Beyond HIV: Lifestyle as a Key Driver of Metabolic Syndrome in Midlife Women with HIV in the INSTIs era

doi:10.21203/rs.3.rs-7766291/v1

Download PDF

Short Report

Beyond HIV: Lifestyle as a Key Driver of Metabolic Syndrome in Midlife Women with HIV in the INSTIs era

https://doi.org/10.21203/rs.3.rs-7766291/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: The contribution of lifestyle factors compared with HIV infection and antiretroviral therapy (ART)-related determinants to Metabolic Syndrome (MetS) in Women with HIV (WoWH) remains uncertain. Objective: To evaluate the prevalence and determinants of MetS in the era of Integrase Strand Transfer Inhibitors (INSTIs).

Design: Monocentric retrospective cohort study.

Methods: We analyzed 297 WoWH to assess the prevalence of MetS and associated HIV-related and lifestyle factors.

Results: MetS was diagnosed in 80 women (26%). Those with MetS showed lower CD4 nadir, longer HIV and ART duration, and greater exposure to protease inhibitors and INSTIs, but none were independently associated with MetS or its components. Lifestyle emerged as the major determinant: physical activity was protective (OR 0.10, 95% CI 0.02–0.44), while physically demanding work increased risk (OR 6.58, 95% CI 2.03–26.26). Family dyslipidemia (OR 5.06, 95% CI 1.67–17.51) and former injection drug use (OR 11.72, 95% CI 1.23–134.03) raised risk.

Conclusions: Lifestyle factors outweighed HIV-related variables in MetS risk, highlighting the need for systematic lifestyle assessment and intervention in WoWH.

HIV

ART

Metabolic syndrome

INSTIs

TAF

Women with HIV

Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that, according to the United States National Cholesterol Education Program/Adult Treatment Panel III (USNCEP ATP-III) criteria [1], includes at least three among the following features: abdominal obesity, low levels of high-density lipoprotein cholesterol (HDL-Chol), insulin resistance, and hypertension. It is well documented that MetS is a strong predictor of type 2 diabetes, cardiovascular disease, renal disease, and premature mortality [2], significantly contributing to increasing the economic burden with increased medical and health care costs [3]. Among People With HIV (PWH), recent data report almost twice the risk of developing MetS than the general population [4]. The causal mechanisms of MetS differ between PHW and the general population, and are primarily attributable to the role of HIV-driven persistent inflammation and immune activation or to the metabolic adverse effects of the oldest Antiretroviral Treatment (ART) medications [5]. These effects add to the conventional risk factors for non-communicable diseases, which include advancing age, tobacco use, excessive alcohol intake, unhealthy dietary habits, and lack of physical activity [6]. Moreover, among women with HIV (WoWH), a combination of biological, hormonal, cultural, and social factors further increases the risk of MetS compared to both women without HIV and to men living with HIV [7,8].

Nevertheless, the relative contribution of lifestyle-related factors versus HIV- and ART-related determinants to the development of MetS has been insufficiently explored in the modern era of Integrase Strand Transfer Inhibitors (INSTIs). In this setting, ART initiation has dramatically changed in both timing—being now recommended soon after diagnosis, thereby reducing virus-driven inflammation [9]—and regimen composition, with current therapies showing a more favorable impact on lipid profiles compared to older antiretroviral drugs, particularly those from the Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) class [10].

In addition, evidence on the burden of individual MetS components is limited [3], and it remains unclear which features are the most prevalent and contribute most to MetS development according to HIV status and ART exposure.

The aim of our study is to assess the prevalence and risk factors for the development of MetS and its individual components in a cohort of middle-aged women with HIV (WoWH), and to evaluate the relative contributions of lifestyle-related versus HIV- and modern ART-related determinants.

This monocentric cohort study aimed to describe the prevalence of MetS, defined according to the 2005 NCEP-ATP III criteria [1], and its diagnostic components in the WoWH cohort receiving stable ART and followed at the outpatient clinic of the Infectious Diseases Department, Policlinic University Hospital in Bari.

We also sought to evaluate the relative contribution of lifestyle-related factors and HIV/ART-related variables in shaping the likelihood of developing MetS and its individual components.

