medical records, not all covariate data were complete. We performed multiple imputation for missing covariates using the random forest method to obtain unbiased estimates of the relationship between the TyG index and CVD incidence. Due to the high levels of missing data, hepatitis B and hepatitis C virus infections were not included in the multiple imputation process. Statistical significance was considered meaningful if the p-value was less than 0.05. Results Participant characteristics Of the 16,122 participants included in the study, 14,600 (90.6%) were male, and the mean (SD) age was 34.0 (10.1) years. The median TyG index was 8.47 (IQR 8.14–8.84). As shown in Table 1 , the baseline characteristics of participants were stratified by quartiles of the TyG index: Q1 (5.84–8.14), Q2 (8.15–8.47), Q3 (8.48–8.84), Q4 (8.85–13.19). Following established model standards, we identified four distinct TyG index trajectories among 15,879 participants: low-stable (n = 4106, 26.1%), low-moderate-stable (n = 6174, 38.3%), high-moderate-stable (n = 4117, 26.2%), and high-increasing (n = 1482, 9.4%) (Fig. 1 A). Notably, the four trajectory groups derived from the TyG index do not correspond directly to the quartile-based stratification (Fig. 1 B). For instance, within quartile 4, 21.4% of individuals belong to the low-moderate-stable group. Meanwhile, in quartile 3, 49.7% and 10.7% of individuals are categorized under the low-moderate-stable and low-stable groups, respectively. Table 2 summarizes the baseline demographics and clinical characteristics of the TyG index trajectories. Participants in higher levels of quartiles and trajectories of TyG index were generally older, more likely to be obese, married, and smokers, and more frequently treated with DTG-containing and EVG/c/FTC/TAF regimens. They had higher rates of hypertension, diabetes, hypercholesterolemia, and a history of heterosexual HIV contact, as well as elevated levels of glucose, TG, TC, CD4 count (< 200 cells/µL), and WBC, while showing lower levels of HDL, AST, ALT, and reduced rates of HBV infection (all P < 0.05, Tables 1 and 2 ). Table 1 Baseline characteristics of study participants according to the TyG index quartiles Characteristics Quartiles of TyG index Total Q1 (5.84–8.14) Q2 (8.15–8.47) Q3 (8.48–8.84) Q4 (8.85–13.19) P value N 16122 4103 4081 3953 3985 Male 14600 (90.6) 3725 (90.8) 3701 (90.7) 3581 (90.6) 3593 (90.2) 0.786 Age, years 34.0 ± 10.1 31.1 ± 9.2 33.0 ± 9.8 34.8 ± 10.1 37.4 ± 10.4 < 0.001 BMI, kg/m2 < 0.001 < 18.5 2494 (15.5) 857 (20.9) 744 (18.2) 529 (13.4) 364 (9.1) 18.5–23.9 10538 (65.4) 2888 (70.4) 2765 (67.8) 2586 (65.4) 2299 (57.7) ≥ 24 3090 (19.2) 358 (8.7) 572 (14.0) 838 (21.2) 1322 (33.2) Marital status < 0.001 Never married 9861 (61.2) 2900 (70.7) 2624 (64.3) 2317 (58.6) 2020 (50.7) Married or cohabiting 4825 (29.9) 960 (23.4) 1147 (28.1) 1257 (31.8) 1461 (36.7) Divorced, separated, or widowed 1436 (8.9) 243 (5.9) 310 (7.6) 379 (9.6) 504 (12.6) HIV transmission route < 0.001 Male-to-male sex contact 10503 (65.1) 2879 (70.2) 2708 (66.4) 2558 (64.7) 2358 (59.2) Heterosexual contact 5002 (31.0) 1101 (26.8) 1217 (29.8) 1244 (31.5) 1440 (36.1) IDU 125 (0.8) 23 (0.6) 35 (0.9) 32 (0.8) 35 (0.9) Other 492 (3.1) 100 (2.4) 121 (3.0) 119 (3.0) 152 (3.8) Smoking 3680 (22.8) 852 (20.8) 933 (22.9) 912 (23.1) 983 (24.7) 0.001 Drinking 3766 (23.4) 956 (23.3) 919 (22.5) 945 (23.9) 946 (23.7) 0.458 Hypertension 333 (2.1) 39 (1.0) 56 (1.4) 83 (2.1) 155 (3.9) < 0.001 Diabetes 521 (3.2) 17 (0.4) 28 (0.7) 88 (2.2) 388 (9.7) < 0.001 Hypercholesterolemia 1866 (11.6) 209 (5.1) 326 (8.0) 487 (12.3) 844 (21.2) < 0.001 Glucose, mg/dL 93.0 ± 23.3 86.9 ± 9.1 90.0 ± 9.3 92.4 ± 11.4 102.7 ± 41.7 < 0.001 High-density lipoprotein cholesterol, mgdL 46.2 ± 11.8 49.9 ± 11.6 47.0 ± 11.2 45.3 ± 11.6 42.6 ± 11.6 < 0.001 Low-density lipoprotein cholesterol, mgdL 98.6 ± 26.4 98.6 ± 26.4 98.5 ± 25.9 98.0 ± 26.6 99.2 ± 26.8 0.262 Triglycerides, mg/dL 104.5 (77.0-147.9) 62.9 (54.0-71.7) 91.2 (83.2–99.2) 124.0 (113.4-137.3) 196.6 (165.6-252.4) < 0.001 Total cholesterol, mg/dL 162.0 ± 67.1 150.9 ± 30.0 160.2 ± 90.4 164.8 ± 80.8 172.5 ± 46.7 < 0.001 Creatinine, mg/dL 0.7 ± 0.4 0.7 ± 0.3 0.7 ± 0.4 0.7 ± 0.4 0.7 ± 0.4 0.064 Aspartate Aminotransferase, U/L < 0.001 Normal 14575 (90.4) 3884 (94.7) 3729 (91.4) 3550 (89.8) 3412 (85.6) ≥ 1ULN 1175 (7.3) 179 (4.4) 280 (6.9) 296 (7.5) 420 (10.5) ≥ 2ULN 372 (2.3) 40 (1.0) 72 (1.8) 107 (2.7) 153 (3.8) Alanine Aminotransferase, U/L < 0.001 Normal 13637 (84.6) 3732 (91.0) 3559 (87.2) 3356 (84.9) 2990 (75.0) ≥ 1ULN 1841 (11.4) 307 (7.5) 383 (9.4) 436 (11.0) 715 (17.9) ≥ 2ULN 644 (4.0) 64 (1.6) 139 (3.4) 161 (4.1) 280 (7.0) WBC, 10^9/L 5.5 ± 1.9 5.4 ± 1.8 5.4 ± 1.8 5.5 ± 1.9 5.7 ± 2.0 < 0.001 Platelet, 10^9/L 213.9 ± 68.8 214.5 ± 62.2 212.2 ± 67.3 215.1 ± 72.3 213.6 ± 73.2 0.252 HBV infection 1362 (11.4) 401 (12.5) 355 (11.7) 336 (11.4) 270 (9.7) 0.007 HCV infection 178 (1.5) 39 (1.2) 59 (1.9) 43 (1.5) 37 (1.3) 0.103 CD4 count, cells/µL < 0.001 < 200 6003 (37.2) 1336 (32.6) 1511 (37.0) 1515 (38.3) 1641 (41.2) 200–349 6027 (37.4) 1713 (41.7) 1590 (39.0) 1436 (36.3) 1288 (32.3) ≥ 350 4092 (25.4) 1054 (25.7) 980 (24.0) 1002 (25.3) 1056 (26.5) HIV RNA, copies/ml < 0.001 < 5000 1381 (8.6) 303 (7.4) 288 (7.1) 337 (8.5) 453 (11.4) 5000–99999 5501 (34.1) 1610 (39.2) 1412 (34.6) 1287 (32.6) 1192 (29.9) ≥ 100000 9240 (57.3) 2190 (53.4) 2381 (58.3) 2329 (58.9) 2340 (58.7) Recent ART treatment regimen < 0.001 3TC + TDF + EFV/NVP 7570 (47.0) 2155 (52.5) 1961 (48.1) 1816 (45.9) 1638 (41.1) DTG-containing 4779 (29.6) 1030 (25.1) 1189 (29.1) 1223 (30.9) 1337 (33.6) 3TC/AZT + EFV/NVP/LPV/r 1440 (8.9) 338 (8.2) 336 (8.2) 347 (8.8) 419 (10.5) 3TC + LPV/r + TDF/AZT/D4T 1074 (6.7) 293 (7.1) 262 (6.4) 269 (6.8) 250 (6.3) EVG/c/FTC/TAF 769 (4.8) 184 (4.5) 192 (4.7) 182 (4.6) 211 (5.3) Other 490 (3.0) 103 (2.5) 141 (3.5) 116 (2.9) 130 (3.3) Continuous variables are expressed as mean ± SD or median and interquartile range (IQR). Categorical variables are expressed as frequency (percentage). TyG, triglyceride-glucose; BMI, body-mass index; IDU, injection drug use; ULN, upper limit of normal (45U/L); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus; ART, antiretroviral therapy; HIV, human immunodeficiency virus; 3TC, lamivudine; TDF, tenofovir disoproxil fumarate; EFV, efavirenz; NVP, nevirapine; DTG, Dolutegravir; AZT, zidovudine; LPVr, lopinavir/ritonavir; D4T, stavudine; EVG, Elvitegravir; FTC, Emtricitabine; TAF, Tenofovir Alafenamide Table 2 Baseline characteristics of study participants according to trajectories of the TyG index Characteristics Trajectories of TyG index Total Low-stable Low- Moderate-stable High- Moderate-stable High-increasing P value N 15879 4106 6174 4117 1482 Male 14400 (90.7) 3640 (88.7) 5589 (90.5) 3791 (92.1) 1380 (93.1) < 0.001 Age, years 34.0 ± 10.1 30.9 ± 9.2 33.8 ± 10.0 35.9 ± 10.1 38.4 ± 10.2 < 0.001 BMI, kg/m2 < 0.001 < 18.5 2435 (15.3) 967 (23.6) 1004 (16.3) 394 (9.6) 70 (4.7) 18.5–23.9 10382 (65.4) 2845 (69.3) 4211 (68.2) 2536 (61.6) 790 (53.3) ≥ 24 3062 (19.3) 294 (7.2) 959 (15.5) 1187 (28.8) 622 (42.0) Marital status < 0.001 Never married 9743 (61.4) 2881 (70.2) 3856 (62.5) 2317 (56.3) 689 (46.5) Married or cohabiting 4720 (29.7) 975 (23.7) 1798 (29.1) 1358 (33.0) 589 (39.7) Divorced, separated, or widowed 1416 (8.9) 250 (6.1) 520 (8.4) 442 (10.7) 204 (13.8) HIV transmission route < 0.001 Male-to-male sex contact 10402 (65.5) 2829 (68.9) 4090 (66.2) 2618 (63.6) 865 (58.4) Heterosexual contact 4902 (30.9) 1160 (28.3) 1868 (30.3) 1336 (32.5) 538 (36.3) IDU 109 (0.7) 17 (0.4) 49 (0.8) 35 (0.9) 8 (0.5) Other 466 (2.9) 100 (2.4) 167 (2.7) 128 (3.1) 71 (4.8) Smoking 3629 (22.9) 825 (20.1) 1401 (22.7) 1006 (24.4) 397 (26.8) < 0.001 Drinking 3715 (23.4) 951 (23.2) 1402 (22.7) 978 (23.8) 384 (25.9) 0.063 Hypertension 328 (2.1) 43 (1.0) 93 (1.5) 110 (2.7) 82 (5.5) < 0.001 Diabetes 512 (3.2) 29 (0.7) 113 (1.8) 166 (4.0) 204 (13.8) < 0.001 Hypercholesterolemia 1846 (11.6) 262 (6.4) 649 (10.5) 657 (16.0) 278 (18.8) < 0.001 Glucose, mg/dL 92.9 ± 22.6 88.2 ± 11.1 91.2 ± 13.1 95.1 ± 29.5 106.8 ± 41.4 < 0.001 High-density lipoprotein cholesterol, mgdL 46.2 ± 11.9 49.9 ± 11.8 46.9 ± 11.7 43.6 ± 11.0 40.0 ± 10.7 < 0.001 Low-density lipoprotein cholesterol, mgdL 98.6 ± 26.4 98.7 ± 25.9 98.6 ± 26.4 97.9 ± 26.0 100.2 ± 29.1 0.038 Triglycerides, mg/dL 104.5 (77.0-148.8) 72.6 (57.6–91.2) 101.0 (79.7-129.3) 140.0 (106.3-183.3) 208.1 (145.2–300.0) < 0.001 Total cholesterol, mg/dL 161.8 ± 67.4 155.6 ± 89.9 161.1 ± 65.2 166.0 ± 48.9 170.3 ± 41.4 < 0.001 Creatinine, mg/dL 0.7 ± 0.4 0.7 ± 0.3 0.7 ± 0.4 0.7 ± 0.4 0.7 ± 0.4 < 0.001 Aspartate Aminotransferase, U/L < 0.001 Normal 14366 (90.5) 3840 (93.5) 5650 (91.5) 3628 (88.1) 1248 (84.2) ≥ 1ULN 1151 (7.2) 208 (5.1) 403 (6.5) 368 (8.9) 172 (11.6) ≥ 2ULN 362 (2.3) 58 (1.4) 121 (2.0) 121 (2.9) 62 (4.2) Alanine Aminotransferase, U/L < 0.001 Normal 13441 (84.6) 3714 (90.5) 5392 (87.3) 3263 (79.3) 1072 (72.3) ≥ 1ULN 1806 (11.4) 298 (7.3) 586 (9.5) 629 (15.3) 293 (19.8) ≥ 2ULN 632 (4.0) 94 (2.3) 196 (3.2) 225 (5.5) 117 (7.9) WBC, 10^9/L 5.5 ± 1.9 5.4 ± 1.8 5.5 ± 1.8 5.6 ± 1.9 5.7 ± 2.0 < 0.001 Platelet, 10^9/L 214.1 ± 68.7 212.7 ± 62.4 214.1 ± 69.3 215.9 ± 72.7 212.5 ± 71.0 0.150 HBV infection 1349 (11.4) 401 (13.0) 552 (12.0) 309 (10.0) 87 (8.2) < 0.001 HCV infection 172 (1.5) 43 (1.4) 80 (1.7) 40 (1.3) 9 (0.8) 0.116 CD4 count, cells/µL < 0.001 < 200 5878 (37.0) 1263 (30.8) 2259 (36.6) 1675 (40.7) 681 (46.0) 200–349 5945 (37.4) 1753 (42.7) 2328 (37.7) 1411 (34.3) 453 (30.6) ≥ 350 4056 (25.5) 1090 (26.5) 1587 (25.7) 1031 (25.0) 348 (23.5) HIV RNA, copies/ml < 0.001 < 5000 1364 (8.6) 317 (7.7) 533 (8.6) 364 (8.8) 150 (10.1) 5000–99999 5421 (34.1) 1573 (38.3) 2096 (33.9) 1338 (32.5) 414 (27.9) ≥ 100000 9094 (57.3) 2216 (54.0) 3545 (57.4) 2415 (58.7) 918 (61.9) Recent ART treatment regimen < 0.001 3TC + TDF + EFV/NVP 7483 (47.1) 2446 (59.6) 3132 (50.7) 1498 (36.4) 407 (27.5) DTG-containing 4742 (29.9) 1116 (27.2) 1761 (28.5) 1329 (32.3) 536 (36.2) 3TC/AZT + EFV/NVP/LPV/r 1419 (8.9) 216 (5.3) 444 (7.2) 502 (12.2) 257 (17.3) 3TC + LPV/r + TDF/AZT/D4T 1045 (6.6) 101 (2.5) 390 (6.3) 422 (10.3) 132 (8.9) EVG/c/FTC/TAF 764 (4.8) 139 (3.4) 284 (4.6) 248 (6.0) 93 (6.3) Other 426 (2.7) 88 (2.1) 163 (2.6) 118 (2.9) 57 (3.8) Continuous variables are expressed as mean ± SD or median and interquartile range (IQR). Categorical variables are expressed as frequency (percentage). TyG, triglyceride-glucose; BMI, body-mass index; IDU, injection drug use; ULN, upper limit of normal (45U/L); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus; ART, antiretroviral therapy; HIV, human immunodeficiency virus; 3TC, lamivudine; TDF, tenofovir disoproxil fumarate; EFV, efavirenz; NVP, nevirapine; DTG, Dolutegravir; AZT, zidovudine; LPVr, lopinavir/ritonavir; D4T, stavudine; EVG, Elvitegravir; FTC, Emtricitabine; TAF, Tenofovir Alafenamide Associations between TyG index and risk of CVD As shown in Fig. 2 A, Kaplan–Meier survival analysis curves demonstrated that higher quartiles of the TyG index were significantly associated with an increased risk of CVD (log-rank P < 0.001). Similarly, in the trajectory analysis, the high-increasing group exhibited the highest cumulative risk of CVD (log-rank P < 0.001, Fig. 2 B). Table 3 demonstrates that the risk of CVD increased significantly with higher TyG index quartiles and across different trajectory groups. After full covariate adjustment, the HRs (95% CIs) were 1.00 (reference), 2.20 (1.22, 3.98), 2.56 (1.43, 4.57), and 3.44 (1.96, 6.07) for the Q1, Q2, Q3, and Q4 groups, respectively ( P < 0.001). Additionally, in the trajectory analysis, compared with the low-stable group, participants in the low-moderate-stable, high-moderate-stable, and high-increasing groups had a higher risk of CVD, with HRs (95% CIs) of 1.32 (0.79, 2.18), 2.18 (1.33, 3.58), and 2.72 (1.58, 