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. Author manuscript; available in PMC: 2025 Dec 11.
Published in final edited form as: AIDS. 2025 Oct 7;40(3):322–329. doi: 10.1097/QAD.0000000000004378

Menopause is associated with faster increases in insulin resistance in women with HIV

Rebecca A Abelman a, Yifei Ma a, C Christina Mehta b, Qian Yang b, Fan Xia a, James B Brock c, Maria L Alcaide d, Anjali Sharma e, Michelle Floris-Moore f, Elizabeth Topper g, Kathleen M Weber h, Seble G Kassaye i, Deborah Gustafson j, Carl Grunfeld a, Cecile D Lahiri b, Phyllis C Tien a
PMCID: PMC12695438  NIHMSID: NIHMS2127825  PMID: 41065426

Abstract

Background:

Switching to an integrase strand transfer inhibitor (INSTI) during the menopausal transition has been associated with accelerated increases in visceral obesity, a risk factor for insulin resistance. Whether switching to an INSTI modifies the association of HIV and menopause with insulin resistance is unknown.

Methods:

From 2006 to 2019, 389 nonpregnant women with HIV (WWH) [133 who switched to an INSTI (INSTI+); 256 who did not switch (INSTI−)] and 334 women without HIV from the Women’s Interagency HIV study without diabetes or hepatitis C virus were included in the analysis. Mixed effect models evaluated the change in insulin resistance estimated through log HOMA-IR by HIV status by menopausal phase. We then compared trajectories by INSTI group. Menopausal phase was determined by anti-Müllerian hormone, a biomarker of ovarian reserve.

Results:

Compared to women without HIV, INSTI+ WWH in premenopause had nonstatistically significant faster annual increases in HOMA-IR [difference in slope: 7.03%; 95% confidence interval (CI): −4.99 to 20.58] whereas INSTI− WWH had nonstatistically significantly faster annual decreases [−1.01% (95% CI: −7.34 to 5.75)]. In late perimenopause, INSTI+ and INSTI− had 4.87% (95% CI:−3.59 to 14.06) and 4.38% (95%CI: −3.10 to 12.44) nonstatistically significantly faster annual increases in HOMA-IR, respectively. In menopause, INSTI+ and INSTI− WWH had 9.18% (95% CI: 1.60 to 17.33) and 11.28% (95% CI: 3.27 to 19.91) statistically significant faster annual increases in HOMA-IR than women without HIV. There was no statistically significant difference between INSTI+ and INSTI− in any menopausal phase.

Conclusion:

Regardless of switching to an INSTI or not, WWH in late perimenopause and menopause have faster increases in insulin resistance when compared to women without HIV. Diabetes screening and prevention in midlife WWH is imperative.

Keywords: menopause, HIV, diabetes, insulin resistance, antiretroviral therapy

Introduction

Women with HIV (WWH) are at higher risk for insulin resistance and diabetes than women without HIV [1,2]. The cause of this HIV-associated risk is likely multifactorial, including increased immune activation, environmental and behavioral exposures, an obesogenic environment, and certain antiretroviral therapy (ART) medications [2–4]. In the contemporary era, integrase strand transfer inhibitors (INSTIs) have been implicated in weight gain in people with HIV, particularly in women [5–7], and has thus been investigated as a driver of insulin resistance and diabetes. However, whether INSTIs increase diabetes risk and if this is exacerbated in WWH has been equivocal [2,7–11]; some studies have demonstrated an increased risk in insulin resistance and diabetes development with INSTI use, likely mediated by weight gain, whereas others have suggested null or decreased risk when compared to other antiretroviral medications [8–10,12–15].

In particular, older antiretroviral medications have been associated with increases in insulin resistance [3]. In a prior study in the Women’s Interagency HIV Study (WIHS), WWH had a higher median homeostasis model assessment of insulin resistance (HOMA-IR) than women without HIV, and WWH who had been exposed to older ART medications, particularly nucleoside reverse transcriptase inhibitors (NRTIs), were found to have higher rates of insulin resistance than their counterparts [2]. In this study, being in postmenopause was also associated with insulin resistance, likely due to increases in visceral adiposity.

Menopause, characterized by the cessation of menses due to the depletion of ovarian follicles, has been associated with increases in visceral adiposity and concomitant metabolic derangements in women without HIV [16–18]. We have previously found that in WWH who were exposed to older ART regimens, HIV appeared to blunt the accelerated increases in BMI and waist circumference, a marker of visceral adiposity, across the transition to menopause [19]. We also found that in a contemporary cohort of women, WWH who switched to an INSTI in late perimenopause and menopause had accelerated increases in waist circumference [20], suggesting that certain ART medications may alter the trajectory of metabolic changes that occur across the menopausal transition. However, whether there are resultant cardiometabolic consequences is unknown.

