Abstract
Objectives
Antiretroviral therapy (ART) has substantially improved survival among people living with human immunodeficiency virus (HIV), resulting in an increasing need to manage age-related chronic diseases. This study examined patterns of multimorbidity clusters and their associated factors among people living with HIV using data from the Korean HIV/AIDS Cohort.
Methods
We analyzed data from 1,606 participants enrolled in the Korean HIV/AIDS Cohort from 2006 to 2023, excluding terminated cases. Participants were categorized as having no morbidity, a single morbidity, or multimorbidity. Variable cluster analysis was performed to identify disease patterns, and multinomial logistic regression was used to identify factors associated with multimorbidity.
Results
Multimorbidity was present in 29.8% of participants, and five disease clusters were identified. Cluster 1 included osteoporosis, cerebral hemorrhage, peripheral vascular disease, and dialysis; Cluster 2 included hepatitis B, hepatitis C, liver cirrhosis, and cancer; Cluster 3 included metabolic syndrome, dyslipidemia, and fatty liver disease; Cluster 4 included hypertension, cerebral infarction, and diabetes mellitus; and Cluster 5 included nongonococcal urethritis, gonorrhea, and condyloma. Cluster 5 was more prevalent among participants aged ≤29 years and declined with advancing age, whereas Clusters 3 and 4 increased with age. Older age at HIV diagnosis, a longer duration since diagnosis, and higher HIV RNA levels were significantly associated with multimorbidity. Initiating ART within 31–90 days after diagnosis was associated with reduced odds of multimorbidity, although baseline differences in ART timing may have contributed to this finding.
Conclusions
Multimorbidity patterns shifted from infection-centered to metabolic and cardio-cerebrovascular disease-centered patterns with advancing age. Integrated management strategies that account for multimorbidity cluster characteristics are essential for people living with HIV.
Keywords: HIV, Multimorbidity, Chronic disease, Cohort studies, Cluster analysis
Key messages
① What is known previously?
As people living with human immunodeficiency virus (HIV) age and achieve longer survival, the burden of chronic diseases—particularly metabolic and cardiovascular diseases—has increased. However, studies evaluating multimorbidity clustering patterns among Korean people living with HIV remain limited.
② What new information is presented?
Approximately 30% of participants in the Korean HIV/AIDS Cohort presented with multimorbidity. With increasing age, the prevalence of Cluster 5 (nongonococcal urethritis, gonorrhea, and condyloma) decreased, whereas the prevalence of Cluster 3 (metabolic syndrome, dyslipidemia, and fatty liver disease) and Cluster 4 (hypertension, cerebral infarction, and diabetes mellitus) increased.
③ What are the implications?
Healthcare strategies for people living with HIV must extend beyond infection-focused care toward integrated chronic disease management that addresses multimorbidity.
Introduction
Since the introduction of antiretroviral therapy (ART), the survival time of people living with human immunodeficiency virus (HIV) has increased, and the number of older people living with HIV (PLWH) is increasing. Accordingly, the burden of chronic diseases, such as obesity, cancer, and cardiovascular disease, is increasing due to the effects of prolonged viral exposure, chronic inflammation, and long-term antiretroviral use [1]. In Western countries, people with HIV are referred to as PLWH, implying that HIV infection is no longer a fatal disease but a chronic disease requiring lifelong management. Accordingly, the prevention and management of various chronic diseases in PLWH has become an important challenge domestically and internationally.
Rather than each disease occurring independently, various chronic diseases exhibit a clustering phenomenon wherein specific disease groups are observed together at a significantly higher frequency [2]. Considering these characteristics, studies have recently been conducted on the co-prevalence of chronic diseases and the clustering patterns among diseases that frequently co-occur.
