Background

Lipodystrophy (LD) is a group of rare disorders of selective deficiency of adipose tissue affecting various areas of the body and is associated with significant metabolic abnormalities [1]. LD is classified into generalized LD (GLD) and partial LD (PLD), which are characterized by near-complete loss or selective loss of adipose tissue, respectively. Both GLD and PLD are sub-divided into genetic (i.e. congenital GLD and familial PLD) and acquired subtypes (i.e. acquired GLD and acquired PLD) based on etiology [2, 3]. HIV-associated LD (HIV-LD) is a distinct subtype of acquired PLD and is the most prevalent type of LD [4, 5]. Although abnormal fat distribution appears in varying presentations, HIV-LD tends to represent peripheral subcutaneous fat loss (i.e. lipoatrophy) with or without central fat accumulation (i.e. lipohypertrophy) [6, 7]. While the exact etiology of HIV-LD is still unknown, there is growing evidence indicating that HIV-associated lipoatrophy is associated with the older thymidine analog-containing highly active antiretroviral therapies stavudine and zidovudine [6, 8, 9]. The presentation of HIV-associated lipoatrophy is similar to that seen in non-HIV-associated LD (non-HIV-LD), while HIV-associated lipohypertrophy shares similar features with metabolic syndrome [10,11,12].

LD is commonly associated with metabolic comorbidities, including dyslipidemia, elevated triglyceride levels, insulin resistance/diabetes, metabolic dysfunction-associated steatotic liver disorder (MASLD), recurrent acute pancreatitis, autoimmune disorders, nephropathy, and reproductive dysfunction [13]. However, the literature has yet to explore the common comorbidities associated with LD as compared to the general populations. Furthermore, with the emergence of new disease modification treatment options for LD, there is growing awareness of the need to understand the prevalence of specific clinical comorbidities commonly associated with LD, and to evaluate whether these are prevalent in the natural history of LD without disease modifying treatment. Therefore, we aim to examine specific LD-associated clinical characteristics in a commercially insured US population using a nationwide administrative claims database between 2018 and 2019.

Methods

Data source and study design

We examined specific LD-associated clinical characteristics using the Clinformatics® Data Mart database from January 1, 2018, to December 31, 2019. Clinformatics Data Mart is an integrated US healthcare claims database that includes privately insured enrollees with commercial or Medicare Advantage plans affiliated with Optum. The database contains statistically de-identified and Health Insurance Portability and Accountability Act-compliant medical claims, including healthcare services performed in inpatient and outpatient settings, and pharmacy claims for approximately 15–20 million individuals annually. The Clinformatics Data Mart population is geographically representative, spanning all 50 US states. In addition to medical and pharmacy claims, the database includes provider data and information on patient enrollment and demographics [14]. Clinformatics Data Mart utilizes internal and external sources to comprehensively capture death information, such as the Death Master File maintained by the Social Security Office, enrollment data files in Medicare data, obituary data, facility claims, member coverage information, and Optum EHR data.

Study population

First, we assembled an all-adult cohort, including all eligible adult individuals (aged ≥ 18 years on January 1, 2018) who continuously enrolled from January 1, 2018 until December 31, 2019 (or until they died, if this was before the end date of the study period), allowing a 45-day enrollment gap (Fig. 1). Next, a cohort comprising patients with a diagnosis of HIV disease was identified to include individuals who had at least 1 inpatient or 2 outpatient HIV diagnoses (i.e. International Classification of Diseases [ICD]-9-CM: 042, 079.53, V08; ICD-10-CM: B20, B97.35, Z21) on separate calendar dates among the all-adult cohort. This identification algorithm was modified from the validated Medicaid-based algorithm to identify people living with HIV [15, 16]. An LD cohort was also created from the all-adult cohort by identifying LD diagnosis (i.e. ICD-9-CM: 272.6; ICD-10-CM: E88.1) in medical claims, or metreleptin prescription (National Drug Code: 66780-310-01, 76431-210-01) in pharmacy claims. In order to increase specificity and decrease misclassification due to rule-out diagnosis, at least 1 inpatient or 2 outpatient LD diagnoses on separate calendar dates were required. The LD cohort was further divided into non-HIV-LD and HIV-LD subgroups. We defined the non-HIV-LD group as individuals without any HIV diagnosis, and the HIV-LD group as individuals with at least 1 inpatient or 2 outpatient HIV diagnoses among the LD cohort. By definition, the non-HIV-LD group included all individuals with GLD or non-HIV-associated PLD, and the HIV-LD group included HIV-associated lipoatrophy and lipohypertrophy.