To address this knowledge gap, we conducted a longitudinal retrospective analysis to evaluate whether switching to an INSTI across the menopausal transition was associated with alterations in insulin resistance trajectories compared to WWH who did not switch and to demographically similar women without HIV. To determine menopausal phase, we used anti-Müllerian hormone (AMH), a biomarker of ovarian reserve.

Methods

Study design

We conducted a longitudinal study of WWH and demographically similar women without HIV enrolled in the WIHS (now part of the MACS-WIHS Combined Cohort Study, or MWCCS). Full details of WIHS recruitment, retention, and demographics have been published elsewhere [21,22]. Women were seen every 6 months for survey administration, biospecimen collection, and physical exam, including anthropometry. Fasting specimens for HOMA-IR were collected annually.

For this analysis, we included women with available HOMA-IR measurements within the Sex-specific Predictors, Pathways, and Cardiometabolic Effects (SPPACE) INSTI sub-study of the WIHS that spanned 2006–2019. This study included WWH who switched to an INSTI, WWH who were not previously exposed to an INSTI, and demographically similar women without HIV who were enrolled in the study during the study period. Women were considered part of the INSTI group if they switched to or added an INSTI and remained on the same INSTI regimen for at least 1 year. Except for INSTI switch, inclusion criteria were the same for all WWH. Further details including additional inclusion criteria of the SPPACE INSTI sub-study have been published previously [20,23].

Women were divided into three distinct groups: WWH who switched to an INSTI (INSTI+), WWH who did not switch to an INSTI (INSTI−), and women without HIV. The INSTI switch visit was the first study visit at which a participant reported INSTI use. The index visits for INSTI− and women without HIV were selected to reflect similar calendar time periods to the switch visit [24]. AMH levels were then used to determine menopausal phase for women at each visit.

For our analysis, we excluded women who did not have a HOMA-IR value prior to the switch visit, who had diabetes at baseline, who had unclear hysterectomy or bilateral oophorectomy status, or who had a history of hepatitis C virus (HCV antibody positive) due to its association with insulin resistance [4,25]].

Study measurements

Menopausal phase was determined using predicted AMH at each visit, as has been described previously [19,20,26]. After measuring serial AMH measures within the WIHS, we previously created prediction models for AMH declines to undetectable levels (<0.08ng/ml). Using these predicted models, we categorized menopausal phase as follows: premenopause, defined as at least 5 years before AMH became undetectable; early perimenopause, defined as 0 to less than 5 years before an undetectable AMH; late perimenopause, defined as 0 to up to 5 years after AMH became undetectable; and menopause, defined as greater than 5 years after AMH became undetectable [19,26].

Women who were 55 years or older and who reported hysterectomy or oophorectomy were categorized as postmenopausal. In women less than 55 years of age, the menopausal status of women with a history of hysterectomy or oophorectomy was determined using the last measured AMH value after hysterectomy or oophorectomy as described previously [20].

The primary outcome was the HOMA-IR, which is an indirect measurement of insulin resistance and is calculated using the formula (insulin×glucose)/405 with insulin measured in μIU/ml and glucose measured in mg/dl [27]. We selected HOMA-IR because it has been widely used in large epidemiologic studies to estimate insulin resistance and has been standardly used across the studies in the MWCCS. A cutoff of greater than 2 has been used mostly in general population epidemiologic studies to define insulin resistance, but cutoffs vary depending on the specific population characteristics. For our population of WWH, we therefore examined HOMA-IR as a continuous measure.

Covariates included race/ethnicity (white, Black, Hispanic, or other), smoking status (never, current, or former), alcohol consumption [abstain, light (>0–7 drinks/week), moderate (>7–12drinks/week), heavy (>12drinks/week)], depressive symptoms [Center for Epidemiological Studies Depression (CESD) scale >16 [28]], hypertension (defined by SBP ≥140mmHg, DBP ≥90mmHg, or use of antihypertensive medication), and chronic kidney disease (defined as EGFR <60ml/min/1.73m2 using CKD-EPI), and waist circumference (cm) due to its known association with insulin resistance [29]. HIV-related parameters included CD4+ T-cell count (nadir and current), HIV RNA (detectable vs. undetectable, <48copies/ml until 2011 then <20copies/ml), and current use of ART (yes/no).