In a study comparing the incidence of chronic diseases between PLWH and sex- and age-matched controls using domestic National Health Insurance Service (NHIS) data, the incidence rates of cancer, chronic kidney disease, depression, osteoporosis, diabetes mellitus, and dyslipidemia were higher and the incidence rates of cardiovascular disease, heart disease, ischemic stroke, and hypertension were lower in PLWH compared to the general population [3]. Additionally, in a study conducting cluster analysis of chronic diseases among PLWH abroad, a substance use and mental illness cluster, a metabolic disease cluster, a liver disease and cancer cluster, and a cerebrovascular disease multimorbidity cluster were identified. In addition, age, sex, route of transmission, race, baseline cluster of differentiation 4 (CD4) cell count, and viral load were significantly associated with multimorbidity among the cohort [4]. In the Republic of Korea (ROK), although some studies have been conducted on the prevalence and incidence of chronic diseases among PLWH [5,6], most are limited to single-disease-oriented analyses, and studies comprehensively analyzing the structure of multimorbidity and clustering patterns between diseases are limited. Therefore, in a situation where the disease burden of PLWH is shifting from a single-disease-centered focus to a multimorbidity structure, the development of integrated management strategies that consider the associations and patterns among diseases is required.
The National Institute of Health, Korea Disease Control and Prevention Agency (KDCA) conducted the “Analysis of Chronic Disease Status and Clinical Information in People Living with HIV” as a general service project to identify the prevalence and incidence patterns of chronic diseases and multimorbidity characteristics among PLWH using data from the Korean HIV/AIDS Cohort and to provide baseline data for establishing future chronic disease management strategies for PLWH. This report aims to introduce the analysis results on the patterns and characteristics of chronic disease multimorbidity among the Korean HIV/AIDS Cohort participants, which are part of the key research contents of the final report of the corresponding general service project.
Methods
1. Study Participants
This study utilized data from the Korean HIV/AIDS Cohort established and operated by the KDCA. This cohort was initiated in 2006 as a policy research service project of the KDCA and is a prospective cohort established for PLWH with the participation of 15 medical institutions nationwide, recruiting approximately 1,600 participants and conducting repeated follow-up surveys at 6-month intervals. It is the largest HIV/AIDS Cohort in the ROK, with a cumulative collection of clinical and epidemiological information on more than 20,000 cases.
In this analysis, a total of 1,606 individuals registered in the Korean HIV/AIDS Cohort from 2006 to 2023 were selected as the final analysis subjects.
2. Definition of Chronic Diseases and Multimorbidity
In the Korean HIV/AIDS Cohort, the presence or absence of chronic diseases, including metabolic diseases such as hypertension diabetes mellitus, as well as bone and joint diseases, and liver diseases, was confirmed based on the medical records of participating hospitals (Table 1). Table 1 presents the chronic disease survey items collected in the cohort, classified into disease groups according to clinical characteristics. This disease group classification is intended to systematically present chronic disease survey items, and its purpose and criteria differ from those of the classification from cluster analysis statistically derived based on co-occurrence patterns between diseases. In this study, focusing on chronic diseases, acquired immunodeficiency syndrome (AIDS)-defining illnesses were excluded. The presence of a chronic disease was defined as having been diagnosed with the corresponding disease from the time of the cohort survey to the last follow-up. Multimorbidity was defined as the co-existence of two or more chronic diseases, and to understand the levels and distribution of multimorbidity, disease status was classified into multimorbidity, single disease, and no disease.
Table 1. Chronic disease variables collected in the Korean HIV/AIDS Cohort.
| Disease category | Disease |
|---|---|
| Metabolic diseases | Hypertension, diabetes mellitus, dyslipidemia, metabolic syndrome |
| Musculoskeletal diseases | Osteoporosis, osteopenia, traumatic fracture, osteoarthritis, osteonecrosis |
| Cardio-cerebrovascular diseases | Myocardial infarction, angina pectoris, cerebral infarction, cerebral hemorrhage, peripheral vascular disease |
| Liver diseases | Hepatitis B, hepatitis C, fatty liver disease, liver cirrhosis |
| Kidney diseases | Chronic kidney disease, hemodialysis, peritoneal dialysis |
| Respiratory diseases | Chronic bronchitis, asthma, COPD |
| STIs | Gonorrhea, nongonococcal urethritis, condyloma (warts), Trichomonas, Chlamydia, Mycoplasmatales, Ureaplasma, Gardnerella, Haemophilus ducreyi, Giardia, HPV |
| Mental health disorders | Depression, anxiety disorder, ADHD, dementia (neurocognitive disorder), schizophrenia |
| Organ transplantation | Organ transplantation |
| Cancer (malignant neoplasms) | Non-AIDS-defining cancer |
HIV=human immunodeficiency virus; AIDS=acquired immunodeficiency syndrome; COPD=chronic obstructive pulmonary disease; STI=sexually transmitted infection; HPV=human papillomavirus; ADHD=attention-deficit/hyperactivity disorder.
