Observational Study Open Access
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 7, 2025; 31(29): 109202
Published online Aug 7, 2025. doi: 10.3748/wjg.v31.i29.109202
Poor sleep and hepatic steatosis contribute to poorer quality of life in people with human immunodeficiency virus
Ana N Hyatt, Jordan E Lake, Department of Medicine, Division of Infectious Diseases, UTHealth Houston, Houston, TX 77030, United States
Sai K Kuchana, Eduardo Vilar-Gomez, Holly Crandall, Samer Gawrieh, Naga Chalasani, Archita P Desai, Department of Medicine, Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, United States
Richard K Sterling, Division of Gastroenterology, Hepatology, and Nutrition, Virginia Commonwealth University Medical Center, Richmond, VA 23219, United States
Susanna Naggie, Department of Internal Medicine, School of Medicine, Clinical Research Institute, Duke University, Durham, NC 27708, United States
Sonya L Heath, Department of Internal Medicine, University of Alabama at Birmingham Medical Center, Birmingham, AL 35233, United States
Jennifer C Price, Department of Medicine, Division of Gastroenterology and Hepatology, University of California San Francisco, San Francisco, CA 94143, United States
Laura A Wilson, Mark S Sulkowski, Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, United States
Rohit Loomba, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
ORCID number: Ana N Hyatt (0000-0001-8041-4331); Richard K Sterling (0000-0002-8637-2475); Samer Gawrieh (0000-0002-2056-4909); Archita P Desai (0000-0001-8731-2370); Jordan E Lake (0000-0002-0640-5514).
Author contributions: Hyatt AN, Desai AP, and Lake JE wrote the manuscript; Hyatt AN, Kuchana SK, Vilar-Gomez E, Desai AP, and Lake JE analyzed the data; Sterling RK, Naggie S, Heath SL, Price JC, Wilson LA, Crandall H, Gawrieh S, Chalasani N, Loomba R, and Sulkowski MS performed the research and revised the manuscript for the human immunodeficiency virus non-alcoholic steatohepatitis clinical research network. All authors have read and approved the final manuscript.
Supported by National Institutes of Health, No. R01DK121378, No. R01DK126042, and No. P30DK120515.
Institutional review board statement: This study was approved by the Committee for the Protection of Human Subjects of UTHealth, No. HSC-MS-16-1064.
Informed consent statement: Each participant provided a signed informed consent prior to any study procedures.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: Some or all datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ana N Hyatt, MD, Department of Medicine, Division of Infectious Diseases, UTHealth Houston, 6431 Fannin Street, Houston, TX 77030, United States. ana.n.hyatt@uth.tmc.edu
Received: May 6, 2025
Revised: May 28, 2025
Accepted: July 9, 2025
Published online: August 7, 2025
Processing time: 95 Days and 0.9 Hours

Abstract
BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) and poor sleep are common among people with human immunodeficiency virus (PWH) and may mediate the impaired health-related quality of life (HRQoL) seen in PWH and in people with MASLD. However, the prevalence and burden of poor sleep in PWH and MASLD is not well described.

AIM

To study the prevalence and multi-faceted relationship between MASLD, poor sleep, and HRQoL in PWH.

METHODS

In this cross-sectional, observational study, adult PWH and no other known cause of liver disease underwent controlled attenuated parameter (for hepatic fat) and liver stiffness measurement via vibration-controlled transient elastography at eight United States. centers (July 2021 to November 2024). Sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI) and HRQoL by Rand 36-Item Short Form Health Survey. Outcomes were compared using standard methods. Multivariate regression examined associations between sleep quality, HRQoL, and clinical factors.

RESULTS

PWH (n = 1005) on suppressive antiretroviral therapy had mean age 55 years and were 65% non-White and 27% cisgender female; 77% had body mass index (BMI) > 25 kg/m2, 44% had MASLD (controlled attenuated parameter ≥ 263 dB/minute), 13% liver fibrosis (liver stiffness measurement ≥ 8 kPa) and 64% poor sleep quality (PSQI > 5). The mean ± SD of PSQI was 6.6 ± 4.1, with no differences by MASLD status; MASLD + fibrosis was associated with poorer sleep. HRQoL was low (< 50) overall: A step-wise decline in physical component summary (PCS) scores was associated with worse liver disease, from no MASLD to MASLD + fibrosis. Among poor sleepers, a similar step-wise PCS worsening occurred. In multivariate analysis, MASLD + fibrosis was associated with lower PCS and poor sleep was associated with worse physical and mental HRQoL.

CONCLUSION

In this cohort of PWH, there was no association between MASLD and sleep quality. Poor sleep, MASLD and liver fibrosis were independently associated with poor HRQoL.

Key Words: Metabolic dysfunction-associated steatotic liver disease; Human immunodeficiency virus; Health-related quality of life; Sleep disorders; Liver fibrosis

Core Tip: The prevalence and burden of poor sleep in people with human immunodeficiency virus and metabolic dysfunction-associated steatotic liver disease (MASLD) and its impact on health-related quality of life (HRQoL) are not well described. We studied these complex relationships in a cohort of 1005 virologically suppressed adult people with human immunodeficiency virus and MASLD, who underwent vibration-controlled transient elastography and self-reported questionnaires at eight United States centers. MASLD prevalence was high, 64% had poor sleep quality and HRQoL was low overall. There was no association between MASLD and sleep quality but poor sleep, MASLD and liver fibrosis were independently associated with poor HRQoL.
INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly non-alcoholic fatty liver disease, is the most common liver disease worldwide[1], with a general population prevalence of up to 39%[2]. MASLD is associated with risk of progression to metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, and hepatocellular carcinoma[3,4], and significantly reduces health-related quality of life (HRQoL) in the general population[5]. People with human immunodeficiency virus (PWH) experience higher prevalence of hepatic steatosis (13%-50%)[6-9], higher rates of progression from MASLD to MASH (63% vs 37%) and greater MASH severity than the general population[10]. Traditional risk factors (older age, sedentary lifestyle) and human immunodeficiency virus (HIV)- and antiretroviral therapy-specific factors (dyslipidemia, increased microbial translocation) likely contribute, and chronic inflammation from both HIV and hepatic steatosis may facilitate tissue injury, fibrosis and, ultimately, organ dysfunction in this population[8].

PWH also have a higher prevalence of sleep disorders (21%-35%) than persons without HIV[11]. Among PWH, insomnia is the most commonly reported sleep disorder and is associated with significantly worse HRQoL[12], similar to the general population[13]. Additionally, comorbidities associated with sleep disorders in PWH include myocardial infarction (insomnia), depressive symptoms (insomnia, restless legs syndrome) and pain (insomnia)[11]. Responses from validated questionnaires assessing dimensions of sleep quality, e.g., sleep dissatisfaction and sleep disruption, report prevalence of poor sleep quality of 45%-70% in some cohorts of PWH[14-17].

A substantial body of epidemiological evidence links sleep disturbances and liver disease bi-directionally[18]. Observational studies have shown an independent association between MASLD and sleep disorders. Indeed, sleep duration, insomnia, sleep-wake disorders and especially obstructive sleep apnea (OSA) may increase MASLD incidence[19,20]. Poor sleep may also mediate the impaired HRQoL seen in people with MASLD[21,22]. Poor sleep quality contributes to the pathogenesis of metabolic disorders including insulin resistance, adipose tissue dysfunction, weight gain, and systemic inflammation that ultimately contribute to chronic, comorbid metabolic disease, particularly the development and progression of MASLD[18]. At the molecular level, relationships between circadian rhythm disruption (which may be triggered by sleep disturbances) and MASLD development have been documented in the general population[23], such that alterations in the hepatic circadian clock can affect hepatic lipid deposition[24,25], leading to accelerated onset and progression of hepatic steatosis. Despite the high prevalence of both poor sleep quality and MASLD in PWH, the prevalence and HRQoL-related burden of poor sleep in PWH and MASLD is not well described. We explored the relationship among prevalence of MASLD, sleep quality, and HRQoL in a multi-ethnic cohort of PWH.

MATERIALS AND METHODS
Study population

Details of the study population have previously been described[9,26]. Briefly, for this cross-sectional, observational study, consecutive PWH aged ≥ 18 years were prospectively enrolled from July 2021 to November 2024 at 8 centers across 7 states in the United States, including Duke University, Indiana University, Johns Hopkins University, University of Alabama Birmingham, University of California San Diego, University of California San Francisco, UTHealth Houston, and Virginia Commonwealth University.