4.69), respectively ( P < 0.001). Each 1-SD increment in the log-transformed values of the TyG index was associated with a 33% increase in the risk of CVD ( P < 0.001). Addition of baseline TyG index to the model that included all significant factors for CVD incidence improved the predictive risk as showed by the metrics of reclassification (NRI 37.47, 95% CI 24.05–50.89, P < 0.001) (Supplemental Table 2). Table 3 Association between TyG index and cardiovascular disease risk in people living with HIV Quartiles of TyG index P value Per 1 SD increase in Log TyG values, P value Quartile 1 Quartile 2 Quartile 3 Quartile 4 Median 7.92 8.32 8.65 9.14 No. of cases/Person-years 15/23944 43/23772 55/23178 101/23990 Model 1, HR (95% CI) 1.00 2.86 (1.59, 5.15) 3.75 (2.12, 6.64) 6.37 (3.70, 10.97) < 0.001 1.56 (1.40–1.73) < 0.001 Model 2, HR (95% CI) 1.00 2.20 (1.22, 3.96) 2.52 (1.42, 4.47) 3.53 (2.05, 6.10) < 0.001 1.34 (1.19–1.52) < 0.001 Model 3, HR (95% CI) 1.00 2.20 (1.22, 3.98) 2.56 (1.43, 4.57) 3.44 (1.96, 6.07) < 0.001 1.33 (1.16–1.53) < 0.001 Trajectories of TyG index P value Per 1 SD increase in Log TyG values , P value Low-stable Low-Moderate-stable High-Moderate-stable High-increasing Median 8.07 8.43 8.79 9.25 No. of cases/Person-years 21/23842 57/35733 82/24689 52/9047 Model 1, HR (95% CI) 1.00 1.81 (1.10, 2.98) 3.68 (2.28, 5.95) 6.04 (3.63, 10.05) < 0.001 1.56 (1.39–1.73) < 0.001 Model 2, HR (95% CI) 1.00 1.32 (0.80, 2.19) 2.31 (1.43, 3.73) 3.09 (1.85, 5.15) < 0.001 1.35 (1.19–1.52) < 0.001 Model 3, HR (95% CI) 1.00 1.32 (0.79, 2.18) 2.18 (1.33, 3.58) 2.72 (1.58, 4.69) < 0.001 1.33 (1.16–1.53) < 0.001 TyG, triglyceride-glucose; HR, hazard ratio; CI, confidence interval; ART, antiretroviral therapy Model 1: an unadjusted model Model 2: adjusted for sex, age Model 3: adjusted for sex, age, body mass index, marital status, smoking, drinking, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, hypertension, CD4 count, HIV transmission route, ART treatment regimen The detection of nonlinear relationships Multivariate-adjusted restricted cubic spline analysis indicated a nonlinear association between the TyG index and CVD incidence, with the adjusted smoothed plot revealing a consistently upward trend ( P for nonlinear association = 0.013, P for overall significance < 0.001, Fig. 3 ). We assessed the association between the baseline TyG index and CVD incidence using two Cox proportional hazards regression models. The standard model showed adjusted HRs and 95% CIs of 1.70 (1.39, 2.08) ( P < 0.001, Table 4 ). Based on a two-piecewise model, we identified an inflection point at 8.479 ( P for Log-likelihood ratio < 0.001). After adjusting for variables including age, sex, BMI, marital status, smoking, drinking, HDL-C, LDL-C, TC, hypertension, CD4 count, HIV transmission route, and ART treatment regimen, the risk of CVD increased by 3.71 (1.18, 11.68) for each unit increase in the TyG index without exceeding the threshold value and by 1.39 (1.03, 1.87) for each unit increase in the TyG index exceeding the threshold value (all P < 0.05). In sensitivity analyses that excluded individuals within the first year of follow-up, the TyG index remained significantly associated with CVD risk, showing consistent patterns of risk increase both below and above the threshold of 8.479 (all P < 0.05, Supplemental Table 3; Supplemental Fig. 2). Table 4 Threshold effect analysis of TyG index on cardiovascular disease risk in people living with HIV Adjusted HR (95% CI) P value TyG index on cardiovascular disease Fitting by the standard Cox proportional risk model 1.70 (1.39, 2.08) < 0.001 Fitting by the two-piecewise Cox proportional risk model Infection point 8.479 TyG index < 8.479 3.71 (1.18, 11.68) 0.025 TyG index ≥ 8.479 1.39 (1.03, 1.87) 0.029 P for Log-likelihood ratio < 0.001 TyG, triglyceride-glucose; HR, hazard ratio; CI, confidence interval; ART, antiretroviral therapy Cox proportional hazard models were used to estimate HR and 95% CI Adjusted for sex, age, body mass index, marital status, smoking, drinking, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, hypertension, CD4 count, HIV transmission route, ART treatment regimen Stratifed analyses In stratified analysis, a TyG index greater than 8.479 was associated with a 59% increase in the overall risk of CVD among PLWH, compared to those with a TyG index less than 8.479 (Fig. 4 ). Elevated risks were notably significant for younger individuals under 35, males, never-married individuals, male-to-male sexual contact, those with hypercholesterolemia, PLWH with lower CD4 counts (< 200 cells/µL) and those with lower body weight (BMI < 18.5), while females and other comorbidity subgroups did not show statistically significant differences. Furthermore, no significant interactions were found across various demographic and clinical variables, except for hypercholesterolemia, indicating that the TyG index's influence on CVD risk is broadly applicable across different strata of the PLWH population. Discussion In this large, prospective, population-based cohort of PLWH, we first explored the relationship between baseline and longitudinal trajectories of the TyG index and CVD risk. Our findings indicated that an elevated baseline TyG index was significantly associated with a higher CVD risk, with individuals in the highest quartile of the TyG index having a 3.44-fold increased risk of developing CVD compared to those in the lowest quartile after adjustment for potential CVD risk factors. We further identified four distinct TyG index trajectories using GBTM over a 17-year follow-up period, with the high-increasing trajectory showing the strongest association with increased CVD risk. Notably, we observed a threshold effect at a TyG index of 8.479, with each unit increase in the TyG index below this point correlating with a 3.71-fold increase in CVD risk, while each unit increase above this point was associated with a more moderate 1.39-fold escalation. Stratified analyses revealed that elevated risks were especially pronounced in younger individuals, males, and those with lower CD4 counts, highlighting the TyG index's potential as a valuable tool for identifying PLWH at increased risk of CVD and guiding targeted preventive measures. Previous research has validated the TyG index as a practical and flexible tool for assessing IR in large-scale or epidemiological studies, offering a favorable alternative to the traditional HOMA-IR [ 12 ]. IR leads to atherogenic dyslipidemia, increases blood pressure, and induces a pro-inflammatory state, making it a risk factor for elevating the CVD incidence [ 18 ]. In recent years, studies on the TyG index’s role in predicting CVD risk across various populations have confirmed its significant predictive value [ 12 – 15 , 19 – 20 ]. However, these studies primarily focused on the baseline or single levels of the TyG index, with limited data on the dynamic changes over time. A North America study categorized TyG trajectories into four distinct groups, demonstrating the baseline and trajectories of the TyG index were significantly associated with the occurrence of major adverse cardiovascular events in patients with type 2 diabetes [ 21 ]. Yan et al. found that elevated levels of the baseline TyG index and a higher long-term trajectory of the TyG index were independently associated with increased arterial stiffness [ 22 ]. These findings highlighted the importance of considering both baseline levels and longitudinal changes of TyG index when assessing CVD risk among PLWH. We first utilized dynamic TyG index data to explore its relationship with CVD in HIV-infected populations and obtained conclusions consistent with those from the general population. Additionally, we presented the varying percentages of participants grouped by baseline TyG index quartiles and trajectory-based groups, revealing partially overlapping distributions. This further underscores the importance of employing trajectory-based grouping for dynamic data analysis. The results support that long-term TyG index trajectories may help to identify HIV- infected individuals at higher risk of CVD who deserve targeted prevention strategies. Another significant finding from our study is the nonlinear association between the TyG index and CVD risk among PLWH, with a threshold of 8.479. Our research indicates a consistent upward trend in CVD risk as the TyG index increases, showing a positive correlation on both sides of this threshold: 3.71-fold below and 1.39-fold above the threshold. This finding contrasts with studies suggesting a U-shaped relationship between the TyG index and CVD risk [ 23 ]. Such discrepancies may result from differences in study design or the specific characteristics of the populations examined. These variations underscore the unique insights of our study into the dynamics of the TyG index, emphasizing its potential to refine CVD risk assessments and inform targeted clinical strategies for PLWH. A large UK cohort study previously found that the highest quartile of the TyG index was associated with a 1.19 times higher risk of total CVD compared to the lowest quartile [ 24 ]. Our research in PLWH shows that the TyG index quartiles predicted a 3.44-fold increase in CVD risk, with the high-increasing trajectory group predicting a 2.72-fold increase, underscoring the pronounced susceptibility of PLWH to CVD. A study by So-Armah demonstrated that younger PLWH faced a significantly higher risk of CVD compared to their uninfected peers, due to the inflammatory and immune-modulating effects of both HIV and ART [ 25 ]. Similarly, a cross-sectional study found a higher prevalence of subclinical atherosclerosis, as measured by carotid ultrasound, in HIV-positive individuals compared to HIV-negative individuals under 35 years old, but not in those between 60 and 75 years [ 26 ]. Interestingly, the FRS was similar between HIV-positive and HIV-negative individuals under 35 years old, suggesting that FRS may not effectively predict early CVD risk in younger PLWH. The measurement of carotid intima-media thickness (cIMT) is costly and requires high-resolution imaging and complex statistical models for accurate data interpretation, making carotid ultrasound unsuitable for widespread use. Therefore, simple indicators for early risk assessment in the younger PLWH are essential. Our study found that the TyG index has greater predictive value in PLWH under 35 years old. This simple and convenient metric enhances the feasibility of CVD risk screening in this unique age-related vulnerable population. Strengths and limitations The primary advantage of this study was the large, longitudinal cohort of HIV-infected individuals, with multiple measurements of TyG index trajectories over time. This prospective design allowed us to establish both baseline and follow-up relationships between the dynamic TyG index and CVD risk. Several limitations need to be addressed. Firstly, the study's single-center setting may limit the broader applicability of the results. Secondly, although we implemented multiple imputations to address missing data, this method could still introduce bias, and the possibility of residual confounding factors may further impact the outcomes, complicating the interpretation of the TyG index's role as a CVD risk predictor. Lastly, despite the large sample size in this study, females constituted a small proportion, which limited the ability to assess sex-specific CVD risk in PLWH. Conclusions Our study identified four distinct trajectory groups of TyG index in PLWH over a 17-year follow-up period. The results indicated that higher baseline TyG levels and the high-increasing trajectory group were associated with increased CVD risk in PLWH. This finding further supports the TyG index as a powerful marker for assessing CVD risk in PLWH, with its effectiveness