Statistical analysis

Sociodemographic and clinical characteristics were summarized using median [interquartile range (IQR)] for continuous variables and percentage (frequency) for categorical variables at the index visit. We compared each characteristic by group using χ2 tests or Fisher’s exact tests for categorical variables and either t tests or Mann–Whitney tests for continuous variables, wherever appropriate. Statistical tests with a p-value of 0.05 were considered significant for all analyses.

We then assessed the change in insulin resistance over time using linear mixed models with a random intercept and random time effect for each participant. HOMA-IR was log transformed to better approximate normality. We fitted each model with a time×group and a time2×group interaction in addition to the main effect of time, assuming a nonlinear longitudinal trajectory. The time2×group interaction was excluded if it was not statistically significant, as this suggested fitting a linear trend was appropriate. None of the time2×group interactions reached statistical significance so a linear trend was used for all models.

Analyses included the visit preceding the index visit and at least one visit after the index visit. Models first evaluated the change in insulin resistance estimated through log HOMA-IR across 5 years of follow-up as a function of study group and time-varying menopausal phase. The models were stratified by the menopausal phase at the index visit. In each model, we adjusted for baseline age, menopausal phase at each visit, and the covariates specified above at each visit. Time intervals of 1 year were used as HOMA-IR was collected annually. HOMA-IR values were back-transformed with coefficients presented in percentages. Given some participants only had one AMH test preceding the index visit, each menopausal phase used the preceding phase as the reference (e.g. the reference for the late perimenopausal phase was early perimenopause). As tenofovir alafenamide (TAF) has been associated with weight gain in WWH [5], we then performed a sensitivity analysis among those who were on tenofovir disoproxil fumarate (TDF). Analyses were conducted using SAS 9.4 (SAS Institute, Cary, North Carolina, USA).

Results

Baseline characteristics

Of the 2063 women enrolled in the SPPACE INSTI substudy, 1026 did not have HOMA-IR values prior to switch, 14 had unclear hysterectomy or oophorectomy status, 137 had hepatitis C antibody positivity, and 166 had diabetes at baseline and thus were excluded from the analysis. Those excluded were slightly older (median age of 48 vs. 46 years), had slightly lower CD4+ nadirs (median 344 vs. 413 cells/μl), nominally higher mean viral loads (26 vs. 21 copies/ml), slightly lower median BMIs (29.3 vs. 29.9 kg/m2), and were more likely to be menopausal (results not shown). Of the 723 included participants, 389 were WWH (133 INSTI+ and 256 INSTI−) (Table 1). The majority of women identified as Black (66%) and non-Hispanic (76%). At the index visit, 40% were premenopausal, 10% were in early perimenopause, 23% were in late perimenopause, and 27% were menopausal. Women without HIV were more likely to drink alcohol and be current cigarette smokers. Women in the INSTI+ group were older than the INSTI− WWH and women without HIV (median age 50 vs. 46 vs. 44 years) and had higher median HOMA-IR values (2.05 vs. 1.83 vs. 1.68). Among WWH, those in the INSTI+ group were more likely to have viral suppression (62.5 vs. 51%) and to be on TAF than the INSTI− group (38 vs. 2.7%). INSTI+ WWH were less likely to be on efavirenz (3.0 vs. 26.6%) but had higher rates of older NRTI use (12 vs. 6.6%). Among the INSTI+ women, 22% were on raltegravir, 38% were on elvitegravir, and 31% were on dolutegravir at the switch visit.

Table 1.

Demographics and clinical characteristics by integrase strand transfer inhibitor group at the index visit.

