Association between low-density lipoprotein cholesterol with cholelithiasis among hospitalized people living with HIV: a retrospective case-control study

Association between low-density lipoprotein cholesterol with cholelithiasis among hospitalized people living with HIV: a retrospective case-control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between low-density lipoprotein cholesterol with cholelithiasis among hospitalized people living with HIV: a retrospective case-control study Chen Tingyu, Wei Guo, Zhao Yong, Wang Hua, Tan Juan, Yang Jing, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9355321/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective To investigate the association between low-density lipoprotein cholesterol (LDL-C) levels and cholelithiasis among hospitalized people living with HIV. Methods We conducted a single-center retrospective case-control study including 259 hospitalized people living with HIV admitted between January 2019 and January 2025. The case group comprised 98 patients with imaging-confirmed cholelithiasis, and the control group comprised 161 patients without imaging evidence of cholelithiasis during the same period. Demographic characteristics, co-infections, antiretroviral therapy-related variables, and laboratory parameters were collected. Logistic regression models were used to evaluate the association between LDL-C and cholelithiasis. Model I was adjusted for sex, age, and body mass index (BMI), and Model II was further adjusted for hepatitis B virus (HBV) co-infection, duration since confirmed HIV diagnosis, albuvirtide (ABT) treatment, and CD4 + T-cell count. Smooth-curve fitting and segmented regression were used to explore a potential nonlinear association. Stratified analyses and interaction tests were conducted as exploratory analyses. Results Compared with controls, patients with cholelithiasis were younger (50.22 ± 11.71 vs. 56.30 ± 16.00 years) and more likely to be female (40.82% vs. 6.21%). BMI was similar between groups. The case group had higher total cholesterol, high-density lipoprotein cholesterol, and LDL-C levels than the control group, with mean LDL-C levels of 3.08 ± 0.96 mmol/L and 2.42 ± 0.75 mmol/L, respectively. In univariable analysis, LDL-C was significantly associated with cholelithiasis (OR = 2.54, 95% CI: 1.80–3.58; P < 0.001). This association remained significant after multivariable adjustment (Model II: OR = 2.71, 95% CI: 1.79–4.11; P < 0.001). Smooth-curve fitting suggested a nonlinear association, with an exploratory inflection point at 2.28 mmol/L. Below this threshold, the association was not statistically significant; above it, the positive association was substantially stronger. No significant interaction was observed for HBV or ABT. Conclusion Among hospitalized people living with HIV, elevated LDL-C was independently associated with cholelithiasis. The data also suggest a possible nonlinear association, with a stronger effect above 2.28 mmol/L. These findings may help identify metabolic risk factors for cholelithiasis in this population, but confirmation in larger prospective studies is required. HIV infection low-density lipoprotein cholesterol cholelithiasis hospitalized patients Figures Figure 1 Figure 2 Introduction Human immunodeficiency virus (HIV) infection has evolved from a rapidly fatal disease into a chronic and increasingly manageable condition. With the widespread use of combination antiretroviral therapy (cART), life expectancy among people living with HIV has increased substantially. At the same time, however, metabolic complications have become an increasingly important clinical concern [ 1 – 3 ]. Previous studies have shown that HIV infection itself, together with the accompanying chronic inflammatory state, can disrupt lipid metabolism and contribute to metabolic abnormalities such as metabolic syndrome and insulin resistance [ 4 – 6 ]. In addition, people living with HIV often exhibit elevated cholesterol and triglyceride levels, some of which are closely related to antiretroviral therapy [ 7 ]. These factors collectively pose greater challenges for metabolic health management in this population. Dyslipidemia is a well-recognized contributor to cholelithiasis. Epidemiological and clinical studies have consistently shown that patients with cholelithiasis often present with abnormal lipid profiles, including elevated total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride levels, along with reduced high-density lipoprotein cholesterol (HDL-C) levels [ 8 – 10 ]. Among these lipid parameters, LDL-C has attracted particular attention. A Mendelian randomization study demonstrated that the effect of free thyroxine (FT4) on cholelithiasis risk was partially mediated by LDL-C and apolipoprotein B, accounting for 17.4% and 13.5% of the total mediating effect, respectively [ 11 ]. Another study reported that genetically proxied LDL-C reduction mediated by single-nucleotide polymorphisms near the HMGCR gene, mimicking the pharmacological effect of statins, was significantly associated with a lower risk of cholelithiasis [ 12 ]. Moreover, multivariable analyses of modifiable risk factors for cholelithiasis have suggested that the relationship between LDL-C and cholelithiasis risk may be more complex than previously appreciated [ 13 ]. Clinical evidence has further shown that patients with cholelithiasis have significantly higher preoperative LDL-C levels than healthy controls, and that lipid profiles tend to normalize after cholecystectomy [ 9 ], further supporting a potential link between LDL-C abnormalities and gallstone formation. Despite these observations, evidence regarding the association between LDL-C and cholelithiasis in people living with HIV remains limited. Previous studies have suggested that HIV-infected individuals with cholelithiasis may exhibit higher LDL-C levels and enhanced bile acid synthetic activity, and that these features may be influenced by factors such as age and cART regimens [ 7 ]. However, the available evidence has largely been derived from small observational studies or has focused on other metabolic indicators. To date, systematic retrospective case-control analyses specifically examining the association between LDL-C levels and cholelithiasis among hospitalized people living with HIV are lacking. Furthermore, HIV-related chronic inflammation, immune reconstitution, and drug–metabolism interactions may lead to a distinct pattern in the relationship between lipid metabolism and cholelithiasis in this population [ 14 ]. Current risk models for cholelithiasis also rarely incorporate HIV infection status as a relevant clinical context [ 15 – 17 ]. Against this background, we conducted a single-center retrospective case-control study among hospitalized people living with HIV admitted between January 2019 and January 2025 to investigate the association between LDL-C levels and cholelithiasis. We further explored whether this association was potentially nonlinear and evaluated the robustness of the findings through stratified analyses and interaction tests. This study aimed to clarify the association between LDL-C levels and cholelithiasis in hospitalized people living with HIV, to further explore the potential nonlinear nature of this relationship, and to provide a clinical basis for future prospective studies. Patients and Methods 1. Study design and participants This was a single-center retrospective case-control study. We included hospitalized people living with HIV who were admitted to the Public Health Clinical Center of Chengdu between January 2019 and January 2025. According to the results of abdominal imaging examinations performed during hospitalization, including ultrasonography, computed tomography (CT), or magnetic resonance imaging (MRI), participants were classified into a case group and a control group. The case group comprised patients with HIV infection who had imaging-confirmed cholelithiasis, whereas the control group comprised hospitalized people living with HIV during the same period who had no imaging evidence of cholelithiasis. The detailed inclusion and exclusion criteria were as follows. 1.1 Inclusion criteria Participants were eligible if they met all of the following criteria: (1) age ≥ 18 years; (2) confirmed HIV infection based on an initial positive HIV antibody screening test and subsequent confirmation by the Chinese Center for Disease Control and Prevention, with the diagnosis of AIDS made in accordance with the Chinese Guidelines for the Diagnosis and Treatment of HIV/AIDS ; (3) complete abdominal imaging data during hospitalization, including ultrasonography, CT, or MRI, sufficient to determine the presence or absence of gallstones; (4) availability of LDL-C results obtained within 24–48 hours after admission; (5) complete clinical data. 1.2 Exclusion criteria Participants were excluded if they met any of the following criteria: (1) age < 18 years; (2) pregnancy or puerperium; (3) presence of biliary tract malignancy, congenital biliary malformations, primary sclerosing cholangitis, or other severe conditions that could interfere with assessment, such as severe cardiovascular or cerebrovascular disease, hematologic disorders, or other malignancies; (4) missing imaging data, LDL-C values, or other key clinical information; (5) LDL-C measured only after biliary intervention or after severe infection/shock; (6) previous cholecystectomy; (7) repeated hospitalizations for the same patient, in which case only the first admission was retained. A total of 259 eligible patients were ultimately included in the analysis, comprising 98 cases and 161 controls (Fig. 1). 2. Data collection and variable definitions Data were collected on demographic characteristics, medical history, co-infections, HIV-related variables, and laboratory findings. Demographic and clinical variables included age, sex, body mass index (BMI), ethnicity, marital status, smoking history, alcohol consumption, hypertension, diabetes mellitus, and coronary heart disease. Co-infections included hepatitis B virus (HBV), hepatitis C virus (HCV), syphilis, and Mycobacterium tuberculosis infection. HIV-related variables included duration since confirmed HIV infection, whether the patient was newly diagnosed with HIV infection, combination antiretroviral therapy (cART) regimen, and albuvirtide (ABT) treatment. Laboratory parameters included complete blood count variables, including white blood cell count (WBC), neutrophil count (Neu), hemoglobin (Hb), and platelet count (PLT); biochemical indicators, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), albumin (Alb), globulin (Glo), total bilirubin (TBIL), urea, creatinine (Cr), calcium (Ca²⁺), sodium (Na⁺), potassium (K⁺), and chloride (Cl⁻); lipid profile variables, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C); coagulation function, represented by prothrombin time (PT); CD4 + T-cell count, CD8 + T-cell count, CD4+/CD8 + ratio, and plasma HIV-1 RNA. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). Duration since confirmed HIV infection was defined as the time from the first confirmed HIV diagnosis to the index hospitalization and was expressed in months. Plasma HIV-1 RNA was categorized as ≤ 20 copies/mL and > 20 copies/mL. Values reported as < 20 copies/mL were classified into the ≤ 20 copies/mL group, indicating that the HIV-1 RNA level was below the lower limit of quantification of the assay. 3. Statistical analysis All statistical analyses were performed using R software (version 4.2.0; The R Foundation for Statistical Computing, Vienna, Austria) and EmpowerStats. Normally distributed continuous variables are presented as mean ± standard deviation (SD), whereas non-normally distributed variables are presented as median and interquartile range (IQR). Categorical variables are presented as number (percentage). Between-group comparisons were performed using the independent-samples t test or Wilcoxon rank-sum test for continuous variables, as appropriate, and the chi-square test or Fisher’s exact test for categorical variables. Univariable logistic regression analyses were first conducted to evaluate the associations between clinical variables and cholelithiasis. Multivariable logistic regression models were then fitted to assess the association between LDL-C and cholelithiasis. Covariates were prespecified on the basis of clinical relevance, prior literature, and model parsimony. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided P value < 0.05 was considered statistically significant; values < 0.001 are reported as P < 0.001. To explore a potential nonlinear association between LDL-C and cholelithiasis, smooth-curve fitting was performed after multivariable adjustment. When a nonlinear trend was suggested, segmented logistic regression was used to estimate the exploratory inflection point and the effect sizes on either side of the threshold. Stratified analyses were conducted according to age, sex, BMI, HBV co-infection status, duration since confirmed HIV diagnosis, receipt of ABT, and CD4 + T-cell count. Interaction terms were incorporated into the final model to explore potential effect modification. Both stratified and interaction analyses were interpreted as exploratory. Results 1. Clinical characteristics 1.1 Demographic and clinical characteristics A total of 259 hospitalized people living with HIV were included, of whom 98 had cholelithiasis and 161 did not. Compared with controls, patients with cholelithiasis were younger (50.22 ± 11.71 vs. 56.30 ± 16.00 years) and more likely to be female (40.82% vs. 6.21%). BMI was similar between the two groups (23.00 ± 2.91 vs. 22.71 ± 3.17 kg/m²). The case group also had a higher prevalence of HBV co-infection (8.16% vs. 2.50%) and a higher proportion of ABT treatment (83.67% vs. 57.76%). The median duration since confirmed HIV diagnosis was similar between groups. Detailed baseline characteristics are presented in Table 1 . Table 1 Baseline demographic and clinical characteristics of 259 hospitalized patients Characteristics Controls(n = 161) Cases(n = 98) Age, Mean ± SD, y 56.30 (16.00) 50.22 (11.71) Sex, N(%) Women 10 (6.21%) 40 (40.82%) Men 151 (93.79%) 58 (59.18%) BMI, Mean ± SD 22.71 (3.17) 23.00 (2.91) Ethnicity, N(%) Han 155 (96.27%) 86 (87.76%) Tibetan 4 (2.48%) 4 (4.08%) Yi 2 (1.24%) 7 (7.14%) Miao 0 (0.00%) 1 (1.02%) Marital Status, N(%) Single 30 (18.63%) 15 (15.31%) Married 119 (73.91%) 76 (77.55%) Divorced 9 (5.59%) 6 (6.12%) Widowed 3 (1.86%) 1 (1.02%) Smoking, N(%) NO 84 (52.17%) 69 (70.41%) YES 77 (47.83%) 29 (29.59%) Alcohol Consumptionn, N(%) NO 122 (75.78%) 85 (86.73%) YES 39 (24.22%) 13 (13.27%) Hypertension, N(%) NO 135 (83.85%) 86 (87.76%) YES 26 (16.15%) 12 (12.24%) Diabetes Mellitus, N(%) Table 1 Continued Characteristics Controls(n = 161) Cases(n = 98) NO 151 (93.79%) 89 (90.82%) YES 10 (6.21%) 9 (9.18%) Coronary Heart Disease, N(%) NO 159 (98.76%) 97 (100.00%) YES 2 (1.24%) 0 (0.00%) HBV, N(%) NO 156 (97.50%) 90 (91.84%) YES 4 (2.50%) 8 (8.16%) HCV, N(%) NO 161 (100.00%) 97 (98.98%) YES 0 (0.00%) 1 (1.02%) Syphilis, N(%) NO 157 (97.52%) 93 (94.90%) YES 4 (2.48%) 5 (5.10%) Tuberculosis, N(%) NO 158 (98.14%) 94 (95.92%) YES 3 (1.86%) 4 (4.08%) Duration since confirmed HIV infection, M (IQR), Months 29.00 (6.00–60.00) 29.50 (5.00–60.00) Patients with newly diagnosed HIV infection, N(%) NO 139 (86.34%) 86 (87.76%) YES 22 (13.66%) 12 (12.24%) cART Regimen, N(%) Not used 22 (13.66%) 16 (16.33%) Potentially LDL-C friendly 3 (1.86%) 1 (1.02%) Potentially LDL-C unfavorable 136(84.47%) 81 (82.65%) Treated with ABT, N(%) NO 68 (42.24%) 16 (16.33%) YES 93 (57.76%) 82 (83.67%) 1.2 Laboratory characteristics Comparison of laboratory parameters showed that patients in the case group had higher levels of TC, HDL-C, and LDL-C than those in the control group [5.09 (1.13) mmol/L vs. 4.36 (0.74) mmol/L; 1.21 (0.32) mmol/L vs. 1.11 (0.35) mmol/L; and 3.08 ± 0.96 mmol/L vs. 2.42 ± 0.75 mmol/L, respectively]. CD4 + T-cell counts were slightly higher in the case group than in the control group [366.50 (245.50-484.50) cells/µL vs. 317.00 (192.00-445.00) cells/µL], whereas CD8 + T-cell counts were comparable between the two groups [494.00 (343.25–709.50) cells/µL vs. 482.00 (329.00-706.00) cells/µL]. In addition, PLT and GGT levels were higher in the case group, whereas urea levels and prothrombin time were lower than those in the control group. Detailed laboratory findings are shown in Table 2 . Table 2 The Laboratory characteristics of 259 hospitalized patients Laboratory Tests Controls(n = 161) Cases(n = 98) Mean ± SD or M (IQR) Mean ± SD or M (IQR) WBC, 10^9/L 5.32 (4.35–7.46) 5.73 (2.16) Neu, 10^9/L 3.26 (2.53–5.40) 3.35 (2.65–4.42) Hb, g/L 136.20 (21.02) 136.63 (18.43) PLT, 10^9/L 166.22 (59.15) 183.92 (56.03) ALT, U/L 26.00 (19.00–40.00) 28.00 (21.00-54.25) AST, U/L 24.00 (20.00–33.00) 26.00 (19.00–38.00) ALP, U/L 95.09 (32.30) 95.50 (77.50-120.75) GGT, U/L 35.00 (24.00–57.00) 53.00 (26.00-137.25) Alb, g/L 40.63 (5.91) 41.18 (4.67) Glo, g/L 29.25 (5.56) 30.09 (7.24) TBIL, umol/L 8.40 (5.80–11.70) 8.00 (5.65–14.60) Urea, mmol/L 5.66 (1.89) 4.75 (1.61) Cr, umol/L 71.69 (18.20) 66.66 (29.13) Ca 2+ , mmol/L 2.21 (0.16) 2.21 (0.14) Na ± , mmol/L 139.34 (11.04) 140.48 (2.87) K + , mmol/L 4.09 (0.35) 4.02 (0.41) CL − , mmol/L 105.21 (3.84) 105.40 (4.22) TC, mmol/L 4.36 (0.74) 5.09 (1.13) TG, mmol/L 1.34 (1.01–1.95) 1.50 (1.08–2.10) HDL-C, mmol/L 1.11 (0.35) 1.21 (0.32) LDL-C, mmol/L 2.42 (0.75) 3.08 (0.96) PT, s 13.26 (1.21) 12.87 (1.49) CD4 ± T cell, cells/ul 317.00 (192.00-445.00) 366.50 (245.50-484.50) CD4 ± T cell, cells/ul, N(%) < 200 42 (26.09%) 19 (19.39%) ≥ 200, < 500 87 (54.04%) 56 (57.14%) ≥ 500 32 (19.88%) 23 (23.47%) CD8 ± T cell, cells/ul 482.00 (329.00-706.00) 494.00 (343.25–709.50) CD4 ± T cell/CD8 ± T cell ratio 0.66 (0.35–1.09) 0.69 (0.41–1.06) CD4 + T cell/CD8 + T cell ratio, N(%) < 1 113 (70.19%) 70 (71.43%) Table 2 Continued Laboratory Tests Controls(n = 161) Cases(n = 98) Mean ± SD or M (IQR) Mean ± SD or M (IQR) ≥ 1 48 (29.81%) 28 (28.57%) Plasma HIV-1 RNA, copies/mL, N(%) ≤ 20 111 (68.94%) 67 (68.37%) > 20 50 (31.06%) 31 (31.63%) 2. Association between LDL-C and cholelithiasis in patients with HIV infection 2.1 Univariable logistic regression analysis for cholelithiasis In univariable logistic regression analysis, LDL-C was significantly and positively associated with cholelithiasis (OR = 2.54, 95% CI: 1.80–3.58, P < 0.001). TC and HDL-C were also positively associated with cholelithiasis, with ORs (95% CIs) of 2.47 (1.77–3.44) and 2.37 (1.12–5.02), respectively (both P < 0.05). HBV co-infection showed a borderline significant positive association with cholelithiasis (OR = 3.47, 95% CI: 1.02–11.84, P = 0.050), and ABT treatment was associated with a higher likelihood of cholelithiasis (OR = 3.75, 95% CI: 2.02–6.97, P < 0.001). Detailed univariable results are provided in Table 3 . Table 3 Univariate analysis for cholelithiasis Covariate Statistics OR (95%CI) p-value Age 54.00 ± 14.80 0.97(0.96, 0.99) < 0.001 Sex Women 50 (19.31%) Reference Men 209 (80.69%) 0.10 (0.05, 0.21) < 0.001 BMI 22.82 ± 3.07 1.03 (0.95, 1.12) 0.46 Ethnicity Han 241 (93.05%) Reference Tibetan 8 (3.09%) 1.80 (0.44, 7.39) 0.41 Yi 9 (3.47%) 6.31 (1.28, 31.04) 0.02 Miao 1 (0.39%) 3817649.96 (0.00, Inf) 0.99 Marital Status Single 45 (17.38%) Reference Married 195 (75.29%) 1.28 (0.65, 2.53) 0.48 Divorced 15 (5.79%) 1.33 (0.40, 4.45) 0.64 Widowed 4 (1.54%) 0.67 (0.06, 6.97) 0.73 Smoking NO 153 (59.07%) Reference YES 106 (40.93%) 0.46 (0.27, 0.78) 0.001 Alcohol Consumptionn NO 207 (79.92%) Reference Table 3 Continued Covariate Statistics OR (95%CI) p-value YES 52 (20.08%) 0.48 (0.24, 0.95) 0.04 Hypertension NO 221 (85.33%) Reference YES 38 (14.67%) 0.73 (0.35, 1.51) 0.39 Diabetes Mellitus NO 240 (92.66%) Reference YES 19 (7.34%) 1.53 (0.60, 3.90) 0.38 Coronary Heart Disease NO 256 (99.22%) Reference YES 2 (0.78%) 0.00 (0.00, Inf) 0.98 HBV NO 246 (95.35%) Reference YES 12 (4.65%) 3.47 (1.02, 11.84) 0.05 HCV NO 258 (99.61%) Reference YES 1 (0.39%) 3515742.02 (0.00, Inf) 0.99 Syphilis NO 250 (96.53%) Reference YES 9 (3.47%) 2.11 (0.55, 8.06) 0.27 Tuberculosis NO 252 (97.30%) Reference YES 7 (2.70%) 2.24 (0.49, 10.23) 0.30 Duration since confirmed HIV infection 29.00 (0.00-168.00) 1.00 (0.99, 1.00) 0.47 Patients with newly diagnosed HIV infection YES 34 (13.13%) Reference NO 225 (86.87%) 1.13 (0.53, 2.41) 0.74 cART Regimen Not used 38 (14.67%) Reference Potentially LDL-C friendly 4 (1.54%) 0.46 (0.04, 4.82) 0.52 Potentially LDL-C unfavorable 217 (83.79%) 0.82 (0.41, 1.65) 0.58 Treated with ABT NO 84 (32.43%) Reference YES 175 (67.57%) 3.75 (2.02, 6.97) 0.001 WBC 5.32 (2.28–22.45) 0.90 (0.83, 0.98) 0.02 Neu 3.26 (1.12–57.70) 0.97 (0.91, 1.04) 0.35 Hb 136.37 ± 20.05 1.00 (0.99, 1.01) 0.87 Table 3 . Continued Covariate Statistics OR (95%CI) p-value PLT 172.92 ± 58.51 1.01 (1.00, 1.00) 0.02 ALT 27.00 (6.00-562.00) 1.01 (1.00, 1.01) 0.01 AST 25.00 (6.00-718.00) 1.01 (1.00, 1.01) 0.04 ALP 92.00 (27.00-1079.00) 1.01 (1.00, 1.01) 0.001 GGT 39.00 (7.00-1182.00) 1.01 (1.00, 1.01) 0.001 Alb 40.84 ± 5.48 1.02 (0.97, 1.07) 0.43 Glo 29.57 ± 6.25 1.02 (0.98, 1.06) 0.30 TBIL 8.20 (2.00-143.40) 1.04 (1.01, 1.06) 0.01 Urea 5.31 ± 1.84 0.73 (0.62, 0.86) 0.001 Cr 69.79 ± 23.03 0.99 (0.97, 1.00) 0.09 Ca 2+ 2.21 ± 0.15 1.20 (0.23, 6.35) 0.83 Na + 139.77 ± 8.89 1.04 (0.95, 1.14) 0.36 K + 4.07 ± 0.38 0.58 (0.30, 1.15) 0.12 CL − 105.29 ± 3.98 1.01 (0.95, 1.08) 0.71 TC 4.64 ± 0.97 2.47 (1.77, 3.44) < 0.001 TG 1.38 (0.51–9.58) 1.23 (0.95, 1.59) 0.11 HDL-C 1.15 ± 0.34 2.37 (1.12, 5.02) 0.02 LDL-C 2.67 ± 0.89 2.54 (1.80, 3.58) <0.001 PT 13.11 ± 1.33 0.79 (0.63, 0.98) 0.03 CD4 + T cell 332.00 (5.00-1369.00) 1.00 (0.99, 1.00) 0.20 CD4 + T cell <200 61 (23.55%) Reference ≥200, < 500 143 (55.21%) 1.42 (0.75, 2.69) 0.28 ≥500 55 (21.24%) 1.57 (0.74, 3.40) 0.23 CD8 + T cell 492.00 (112.00-1651.00) 1.00 (0.99, 1.00) 0.30 CD4 + T cell/CD8 + T cell ratio 0.67 (0.01–3.55) 0.91(0.61, 1.35) 0.63 CD4 + T cell/CD8 + T cell ratio 20 81 (31.27%) 1.03 (0.6.0, 1.76) 0.92 2.2 Multivariable logistic regression analysis of the association between LDL-C and cholelithiasis In the unadjusted model, LDL-C was significantly associated with cholelithiasis (OR = 2.54, 95% CI: 1.80–3.58, P < 0.001). In Model I, after adjustment for sex, age, and BMI, the association remained significant (OR = 2.54, 95% CI: 1.73–3.71, P < 0.001). In Model II, after further adjustment for HBV co-infection, ABT treatment, duration since confirmed HIV infection, and CD4 + T-cell count, the association remained stable and the effect estimate was slightly strengthened (OR = 2.71, 95% CI: 1.79–4.11, P < 0.001). These findings support an independent association between elevated LDL-C and cholelithiasis in this inpatient HIV population (Table 4 ). Table 4 Relationship between LDL-C and cholelithiasis Outcome Crude Model Model Ⅰ Model Ⅱ OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value LDL-C 2.54 (1.80, 3.58) < 0.001 2.54 (1.73, 3.71) <0.001 2.71 (1.79, 4.11) < 0.001 2.3 Nonlinear relationship and threshold effect analysis of LDL-C and cholelithiasis Smooth-curve fitting suggested a possible nonlinear association between LDL-C and cholelithiasis. Segmented logistic regression identified an exploratory inflection point at 2.28 mmol/L. Below this threshold, LDL-C was not significantly associated with cholelithiasis (effect estimate = 0.66, 95% CI: 0.194–2.23; P = 0.50). At or above 2.28 mmol/L, LDL-C was strongly and positively associated with cholelithiasis (effect estimate = 4.31, 95% CI: 2.324–7.994; P < 0.001). These findings suggest that the association may become more pronounced above a certain LDL-C level (Fig. 2 and Table 5 ). A threshold, nonlinear association between LDL-C and cholelithiasis was found in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. All adjusted for sex, age, and BMI, HBV co-infection, ABT treatment, duration since confirmed HIV infection, and CD4 + T-cell count. Table 5 Threshold effect analysis of LDL-C and cholelithiasis using piecewise linear regression. Inflection point of LDL-C Effect size(β) 95%CI P value < 2.28 0.66 0.194 to 2.23 0.50 ≥ 2.28 4.31 2.324 to 7.994 < 0.001 2.4 Stratified and interaction analyses In exploratory stratified analyses, the positive association between LDL-C and cholelithiasis was directionally consistent across most subgroups. The association was statistically significant across all age strata and among men, patients with BMI > = 18 kg/m², those with longer duration since confirmed HIV diagnosis, and those with CD4 + T-cell counts across all prespecified categories ( P < 0.05). In the HBV co-infection and ABT treatment stratified analyses, the association was more pronounced in the non-HBV-infected subgroup and the ABT-treated subgroup. Although the direction of association remained consistent in the HBV-infected subgroup and the non-ABT-treated subgroup, statistical significance was not reached (Table 6 ). Interaction analyses did not show significant effect modification by HBV or ABT. In the fully adjusted model, the association between LDL-C and cholelithiasis remained significant in the non-HBV-infected subgroup and in the ABT-treated subgroup; however, the P values for interaction were 0.862 and 0.212, respectively (Table 7 ). Table 6 Stratified Analysis of the Association Between LDL-C and Cholelithiasis Characteristic Number of cases OR (95%CI) p-value Age, y 21–48 85 2.29 (1.27, 4.13) 0.006 49–62 86 4.33 (2.02, 9.28) 0.000 63–85 88 2.12 (1.12, 3.98) 0.020 Sex Women 50 2.11 (0.83, 5.35) 0.116 Men 209 2.57 (1.72, 3.85) < 0.0001 BMI, kg/m 2 =18, < 24 163 2.53 (1.59, 4.02) =24 83 2.37 (1.38, 