Included participants had a documented history of HIV-1 on suppressive antiretroviral therapy (ART) for 6 months with HIV-1 RNA < 200 copies/mL and no other known cause of liver disease, defined as positive hepatitis B virus surface antigen in serum at any time before enrollment, evidence of recent or current hepatitis C virus (HCV), defined as presence of anti-HCV antibody with detectable HCV RNA in serum within 3 years prior to enrollment, alpha-1-antitripsin deficiency, Wilson disease, hemochromatosis, polycystic liver disease, autoimmune hepatitis or primary biliary cholangitis. For this analysis, participants with excessive alcohol intake, defined as Alcohol Use Disorder Identification Test (AUDIT) score ≥ 8 and/or meeting criteria for alcohol-related liver disease (ALD) or metabolic ALD[27], and those who were currently pregnant, had disseminated or advanced malignancy or were unable to complete study procedures were excluded. Each participant provided a signed informed consent prior to any study procedures. A single Institutional Review Board reviewed and approved the study protocol.

Study procedures

Clinical assessments: Sociodemographic characteristics, including age, gender, race, ethnicity, education level and marital status, smoking status, medical and medications history, and ART history were self-reported; area deprivation index (ADI), which ranks neighborhoods by socioeconomic disadvantage (a ranking of 1 indicates the lowest level of “disadvantage” within the nation and an ADI with a ranking of 100 indicates the highest level of “disadvantage”), was calculated by inputting participant’s home address in the Neighborhood Atlas from the University of Wisconsin Center for Health Disparities Research map[28,29]; basic laboratory and HIV (HIV-1 RNA, CD4+ T-cell count) variables were measured at local certified laboratories; anthropometrics (BMI, waist circumference) were measured by study staff according to standardized protocols; food insecurity was assessed using the six-item short form United States Department of Agriculture Household Food Security Survey, which is validated in the general population and used extensively in PWH[26,30-32]. We grouped low and very low food security into a combined “food insecure” category, consistent with prior food insecurity analyses[26,33]. Elevated waist circumference was defined as > 94 cm for persons assigned female sex at birth or > 95 cm for persons assigned male sex at birth. Hypertension was defined as blood pressure ≥ 130/85 mmHg or antihypertensive drug treatment. Dysglycemia was defined as fasting glucose ≥ 100 mg/dL, hemoglobin A1c ≥ 5.7 mg/dL, diagnosed type 2 diabetes (T2D) or on T2D medications. Dyslipidemia was defined as plasma triglycerides ≥ 150 mg/dL or plasma high-density lipoprotein ≤ 40/50 mg/dL for persons assigned male/female sex at birth or on lipid-lowering treatment.

Steatosis and fibrosis assessments: Hepatic fat content (controlled attenuated parameter) and liver stiffness measurement (LSM) were obtained non-invasively using vibration controlled transient elastography (VCTE, FibroScanÒ, Echosens, Paris, France) by trained study staff. Steatosis was defined as controlled attenuated parameter ≥ 263 dB/m and clinically significant liver fibrosis (CSF) as LSM ≥ 8 kPa, similar to previous analyses[9,34,35]. M or XL probes could be employed, where appropriate. Participants were instructed to fast for ≥ 3 hours before VCTE. Unreliable LSM was defined as interquartile range (IQR) > 30% of the median[36].

Per the recent multi-society Delphi consensus, cardiometabolic risk factors considered in MASLD definition included: (1) BMI ≥ 25 kg/m2 or waist circumference ≥ 94 cm (men) and 80 cm (women), or ethnically adjusted equivalents; (2) Fasting blood sugar ≥ 100 mg/dL, hemoglobin A1c ≥ 5.7%, T2D or treatment for T2D; (3) Blood pressure ≥ 130/85 mmHg or anti-hypertensive treatment; (4) Plasma triglycerides ≥ 150 mg/dL or lipid-lowering treatment; and (5) High-density lipoprotein cholesterol ≤ 40 mg/dL (persons assigned male sex at birth) and 50 mg/dL (persons assigned female sex at birth) or lipid-lowering treatment[27]. MASLD was defined as steatosis with ≥ 1 cardiometabolic risk factor and low alcohol intake (≤ 2 standard drinks/day).

Questionnaires: (1) Alcohol use: The AUDIT questionnaire was developed by the World Health Organization to assess alcohol consumption in the past year[37]. The AUDIT is a simple method of screening for excessive drinking and has become the world’s most widely used alcohol screening instrument since its publication in 1989. The AUDIT has been extensively validated, and a score of ≥ 8 indicates a strong likelihood of risky drinking or harmful alcohol use[38]; (2) HRQoL: HRQoL was assessed using the Rand 36-Item Short Form Health Survey (SF-36) version 1.0, a widely used and validated health survey to evaluate the impact of chronic diseases, including MASLD and HIV, on several physical and mental domains of health[39-42]. SF-36 measures 8 dimensions of HRQoL: Physical functioning, role limitations due to physical health, emotional well-being, role limitations due to emotional problems, energy/fatigue, social functioning, pain and general health. The SF-36 dimensions are summarized using the physical component summary (PCS) and mental component summary (MCS) scores. PCS is a summary measure of self-reported physical function, role limitations due to physical function, pain and general health. MCS is a summary measure of self-reported emotional function, role limitations due to emotional problems, social function and energy/fatigue. SF-36 scores are continuous, ranging 0-100. In the United States general population, the mean ± SD for the PCS and MCS is 50 ± 10[43]; and (3) Sleep quality: Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), a self-report questionnaire that evaluates sleep quality over a one-month time interval[44]. The 19 self-reported items belong to 1 of 7 subcategories: Subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction. Scores for each question range from 0-3, with higher scores indicating more severe sleep disturbances. The PSQI total score ranges from 0 (best) to 21 (worst). A global PSQI score > 5 yields a sensitivity of 89.6% and specificity of 86.5% (κ = 0.75, P < 0.001) in distinguishing good and poor sleepers[44,45].

Statistical analysis

Baseline characteristics were summarized and compared by MASLD status (MASLD vs non-steatotic liver disease), presence of CSF (LSM < 8 vs ≥ 8 kPa) and sleep quality (PSQI ≤ 5 vs > 5). Continuous variables were reported as mean ± SD for normally distributed data and medians with IQR for non-normally distributed data. Categorical variables were expressed as frequencies and percentages. The Shapiro-Wilk test was used to assess the normality of data distributions. Missing data accounted for less than 12% and imputation was performed to address the missing values.

For group comparisons, independent t-tests were used for normally distributed continuous variables and Mann-Whitney U tests were applied for non-normally distributed variables. Categorical variables were compared using χ2 tests, with Fisher’s exact test employed when appropriate. For comparisons involving three or more groups, ANOVA was utilized. A P value < 0.05 was considered statistically significant. Pearson correlation analysis was performed to evaluate relationships between sleep quality (PSQI subcategories) and HRQoL (PCS and MCS scores). Correlation coefficients were tested for significance, with P values < 0.01 considered statistically significant.

Multivariate linear regression analyses were conducted to examine the associations between HRQoL (PCS and MCS scores), sleep quality and other clinical predictors. For the multivariable regression models, we selected variables based on their significance (P < 0.05) in the descriptive analysis or with a previously recognized association with exposures or study outcomes. Only variables with P < 0.05 in the univariate regression analysis were included in the multivariate models. To address potential multicollinearity, variance inflation factor analysis was performed, and variables with a variance inflation factor > 0.5 were excluded from the final models.

For analysis focusing on sleep quality, only participants with PSQI > 5 were included. Within this subset, comparisons of HRQoL subdomains were conducted across three groups stratified by MASLD status and presence of CSF. Differences across groups were assessed using one-way ANOVA. Box plots were generated to represent the distributions of PCS and MCS by MASLD status. All statistical analyses and visualizations were performed using Python (version 3.9) within Jupyter Notebook (version 7.2.2), leveraging the pandas, NumPy, statsmodels, SciPy and matplotlib libraries for data processing and visualization.