consistently recognized across various patient populations. Monitoring longitudinal TyG levels and integrating the index into routine clinical assessments could enhance CVD prevention strategies for PLWH. Future research is needed to validate these findings and explore the benefits of interventions through large multicenter studies. Abbreviations TyG Triglyceride-glucose CVD Cardiovascular diseases HIV Human immunodeficiency virus ART Antiretroviral therapy PLWH People living with HIV HOMA-IR Homeostatic model assessment of insulin resistance BMI Body mass index HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol TC Total cholesterol Cr Creatinine AST Aspartate aminotransferase ALT Alanine aminotransferase WBC White blood cells HBV Hepatitis B virus HCV Hepatitis C virus NRI Net reclassification improvement IDI Integrated discrimination improvement HR Hazard ratio CI Confidence interval SD Standard deviation DTG Dolutegravir Declarations Acknowledgements The authors are deeply grateful to all participants at the Shenzhen Third People's Hospital for their crucial contributions to this study. Author contributions Conceptualization: JL and HL; Methodology: YL and LS; Formal analysis and investigation: JL and YL; Writing—original draft preparation: YL; Writing—review and editing: YH and FZ; Resources: LG, XL, YH and XX; Funding acquisition: JL and HL; Supervision: FB and DS. All authors contributed to subsequent revisions and approved the final version. All authors approved the final manuscript. Funding This work was supported by the Science and Technology Innovation Committee of Shenzhen Municipality (No. JCYJ20220531102202005), the Natural Science Foundation of Guangdong Province (No. 2024A1515012118), the Shenzhen High-level Hospital Construction Fund (No. G2022153), and the Shenzhen Fund for Guangdong Provincial High-level Clinical Key Spcialties (No: SZGSP011). Availability of data and materials The derived data that were generated in the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study protocol was compliant with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Shenzhen Third People’s Hospital (No. 2022-143). All participants provided written informed consent. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. References Global AIDSupdate. 2023. Joint United Nations Programme on HIV/AIDS; 2023. https://www.unaids.org/en/resources/documents/2023/global-aids-update-2023 . Accessed 3 June 2024. Nazari I, Feinstein MJ. Evolving mechanisms and presentations of cardiovascular disease in people with HIV: implications for management. Clin Microbiol Rev. 2024;37(1):e0009822. Wu PY, Chen MY, Sheng WH, Hsieh SM, Chuang YC, Cheng A, et al. Estimated risk of cardiovascular disease among the HIV-positive patients aged 40 years or older in Taiwan. J Microbiol Immunol Infect. 2019;52(4):549–55. 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HIV and cardiovascular disease. Lancet HIV. 2020;7(4):e279–93. Lin H, Ding Y, Ning C, Qiao X, Chen X, Chen X, et al. Age-specific associations between HIV infection and carotid artery intima-media thickness in China: a cross-sectional evaluation of baseline data from the CHART cohort. Lancet HIV. 2019;6(12):e860–8. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial.docx Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 30 Oct, 2024 Editor assigned by journal 29 Oct, 2024 Submission checks completed at journal 29 Oct, 2024 First submitted to journal 29 Oct, 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. 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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-5352658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372118350,"identity":"ccc910ce-929d-4c70-9efa-7a6eac5ece63","order_by":0,"name":"Yinsong Luo","email":"","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yinsong","middleName":"","lastName":"Luo","suffix":""},{"id":372118352,"identity":"decd6e55-11c7-4dae-a53f-d8be6f076e12","order_by":1,"name":"Liqin Sun","email":"","orcid":"","institution":"National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liqin","middleName":"","lastName":"Sun","suffix":""},{"id":372118353,"identity":"6f72cbea-8141-4e27-86bb-10c8e9523f77","order_by":2,"name":"Yun He","email":"","orcid":"","institution":"National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"He","suffix":""},{"id":372118354,"identity":"2e024b66-3da7-4932-b5a3-cf778a926e22","order_by":3,"name":"Fang Zhao","email":"","orcid":"","institution":"National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Zhao","suffix":""},{"id":372118355,"identity":"d9e97580-15ae-4fbb-b27f-90b3d2c57417","order_by":4,"name":"Duo Shan","email":"","orcid":"","institution":"National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Duo","middleName":"","lastName":"Shan","suffix":""},{"id":372118356,"identity":"3d0b6554-89bd-43c6-89ee-bbfd9c34d4b6","order_by":5,"name":"Fan Bu","email":"","orcid":"","institution":"The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Bu","suffix":""},{"id":372118357,"identity":"f0859ced-6862-4042-a988-be4f11fb511c","order_by":6,"name":"Lingyun Ge","email":"","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Ge","suffix":""},{"id":372118358,"identity":"c06fac81-f4e7-459a-bf46-062db992ac1d","order_by":7,"name":"Xiaorui Li","email":"","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaorui","middleName":"","lastName":"Li","suffix":""},{"id":372118359,"identity":"71e01e12-b424-44ee-8f09-03ba78bf7b95","order_by":8,"name":"Yiyao Hu","email":"","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiyao","middleName":"","lastName":"Hu","suffix":""},{"id":372118360,"identity":"e882ad31-f9f0-49b2-adb7-17c438bb8b71","order_by":9,"name":"Xi Xiao","email":"","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Xiao","suffix":""},{"id":372118361,"identity":"15cd1baf-7d74-41ab-be1a-3bfdae6967e6","order_by":10,"name":"Hongzhou Lu","email":"","orcid":"","institution":"National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongzhou","middleName":"","lastName":"Lu","suffix":""},{"id":372118362,"identity":"b7343033-7936-4746-904d-754469177ee6","order_by":11,"name":"Jiaye Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie2ROwrCQBBARwK7FkvSpjJXmBBQweBZEoS1SRF7UUGIjVh7DEGwFgdzBgsLQbAOCCIoYlIErNyUgvuqGZjH/AA0mh/ENAAwyAPGp1vM0O8qFVYqpkiDwTKWPbVSBg07wqvIdrWJUuEC49OQGsyW4drHrQGc9qvvgwnEICWPiTN5ER5NEFIe1AqjMOFS5srFAFs0KygvGicQtR5tpNqkkhImFLB6hB5UU1iM4aLvJvmR3TnKHlPtYlm0ce+3juPM8lfen37X4pR+VYpGCDD9SBXlBcYJYFShTqPRaP6WNzG2RHIrfJqKAAAAAElFTkSuQmCC","orcid":"","institution":"Shenzhen University Medical School","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jiaye","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-29 08:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5352658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5352658/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21744-1","type":"published","date":"2025-02-05T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70209424,"identity":"91b6fbe7-e164-48a2-874e-f2a48663ffc6","added_by":"auto","created_at":"2024-11-29 14:22:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313930,"visible":true,"origin":"","legend":"\u003cp\u003eTyG index trajectories groups (\u003cstrong\u003eA\u003c/strong\u003e) and percentage of the trajectories across TyG quartiles (\u003cstrong\u003eB\u003c/strong\u003e) in PLWH.\u003c/p\u003e\n\u003cp\u003eTyG, triglyceride–glucose; PLWH, people living with HIV\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/9015b644841884b54199b41a.jpg"},{"id":70209188,"identity":"c3819faf-342a-429b-8575-fd591805f957","added_by":"auto","created_at":"2024-11-29 14:14:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":418365,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for cumulative incidence of cardiovascular diseases by quartiles (\u003cstrong\u003eA\u003c/strong\u003e) and trajectories (\u003cstrong\u003eB\u003c/strong\u003e) of TyG index in PLWH.\u003c/p\u003e\n\u003cp\u003eQuartiles are defined as follows: Q1 (5.84-8.14), Q2 (8.15-8.47), Q3 (8.48-8.84), and Q4 (8.85-13.19). TyG, triglyceride–glucose; CVD, cardiovascular diseases; PLWH, people living with HIV\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/8b408f3241effa785848c8dd.jpg"},{"id":70209186,"identity":"14dd514c-2373-46ac-9cff-9fb551c92424","added_by":"auto","created_at":"2024-11-29 14:14:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121973,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable adjusted spline analysis of the TyG index and cardiovascular diseases risk in PLWH.\u003c/p\u003e\n\u003cp\u003eThe solid line indicates the HR for CVD, and the pink shaded area represents the 95% CI. A nonlinear association is evident, and the CVD risk is elevated, with an inflection point at a TyG index of 8.479. Covariates adjusted in the model include sex, age, BMI, marital status, smoking, drinking, HDL-C, LDL-C, TC, hypertension, CD4 count, HIV transmission route, ART treatment regimen.\u003c/p\u003e\n\u003cp\u003eTyG, triglyceride–glucose; CVD, cardiovascular diseases; PLWH, people living with HIV; HR, hazard ratio; CI, confidence interval; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; ART, antiretroviral therapy\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/ea8bbed617b6ce71c5538316.jpg"},{"id":70209187,"identity":"07eb8fde-ffe6-43ee-bb15-68d4d5f924a6","added_by":"auto","created_at":"2024-11-29 14:14:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":657216,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the TyG index and cardiovascular diseases risk in PLWH.\u003c/p\u003e\n\u003cp\u003eThis fig depicts HR and 95%CI for CVD incidence associated with TyG index levels above and below the inflection point of 8.479 among various subgroups of PLWH, with adjustments made for confounding factors. The vertical dashed line represents the TyG index threshold, and the horizontal dashed line represents an HR of 1, indicating no increased risk.\u003c/p\u003e\n\u003cp\u003eTyG, triglyceride-glucose; CVD, cardiovascular diseases; PLWH, people living with HIV; HR, hazard ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/d21ac185c203a9dfd12b842e.jpg"},{"id":75929938,"identity":"cfc586d4-b5dc-4a88-b1d9-077386318c60","added_by":"auto","created_at":"2025-02-10 16:05:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5790249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/468eb5cb-1f6e-4338-8fb8-ad05875b197f.pdf"},{"id":70209190,"identity":"c78a3082-08fa-45d7-990f-36fdfdb51960","added_by":"auto","created_at":"2024-11-29 14:14:30","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":230276,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5352658/v1/0dc722bc18276211a628e81b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The triglyceride-glucose index trajectories are associated with cardiovascular diseases in people living with HIV: evidence from a prospective cohort study in China, 2005-2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, there were 1.3\u0026nbsp;million new HIV infections in 2022, and 29.8\u0026nbsp;million of the 39\u0026nbsp;million people living with HIV (PLWH) were on antiretroviral therapy (ART) by 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While ART has transformed HIV from a fatal disease to a chronic, manageable condition, it has also introduced new health challenges, particularly a heightened risk of cardiovascular disease (CVD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This risk is notably higher in PLWH compared to that in uninfected population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], posing a major concern for the long-term management of HIV.