Divergent trajectories and risk profiles of multimorbidity among people living with HIV in China

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Yet its long-term evolution and risk factors remain insufficiently described. Methods: We conducted a retrospective cohort study of 5,950 PLWH initiating antiretroviral therapy (ART) in Shenzhen, China, between 2009 and 2016, with follow-up through 2024. Multimorbidity was defined as ≥2 predefined 26 conditions and categorized into six evolving patterns. Longitudinal changes were assessed using Sankey plots and multivariable regression models. Results: At ART initiation, 25.9% had multimorbidity. Over 8 years, the overall prevalence rose to 42.5%, driven mainly by a threefold increase in incurable multimorbidity (7.7% to 25.5%). Nearly 36% of initially disease-free individuals developed multimorbidity during follow-up. Younger PLWH experienced the faster decline in comorbidity-free status over time, while older individuals more frequently progressed to multimorbidity. Dyslipidemia was the most frequent and persistent condition. Risk factors for metabolic multimorbidity included age at diagnosis ≥ 46 years (HR = 3.44, P < 0.01), body mass index (BMI) ≥ 24 (HR=1.69, P < 0.01), etc. In contrast, higher BMI appeared protective against mixed infectious–non-infectious patterns (HR = 0.72, P < 0.01). Conclusions: Multimorbidity is increasingly prevalent among PLWH in the ART era, with a shift from infectious to metabolic and behavioral comorbidities. Distinct risk profiles highlight the need for early identification and tailored interventions, especially among high-risk subgroups. Early identification and tailored management of key risk factors, particularly dyslipidemia, are essential for integrated HIV care. PLWH HIV multimorbidity comorbidity cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With the widespread adoption and ongoing optimization of antiretroviral therapy (ART), the life expectancy of people living with HIV (PLWH) has markedly increased [ 1 – 3 ]. However, this success has introduced new challenges. PLWH are now developing chronic comorbidities at younger ages than their HIV-negative counterparts, making multimorbidity a growing concern [ 2 ]. Multimorbidity, defined as the simultaneous or sequential presence of two or more health conditions within a giving timeframe [ 4 , 5 ], is of particular relevance in the context of HIV. Among PLWH, multimorbidity refers to the presence of at least two conditions other than HIV infection itself. Due to the combined effects of chronic HIV-related inflammation, immune dysregulation, and ART-realted toxicities, multimorbidity patterns in PLWH differ substantially from those seen in the general population. In addition to an increased risk of non-communicable diseases (NCDs)—such as hypertension, diabetes, and dyslipidemia—PLWH also face a persistently high burden of chronic or recurrent infections, including hepatitis B, syphilis, and various opportunistic infections. These arise from ongoing immune dysfunction, high-risk behaviors, and complex interactions between pathogen latency, immune activation, and systemic metabolic dysregulation. Unlike the general population, in whom multimorbidity typically occurs at older ages, PLWH often experience multiple comorbidities at a younger age [ 6 , 7 ]. Moreover, the prevalence of multimorbidity rises steadily with age [ 8 ], underscoring the importance of investigating multimorbidity not only older but also younger PLWH. Although several large cohort studies have provided valuable insights into the burden and risk factors of multimorbidity among PLWH [ 9 , 10 ], much of the literature remains cross-sectional, focusing on a limited number of conditions. Even longitudinal studies have typically examined incidence and prevalence of comorbidities without capturing their long-term evolution over time [ 8 , 11 ]. As HIV care increasingly shifts from a focus on viral suppression to lifelong chronic disease management, a deeper understanding of multimorbidity dynamics is urgently needed. In this study, we aimed to characterize the longitudinal evolution of multimorbidity patterns over an 8-year period in a cohort of PLWH in Shenzhen, China. We further identified the most prevalent and clinically significant multimorbidity clusters, described their temporal trajectories, and examined risk factors associated with the development of metabolic-related and mixed infectious–non-infectious multimorbidity patterns. Methods Study design and population We conducted a retrospective cohort study to investigate the longitudinal evolution of multimorbidity patterns among PLWH initiating ART in Shenzhen, China. Participants were eligible for inclusion if they had a confirmed HIV diagnosis, initiated ART between January 1, 2009, and June 30, 2016, and had available follow-up data through June 30, 2024, allowing for a minimum of 8 years of observation. We excluded individuals who lacked documented ART initiation dates and had fewer than three recorded diagnostic encounters during the follow-up period, in order to reduce potential bias due to insufficient clinical information. Disease ascertainment and classification Comorbid conditions were identified primarily through ICD-10 diagnostic codes and clinician-documented diagnoses recorded in the electronic medical record system. A total of 26 chronic conditions were selected for inclusion in the analysis. These conditions were chosen based on their prevalence in the general population and clinical relevance among PLWH. The selected conditions included: (1) Hypertension, (2) Diabetes mellitus, (3) Dyslipidemia, (4) Cardiovascular disease (CVD), (5) Chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis, (6) Esophageal, gastric, or duodenal diseases, (7) Hepatitis B (HBV), (8) Hepatitis C (HCV), (9) Chronic kidney disease (CKD), (10) Non-AIDS-related cancers, (11) Osteoporosis, (12) Mental health disorders, (13) Sleep disorders, (14) Syphilis, (15) Human papillomavirus (HPV) infection, (16) Chronic liver disease (CLD) excluding viral hepatitis, (17) AIDS-defining cancers, (18) HIV-associated encephalopathy, (19) Pneumocystis jirovecii pneumonia (PJP), (20) Nontuberculous mycobacterial infections, (21) Cytomegalovirus (CMV) infection, (22) Herpes simplex virus (HSV) infection, (23) Varicella-zoster virus (VZV) infection, (24) Toxoplasmosis, (25) Fungal infections, (26) Tuberculosis (TB). Full details on disease definitions and classification are available in the Supplementary Appendix. Follow-up and multimorbidity patterns evolution ART initiation was defined as the study baseline for describing participant demographics and clinical characteristics. To ensure stabilization after ART initiation and reduce potential misclassification of early-onset conditions, outcome follow-up began at three months post-ART. All multimorbidity assessments and transitions were measured from this point onward. The total follow-up duration was eight years, divided into three time periods: baseline, Wave1 (from 3-month to 4-year follow-up), and Wave2 (from 4-year to 8-year follow-up). Multimorbidity patterns were categorized into six mutually exclusive modules based on disease count and curability: incurable multimorbidity, curable multimorbidity, mixed curable multimorbidity, single incurable disease, single curable disease, and no disease. Follow-up was conducted at approximately three-month intervals. Participants were classified as dropouts if none of the 26 conditions were recorded during a follow-up wave and if no medical records were available for more than six consecutive months prior to the end of that wave. Curable conditions were not expected to persist throughout the study period. In contrast, incurable conditions were recorded at the first time and were assumed to persist for the remainder of the follow-up. Covariates We extracted key covariates from the hospital records and laboratory results. Demographic variables included age group, sex, and marital status. Laboratory parameters included HDL-C, LDL-C, fasting glucose (FPG), platelet count (PLT), alanine aminotransferase (ALT), and estimated glomerular filtration rate (eGFR), all obtained via standardized clinical protocols. HIV-related factors comprised baseline CD4 count, HIV transmission route, and the presence of opportunistic infections at baseline. Behavioral characteristics included smoking and alcohol use. Statistical analysis All analyses were performed using R version 4.4.1 and RStudio version 2024.12.0. Descriptive statistics were used to summarize participant characteristics and the distribution of multimorbidity modules across waves. Sankey diagrams were generated using the ggsankey, ggplot2, and dplyr packages in R to visualize transitions in multimorbidity patterns over time. Retrospective Sankey plots were also constructed to trace prior disease profiles among individuals who developed incurable or mixed multimorbidity patterns. Stacked bar charts were drawn to compare the distribution of multimorbidity patterns by sex and age groups across waves. Differences between subgroups were assessed using Chi-square or Fisher’s exact tests. Two clinically relevant patterns—metabolic and mixed infectious–non-infectious multimorbidity—were modeled due to their prevalence and clinical relevance. Cox regression assessed risk factors for metabolic multimorbidity, and logistic regression was used for the mixed pattern. Results were reported as HRs or ORs with 95% CIs, adjusting for covariates. P-values < 0.05 were considered significant. Results Baseline characteristics of participants A total of 5,950 participants were included in the final analysis. At enrollment, 25.9% of participants had multimorbidity (Table 1 ). Compared with those without multimorbidity, participants with multimorbidity were older (median age: 35.0 vs. 31.0 years; interquartile range [IQR]: 29–43 vs. 27–38 years; P < 0.001), and more likely to be male (90.5% vs. 85.6%; P < 0.001). Significant differences in body mass index (BMI) were observed between the two groups ( P = 0.001), with a higher proportion of underweight individuals among those with multimorbidity. Marital status also showed a significant difference between groups (P < 0.001), with a higher proportion of divorced, separated, or widowed individuals in the multimorbidity group (12.3% vs. 8.0%). Unhealthy lifestyle behaviors, including smoking and alcohol use, were more common in the