RESULTS
Study population

Of 1126 participants, 92 were excluded for metabolic ALD, 28 for ALD and 1 for missing data, leaving 1005 participants in the analysis population. Demographic and clinical characteristics are presented in Table 1. Briefly, age was 55 ± 12 years; 27% were cisgender women and 5% transgender/gender diverse; 35% were non-Hispanic White, 52% non-Hispanic Black and 13% Hispanic; 84% attended the equivalent of ≥ 12 years of school, 22% had a long-term partner, 33% reported food insecurity and ADI was 56 ± 31. All PWH had HIV-1 RNA < 200 copies/mL, had been living with HIV for 21 ± 11 years, had median (IQR) CD4+ T-cell count 675 (516, 917) cells/mm3 and 86% were receiving integrase strand-transfer inhibitors as part of their ART. Overall, 77% had BMI > 25 kg/m2 (41% ≥ 30 kg/m2) and 59% had elevated waist circumference. Nearly half (44%) had MASLD, 13% CSF, 75% hypertension, 58% dysglycemia, 71% dyslipidemia, 23% were current smokers and 20% had OSA.

Table 1 Demographics and clinical characteristics of the analysis population by metabolic-associated steatotic liver disease status, n (%).
Entire cohort, N = 1005
Non-SLD, N = 562
MASLD, N = 443
P value
Age, years55 ± 12.354 ± 13.156 ± 11.10.005
Gender0.02
Male683 (68)389 (69)294 (67)
Female274 (27)138 (25)136 (31)
Transgender/gender diverse48 (5.1)35 (7)13 (3.2)
Race/ethnicity< 0.001
Non-Hispanic White351 (35)179 (32)172 (39)
Non-Hispanic Black527 (52)329 (59)198 (45)
Hispanic127 (13)54 (10)73 (17)
Education: High school or above844 (84)476 (85)368 (83)0.01
Married/long-term partner216 (21.5)122 (22)94 (21)0.7
Food insecurity 333 (33)199 (35)134 (30)0.09
Area deprivation index55.8 (30.5)53.5 (31.3)58.7 (29.1)0.001
Body mass index, kg/m2< 0.01
< 25232 (23)202 (36)30 (7)
25-29.9357 (36)216 (38)141 (32)
≥ 30416 (41)144 (26)272 (61)
Waist circumference, cm99.5 ± 1692.9 ± 13107.7 ± 15< 0.01
Elevated waist circumference589 (59)324 (58)265 (60)0.55
Hypertension751 (74.7)399 (71.0)352 (79.5)< 0.001
Dysglycemia581 (57.8)274 (48.8)307 (69.3)< 0.01
Dyslipidemia710 (71)342 (61)368 (83)< 0.001
Current smoker226 (23)161 (29)65 (15)< 0.01
Obstructive sleep apnea< 0.01
Never779 (78)458 (81)321 (72)
Treated142 (14)52 (9)90 (20)
Symptomatic, not treated84 (8)52 (9)32 (7)
HIV-related characteristics
Living with HIV, years20.6 ± 10.520.5 ± 10.820.7 ± 10.00.7
CD4+ T-cell count675 (516, 917)672 (497, 907)686 (539, 931)0.3
Antiretroviral therapy
NRTI885 (88)485 (86)400 (90)0.07
NNRTI201 (20)116 (21)85 (19)0.6
Efavirenz23 (2)9 (2)14 (3)0.1
PI124 (12)66 (12)58 (13)0.6
INSTI867 (86)486 (87)381 (86)0.9
Laboratory
Total cholesterol, mmol/L4.5 ± 1.14.4 ± 1.14.5 ± 1.00.1
Triglycerides, mmol/L1.56 ± 1.391.28 ± 0.71.9 ± 1.8< 0.01
LDL cholesterol, mmol/L2.6 ± 0.92.5 ± 0.92.7 ± 1.00.07
HDL cholesterol, mmol/L1.3 ± 0.41.3 ± 0.41.2 (0.4< 0.01
ALT, U/L 27 ± 2224 ± 1932 ± 24< 0.01
AST, U/L27 ± 1725 ± 1728 ± 180.01
Alkaline phosphatase, U/L79 ± 2977 ± 2782 ± 300.01
Bilirubin, direct, μmol/L3.08 ± 1.73.25 ± 1.72.91 ± 1.370.06
Bilirubin, total, μmol/L10.4 ± 8.610.6 ± 8.510.1 ± 8.60.1
Albumin, mmol/L0.6 ± 0.060.6 ± 0.060.6 ± 0.060.8
Platelet count/μL242901 ± 71174240215 ± 70614246303 ± 718130.1
Creatinine, μg/L97.3 ± 79106.1 ± 7997.3 ± 790.7
Serum glucose, mmol/L5.6 ± 1.95.3 ± 1.46.1 ± 2.3< 0.01
HbA1c, mmol/mol40 ± 138 ± 143 ± 1< 0.01
Vibration-controlled transient elastography
CAP, dB/m253 (214, 298)218 (191, 240)305 (281, 337)< 0.01
LSM, kPa5.0 (4.1-6.5)4.8 (3.9-6.1)5.3 (4.3-6.9)< 0.01
LSM ≥ 8 kPa 135 (13)49 (9)86 (19)< 0.01
LSM ≥ 12 kPa38 (4)12 (2)26 (6)0.001
MASLD: Metabolic dysfunction-associated steatotic liver disease; Non-SLD: Non-steatotic liver disease; HIV: Human immunodeficiency virus; NRTI: Nucleoside reverse transcriptase inhibitor; NNRTI: Non-nucleoside reverse transcriptase inhibitor; PI: Protease inhibitor; INSTI: Integrase strand transfer inhibitor; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HbA1c: Hemoglobin A1c; CAP: Controlled attenuation parameter; LSM: Liver stiffness measurement.
PWH and MASLD

Compared to PWH without SLD, PWH and MASLD were more likely to be older, female, to live in a more resource-deprived area and had higher rates of obesity, hypertension, dysglycemia, dyslipidemia and OSA (Table 1). PWH and MASLD were less likely to be non-Hispanic Black or have completed the equivalent of ≥ 12 years of school and there were no differences in the frequency of elevated waist circumference, years living with HIV, CD4+ T-cell count or ART. Compared to PWH and MASLD without CSF, PWH, MASLD, and CSF were more likely to be Hispanic and to have obesity, elevated waist circumference, hypertension, dysglycemia, dyslipidemia, and treated OSA (Supplementary Table 1).

Sleep quality in PWH by MASLD status

Overall, 64% of PWH had PSQI > 5, with mean ± SD of 6.6 ± 4.1 and no differences in total score or individual domains by MASLD status (Table 2). Compared to PWH with PSQI ≤ 5, PWH with PSQI > 5 were more likely to be current smokers and to have OSA, had higher rates of elevated waist circumference and food insecurity and were less likely to have a long-term partner (Supplementary Table 2). However, the subgroup of PWH, MASLD, and CSF had poorer sleep quality than PWH and MASLD without CSF (total PSQI 7.8 ± 4.2 vs 6.5 ± 4.0, P = 0.02). There were significant differences in sleep disturbances (1.6 ± 0.7 vs 1.3 ± 0.7, P = 0.01) and sleep latency (1.8 ± 1.0 vs 1.2 ± 1.1, P < 0.001) (Table 2).