Between January 1 and July 30, 2025, all women on ART for at least 6 months, in active follow-up (≥ 1 visit or blood test in the previous year), and able to provide written informed consent underwent a structured clinical assessment for MetS and a standardized interview to collect information on self-reported menopause status, occupational and physical activity, lifestyle habits, and family history of cardio metabolic co morbidities. Biochemical parameters (HDL- and LDL-cholesterol, triglycerides -TGL, and fasting glucose) were extracted from the Institutional electronic database. A descriptive analysis was performed, using Chi-square and Mann–Whitney U tests to compare WoWH with and without MetS. Uni- and multivariate Cox regression models were adjusted for lifestyle-, HIV-, and ART-related variables known from the literature to be associated with metabolic diseases, including chronological age, self-reported menopausal status, physical activity, type of occupation, smoking and alcohol habits, mode of HIV transmission, CD4 nadir, duration of HIV infection and ART, and prior exposure to Tenofovir Alafenamide (TAF), Protease Inhibitors (PIs), and INSTIs. These variables were evaluated in both univariate and multivariate analyses, first in relation to MetS and subsequently to its individual components (hypertension, hyperglycemia, HDL-Chol < 50 mg/dL, triglycerides > 150 mg/dL, waist circumference > 88 cm) as outcomes. Statistical analyses were performed using Jamovi v2.6.44. The study was approved by the Local Ethics Committee on June 16, 2025 (Prot. 2241/CEI).

A total of 297 WoWH were enrolled, with a median age of 58 (50–61) years.

Among them, 141 (55%) had a waist circumference > 88cm, 130 (46%) had blood pressure ≥ 130/80 mmHg, 101 (34%) had low levels of HDL-Chol, and 58 (20%) had fasting glucose levels > 100mg/dL.

MetS was diagnosed in 80 women, with a prevalence of 26%. Of these, 62 (80%) were postmenopausal and 14 (18%) premenopausal according to self-reported menopause status. Women with MetS had a slightly older age at menopause (52 vs 51 years, p = 0.036) and longer menopausal duration (8 [4–15] vs 4 [0–10] years) compared to those without MetS. In the MetS group, median body weight was 68 kg (Inter Quartile Range, IQR 73–82), waist circumference 94 cm (IQR 90–106), TGL 130 mg/dL (IQR 101–165), fasting glucose 101 mg/dL (IQR 88–113), HDL-Chol 46 mg/dL (IQR 40–50), and LDL Cholesterol (LDL-Chol) was 107 mg/dL (IQR 80–137). Women with MetS were older, had lower CD4 counts at diagnosis, and longer histories of HIV and ART. Although current ART regimens did not differ between groups, cumulative exposure to PIs and INSTIs was significantly longer among those with MetS (Table 1).