\u003c/p\u003e \u003cp\u003eHIV-infected individuals face a complex range of CVD risk factors, including virological factors, ART-related factors, and traditional risk factors such as smoking, hypertension, diabetes, dyslipidemia, and obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The D:A:D model, developed specifically for HIV-infected individuals, offers a 5-year CVD risk assessment tool by integrating HIV-specific and traditional CVD risk factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, its external applicability is limited, performing adequately only in HIV populations with characteristics similar to its initial European cohort [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, the model's validity may have diminished over time due to advances in ART, making it less reflective of current HIV management. General population models, such as the Framingham Risk Score (FRS) and Pooled Cohort Equations, also prove inadequate for assessing CVD risk in HIV-infected populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, exploring new, accurate, and specific markers is essential for comprehensive risk assessment in this population.\u003c/p\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index is a composite marker that integrates measurements of triglycerides and glucose, providing a straightforward and effective way to assess insulin resistance and CVD risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Unlike traditional indices such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), which relies on stable insulin levels, the TyG index circumvents this limitation, offering a more adaptable and reliable measure for populations with fluctuating insulin levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This makes the TyG index a promising marker for comprehensive CVD risk assessment in diverse populations. However, most previous studies in the general population have primarily focused on elevated baseline levels of the TyG index rather than dynamic changes over time [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Exploring the relationship between the trajectory of TyG index fluctuates over time and CVD risk could help identify disease risks in HIV-infected individuals, thereby informing tailored prevention strategies.\u003c/p\u003e \u003cp\u003eTherefore, we conducted this large cohort study to evaluate the predictive value of the TyG index for CVD in PLWH, exploring the associations between baseline levels and the trajectory of the TyG index with CVD risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e We conducted a prospective cohort study of treatment-naive PLWH who sought care at the Third People\u0026rsquo;s Hospital of Shenzhen during the period 2005\u0026ndash;2022. This hospital is the only designated facility for HIV treatment and management in Shenzhen. Detailed information about this cohort is provided in the supplementary material (Brief introduction of the cohort). After excluding participants with missing TyG index data (n\u0026thinsp;=\u0026thinsp;163), those diagnosed with prevalent CVD prior to, at, or within three months of study enrollment (n\u0026thinsp;=\u0026thinsp;139), and those followed for less than three months (n\u0026thinsp;=\u0026thinsp;1824), a total of 16,122 individuals remained for the final analysis. After further excluding participants with data quality issues and outliers (n\u0026thinsp;=\u0026thinsp;243), 15,879 individuals were included in the trajectory group analysis.\u003c/p\u003e \u003cp\u003eWe obtained baseline and follow-up data, including socio-demographic, clinical information (exposures and outcomes), and laboratory test results from survey questionnaires and the hospital\u0026rsquo;s routine diagnosis and treatment information systems. Patients were followed up every three months.\u003c/p\u003e \u003cp\u003e The study protocol was compliant with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Shenzhen Third People\u0026rsquo;s Hospital (No. 2022\u0026thinsp;\u0026minus;\u0026thinsp;143). All participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eOur study included the following categories of covariates from the hospital routine diagnosis and treatment information system: (1) Demographic characteristics included age, sex, marital status (married or cohabiting, divorced or separated, widowed, never married), body mass index (BMI), which is calculated by dividing weight in kilograms by height in meters squared, smoking (ever vs. never), and drinking (yes or no). (2) Laboratory parameters included glucose, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), white blood cells (WBC), and platelet count. (3) Comorbidities included hypertension (identified through clinical diagnostic records or prescriptions of antihypertensive drugs), diabetes (diagnosed through clinical records, prescriptions of diabetes medications, or a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;7mmol/L), and hypercholesterolemia (total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2 mmol/L or use of lipid-modifying agents), hepatitis B virus (HBV) infection (positive HBV surface-antigen test or positive envelope-antigen test, or a detectable HBV DNA), and hepatitis C virus (HCV) infection (positive anti-HCV antibody test or detectable HCV RNA). (4) HIV-related factors included the route of HIV transmission (male-to-male sexual contact, heterosexual contact, injection drug use, or other), the CD4 count at initiation of ART, achievement of viral suppression within 3 months post-ART initiation, and the time interval between HIV diagnosis and ART initiation. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) \u0026times; fasting glucose (mg/dL)/2]. The levels of triglycerides and glucose were measured every three months in patients who had fasted for at least eight hours but less than 24 hours.\u003c/p\u003e\n\u003ch3\u003eFollow-up and outcomes\u003c/h3\u003e\n\u003cp\u003eThe outcome of this study was CVD, defined as myocardial infarction, coronary heart disease, stroke, peripheral arterial disease, heart failure, and transient ischemic attack. All these CVDs were comprehensively determined by inpatient and outpatient records, medical history, and relevant laboratory results, adhering to established criteria such as the World Health Organization guidelines. This ensures standardized diagnostic confirmation across the study.\u003c/p\u003e \u003cp\u003eThe baseline was established as the date PLWH first received ART at the participating hospital. We identified outcomes occurring from three months after PLWH entry into the cohort to death, loss to follow-up, or Sep 30, 2022, the final date of the follow-up, whichever came first. Patients were considered lost to follow-up if they failed to attend scheduled appointments for more than three months, based on standard clinical practice. Participants might develop two or more CVD outcomes during the study period, and the time of the earliest disease occurrence is considered the time of the outcome.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.3) and SAS software (version 9.4). Participants were divided into four groups based on the quartiles of the TyG index. Continuous variables were described as mean and standard deviation (SD), while categorical variables were expressed as frequency and percentage. Statistical methods included one-way ANOVA for continuous variables and chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eGroup-based trajectory modeling (GBTM), implemented through SAS Proc Traj, was used to identify subgroups with similar TyG index trajectories over time, utilizing finite mixture modeling to group individuals with comparable variable trajectories [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Before fitting the model, likelihood ratio testing was conducted to determine the best-fit polynomial shape for each vital sign (i.e., linear, quadratic, cubic) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The optimal GBTM model was selected by combining the following criteria: (1) the lowest BIC value; (2) higher average posterior probabilities of group membership (\u0026gt;\u0026thinsp;0.7); and (3) adequate patient representation (\u0026gt;\u0026thinsp;5% in proportion) in each group. Ultimately, four distinct groups were identified as the best-fit model, and details regarding model selection and strategies are provided in Supplemental Table\u0026nbsp;1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Kaplan-Meier cumulative incidence plot was used to evaluate the relationship between the TyG index and CVD incidence during the follow-up period, and a log-rank test was used for statistical evaluation. We developed a multivariate Cox proportional hazards model to examine the association between the TyG index and CVD risk, while also providing hazard ratios (HR) and 95% confidence intervals (CI) for each 1-SD increment in the log-transformed TyG index. We built three models to adjust for potential confounders in CVD: Model 1 was unadjusted, serving as a baseline for comparison. Model 2 was adjusted for age and sex, acknowledging basic demographic influences on CVD risk. Model 3 included further adjustments for age, sex, BMI, marital status, smoking, drinking, HDL-C, LDL-C, TC, hypertension, CD4 count, HIV transmission route, and ART treatment regimen. These variables were chosen based on their established or potential roles as confounders or modifiers in CVD pathogenesis. Increase in prediction risk upon addition of TyG index to the conventional risk model was examined by change in Net Reclassification Improvement (NRI)and Integrated Discrimination Improvement (IDI).\u003c/p\u003e \u003cp\u003eTo confirm and further explore the relationship between the TyG index and CVD incidence, we utilized a restricted cubic spline plot to assess the potential nonlinearity of this relationship. After identifying a nonlinear relationship, we applied a two-piecewise multivariate Cox proportional hazards model to both segments divided at the inflection point of the TyG index. The threshold was estimated using the maximum likelihood method. Subsequently, stratification and interaction analyses were performed by age, sex, marital status, HIV transmission route, BMI, smoking, drinking, hypertension, diabetes, hypercholesterolemia, and CD4 counts. In sensitivity analyses, we excluded individuals within the first year of follow-up, regardless of the outcome with or without CVD.\u003c/p\u003e \u003cp\u003eSince our data were derived from hospital electronic medical records, not all covariate data were complete. We performed multiple imputation for missing covariates using the random forest method to obtain unbiased estimates of the relationship between the TyG index and CVD incidence. Due to the high levels of missing data, hepatitis B and hepatitis C virus infections were not included in the multiple imputation process. Statistical significance was considered meaningful if the p-value was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eOf the 16,122 participants included in the study, 14,600 (90.6%) were male, and the mean (SD) age was 34.0 (10.1) years. The median TyG index was 8.47 (IQR 8.14\u0026ndash;8.84). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the baseline characteristics of participants were stratified by quartiles of the TyG index: Q1 (5.84\u0026ndash;8.14), Q2 (8.15\u0026ndash;8.47), Q3 (8.48\u0026ndash;8.84), Q4 (8.85\u0026ndash;13.19). Following established model standards, we identified four distinct TyG index trajectories among 15,879 participants: low-stable (n\u0026thinsp;=\u0026thinsp;4106, 26.1%), low-moderate-stable (n\u0026thinsp;=\u0026thinsp;6174, 38.3%), high-moderate-stable (n\u0026thinsp;=\u0026thinsp;4117, 26.2%), and high-increasing (n\u0026thinsp;=\u0026thinsp;1482, 9.