Fig. 1
figure 1

Flow chart for clinical characteristic analysis in 2018–2019. aIndividuals who had at least 1 inpatient or 2 outpatient HIV diagnoses (i.e. ICD-9-CM: 042, 079.53, V08; ICD-10-CM: B20, B97.35, Z21) on separate calendar dates are qualified to have HIV. Abbreviations: HIV-LD, lipodystrophy associated with HIV; ICD, International Classification of Diseases; non-HIV-LD, lipodystrophy not associated with HIV; PWHD, people living with HIV disease

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Individuals in the non-HIV-LD group were matched by age and sex in a 1:4 ratio to controls from the all-adult cohort without HIV. The controls were required to have neither an LD nor an HIV diagnosis. To minimize the effect of fat alterations associated with HIV infection and antiretroviral therapy, individuals in the HIV-LD group were matched by age and sex in a 1:4 ratio to controls from the people living with HIV cohort. The people living with HIV controls were required not to have an LD diagnosis.

Descriptive data analysis 2018–2019

We examined patient demographics (i.e. age, sex, and race/ethnicity), estimated their Elixhauser Comorbidity Index [17] (identification of 38 different pre-existing conditions based on secondary diagnoses) and estimated the prevalence of clinical characteristics among the non-HIV-LD and HIV-LD cohorts. The specific clinical characteristics evaluated included hyperlipidemia, diabetes mellitus, hypertension, acute myocardial infarction (AMI), MASLD or metabolic dysfunction-associated steatohepatitis (MASH), liver fibrosis or cirrhosis, acute pancreatitis, kidney disease (i.e. acute or chronic glomerulonephritis, acute or chronic renal failure, nephritis or nephrotic syndrome, renal failure or proteinuria), autoimmune diseases (i.e. systematic lupus erythematosus [SLE], rheumatoid arthritis, autoimmune thyroiditis), cancers excluding non-melanoma skin cancers, and serious infections (i.e. bacteremia, pneumonia, skin/soft tissue infection, gastrointestinal infection, acute osteomyelitis, acute pyelonephritis, acute meningitis) resulting in hospitalization. We examined the prevalence of overall cancer and specific types of cancers (i.e. lymphoma, breast cancer, prostate cancer). Lymphoma was identified with at least 1 inpatient diagnosis or 2 outpatient lymphoma diagnoses on separate calendar dates occurring at least 2 months apart. This approach was a modified version of a validated algorithm developed by Setoguchi et al., expecting > 80% sensitivity and > 90% specificity [18]. We also applied the same identification algorithm to other cancers [18]. To identify AMI, we applied an identification algorithm yielding 86.0% positive predictive value from a primary hospital discharge diagnosis of AMI [19]. Acute pancreatitis was identified from a primary hospital discharge diagnosis of acute pancreatitis [20]. To identify SLE, we modified a claims-based identification algorithm by using at least 1 inpatient diagnosis or 2 outpatient diagnoses at least 2 months apart [21].

Furthermore, we used a case definition of serious infections resulting in hospitalizations [22]. For hyperlipidemia, diabetes mellitus, and hypertension, we used the Centers for Medicare and Medicaid Services (CMS) Chronic Condition Warehouse (CCW) algorithms using at least 1 inpatient or 2 outpatient diagnoses on separate calendar dates [23].