Table 2 Sleep quality and health related quality of life in people with human immunodeficiency virus by metabolic dysfunction-associated steatotic liver disease status.
Entire cohort, N = 1005
Non-SLD, N = 562
MASLD, N = 443
P value
MASLD and LSM < 8, N = 369
MASLD and LSM ≥ 8, N = 74
P value
Sleep quality: PSQI
PSQI >563.6%63.0%64.3%0.763.4%68.9%0.4
Total PSQI score6.6 ± 4.16.5 ± 4.16.7 ± 4.10.56.5 ± 4.07.8 ± 4.20.02
Daytime dysfunction0.6 ± 0.80.6 ± 0.80.5 ± 0.80.070.5 ± 0.80.6 ± 0.70.4
Sleep disturbances1.4 ± 0.71.4 ± 0.71.4 ± 0.70.91.3 ± 0.71.6 ± 0.70.01
Sleep duration0.7 ± 0.90.7 ± 0.90.7 ± 0.90.50.7 ± 0.90.7 ± 0.90.9
Habitual sleep efficiency0.8 ± 1.00.8 ± 1.00.8 ± 1.00.30.8 ± 1.01.0 ± 1.00.08
Sleep latency1.3 ± 1.11.3 ± 1.11.3 ± 1.10.51.2 ± 1.11.8 ± 1.0< 0.001
Needs medication0.8 ± 1.20.8 ± 1.20.9 ± 1.30.40.9 ± 1.20.9 ± 1.40.8
Sleep quality1.0 ± 0.81.0 ± 0.81.1 ± 0.80.61.02 ± 0.81.2 ± 0.80.1
Health-related quality of life: SF-36
Physical component summary score47.3 ± 10.948.4 ± 10.545.9 ± 11.2< 0.00146.6 ± 10.942.5 ± 12.40.004
Physical functioning76.6 ± 28.478.9 ± 27.273.6 ± 29.70.00375.3 ± 28.664.9 ± 33.50.01
Role limitations due to physical health71.04 ± 40.073.2 ± 39.268.4 ± 41.10.0669.8 ± 40.261.0 ± 44.90.1
Pain 74.5 ± 27.776.2 ± 26.772.3 ± 28.90.0273.2 ± 28.868.0 ± 29.10.2
General health65.0 ± 22.566.3 ± 22.163.4 ± 22.90.0465.2 ± 22.054.5 ± 25.30.001
Mental component summary score48.7 ± 11.548.1 ± 12.349.5 ± 10.50.0749.7 ± 10.648.3 ± 9.90.3
Role limitations due to emotional health70.4 ± 40.671.0 ± 40.669.6 ± 40.60.670.5 ± 39.965.3 ± 43.90.4
Energy/fatigue60.9 ± 23.561.10 ± 24.160.7 ± 22.80.861.8 ± 22.455.1 ± 24.10.03
Emotional well-being73.1 ± 20.572.2 ± 21.474.2 ± 19.30.174.9 ± 19.170.7 ± 19.70.09
Social functioning78.5 ± 26.078.0 ± 27.179.1 ± 24.60.579.9 ± 24.275.3 ± 26.60.2
Non-SLD: Non-steatotic liver disease; MASLD: Metabolic dysfunction-associated steatotic liver disease; PSQI: Pittsburgh Sleep Quality Index; SF-36: 36-Item Short Form Health Survey.
Factors associated with sleep quality in PWH

In univariate analysis (Supplementary Table 3), MASLD + CSF, being a cisgender female, not having a long-term partner, larger waist circumference, OSA, current smoker status and food insecurity were associated with higher PSQI scores (worse sleep quality). Lower PCS and MCS scores (worse HRQoL) were also associated with higher PSQI scores (worse sleep quality). Age, race/ethnicity, ADI and MASLD without CSF were not associated with sleep quality. In multivariate analysis (Table 3), being a cisgender female, not having a long-term partner, high-school education level or above, OSA and food insecurity were positively associated with PSQI (worse sleep quality). Lower PCS and MCS scores (worse HRQoL) remained independently associated with higher PSQI scores (worse sleep quality). Waist circumference, current smoker status, and presence of MASLD + CSF became non-significant.

Table 3 Factors associated with sleep quality in people with human immunodeficiency virus based on multivariate analysis.
Variable
β estimate
95%CI
P value
Age0.02-0.001 to 0.040.07
Gender, ref: Male
Female0.680.12-1.240.02
Transgender/gender diverse0.58-0.53 to 10710.3
Race/ethnicity, ref: Non-Hispanic White
Black0.09-0.39 to 0.590.7
Hispanic0.18-0.76 to 1.210.7
No long-term partner0.610.03-1.180.04
Education: High school or above1.000.34-1.670.002
Waist circumference, by 5 cm-0.06-0.14 to 0.030.2
Obstructive sleep apnea, ref: No sleep apnea
Not treated1.270.40-2.130.004
Treated1.330.61-2.04< 0.001
Current smoker0.31-0.27 to 0.900.3
Food insecurity0.970.44-1.49< 0.001
Area deprivation index-0.002-0.01 to 0.0050.5
MASLD, ref: Non-SLD
MASLD and LSM ≥ 8 kPa0.26-0.68 to 1.210.6
MASLD and LSM < 8 kPa-0.30-0.83 to 0.220.3
Health related quality of life
PCS-0.13-0.16 to -0.11< 0.01
MCS-0.15-0.17 to -0.12< 0.01
CI: Confidence interval; MASLD: Metabolic dysfunction-associated steatotic liver disease; Non-SLD: Non-steatotic liver disease; LSM: Liver stiffness measurement; PCS: Physical component summary score; MCS: Mental component summary score.
HRQoL in PWH by MASLD status

PWH had low (≤ 50) PCS (47.3 ± 10.9) and MCS (48.7 ± 11.5) scores (Table 2). PWH and MASLD had lower PCS total and subcomponent scores than PWH without MASLD (all P < 0.05, except for role limitations due to physical health, P = 0.06). In addition, there was a step-wise decline in PCS total and subcomponent scores from PWH without MASLD (48.4 ± 10.5) to PWH and MASLD without CSF (46.6 ± 10.9) to PWH, MASLD + CSF (42.5 ± 12.4, P < 0.01; Figure 1A and Table 2). MCS total and subcomponent scores did not differ by MASLD status (Figure 1B) in PWH. Although PWH with MASLD + CSF had similar total MCS scores to those with MASLD without CSF, the subcomponent scores trended lower in PWH and MASLD + CSF compared to those without CSF. Of all SF-36 domains, the lowest scores in PWH with MASLD + CSF were energy level and general health, and only the energy subcomponent score was statistically significant (P < 0.05).

Figure 1
Open in New Tab   Full Size Figure   Download Figure
Figure 1 Health related quality of life in people living with human immunodeficiency virus by metabolic-associated steatotic liver disease status. A: Box-whisker plot displaying the distribution of physical component summary scores; B: Box-whisker plot displaying the distribution of mental component summary scores. PCS: Physical component summary; Non-SLD: Non-steatotic liver disease; MASLD: Metabolic-associated steatotic liver disease; LSM: Liver stiffness measurement; MCS: Mental component summary. aP value < 0.05; bP value < 0.01; cP value < 0.001.
Health related quality of life in PWH with poor sleep by MASLD status

Among 639 PWH with PSQI > 5, there was a step-wise decline in PCS score from PWH without MASLD (46.3 ± 11.3) to PWH and MASLD without CSF (44.4 ± 11.2) to PWH, MASLD + CSF (38.6 ± 11.8, P < 0.001; Figure 2A; Supplementary Table 4). Similar declines were seen in the physical functioning and general health subcomponents (all P < 0.001), but not in role limitations due to physical health or pain. PWH with MASLD were more likely to have higher MCS score than PWH without MASLD, regardless of presence of CSF, but this did not reach statistical significance (Figure 2B; Supplementary Table 4). PWH and MASLD without CSF had higher subcomponent scores than both PWH without MASLD and PWH, MASLD and CSF.

Figure 2
Open in New Tab   Full Size Figure   Download Figure
Figure 2 Health related quality of life in people living with human immunodeficiency virus with poor sleep by metabolic-associated steatotic liver disease status. A: Box-whisker plot displaying the distribution in people with human immunodeficiency virus with poor sleep by metabolic-associated steatotic liver disease status of physical component summary scores; B: Box-whisker plot displaying the distribution in people with human immunodeficiency virus with poor sleep by metabolic-associated steatotic liver disease status of mental component summary scores. PCS: Physical component summary; Non-SLD: Non-steatotic liver disease; MASLD: Metabolic-associated steatotic liver disease; LSM: Liver stiffness measurement; MCS: Mental component summary. aP value < 0.05; bP value < 0.01; cP value < 0.001.
Factors associated with HRQoL in PWH

In univariate analysis (Supplementary Table 3), MASLD without CSF, higher PSQI score, being a cisgender female, not having a long-term partner, larger waist circumference, OSA, current smoker status, food insecurity and higher ADI were associated with worse PCS scores in PWH. Having the equivalent of ≥ 12 years of education and self-identification as transgender/gender diverse were associated with better PCS score. MASLD without CSF, age and race/ethnicity were not associated with PCS score. Being a cisgender female, not having a long-term partner, treated OSA and higher PSQI were associated with worse MCS score, whereas MASLD without CSF and self-identification as transgender/gender diverse were associated with better MCS score.