multimorbidity group ( P = 0.003 and P = 0.045, respectively). The prevalence of hypertension, diabetes, and hypercholesterolemia was significantly higher among participants with multimorbidity (all P < 0.001). Additionally, these participants had lower baseline CD4 cell counts, higher HIV RNA levels, and a greater prevalence of HBV or HCV co-infection (all P < 0.001). Table 1 Baseline characteristics of study participants according to multimorbidity status Characteristics Total No multimorbidity Multimorbidity P -value N 5950 4411 1539 Age, years 32.0 (27.0–39.0) 31.0 (27.0–38.0) 35.0 (29.0–43.0) < 0.001 Sex Male 5171 (86.9) 3778 (85.6) 1393 (90.5) < 0.001 Female 779 (13.1) 633 (14.4) 146 (9.5) BMI, kg/m 2 < 18.5 930 (15.6) 647 (14.7) 283 (18.4) 0.001 18.5–23.9 4039 (67.9) 3052 (69.2) 987 (64.1) ≥ 24 981 (16.5) 712 (16.1) 269 (17.5) Marital status Never married 3181 (53.5) 2441 (55.3) 740 (48.1) < 0.001 Married or cohabiting 2229 (37.5) 1619 (36.7) 610 (39.6) Divorced, separated, or widowed 540 (9.1) 351 (8.0) 189 (12.3) HIV transmission route Male-to-male sex contact 3518 (59.1) 2629 (59.6) 889 (57.8) 0.049 Heterosexual contact 2201 (37) 1619 (36.7) 582 (37.8) IDU 74 (1.2) 45 (1.0) 29 (1.9) Other 157 (2.6) 118 (2.7) 39 (2.5) Smoking 1292 (21.7) 918 (20.8) 374 (24.3) 0.003 Drinking 1409 (23.7) 1015 (23.0) 394 (25.6) 0.045 Hypertension 82 (1.4) 6 (0.1) 76 (4.9) < 0.001 Diabetes 219 (3.7) 18 (0.4) 201 (13.1) < 0.001 Hypercholesterolemia 4056 (68.2) 2645 (60.0) 1411 (91.7) < 0.001 Glucose, mg/dL 90.3 (84.7–96.3) 89.9 (84.7–95.9) 90.8 (84.9–98.2) < 0.001 High-density lipoprotein cholesterol, mg/dL 48.7 (41.4–56.5) 49.11 (41.8–56.8) 48.0 (40.0–55.3) < 0.001 Low-density lipoprotein cholesterol, mg/dL 94.0 (78.5–111.8) 93.19 (78.5–111.0) 95.9 (79.7–114.5) 0.005 Triglycerides, mg/dL 113.3 (80.6–167.4) 105.4 (76.2–156.7) 135.5 (93.9–193.0) < 0.001 Total cholesterol, mg/dL 162.0 (141.5–185.6) 161.3 (141.9–184.3) 163.6 (141.2–188.5) 0.156 TyG index 8.5 (8.2–9.0) 8.5 (8.1–8.9) 8.8 (8.4–9.1) < 0.001 Creatinine, mg/dL 0.8 (0.7–0.9) 0.8 (0.7–0.9) 0.8 (0.7–0.9) < 0.001 Aspartate Aminotransferase, U/L < 0.001 Normal 5422 (91.1) 4113 (93.2) 1309 (85.1) ≥ 1×ULN 406 (6.8) 244 (5.5) 162 (10.5) ≥ 2×ULN 122 (2.1) 54 (1.2) 68 (4.4) Alanine Aminotransferase, U/L < 0.001 Normal 5093 (85.6) 3865 (87.6) 1228 (79.8) ≥ 1×ULN 646 (10.9) 420 (9.5) 226 (14.7) ≥ 2×ULN 211 (3.5) 126 (2.9) 85 (5.5) WBC, 10^9/L 5.2 (4.3–6.3) 5.2 (4.3–6.3) 5.2 (4.2–6.4) 0.463 Platelet, 10^9/L 201.0 (167.0–240.0) 201.0 (168.0–237.0) 203.0 (164.0–249.0) 0.219 HBV infection 327 (5.5) 52 (1.2) 275 (17.9) < 0.001 HCV infection 67 (1.1) 6 (0.1) 61 (4) < 0.001 Baseline CD4 count, cells/µL < 0.001 < 200 2137 (35.9) 1437 (32.6) 700 (45.5) 200–349 2484 (41.7) 1960 (44.4) 524 (34) 350–499 1006 (16.9) 768 (17.4) 238 (15.5) ≥ 500 323 (5.4) 246 (5.6) 77 (5) HIV RNA, copies/mL < 0.001 < 5000 524 (8.8) 433 (9.8) 91 (5.9) 5000–99999 3538 (59.5) 2454 (55.6) 1084 (70.4) ≥ 100000 1888 (31.7) 1524 (34.5) 364 (23.7) Continuous variables are presented as median (IQR: 25th–75th percentile) and compared using the Mann–Whitney U test. Categorical variables are presented as frequency (%) and compared using Pearson’s χ² test. BMI, body-mass index; HIV, human immunodeficiency virus; IDU, injection drug use; TyG index, triglyceride-glucose index (calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) ÷ 2]); ULN, upper limit of normal; ALT ULN: 30 U/L (male), 19 U/L (female); AST ULN: 35 U/L (male), 31 U/L (female); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus Evolution of multimorbidity patterns during 8-Year follow-up in PLWH Over the 8-year follow-up period, the distribution of multimorbidity patterns among PLWH exhibited notable temporal shifts. At baseline, most participants were classified as having either no comorbidity (24.61%) or a single incurable condition (46.03%) among the 5,950 individuals included. The overall prevalence of multimorbidity—including incurable, curable, and mixed patterns—increased from 25.87% at baseline to 42.49% by Wave2 (Fig. 1 , Table S4). The most substantial increase was observed in the incurable multimorbidity pattern, which rose from 7.66% at baseline to 18.82% in Wave1, and further to 25.50% by Wave2—representing a more than threefold increase. The mixed multimorbidity pattern, characterized by the coexistence of both curable and incurable conditions (e.g., syphilis or HPV alongside dyslipidemia or diabetes), remained relatively stable over time, consistently affecting approximately 1,000 participants (16–18% of the cohort). In contrast, the curable multimorbidity and single curable disease patterns steadily declined, with their combined prevalence decreasing from 4.27% to just 0.3%. Among the 1,464 participants who were free of all 26 predefined conditions at baseline, 528 (36.07%) developed multimorbidity during the follow-up period. Of these, 277 transitioned during Wave1, and an additional 251 during Wave2. Distribution of multimorbidity patterns by sex and age In the sex-stratified analysis, females consistently exhibited a lower prevalence of multimorbidity across all waves compared to males. The proportion of participants without any comorbidity declined markedly in both sexes, with a sharper decrease observed in females (from 34.27–4.36%) than in males (from 23.15–3.06%) ( P < 0.01 for both). In contrast, males showed a greater increase in incurable multimorbidity over time, rising from 7.95–25.91%, compared with an increase from 5.78–22.72% in females ( P < 0.01 for both). Meanwhile, the mixed multimorbidity pattern remained relatively stable in males (18.14–17.95%) but declined modestly in females (12.71–10.14%) (Fig. 2 A, Tables S6–S7). Age-stratified analysis showed that individuals aged > 32 years consistently had a higher burden of multimorbidity across all waves (Fig. 2 B, Table S8). At baseline, 31.36% of older participants had multimorbidity compared to 20.70% in those aged ≤ 32 years (P < 0.01). As follow-up progressed, multimorbidity increased significantly in both groups (P < 0.01; Tables S9–S10), mainly driven by the rise in incurable multimorbidity. By Wave2, the prevalence of incurable multimorbidity reached 32.56% in the older group and 19.90% in the younger group (P < 0.01). While the older group initially had a higher prevalence of mixed multimorbidity (20.29% vs. 14.74%; P < 0.01), this difference diminished over time, and by Wave2 the proportions had converged (16.20% vs. 17.61%; P = 0.16). The proportion of participants with no comorbidity declined in both age groups, with a more substantial reduction among younger participants (from 28.66–4.37%; P < 0.01). Evolution and origins of top incurable and mixed curable multimorbidity patterns over time In the top three incurable multimorbidity clusters, most individuals maintained the same disease combinations through follow-up, reflecting considerable structural stability. Specifically, individuals classified under the “diabetes–dyslipidemia,” “dyslipidemia–HBV,” or “dyslipidemia–non-viral CLD” at baseline, including those with coexisting curable diseases, largely remained within the same top three incurable disease clusters in Wave1. Within these groups, 18.00%, 37.21%, and 46.00% of individuals, respectively, had coexisting curable diseases, primarily infections. Among those with curable diseases at baseline, 22.22%, 31.25%, and 53.97% had two or more curable conditions, respectively. By Wave 2, among individuals who remained in the same incurable multimorbidity clusters, the proportions with coexisting curable diseases had declined to 15.07%, 20.10%, and 27.86%, respectively (Fig. 3 ). In the mixed curable pattern, more variation was observed. “Dyslipidemia–syphilis” remained the most common across waves (18.71%, 16.51%, and 19.17%), while “dyslipidemia–HPV” declined from 11.13% in Wave 1 to 4.47% in Wave 2. In contrast, “dyslipidemia–sleep disorders” increased to 12.02% in Wave 2, becoming the second most frequent combination (Table 2 , Fig. 4 ). Table 2 Distribution of the top three incurable and mixed curable multimorbidity patterns Time period Incurable multimorbidity Mixed curable multimorbidity Pattern N Proportion, % pattern N Proportion, % Baseline Total individuals 456 Total individuals 1037 Diabetes & Dyslipidemia 115 25.22 Dyslipidemia & Syphilis 194 18.71 Dyslipidemia & HBV 114 25.00 Dyslipidemia & TB 101 9.74 Dyslipidemia & Other CLD 98 21.49 Dyslipidemia & HPV 76 7.33 Baseline to 4-year Total individuals 1143 Total individuals 1042 Diabetes & Dyslipidemia 309 27.03 Dyslipidemia & Syphilis 172 16.51 Dyslipidemia & Other CLD 222 19.42 Dyslipidemia & HPV 116 11.13 Dyslipidemia & HBV 208 18.20 Dyslipidemia & Sleep Disorders 75 7.20 4-year to 8-year Total individuals 1651 Total individuals 1007 Diabetes & Dyslipidemia 431 26.11 Dyslipidemia & Syphilis 193 19.17 Dyslipidemia & HBV 250 15.14 Dyslipidemia & Sleep Disorders 121 12.02 Dyslipidemia & Other CLD 235 14.23 Dyslipidemia & HPV 45 4.47 HBV, hepatitis B virus; CLD, chronic liver disease; HPV, human papillomavirus; TB, tuberculosis. Distinct risk factors for metabolic versus infectious–non-infectious multimorbidity pattern In the metabolic multimorbidity model (Fig. 5 ), older age was a strong predictor: individuals aged ≥ 46 had a 3.44-fold increased risk compared to those ≤ 25 (HR = 3.44, 95% CI: 2.61–4.52, P < 0.01). Overweight or obesity (BMI ≥ 24) was associated with higher risk (HR = 1.69, 95% CI: 1.46–1.95, P < 0.01), as was male sex (HR = 1.41, 95% CI: 1.13–1.76, P < 0.01). Elevated HDL-C (≥ 1.0 mmol/L) and FPG (≥ 6.1 mmol/L) were also significant (HR = 1.21, 95% CI: 1.03–1.42, P < 0.05; HR = 2.63, 95% CI: 2.09–3.31, P < 0.01). ALT ≥ 1× ULN and ≥ 2× ULN increased the risk by 50% and 47%, respectively (both P < 0.05), while reduced eGFR (< 60 mL/min/1.73 m²) was associated with higher risk (HR = 2.50, 95% CI: 1.39–4.47, P < 0.01). Compared to MSM, those with IDU had elevated risk (HR = 1.82, 95% CI: 1.31–2.52, P < 0.01). Baseline CD4 count, smoking, and alcohol use were not significantly associated. In the mixed infectious–non-infectious model (Fig. 6 ), male sex (OR = 1.89, 95% CI: 1.48–2.43, P < 0.01) and IDU (OR = 2.00, 95% CI: 1.12–3.55, P < 0.05) were strong risk factors. In contrast, BMI ≥ 24 was protective (OR = 0.77, 95% CI: 0.58–0.90, P < 0.01), as was being married (OR = 0.75, 95% CI: 0.63–0.90, P < 0.01). CD4 ≤ 100 cells/µL (OR = 1.31, 95% CI: 1.07–1.60, P < 0.01), PLT < 100×10⁹/L (OR = 1.63, 95% CI: 1.14–2.31, P < 0.01), and a history of opportunistic infections (OR = 2.16, 95% CI: 1.73–2.69, P < 0.01) were all independently associated. Alcohol consumption was modestly associated (OR = 1.19, P < 0.05), while age, HDL-C, and eGFR were not significant in this model. Discussion This study presents an 8-year longitudinal cohort analysis examining the evolution and risk factors of multimorbidity among PLWH in Shenzhen, China. Over the follow-up period, we observed a significant increase in multimorbidity prevalence, notably, the prevalence of incurable multimorbidity nearly tripled and became the dominant pattern. Although males consistently exhibited higher multimorbidity burdens, females experienced a faster rate of increase, and younger individuals (≤ 32 years) showed steeper rises despite lower baseline burdens. Most multimorbidity patterns involved combinations of non-communicable diseases with curable infectious conditions, highlighting a dual burden of disease. Additionally, distinct risk profiles were identified for metabolic versus combined infectious–non-infectious multimorbidity patterns. These findings underscore the necessity of continuous monitoring and targeted interventions tailored to specific multimorbidity trajectories, emphasizing the importance of personalized chronic disease management for PLWH. The sharp increase in multimorbidity, particularly incurable patterns, reflects a shifting disease landscape in the ART era. While the incidence of infectious comorbidities declined with prolonged ART, the burden of non-AIDS-related chronic conditions—such as dyslipidemia, diabetes, and non-viral chronic liver disease—increased substantially, in line with previous findings [ 2 ]. This transition may be driven by population aging [ 12 ], chronic immune activation [ 13 – 15 ], HIV-induced mitochondrial dysfunction [ 16 – 18 ], gut microbiota dysbiosis [ 19 ], and long-term ART-related toxicities [ 20 , 21 ]. Dyslipidemia emerged as a key factor in both incurable and mixed-curability patterns, likely due to HIV-related immune activation, ART-induced metabolic effects, and its impact on cardiovascular, liver, and glucose metabolism [ 20 , 22 – 26 ]. Its frequent co-occurrence with both metabolic and infectious comorbidities underscores its potential as an early marker and modifiable entry point for multimorbidity surveillance and prevention. Collectively, these shifts call for integrated models of HIV care that anticipate evolving chronic disease risks and adapt accordingly across the treatment continuum. Subgroup analyses revealed sex- and age-related disparities in multimorbidity trends. Although men consistently bore a greater burden of multimorbidity, women experienced a steeper increase over time. This may reflect sex-based immunological differences as well as gender-specific barriers to health service access, delayed diagnosis, or reduced screening uptake [ 7 , 27 , 28 ]. Age was another consistent predictor of multimorbidity; older PLWH had significantly greater burdens across all patterns, corroborating prior cohort studies [ 8 ]. Notably, despite a relatively young mean age of just over 30 years in this cohort, a substantial multimorbidity burden was already present at baseline, and younger individuals (≤ 32 years) showed a faster decline in comorbidity-free status over time. This accelerated accumulation may reflect early onset of immunosenescence, chronic low-grade inflammation, and ART-related metabolic disturbances. It may also signal insufficient attention to routine screening and health maintenance in younger PLWH [ 28 , 29 ]. These findings underscore the need to prioritize early screening and tailored interventions for older males, while also emphasizing the importance of proactive chronic disease surveillance among younger populations. The contrasting risk profiles of metabolic versus infectious–non-infectious multimorbidity highlight important differences in etiology and management priorities. Metabolic multimorbidity was primarily associated with older age, overweight or obesity, elevated fasting glucose, and organ dysfunction, likely reflecting cumulative effects of ART toxicity, systemic inflammation, and metabolic derangements [ 30 , 31 ]. Interestingly, elevated HDL-C also conferred increased risk, echoing previous studies suggesting a potential U-shaped relationship with cardiovascular risk in PLWH [ 32 , 33 ]. In contrast, higher BMI appeared protective in the mixed-infectious model, possibly due to improved immune reconstitution in individuals with greater baseline nutritional reserves [ 8 , 34 ]. Furthermore, PLWH with a history of IDU faced substantially higher risks in both patterns, likely driven by higher rates of co-infections, lower ART adherence, and greater cumulative immune damage [ 35 – 38 ]. These findings support the use of risk stratification tools and differentiated care pathways—metabolic screening and lifestyle modification for at-risk individuals, and early immune recovery and infection control for those with high behavioral vulnerability. This study offers several important contributions. Its extended 8-year follow-up allowed for capturing the dynamic evolution of multimorbidity patterns, rather than relying solely on cross-sectional snapshots. The inclusion of both curable and incurable conditions provides a more nuanced view of disease trajectories, particularly in recognizing dual disease burdens that combine infections with chronic metabolic conditions. Several limitations should be acknowledged. First, the study was conducted at a single HIV treatment center, which may limit the generalizability of findings to other regions or healthcare settings. Second, certain confounders—such as detailed ART regimens, adherence data, and behavioral or psychosocial factors—were unavailable, potentially limiting the precision of risk estimates. Third, despite efforts to standardize diagnoses using laboratory data, underdiagnosis or underdocumentation of conditions such as mental health disorders and behavioral diseases may persist. Future multicenter studies with prospective designs and richer behavioral datasets are needed to validate and expand upon these findings. In conclusion, this study reveals a rising burden of multimorbidity among PLWH in the ART era, characterized by dynamic shifts from infectious conditions to metabolic and behavioral comorbidities. Distinct multimorbidity trajectories and risk profiles underscore the need for tailored prevention and management strategies, particularly among high-risk subgroups such as older males and those with a history of injection drug use. Moving forward, research should focus on elucidating the mechanisms underlying multimorbidity clustering, evaluating the effectiveness of early interventions, and building integrated care models that address both infectious and non-communicable diseases across the life course of PLWH. Declarations Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Contributions CL, XX, and LS contributed to the conceptualization of the study. CL, XX, YH, and FZ were responsible for methodology. Formal analysis and investigation were performed by CL, XX, YL, DZ, and YJ. The original draft was prepared by CL, XX, and LS, while review and editing were conducted by CL, XX, XL, TK, HL, KG, and JL. Resources were provided by LS, YH, FZ and HL. Funding acquisition was handled by HL and JL, who also supervised the project. All authors contributed to subsequent revisions and approved the final version of the manuscript. Ethics approval and consent to participate The study protocol adhered to the ethical principles outlined in the Declaration of Helsinki (1975 revision) and was approved by the Institutional Review Board of the Third People’s Hospital of Shenzhen (No. 2022-143). Written informed consent was obtained from all participants. Competing interests The authors declare that they have no competing interests. Acknowledgements The authors are deeply grateful to all participants and healthcare workers at the Third People’s Hospital of Shenzhen for their crucial contributions to this study. Funding This work was supported by the project of the Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515012118)，the Guangdong Provincial Medical Science and Technology Research Fund Project (No.A2025250), the Science and Technology Innovation Committee of Shenzhen Municipality (No. JCYJ20220531102202005), Shenzhen Clinical Research Center for Emerging Infectious Diseases (No. LCYSSQ20220823091203007), Shenzhen High-level Hospital Construction Fund (No. G2022153，XKJS-GRMYK-001, XKJS-GRMYK-002), and Sanming Project of Medicine in Shenzhen (SZSM202311033). References Smit M, Brinkman K, Geerlings S, et al. Changes in first-line cART regimens and short-term clinical outcome between 1996 and 2010 in The Netherlands. PLoS One. 2013;8(9):e76071. Marcus JL, Chao CR, Leyden WA, et al. Comparison of overall and comorbidity-free life expectancy between insured adults with and without HIV infection, 2000-2016. JAMA Netw Open. 2020;3(6):e207954. Samji H, Cescon A, Hogg RS, et al. 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Clusterization of co-morbidities and multi-morbidities among persons living with HIV: a cross-sectional study. BMC Infect Dis. 2019;19(1):555. Published 2019 Jun 25. Yang X, Zhang J, Chen S, Weissman S, Olatosi B, Li X. Comorbidity patterns among people living with HIV: a hierarchical clustering approach through integrated electronic health records data in South Carolina. AIDS Care. 2021;33(5):594-606. Chauvin M, Sauce D. Mechanisms of immune aging in HIV. Clin Sci (Lond). 2022;136(1):61-80. Wallis ZK, Williams KC. Monocytes in HIV and SIV Infection and Aging: Implications for Inflamm-Aging and Accelerated Aging. Viruses. 2022;14(2):409. Published 2022 Feb 17. Hudson P, Woudberg NJ, Kamau F, Strijdom H, Frias MA, Lecour S. HIV-related cardiovascular disease: any role for high-density lipoproteins?. Am J Physiol Heart Circ Physiol. 2020;319(6):H1221-H1226. Trimarco V, Izzo R, Morisco C, et al. High HDL (High-Density Lipoprotein) Cholesterol Increases Cardiovascular Risk in Hypertensive Patients. Hypertension. 