3. Analytical Methods
To identify the characteristics of the participants in the HIV/AIDS Cohort data, the chi-square test, analysis of variance, and Kruskal–Wallis test were performed. To evaluate co-occurrence patterns between diseases, the presence or absence of each chronic disease was coded as a binary variable for analysis, and variable cluster analysis was performed to identify the structural patterns of co-occurrence. Each disease was assigned to the most appropriate cluster using Own Cluster R2 and Nearest Cluster R2, and cluster fit was evaluated using Own Cluster R2. After performing clustering using various numbers of clusters, the final number of clusters was determined by comprehensively considering the epidemiological associations and clinical interpretability of the diseases included in each cluster, and key multimorbidity patterns were derived based on these results. In addition, differences in disease patterns over time were compared through age-stratified analysis. To analyze factors associated with chronic diseases and multimorbidity, sociodemographic factors (age and sex), clinical factors (CD4 cell count, HIV RNA, ART status and timing, ART adherence, and presence of AIDS-defining illnesses), and health behavior factors (smoking, drinking, etc.) were included, and multinomial logistic regression was performed to analyze the factors associated with the presence or absence of multimorbidity to calculate odds ratios (OR) and 95% confidence intervals (CI). All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc.).
Results
1. Characteristics of Enrolled Participants and Lifetime Prevalence of Chronic Diseases in the HIV/AIDS Cohort
The general characteristics of 1,606 PLWH in the Korean HIV/AIDS Cohort were analyzed by classifying chronic disease prevalence status into multimorbidity, single-disease, and no-disease groups (Table 2). Among the participants, 29.8%, 34.5%, and 35.7% of the participants were classified into multimorbidity, single-disease, and no-disease groups, respectively. Most of the study participants were male (93.6%), and there was no significant difference in sex distribution between the groups (p=0.528). The mean age at HIV diagnosis was 38.92±12.4 years, and the mean age was higher in the multimorbidity group (42.85±11.67 years) than in the no-disease group (35.15±12.06 years) (p<0.001). By year of diagnosis, the proportion of those diagnosed before 2005 was higher in the multimorbidity group, whereas the proportion of those diagnosed in recent years was significantly higher in the no-disease group (p<0.001). At diagnosis, CD4 cell counts were comparable across the groups (p=0.222), whereas HIV RNA levels differed significantly, with a higher proportion of individuals with high HIV RNA levels in the multimorbidity group (p=0.013). In the initial ART regimen, most patients received the 2 nucleoside reverse transcriptase inhibitor+protease inhibitor regimen, and the distribution of treatment regimens between groups was significantly different (p<0.001). In contrast, the proportion of ART-untreated participants was relatively high in the no-disease group. The follow-up period was the longest in the multimorbidity group, with a mean of 12.5 years, and was significantly longer than that in the no-disease group (7.0 years) (p<0.001) (Table 2).
Table 2. General characteristics of participants in the Korean HIV/AIDS Cohort according to multimorbidity status.