4.07) 0.002 HBV NO 246 2.65 (1.84, 3.80) <0.0001 YES 12 1.65 (0.52, 5.29) 0.396 Duration since confirmed HIV infection, Months Table 6 Continued Characteristic Number of cases OR (95%CI) p-value 0–11 81 1.82 (1.11, 3.00) 0.017 12–46 82 2.91 (1.50, 5.66) 0.002 48–168 96 3.63 (1.85, 7.12) 0.000 Treated with ABT NO 84 1.76 (0.92, 3.39) 0.089 YES 175 2.97 (1.91, 4.60) <0.0001 CD4 + T cell, cells/ul <200 61 2.12 (1.13, 3.97) 0.019 ≥200, < 500 143 2.68 (1.64, 4.39) < 0.0001 ≥500 55 2.85 (1.38, 5.86) 0.005 Table 7 Interaction analyses of LDL-C with HBV and ABT in relation to cholelithiasis Effect Modifier Category Adjusted OR (95% CI) P value P for interaction HBV NO 2.82 (1.81–4.38) < 0.001 0.862 YES 3.40 (0.41–27.99) 0.26 ABT NO 1.79 (0.87–3.65) 0.11 0.212 YES 3.18 (1.86–5.42) < 0.001 Discussion In this single-center retrospective case-control study of hospitalized people living with HIV, We systematically evaluated the association between LDL-C and cholelithiasis in hospitalized people living with HIV and further explored its potential nonlinear pattern, threshold effect, and consistency across subgroups. The results showed that patients in the case group were younger, more likely to be female, and had higher proportions of HBV co-infection and albuvirtide (ABT) treatment than those in the control group. Previous studies have shown that cholelithiasis is more common in women and in individuals with metabolic abnormalities, which may be related to disturbances in cholesterol metabolism, hormonal factors, and impaired gallbladder motility [18–22]. In addition, age and co-infection status may indirectly affect cholesterol homeostasis and bile composition through metabolic pathways [7, 23, 24]. In the present study, LDL-C levels were significantly higher in the case group than in the control group, a finding generally consistent with previous observations of elevated LDL-C levels and enhanced bile acid synthesis in women with HIV infection and cholelithiasis [7]. Taken together, these factors may partly explain the lower risk of cholelithiasis observed in the control group. In the association analyses, we found that LDL-C was significantly positively associated with cholelithiasis (OR = 2.54, 95% CI: 1.80–3.58, P < 0.001). This positive association remained statistically significant and robust after adjustment for potential confounders, including sex, age, BMI, HBV co-infection, ABT treatment, duration since HIV diagnosis, and CD4 + T-cell count. It should be noted that LDL-C, TC, HDL-C, and triglycerides are all lipid-related parameters and are physiologically highly correlated [25]. Simultaneous inclusion of these variables in the same multivariable model could therefore introduce multicollinearity [26, 27]. For this reason, LDL-C was treated as the primary exposure of interest, whereas TC, HDL-C, and TG were not included simultaneously in the final multivariable model. Similarly, although univariable analyses suggested that some liver function indicators, including ALT, AST, ALP, GGT, and TBIL, might be associated with cholelithiasis in patients with HIV infection, these markers may also lie downstream of the LDL-C–cholelithiasis relationship or may be affected by cholelithiasis itself [28–30]. To avoid overadjustment or collider bias, these liver function parameters were not included in the primary multivariable model [31, 32]. From a biological perspective, cholesterol is a major constituent of bile, and its homeostasis is regulated through multiple pathways, including endogenous synthesis, exogenous uptake, and biliary secretion [33–35]. Previous studies have shown that people living with HIV are prone to developing hypercholesterolemia after receiving combination antiretroviral therapy (cART) [6, 36, 37]. Meanwhile, patients with cholelithiasis often exhibit upregulated expression of key molecules involved in bile acid synthesis, such as CYP7A1 , HNF1α , and LXRβ , suggesting that disordered cholesterol metabolism and abnormal bile acid synthesis may jointly contribute to gallstone formation [7, 38, 39]. When circulating LDL-C levels increase, more cholesterol may be delivered to the liver and enter biliary metabolic pathways, thereby increasing the risk of biliary cholesterol supersaturation and cholesterol crystal precipitation, which is one of the key pathological bases of cholesterol gallstone formation [40–42]. Previous studies have also supported a positive association between elevated LDL-C levels and an increased risk of cholelithiasis [43]. Nevertheless, the existing evidence is not entirely consistent. Some Mendelian randomization studies have suggested that genetically predicted elevations in LDL-C do not necessarily directly increase the risk of cholelithiasis [12], indicating that the relationship may not represent a simple linear causal pathway. Rather, it may be influenced by multiple factors, including population heterogeneity [44], gallstone subtype [45], and differences in LDL-C regulatory pathways [46]. For example, HMGCR variants that mimic the effects of statins have been associated with a reduced risk of cholelithiasis, whereas ezetimibe, which targets NPC1L1 , and PCSK9 inhibitors do not appear to confer the same effect despite achieving comparable reductions in LDL-C. This suggests that the molecular target of lipid-lowering therapy, rather than a change in LDL-C level alone, may have a greater influence on gallstone formation [43]. Accordingly, the association observed in the present study is more likely to reflect a complex metabolic phenotype in people living with HIV, shaped by the combined effects of cART exposure, changes in immune status, and metabolic disturbances, rather than a direct causal effect of LDL-C alone. To further characterize the relationship between LDL-C and cholelithiasis in patients with HIV infection, we performed smooth curve fitting and threshold effect analyses, which suggested a potential nonlinear association between the two. Specifically, in this special population of people living with HIV, the risk of cholelithiasis appeared to increase markedly when LDL-C reached or exceeded approximately 2.28 mmol/L, whereas no significant association was observed below this threshold. This threshold effect suggests that the contribution of LDL-C to cholelithiasis risk may not accumulate in a simple linear manner, but may instead follow a pattern of threshold-dependent or accelerated risk. It should be emphasized that this inflection point was derived from an exploratory analysis within the present sample and therefore primarily reflects a statistically identified pattern rather than a clinically generalizable diagnostic cutoff. Nevertheless, previous studies examining LDL-C in relation to other disease outcomes have also reported evidence of increased risk once LDL-C exceeds specific thresholds. For example, in a study of young adults in the United States, LDL-C ≥ 190 mg/dL was identified as a high-risk subgroup and was associated with a significantly increased risk of atherosclerotic cardiovascular disease (ASCVD) [47]. Similarly, in the acute phase of ischemic stroke, LDL-C ≥ 5.0 mmol/L (approximately 193 mg/dL) was associated with an increased risk of all-cause mortality (adjusted OR = 1.22, 95% CI: 0.98–1.50); although the confidence interval was close to the null, the overall trend analysis still suggested increased risk at higher LDL-C levels [48]. To our knowledge, the present study is the first to identify such a threshold pattern in people living with HIV. This finding provides a useful direction for future research, although larger studies are needed to determine whether this threshold is reproducible and broadly applicable. In addition to the above considerations, the development of cholelithiasis involves the interaction of multiple factors. Previous studies have shown that metabolic syndrome (MetS) is a well-established risk factor for cholelithiasis, and that low HDL-C may be the MetS component most strongly associated with gallstone risk [19, 41]. Higher BMI may also indirectly increase the risk of cholelithiasis through lowering HDL-C and increasing triglyceride (TG) levels [41, 49]. Although the present study focused on LDL-C, our stratified analyses showed that the positive association between LDL-C and cholelithiasis was directionally consistent across most subgroups, including those defined by age, sex, BMI, HBV co-infection status, and immune status, as reflected by CD4 + T-cell count. This finding suggests that the risk associated with LDL-C is not confined to a single subgroup, but may be relatively broadly present among patients with HIV infection. Notably, the association between LDL-C and cholelithiasis appeared to be stronger among patients with a longer duration of HIV infection and among those receiving ABT treatment, indirectly suggesting that long-term infection, treatment exposure, and metabolic remodeling may jointly contribute to gallstone development [7]. However, the interaction tests for HBV and ABT were not statistically significant (both P for interaction > 0.05), indicating that the current sample does not provide sufficient evidence to support a clear effect-modifying role for either factor in the association between LDL-C and cholelithiasis. Given the limited sample size in some subgroups, statistical power may have been insufficient, and these subgroup findings should therefore be interpreted with caution. Further validation in larger studies is warranted. This study still has several limitations. First, because of its retrospective design, causal inference is inherently limited, and we cannot definitively establish whether elevated LDL-C is a direct cause of gallstone formation. Second, this was a single-center analysis, which may limit the generalizability of the findings to other populations or geographic settings. In addition, several potential confounding factors, including dietary patterns, genetic background, gallbladder motility, and the gut microbiota, could not be fully accounted for, all of which may influence cholesterol metabolism and the risk of cholelithiasis [50, 51]. Finally, some subgroups, such as patients with HBV co-infection, had relatively small sample sizes, which may have reduced statistical power and limited our ability to detect potential effect modification. Therefore, the subgroup analyses, interaction analyses, and the identified inflection point of 2.28 mmol/L should all be interpreted as exploratory findings rather than definitive evidence. Future studies should employ prospective cohort designs or large multicenter investigations to further examine the causal relationship between LDL-C and cholelithiasis and to clarify the underlying biological mechanisms. In particular, a more comprehensive risk prediction framework could be developed by integrating multiple dimensions, including bile composition, hepatic cholesterol transport pathways, metabolic syndrome-related indicators, and immune-inflammatory status. In addition, evaluating whether LDL-C-targeted interventions can reduce the risk of cholelithiasis would help clarify the practical clinical value of LDL-C in gallstone prevention. In summary, the present study suggests that elevated LDL-C levels are significantly associated with cholelithiasis among hospitalized people living with HIV, and that this association persists even after adjustment for multiple covariates. We further observed a possible nonlinear relationship in this dataset, with a stronger association above the exploratory inflection point of 2.28 mmol/L. These findings provide additional evidence regarding metabolic factors associated with cholelithiasis in people living with HIV. Nevertheless, future multicenter prospective studies, together with more comprehensive covariate adjustment, external validation, and mechanistic investigation, are still needed to determine whether LDL-C can offer incremental value in gallstone risk assessment in this special population. Conclusion Elevated LDL-C was independently associated with cholelithiasis among hospitalized people living with HIV, even after adjustment for multiple potential confounders. A possible nonlinear relationship was also observed, with a stronger association above an exploratory inflection point of 2.28 mmol/L. The association was directionally consistent across most subgroups, and no significant interaction was detected for HBV or ABT. Further large-scale prospective studies are warranted to validate these findings and to determine the potential clinical utility of LDL-C in gallstone risk stratification among people living with HIV. Declarations Ethics approval and consent to participate This study was approved by the institutional ethics committee of the Public Health Clinical Center of Chengdu (approval number: YJ-K2024-94-01). Given the retrospective design and the use of anonymized data extracted from existing medical records, the requirement for informed consent was waived by the ethics committee. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions Authors’ contributions Cheng Xingzhen and Chen Tingyu conceived and designed the study. Liu Dongxu, Gui Fuqiang, Yang Lei and Chen Jidong collected the data. 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10:08:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9355321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9355321/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109216559,"identity":"33380bc7-25aa-4a78-9937-8b1a5fb3b06a","added_by":"auto","created_at":"2026-05-13 18:06:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96335,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9355321/v1/ab7228c4557e5bcf10216dcb.png"},{"id":109216560,"identity":"9cd7c3c8-e850-4a3c-87a2-386be1109eb9","added_by":"auto","created_at":"2026-05-13 18:06:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between LDL-C and cholelithiasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA threshold, nonlinear association between LDL-C and cholelithiasis was found in a generalized additive model (GAM) . Solid rad line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. All adjusted for sex, age, and BMI, HBV co-infection, ABT treatment, duration since confirmed HIV infection, and CD4+ T-cell count.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9355321/v1/206513af3dbf8c381b6c499f.png"},{"id":109249120,"identity":"fe09e5f2-77a1-4988-9409-dd710161173b","added_by":"auto","created_at":"2026-05-14 08:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9355321/v1/d3c57b11-6479-4714-b472-12d3083cdfdb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between low-density lipoprotein cholesterol with cholelithiasis among hospitalized people living with HIV: a retrospective case-control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman immunodeficiency virus (HIV) infection has evolved from a rapidly fatal disease into a chronic and increasingly manageable condition. With the widespread use of combination antiretroviral therapy (cART), life expectancy among people living with HIV has increased substantially. At the same time, however, metabolic complications have become an increasingly important clinical concern [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Previous studies have shown that HIV infection itself, together with the accompanying chronic inflammatory state, can disrupt lipid metabolism and contribute to metabolic abnormalities such as metabolic syndrome and insulin resistance [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, people living with HIV often exhibit elevated cholesterol and triglyceride levels, some of which are closely related to antiretroviral therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These factors collectively pose greater challenges for metabolic health management in this population.\u003c/p\u003e \u003cp\u003eDyslipidemia is a well-recognized contributor to cholelithiasis. Epidemiological and clinical studies have consistently shown that patients with cholelithiasis often present with abnormal lipid profiles, including elevated total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride levels, along with reduced high-density lipoprotein cholesterol (HDL-C) levels [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among these lipid parameters, LDL-C has attracted particular attention. A Mendelian randomization study demonstrated that the effect of free thyroxine (FT4) on cholelithiasis risk was partially mediated by LDL-C and apolipoprotein B, accounting for 17.4% and 13.5% of the total mediating effect, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another study reported that genetically proxied LDL-C reduction mediated by single-nucleotide polymorphisms near the \u003cem\u003eHMGCR\u003c/em\u003e gene, mimicking the pharmacological effect of statins, was significantly associated with a lower risk of cholelithiasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, multivariable analyses of modifiable risk factors for cholelithiasis have suggested that the relationship between LDL-C and cholelithiasis risk may be more complex than previously appreciated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Clinical evidence has further shown that patients with cholelithiasis have significantly higher preoperative LDL-C levels than healthy controls, and that lipid profiles tend to normalize after cholecystectomy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], further supporting a potential link between LDL-C abnormalities and gallstone formation.\u003c/p\u003e \u003cp\u003eDespite these observations, evidence regarding the association between LDL-C and cholelithiasis in people living with HIV remains limited. Previous studies have suggested that HIV-infected individuals with cholelithiasis may exhibit higher LDL-C levels and enhanced bile acid synthetic activity, and that these features may be influenced by factors such as age and cART regimens [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the available evidence has largely been derived from small observational studies or has focused on other metabolic indicators. To date, systematic retrospective case-control analyses specifically examining the association between LDL-C levels and cholelithiasis among hospitalized people living with HIV are lacking. Furthermore, HIV-related chronic inflammation, immune reconstitution, and drug\u0026ndash;metabolism interactions may lead to a distinct pattern in the relationship between lipid metabolism and cholelithiasis in this population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Current risk models for cholelithiasis also rarely incorporate HIV infection status as a relevant clinical context [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgainst this background, we conducted a single-center retrospective case-control study among hospitalized people living with HIV admitted between January 2019 and January 2025 to investigate the association between LDL-C levels and cholelithiasis. We further explored whether this association was potentially nonlinear and evaluated the robustness of the findings through stratified analyses and interaction tests. This study aimed to clarify the association between LDL-C levels and cholelithiasis in hospitalized people living with HIV, to further explore the potential nonlinear nature of this relationship, and to provide a clinical basis for future prospective studies.\u003c/p\u003e "},{"header":"Patients and Methods","content":"\u003ch3\u003e1. Study design and participants\u003c/h3\u003e\n\u003cp\u003eThis was a single-center retrospective case-control study. We included hospitalized people living with HIV who were admitted to the Public Health Clinical Center of Chengdu between January 2019 and January 2025. According to the results of abdominal imaging examinations performed during hospitalization, including ultrasonography, computed tomography (CT), or magnetic resonance imaging (MRI), participants were classified into a case group and a control group. The case group comprised patients with HIV infection who had imaging-confirmed cholelithiasis, whereas the control group comprised hospitalized people living with HIV during the same period who had no imaging evidence of cholelithiasis. The detailed inclusion and exclusion criteria were as follows.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Inclusion criteria\u003c/h2\u003e\n \u003cp\u003eParticipants were eligible if they met all of the following criteria: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) confirmed HIV infection based on an initial positive HIV antibody screening test and subsequent confirmation by the Chinese Center for Disease Control and Prevention, with the diagnosis of AIDS made in accordance with the \u003cem\u003eChinese Guidelines for the Diagnosis and Treatment of HIV/AIDS\u003c/em\u003e; (3) complete abdominal imaging data during hospitalization, including ultrasonography, CT, or MRI, sufficient to determine the presence or absence of gallstones; (4) availability of LDL-C results obtained within 24\u0026ndash;48 hours after admission; (5) complete clinical data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2 Exclusion criteria\u003c/h2\u003e\n \u003cp\u003eParticipants were excluded if they met any of the following criteria: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) pregnancy or puerperium; (3) presence of biliary tract malignancy, congenital biliary malformations, primary sclerosing cholangitis, or other severe conditions that could interfere with assessment, such as severe cardiovascular or cerebrovascular disease, hematologic disorders, or other malignancies; (4) missing imaging data, LDL-C values, or other key clinical information; (5) LDL-C measured only after biliary intervention or after severe infection/shock; (6) previous cholecystectomy; (7) repeated hospitalizations for the same patient, in which case only the first admission was retained.\u003c/p\u003e\n \u003cp\u003eA total of 259 eligible patients were ultimately included in the analysis, comprising 98 cases and 161 controls (Fig. 1).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. Data collection and variable definitions\u003c/h3\u003e\n\u003cp\u003eData were collected on demographic characteristics, medical history, co-infections, HIV-related variables, and laboratory findings. Demographic and clinical variables included age, sex, body mass index (BMI), ethnicity, marital status, smoking history, alcohol consumption, hypertension, diabetes mellitus, and coronary heart disease. Co-infections included hepatitis B virus (HBV), hepatitis C virus (HCV), syphilis, and \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e infection. HIV-related variables included duration since confirmed HIV infection, whether the patient was newly diagnosed with HIV infection, combination antiretroviral therapy (cART) regimen, and albuvirtide (ABT) treatment.\u003c/p\u003e\n\u003cp\u003eLaboratory parameters included complete blood count variables, including white blood cell count (WBC), neutrophil count (Neu), hemoglobin (Hb), and platelet count (PLT); biochemical indicators, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), albumin (Alb), globulin (Glo), total bilirubin (TBIL), urea, creatinine (Cr), calcium (Ca\u0026sup2;⁺), sodium (Na⁺), potassium (K⁺), and chloride (Cl⁻); lipid profile variables, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C); coagulation function, represented by prothrombin time (PT); CD4\u0026thinsp;+\u0026thinsp;T-cell count, CD8\u0026thinsp;+\u0026thinsp;T-cell count, CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio, and plasma HIV-1 RNA.\u003c/p\u003e\n\u003cp\u003eBMI was calculated as weight in kilograms divided by height in meters squared (kg/m\u0026sup2;). Duration since confirmed HIV infection was defined as the time from the first confirmed HIV diagnosis to the index hospitalization and was expressed in months. Plasma HIV-1 RNA was categorized as \u0026le;\u0026thinsp;20 copies/mL and \u0026gt;\u0026thinsp;20 copies/mL. Values reported as \u0026lt;\u0026thinsp;20 copies/mL were classified into the \u0026le;\u0026thinsp;20 copies/mL group, indicating that the HIV-1 RNA level was below the lower limit of quantification of the assay.\u003c/p\u003e\n\u003ch3\u003e3. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.2.0; The R Foundation for Statistical Computing, Vienna, Austria) and EmpowerStats. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas non-normally distributed variables are presented as median and interquartile range (IQR). Categorical variables are presented as number (percentage). Between-group comparisons were performed using the independent-samples \u003cem\u003et\u003c/em\u003e test or Wilcoxon rank-sum test for continuous variables, as appropriate, and the chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression analyses were first conducted to evaluate the associations between clinical variables and cholelithiasis. Multivariable logistic regression models were then fitted to assess the association between LDL-C and cholelithiasis. Covariates were prespecified on the basis of clinical relevance, prior literature, and model parsimony. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant; values\u0026thinsp;\u0026lt;\u0026thinsp;0.001 are reported as P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003cp\u003eTo explore a potential nonlinear association between LDL-C and cholelithiasis, smooth-curve fitting was performed after multivariable adjustment. When a nonlinear trend was suggested, segmented logistic regression was used to estimate the exploratory inflection point and the effect sizes on either side of the threshold. Stratified analyses were conducted according to age, sex, BMI, HBV co-infection status, duration since confirmed HIV diagnosis, receipt of ABT, and CD4\u0026thinsp;+\u0026thinsp;T-cell count. Interaction terms were incorporated into the final model to explore potential effect modification. Both stratified and interaction analyses were interpreted as exploratory.