In multivariate analysis (Table 4), MASLD with or without CSF, PSQI score, being a cisgender female, larger waist circumference and food insecurity were negatively associated with PCS score, whereas self-identification as transgender/gender diverse, Hispanic ethnicity and having the equivalent of ≥ 12 years of education were positively associated with PCS score. Not having a long-term partner, OSA (treated or untreated), current smoker status and ADI were not independently associated with PCS score. PSQI scores and food insecurity were also negatively associated with MCS score, whereas self-identification as transgender/gender diverse, higher ADI and MASLD without fibrosis were positively associated with MCS score. Being a cisgender female, not having a long-term partner and treated OSA were not independently associated with MCS score.

Table 4 Factors associated with health-related quality of life in people with human immunodeficiency virus based on multivariate analysis.
VariablePCS
MCS
β estimate
95%CI
P value
β estimate
95%CI
P value
Age0.03-0.02 to 0.080.2-0.02-0.08 to 0.030.4
Gender, ref: Male
Female-3.10-4.5 to -1.69< 0.01-0.65-2.17 to 0.870.4
Others2.807-0.06 to 5.670.053.690.61-6.750.02
Race/ethnicity, ref: Non-Hispanic White
Black0.54-0.72 to 1.790.4-0.6-1.95 to 0.750.4
Hispanic2.520.003-5.020.04-1.49-4.19 to 1.190.3
No long-term partner-0.33-1.80 to 1.140.7-1.36-2.9 to 0.210.09
Education: High school or above2.550.86-4.240.0030.22-1.59 to 2.030.8
Waist circumference, by 5 cm-0.60-0.81 to -0.39< 0.01-0.07-0.29 to 0.150.5
Obstructive sleep apnea, ref: No sleep apnea
Not treated-1.12-3.34 to 1.100.30.69-1.69 to 3.070.57
Treated-0.78-2.62 to 1.050.40.01-1.95 to 1.980.9
Current smoker-0.59-2.09 to 0.910.40.11-1.49 to 1.720.9
Food insecurity-2.9-4.25 to -1.56< 0.01-3.77-5.21 to -2.33< 0.01
Area deprivation index0.003-0.01 to 0.020.70.030.007-0.050.007
MASLD, ref: Non-SLD
MASLD and LSM ≥ 8 kPa-3.35-5.76 to -0.950.0061.83-0.75 to 4.410.1
MASLD and LSM < 8 kPa-1.82-3.15 to -0.480.0071.580.15 to 3.010.03
PSQI score-0.86-1.01 to -0.71< 0.01-1.17-1.33 to -1.01< 0.01
PCS: Physical component summary score; MCS: Mental component summary score; CI: Confidence interval; MASLD: Metabolic dysfunction-associated steatotic liver disease; Non-SLD: Non-steatotic liver disease; LSM: Liver stiffness measurement; PSQI: Pittsburgh Sleep Quality Index.
DISCUSSION

In this cross-sectional analysis of a large, multi-center cohort of diverse, middle-aged PWH on suppressive ART, we highlight the high prevalence of poor sleep in PWH, with the majority of our participants reporting deficits in various areas of sleep quality. Our study is the first to establish that: (1) MASLD and resulting liver fibrosis compound poor quality of sleep in PWH; and (2) Both MASLD status and sleep quality are independently associated with HRQoL (and vice versa). In fact, among those with poor sleep, the presence of MASLD and CSF were related to a progressive decline in physical components of HRQoL. With newly available treatments for MASH and related CSF, our study identifies an important pathway to improving HRQoL in PWH. In addition to treatment of MASLD and CSF, our data also highlight other modifiable factors such as obesity, treatment of sleep apnea and food insecurity that can serve as targets to improve both sleep quality and HRQoL in PWH. Taken together, our study provides important data on prevalence of sleep disturbances, MASLD and CSF in PWH while documenting its significant and detrimental impact on HRQoL.

Our study provides important epidemiological data within a unique, large, multi-center cohort of diverse, middle-aged PWH on suppressive ART. MASLD prevalence was higher (44%) than in the general population[3,4], but within the range of what has been previously reported in PWH[6,7,9,34,46], and the population had high rates of other metabolic comorbidities. Sleep plays a well-established and critical role in health outcomes and overall wellbeing[47]. Prior studies have associated circadian rhythm disruptions with development and progression of metabolic diseases, including MASLD[23]. For example, short sleep duration, poor sleep quality, insomnia and sleep-wake disorders, particularly OSA, have been robustly linked with MASLD[19,20,48,49] and advanced liver disease[18]. However, there is less evidence for how MASLD/MASH may disrupt the molecular clock, with most of the evidence pertaining to patients with cirrhosis and/or hepatocellular carcinoma[18,50]. In this cohort, prevalence of poor sleep quality, defined as PSQI > 5, was notably higher than in previous reports in PWH[11,14,15], although there were no differences in total score or individual domains by MASLD status, aligned with observational studies in the general population[51]. Additionally, MASLD was not independently associated with sleep quality once adjusted for other comorbid conditions such as obesity and OSA. Factors independently associated with worse sleep quality included being a cisgender female, not having a long-term partner, having the equivalent of ≥ 12 years of education, treated and untreated OSA and food insecurity. Age, race/ethnicity, waist circumference, current smoker status, and ADI were not associated with sleep quality. However, among PWH and MASLD, those with CSF reported poorer sleep quality than those without, with the highest mean PSQI score, indicating the poorest sleep quality, observed in a subset of participants with MASLD and LSM ≥ 12 kPa (data not shown). These findings are consistent with hypothesis in the general population that progression to liver fibrosis may involve inflammatory and metabolic pathways that adversely affect sleep quality. While similar mechanisms likely apply to PWH, the cross-sectional design of this study limits mechanistic exploration.

Our study also contributes important data on the HRQoL-burden in PWH and MASLD, for which there are currently limited available data. In this cohort, PWH demonstrated low (≤ 50) mean HRQoL summary scores and PWH and MASLD had clinically significant lower PCS total and subcomponent scores than those without MASLD[52]. Among them, those with CSF had the lowest scores, consistent with studies in the general population[22,53]. Of all SF-36 domains, the lowest scores were energy level and general health, similar to previous studies among PWH[54]. Interestingly, MCS total and subcomponent scores did not differ by MASLD or CSF status, possibly explained by the fact that this cohort is unique in that all are long-time survivors of their HIV diagnosis, have HIV that is well-managed and have significant access to resources to manage their chronic disease; perhaps this cohort is better able to manage the psychological burden of an additional chronic disease. In multivariate analysis, MASLD with or without CSF was associated with lower PCS scores, while MASLD without fibrosis was associated with higher MCS scores. Interestingly, MASLD with or without CSF was not associated with HRQoL in previous studies[55,56], including our own recent cross-sectional, prospective study of HRQoL in another cohort of PWH and MASLD, possibly explained by the differences in sample size[57].

We also examined the incremental impact of poor sleep and MASLD on HRQoL in PWH. Our findings demonstrating the impact of poor sleep quality on HRQoL in PWH is consistent with existing knowledge in the field[58-61]. Adding to this body of literature, we show that among PWH with poor sleep quality (PSQI > 5), there was a step-wise worsening in self-reported HRQoL, specifically in the PCS score, from PWH without MASLD to PWH and MASLD without CSF to PWH, MASLD and CSF, with no difference seen in the MCS component. Furthermore, in multivariate analysis, poor sleep quality, MASLD and CSF status were independently associated with worse physical and mental components of HRQoL. These data identify important modifiable targets for reducing the burden of poor HRQoL faced by PWH. Future studies should study the impact of newly available treatments for MASLD and CSF on HQROL in PWH. In addition, comprehensive care models for PWH should include targeted treatment of sleep disturbances, especially in those with metabolic comorbidities, where the prevalence of poor sleep is the highest and may be fueling the development of metabolic dysfunction-related complications.