2022;79(10):2355-2363. Wilding JPH, Batterham RL, Calanna S, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384(11):989-1002. Lesko CR, Moore RD, Tong W, Lau B. Association of injection drug use with incidence of HIV-associated non-AIDS-related morbidity by age, 1995-2014. AIDS. 2016;30(9):1447-1455. Hileman CO, McComsey GA. The Opioid Epidemic: Impact on Inflammation and Cardiovascular Disease Risk in HIV. Curr HIV/AIDS Rep. 2019;16(5):381-388. Althoff KN, Stewart C, Humes E, et al. The forecasted prevalence of comorbidities and multimorbidity in people with HIV in the United States through the year 2030: A modeling study. PLoS Med. 2024;21:e1004325. Safren SA, O'Cleirigh CM, Bullis JR, Otto MW, Stein MD, Pollack MH. Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: a randomized controlled trial. J Consult Clin Psychol. 2012;80(3):404-415. Additional Declarations No competing interests reported. Supplementary Files Highlight.docx graphicalabstract.png SupplementaryAppendix.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:27:16","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47420,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/e82f8d39f213bd8af309f643.png"},{"id":93681300,"identity":"941345f6-63a1-495b-a17e-6599a99cd620","added_by":"auto","created_at":"2025-10-16 12:11:16","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39759,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/befdd14439b6ff92813384de.png"},{"id":93681303,"identity":"d991d2c6-d2c0-400e-9493-f2ead8ed52c2","added_by":"auto","created_at":"2025-10-16 12:11:16","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155152,"visible":true,"origin":"","legend":"","description":"","filename":"827765c62d5e475c8b0683a71a15d92a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/199d6979d7affc29a699bd77.xml"},{"id":93681305,"identity":"c3c0c513-e902-4597-a534-b679d11f3430","added_by":"auto","created_at":"2025-10-16 12:11:16","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168783,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/8e5f673289120f2c01993b05.html"},{"id":93681461,"identity":"9ab2e9b7-0510-4771-b263-866c4f67ee10","added_by":"auto","created_at":"2025-10-16 12:19:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEvolution of PLWH across six multimorbidity patterns and dropout to follow-up during three time periods over an 8-year follow-up (N = 5,950).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe height of the boxes and the thickness of the stripes are proportional to the amount of PLWH belonging to the pattern and moving from the pattern, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/c4f2f3566f111c347d626fb9.png"},{"id":93681463,"identity":"5821170c-5ba6-47bf-9625-9fd7743a45cf","added_by":"auto","created_at":"2025-10-16 12:19:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of multimorbidity patterns by sex and age across different time periods.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Proportions of multimorbidity patterns in different time periods stratified by sex. (B) Proportions of multimorbidity patterns in different time periods stratified by age at diagnosis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/4543f367646fba108bce2514.png"},{"id":93681270,"identity":"530fc86f-bc84-4532-b663-91df2c52919e","added_by":"auto","created_at":"2025-10-16 12:11:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRetrospective Sankey diagrams illustrating the top three clusters’ transition for incurable multimorbidity patterns.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNumbers nearby the clusters represent different 26 predefined conditions.\u003c/p\u003e\n\u003cp\u003e(A) Transitions of Wave1’s top three clusters from baseline (n = 739). (B) Transitions of Wave2’s top three clusters from Wave1 (n = 916).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/e11b8068360a84984dffbdee.png"},{"id":93681272,"identity":"cb454c07-7dbe-486f-b1f1-a06184191dce","added_by":"auto","created_at":"2025-10-16 12:11:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRetrospective Sankey diagrams illustrating the top three clusters’ transition for mixed curable multimorbidity patterns.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNumbers nearby the clusters represent different 26 predefined conditions.\u003c/p\u003e\n\u003cp\u003e(A) Transitions of Wave1’s top three clusters from baseline (n = 363). (B) Transitions of Wave2’s top three clusters from Wave1 (n = 359).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/15aaba4a073f1fb8464ada6a.png"},{"id":93681274,"identity":"38807045-8519-4b56-93c4-d5adb705b36d","added_by":"auto","created_at":"2025-10-16 12:11:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":237733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of metabolic-related multimorbidity outcome derived from multivariate Cox regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; PLT, platelet count; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose; ALT, alanine aminotransferase; eGFR, estimated glomerular filtration rate.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/f8e3f61dc7ea8698e28c0e84.png"},{"id":93681464,"identity":"a203bf3d-c4cb-43a4-9ff4-01b501b4ec32","added_by":"auto","created_at":"2025-10-16 12:19:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":182338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of mixed infectious–non-infectious multimorbidity outcome derived from multivariate Cox regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; PLT, platelet count; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/931a4df470bd474646dcc805.png"},{"id":96913486,"identity":"f2d1f5af-8977-4ddd-beef-74fef90d0906","added_by":"auto","created_at":"2025-11-27 14:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2086690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/ada71335-7a70-4351-b9ca-d51c47272602.pdf"},{"id":93681268,"identity":"38e0fdec-4cb3-4585-b380-f31669ec19fd","added_by":"auto","created_at":"2025-10-16 12:11:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10615,"visible":true,"origin":"","legend":"","description":"","filename":"Highlight.docx","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/d0cc37d6ce64d94390e1b06f.docx"},{"id":93681887,"identity":"4f4003d5-0a7d-43b4-b563-f10f6d13c111","added_by":"auto","created_at":"2025-10-16 12:27:16","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":278625,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/6a360a96a28b79cabae19f4f.png"},{"id":93681277,"identity":"b83a9eee-b83d-4067-a122-bc621b3b4591","added_by":"auto","created_at":"2025-10-16 12:11:15","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":431122,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7498811/v1/74e7371ecde5c081ad4f1c00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Divergent trajectories and risk profiles of multimorbidity among people living with HIV in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the widespread adoption and ongoing optimization of antiretroviral therapy (ART), the life expectancy of people living with HIV (PLWH) has markedly increased [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, this success has introduced new challenges. PLWH are now developing chronic comorbidities at younger ages than their HIV-negative counterparts, making multimorbidity a growing concern [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multimorbidity, defined as the simultaneous or sequential presence of two or more health conditions within a giving timeframe [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], is of particular relevance in the context of HIV. Among PLWH, multimorbidity refers to the presence of at least two conditions other than HIV infection itself. Due to the combined effects of chronic HIV-related inflammation, immune dysregulation, and ART-realted toxicities, multimorbidity patterns in PLWH differ substantially from those seen in the general population.\u003c/p\u003e\u003cp\u003eIn addition to an increased risk of non-communicable diseases (NCDs)\u0026mdash;such as hypertension, diabetes, and dyslipidemia\u0026mdash;PLWH also face a persistently high burden of chronic or recurrent infections, including hepatitis B, syphilis, and various opportunistic infections. These arise from ongoing immune dysfunction, high-risk behaviors, and complex interactions between pathogen latency, immune activation, and systemic metabolic dysregulation. Unlike the general population, in whom multimorbidity typically occurs at older ages, PLWH often experience multiple comorbidities at a younger age [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the prevalence of multimorbidity rises steadily with age [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], underscoring the importance of investigating multimorbidity not only older but also younger PLWH.\u003c/p\u003e\u003cp\u003eAlthough several large cohort studies have provided valuable insights into the burden and risk factors of multimorbidity among PLWH [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], much of the literature remains cross-sectional, focusing on a limited number of conditions. Even longitudinal studies have typically examined incidence and prevalence of comorbidities without capturing their long-term evolution over time [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As HIV care increasingly shifts from a focus on viral suppression to lifelong chronic disease management, a deeper understanding of multimorbidity dynamics is urgently needed.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to characterize the longitudinal evolution of multimorbidity patterns over an 8-year period in a cohort of PLWH in Shenzhen, China. We further identified the most prevalent and clinically significant multimorbidity clusters, described their temporal trajectories, and examined risk factors associated with the development of metabolic-related and mixed infectious\u0026ndash;non-infectious multimorbidity patterns.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study to investigate the longitudinal evolution of multimorbidity patterns among PLWH initiating ART in Shenzhen, China. Participants were eligible for inclusion if they had a confirmed HIV diagnosis, initiated ART between January 1, 2009, and June 30, 2016, and had available follow-up data through June 30, 2024, allowing for a minimum of 8 years of observation. We excluded individuals who lacked documented ART initiation dates and had fewer than three recorded diagnostic encounters during the follow-up period, in order to reduce potential bias due to insufficient clinical information.