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Clinical characteristics\u003c/h3\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Demographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 259 hospitalized people living with HIV were included, of whom 98 had cholelithiasis and 161 did not. Compared with controls, patients with cholelithiasis were younger (50.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.71 vs. 56.30\u0026thinsp;\u0026plusmn;\u0026thinsp;16.00 years) and more likely to be female (40.82% vs. 6.21%). BMI was similar between the two groups (23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91 vs. 22.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17 kg/m\u0026sup2;). The case group also had a higher prevalence of HBV co-infection (8.16% vs. 2.50%) and a higher proportion of ABT treatment (83.67% vs. 57.76%). The median duration since confirmed HIV diagnosis was similar between groups. Detailed baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\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 demographic and clinical characteristics of 259 hospitalized patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eControls(n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e56.30 (16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e50.22 (11.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSex, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10 (6.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e40 (40.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e151 (93.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e58 (59.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22.71 (3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e23.00 (2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eEthnicity, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e155 (96.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e86 (87.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibetan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4 (2.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e4 (4.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2 (1.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e7 (7.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e1 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarital Status, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30 (18.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (15.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e119 (73.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (77.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9 (5.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (6.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSmoking, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e84 (52.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (70.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e77 (47.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (29.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAlcohol Consumptionn, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e122 (75.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (86.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e39 (24.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (13.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHypertension, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e135 (83.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86 (87.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e26 (16.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (12.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDiabetes Mellitus, N(%)\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls(n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (93.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e89 (90.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (6.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9 (9.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (98.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e97 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHBV, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (97.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90 (91.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8 (8.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHCV, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e97 (98.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSyphilis, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (97.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e93 (94.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5 (5.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTuberculosis, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (98.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e94 (95.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4 (4.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration since confirmed HIV infection, M (IQR), Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.00 (6.00\u0026ndash;60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29.50 (5.00\u0026ndash;60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePatients with newly diagnosed HIV infection, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e139 (86.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86 (87.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22 (13.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (12.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ecART Regimen, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e22 (13.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16 (16.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotentially LDL-C friendly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotentially LDL-C unfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e136(84.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e81 (82.65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTreated with ABT, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e68 (42.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16 (16.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e93 (57.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e82 (83.67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Laboratory characteristics\u003c/h2\u003e \u003cp\u003eComparison of laboratory parameters showed that patients in the case group had higher levels of TC, HDL-C, and LDL-C than those in the control group [5.09 (1.13) mmol/L vs. 4.36 (0.74) mmol/L; 1.21 (0.32) mmol/L vs. 1.11 (0.35) mmol/L; and 3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 mmol/L vs. 2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 mmol/L, respectively]. CD4\u0026thinsp;+\u0026thinsp;T-cell counts were slightly higher in the case group than in the control group [366.50 (245.50-484.50) cells/\u0026micro;L vs. 317.00 (192.00-445.00) cells/\u0026micro;L], whereas CD8\u0026thinsp;+\u0026thinsp;T-cell counts were comparable between the two groups [494.00 (343.25\u0026ndash;709.50) cells/\u0026micro;L vs. 482.00 (329.00-706.00) cells/\u0026micro;L]. In addition, PLT and GGT levels were higher in the case group, whereas urea levels and prothrombin time were lower than those in the control group. Detailed laboratory findings are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Laboratory characteristics of 259 hospitalized patients\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLaboratory Tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls(n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or M (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or M (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32 (4.35\u0026ndash;7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.73 (2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu, 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.26 (2.53\u0026ndash;5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35 (2.65\u0026ndash;4.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.20 (21.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.63 (18.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166.22 (59.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.92 (56.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.00 (19.00\u0026ndash;40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.00 (21.00-54.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.00 (20.00\u0026ndash;33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.00 (19.00\u0026ndash;38.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.09 (32.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.50 (77.50-120.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.00 (24.00\u0026ndash;57.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.00 (26.00-137.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.63 (5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.18 (4.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlo, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.25 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.09 (7.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.40 (5.80\u0026ndash;11.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 (5.65\u0026ndash;14.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.66 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75 (1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.69 (18.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.66 (29.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21 (0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e\u0026plusmn;\u003c/sup\u003e, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.34 (11.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.48 (2.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.09 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02 (0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003csup\u003e\u0026minus;\u003c/sup\u003e, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.21 (3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.40 (4.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09 (1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.01\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.08\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (0.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.42 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08 (0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.26 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.87 (1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e\u0026plusmn;\u003c/sup\u003eT cell, cells/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317.00 (192.00-445.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e366.50 (245.50-484.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e\u0026plusmn;\u003c/sup\u003eT cell, cells/ul, N(%)\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\u003e42 (26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (19.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;200, \u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (54.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (57.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (19.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (23.47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003csup\u003e\u0026plusmn;\u003c/sup\u003eT cell, cells/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482.00 (329.00-706.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e494.00 (343.25\u0026ndash;709.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e\u0026plusmn;\u003c/sup\u003eT cell/CD8\u003csup\u003e\u0026plusmn;\u003c/sup\u003eT cell ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.35\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.41\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T cell/CD8\u0026thinsp;+\u0026thinsp;T cell ratio, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (70.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (71.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLaboratory Tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls(n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or M (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or M (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (29.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (28.