Interestingly, better HRQoL was independently associated with self-identification as transgender/gender diverse, Hispanic ethnicity and having the equivalent of ≥ 12 years of education, while worse HRQoL was associated with larger waist circumference, being a cisgender female, higher ADI and food insecurity. Previously, social determinants of health have been largely associated with prevalence of advanced liver disease[26,62] and impaired HRQoL[54,63], although mechanisms underlying these associations remain uncertain[53]. These results highlight additional modifiable targets for health delivery interventions, especially community-based interventions, in PWH and MASLD to improve their HRQoL.

Related, we observed independent associations between gender and both sleep quality and HRQoL. Being a cisgender female was independently associated with poorer sleep quality and lower PCS score; in univariate analysis, being a cisgender female was associated with lower MCS, but the association became non-significant in multivariate analysis. Self-identification as transgender/gender diverse was independently associated with better HRQoL (higher PCS and MCS scores) but not sleep quality. Of note, we did not capture details of gender-affirming therapy or lack thereof; as such, we cannot perform further analysis.

This study has important strengths, including a large and diverse sample size prospectively enrolled from multiple centers; the comprehensive collection of socio-economic status details; the availability of a control group composed exclusively of PWH; the use of all element scores of the PSQI and SF-36 questionnaires to assess sleep quality and HRQoL, respectively; and the consistent use of VCTE to evaluate liver fat content and fibrosis. As in any study, we also note a few limitations; first, the cross-sectional study design, which precludes the inference of causal relationships. Second, analyses were based on self-reported responses to questionnaires (PSQI and SF-36) at a single time point; while these instruments are well-validated, the observed associations between sleep quality, HRQoL, and MASLD need to be confirmed by longitudinal studies, possibly using objective measures of sleep quality. Third, we acknowledge that HIV-related chronic inflammation may influence both sleep quality and hepatic steatosis, complicating interpretation of our findings. However, the cross-sectional design and lack of an HIV-negative control group limit mechanistic insight. Future studies are needed to clarify these pathways. Fourth, our results may not apply to PWH not suppressed on ART.

CONCLUSION

In conclusion, we observed a high prevalence of MASLD and poorer sleep quality than previously reported among PWH. We also characterized the associations of sleep quality with MASLD and its impact on HRQoL in this vulnerable population, advancing the understanding of these complex interactions. Longitudinal studies are needed to further clarify relationships between MASLD, sleep, and quality of life for PWH.

ACKNOWLEDGEMENTS

The authors would like to thank the participants and study staff for their time and effort. The views expressed here are those of the authors and do not necessarily reflect those of the National Institutes of Health or the United States Government.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country of origin: United States

Peer-review report’s classification

Scientific Quality: Grade A, Grade A, Grade A, Grade B, Grade C

Novelty: Grade A, Grade A, Grade B, Grade C, Grade D

Creativity or Innovation: Grade A, Grade B, Grade B, Grade C, Grade D

Scientific Significance: Grade A, Grade A, Grade B, Grade C, Grade D

P-Reviewer: Liang GD; Zhang JF; Zhou HX S-Editor: Wu S L-Editor: A P-Editor: Zhang XD