| Characteristics | Total | Multi morbidity | Single morbidity | No morbidity | p-value |
|---|---|---|---|---|---|
| Total | 1,606 (100.0) | 478 (29.8) | 554 (34.5) | 574 (35.7) | |
| Sex | |||||
| Male | 1,503 (93.6) | 443 (92.7) | 523 (94.4) | 537 (93.6) | 0.528 |
| Female | 103 (6.4) | 35 (7.3) | 31 (5.6) | 37 (6.4) | |
| Age at HIV diagnosis (yr) | 38.92±12.4 | 42.85±11.67 | 39.43±12.25 | 35.15±12.06 | <0.001 |
| ≤29 | 405 (25.2) | 60 (12.6) | 129 (23.3) | 216 (37.6) | <0.001 |
| 30–39 | 478 (29.8) | 141 (29.5) | 159 (28.7) | 178 (31.0) | |
| 40–49 | 393 (24.5) | 148 (31.0) | 145 (26.2) | 100 (17.4) | |
| ≥50 | 330 (20.5) | 129 (27.0) | 121 (21.8) | 80 (13.9) | |
| Year of HIV diagnosis | |||||
| ≤2005 | 342 (21.3) | 147 (30.8) | 106 (19.1) | 89 (15.5) | <0.001 |
| 2006–2010 | 713 (44.4) | 196 (41.0) | 239 (43.1) | 278 (48.4) | |
| 2011–2021 | 551 (34.3) | 135 (28.2) | 209 (37.7) | 207 (36.1) | |
| Transmission route | |||||
| Sexual contact | 1,506 (93.8) | 458 (95.8) | 522 (94.2) | 526 (91.6) | 0.017 |
| Other/unknown | 100 (6.2) | 20 (4.2) | 32 (5.8) | 48 (8.4) | |
| CD4 cell count at HIV diagnosis | |||||
| <200 | 627 (39.0) | 187 (39.1) | 226 (40.8) | 214 (37.3) | 0.222 |
| 200–349 | 459 (28.6) | 144 (30.1) | 166 (30.0) | 149 (26.0) | |
| 350–499 | 260 (16.2) | 73 (15.3) | 83 (15.0) | 104 (18.1) | |
| ≥500 | 236 (14.7) | 69 (14.4) | 73 (13.2) | 94 (16.4) | |
| Missing | 24 (1.5) | 5 (1.0) | 6 (1.1) | 13 (2.3) | |
| HIV RNA at HIV diagnosis | |||||
| <100,000 | 1,104 (68.7) | 318 (66.5) | 386 (69.7) | 400 (69.7) | 0.013 |
| ≥100,000 | 486 (30.3) | 160 (33.5) | 163 (29.4) | 163 (28.4) | |
| Missing | 16 (1.0) | 0 (0.0) | 5 (0.9) | 11 (1.9) | |
| Initial ART regimen | |||||
| 2NRTI+INI | 62 (3.9) | 17 (3.6) | 20 (3.6) | 25 (4.4) | <0.001 |
| 2NRTI+NNRTI | 394 (24.5) | 135 (28.2) | 126 (22.7) | 133 (23.2) | |
| 2NRTI+PI | 751 (46.8) | 263 (55.0) | 255 (46.0) | 233 (40.6) | |
| Others | 294 (18.3) | 58 (12.1) | 124 (22.4) | 112 (19.5) | |
| No | 105 (6.5) | 5 (1.0) | 29 (5.2) | 71 (12.4) | |
| Smoking history at HIV diagnosis | |||||
| Yes | 943 (58.7) | 312 (65.3) | 340 (61.4) | 291 (50.7) | <0.001 |
| No | 447 (27.8) | 141 (29.5) | 159 (28.7) | 147 (25.6) | |
| Refused/missing | 216 (13.5) | 25 (5.2) | 55 (9.9) | 136 (23.7) | |
| Alcohol use at HIV diagnosis | |||||
| Yes | 978 (60.9) | 329 (68.8) | 352 (63.5) | 297 (51.7) | <0.001 |
| No | 391 (24.4) | 116 (24.3) | 141 (25.5) | 134 (23.3) | |
| Refused/missing | 237 (14.8) | 33 (6.9) | 61 (11.1) | 143 (24.9) | |
| Time from HIV diagnosis to ART initiation (day) | |||||
| 0–30 | 988 (65.8) | 327 (69.1) | 327 (65.0) | 334 (63.6) | <0.001 |
| 31–90 | 184 (12.3) | 44 (9.3) | 59 (11.7) | 81 (15.4) | |
| >90 | 329 (21.9) | 102 (21.6) | 117 (23.3) | 110 (21.0) | |
| Mean (range) | 201.2 (0–6246) | 224.4 (0–6246) | 171.8 (0–4448) | 210.1 (0–5651) | 0.2396 |
| Median (IQR) | 7 (0–30) | 0 (0–53) | 9 (0–61) | 7 (0–65) | |
| Follow-up duration to last visit (yr) | |||||
| Mean (range) | 9.3 (0–28.9) | 12.5 (0.4–28.9) | 9.0 (0.02–25.7) | 7.0 (0–26.2) | <0.001 |
| Median (IQR) | 8.6 (4.9–13.4) | 13.1 (8.6–16.1) | 8.5 (5.1–12.1) | 6.2 (3.3–9.9) | |
Values are presented as number (%) or mean±standard deviation. Continuous variables were compared using the Kruskal–Wallis test. HIV=human immunodeficiency virus; AIDS=acquired immunodeficiency syndrome; CD4=cluster of differentiation 4; RNA=ribonucleic acid; ART=antiretroviral therapy; NRTI=nucleoside reverse transcriptase inhibitor; INI=integrase strand transfer inhibitor; NNRTI=non-nucleoside reverse transcriptase inhibitor; PI=protease inhibitor; IQR=interquartile range.