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePlasma HIV-1 RNA, copies/mL, N(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (68.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (68.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (31.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (31.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Association between LDL-C and cholelithiasis in patients with HIV infection\u003c/h3\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Univariable logistic regression analysis for cholelithiasis\u003c/h2\u003e \u003cp\u003eIn univariable logistic regression analysis, LDL-C was significantly and positively associated with cholelithiasis (OR\u0026thinsp;=\u0026thinsp;2.54, 95% CI: 1.80\u0026ndash;3.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TC and HDL-C were also positively associated with cholelithiasis, with ORs (95% CIs) of 2.47 (1.77\u0026ndash;3.44) and 2.37 (1.12\u0026ndash;5.02), respectively (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HBV co-infection showed a borderline significant positive association with cholelithiasis (OR\u0026thinsp;=\u0026thinsp;3.47, 95% CI: 1.02\u0026ndash;11.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050), and ABT treatment was associated with a higher likelihood of cholelithiasis (OR\u0026thinsp;=\u0026thinsp;3.75, 95% CI: 2.02\u0026ndash;6.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed univariable results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis for cholelithiasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97(0.96, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eSex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (19.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (80.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.05, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.95, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (93.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibetan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (3.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (0.44, 7.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.31 (1.28, 31.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3817649.96 (0.00, Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (17.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (75.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.65, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (5.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (0.40, 4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.06, 6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (59.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (40.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.27, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumptionn\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (79.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (20.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.24, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (85.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (14.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.35, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240 (92.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (7.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53 (0.60, 3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256 (99.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246 (95.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47 (1.02, 11.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (99.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3515742.02 (0.00, Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (96.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 (0.55, 8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (97.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24 (0.49, 10.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration since confirmed HIV infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.00 (0.00-168.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePatients with newly diagnosed HIV infection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (13.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (86.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.53, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecART Regimen\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (14.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotentially LDL-C friendly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.04, 4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotentially LDL-C unfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (83.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.41, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreated with ABT\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (32.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (67.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75 (2.02, 6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32 (2.28\u0026ndash;22.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.83, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.26 (1.12\u0026ndash;57.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.91, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.37\u0026thinsp;\u0026plusmn;\u0026thinsp;20.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eContinued\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.92\u0026thinsp;\u0026plusmn;\u0026thinsp;58.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 (6.00-562.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00 (6.00-718.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.00 (27.00-1079.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00 (7.00-1182.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.97, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.98, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.20 (2.00-143.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (1.01, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.62, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.79\u0026thinsp;\u0026plusmn;\u0026thinsp;23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.97, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.23, 6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.77\u0026thinsp;\u0026plusmn;\u0026thinsp;8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.95, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.30, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.95, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47 (1.77, 3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (0.51\u0026ndash;9.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (0.95, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37 (1.12, 5.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54 (1.80, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.63, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332.00 (5.00-1369.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T cell\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (23.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;200, \u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (55.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (0.75, 2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (21.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (0.74, 3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492.00 (112.00-1651.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T cell/CD8\u0026thinsp;+\u0026thinsp;T cell ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.01\u0026ndash;3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.61, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T cell/CD8\u0026thinsp;+\u0026thinsp;T cell ratio\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (70.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (29.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.54, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma HIV-1 RNA\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (68.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (31.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.6.0, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Multivariable logistic regression analysis of the association between LDL-C and cholelithiasis\u003c/h2\u003e \u003cp\u003eIn the unadjusted model, LDL-C was significantly associated with cholelithiasis (OR\u0026thinsp;=\u0026thinsp;2.54, 95% CI: 1.80\u0026ndash;3.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model I, after adjustment for sex, age, and BMI, the association remained significant (OR\u0026thinsp;=\u0026thinsp;2.54, 95% CI: 1.73\u0026ndash;3.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model II, after further adjustment for HBV co-infection, ABT treatment, duration since confirmed HIV infection, and CD4\u0026thinsp;+\u0026thinsp;T-cell count, the association remained stable and the effect estimate was slightly strengthened (OR\u0026thinsp;=\u0026thinsp;2.71, 95% CI: 1.79\u0026ndash;4.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings support an independent association between elevated LDL-C and cholelithiasis in this inpatient HIV population (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between LDL-C and cholelithiasis\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel Ⅰ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel Ⅱ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54 (1.80, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54 (1.73, 3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71 (1.79, 4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nonlinear relationship and threshold effect analysis of LDL-C and cholelithiasis\u003c/h2\u003e \u003cp\u003eSmooth-curve fitting suggested a possible nonlinear association between LDL-C and cholelithiasis. Segmented logistic regression identified an exploratory inflection point at 2.28 mmol/L. Below this threshold, LDL-C was not significantly associated with cholelithiasis (effect estimate\u0026thinsp;=\u0026thinsp;0.66, 95% CI: 0.194\u0026ndash;2.23; P\u0026thinsp;=\u0026thinsp;0.50). At or above 2.28 mmol/L, LDL-C was strongly and positively associated with cholelithiasis (effect estimate\u0026thinsp;=\u0026thinsp;4.31, 95% CI: 2.324\u0026ndash;7.994; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that the association may become more pronounced above a certain LDL-C level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA threshold, nonlinear association between LDL-C and cholelithiasis was found in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. All adjusted for sex, age, and BMI, HBV co-infection, ABT treatment, duration since confirmed HIV infection, and CD4\u0026thinsp;+\u0026thinsp;T-cell count.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of LDL-C and cholelithiasis using piecewise linear regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point of LDL-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect size(β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP 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\u0026lt;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194 to 2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.324 to 7.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Stratified and interaction analyses\u003c/h2\u003e \u003cp\u003eIn exploratory stratified analyses, the positive association between LDL-C and cholelithiasis was directionally consistent across most subgroups. The association was statistically significant across all age strata and among men, patients with BMI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;18 kg/m\u0026sup2;, those with longer duration since confirmed HIV diagnosis, and those with CD4\u0026thinsp;+\u0026thinsp;T-cell counts across all prespecified categories (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the HBV co-infection and ABT treatment stratified analyses, the association was more pronounced in the non-HBV-infected subgroup and the ABT-treated subgroup. Although the direction of association remained consistent in the HBV-infected subgroup and the non-ABT-treated subgroup, statistical significance was not reached (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInteraction analyses did not show significant effect modification by HBV or ABT. In the fully adjusted model, the association between LDL-C and cholelithiasis remained significant in the non-HBV-infected subgroup and in the ABT-treated subgroup; however, the P values for interaction were 0.862 and 0.212, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified Analysis of the Association Between LDL-C and Cholelithiasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29 (1.27, 4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u0026ndash;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.33 (2.02, 9.