Statistical analysis

The patient demographics and clinical characteristics among non-HIV-LD and HIV-LD cases were presented as percentages for categorical variables, and as means and standard deviations for continuous variables. We compared demographics and clinical characteristics between the LD cohorts and matched controls using the Chi-square test, Fisher’s exact test, and student t-test, as appropriate. Logistic regression models were used to estimate the odds ratios (ORs) and accompanying 95% confidence intervals (CIs) for each clinical characteristic among non-HIV-LD and HIV-LD cohorts compared with the matched controls. All analyses were performed using Health Data software (Panalgo, Boston, MA, USA) with a two-sided level of statistical significance of 0.05.

Data and resource availability

All data analyzed during this study are from the Clinformatics Data Mart database that is cited in the Methods section. All analyses conducted in this study are presented in the manuscript.

Results

Clinical characteristics of individuals with non-HIV-LD versus matched controls from the all-adult cohort without HIV

We identified 546 individuals with non-HIV-LD (mean age, 60.3 ± 14.9 years; female, 67.6%; non-Hispanic White, 53.8%) and 2184 age- and sex-matched controls from the all-adult cohort without HIV between 2018 and 2019. Individuals with non-HIV-LD were more likely to use healthcare services compared with matched controls as follows: having ≥ 2 hospitalizations (17.9% vs 4.3%; P < 0.001), having ≥ 2 emergency department visits (29.7% vs 12.0%; P < 0.001), and having ≥ 10 outpatient visits (93.6% vs 63.7%; P < 0.001) during the 2-year study period (Table 1). Compared to matched controls, individuals with non-HIV-LD had higher odds of having all of the identified metabolic comorbidities and complications (i.e. hypertension, hyperlipidemia, diabetes mellitus, MASLD/MASH, AMI, liver fibrosis or cirrhosis, and acute pancreatitis; Table 2). In addition, those with non-HIV-LD had increased odds of having kidney diseases, identified autoimmune diseases, serious infections resulting in hospitalization, and any cancer, mostly breast cancer and prostate cancer (Table 2). The Elixhauser index was greater in individuals with non-HIV-LD (6.5 ± 4.1) than in matched controls (3.2 ± 3.5; P < 0.001; Table 1).

Table 1 Demographics and clinical characteristics of individuals with lipodystrophy and age- and sex-matched controls in 2018–2019
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Table 2 Odds ratios for the association between lipodystrophy and clinical characteristics for individuals with non-HIV-LD and HIV-LD compared to age- and sex-matched controls in 2018–2019
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Clinical characteristics of individuals with HIV-LD versus matched controls from the people living with HIV cohort

We identified 334 individuals with HIV-LD (mean age, 59.2 ± 8.3 years; female, 15.0%; non-Hispanic White, 56.9%) and 1336 age- and sex-matched controls from the people living with HIV cohort between 2018 and 2019. Individuals with HIV-LD were more likely to have ≥ 10 outpatient visits compared to matched people living with HIV controls during the 2-year study period (92.2% vs 81.3%; P < 0.001); however, there were no statistically significant differences between those having ≥ 2 hospitalizations (9.3% vs 9.4%; P = 0.80) and those having ≥ 2 emergency department visits (21.0% vs 22.8%; P = 0.51; Table 1). Compared to matched people living with HIV individuals, those with HIV-LD had higher odds of having hypertension (68.6% vs 59.7%; P = 0.004, OR: 1.47; 95% CI: 1.13–1.92), hyperlipidemia (75.7% vs 55.9%; P < 0.001, OR: 2.46, 95% CI: 1.86–3.28), and diabetes mellitus (31.1% vs 24.9%; P = 0.02, OR: 1.37, 95% CI: 1.04–1.79; Table 2). The Elixhauser index was greater in individuals with HIV-LD (6.1 ± 3.7) than in matched people living with HIV controls (5.5 ± 3.8; P = 0.01; Table 1). However, the prevalence of other clinical characteristics was comparable between those with HIV-LD and matched people living with HIV controls (Tables 1 and 2).

Discussion

Our study yielded 2 important findings using a US nationwide cohort of the commercially insured adult population. The findings of this study demonstrate the high prevalence of metabolic abnormalities and comorbidities in individuals with LD. The study findings may also shed light on the suspected association between immune dysregulation, cancer development, and LD.