Analysis of the prevalence distribution of single disease and multimorbidity among the participants showed that among individuals with a single disease, dyslipidemia (10.5%), condyloma acuminatum (4.5%), and hypertension (4.2%) accounted for the highest proportions, in that order, and disease combinations including dyslipidemia were also observed with a high frequency among those with multimorbidity. The most common multimorbidity combination was hypertension and dyslipidemia (3.6%), followed by hypertension+diabetes mellitus+dyslipidemia (1.4%) and dyslipidemia+condyloma acuminatum (1.3%). Overall, patterns of co-occurrence of other metabolic diseases centered on hypertension and dyslipidemia were commonly observed (Figure 1).
Figure 1. Lifetime chronic disease and multimorbidity patterns among participants in the Korean HIV/AIDS Cohort (N=1,606).
Only the seven most frequent single disease conditions and multimorbidity combinations are presented. HIV=human immunodeficiency virus; AIDS= acquired immunodeficiency syndrome.
2. Age-Specific Distribution of Multimorbidity Clusters Based on Representative Clusters
An analysis was conducted by deriving representative clusters based on the chronic disease prevalence among patients with HIV identified from the HIV/AIDS Cohort data. As a result of the cluster analysis of chronic diseases using data from the Korean HIV/AIDS Cohort, a total of five clusters were derived. Cluster 1 included osteoporosis, cerebral hemorrhage, peripheral vascular disease, dialysis, etc.; Cluster 2 included hepatitis B, hepatitis C, liver cirrhosis, and cancer; Cluster 3 included metabolic syndrome, dyslipidemia, fatty liver, etc.; Cluster 4 included hypertension, cerebral infarction, and diabetes mellitus; and Cluster 5 included non-gonococcal urethritis, gonorrhea, condyloma acuminatum, etc. Among participants aged 29 years or younger, most were in the no-disease group (53.3%), whereas among participants aged 50 years or older, the no-disease group accounted for 24.2%, with 75.8% belonging to one or more disease clusters.
With increasing age, distinct changes in the distribution of chronic disease clusters were observed. Among those aged 29 years or younger, the proportion of Cluster 5 (24.4%), which included non-gonococcal urethritis, gonorrhea, and condyloma acuminatum, was high, but the proportion of this cluster showed a decreasing trend with increasing ages. In contrast, Cluster 3, which included metabolic syndrome, dyslipidemia, and fatty liver, was more common among those in their 30s and 40s, with the highest proportion (48.6%) observed particularly among those aged 40–49 years. Cluster 4, which included hypertension, cerebral infarction, and diabetes mellitus, consistently increased with increasing age, showing the highest proportion (48.5%) among those aged 50 years or older. In addition, the proportions of Clusters 1 and 2 also showed an increasing trend with increasing age. These results showed a pattern wherein the chronic disease structure in PLWH shifted from an infectious disease–centered structure to a chronic disease–centered structure with increasing age (Figure 2).
Figure 2. Age-group distribution of representative multimorbidity clusters in the Korean HIV/AIDS Cohort (N=1,606).