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDisease ascertainment and classification\u003c/h3\u003e\n\u003cp\u003eComorbid conditions were identified primarily through ICD-10 diagnostic codes and clinician-documented diagnoses recorded in the electronic medical record system. A total of 26 chronic conditions were selected for inclusion in the analysis. These conditions were chosen based on their prevalence in the general population and clinical relevance among PLWH. The selected conditions included: (1) Hypertension, (2) Diabetes mellitus, (3) Dyslipidemia, (4) Cardiovascular disease (CVD), (5) Chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis, (6) Esophageal, gastric, or duodenal diseases, (7) Hepatitis B (HBV), (8) Hepatitis C (HCV), (9) Chronic kidney disease (CKD), (10) Non-AIDS-related cancers, (11) Osteoporosis, (12) Mental health disorders, (13) Sleep disorders, (14) Syphilis, (15) Human papillomavirus (HPV) infection, (16) Chronic liver disease (CLD) excluding viral hepatitis, (17) AIDS-defining cancers, (18) HIV-associated encephalopathy, (19) Pneumocystis jirovecii pneumonia (PJP), (20) Nontuberculous mycobacterial infections, (21) Cytomegalovirus (CMV) infection, (22) Herpes simplex virus (HSV) infection, (23) Varicella-zoster virus (VZV) infection, (24) Toxoplasmosis, (25) Fungal infections, (26) Tuberculosis (TB). Full details on disease definitions and classification are available in the Supplementary Appendix.\u003c/p\u003e\n\u003ch3\u003eFollow-up and multimorbidity patterns evolution\u003c/h3\u003e\n\u003cp\u003eART initiation was defined as the study baseline for describing participant demographics and clinical characteristics. To ensure stabilization after ART initiation and reduce potential misclassification of early-onset conditions, outcome follow-up began at three months post-ART. All multimorbidity assessments and transitions were measured from this point onward. The total follow-up duration was eight years, divided into three time periods: baseline, Wave1 (from 3-month to 4-year follow-up), and Wave2 (from 4-year to 8-year follow-up).\u003c/p\u003e\u003cp\u003eMultimorbidity patterns were categorized into six mutually exclusive modules based on disease count and curability: incurable multimorbidity, curable multimorbidity, mixed curable multimorbidity, single incurable disease, single curable disease, and no disease. Follow-up was conducted at approximately three-month intervals. Participants were classified as dropouts if none of the 26 conditions were recorded during a follow-up wave and if no medical records were available for more than six consecutive months prior to the end of that wave. Curable conditions were not expected to persist throughout the study period. In contrast, incurable conditions were recorded at the first time and were assumed to persist for the remainder of the follow-up.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eWe extracted key covariates from the hospital records and laboratory results. Demographic variables included age group, sex, and marital status. Laboratory parameters included HDL-C, LDL-C, fasting glucose (FPG), platelet count (PLT), alanine aminotransferase (ALT), and estimated glomerular filtration rate (eGFR), all obtained via standardized clinical protocols. HIV-related factors comprised baseline CD4 count, HIV transmission route, and the presence of opportunistic infections at baseline. Behavioral characteristics included smoking and alcohol use.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed using R version 4.4.1 and RStudio version 2024.12.0. Descriptive statistics were used to summarize participant characteristics and the distribution of multimorbidity modules across waves.\u003c/p\u003e\u003cp\u003eSankey diagrams were generated using the ggsankey, ggplot2, and dplyr packages in R to visualize transitions in multimorbidity patterns over time. Retrospective Sankey plots were also constructed to trace prior disease profiles among individuals who developed incurable or mixed multimorbidity patterns.\u003c/p\u003e\u003cp\u003eStacked bar charts were drawn to compare the distribution of multimorbidity patterns by sex and age groups across waves. Differences between subgroups were assessed using Chi-square or Fisher\u0026rsquo;s exact tests.\u003c/p\u003e\u003cp\u003eTwo clinically relevant patterns\u0026mdash;metabolic and mixed infectious\u0026ndash;non-infectious multimorbidity\u0026mdash;were modeled due to their prevalence and clinical relevance. Cox regression assessed risk factors for metabolic multimorbidity, and logistic regression was used for the mixed pattern. Results were reported as HRs or ORs with 95% CIs, adjusting for covariates. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e\n \u003cp\u003eA total of 5,950 participants were included in the final analysis. At enrollment, 25.9% of participants had multimorbidity (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with those without multimorbidity, participants with multimorbidity were older (median age: 35.0 vs. 31.0 years; interquartile range [IQR]: 29\u0026ndash;43 vs. 27\u0026ndash;38 years; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more likely to be male (90.5% vs. 85.6%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences in body mass index (BMI) were observed between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), with a higher proportion of underweight individuals among those with multimorbidity. Marital status also showed a significant difference between groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a higher proportion of divorced, separated, or widowed individuals in the multimorbidity group (12.3% vs. 8.0%). Unhealthy lifestyle behaviors, including smoking and alcohol use, were more common in the multimorbidity group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045, respectively). The prevalence of hypertension, diabetes, and hypercholesterolemia was significantly higher among participants with multimorbidity (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, these participants had lower baseline CD4 cell counts, higher HIV RNA levels, and a greater prevalence of HBV or HCV co-infection (all\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of study participants according to multimorbidity status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo multimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.0 (27.0\u0026ndash;39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.0 (27.0\u0026ndash;38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0 (29.0\u0026ndash;43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5171 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3778 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1393 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e633 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e930 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e647 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4039 (67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3052 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e987 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e981 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3181 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2441 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e740 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried or cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2229 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1619 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e610 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced, separated, or widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV transmission route\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale-to-male sex contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3518 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2629 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e889 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeterosexual contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2201 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1619 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e582 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1292 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e918 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1409 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1015 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypercholesterolemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4056 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2645 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1411 (91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.3 (84.7\u0026ndash;96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.9 (84.7\u0026ndash;95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.8 (84.9\u0026ndash;98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-density lipoprotein cholesterol, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.7 (41.4\u0026ndash;56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.11 (41.8\u0026ndash;56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.0 (40.0\u0026ndash;55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-density lipoprotein cholesterol, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.0 (78.5\u0026ndash;111.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.19 (78.5\u0026ndash;111.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.9 (79.7\u0026ndash;114.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.3 (80.6\u0026ndash;167.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.4 (76.2\u0026ndash;156.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.5 (93.9\u0026ndash;193.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.0 (141.5\u0026ndash;185.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.3 (141.9\u0026ndash;184.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.6 (141.2\u0026ndash;188.