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Notably, the four trajectory groups derived from the TyG index do not correspond directly to the quartile-based stratification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For instance, within quartile 4, 21.4% of individuals belong to the low-moderate-stable group. Meanwhile, in quartile 3, 49.7% and 10.7% of individuals are categorized under the low-moderate-stable and low-stable groups, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the baseline demographics and clinical characteristics of the TyG index trajectories. Participants in higher levels of quartiles and trajectories of TyG index were generally older, more likely to be obese, married, and smokers, and more frequently treated with DTG-containing and EVG/c/FTC/TAF regimens. They had higher rates of hypertension, diabetes, hypercholesterolemia, and a history of heterosexual HIV contact, as well as elevated levels of glucose, TG, TC, CD4 count (\u0026lt;\u0026thinsp;200 cells/\u0026micro;L), and WBC, while showing lower levels of HDL, AST, ALT, and reduced rates of HBV infection (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eBaseline characteristics of study participants according to the TyG index quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of TyG index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (5.84\u0026ndash;8.14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (8.15\u0026ndash;8.47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (8.48\u0026ndash;8.84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (8.85\u0026ndash;13.19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14600 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3725 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3701 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3581 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3593 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2494 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e857 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e744 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e529 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e364 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10538 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2888 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2765 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2586 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2299 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3090 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e572 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e838 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1322 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9861 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2900 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2624 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2317 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4825 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e960 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1147 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1257 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1461 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced, separated, or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1436 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e379 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e504 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV transmission route\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-to-male sex contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10503 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2879 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2708 (66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2558 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2358 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e5002 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1101 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1217 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1244 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1440 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3680 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e852 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e933 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e912 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e983 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3766 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e919 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e945 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e946 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e521 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e388 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1866 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e326 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e487 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.7\u0026thinsp;\u0026plusmn;\u0026thinsp;41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-density lipoprotein cholesterol, mgdL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-density lipoprotein cholesterol, mgdL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.2\u0026thinsp;\u0026plusmn;\u0026thinsp;26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.5 (77.0-147.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.9 (54.0-71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.2 (83.2\u0026ndash;99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.0 (113.4-137.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196.6 (165.6-252.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.0\u0026thinsp;\u0026plusmn;\u0026thinsp;67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.9\u0026thinsp;\u0026plusmn;\u0026thinsp;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.2\u0026thinsp;\u0026plusmn;\u0026thinsp;90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164.8\u0026thinsp;\u0026plusmn;\u0026thinsp;80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172.5\u0026thinsp;\u0026plusmn;\u0026thinsp;46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspartate Aminotransferase, U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14575 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3884 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3729 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3550 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3412 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1175 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e420 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlanine Aminotransferase, U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13637 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3732 (91.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3559 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3356 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2990 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1841 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e436 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e715 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e644 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e280 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC, 10^9/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet, 10^9/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.9\u0026thinsp;\u0026plusmn;\u0026thinsp;68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212.2\u0026thinsp;\u0026plusmn;\u0026thinsp;67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e215.1\u0026thinsp;\u0026plusmn;\u0026thinsp;72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e213.6\u0026thinsp;\u0026plusmn;\u0026thinsp;73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBV infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1362 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e270 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCV infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD4 count, cells/\u0026micro;L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e6003 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1336 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1511 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1515 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1641 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6027 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1713 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1590 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1436 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1288 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4092 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1054 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e980 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1002 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1056 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV RNA, copies/ml\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1381 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e453 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026ndash;99999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5501 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1610 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1412 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1287 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1192 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9240 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2190 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2381 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2329 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2340 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecent ART treatment regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC\u0026thinsp;+\u0026thinsp;TDF\u0026thinsp;+\u0026thinsp;EFV/NVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7570 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2155 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1961 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1816 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1638 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTG-containing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4779 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1030 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1189 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1223 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1337 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC/AZT\u0026thinsp;+\u0026thinsp;EFV/NVP/LPV/r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1440 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e419 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC\u0026thinsp;+\u0026thinsp;LPV/r\u0026thinsp;+\u0026thinsp;TDF/AZT/D4T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1074 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVG/c/FTC/TAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e769 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median and interquartile range (IQR). Categorical variables are expressed as frequency (percentage).\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eTyG, triglyceride-glucose; BMI, body-mass index; IDU, injection drug use; ULN, upper limit of normal (45U/L); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus; ART, antiretroviral therapy; HIV, human immunodeficiency virus; 3TC, lamivudine; TDF, tenofovir disoproxil fumarate; EFV, efavirenz; NVP, nevirapine; DTG, Dolutegravir; AZT, zidovudine; LPVr, lopinavir/ritonavir; D4T, stavudine; EVG, Elvitegravir; FTC, Emtricitabine; TAF, Tenofovir Alafenamide\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\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\u003eBaseline characteristics of study participants according to trajectories of the TyG index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eTrajectories of TyG index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-\u003c/p\u003e \u003cp\u003eModerate-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-\u003c/p\u003e \u003cp\u003eModerate-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14400 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3640 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5589 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3791 (92.