N (%) INSTI+ INSTI- WWOH
[median (IQR)] (n = 133) (n = 256) (n = 334) P valueb
Age (years) 50 (43–56) 46 (40–53) 44 (36–51) <0.0001
Race/ethnicity
 White 26 (20%) 61 (24%) 56 (17%) 0.063
 Black 89 (67%) 151 (59%) 236 (71%)
 Othera 18 (14%) 44 (17%) 42 (13%)
 Hispanic 27 (20%) 73 (29%) 71 (21%) 0.073
Menopausal phase
 Premenopause 31 (23%) 94 (37%) 163 (49%) <0.0001
 Early perimenopause 9 (6.8%) 29 (11%) 37 (11%)
 Late perimenopause 36 (27%) 61 (24%) 67 (20%)
 Menopause 57 (43%) 72 (28%) 67 (20%)
Waist circumference (cm) 92.5 (85.7–111) 91.5 (83.4–103) 98.2 (86.6–109) 0.046
Current smoking 42 (32%) 78 (31%) 140 (45%) 0.0008
Drinking
 Abstainer 68 (52%) 157 (62%) 118 (38%) <0.0001
 1–7 drinks/week 54 (42%) 77 (30%) 121 (39%)
 8–12 drinks/week 1 (0.8%) 7 (2.8%) 22 (7.1%)
 >12 drinks/week 7 (5.4%) 13 (5.1%) 49 (16%)
Depression (CESD score) 6 (2–16) 7 (2–16) 8 (3–16) 0.360
Hypertension 59 (44%) 86 (34%) 117 (35%) 0.091
Chronic kidney disease 19 (15%) 26 (10%) 7 (2.5%) <0.0001
HOMA-IR 2.05 (1.32–3.68) 1.83 (1.09–3.09) 1.68 (0.96–2.81) 0.026
HIV-related factors
Undetectable HIV RNA 80 (63%) 115 (51%) – 0.031
CD4+ T cells (cells/μl) 715 (554–853) 617 (450–832) 944 (636–1160) <0.0001
Current tenofovir disoproxil fumarate (TDF) use 37 (28%) 214 (84%) –
Current Tenofovir alafenamide (TAF) use 50 (38%) 7 (2.7%) –
Current efavirenz use (EFV) 4 (3.0%) 68 (27%) –
Prior DDI, D4T, and AZT use 16 (12%) 17 (6.6%)
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Column percents may not add up to 100% due to rounding. Missing values were excluded when calculating frequencies/percentages. CESD, Center for Epidemiological Studies-Depression scale; IQR, interquartile; INSTI, integrase strand transfer inhibitor.

a

Includes Asian, Pacific Islander, Native American, Alaskan and other study participants.

b

P value from Pearson χ2 test, Wilcoxon rank-sum test or Fisher’s exact test.

In the evaluation of baseline characteristics and demographics by menopausal phase, median HOMA-IR increased from premenopause to menopause (Supplementary Table 1, http://links.lww.com/QAD/D699). TDF use also increased across menopausal phases.

Associations of menopausal phase with insulin resistance by INSTI status

Figure 1a–d shows the change in insulin resistance by INSTI group for each menopausal phase at the time of switch, adjusting for sociodemographic and clinical factors including waist circumference. Figures demonstrate the absolute changes in HOMA-IR over time.

Fig. 1.

Fig. 1.

Open in a new tab

Change in HOMA-IR by menopausal phase by integrase strand transfer inhibitor status.

In premenopause, when compared to women without HIV, WWH in the INSTI+ group had faster annual increases in HOMA-IR (difference in slope: 7.03%; 95% CI: −4.99 to 20.58), whereas women in the INSTI− group had annual decreases (−1.01%; 95%CI: −7.34 to 5.75), but the differences were not statistically significant for either comparison (Fig. 1a). There was also no statistically significant difference when comparing INSTI + and INSTI−.

In early perimenopause, when compared to women without HIV, the INSTI+ group had nonstatistically significant annual decreases (−8.10%; 95%CI: −19.97 to 5.53) and INSTI− had nonstatistically significant faster annual increases in HOMA-IR (3.68%; 95% CI: −7.80 to 16.59) (Fig. 1b). There was also no statistically significant difference when comparing INSTI+ and INSTI−.

In late perimenopause, when compared to women without HIV, INSTI+ and INSTI− women had 4.87% (95% CI: −3.59 to 14.06) and 4.38% (95% CI: −3.10 to 12.44) annual increases in HOMA-IR, respectively, but the difference was not statistically significant for either group (Fig. 1c). There was also no statistically significant difference between INSTI+ and INSTI−.

In menopause, when compared to women without HIV, the INSTI+ group had 9.18% (95% CI:1.60 to 17.33) and INSTI− women had 11.28% (95% CI: 3.27 to 19.91) statistically significantly faster annual increases in HOMA-IR (Fig. 1d). There was no statistically significant difference between INSTI+ and INSTI−.

Sensitivity analysis among those on tenofovir disoproxil fumarate

When limited to just those on a TDF-containing regimen, the trajectories appeared similar and were in the same direction (Supplementary Table 2, http://links.lww.com/QAD/D700). When comparing INSTI+ to INSTI−, there was no statistically significant difference between groups across all menopausal phases.

Discussion

We found that WWH in late perimenopause and especially menopause had a faster increase in insulin resistance compared to women without HIV. Notably, this increase was independent of visceral obesity as assessed by waist circumference. Our findings suggest that during the menopausal transition, WWH are at greater risk for faster increases in insulin resistance, which could increase the risk for type 2 diabetes, and it is not related to the use of INSTIs.