Table 1
Characteristics of 297 WoWH and Without Metabolic Syndrome (MetS).
Variables	WoWH without MetS (N = 217 )	WoWH with MetS (N = 80 )	Total (N = 297)	p-value§
Non Italian Nationality, n (%)	26 (12)	11 (14)	37 (12)	.682
Age, years, median (IQR)	56 (49–60)	60 (54–65)	56 (50–61)	< .001
< 40 years, n (%)	28 (13)	5 (6)	33 (11)	< .001
40–49 years, n%	27 /12)	3 (4)	30 (10)
51–59 years, n (%)	107 (49)	30 (37)	137 (46)
≥ 60 years, n (%)	55 (25)	42 (52)	97 (33)
HIV transmission route, n (%)
Heterosexual contact	128 (59)	40 (50)	168 (56)	.259
PWID	9 (4)	7 (9)	16 (5)
Blood products	3 (1)	1 (1)	4 (1)
Vertical	4 (2)	0 (0)	4 (1)
Unknown	73 (34)	32 (40)	35 (105)
HBsAg positive, n (%)	8 (4)	3 (4)	11 (4)	.998
HCV-Ab positive, n (%)	29 (13)	14 (18)	43 (15)	.388
CD4 nadir, cells/mL, median (IQR)	193 (107–289)	119 (70–123)	183 (90–274)	.003
CD4 nadir < 200 cells/mL, median (IQR)	109 (53)	50 (68)	159 (57)	.035
HIV RNA zenith, cp/mL, median (IQR)	81,500 (21,750 − 285,000)	88,000 (17,000-287,000)	86,000 (21,000-310,000)	.751
AIDS-defining conditions at baseline, n (%)	32 (15)	18 (23)	50 (17)	.107
History of HIV infection, years, median (IQR)	22 (14–31)	27 (21–33)	25 (15–32)	.006
> 10 years of HIV, n (%)	188 (87)	74 (92)	262 (88)	.164
History of ART, years, median (IQR)	20 (12–27)	25 (20–28)	22 (13–28)	< .001
> 10 years of ART, n (%)	173 (809)	71 (89)	244 (82)	.071
Undetectable, n (%)	172 (79)	65 (81)	237 (80)	.705
Current ART regimen, n (%)				.599
2 NRTIs + NNRTI	38 (17)	18 (22)	56 (19)
2 NRTIs + PI	6 (3)	6 (7)	12 (4)
2 NRTIs + INSTI	71 (34)	27 (34)	98 (33)
NRTI + INSTI	50 (23)	13 (16)	63 (21)
NNRTI + INSTI	42 (19)	11 (14)	53 (18)
Other	10 (5)	5 (6)	15 (5)
Currently on STR, n (%)	176 (81)	65 (81)	241 (81)	.978
Currently on LA ART regimen, n (%)	10 (5)	0 (0)	10 (3)	.051
Currently on 2DR regimen, n (%)	100 (46)	29 (36)	129 (43)	.129
Currently of TAF- based regimens, n (%)	87 (49)	38 (47)	60 (77)	.251
Currently on TDF-based regimens, n (%)	26 (12)	12 (15)	38 (12)	.49
History of TAF, n (%)	149 (70)	77 (52)	206 (70)	.599
Duration of TAF use, years, median (IQR)	3 (0–6)	4 (0–7)	4 (0–7)	.230
≥ 5 years TAF, n (%)	74 (34)	33 (41)	107 (36)	.255
History of PIs, n (%)	142 (67)	60 (76)	202 (70)	.009
Duration of PIs use, years, median (IQR)	3 (0–9)	7 (0–14)	4 (0–12)	.008
≥ 5 years PIs, n (%)	130 (62)	32 (43)	162 (57)	.005
≥ 10 years PIs, n (%)	54 (25)	33 (41)	97 (29)	.006
History of INSTIs, n (%)	169 (78)	57 (61)	226 (76)	.235
Duration of INSTIs use, years, median (IQR)	6 (2–9)	7 (3–9)	6 (2–9)	.440
≥ 5 years INSTIs, n (%)	112 (52)	42 (54)	154 (53)	.791
≥ 10 years INSTIs, n (%)	27 (13)	16 (21)	43 (15)	.009
§Chi-square Test/Mann-Whitney U-test as appropriate
WoWH: Women With HIV; MetS: Metabolic Syndrome; IQR: InterQuartile Range; HIV: Human Immunodeficency Virus; HBsAg: Hepatitis B virus Surface Antigen; HCVAb: Hepatitis C Virus Antibodies; PWID: people who inject drugs; AIDS: Acquired Immunodeficiency Syndrome; ART: Antiretroviral Regimen; NRTI: Nucleoside Reverse Transcriptase Inhibitor; LA ART: Long Acting Antiretroviral Treatment; STR: Single Tablet Regimen; TAF: Tenofovir Alafenamide; TDF: Tenofovir Disoproxil; 2DR: Two Drug Regimen

Family history of metabolic disease was frequent: 45 women (62%) reported dyslipidemia, 22 (31%) ischemic heart disease, and 20 (28%) DM, all more prevalent than in women without MetS. Alcohol abuse was rare (12% vs 11%, p = 0.90), while smoking was common (47% vs 48%, p = 0.87) in both groups. Most women with MetS (73%) held arduous jobs (farm/factory laborer, domestic worker, healthcare assistant, cleaner, nurse), and only 7 (9%) engaged in physical activity.

Risk factor analysis showed higher odds ratio (OR) of MetS with arduous work (OR 6.58, 95% confidence interval [CI] 2.03–26.26, p = 0.003), family history of dyslipidemia ( OR 5.06, 95% CI 1.67–17.51, p = 0.006) and former history of injection drug abuse (PWID) (OR 11.72, 95% CI 1.23-134.03, p = 0.033) and lower risk with physical activity (OR 0.10, 95% CI 0.02–0.44, p = 0.005). (Table 2). When the same multivariate model was applied to the separate MetS components, physical activity reduced the likelihood of waist circumference > 88 cm (OR 0.39, 95% CI 0.15–0.97, p = 0.04) and hyperglycemia (OR 0.16, 95% CI 0.03–0.64, p = 0.02). Conversely, hyperglycemia appeared more likely in women on INSTIs for > 10 years (OR 6.51, 95% CI 1.48–32.72, p = 0.02). PWID showed markedly higher odds of hypertriglyceridemia (OR 13.73, 95% CI 1.45–157.55, p = 0.02) while having a family history of dyslipidemia both increased the risk of having triglycerides above the cut-off (OR 3.90, 95% CI 1.17–14.72, p = 0.03), lower HDL-Chol (OR 5.26, 95% CI 5.04–16.13). Low levels of HDL-chol were also related to arduous work (OR 5.01, 95% CI 1.88–14,85, p = 0.002). None of the explored variables significantly increased the risk of hypertension.