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1380 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2435 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e967 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1004 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e394 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10382 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2845 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4211 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2536 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e790 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3062 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e959 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1187 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e622 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9743 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2881 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3856 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2317 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e689 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4720 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e975 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1798 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1358 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e589 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced, separated, or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1416 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e442 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV transmission route\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-to-male sex contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10402 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2829 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4090 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2618 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e865 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e4902 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1160 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1868 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1336 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e538 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e466 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3629 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1401 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1006 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e397 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3715 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e951 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1402 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e978 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e384 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1846 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e657 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e278 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.1\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106.8\u0026thinsp;\u0026plusmn;\u0026thinsp;41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-density lipoprotein cholesterol, mgdL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-density lipoprotein cholesterol, mgdL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.2\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.5 (77.0-148.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.6 (57.6\u0026ndash;91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.0 (79.7-129.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.0 (106.3-183.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208.1 (145.2\u0026ndash;300.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.8\u0026thinsp;\u0026plusmn;\u0026thinsp;67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.6\u0026thinsp;\u0026plusmn;\u0026thinsp;89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.1\u0026thinsp;\u0026plusmn;\u0026thinsp;65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166.0\u0026thinsp;\u0026plusmn;\u0026thinsp;48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170.3\u0026thinsp;\u0026plusmn;\u0026thinsp;41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspartate Aminotransferase, U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14366 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3840 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5650 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3628 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1248 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1151 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e403 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlanine Aminotransferase, U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13441 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3714 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5392 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3263 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1072 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1806 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e586 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e629 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e632 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC, 10^9/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet, 10^9/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.1\u0026thinsp;\u0026plusmn;\u0026thinsp;68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212.7\u0026thinsp;\u0026plusmn;\u0026thinsp;62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214.1\u0026thinsp;\u0026plusmn;\u0026thinsp;69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e215.9\u0026thinsp;\u0026plusmn;\u0026thinsp;72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212.5\u0026thinsp;\u0026plusmn;\u0026thinsp;71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBV infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1349 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e309 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCV infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD4 count, cells/\u0026micro;L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e5878 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1263 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2259 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1675 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e681 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5945 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1753 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2328 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1411 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e453 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4056 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1090 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1587 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1031 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e348 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV RNA, copies/ml\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1364 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e533 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e364 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026ndash;99999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5421 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1573 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2096 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1338 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e414 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9094 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2216 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3545 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2415 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e918 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecent ART treatment regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC\u0026thinsp;+\u0026thinsp;TDF\u0026thinsp;+\u0026thinsp;EFV/NVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7483 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2446 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3132 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1498 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e407 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTG-containing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4742 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1116 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1761 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1329 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e536 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC/AZT\u0026thinsp;+\u0026thinsp;EFV/NVP/LPV/r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1419 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e502 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e257 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3TC\u0026thinsp;+\u0026thinsp;LPV/r\u0026thinsp;+\u0026thinsp;TDF/AZT/D4T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1045 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e422 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVG/c/FTC/TAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e764 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e248 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median and interquartile range (IQR). Categorical variables are expressed as frequency (percentage).\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eTyG, triglyceride-glucose; BMI, body-mass index; IDU, injection drug use; ULN, upper limit of normal (45U/L); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus; ART, antiretroviral therapy; HIV, human immunodeficiency virus; 3TC, lamivudine; TDF, tenofovir disoproxil fumarate; EFV, efavirenz; NVP, nevirapine; DTG, Dolutegravir; AZT, zidovudine; LPVr, lopinavir/ritonavir; D4T, stavudine; EVG, Elvitegravir; FTC, Emtricitabine; TAF, Tenofovir Alafenamide\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between TyG index and risk of CVD\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Kaplan\u0026ndash;Meier survival analysis curves demonstrated that higher quartiles of the TyG index were significantly associated with an increased risk of CVD (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in the trajectory analysis, the high-increasing group exhibited the highest cumulative risk of CVD (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates that the risk of CVD increased significantly with higher TyG index quartiles and across different trajectory groups. After full covariate adjustment, the HRs (95% CIs) were 1.00 (reference), 2.20 (1.22, 3.98), 2.56 (1.43, 4.57), and 3.44 (1.96, 6.07) for the Q1, Q2, Q3, and Q4 groups, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, in the trajectory analysis, compared with the low-stable group, participants in the low-moderate-stable, high-moderate-stable, and high-increasing groups had a higher risk of CVD, with HRs (95% CIs) of 1.32 (0.79, 2.18), 2.18 (1.33, 3.58), and 2.72 (1.58, 4.69), respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each 1-SD increment in the log-transformed values of the TyG index was associated with a 33% increase in the risk of CVD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Addition of baseline TyG index to the model that included all significant factors for CVD incidence improved the predictive risk as showed by the metrics of reclassification (NRI 37.47, 95% CI 24.05\u0026ndash;50.