The association of treated HIV and menopause with greater HOMA-IR is suggested by our prior studies in WWH who had been exposed to older ART regimens. In an early study in WIHS, we found that HIV and menopause were independently associated with greater HOMA-IR, but most were on protease inhibitor-based regimens and the proportion reporting menopause was low [2]. In this more contemporary cohort of WWH, we found that INSTI+ women had faster increases in waist circumference in late perimenopause and menopause, a finding that was not seen in the INSTI− group [20]. Interestingly, in this study, we did not find commensurate INSTI-associated changes in insulin resistance. Rather, it was menopausal WWH who distinctly demonstrated increases in insulin resistance, whereas menopausal women without HIV had stable levels of insulin resistance followed by declines. The latter findings are consistent with the literature in the general population, which has not found menopause-associated changes in insulin resistance after adjustment for chronologic age [17,30]. Taken together, our findings suggest that there is neither a direct effect of INSTI on insulin resistance nor an indirect effect through visceral adiposity. Additional research is needed to understand the mechanism by which menopause affects insulin resistance.

Prior studies have yielded inconclusive findings regarding the effects of INSTI use on insulin resistance and diabetes. In a meta-analysis evaluating whether INSTIs were associated with a higher risk of incident diabetes or higher rates of insulin resistance in people with HIV, the authors found that INSTI therapy was associated with a lower risk of incident diabetes, especially when compared to NNRTIs [8]. Similarly, in a large study of ART-naïve individuals, people starting an INSTI vs. an NNRTI had a small-to-null association with incident diabetes when accounting for 12-month weight gain [10]. Notably, the majority of participants in these studies were male, and in the latter study, men were more likely to be started on an INSTI than women. By contrast, a prior study by Summers et al.[9] in the WIHS, including predominantly midlife WWH, found that women who switched to an INSTI had greater increases in HgbA1c when compared to WWH who did not switch. Although we did not see an INSTI-associated difference in our study of women, the Summers et al. analysis did not incorporate menopausal phase, which may explain this discrepancy.

Menopause is associated with body composition change and concomitant metabolic derangements in the women without HIV [17,31]], but whether similar effects are seen in WWH is incompletely characterized. Other general population studies have suggested that metabolic parameters begin to worsen in the perimenopausal period, such as acceleration of markers of adiposity [31], increases in lipid profiles [30], and elevated risk of metabolic syndrome development [32,33]. We have also previously found that in both women with and without HIV, late perimenopause and menopause were associated with shorter relative times to diabetes, independent of BMI [26]. In this analysis, we similarly found that HOMA-IR trajectories began to accelerate beginning in late perimenopause and spanning into menopause, and that the HIV effect was most pronounced in menopause. Although there were elevations in HOMA-IR in early perimenopause, we cannot make inferences from these data based on the small sample size; however, this may suggest a potential imprinting effect starting in the perimenopausal period. Whereas the trajectories of HOMA-IR in women without HIV began to decrease about a year after menopause, insulin resistance trajectories remained flat in WWH. Taken together, this suggests menopause may differentially impact insulin resistance in WWH.

The absence of an INSTI effect in insulin resistance among WWH may be due to sex differences in INSTI effects on adipose tissue. Prior in-vitro and in-vivo studies have found that INSTI exposure to adipose tissue leads to alterations contributing to insulin resistance and impaired insulin-mediated glucose uptake [34,35]. Building on this work, another study found that INSTI-related effects on adipocytes are altered by estrogen exposure [36], suggesting that INSTIs may impact adipose tissue and glucose uptake differentially by sex. The mechanism by which estrogen depletion in the setting of HIV might affect a possible association of INSTI with insulin resistance merits further study.

We did not find that TDF use altered HOMA-IR trajectories in WWH by menopausal phase. Prior studies have suggested that either TAF [5] or the cessation of TDF, which may have catabolic effects [37–39], contributes to the weight gain seen in WWH, which would serve as a driver of insulin resistance. For example, in a study applying diabetes predictive models to the ADVANCE cohort, an additional six cases of diabetes were predicted when comparing TAF to TDF-based regimens with an INSTI backbone at 96weeks [3,40], likely due to weight gain. However, in our study, when limiting to those on TDF, we did not see any changes in HOMA-IR trajectories. While there was low TAF uptake in our cohort precluding an analysis among those just on TAF, when adjusting for visceral obesity, the effects of TDF were small or null on insulin resistance levels, suggesting menopausal phase was a more important predictor of insulin resistance than tenofovir exposure.