Table 2
Cox regression of HIV-related and lifestyle factors associated with metabolic syndrome (MetS) in 297 women living with HIV: O.R. and 95% C.I.
Covariates	Univariate O.R. (95% C.I., p-value)	Multivariate O.R. (95% C.I., p-value)
Age (x 1 year increase)	1.03 (0.98–1.08, p = 0.246)	0.99 (0.90–1.08, p = 0.812)
Menopause	1.57 (0.60–4.65, p = 0.380)	1.31 (0.19–9.47, p = 0.786)
Pyshical Activity	0.19 (0.04–0.59, p = 0.010)	0.13 (0.02–0.51, p = 0.007)
Arduous work	4.11 (1.65–11.33, p = 0.004)	6.04 (1.93–22.27, p = 0.003)
Active/Former Smoker	0.95 (0.40–2.22, p = 0.901)	0.72 (0.22–2.18, p = 0.564)
Alcohol abuse	1.08 (0.15–5.01, p = 0.930)	0.67 (0.05–5.91, p = 0.738)
Family history of dyslipidemia	2.02 (0.86–4.92, p = 0.111)	5.06 (1.67–17.51, p = 0.006)
PWID	5.28 (0.83–41.78, p = 0.077)	11.72 (1.23-134.03, p = 0.033)
≥ 10Years of HIV	2.00 (0.50-13.39, p = 0.384)	0.13 (0.00-5.40, p = 0.356)
≥ 10Years of ART	2.81 (0.73–18.54, p = 0.187)	5.68 (0.21-746.09, p = 0.422)
NADIR CD4 < 200cell/mm3	1.77 (0.74–4.49, p = 0.213)	1.24 (0.42–3.78, p = 0.693)
≥ 5Years of TAF	1.17 (0.48–2.79, p = 0.720)	0.73 (0.24–2.12, p = 0.572)
≥ 10Years of PIs	1.83 (0.72–4.52, p = 0.197)	1.29 (0.37–4.41, p = 0.682)
≥ 10Years of INSTIs	1.20 (0.31–3.87, p = 0.775)	1.31 (0.25–6.41, p = 0.737)
O.R.: Odds Ratio; C.I.: Confidence Interval; HIV: Human Immunodeficiency Virus; PWID: People Who Inject Drugs; AIDS: Acquired Immunodeficiency Syndrome; HBsAg: Hepatitis B Surface Antigen; HCVAb: Hepatitis C Virus Antibodies; ART: Antiretroviral Treatment; TAF: Tenofovir Alafenamide; PIs: Protease Inhibitors; INSTIs: Integrase Strand Transfer Inhibitors

In our cohort of WoWH, the prevalence of MetS was 26%. Published estimates vary widely due to historical period, geographic region, age, and diagnostic criteria, but recent pooled data indicate a prevalence of 25.26% (95% CI, 22.95–27.58) among HIV-infected individuals [4]. Evidence specifically in women remains limited and mostly derives from the pre-INSTIs era. For instance, in 2008, the Women’s Interagency HIV Study reported a MetS prevalence of 33% in HIV-positive women versus 22% in HIV-negative women [8]. Previously, hypertriglyceridemia, low HDL cholesterol, and hypertension were the most common MetS components among PWH, whereas increased waist circumference predominated in HIV-negative subjects, suggesting distinct pathophysiological mechanisms [11]. In contrast, our analysis highlighted hypertension, increased waist circumference, and reduced HDL cholesterol as the main MetS components. Recent studies similarly identify increased waist circumference and low LDL cholesterol as predominant features among WoWH [7,12], indicating an evolving role of HIV infection, lifestyle, and ART-related toxicity in MetS development. Meta-analytic evidence suggests ART exposure increases MetS risk by approximately 1.5-fold [4]. Early ART regimens—including NRTIs (didanosine, stavudine, abacavir), NNRTIs (efavirenz), and PIs (ritonavir, lopinavir, darunavir, atazanavir, nelfinavir, saquinavir)—were associated with dyslipidemia, insulin resistance, and fat redistribution [8,11,13–15]. More recent studies, including the SMART trial [16], indicate that modern ART may have neutral or even beneficial metabolic effects. In our cohort, over 70% of women were on INSTI-based regimens, which generally do not have adverse effects.