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplemental Table\u0026nbsp;2).\u003c/p\u003e \u003cp\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\u003eAssociation between TyG index and cardiovascular disease risk in people living with HIV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of TyG index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePer 1 SD increase in\u003c/p\u003e \u003cp\u003eLog TyG values, \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cases/Person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/23944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43/23772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55/23178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e101/23990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86 (1.59, 5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.75 (2.12, 6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.37 (3.70, 10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56 (1.40\u0026ndash;1.73)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20 (1.22, 3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.52 (1.42, 4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.53 (2.05, 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.34 (1.19\u0026ndash;1.52)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20 (1.22, 3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56 (1.43, 4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.44 (1.96, 6.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33 (1.16\u0026ndash;1.53)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTrajectories of TyG index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePer 1 SD increase in\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLog TyG values\u003c/b\u003e, \u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow-stable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLow-Moderate-stable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHigh-Moderate-stable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHigh-increasing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cases/Person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21/23842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57/35733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82/24689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e52/9047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81 (1.10, 2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68 (2.28, 5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.04 (3.63, 10.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56 (1.39\u0026ndash;1.73)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.80, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31 (1.43, 3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.09 (1.85, 5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35 (1.19\u0026ndash;1.52)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3, HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.79, 2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18 (1.33, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.72 (1.58, 4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33 (1.16\u0026ndash;1.53)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eTyG, triglyceride-glucose; HR, hazard ratio; CI, confidence interval; ART, antiretroviral therapy\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eModel 1: an unadjusted model\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eModel 2: adjusted for sex, age\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eModel 3: adjusted for sex, age, body mass index, marital status, smoking, drinking, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, hypertension, CD4 count, HIV transmission route, ART treatment regimen\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe detection of nonlinear relationships\u003c/h3\u003e\n\u003cp\u003eMultivariate-adjusted restricted cubic spline analysis indicated a nonlinear association between the TyG index and CVD incidence, with the adjusted smoothed plot revealing a consistently upward trend (\u003cem\u003eP\u003c/em\u003e for nonlinear association\u0026thinsp;=\u0026thinsp;0.013, \u003cem\u003eP\u003c/em\u003e for overall significance\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe assessed the association between the baseline TyG index and CVD incidence using two Cox proportional hazards regression models. The standard model showed adjusted HRs and 95% CIs of 1.70 (1.39, 2.08) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on a two-piecewise model, we identified an inflection point at 8.479 (\u003cem\u003eP\u003c/em\u003e for Log-likelihood ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for variables including age, sex, BMI, marital status, smoking, drinking, HDL-C, LDL-C, TC, hypertension, CD4 count, HIV transmission route, and ART treatment regimen, the risk of CVD increased by 3.71 (1.18, 11.68) for each unit increase in the TyG index without exceeding the threshold value and by 1.39 (1.03, 1.87) for each unit increase in the TyG index exceeding the threshold value (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In sensitivity analyses that excluded individuals within the first year of follow-up, the TyG index remained significantly associated with CVD risk, showing consistent patterns of risk increase both below and above the threshold of 8.479 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplemental Table\u0026nbsp;3; Supplemental Fig.\u0026nbsp;2).\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\u003eThreshold effect analysis of TyG index on cardiovascular disease risk in people living with HIV\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index on cardiovascular disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by the standard Cox proportional risk model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.39, 2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by the two-piecewise Cox proportional risk model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u0026thinsp;\u0026lt;\u0026thinsp;8.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.71 (1.18, 11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u0026thinsp;\u0026ge;\u0026thinsp;8.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.03, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for Log-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eTyG, triglyceride-glucose; HR, hazard ratio; CI, confidence interval; ART, antiretroviral therapy\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eCox proportional hazard models were used to estimate HR and 95% CI\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eAdjusted for sex, age, body mass index, marital status, smoking, drinking, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, hypertension, CD4 count, HIV transmission route, ART treatment regimen\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStratifed analyses\u003c/h2\u003e \u003cp\u003eIn stratified analysis, a TyG index greater than 8.479 was associated with a 59% increase in the overall risk of CVD among PLWH, compared to those with a TyG index less than 8.479 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Elevated risks were notably significant for younger individuals under 35, males, never-married individuals, male-to-male sexual contact, those with hypercholesterolemia, PLWH with lower CD4 counts (\u0026lt;\u0026thinsp;200 cells/\u0026micro;L) and those with lower body weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), while females and other comorbidity subgroups did not show statistically significant differences. Furthermore, no significant interactions were found across various demographic and clinical variables, except for hypercholesterolemia, indicating that the TyG index's influence on CVD risk is broadly applicable across different strata of the PLWH population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, prospective, population-based cohort of PLWH, we first explored the relationship between baseline and longitudinal trajectories of the TyG index and CVD risk. Our findings indicated that an elevated baseline TyG index was significantly associated with a higher CVD risk, with individuals in the highest quartile of the TyG index having a 3.44-fold increased risk of developing CVD compared to those in the lowest quartile after adjustment for potential CVD risk factors. We further identified four distinct TyG index trajectories using GBTM over a 17-year follow-up period, with the high-increasing trajectory showing the strongest association with increased CVD risk. Notably, we observed a threshold effect at a TyG index of 8.479, with each unit increase in the TyG index below this point correlating with a 3.71-fold increase in CVD risk, while each unit increase above this point was associated with a more moderate 1.39-fold escalation. Stratified analyses revealed that elevated risks were especially pronounced in younger individuals, males, and those with lower CD4 counts, highlighting the TyG index's potential as a valuable tool for identifying PLWH at increased risk of CVD and guiding targeted preventive measures.\u003c/p\u003e \u003cp\u003ePrevious research has validated the TyG index as a practical and flexible tool for assessing IR in large-scale or epidemiological studies, offering a favorable alternative to the traditional HOMA-IR [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. IR leads to atherogenic dyslipidemia, increases blood pressure, and induces a pro-inflammatory state, making it a risk factor for elevating the CVD incidence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, studies on the TyG index\u0026rsquo;s role in predicting CVD risk across various populations have confirmed its significant predictive value [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, these studies primarily focused on the baseline or single levels of the TyG index, with limited data on the dynamic changes over time. A North America study categorized TyG trajectories into four distinct groups, demonstrating the baseline and trajectories of the TyG index were significantly associated with the occurrence of major adverse cardiovascular events in patients with type 2 diabetes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Yan et al. found that elevated levels of the baseline TyG index and a higher long-term trajectory of the TyG index were independently associated with increased arterial stiffness [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings highlighted the importance of considering both baseline levels and longitudinal changes of TyG index when assessing CVD risk among PLWH. We first utilized dynamic TyG index data to explore its relationship with CVD in HIV-infected populations and obtained conclusions consistent with those from the general population. Additionally, we presented the varying percentages of participants grouped by baseline TyG index quartiles and trajectory-based groups, revealing partially overlapping distributions. This further underscores the importance of employing trajectory-based grouping for dynamic data analysis. The results support that long-term TyG index trajectories may help to identify HIV- infected individuals at higher risk of CVD who deserve targeted prevention strategies.