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e63\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12 (1.12, 3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 (0.83, 5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57 (1.72, 3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03 (0.58, 15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=18, \u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53 (1.59, 4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37 (1.38, 4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65 (1.84, 3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 (0.52, 5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eDuration since confirmed HIV infection, Months\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=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.11, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.91 (1.50, 5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63 (1.85, 7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreated with ABT\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76 (0.92, 3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97 (1.91, 4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cell, cells/ul\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12 (1.13, 3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;200, \u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 (1.64, 4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85 (1.38, 5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction analyses of LDL-C with HBV and ABT in relation to cholelithiasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Modifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82 (1.81\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40 (0.41\u0026ndash;27.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eABT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (0.87\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18 (1.86\u0026ndash;5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center retrospective case-control study of hospitalized people living with HIV, We systematically evaluated the association between LDL-C and cholelithiasis in hospitalized people living with HIV and further explored its potential nonlinear pattern, threshold effect, and consistency across subgroups. The results showed that patients in the case group were younger, more likely to be female, and had higher proportions of HBV co-infection and albuvirtide (ABT) treatment than those in the control group. Previous studies have shown that cholelithiasis is more common in women and in individuals with metabolic abnormalities, which may be related to disturbances in cholesterol metabolism, hormonal factors, and impaired gallbladder motility [18–22]. In addition, age and co-infection status may indirectly affect cholesterol homeostasis and bile composition through metabolic pathways [7, 23, 24]. In the present study, LDL-C levels were significantly higher in the case group than in the control group, a finding generally consistent with previous observations of elevated LDL-C levels and enhanced bile acid synthesis in women with HIV infection and cholelithiasis [7]. Taken together, these factors may partly explain the lower risk of cholelithiasis observed in the control group.\u003c/p\u003e\n\u003cp\u003eIn the association analyses, we found that LDL-C was significantly positively associated with cholelithiasis (OR = 2.54, 95% CI: 1.80–3.58, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). This positive association remained statistically significant and robust after adjustment for potential confounders, including sex, age, BMI, HBV co-infection, ABT treatment, duration since HIV diagnosis, and CD4 + T-cell count. It should be noted that LDL-C, TC, HDL-C, and triglycerides are all lipid-related parameters and are physiologically highly correlated [25]. Simultaneous inclusion of these variables in the same multivariable model could therefore introduce multicollinearity [26, 27]. For this reason, LDL-C was treated as the primary exposure of interest, whereas TC, HDL-C, and TG were not included simultaneously in the final multivariable model. Similarly, although univariable analyses suggested that some liver function indicators, including ALT, AST, ALP, GGT, and TBIL, might be associated with cholelithiasis in patients with HIV infection, these markers may also lie downstream of the LDL-C–cholelithiasis relationship or may be affected by cholelithiasis itself [28–30]. To avoid overadjustment or collider bias, these liver function parameters were not included in the primary multivariable model [31, 32].\u003c/p\u003e\n\u003cp\u003eFrom a biological perspective, cholesterol is a major constituent of bile, and its homeostasis is regulated through multiple pathways, including endogenous synthesis, exogenous uptake, and biliary secretion [33–35]. Previous studies have shown that people living with HIV are prone to developing hypercholesterolemia after receiving combination antiretroviral therapy (cART) [6, 36, 37]. Meanwhile, patients with cholelithiasis often exhibit upregulated expression of key molecules involved in bile acid synthesis, such as \u003cem\u003eCYP7A1\u003c/em\u003e, \u003cem\u003eHNF1α\u003c/em\u003e, and \u003cem\u003eLXRβ\u003c/em\u003e, suggesting that disordered cholesterol metabolism and abnormal bile acid synthesis may jointly contribute to gallstone formation [7, 38, 39]. When circulating LDL-C levels increase, more cholesterol may be delivered to the liver and enter biliary metabolic pathways, thereby increasing the risk of biliary cholesterol supersaturation and cholesterol crystal precipitation, which is one of the key pathological bases of cholesterol gallstone formation [40–42]. Previous studies have also supported a positive association between elevated LDL-C levels and an increased risk of cholelithiasis [43]. Nevertheless, the existing evidence is not entirely consistent. Some Mendelian randomization studies have suggested that genetically predicted elevations in LDL-C do not necessarily directly increase the risk of cholelithiasis [12], indicating that the relationship may not represent a simple linear causal pathway. Rather, it may be influenced by multiple factors, including population heterogeneity [44], gallstone subtype [45], and differences in LDL-C regulatory pathways [46]. For example, \u003cem\u003eHMGCR\u003c/em\u003e variants that mimic the effects of statins have been associated with a reduced risk of cholelithiasis, whereas ezetimibe, which targets \u003cem\u003eNPC1L1\u003c/em\u003e, and PCSK9 inhibitors do not appear to confer the same effect despite achieving comparable reductions in LDL-C. This suggests that the molecular target of lipid-lowering therapy, rather than a change in LDL-C level alone, may have a greater influence on gallstone formation [43]. Accordingly, the association observed in the present study is more likely to reflect a complex metabolic phenotype in people living with HIV, shaped by the combined effects of cART exposure, changes in immune status, and metabolic disturbances, rather than a direct causal effect of LDL-C alone.\u003c/p\u003e\n\u003cp\u003eTo further characterize the relationship between LDL-C and cholelithiasis in patients with HIV infection, we performed smooth curve fitting and threshold effect analyses, which suggested a potential nonlinear association between the two. Specifically, in this special population of people living with HIV, the risk of cholelithiasis appeared to increase markedly when LDL-C reached or exceeded approximately 2.28 mmol/L, whereas no significant association was observed below this threshold. This threshold effect suggests that the contribution of LDL-C to cholelithiasis risk may not accumulate in a simple linear manner, but may instead follow a pattern of threshold-dependent or accelerated risk. It should be emphasized that this inflection point was derived from an exploratory analysis within the present sample and therefore primarily reflects a statistically identified pattern rather than a clinically generalizable diagnostic cutoff. Nevertheless, previous studies examining LDL-C in relation to other disease outcomes have also reported evidence of increased risk once LDL-C exceeds specific thresholds. For example, in a study of young adults in the United States, LDL-C ≥ 190 mg/dL was identified as a high-risk subgroup and was associated with a significantly increased risk of atherosclerotic cardiovascular disease (ASCVD) [47]. Similarly, in the acute phase of ischemic stroke, LDL-C ≥ 5.0 mmol/L (approximately 193 mg/dL) was associated with an increased risk of all-cause mortality (adjusted OR = 1.22, 95% CI: 0.98–1.50); although the confidence interval was close to the null, the overall trend analysis still suggested increased risk at higher LDL-C levels [48]. To our knowledge, the present study is the first to identify such a threshold pattern in people living with HIV. This finding provides a useful direction for future research, although larger studies are needed to determine whether this threshold is reproducible and broadly applicable.\u003c/p\u003e\n\u003cp\u003eIn addition to the above considerations, the development of cholelithiasis involves the interaction of multiple factors. Previous studies have shown that metabolic syndrome (MetS) is a well-established risk factor for cholelithiasis, and that low HDL-C may be the MetS component most strongly associated with gallstone risk [19, 41]. Higher BMI may also indirectly increase the risk of cholelithiasis through lowering HDL-C and increasing triglyceride (TG) levels [41, 49]. Although the present study focused on LDL-C, our stratified analyses showed that the positive association between LDL-C and cholelithiasis was directionally consistent across most subgroups, including those defined by age, sex, BMI, HBV co-infection status, and immune status, as reflected by CD4 + T-cell count. This finding suggests that the risk associated with LDL-C is not confined to a single subgroup, but may be relatively broadly present among patients with HIV infection. Notably, the association between LDL-C and cholelithiasis appeared to be stronger among patients with a longer duration of HIV infection and among those receiving ABT treatment, indirectly suggesting that long-term infection, treatment exposure, and metabolic remodeling may jointly contribute to gallstone development [7]. However, the interaction tests for HBV and ABT were not statistically significant (both \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05), indicating that the current sample does not provide sufficient evidence to support a clear effect-modifying role for either factor in the association between LDL-C and cholelithiasis. Given the limited sample size in some subgroups, statistical power may have been insufficient, and these subgroup findings should therefore be interpreted with caution. Further validation in larger studies is warranted.\u003c/p\u003e\n\u003cp\u003eThis study still has several limitations. First, because of its retrospective design, causal inference is inherently limited, and we cannot definitively establish whether elevated LDL-C is a direct cause of gallstone formation. Second, this was a single-center analysis, which may limit the generalizability of the findings to other populations or geographic settings. In addition, several potential confounding factors, including dietary patterns, genetic background, gallbladder motility, and the gut microbiota, could not be fully accounted for, all of which may influence cholesterol metabolism and the risk of cholelithiasis [50, 51]. Finally, some subgroups, such as patients with HBV co-infection, had relatively small sample sizes, which may have reduced statistical power and limited our ability to detect potential effect modification. Therefore, the subgroup analyses, interaction analyses, and the identified inflection point of 2.28 mmol/L should all be interpreted as exploratory findings rather than definitive evidence. Future studies should employ prospective cohort designs or large multicenter investigations to further examine the causal relationship between LDL-C and cholelithiasis and to clarify the underlying biological mechanisms. In particular, a more comprehensive risk prediction framework could be developed by integrating multiple dimensions, including bile composition, hepatic cholesterol transport pathways, metabolic syndrome-related indicators, and immune-inflammatory status. In addition, evaluating whether LDL-C-targeted interventions can reduce the risk of cholelithiasis would help clarify the practical clinical value of LDL-C in gallstone prevention.