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyG index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5 (8.2\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5 (8.1\u0026ndash;8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8 (8.4\u0026ndash;9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (0.7\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (0.7\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (0.7\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspartate Aminotransferase, U/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5422 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4113 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1309 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e244 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlanine Aminotransferase, U/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5093 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3865 (87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1228 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e646 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC, 10^9/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2 (4.3\u0026ndash;6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2 (4.3\u0026ndash;6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2 (4.2\u0026ndash;6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet, 10^9/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.0 (167.0\u0026ndash;240.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.0 (168.0\u0026ndash;237.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.0 (164.0\u0026ndash;249.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBV infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCV infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline CD4 count, cells/\u0026micro;L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2137 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1437 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u0026ndash;349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2484 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1960 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e524 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u0026ndash;499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1006 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e768 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV RNA, copies/mL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e524 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e433 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000\u0026ndash;99999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3538 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2454 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1084 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;100000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1888 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1524 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eContinuous variables are presented as median (IQR: 25th\u0026ndash;75th percentile) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables are presented as frequency (%) and compared using Pearson\u0026rsquo;s \u0026chi;\u0026sup2; test. BMI, body-mass index; HIV, human immunodeficiency virus; IDU, injection drug use; TyG index, triglyceride-glucose index (calculated as ln [fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL)\u0026thinsp;\u0026divide;\u0026thinsp;2]); ULN, upper limit of normal; ALT ULN: 30 U/L (male), 19 U/L (female); AST ULN: 35 U/L (male), 31 U/L (female); WBC, white blood cells; HBV, hepatitis B virus; HCV, hepatitis C virus\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvolution of multimorbidity patterns during 8-Year follow-up in PLWH\u003c/h3\u003e\n\u003cp\u003eOver the 8-year follow-up period, the distribution of multimorbidity patterns among PLWH exhibited notable temporal shifts. At baseline, most participants were classified as having either no comorbidity (24.61%) or a single incurable condition (46.03%) among the 5,950 individuals included. The overall prevalence of multimorbidity\u0026mdash;including incurable, curable, and mixed patterns\u0026mdash;increased from 25.87% at baseline to 42.49% by Wave2 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S4). The most substantial increase was observed in the incurable multimorbidity pattern, which rose from 7.66% at baseline to 18.82% in Wave1, and further to 25.50% by Wave2\u0026mdash;representing a more than threefold increase. The mixed multimorbidity pattern, characterized by the coexistence of both curable and incurable conditions (e.g., syphilis or HPV alongside dyslipidemia or diabetes), remained relatively stable over time, consistently affecting approximately 1,000 participants (16\u0026ndash;18% of the cohort). In contrast, the curable multimorbidity and single curable disease patterns steadily declined, with their combined prevalence decreasing from 4.27% to just 0.3%.\u003c/p\u003e\n\u003cp\u003eAmong the 1,464 participants who were free of all 26 predefined conditions at baseline, 528 (36.07%) developed multimorbidity during the follow-up period. Of these, 277 transitioned during Wave1, and an additional 251 during Wave2.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDistribution of multimorbidity patterns by sex and age\u003c/h2\u003e\n \u003cp\u003eIn the sex-stratified analysis, females consistently exhibited a lower prevalence of multimorbidity across all waves compared to males. The proportion of participants without any comorbidity declined markedly in both sexes, with a sharper decrease observed in females (from 34.27\u0026ndash;4.36%) than in males (from 23.15\u0026ndash;3.06%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for both). In contrast, males showed a greater increase in incurable multimorbidity over time, rising from 7.95\u0026ndash;25.91%, compared with an increase from 5.78\u0026ndash;22.72% in females (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for both). Meanwhile, the mixed multimorbidity pattern remained relatively stable in males (18.14\u0026ndash;17.95%) but declined modestly in females (12.71\u0026ndash;10.14%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, Tables S6\u0026ndash;S7).\u003c/p\u003e\n \u003cp\u003eAge-stratified analysis showed that individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;32 years consistently had a higher burden of multimorbidity across all waves (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, Table S8). At baseline, 31.36% of older participants had multimorbidity compared to 20.70% in those aged\u0026thinsp;\u0026le;\u0026thinsp;32 years (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). As follow-up progressed, multimorbidity increased significantly in both groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Tables S9\u0026ndash;S10), mainly driven by the rise in incurable multimorbidity. By Wave2, the prevalence of incurable multimorbidity reached 32.56% in the older group and 19.90% in the younger group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). While the older group initially had a higher prevalence of mixed multimorbidity (20.29% vs. 14.74%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), this difference diminished over time, and by Wave2 the proportions had converged (16.20% vs. 17.61%; P\u0026thinsp;=\u0026thinsp;0.16). The proportion of participants with no comorbidity declined in both age groups, with a more substantial reduction among younger participants (from 28.66\u0026ndash;4.37%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEvolution and origins of top incurable and mixed curable multimorbidity patterns over time\u003c/h2\u003e\n \u003cp\u003eIn the top three incurable multimorbidity clusters, most individuals maintained the same disease combinations through follow-up, reflecting considerable structural stability. Specifically, individuals classified under the \u0026ldquo;diabetes\u0026ndash;dyslipidemia,\u0026rdquo; \u0026ldquo;dyslipidemia\u0026ndash;HBV,\u0026rdquo; or \u0026ldquo;dyslipidemia\u0026ndash;non-viral CLD\u0026rdquo; at baseline, including those with coexisting curable diseases, largely remained within the same top three incurable disease clusters in Wave1. Within these groups, 18.00%, 37.21%, and 46.00% of individuals, respectively, had coexisting curable diseases, primarily infections. Among those with curable diseases at baseline, 22.22%, 31.25%, and 53.97% had two or more curable conditions, respectively. By Wave 2, among individuals who remained in the same incurable multimorbidity clusters, the proportions with coexisting curable diseases had declined to 15.07%, 20.10%, and 27.86%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the mixed curable pattern, more variation was observed. \u0026ldquo;Dyslipidemia\u0026ndash;syphilis\u0026rdquo; remained the most common across waves (18.71%, 16.51%, and 19.17%), while \u0026ldquo;dyslipidemia\u0026ndash;HPV\u0026rdquo; declined from 11.13% in Wave 1 to 4.47% in Wave 2. In contrast, \u0026ldquo;dyslipidemia\u0026ndash;sleep disorders\u0026rdquo; increased to 12.02% in Wave 2, becoming the second most frequent combination (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of the top three incurable and mixed curable multimorbidity patterns\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTime period\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIncurable multimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMixed curable multimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePattern\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epattern\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion, %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes \u0026amp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Syphilis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Other CLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline to 4-year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes \u0026amp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Syphilis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Other CLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Sleep Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-year to 8-year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes \u0026amp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Syphilis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Sleep Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; Other CLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia \u0026amp; HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eHBV, hepatitis B virus; CLD, chronic liver disease; HPV, human papillomavirus; TB, tuberculosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDistinct risk factors for metabolic versus infectious\u0026ndash;non-infectious multimorbidity pattern\u003c/h2\u003e\n \u003cp\u003eIn the metabolic multimorbidity model (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), older age was a strong predictor: individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;46 had a 3.44-fold increased risk compared to those\u0026thinsp;\u0026le;\u0026thinsp;25 (HR\u0026thinsp;=\u0026thinsp;3.44, 95% CI: 2.61\u0026ndash;4.52, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Overweight or obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;24) was associated with higher risk (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.46\u0026ndash;1.95, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as was male sex (HR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.13\u0026ndash;1.76, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Elevated HDL-C (\u0026ge;\u0026thinsp;1.0 mmol/L) and FPG (\u0026ge;\u0026thinsp;6.1 mmol/L) were also significant (HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.03\u0026ndash;1.