\u003c/p\u003e \u003cp\u003eAnother significant finding from our study is the nonlinear association between the TyG index and CVD risk among PLWH, with a threshold of 8.479. Our research indicates a consistent upward trend in CVD risk as the TyG index increases, showing a positive correlation on both sides of this threshold: 3.71-fold below and 1.39-fold above the threshold. This finding contrasts with studies suggesting a U-shaped relationship between the TyG index and CVD risk [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Such discrepancies may result from differences in study design or the specific characteristics of the populations examined. These variations underscore the unique insights of our study into the dynamics of the TyG index, emphasizing its potential to refine CVD risk assessments and inform targeted clinical strategies for PLWH.\u003c/p\u003e \u003cp\u003eA large UK cohort study previously found that the highest quartile of the TyG index was associated with a 1.19 times higher risk of total CVD compared to the lowest quartile [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our research in PLWH shows that the TyG index quartiles predicted a 3.44-fold increase in CVD risk, with the high-increasing trajectory group predicting a 2.72-fold increase, underscoring the pronounced susceptibility of PLWH to CVD. A study by So-Armah demonstrated that younger PLWH faced a significantly higher risk of CVD compared to their uninfected peers, due to the inflammatory and immune-modulating effects of both HIV and ART [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, a cross-sectional study found a higher prevalence of subclinical atherosclerosis, as measured by carotid ultrasound, in HIV-positive individuals compared to HIV-negative individuals under 35 years old, but not in those between 60 and 75 years [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Interestingly, the FRS was similar between HIV-positive and HIV-negative individuals under 35 years old, suggesting that FRS may not effectively predict early CVD risk in younger PLWH. The measurement of carotid intima-media thickness (cIMT) is costly and requires high-resolution imaging and complex statistical models for accurate data interpretation, making carotid ultrasound unsuitable for widespread use. Therefore, simple indicators for early risk assessment in the younger PLWH are essential. Our study found that the TyG index has greater predictive value in PLWH under 35 years old. This simple and convenient metric enhances the feasibility of CVD risk screening in this unique age-related vulnerable population.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe primary advantage of this study was the large, longitudinal cohort of HIV-infected individuals, with multiple measurements of TyG index trajectories over time. This prospective design allowed us to establish both baseline and follow-up relationships between the dynamic TyG index and CVD risk. Several limitations need to be addressed. Firstly, the study's single-center setting may limit the broader applicability of the results. Secondly, although we implemented multiple imputations to address missing data, this method could still introduce bias, and the possibility of residual confounding factors may further impact the outcomes, complicating the interpretation of the TyG index's role as a CVD risk predictor. Lastly, despite the large sample size in this study, females constituted a small proportion, which limited the ability to assess sex-specific CVD risk in PLWH.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identified four distinct trajectory groups of TyG index in PLWH over a 17-year follow-up period. The results indicated that higher baseline TyG levels and the high-increasing trajectory group were associated with increased CVD risk in PLWH. This finding further supports the TyG index as a powerful marker for assessing CVD risk in PLWH, with its effectiveness consistently recognized across various patient populations. Monitoring longitudinal TyG levels and integrating the index into routine clinical assessments could enhance CVD prevention strategies for PLWH. Future research is needed to validate these findings and explore the benefits of interventions through large multicenter studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eHIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eART \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003ePLWH \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;People living with HIV\u003c/p\u003e\n\u003cp\u003eHOMA-IR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Homeostatic model assessment of insulin resistance\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003eCr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Creatinine\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;White blood cells\u003c/p\u003e\n\u003cp\u003eHBV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hepatitis B virus\u003c/p\u003e\n\u003cp\u003eHCV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hepatitis C virus\u003c/p\u003e\n\u003cp\u003eNRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Net reclassification improvement\u003c/p\u003e\n\u003cp\u003eIDI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Integrated discrimination improvement\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hazard ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standard deviation\u003c/p\u003e\n\u003cp\u003eDTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dolutegravir\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are deeply grateful to all participants at the Shenzhen Third People\u0026apos;s Hospital for their crucial contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: JL and HL; Methodology: YL and LS; Formal analysis and investigation: JL and YL; Writing\u0026mdash;original draft preparation: YL; Writing\u0026mdash;review and editing: YH and FZ; Resources: LG, XL, YH and XX; Funding acquisition: JL and HL; Supervision: FB and DS. All authors contributed to subsequent revisions and approved the final version. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and Technology Innovation Committee of Shenzhen Municipality (No. JCYJ20220531102202005), the Natural Science Foundation of Guangdong Province (No. 2024A1515012118), the Shenzhen High-level Hospital Construction Fund (No. G2022153), and the Shenzhen Fund for Guangdong Provincial High-level Clinical Key Spcialties (No: SZGSP011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe derived data that were generated in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was compliant with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Shenzhen Third People\u0026rsquo;s Hospital (No. 2022-143). All participants provided written informed consent.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal AIDSupdate. 2023. Joint United Nations Programme on HIV/AIDS; 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/en/resources/documents/2023/global-aids-update-2023\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/en/resources/documents/2023/global-aids-update-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 3 June 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazari I, Feinstein MJ. 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Cardiovasc Diabetol. 2023;22(1):34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSo-Armah K, Benjamin LA, Bloomfield GS, Feinstein MJ, Hsue P, Njuguna B, et al. HIV and cardiovascular disease. Lancet HIV. 2020;7(4):e279\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin H, Ding Y, Ning C, Qiao X, Chen X, Chen X, et al. Age-specific associations between HIV infection and carotid artery intima-media thickness in China: a cross-sectional evaluation of baseline data from the CHART cohort. Lancet HIV. 2019;6(12):e860\u0026ndash;8.\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":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Cohort study, Cardiovascular disease, HIV, Incidence","lastPublishedDoi":"10.21203/rs.3.rs-5352658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5352658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index has been validated as a novel biomarker for cardiovascular disease (CVD) risk. However, the prospective relationship between baseline and long-term trajectories of the TyG index and CVD risk in people living with HIV (PLWH) remains unexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cohort study included 16,122 treatment-naive PLWH who initiated antiretroviral therapy (ART) at the Third People's Hospital of Shenzhen from 2005 to 2022. The TyG index was calculated as Ln [fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL)/2]. Group-based trajectory modeling (GBTM) was used to identify distinct TyG index trajectories over the follow-up period. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariate Cox proportional hazards models to assess the association between TyG index trajectories and CVD risk. Nonlinear relationships were investigated using a restricted cubic spline plot. Subgroup analysis was conducted to examine whether the association between the TyG index and CVD risk differed across various subgroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 70 months, 214 PLWH developed CVD. Each 1-standard deviation (SD) increase in the baseline TyG index was associated with a 33% higher risk of CVD (HR\u0026thinsp;=\u0026thinsp;1.33, 95% CI 1.18\u0026ndash;1.51) after adjusting for traditional CVD risk factors. Participants were categorized into four distinct TyG trajectory groups: low-stable, low-moderate-stable, high-moderate-stable, and high-increasing. After multivariate adjustment, the high-increasing trajectory group had a 2.72-fold (95% CI 1.58\u0026ndash;4.69) increased risk of CVD compared to the low-stable group. The restriction cubic spline plot showed an upward trend between the baseline TyG index and the CVD occurrence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the threshold at 8.479. Significantly positive correlations between the TyG index and CVD were observed both below the TyG threshold with HR 3.71 (95% CI 1.18, 11.68) and above the threshold with HR 1.39 (95% CI 1.03, 1.87) (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher baseline TyG index levels and the high-increasing trajectory were significantly associated with increased CVD risk in PLWH. Long-term trajectories of the TyG index could aid in identifying individuals at higher risk of CVD, highlighting the importance of incorporating TyG index monitoring into routine clinical assessments for tailored prevention strategies.\u003c/p\u003e","manuscriptTitle":"The triglyceride-glucose index trajectories are associated with cardiovascular diseases in people living with HIV: evidence from a prospective cohort study in China, 2005-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 14:14:25","doi":"10.21203/rs.3.rs-5352658/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T08:24:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-30T01:35:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-30T01:34:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-10-29T08:42:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc800027-961d-45ad-b3f0-88cc07187d22","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T15:58:47+00:00","versionOfRecord":{"articleIdentity":"rs-5352658","link":"https://doi.org/10.1186/s12889-025-21744-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-02-05 15:56:53","publishedOnDateReadable":"February 5th, 2025"},"versionCreatedAt":"2024-11-29 14:14:25","video":"","vorDoi":"10.1186/s12889-025-21744-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21744-1","workflowStages":[]},"version":"v1","identity":"rs-5352658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5352658","identity":"rs-5352658","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}