Limitations of this study include examining INSTIs as a drug class rather than by individual agent, as there may be differential effects of INSTIs on insulin resistance and diabetes [3]. Due to attrition in longitudinal follow-up, we had fewer participants beyond 5 years, limiting our ability to evaluate longer term changes after INSTI switch. Due to the observational nature of this study, we could not determine causality, nor could we exclude that the demographic differences between the WWH and the women without HIV or other potential confounders, such as physical activity and diet, are contributing to the results seen here.

This study had several strengths. We leveraged a large geographically and racially/ethnically diverse US cohort of women with and without HIV across the reproductive lifespan. We had detailed ART characterization at the index visit as well as serial HOMA-IR and AMH measurements. This study includes women, particularly midlife WWH, who may require distinct screening or therapeutic interventions as they undergo the transition to menopause.

In conclusion, we found that WWH in late perimenopause and menopause have faster increases in insulin resistance when compared to women without HIV, and this increase was independent of switching to an INSTI or tenofovir exposure. These findings suggest that diabetes screening and prevention in midlife WWH is imperative, regardless of ART regimen.

Supplementary Material

Supplementary Table 1
Supplementary Table 2

Acknowledgements

The authors gratefully acknowledge the contributions of the study participants and dedication of the staff at the MWCCS sites.

The MWCCS is funded primarily by the National Institutes of Health (NIH), in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR).

Additional funding was provided through NHLBI (K24 HL176324, PI: CL), NIDDK (R01 DK125246, PI: CL) and NIAID (K24AI 108516, PI: PCT). Additional institutional grant support was provided through Merck.

Data in this manuscript were collected by the Women’s Interagency HIV Study (WIHS), now the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos, David Hanna, and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D’Souza, Stephen Gange and Elizabeth Topper), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen, Audrey French, and Ryan Ross), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky, Frank Palella, and Valentina Stosor), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, James B. Brock, Emily Levitan, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (M. Bradley Drummond and Michelle Floris-Moore), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), National Institute on Aging (NIA), National Institute of Dental & Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Institute of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), P30-AI-124414 (ERC-CFAR), P30-MH-116867 (Miami CHARM), UL1-TR001409 (DC CTSA), KL2-TR001432 (DC CTSA), and TL1-TR001431 (DC CTSA).

Conflicts of interest

R.A.A., institutional grant support from Merck. J.B. contracted research support from Gilead Sciences. M.A., Advisory board for Gilead Sciences. A.S., grant support from Gilead Sciences. M.F.-M., Viiv Healthcare Advisory Board. S.K., HIV-related medical education materials for Vindico CME. C.L., Institutional grant support from Merck; Advisory Board Member and Consultant for Theratechnologies, Inc. P.T., Institutional grant support from Merck.