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; HR\u0026thinsp;=\u0026thinsp;2.63, 95% CI: 2.09\u0026ndash;3.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). ALT\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026times; ULN and \u0026ge;\u0026thinsp;2\u0026times; ULN increased the risk by 50% and 47%, respectively (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while reduced eGFR (\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;) was associated with higher risk (HR\u0026thinsp;=\u0026thinsp;2.50, 95% CI: 1.39\u0026ndash;4.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Compared to MSM, those with IDU had elevated risk (HR\u0026thinsp;=\u0026thinsp;1.82, 95% CI: 1.31\u0026ndash;2.52, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Baseline CD4 count, smoking, and alcohol use were not significantly associated.\u003c/p\u003e\n \u003cp\u003eIn the mixed infectious\u0026ndash;non-infectious model (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), male sex (OR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.48\u0026ndash;2.43, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and IDU (OR\u0026thinsp;=\u0026thinsp;2.00, 95% CI: 1.12\u0026ndash;3.55, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were strong risk factors. In contrast, BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 was protective (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.58\u0026ndash;0.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as was being married (OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.63\u0026ndash;0.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). CD4\u0026thinsp;\u0026le;\u0026thinsp;100 cells/\u0026micro;L (OR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.07\u0026ndash;1.60, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), PLT\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026times;10⁹/L (OR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.14\u0026ndash;2.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a history of opportunistic infections (OR\u0026thinsp;=\u0026thinsp;2.16, 95% CI: 1.73\u0026ndash;2.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were all independently associated. Alcohol consumption was modestly associated (OR\u0026thinsp;=\u0026thinsp;1.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while age, HDL-C, and eGFR were not significant in this model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents an 8-year longitudinal cohort analysis examining the evolution and risk factors of multimorbidity among PLWH in Shenzhen, China. Over the follow-up period, we observed a significant increase in multimorbidity prevalence, notably, the prevalence of incurable multimorbidity nearly tripled and became the dominant pattern. Although males consistently exhibited higher multimorbidity burdens, females experienced a faster rate of increase, and younger individuals (\u0026le;\u0026thinsp;32 years) showed steeper rises despite lower baseline burdens. Most multimorbidity patterns involved combinations of non-communicable diseases with curable infectious conditions, highlighting a dual burden of disease. Additionally, distinct risk profiles were identified for metabolic versus combined infectious\u0026ndash;non-infectious multimorbidity patterns. These findings underscore the necessity of continuous monitoring and targeted interventions tailored to specific multimorbidity trajectories, emphasizing the importance of personalized chronic disease management for PLWH.\u003c/p\u003e\u003cp\u003eThe sharp increase in multimorbidity, particularly incurable patterns, reflects a shifting disease landscape in the ART era. While the incidence of infectious comorbidities declined with prolonged ART, the burden of non-AIDS-related chronic conditions\u0026mdash;such as dyslipidemia, diabetes, and non-viral chronic liver disease\u0026mdash;increased substantially, in line with previous findings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This transition may be driven by population aging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], chronic immune activation [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], HIV-induced mitochondrial dysfunction [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], gut microbiota dysbiosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and long-term ART-related toxicities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Dyslipidemia emerged as a key factor in both incurable and mixed-curability patterns, likely due to HIV-related immune activation, ART-induced metabolic effects, and its impact on cardiovascular, liver, and glucose metabolism [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Its frequent co-occurrence with both metabolic and infectious comorbidities underscores its potential as an early marker and modifiable entry point for multimorbidity surveillance and prevention. Collectively, these shifts call for integrated models of HIV care that anticipate evolving chronic disease risks and adapt accordingly across the treatment continuum.\u003c/p\u003e\u003cp\u003eSubgroup analyses revealed sex- and age-related disparities in multimorbidity trends. Although men consistently bore a greater burden of multimorbidity, women experienced a steeper increase over time. This may reflect sex-based immunological differences as well as gender-specific barriers to health service access, delayed diagnosis, or reduced screening uptake [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Age was another consistent predictor of multimorbidity; older PLWH had significantly greater burdens across all patterns, corroborating prior cohort studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notably, despite a relatively young mean age of just over 30 years in this cohort, a substantial multimorbidity burden was already present at baseline, and younger individuals (\u0026le;\u0026thinsp;32 years) showed a faster decline in comorbidity-free status over time. This accelerated accumulation may reflect early onset of immunosenescence, chronic low-grade inflammation, and ART-related metabolic disturbances. It may also signal insufficient attention to routine screening and health maintenance in younger PLWH [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings underscore the need to prioritize early screening and tailored interventions for older males, while also emphasizing the importance of proactive chronic disease surveillance among younger populations.\u003c/p\u003e\u003cp\u003eThe contrasting risk profiles of metabolic versus infectious\u0026ndash;non-infectious multimorbidity highlight important differences in etiology and management priorities. Metabolic multimorbidity was primarily associated with older age, overweight or obesity, elevated fasting glucose, and organ dysfunction, likely reflecting cumulative effects of ART toxicity, systemic inflammation, and metabolic derangements [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interestingly, elevated HDL-C also conferred increased risk, echoing previous studies suggesting a potential U-shaped relationship with cardiovascular risk in PLWH [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, higher BMI appeared protective in the mixed-infectious model, possibly due to improved immune reconstitution in individuals with greater baseline nutritional reserves [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, PLWH with a history of IDU faced substantially higher risks in both patterns, likely driven by higher rates of co-infections, lower ART adherence, and greater cumulative immune damage [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings support the use of risk stratification tools and differentiated care pathways\u0026mdash;metabolic screening and lifestyle modification for at-risk individuals, and early immune recovery and infection control for those with high behavioral vulnerability.\u003c/p\u003e\u003cp\u003eThis study offers several important contributions. Its extended 8-year follow-up allowed for capturing the dynamic evolution of multimorbidity patterns, rather than relying solely on cross-sectional snapshots. The inclusion of both curable and incurable conditions provides a more nuanced view of disease trajectories, particularly in recognizing dual disease burdens that combine infections with chronic metabolic conditions.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the study was conducted at a single HIV treatment center, which may limit the generalizability of findings to other regions or healthcare settings. Second, certain confounders\u0026mdash;such as detailed ART regimens, adherence data, and behavioral or psychosocial factors\u0026mdash;were unavailable, potentially limiting the precision of risk estimates. Third, despite efforts to standardize diagnoses using laboratory data, underdiagnosis or underdocumentation of conditions such as mental health disorders and behavioral diseases may persist. Future multicenter studies with prospective designs and richer behavioral datasets are needed to validate and expand upon these findings.