3. Analysis of Risk and Factors Associated with Chronic Disease Multimorbidity among People Living with HIV in the Republic of Korea
Using data from the Korean HIV/AIDS Cohort, factors associated with the prevalence of chronic disease multimorbidity were analyzed, and the likelihood of belonging to the no-disease group and the cluster-based multimorbidity group was compared with the single-disease group as the reference group. The analysis showed that age at HIV diagnosis and the time elapsed since diagnosis were identified as key factors associated with multimorbidity. Regarding age at HIV diagnosis, with those aged 29 years or younger as the reference group, the likelihood of belonging to the multimorbidity group increased significantly with increasing age, particularly showing the highest likelihood among those aged 50 years or older. In addition, those diagnosed with HIV before 2005 were more likely to belong to the multimorbidity group compared to the single-disease group, confirming a trend where a longer period elapsed since HIV infection was associated with an increased prevalence of multimorbidity. Regarding clinical characteristics, CD4 cell count at diagnosis did not differ significantly between the groups; however, a high HIV RNA level at diagnosis (≥100,000) was associated with an increased likelihood of multimorbidity (OR=1.56, 95% CI=1.11–2.20). Regarding treatment-related factors, differences according to the type of ART were generally limited. Those who initiated ART within 31–90 days after HIV diagnosis were less likely to belong to the multimorbidity group (OR=0.49, 95% CI=0.33–0.75). In summary, chronic disease multimorbidity in PLWH is closely associated with increasing age and the time elapsed since diagnosis, and certain clinical and treatment-related factors were shown to affect the prevalence of multimorbidity (Table 3).
Table 3. Factors associated with multimorbidity status among participants in the Korean HIV/AIDS Cohort.
| Characteristics | No morbidity | Clustered multimorbidity | |||
|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | ||
| Sex | |||||
| Male | Reference | Reference | |||
| Female | 1.27 (0.74–2.15) | 0.386 | 1.11 (0.65–1.92) | 0.7 | |
| Age at HIV diagnosis (yr) | |||||
| ≤29 | Reference | Reference | |||
| 30–39 | 0.63 (0.45–0.86) | 0.005 | 1.72 (1.16–2.55) | 0.007 | |
| 40–49 | 0.39 (0.27–0.56) | <0.001 | 2.19 (1.47–3.27) | <0.001 | |
| ≥50 | 0.30 (0.20–0.45) | <0.001 | 2.28 (1.50–3.45) | <0.001 | |
| Year of HIV diagnosis | |||||
| ≤2005 | 0.66 (0.44–1.02) | 0.058 | 2.13 (1.42–3.20) | <0.001 | |
| 2006–2010 | 0.98 (0.69–1.38) | 0.905 | 1.12 (0.78–1.60) | 0.552 | |
| 2011–2021 | Reference | Reference | |||
| Transmission route | |||||
| Sexual contact | Reference | Reference | |||
| Other/unknown | 1.17 (0.70–1.97) | 0.548 | 0.67 (0.36–1.26) | 0.21 | |
| CD4 cell count at HIV diagnosis | |||||
| <200 | 1.02 (0.67–1.56) | 0.917 | 0.77 (0.46–1.29) | 0.322 | |
| 200–349 | 0.91 (0.60–1.38) | 0.646 | 1.03 (0.61–1.73) | 0.914 | |
| 350–499 | 1.27 (0.80–2.00) | 0.314 | 1.46 (0.83–2.58) | 0.193 | |
| ≥500 | Reference | Reference | |||
| HIV RNA at HIV diagnosis | |||||
| <100,000 | Reference | Reference | |||
| ≥100,000 | 1.01 (0.76–1.35) | 0.944 | 1.56 (1.11–2.20) | 0.011 | |
| Initial ART regimen | |||||
| 2NRTI+INI | 1.26 (0.63–2.54) | 0.514 | 1.05 (0.50–2.19) | 0.902 | |
| 2NRTI+NNRTI | 1.12 (0.81–1.54) | 0.503 | 1.15 (0.84–1.57) | 0.38 | |
| 2NRTI+PI | Reference | Reference | |||
| Others | 0.87 (0.59–1.28) | 0.485 | 0.49 (0.32–0.75) | 0.001 | |
| No | 1.69 (0.98–2.91) | 0.06 | 0.19 (0.07–0.50) | 0.001 | |
| Smoking history at HIV diagnosis | |||||
| Yes | 0.96 (0.72–1.28) | 0.764 | 0.73 (0.52–1.03) | 0.074 | |
| No | Reference | Reference | |||
| Refused/missing | 2.18 (0.88–5.41) | 0.093 | 0.30 (0.10–0.87) | 0.027 | |
| Alcohol use at HIV diagnosis | |||||
| Yes | 0.84 (0.61–1.14) | 0.253 | 1.14 (0.83–1.56) | 0.415 | |
| No | Reference | Reference | |||
| Refused/missing | 1.19 (0.49–2.86) | 0.704 | 1.16 (0.44–3.04) | 0.771 | |
| Time from HIV diagnosis to ART initiation (day) | |||||
| 0–30 | Reference | Reference | |||