References
1.  Younossi ZM, Marchesini G, Pinto-Cortez H, Petta S. Epidemiology of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis: Implications for Liver Transplantation. Transplantation. 2019;103:22-27.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 196]  [Cited by in RCA: 272]  [Article Influence: 45.3]  [Reference Citation Analysis (0)]
2.  Riazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, Swain MG, Congly SE, Kaplan GG, Shaheen AA. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851-861.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 149]  [Cited by in RCA: 1160]  [Article Influence: 386.7]  [Reference Citation Analysis (1)]
3.  Anstee QM, Reeves HL, Kotsiliti E, Govaere O, Heikenwalder M. From NASH to HCC: current concepts and future challenges. Nat Rev Gastroenterol Hepatol. 2019;16:411-428.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 814]  [Cited by in RCA: 961]  [Article Influence: 160.2]  [Reference Citation Analysis (0)]
4.  Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64:73-84.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 5322]  [Cited by in RCA: 7512]  [Article Influence: 834.7]  [Reference Citation Analysis (0)]
5.  Younossi Z, Aggarwal P, Shrestha I, Fernandes J, Johansen P, Augusto M, Nair S. The burden of non-alcoholic steatohepatitis: A systematic review of health-related quality of life and patient-reported outcomes. JHEP Rep. 2022;4:100525.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 36]  [Cited by in RCA: 57]  [Article Influence: 19.0]  [Reference Citation Analysis (0)]
6.  Verna EC. Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in patients with HIV. Lancet Gastroenterol Hepatol. 2017;2:211-223.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 24]  [Cited by in RCA: 49]  [Article Influence: 6.1]  [Reference Citation Analysis (0)]
7.  Nishijima T, Gatanaga H, Shimbo T, Komatsu H, Nozaki Y, Nagata N, Kikuchi Y, Yanase M, Oka S. Traditional but not HIV-related factors are associated with nonalcoholic fatty liver disease in Asian patients with HIV-1 infection. PLoS One. 2014;9:e87596.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 32]  [Cited by in RCA: 44]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
8.  Vallet-Pichard A, Mallet V, Pol S. Nonalcoholic fatty liver disease and HIV infection. Semin Liver Dis. 2012;32:158-166.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 42]  [Cited by in RCA: 42]  [Article Influence: 3.2]  [Reference Citation Analysis (0)]
9.  Sebastiani G, Milic J, Tsochatzis EA, Marzolini C, Betel M, Bhagani S, Morse CG, Cinque F, Maurice JB, Ingiliz P, Price J, Lemoine M, Rockstroh JK, Guaraldi G; Steatohepatitis in HIV Emerging Research (SHIVER) Network. Letter to the Editor: People living with HIV and NAFLD-A population left behind in the global effort for liver fibrosis screening? Hepatology. 2023;78:E87-E88.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Reference Citation Analysis (0)]
10.  Vodkin I, Valasek MA, Bettencourt R, Cachay E, Loomba R. Clinical, biochemical and histological differences between HIV-associated NAFLD and primary NAFLD: a case-control study. Aliment Pharmacol Ther. 2015;41:368-378.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 75]  [Cited by in RCA: 105]  [Article Influence: 10.5]  [Reference Citation Analysis (0)]
11.  Kunisaki KM. Do sleep disturbances contribute to comorbidities in HIV? Curr Opin HIV AIDS. 2023;18:81-86.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 8]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
12.  Kunisaki KM, De Francesco D, Sabin CA, Winston A, Mallon PWG, Anderson J, Bagkeris E, Boffito M, Doyle N, Haddow L, Post FA, Sachikonye M, Vera J, Khalil W, Redline S. Sleep Disorders in Human Immunodeficiency Virus: A Substudy of the Pharmacokinetics and Clinical Observations in People Over Fifty (POPPY) Study. Open Forum Infect Dis. 2021;8:ofaa561.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 7]  [Cited by in RCA: 26]  [Article Influence: 5.2]  [Reference Citation Analysis (0)]
13.  Plotogea OM, Gheorghe G, Stan-Ilie M, Constantinescu G, Bacalbasa N, Bungau S, Diaconu CC. Assessment of Sleep among Patients with Chronic Liver Disease: Association with Quality of Life. J Pers Med. 2021;11:1387.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 7]  [Cited by in RCA: 3]  [Article Influence: 0.8]  [Reference Citation Analysis (0)]
14.  Milinkovic A, Singh S, Simmons B, Pozniak A, Boffito M, Nwokolo N. Multimodality assessment of sleep outcomes in people living with HIV performed using validated sleep questionnaires. Int J STD AIDS. 2020;31:996-1003.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 3]  [Cited by in RCA: 16]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
15.  Mohammadnejhad S, Najafi A, Earnshaw VA, Mousavi ME, Fotouhi A, Akbarpour S. Sleep traits and associated factors among people living with HIV/AIDS in Iran: a two-step clustering analysis. Sci Rep. 2024;14:5076.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Reference Citation Analysis (0)]
16.  Bedaso A, Abraham Y, Temesgen A, Mekonnen N. Quality of sleep and associated factors among people living with HIV/AIDS attending ART clinic at Hawassa University comprehensive specialized Hospital, Hawassa, SNNPR, Ethiopia. PLoS One. 2020;15:e0233849.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 24]  [Cited by in RCA: 30]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
17.  Mahboobi M, Najafi A, Nakhostin-Ansari A, Kazerooni PA, Bazargani M, Navaiian F, Akbarpour S. Depression, sleep quality and condom use amongst Iranian people living with human immunodeficiency virus. South Afr J HIV Med. 2020;21:1150.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 2]  [Reference Citation Analysis (0)]
18.  Marjot T, Ray DW, Williams FR, Tomlinson JW, Armstrong MJ. Sleep and liver disease: a bidirectional relationship. Lancet Gastroenterol Hepatol. 2021;6:850-863.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 17]  [Cited by in RCA: 58]  [Article Influence: 14.5]  [Reference Citation Analysis (0)]
19.  Bu LF, Xiong CY, Zhong JY, Xiong Y, Li DM, Hong FF, Yang SL. Non-alcoholic fatty liver disease and sleep disorders. World J Hepatol. 2024;16:304-315.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 5]  [Reference Citation Analysis (0)]
20.  Wijarnpreecha K, Thongprayoon C, Panjawatanan P, Ungprasert P. Short sleep duration and risk of nonalcoholic fatty liver disease: A systematic review and meta-analysis. J Gastroenterol Hepatol. 2016;31:1802-1807.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 50]  [Cited by in RCA: 52]  [Article Influence: 5.8]  [Reference Citation Analysis (0)]
21.  Papatheodoridi M, Pallini G, Aithal G, Lim HK, Cobbold J, Plaz Torres MC, Misas MG, Ryan J, Tomlinson J, Allison M, Longworth L, Tsochatzis EA. Health-related Quality of Life in Patients With Nonalcoholic Fatty Liver Disease: A Prospective Multi-center UK Study. Clin Gastroenterol Hepatol. 2023;21:3107-3114.e3.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 1]  [Cited by in RCA: 5]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
22.  Golabi P, Otgonsuren M, Cable R, Felix S, Koenig A, Sayiner M, Younossi ZM. Non-alcoholic Fatty Liver Disease (NAFLD) is associated with impairment of Health Related Quality of Life (HRQOL). Health Qual Life Outcomes. 2016;14:18.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 75]  [Cited by in RCA: 110]  [Article Influence: 12.2]  [Reference Citation Analysis (0)]
23.  de Assis LVM, Demir M, Oster H. The role of the circadian clock in the development, progression, and treatment of non-alcoholic fatty liver disease. Acta Physiol (Oxf). 2023;237:e13915.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 15]  [Reference Citation Analysis (0)]
24.  Fekry B, Ribas-Latre A, Baumgartner C, Mohamed AMT, Kolonin MG, Sladek FM, Younes M, Eckel-Mahan KL. HNF4α-Deficient Fatty Liver Provides a Permissive Environment for Sex-Independent Hepatocellular Carcinoma. Cancer Res. 2019;79:5860-5873.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 15]  [Cited by in RCA: 25]  [Article Influence: 4.2]  [Reference Citation Analysis (0)]
25.  Fekry B, Ribas-Latre A, Baumgartner C, Deans JR, Kwok C, Patel P, Fu L, Berdeaux R, Sun K, Kolonin MG, Wang SH, Yoo SH, Sladek FM, Eckel-Mahan K. Incompatibility of the circadian protein BMAL1 and HNF4α in hepatocellular carcinoma. Nat Commun. 2018;9:4349.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 71]  [Cited by in RCA: 79]  [Article Influence: 11.3]  [Reference Citation Analysis (0)]
26.  Kardashian A, Lloyd A, Vilar-Gomez E, Naggie S, Sulkowski MS, Woreta T, Lake JE, Crandall H, Loomba R, Wilson LA, Sterling RK, Heath S, Gawrieh S, Chalasani NP, Price JC; HIV NASH Clinical Research Network. Effects of Food Insecurity on Hepatic Steatosis and Fibrosis in People With HIV. Clin Gastroenterol Hepatol. 2024;22:1427-1435.e6.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Reference Citation Analysis (0)]
27.  Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, Romero D, Abdelmalek MF, Anstee QM, Arab JP, Arrese M, Bataller R, Beuers U, Boursier J, Bugianesi E, Byrne CD, Castro Narro GE, Chowdhury A, Cortez-Pinto H, Cryer DR, Cusi K, El-Kassas M, Klein S, Eskridge W, Fan J, Gawrieh S, Guy CD, Harrison SA, Kim SU, Koot BG, Korenjak M, Kowdley KV, Lacaille F, Loomba R, Mitchell-Thain R, Morgan TR, Powell EE, Roden M, Romero-Gómez M, Silva M, Singh SP, Sookoian SC, Spearman CW, Tiniakos D, Valenti L, Vos MB, Wong VW, Xanthakos S, Yilmaz Y, Younossi Z, Hobbs A, Villota-Rivas M, Newsome PN; NAFLD Nomenclature consensus group. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966-1986.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 1212]  [Cited by in RCA: 1275]  [Article Influence: 637.5]  [Reference Citation Analysis (0)]
28.  Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med. 2018;378:2456-2458.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 752]  [Cited by in RCA: 1983]  [Article Influence: 283.3]  [Reference Citation Analysis (0)]
29.  Center for Health Disparities Research, University of Wisconsin School of Medicine and Public Health.   Neighborhood Atlas®. [cited 8 January 2024]. Available from: https://www.neighborhoodatlas.medicine.wisc.edu/.  [PubMed]  [DOI]
30.  United States Department of Agriculture.   Guide to measuring household food security, revised 2000. [cited 8 January 2024]. Available from: https://fns-prod.azureedge.us/sites/default/files/FSGuide.pdf.  [PubMed]  [DOI]
31.  Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the Household Food Security Scale. Am J Public Health. 1999;89:1231-1234.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 507]  [Cited by in RCA: 667]  [Article Influence: 25.7]  [Reference Citation Analysis (0)]