References

  • 1.Tien PC, Schneider MF, Cox C, Karim R, Cohen M, et al. Association of HIV infection with incident diabetes mellitus: impact of using hemoglobin A1C as a criterion for diabetes. J Acquir Immune Defic Syndr 2012; 61:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Tien PC, Schneider MF, Cole SR, Levine AM, Cohen M, DeHovitz J, et al. Antiretroviral therapy exposure and insulin resistance in the Women’s Interagency HIV Study. JAIDS J Acquir Immune Defic Syndr 2008; 49:369–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bailin SS, Koethe JR. Diabetes in HIV: the link to weight gain. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Howard AA, Hoover DR, Anastos K, Wu X, Shi Q, Strickler HD, et al. The effects of opiate use and hepatitis C virus infection on risk of diabetes mellitus in the Women’s Interagency HIV Study. J Acquir Immune Defic Syndr 2010; 54:152–159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Sax PE, Erlandson KM, Lake JE, Mccomsey GA, Orkin C, Esser S, et al. Weight gain following initiation of antiretroviral therapy: risk factors in randomized comparative clinical trials. Clin Infect Dis 2020; 71:1379–1389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Venter WDF, Moorhouse M, Sokhela S, Fairlie L, Mashabane N, Masenya M, et al. Dolutegravir plus two different prodrugs of tenofovir to treat HIV. N Engl J Med 2019; 381:803–815. [DOI] [PubMed] [Google Scholar]
  • 7.Alvarez JA, Yang C-A, Ojuri V, Buckley K, Bedi B, Musonge-Effoe J, et al. Sex differences in metabolic disorders of aging and obesity in people with HIV. Curr HIV/AIDS Rep 2025; 22:3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Mulindwa F, Kamal H, Castelnuovo B, Byonanebye DM, Schwarz J-M, Bollinger R, Brusselaers N. Association between integrase strand transfer inhibitor use with insulin resistance and incident diabetes mellitus in persons living with HIV: a systematic review and meta-analysis. BMJ Open Diabetes Res Care 2023; 11:e003136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Summers NA, Lahiri CD, Angert CD, Aldredge A, Mehta CC, Ofotokun I, et al. Metabolic changes associated with the use of integrase strand transfer inhibitors among virally controlled women. J Acquir Immune Defic Syndr 2020; 85:355–362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Rebeiro PF, Jenkins CA, Bian A, Lake JE, Bourgi K, Moore RD, et al. Risk of incident diabetes mellitus, weight gain, and their relationships with integrase inhibitor–based initial antiretroviral therapy among persons with human immunodeficiency virus in the United States and Canada. Clin Infect Dis 2021; 73: e2234–e2242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Milic J, Renzetti S, Ferrari D, Barbieri S, Menozzi M, Carli F, et al. Relationship between weight gain and insulin resistance in people living with HIV switching to integrase strand transfer inhibitors-based regimens. AIDS 2022; 36:1643–1653. [DOI] [PubMed] [Google Scholar]
  • 12.O’Halloran JA, Sahrmann J, Parra-Rodriguez L, Vo DT, Butler AM, Olsen MA, et al. Integrase strand transfer inhibitors are associated with incident diabetes mellitus in people with human immunodeficiency virus. Clin Infect Dis 2022; 75: 2060–2065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Bares SH, Wu X, Tassiopoulos K, Lake JE, Koletar SL, Kalayjian R, et al. Weight gain after antiretroviral therapy initiation and subsequent risk of metabolic and cardiovascular disease. Clin Infect Dis 2024; 78:395–401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Batterham RL, Espinosa N, Katlama C, McKellar M, Scholten S, Smith DE, et al. Cardiometabolic parameters 3 years after switch to dolutegravir/lamivudine vs maintenance of tenofovir alafenamide–based regimens. Open Forum Infect Dis 2023; 10: ofad359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Gianotti N, Muccini C, Galli L, Poli A, Spagnuolo V, Andolina A, et al. Homeostatic model assessment for insulin resistance index trajectories in HIV-infected patients treated with different first-line antiretroviral regimens. J Med Virol 2019; 91: 1937–1943. [DOI] [PubMed] [Google Scholar]
  • 16.Donato GB, Fuchs SC, Oppermann K, Bastos C, Spritzer PM. Association between menopause status and central adiposity measured at different cutoffs of waist circumference and waistto-hip ratio. Menopause 2006; 13:280–285. [DOI] [PubMed] [Google Scholar]
  • 17.El Khoudary SR, Greendale G, Crawford SL, Avis NE, Brooks MM, Thurston RC, et al. The menopause transition and women’s health at midlife: a progress report from the Study of Women’s Health Across the Nation (SWAN). Menopause 2019; 26: 1213–1227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Carr MC. The emergence of the metabolic syndrome with menopause. J Clin Endocrinol Metab 2003; 88:2404–2411. [DOI] [PubMed] [Google Scholar]