\u003c/p\u003e\n\u003cp\u003eIn summary, the present study suggests that elevated LDL-C levels are significantly associated with cholelithiasis among hospitalized people living with HIV, and that this association persists even after adjustment for multiple covariates. We further observed a possible nonlinear relationship in this dataset, with a stronger association above the exploratory inflection point of 2.28 mmol/L. These findings provide additional evidence regarding metabolic factors associated with cholelithiasis in people living with HIV. Nevertheless, future multicenter prospective studies, together with more comprehensive covariate adjustment, external validation, and mechanistic investigation, are still needed to determine whether LDL-C can offer incremental value in gallstone risk assessment in this special population.\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eElevated LDL-C was independently associated with cholelithiasis among hospitalized people living with HIV, even after adjustment for multiple potential confounders. A possible nonlinear relationship was also observed, with a stronger association above an exploratory inflection point of 2.28 mmol/L. The association was directionally consistent across most subgroups, and no significant interaction was detected for HBV or ABT. Further large-scale prospective studies are warranted to validate these findings and to determine the potential clinical utility of LDL-C in gallstone risk stratification among people living with HIV.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics committee of the Public Health Clinical Center of Chengdu (approval number: YJ-K2024-94-01). Given the retrospective design and the use of anonymized data extracted from existing medical records, the requirement for informed consent was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCheng Xingzhen and Chen Tingyu conceived and designed the study. Liu Dongxu, Gui Fuqiang, Yang Lei and Chen Jidong collected the data. Cheng Xingzhen and Yang Jing performed the statistical analysis. Tan Juan and Feng Shifeng interpreted the data. Chen Tingyu drafted the manuscript. Wei Guo, Zhao Yong and Wang Hua revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTHOMAS A, HOY J F. Challenges of HIV Management in an Aging Population[J]. Curr HIV/AIDS Rep, 2024,22(1):8.\u003c/li\u003e\n\u003cli\u003eTHE L H. Preparing for an ageing HIV epidemic[J]. 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Association between exposure to brominated flame retardants (BFRs) and blood lipid profiles in American adults: a cross-sectional study[J]. Lipids Health Dis, 2025,24(1):120.\u003c/li\u003e\n\u003cli\u003eBOLOGHEANU R, BILIR A, KAPRAL L, et al. New Persistent Opioid Use After Surgery[J]. JAMA Netw Open, 2025,8(2):e2460794.\u003c/li\u003e\n\u003cli\u003eLASH M T, SAJEESH S, ARAZ O M. Predicting mobility using limited data during early stages of a pandemic[J]. J Bus Res, 2023,157:113413.\u003c/li\u003e\n\u003cli\u003eTIAN W, WU Z, YANG W, et al. Investigating the shared genetic information between serum concentration levels of liver enzymes and cholelithiasis[J]. BMC Gastroenterol, 2025,25(1):564.\u003c/li\u003e\n\u003cli\u003eNALADO A M, WAZIRI B, ISMAIL A, et al. Prevalence and Determinants of Endothelial Dysfunction among Adults Living with HIV in Northwest Nigeria[J]. Glob Heart, 2023,18(1):57.\u003c/li\u003e\n\u003cli\u003eHAMASAKI M, SAKANE N, KOTANI K. Nonalcoholic Fatty Liver Disease Risk and Proprotein Convertase Subtilisin Kexin 9 in Familial Hypercholesterolemia Under Statin Treatment[J]. Nutrients, 2024,16(21).\u003c/li\u003e\n\u003cli\u003eKHAN A, KIRYLUK K. Kidney disease progression and collider bias in GWAS[J]. Kidney Int, 2022,102(3):476-478.\u003c/li\u003e\n\u003cli\u003eMAHMOUD O, DUDBRIDGE F, DAVEY S G, et al. A robust method for collider bias correction in conditional genome-wide association studies[J]. Nat Commun, 2022,13(1):619.\u003c/li\u003e\n\u003cli\u003eOZTURK D, SIVASLIOGLU A, BULUS H, et al. TyG index is positively associated with HOMA-IR in cholelithiasis patients with insulin resistance: Based on a retrospective observational study[J]. Asian J Surg, 2024,47(6):2579-2583.\u003c/li\u003e\n\u003cli\u003eZHANG C, DAI W, YANG S, et al. Resistance to Cholesterol Gallstone Disease: Hepatic Cholesterol Metabolism[J]. J Clin Endocrinol Metab, 2024,109(4):912-923.\u003c/li\u003e\n\u003cli\u003eSHAFIK C A, DAS A, SREE A, et al. Recent Advances in Possible Treatment Options Including Herbal Remedies for the Management of Cholelithiasis[J]. Curr Pharm Des, 2025.\u003c/li\u003e\n\u003cli\u003eABOU H F, BOU H M, EL A K, et al. Trends \u0026amp; predictors of non-AIDS comorbidities among people living with HIV and receiving antiretroviral therapy in Lebanon[J]. Medicine (Baltimore), 2022,101(13):e29162.\u003c/li\u003e\n\u003cli\u003eSZYMAŃSKA B, KNYSZ B, CIEPŁUCHA H, et al. Assessment of Metabolic, Inflammatory, and Immunological Disorders Using a New Panel of Plasma Parameters in People Living with HIV Undergoing Antiretroviral Therapy-A Retrospective Study[J]. J Clin Med, 2024,13(15).\u003c/li\u003e\n\u003cli\u003eZHANG M, MAO M, ZHANG C, et al. Blood lipid metabolism and the risk of gallstone disease: a multi-center study and meta-analysis[J]. Lipids Health Dis, 2022,21(1):26.\u003c/li\u003e\n\u003cli\u003eHUANG C, XIAO W, ZHAO J, et al. Gut Microbiome Dysbiosis Promotes Gallstone Formation via Bile Acid Metabolic Disorder: A Multiomics Study[J]. FASEB J, 2026,40(6):e71656.\u003c/li\u003e\n\u003cli\u003eMO P, CHEN H, JIANG X, et al. Effect of hepatic NPC1L1 on cholesterol gallstone disease and its mechanism[J]. Heliyon, 2023,9(5):e15757.\u003c/li\u003e\n\u003cli\u003eXU X, GAO J, SUN J, et al. The role of metabolic factors in the association between obesity and cholelithiasis: A two-step, two-sample multivariable mendelian randomization study[J]. Clinics (Sao Paulo), 2024,79:100520.\u003c/li\u003e\n\u003cli\u003eDONG H, CHEN R, XU F, et al. Can Lipid-Lowering Drugs Reduce the Risk of Cholelithiasis? A Mendelian Randomization Study[J]. Clin Epidemiol, 2024,16:131-141.\u003c/li\u003e\n\u003cli\u003eCHEN L, QIU W, SUN X, et al. Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis[J]. Gut, 2024,73(3):521-532.\u003c/li\u003e\n\u003cli\u003eLIU X, SHI L, ZHANG S, et al. Exploring potential plasma drug targets for cholelithiasis through multiancestry Mendelian randomization[J]. Int J Surg, 2025,111(1):302-310.\u003c/li\u003e\n\u003cli\u003eHU H, SHAO W, LIU Q, et al. Gut microbiota promotes cholesterol gallstone formation by modulating bile acid composition and biliary cholesterol secretion[J]. Nat Commun, 2022,13(1):252.\u003c/li\u003e\n\u003cli\u003eXIA Y, XU Y, LIU Q, et al. Glutaredoxin 1 regulates cholesterol metabolism and gallstone formation by influencing protein S-glutathionylation[J]. Metabolism, 2023,145:155610.\u003c/li\u003e\n\u003cli\u003eHARRISON T N, ZHANG Y, CHOI S K, et al. Follow-Up Lipid Testing and Statin Initiation Among Young Adults in a U.S. Health Care System[J]. J Am Coll Cardiol, 2025.\u003c/li\u003e\n\u003cli\u003eCHEN Z M, GU H Q, MO J L, et al. U-shaped association between low-density lipoprotein cholesterol levels and risk of all-cause mortality mediated by post-stroke infection in acute ischemic stroke[J]. Sci Bull (Beijing), 2023,68(12):1327-1335.\u003c/li\u003e\n\u003cli\u003eZHAO F, YANG Y, YANG W. Exploring the causal impact of body mass index on metabolic biomarkers and cholelithiasis risk: a Mendelian randomization analysis[J]. Sci Rep, 2025,15(1):415.\u003c/li\u003e\n\u003cli\u003eCHEN H, JIANG X, LI Y, et al. A Gallbladder-Specific Hydrophobic Bile Acid-FXR-MUC1 Signaling Axis Mediates Cholesterol Gallstone Formation[J]. Adv Sci (Weinh), 2025,12(13):e2401956.\u003c/li\u003e\n\u003cli\u003eFAIRFIELD C J, DRAKE T M, PIUS R, et al. Genome-wide analysis identifies gallstone-susceptibility loci including genes regulating gastrointestinal motility[J]. Hepatology, 2022,75(5):1081-1094.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV infection, low-density lipoprotein cholesterol, cholelithiasis, hospitalized patients","lastPublishedDoi":"10.21203/rs.3.rs-9355321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9355321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the association between low-density lipoprotein cholesterol (LDL-C) levels and cholelithiasis among hospitalized people living with HIV.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a single-center retrospective case-control study including 259 hospitalized people living with HIV admitted between January 2019 and January 2025. The case group comprised 98 patients with imaging-confirmed cholelithiasis, and the control group comprised 161 patients without imaging evidence of cholelithiasis during the same period. Demographic characteristics, co-infections, antiretroviral therapy-related variables, and laboratory parameters were collected. Logistic regression models were used to evaluate the association between LDL-C and cholelithiasis. Model I was adjusted for sex, age, and body mass index (BMI), and Model II was further adjusted for hepatitis B virus (HBV) co-infection, duration since confirmed HIV diagnosis, albuvirtide (ABT) treatment, and CD4\u0026thinsp;+\u0026thinsp;T-cell count. Smooth-curve fitting and segmented regression were used to explore a potential nonlinear association. Stratified analyses and interaction tests were conducted as exploratory analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCompared with controls, patients with cholelithiasis were younger (50.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.71 vs. 56.30\u0026thinsp;\u0026plusmn;\u0026thinsp;16.00 years) and more likely to be female (40.82% vs. 6.21%). BMI was similar between groups. The case group had higher total cholesterol, high-density lipoprotein cholesterol, and LDL-C levels than the control group, with mean LDL-C levels of 3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 mmol/L and 2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 mmol/L, respectively. In univariable analysis, LDL-C was significantly associated with cholelithiasis (OR\u0026thinsp;=\u0026thinsp;2.54, 95% CI: 1.80\u0026ndash;3.58; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained significant after multivariable adjustment (Model II: OR\u0026thinsp;=\u0026thinsp;2.71, 95% CI: 1.79\u0026ndash;4.11; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Smooth-curve fitting suggested a nonlinear association, with an exploratory inflection point at 2.28 mmol/L. Below this threshold, the association was not statistically significant; above it, the positive association was substantially stronger. No significant interaction was observed for HBV or ABT.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong hospitalized people living with HIV, elevated LDL-C was independently associated with cholelithiasis. The data also suggest a possible nonlinear association, with a stronger effect above 2.28 mmol/L. These findings may help identify metabolic risk factors for cholelithiasis in this population, but confirmation in larger prospective studies is required.\u003c/p\u003e","manuscriptTitle":"Association between low-density lipoprotein cholesterol with cholelithiasis among hospitalized people living with HIV: a retrospective case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 18:06:31","doi":"10.21203/rs.3.rs-9355321/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"18617478144926276178899151278888146457","date":"2026-05-13T04:20:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311625198115808461113296148814272322054","date":"2026-05-11T09:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3271752192971395669950800543687083037","date":"2026-05-06T08:11:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T11:44:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T05:12:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T14:48:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T14:47:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-04-08T10:01:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3063a23a-32da-4430-b50e-206249426db7","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"18617478144926276178899151278888146457","date":"2026-05-13T04:20:16+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"311625198115808461113296148814272322054","date":"2026-05-11T09:47:57+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"3271752192971395669950800543687083037","date":"2026-05-06T08:11:43+00:00","index":45,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-05T11:44:31+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T18:06:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 18:06:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9355321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9355321","identity":"rs-9355321","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}