32.  Hatsu I, Hade E, Campa A. Food Security Status is Related to Mental Health Quality of Life Among Persons Living with HIV. AIDS Behav. 2017;21:745-753.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 13]  [Cited by in RCA: 14]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
33.  Seligman HK, Laraia BA, Kushel MB. Food insecurity is associated with chronic disease among low-income NHANES participants. J Nutr. 2010;140:304-310.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 962]  [Cited by in RCA: 991]  [Article Influence: 66.1]  [Reference Citation Analysis (0)]
34.  Gawrieh S, Lake JE, Debroy P, Sjoquist JA, Robison M, Tann M, Akisik F, Bhamidipalli SS, Saha CK, Zachary K, Robbins GK, Gupta SK, Chung RT, Chalasani N, Corey KE. Burden of fatty liver and hepatic fibrosis in persons with HIV: A diverse cross-sectional US multicenter study. Hepatology. 2023;78:578-591.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 12]  [Cited by in RCA: 16]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
35.  Siddiqui MS, Vuppalanchi R, Van Natta ML, Hallinan E, Kowdley KV, Abdelmalek M, Neuschwander-Tetri BA, Loomba R, Dasarathy S, Brandman D, Doo E, Tonascia JA, Kleiner DE, Chalasani N, Sanyal AJ; NASH Clinical Research Network. Vibration-Controlled Transient Elastography to Assess Fibrosis and Steatosis in Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2019;17:156-163.e2.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 335]  [Cited by in RCA: 449]  [Article Influence: 74.8]  [Reference Citation Analysis (0)]
36.  Castera L. Noninvasive methods to assess liver disease in patients with hepatitis B or C. Gastroenterology. 2012;142:1293-1302.e4.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 438]  [Cited by in RCA: 451]  [Article Influence: 34.7]  [Reference Citation Analysis (0)]
37.  World Health Organization.   AUDIT: the Alcohol Use Disorders Identification Test: guidelines for use in primary health care. [cited 8 January 2024]. Available from: https://www.who.int/publications/i/item/WHO-MSD-MSB-01.6a..  [PubMed]  [DOI]
38.  Knight JR, Sherritt L, Harris SK, Gates EC, Chang G. Validity of brief alcohol screening tests among adolescents: a comparison of the AUDIT, POSIT, CAGE, and CRAFFT. Alcohol Clin Exp Res. 2003;27:67-73.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 120]  [Reference Citation Analysis (0)]
39.  McHorney CA, Ware JE Jr, Lu JF, Sherbourne CD. The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care. 1994;32:40-66.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 3052]  [Cited by in RCA: 3200]  [Article Influence: 103.2]  [Reference Citation Analysis (0)]
40.  Ware JE, Kosinski M. Interpreting SF-36 summary health measures: a response. Qual Life Res. 2001;10:405-413; discussion 415.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 397]  [Cited by in RCA: 428]  [Article Influence: 18.6]  [Reference Citation Analysis (0)]
41.  Assimakopoulos K, Karaivazoglou K, Tsermpini EE, Diamantopoulou G, Triantos C. Quality of life in patients with nonalcoholic fatty liver disease: A systematic review. J Psychosom Res. 2018;112:73-80.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 26]  [Cited by in RCA: 37]  [Article Influence: 5.3]  [Reference Citation Analysis (0)]
42.  Wen H, Yang Z, Zhu Z, Han S, Zhang L, Hu Y. Psychometric properties of self-reported measures of health-related quality of life in people living with HIV: a systematic review. Health Qual Life Outcomes. 2022;20:5.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 3]  [Cited by in RCA: 15]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
43.  Ware JE, Snow KK, Kosinski MA, Gandek BG.   SF-36 Health Survey: Manual and Interpretation Guide. Boston: Nimrod Press, 1993.  [PubMed]  [DOI]
44.  Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193-213.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 17520]  [Cited by in RCA: 21921]  [Article Influence: 608.9]  [Reference Citation Analysis (0)]
45.  Shahid A, Wilkinson K, Marcu S, Shapiro CM.   Pittsburgh Sleep Quality Index (PSQI). In: Shahid A, Wilkinson K, Marcu S, Shapiro CM. STOP, THAT and One Hundred Other Sleep Scales. New York: Springer, 2011: 279-283.  [PubMed]  [DOI]  [Full Text]
46.  Lemoine M, Assoumou L, Girard PM, Valantin MA, Katlama C, De Wit S, Campa P, Rougier H, Meynard JL, Necsoi C, Huefner AD, Van Luzen J, Schulze Zur Wiesch J, Bastard JP, Fellahi S, Mauss S, Stankov MV, Baumgarten A, Post G, Serfaty L, Ratziu V, Menu Y, Schlue J, Bedossa P, Capeau J, Costagliola D, Behrens G, Ingiliz P; ANRS-ECHAM group. Screening HIV Patients at Risk for NAFLD Using MRI-PDFF and Transient Elastography: A European Multicenter Prospective Study. Clin Gastroenterol Hepatol. 2023;21:713-722.e3.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 4]  [Cited by in RCA: 15]  [Article Influence: 7.5]  [Reference Citation Analysis (0)]
47.  Ramos AR, Wheaton AG, Johnson DA. Sleep Deprivation, Sleep Disorders, and Chronic Disease. Prev Chronic Dis. 2023;20:E77.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 22]  [Reference Citation Analysis (0)]
48.  Kim D, Kim HJ, Kushida CA, Heo NY, Ahmed A, Kim WR. Short Sleep Duration Is Associated With Abnormal Serum Aminotransferase Activities and Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2018;16:588-590.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 19]  [Cited by in RCA: 17]  [Article Influence: 2.4]  [Reference Citation Analysis (0)]
49.  Peng K, Lin L, Wang Z, Ding L, Huang Y, Wang P, Xu Y, Lu J, Xu M, Bi Y, Wang W, Chen Y, Ning G. Short sleep duration and longer daytime napping are associated with non-alcoholic fatty liver disease in Chinese adults. J Diabetes. 2017;9:827-836.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 29]  [Cited by in RCA: 48]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
50.  Montagnese S, De Pittà C, De Rui M, Corrias M, Turco M, Merkel C, Amodio P, Costa R, Skene DJ, Gatta A. Sleep-wake abnormalities in patients with cirrhosis. Hepatology. 2014;59:705-712.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 70]  [Cited by in RCA: 74]  [Article Influence: 6.7]  [Reference Citation Analysis (0)]
51.  Takahashi A, Anzai Y, Kuroda M, Kokubun M, Kondo Y, Ogata T, Fujita M, Hayashi M, Imaizumi H, Abe K, Tanji N, Ohira H. Effects of sleep quality on non-alcoholic fatty liver disease: a cross-sectional survey. BMJ Open. 2020;10:e039947.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 7]  [Cited by in RCA: 15]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
52.  Hays RD, Morales LS. The RAND-36 measure of health-related quality of life. Ann Med. 2001;33:350-357.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 845]  [Cited by in RCA: 1025]  [Article Influence: 42.7]  [Reference Citation Analysis (0)]
53.  Czapla BC, Dalvi A, Hu J, Moran IJ, Wijarnpreecha K, Chen VL. Physical activity, diet, and social determinants of health associate with health related quality of life and fibrosis in MASLD. Sci Rep. 2025;15:7976.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 1]  [Cited by in RCA: 1]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
54.  George S, Bergin C, Clarke S, Courtney G, Codd MB. Health-related quality of life and associated factors in people with HIV: an Irish cohort study. Health Qual Life Outcomes. 2016;14:115.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 42]  [Cited by in RCA: 59]  [Article Influence: 6.6]  [Reference Citation Analysis (0)]
55.  Michel M, Labenz C, Anders M, Wahl A, Girolstein L, Kaps L, Kremer WM, Huber Y, Galle PR, Sprinzl M, Schattenberg JM. Effect of hepatic steatosis and associated metabolic comorbidities on health-related quality of life in people living with HIV. Hepatol Commun. 2022;6:2011-2021.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 5]  [Cited by in RCA: 11]  [Article Influence: 3.7]  [Reference Citation Analysis (0)]
56.  Michel M, Wahl A, Anders M, Alqahtani SA, Kremer WM, Galle PR, Labenz C, Grimm D, Sprinzl M, Schattenberg JM. Significant liver fibrosis is a predictor of poor health-related quality of life in people living with HIV. Qual Life Res. 2023;32:401-411.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Reference Citation Analysis (0)]
57.  Gawrieh S, Corey KE, Lake JE, Samala N, Desai AP, Debroy P, Sjoquist JA, Robison M, Tann M, Akisik F, Bhamidipalli SS, Saha CK, Zachary K, Robbins GK, Gupta SK, Chung RT, Chalasani N. Non-alcoholic fatty liver disease is not associated with impairment in health-related quality of life in virally suppressed persons with human immune deficiency virus. PLoS One. 2023;18:e0279685.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 2]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
58.  Paunio T, Korhonen T, Hublin C, Partinen M, Kivimäki M, Koskenvuo M, Kaprio J. Longitudinal study on poor sleep and life dissatisfaction in a nationwide cohort of twins. Am J Epidemiol. 2009;169:206-213.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 69]  [Cited by in RCA: 80]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
59.  Groeger JA, Zijlstra FR, Dijk DJ. Sleep quantity, sleep difficulties and their perceived consequences in a representative sample of some 2000 British adults. J Sleep Res. 2004;13:359-371.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 214]  [Cited by in RCA: 218]  [Article Influence: 10.9]  [Reference Citation Analysis (0)]
60.  Kudrnáčová M, Kudrnáč A. Better sleep, better life? testing the role of sleep on quality of life. PLoS One. 2023;18:e0282085.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 13]  [Cited by in RCA: 24]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
61.  Pujasari H, Levy J, Culbert G, Steffen A, Carley D, Kapella M. Sleep disturbance, associated symptoms, and quality of life in adults living with HIV in Jakarta, Indonesia. AIDS Care. 2021;33:39-46.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 6]  [Article Influence: 1.2]  [Reference Citation Analysis (0)]
62.  Ivancovsky-Wajcman D, Brennan PN, Kopka CJ, Zelber-Sagi S, Younossi ZM, Allen AM, Flórez KR, Lazarus JV. Integrating social nutrition principles into the treatment of steatotic liver disease. Commun Med (Lond). 2023;3:165.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 11]  [Reference Citation Analysis (0)]
63.  Reid RJ, Safford M, Lambert WM, Bryan J, Pinheiro LC, Sterling MR, Bowling CB, Levitan EB, Banerjee S, Durant R, Kim M, Lau JD, Goyal P. The Cumulative Burden of Social Risk Factors and 10-Year Change in Quality of Life. J Gerontol A Biol Sci Med Sci. 2024;80:glae222.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Reference Citation Analysis (0)]