The association between LD and metabolic comorbidities is well recognized in the literature. Our results were consistent with previous research, as we observed a high burden of metabolic comorbidities among individuals with non-HIV-LD, although due to the design of our study we were not able to explore the incidence of these comorbidities. Similar to the findings from the study by Gonzaga-Jauregui and colleagues [24], we observed higher ORs of having hypertension, hyperlipidemia, diabetes mellitus, and MASLD/MASH among individuals with non-HIV-LD compared with age- and sex-matched controls from the all-adult cohort without HIV. Our study also demonstrated the higher odds between non-HIV-LD and complications of metabolic comorbidities, such as AMI, liver fibrosis or cirrhosis, and acute pancreatitis as compared to the general population. An increased risk of metabolic comorbidities can lead to accelerated coronary artery disease [25]. MASH is one of the leading causes of advanced liver disease, such as liver fibrosis and cirrhosis, and type 2 diabetes is identified as a strong risk factor for progressive liver disease [26,27,28]. In addition, hypertriglyceridemia is an infrequent but well-established cause of acute pancreatitis [29]. The aforementioned evidence indicates that individuals with LD are at higher risk of AMI, liver fibrosis or cirrhosis, and acute pancreatitis. Consistent with non-HIV-LD, individuals with HIV-LD are more likely to have hypertension, hyperlipidemia, or diabetes mellitus compared with age- and sex-matched controls from the people living with HIV cohort.

Some studies are suggestive of a potential association between LD and autoimmune diseases [30,31,32]. We observed higher odds of autoimmune diseases in the non-HIV-LD cohort, as higher odds were observed for rheumatoid arthritis, autoimmune thyroiditis, and lupus compared with the age- and sex-matched controls from the all-adult cohort without HIV.

We observed that individuals with non-HIV-LD and HIV-LD have an increased risk of cancer compared with matched controls. Specifically, individuals with non-HIV-LD are more likely to have breast cancer and prostate cancer, whereas individuals with HIV-LD had numerically higher odds of having lymphoma compared with the people living with HIV cohort; however, this was not statistically significant.

All studies utilizing secondary data sources have inherent limitations, and our study is no exception. We did not perform subgroup analyses for GLD and PLD, since there are no specific ICD-9/10-CM codes for LD subtypes, or validated algorithms currently available. However, we categorized LD into non-HIV-LD and HIV-LD using a combination of ICD-9/10-CM codes for LD and HIV, which is consistent with previous studies [24, 33]. Due to its rarity, heterogeneity, and similarity in disease manifestation with metabolic syndrome (particularly for PLD) [13], LD is commonly underrecognized and misdiagnosed in clinical practice. This may have contributed to the under-identification of LD cases in our study. However, the use of the ICD-9/10-CM codes provides a standard approach to identify the frequency of LD diagnosis in real-world settings. In addition, there is a potential for misclassification of comorbidities due to improper coding or rule-out diagnosis. Therefore, we conducted a thorough search to identify validated claims-based algorithms. Where no such algorithm existed, we adopted the “1 inpatient or 2 outpatient diagnoses” approach to increase specificity and minimize misclassification resulting from rule-out diagnosis. This approach was also consistent with most CCW algorithms to identify chronic conditions in the CMS administrative claims data [23]. As a limitation inherent in cross-sectional studies, we did not investigate the temporal dynamic of clinical characteristics; thus, the findings of this study cannot be used to establish causality between LD and clinical characteristics. Additionally, we did not adjust for medications that have known associations with LD. However, it is still meaningful to understand the general prevalence of comorbidities in individuals with LD. To the best of our knowledge, this study is the first large claims-based study to explore clinical characteristics observed in patients with LD.

Conclusions

In summary, although LD is a group of rare diseases, it imposes a substantially high burden on affected individuals, and has an increased odds with a range of comorbidities and complications (metabolic abnormalities, autoimmune diseases, cancers, serious infections resulting in hospital admissions). Future longitudinal follow-up studies are warranted to investigate the temporal dynamics between LD and observed clinical characteristics.