\u003c/p\u003e\u003cp\u003eIn conclusion, this study reveals a rising burden of multimorbidity among PLWH in the ART era, characterized by dynamic shifts from infectious conditions to metabolic and behavioral comorbidities. Distinct multimorbidity trajectories and risk profiles underscore the need for tailored prevention and management strategies, particularly among high-risk subgroups such as older males and those with a history of injection drug use. Moving forward, research should focus on elucidating the mechanisms underlying multimorbidity clustering, evaluating the effectiveness of early interventions, and building integrated care models that address both infectious and non-communicable diseases across the life course of PLWH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCL, XX, and LS contributed to the conceptualization of the study. CL, XX, YH, and FZ were responsible for methodology. Formal analysis and investigation were performed by CL, XX, YL, DZ, and YJ. The original draft was prepared by CL, XX, and LS, while review and editing were conducted by CL, XX, XL, TK, HL, KG, and JL. Resources were provided by LS, YH, FZ and HL. Funding acquisition was handled by HL and JL, who also supervised the project. All authors contributed to subsequent revisions and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol adhered to the ethical principles outlined in the Declaration of Helsinki (1975 revision) and was approved by the Institutional Review Board of the Third People’s Hospital of Shenzhen (No. 2022-143). Written informed consent was obtained from all participants.\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\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are deeply grateful to all participants and healthcare workers at the Third People’s Hospital of Shenzhen for their crucial contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the project of the Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515012118)，the Guangdong Provincial Medical Science and Technology Research Fund Project (No.A2025250), the Science and Technology Innovation Committee of Shenzhen Municipality (No. JCYJ20220531102202005), \u0026nbsp; Shenzhen Clinical Research Center for Emerging Infectious Diseases (No. LCYSSQ20220823091203007), Shenzhen High-level Hospital Construction Fund (No. G2022153，XKJS-GRMYK-001, XKJS-GRMYK-002), and Sanming Project of Medicine in Shenzhen (SZSM202311033).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmit M, Brinkman K, Geerlings S, et al. 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London: The Academy of Medical Sciences; 2018. https://acmedsci.ac.uk/policy/policy-projects/multimorbidity. Accessed 8 June 2025.\u003c/li\u003e\n\u003cli\u003eNanditha, N. G. A., Paiero, A., Tafessu, H. M., et al. Excess burden of age-associated comorbidities among people living with HIV in British Columbia, Canada: a population-based cohort study. BMJ open.2021;11(1), e041734.\u003c/li\u003e\n\u003cli\u003eGuaraldi G, Orlando G, Zona S, et al. Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis. 2011;53(11):1120\u0026ndash;1126.\u003c/li\u003e\n\u003cli\u003eWong C, Gange SJ, Moore RD, et al. Multimorbidity among persons living with human immunodeficiency virus in the United States. Clin Infect Dis. 2018;66(8):1230\u0026ndash;1238.\u003c/li\u003e\n\u003cli\u003eHasse B, Ledergerber B, Furrer H, et al. Morbidity and aging in HIV-infected persons: The Swiss HIV Cohort Study. 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Comparison of metabolic alterations, hepatic and cardiovascular damage between primary NAFLD and HIV-associated NAFLD: Role of low visceral adiposity. Dig Liver Dis. 2023;55(3):365\u0026ndash;372.\u003c/li\u003e\n\u003cli\u003eNakaranurack C, Manosuthi W. Prevalence of non-AIDS comorbidities and factors associated with metabolic complications among HIV-infected patients at a Thai referral hospital. J Int Assoc Provid AIDS Care. 2018;17:2325957417752256.\u003c/li\u003e\n\u003cli\u003eBastard JP, Couffignal C, Fellahi S, et al. Diabetes and dyslipidaemia are associated with oxidative stress independently of inflammation in long-term antiretroviral-treated HIV-infected patients. Diabetes Metab. 2019;45(6):594\u0026ndash;602.\u003c/li\u003e\n\u003cli\u003eDe Francesco D, Verboeket S, Underwood J, et al. Patterns of co-occurring comorbidities in people living with HIV. Open Forum Infect Dis. 2018;5(11):ofy272.\u003c/li\u003e\n\u003cli\u003eMazzitelli M, Fusco P, Brogna M, et al. 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HIV protein Nef causes dyslipidemia and formation of foam cells in mouse models of atherosclerosis. FASEB J. 2014;28(7):2828-2839.\u003c/li\u003e\n\u003cli\u003eKaur S U, Oyeyemi BF, Shet A, et al. Plasma metabolomic study in perinatally HIV-infected children using 1H NMR spectroscopy reveals perturbed metabolites that sustain during therapy. PLoS One. 2020;15(8):e0238316. Published 2020 Aug 31.\u003c/li\u003e\n\u003cli\u003eGiannarelli C, Klein RS, Badimon JJ. Cardiovascular implications of HIV-induced dyslipidemia. Atherosclerosis. 2011;219(2):384-389.\u003c/li\u003e\n\u003cli\u003eCurrier JS, Lundgren JD, Carr A, et al. Epidemiological evidence for cardiovascular disease in HIV-infected patients and relationship to highly active antiretroviral therapy. Circulation. 2008;118(2):e29\u0026ndash;e35.\u003c/li\u003e\n\u003cli\u003eHan WM, Apornpong T, Lwin HMS, et al. Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis With Liver Fibrosis as Predictors of New-Onset Diabetes Mellitus in People With HIV: A Longitudinal Cohort Study. Clin Infect Dis. 2023;77(12):1687-1695.\u003c/li\u003e\n\u003cli\u003eGuaraldi G, Malagoli A, Calcagno A, et al. The increasing burden and complexity of multi-morbidity and polypharmacy in geriatric HIV patients: a cross sectional study of people aged 65 - 74 years and more than 75 years. BMC Geriatr. 2018;18(1):99. Published 2018 Apr 20.\u003c/li\u003e\n\u003cli\u003eMaggi P, Santoro CR, Nofri M, et al. Clusterization of co-morbidities and multi-morbidities among persons living with HIV: a cross-sectional study. BMC Infect Dis. 2019;19(1):555. Published 2019 Jun 25.\u003c/li\u003e\n\u003cli\u003eYang X, Zhang J, Chen S, Weissman S, Olatosi B, Li X. Comorbidity patterns among people living with HIV: a hierarchical clustering approach through integrated electronic health records data in South Carolina. AIDS Care. 2021;33(5):594-606.\u003c/li\u003e\n\u003cli\u003eChauvin M, Sauce D. Mechanisms of immune aging in HIV. Clin Sci (Lond). 2022;136(1):61-80.\u003c/li\u003e\n\u003cli\u003eWallis ZK, Williams KC. Monocytes in HIV and SIV Infection and Aging: Implications for Inflamm-Aging and Accelerated Aging. Viruses. 2022;14(2):409. Published 2022 Feb 17.\u003c/li\u003e\n\u003cli\u003eHudson P, Woudberg NJ, Kamau F, Strijdom H, Frias MA, Lecour S. HIV-related cardiovascular disease: any role for high-density lipoproteins?. Am J Physiol Heart Circ Physiol. 2020;319(6):H1221-H1226.\u003c/li\u003e\n\u003cli\u003eTrimarco V, Izzo R, Morisco C, et al. High HDL (High-Density Lipoprotein) Cholesterol Increases Cardiovascular Risk in Hypertensive Patients. Hypertension. 2022;79(10):2355-2363.\u003c/li\u003e\n\u003cli\u003eWilding JPH, Batterham RL, Calanna S, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384(11):989-1002.\u003c/li\u003e\n\u003cli\u003eLesko CR, Moore RD, Tong W, Lau B. Association of injection drug use with incidence of HIV-associated non-AIDS-related morbidity by age, 1995-2014. AIDS. 2016;30(9):1447-1455.\u003c/li\u003e\n\u003cli\u003eHileman CO, McComsey GA. The Opioid Epidemic: Impact on Inflammation and Cardiovascular Disease Risk in HIV. Curr HIV/AIDS Rep. 2019;16(5):381-388.\u003c/li\u003e\n\u003cli\u003eAlthoff KN, Stewart C, Humes E, et al. The forecasted prevalence of comorbidities and multimorbidity in people with HIV in the United States through the year 2030: A modeling study. PLoS Med. 2024;21:e1004325.\u003c/li\u003e\n\u003cli\u003eSafren SA, O\u0026apos;Cleirigh CM, Bullis JR, Otto MW, Stein MD, Pollack MH. Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: a randomized controlled trial. J Consult Clin Psychol. 2012;80(3):404-415.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PLWH, HIV, multimorbidity, comorbidity, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7498811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7498811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWith increased longevity among people living with HIV (PLWH), multimorbidity has emerged as a critical challenge, occurring at younger ages and involving both non-communicable diseases and persistent infections. Yet its long-term evolution and risk factors remain insufficiently described.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a retrospective cohort study of 5,950 PLWH initiating antiretroviral therapy (ART) in Shenzhen, China, between 2009 and 2016, with follow-up through 2024. Multimorbidity was defined as ≥2 predefined 26 conditions and categorized into six evolving patterns. Longitudinal changes were assessed using Sankey plots and multivariable regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAt ART initiation, 25.9% had multimorbidity. Over 8 years, the overall prevalence rose to 42.5%, driven mainly by a threefold increase in incurable multimorbidity (7.7% to 25.5%). Nearly 36% of initially disease-free individuals developed multimorbidity during follow-up. Younger PLWH experienced the faster decline in comorbidity-free status over time, while older individuals more frequently progressed to multimorbidity. Dyslipidemia was the most frequent and persistent condition. Risk factors for metabolic multimorbidity included age at diagnosis ≥ 46 years (HR = 3.44, P \u0026lt; 0.01), body mass index (BMI) ≥ 24 (HR=1.69, P \u0026lt; 0.01), etc. In contrast, higher BMI appeared protective against mixed infectious–non-infectious patterns (HR = 0.72, P \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMultimorbidity is increasingly prevalent among PLWH in the ART era, with a shift from infectious to metabolic and behavioral comorbidities. Distinct risk profiles highlight the need for early identification and tailored interventions, especially among high-risk subgroups. Early identification and tailored management of key risk factors, particularly dyslipidemia, are essential for integrated HIV care.\u003c/p\u003e","manuscriptTitle":"Divergent trajectories and risk profiles of multimorbidity among people living with HIV in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 12:11:10","doi":"10.21203/rs.3.rs-7498811/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc800027-961d-45ad-b3f0-88cc07187d22","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T14:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 12:11:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7498811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7498811","identity":"rs-7498811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}