| 31–90 | 0.70 (0.47–1.04) | 0.074 | 0.49 (0.33–0.75) | <0.001 | |
| >90 | 1.05 (0.75–1.48) | 0.77 | 0.72 (0.51–1.02) | 0.063 | |
Reference group: single morbidity. HIV=human immunodeficiency virus; AIDS=acquired immunodeficiency syndrome; OR=odds ratio; CI=confidence interval; CD4=cluster of differentiation 4; RNA=ribonucleic acid; ART=antiretroviral therapy; NRTI=nucleoside reverse transcriptase inhibitor; INI=integrase strand transfer inhibitor; NNRTI=non-nucleoside reverse transcriptase inhibitor; PI=protease inhibitor.
Discussion
This study utilized data from the Korean HIV/AIDS Cohort to identify chronic disease co-occurrence patterns and multimorbidity characteristics among PLWH in the ROK. As a result, multimorbidity was identified in approximately 30% of PLWH, and a tendency toward higher multimorbidity prevalence with increasing age and longer time elapsed since HIV diagnosis was observed. This finding suggests that the burden of chronic diseases is increasing along with the increased survival rate of PLWH.
In particular, this study identified a cluster structure wherein multiple diseases co-occurred, rather than chronic diseases occurring alone. As a result of the cluster analysis, a total of five clusters (Clusters 1–5) were derived. Previous studies have commonly reported multimorbidity patterns associated with cardiovascular diseases, metabolic diseases, and mental health disorders among PLWH, as well as patterns wherein lifestyle-related diseases, such as sexually transmitted infections, co-occur [7]. In this study, Cluster 3, which included metabolic syndrome, dyslipidemia, and fatty liver; Cluster 4, which included hypertension, cerebral infarction, and diabetes mellitus; and Cluster 5, which included sexually transmitted infections, were also identified, showing patterns partially similar to those reported in previous studies. The proportion of individuals with one or more clusters increased with increasing age, and a pattern of transitioning from sexually transmitted infection–centered clusters to metabolic disease–and cardiovascular disease–centered clusters was observed. This finding suggests persistent immune activation and a chronic inflammatory state following HIV infection, acting in combination, to increase the risk of metabolic and cardiovascular diseases.
Furthermore, in the analysis of factors associated with multimorbidity, age and the time elapsed since diagnosis were identified as major influencing factors, whereas immunological markers, such as CD4 cell count, did not show a significant association. Chronic diseases and multimorbidity in PLWH are reportedly influenced by a complex interplay of various factors, including not only aging but also long-term HIV infection and long-term exposure to ART, chronic inflammation, immune activation, and lifestyle [8]. Therefore, the results of this study also need to be interpreted by comprehensively considering various clinical and lifestyle factors rather than a single immunological marker. Regarding treatment-related factors, it was also confirmed that the group that initiated ART within 31–90 days of HIV diagnosis had a lower likelihood of multimorbidity. However, these results may reflect differences in the timing of treatment initiation based on past treatment guidelines and patients’ baseline health status, rather than the timing of ART initiation itself. In the past, the timing of ART initiation was determined by the patient’s clinical status and the treatment guidelines at the time, and because ART initiation may have been delayed in patients with relatively good baseline health status, caution is required in interpreting causal relationships. In this regard, although this study included the initial ART type in the analysis, the cumulative exposure duration of individual drugs and whether drugs were changed during treatment were not reflected. Therefore, to more clearly evaluate the association between ART and multimorbidity, further analysis considering the cumulative exposure duration of each drug and the history of treatment changes is required in the future.