  • 19.Abelman RA, Nguyen TTJ, Ma Y, Bacchetti P, Messerlian G, French AL, et al. Body composition changes over the menopausal transition in women with and without HIV. Clin Infect Dis 2023; 77:265–271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Abelman RA, Ma Y, Mehta CC, Yang Q, Xia F, Brock JB, et al. Switch to integrase strand transfer inhibitors during the menopausal transition is associated with accelerated body composition change in women with HIV. Clin Infect Dis 2024; 80: ciae540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Adimora AA, Ramirez C, Benning L, Greenblatt RM, Kempf M-C, Tien PC, et al. Cohort Profile: The Women’s Interagency HIV Study (WIHS). Int J Epidemiol 2018; 47:393i–394i. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.D’Souza G, Bhondoekhan F, Benning L, Margolick JB, Adedimeji AA, Adimora AA, et al. Characteristics of the MACS/WIHS Combined Cohort Study: opportunities for research on aging with HIV in the Longest US Observational Study of HIV. Am J Epidemiol 2021; 190:1457–1475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lahiri CD, Mehta CC, Yang Q, Maramba T, Musonge-Effoe J, Yang C-A, et al. Short and long-term body weight change following the switch to or the addition of integrase inhibitors in persons with HIV differs by sex. Clin Infect Dis 2025; 80: 575–584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Yu MA, Gerig L, Mehta CC, Musonge-Effoe J, Alvarez JA, Ofotokun I, et al. Noninvasive markers of hepatic steatosis and fibrosis following INSTI initiation in women with HIV. Clin Infect Dis 2025; 81:304–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Reid M, Ma Y, Scherzer R, Price JC, French AL, Plankey MW, et al. Higher CD163 levels are associated with insulin resistance in hepatitis C virus-infected and HIV-infected adults. AIDS 2017; 31:385–393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Abelman RA, Schneider MF, Cox C, Messerlian G, Cohen M, Gustafson D, et al. Association of androgen hormones, sex hormone binding globulin, and the menopausal transition with incident diabetes mellitus in women with and without HIV. J Acquir Immune Defic Syndr 2024; 95:486–493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28:412–419. [DOI] [PubMed] [Google Scholar]
  • 28.Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Measurement 1977; 1:385–401. [Google Scholar]
  • 29.Racette SB, Evans EM, Weiss EP, Hagberg JM, Holloszy JO. Abdominal adiposity is a stronger predictor of insulin resistance than fitness among 50–95year olds. Diabetes Care 2006; 29: 673–678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Matthews KA, Crawford SL, Chae CU, Everson-Rose SA, Sowers MF, Sternfeld B, et al. Are changes in cardiovascular disease risk factors in midlife women due to chronological aging or to the menopausal transition? J Am Coll Cardiol 2009; 54:2366–2373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Greendale GA, Sternfeld B, Huang M, Han W, Karvonen-Gutierrez C, Ruppert K, et al. Changes in body composition and weight during the menopause transition. JCI Insight 2019; 4: 124865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Mehta JM, Manson JE. The menopausal transition period and cardiovascular risk. Nat Rev Cardiol 2024; 21:203–211. [DOI] [PubMed] [Google Scholar]
  • 33.Janssen I, Powell LH, Crawford S, Lasley B, Sutton-Tyrrell K. Menopause and the metabolic syndrome: the Study of Women’s Health Across the Nation. Arch Intern Med 2008; 168: 1568–1575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Gorwood J, Bourgeois C, Pourcher V, Pourcher G, Charlotte F, Mantecon M, et al. The integrase inhibitors dolutegravir and raltegravir exert proadipogenic and profibrotic effects and induce insulin resistance in human/simian adipose tissue and human adipocytes. Clin Infect Dis 2020; 71:e549–e560. [DOI] [PubMed] [Google Scholar]
  • 35.Jung I, Tu-Sekine B, Jin S, Anokye-Danso F, Ahima RS, Brown TT, Kim SF. Dolutegravir suppresses thermogenesis via disrupting uncoupling protein 1 expression and mitochondrial function in brown/beige adipocytes in preclinical models. J Infect Dis 2022; 226:1626–1636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Jung I, Jin S, Tu-Sekine B, Anokye-Danso F, Brown TT, Kim SW. A loss of ER attenuates DTG-mediated disruption of thermogenesis in brown adipocytes. Conference on Retroviruses and Opportunistic Infections. Seattle, WA; 2023. [Google Scholar]
  • 37.Leonard MA, Cindi Z, Bradford Y, Bourgi K, Koethe J, Turner M, et al. Efavirenz pharmacogenetics and weight gain following switch to integrase inhibitor–containing regimens. Clin Infect Dis 2021; 73:e2153–e2163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.McComsey GA, Daar ES, O’Riordan M, Collier AC, Kosmiski L, Santana JL, et al. Changes in fat mitochondrial DNA and function in subjects randomized to abacavir-lamivudine or tenofovir DF–emtricitabine with atazanavir-ritonavir or efavirenz: AIDS Clinical Trials Group Study A5224s, Substudy of A5202. J Infect Dis 2013; 207:604–611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Shah S, Pilkington V, Hill A. Is tenofovir disoproxil fumarate associated with weight loss? AIDS 2021; 35 (Suppl 2): S189–S195. [DOI] [PubMed] [Google Scholar]
  • 40.McCann K, Shah S, Hindley L, Hill A, Qavi A, Simmons B, et al. Implications of weight gain with newer antiretrovirals: 10-year predictions of cardiovascular disease and diabetes. AIDS 2021; 35:1657–1665. [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Supplementary Table 1
NIHMS2127825-supplement-Supplementary_Table_1.docx (21KB, docx)
Supplementary Table 2
NIHMS2127825-supplement-Supplementary_Table_2.docx (15.2KB, docx)

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