According to the Korea National Health and Nutrition Examination Survey (KNHANES), the prevalences of hypertension, diabetes mellitus, and dyslipidemia among adults as of 2023 were 20.0%, 9.4%, and 20.9%, respectively [9]. In comparison, the prevalences of hypertension (21.1%) and diabetes mellitus (11.1%) identified in the Korean HIV/AIDS Cohort were comparable to those reported in KNHANES, but the prevalence of dyslipidemia (35.7%) was relatively high. However, this comparison is a simple descriptive comparison without standardization for age and sex, and because the two datasets differ in participant composition, study design, disease definitions, and methods of calculating prevalence, there are limitations in directly interpreting the prevalence of dyslipidemia in PLWH as being higher than that in the general population.
This study has several limitations. First, because the analysis was conducted using data from the Korean HIV/AIDS Cohort, the number of participants was relatively limited, and there were limitations in securing a sufficient number of cases for some chronic diseases. Currently, an additional analysis using NHIS data is underway for a more representative analysis, which might enable a more sophisticated evaluation of the chronic disease and multimorbidity structure of PLWH in the ROK in the future. Second, the Korean HIV/AIDS Cohort is a database established while focusing on major tertiary medical institutions nationwide, and there may be selection bias in representing the entire population of PLWH because the cohort might have included patients who had relatively good access to medical care and were well managed. In addition, because weighting adjustments reflecting the characteristics of the population of PLWH in the ROK could not be applied, caution is required when generalizing the prevalence of multimorbidity identified in this study to the prevalence among the entire population of PLWH in the ROK. Third, only limited information on some mental disorders, such as depression and anxiety disorders, was collected in the cohort data used in this study. Therefore, there was a limitation in securing a sufficient number of cases to include in the cluster analysis, and the multimorbidity characteristics of the mental health domain, which is important for PLWH, were not fully reflected. Additionally, it is necessary to consider the possibility that the actual mental health problems of PLWH were not fully reflected in the cohort survey data. In overseas studies, mental health disorders have been reported as one of the major multimorbidity patterns among PLWH [7], and further analysis including mental health disorders is needed. Fourth, because information on some chronic diseases was collected via medical records and survey data, there may have been differences in diagnostic and recording methods among medical institutions, which may have affected estimates of the prevalence of specific chronic diseases and multimorbidity. However, the Korean HIV/AIDS Cohort has the strength of including detailed clinical information, such as CD4 cell count, HIV RNA level, type of ART, and ART adherence, allowing for a more accurate and comprehensive evaluation of the disease characteristics and multimorbidity patterns of PLWH. The results of this study represent an initial analysis of multimorbidity in PLWH using data from the Korean HIV/AIDS Cohort, and need to be further refined through additional analyses using large-scale data and long-term follow-up data to obtain more sophisticated and reliable results.
This study is significant in that it comprehensively analyzed the chronic disease cluster structure and multimorbidity characteristics of PLWH using data from a large-scale domestic HIV/AIDS cohort. These results suggest that health management for PLWH needs to expand beyond an infectious disease–centered approach to integrated management that includes chronic diseases, and can be used as evidence supporting the need for management strategies that consider linkages between disease groups, beyond an individual disease–centered approach to the chronic disease management of PLWH. In particular, to prepare for the increase in older PLWH, it is necessary to establish multimorbidity-centered management strategies, including metabolic diseases and cardiocerebrovascular diseases, and prevention-oriented public health policies.
Acknowledgments
None.
Declarations
Ethics Statement: Not applicable.
Funding Source: This research was supported by the Korea National Institute of Health (KNIH) research project.
Conflict of Interest: The authors have no conflicts of interest to declare.
Author Contributions: Conceptualization: SMC, BYP, SMR, MJG. Data curation: KHA, SHH. Formal analysis: KHA, SHH. Supervision: BYP, MJG, JHL. Writing – original draft: SMC, SMR. Writing – review & editing: SMC, BYP, SMR, MJG, JGL, JHL.
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