affect MetS and may exert a protective effect [17]. While some reports suggest dolutegravir may slightly increase risk, cabotegravir/rilpivirine injections might be protective, though long-term confirmation is needed [18]. A novelty of our study is that we analyzed not only the impact of the current ART regimen on MetS prevalence—potentially biased by channeling, since patients at perceived high risk for MetS may have been preferentially started on or switched to supposedly protective regimens—but also the cumulative years of exposure to PIs and INSTIs, finding that neither class significantly increased MetS risk. Lifestyle remains a key determinant of MetS. Physical activity was protective, consistent with findings in HIV-negative populations [19]. Conversely, women in physically demanding occupations showed higher MetS risk, potentially reflecting socioeconomic factors, diet, and healthcare access, already related to a higher incidence of MetS in PWH [20].

Finally, unlike previous studies linking reproductive status to MetS risk [12], our findings suggest that chronological age is a stronger determinant, underscoring the need for comprehensive metabolic screening and prevention strategies in older women with HIV, beyond considerations related solely to ART regimen switching. Our study represents, to date, the most up-to-date investigation on this topic in women living with HIV. However, its limitations include the single-center design, which restricts the generalizability of our findings to the Italian context, as MetS is strongly influenced by regional lifestyle habits. Additionally, the relatively small sample size may have affected the statistical significance of some results. Therefore, larger multicenter studies are essential to confirm and strengthen the validity of our conclusions.

Acknowledgements

Not applicable.

Author contributions

AS and FDG contributed to the study design and critical revision. MP participated in the research design, data analysis, data interpretation, and manuscript writing. GB, CD, CA, CN, GV, LV, MG, ME, PL, SRC, and VA contributed to data collection. MP, AS, and FDG were involved in editing the manuscript. All authors read and approved the final version of the manuscript.

Statements and declarations

Ethics approval and consent to participate

The study was approved by the Local Ethics Committee on June 16, 2025 (Prot. 2241/CEI). All patients provided written informed consent for the use of anonymized data for research purposes.

Consent for publication

All patients provided written informed consent for the use of anonymized data for research purposes.

Competing interests

The authors declare no competing interests.

Funding statement

The authors reported that there is no funding associated with the work featured in this article.

Availability of data and material

The datasets generated and/or analyzed during the current study are not publicly available due to privacy and confidentiality restrictions, but are available from the corresponding author on reasonable request.

National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the NCEP Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002 Dec 17;106(25):3143–421. PMID: 12485966.
Shin JA, Lee JH, Lim SY, et al. Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. J Diabetes Investig. 2013;4:334–43.
Wilson PW, D’Agostino RB, Parise H, et al. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112:3066–72.
Curtis LH, Hammill BG, Bethel MA, et al. Costs of the metabolic syndrome in elderly individuals: findings from the Cardiovascular Health Study. Diabetes Care. 2007;30:2553–8.
Trachunthong D, Tipayamongkholgul M, Chumseng S, et al. Burden of metabolic syndrome in the global adult HIV-infected population: a systematic review and meta-analysis. BMC Public Health. 2024;24(1):2657. doi: 10.1186/s12889-024-20118-3. PMID: 39342258; PMCID: PMC11438355.
Hsu DC, Sereti I. Serious non-AIDS events: therapeutic targets of immune activation and chronic inflammation in HIV infection. Drugs. 2016;76:533–49.
Barbaro G. Highly active antiretroviral therapy–associated metabolic syndrome: pathogenesis and cardiovascular risk. Am J Ther. 2006;13:248–60.
World Health Organization (WHO). Noncommunicable diseases. 2024. Available at: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Last accessed: Sep 14, 2025.
Hirigo AT, Tesfaye DY. Influences of gender in metabolic syndrome and its components among people living with HIV using antiretroviral treatment in Hawassa, southern Ethiopia. BMC Res Notes. 2016;9:145. doi: 10.1186/s13104-016-1953-2. PMID: 26945987; PMCID: PMC4779577.
Sobieszczyk ME, Hoover DR, Anastos K, et al. Prevalence and predictors of metabolic syndrome among HIV-infected and HIV-uninfected women in the Women’s Interagency HIV Study. J Acquir Immune Defic Syndr. 2008;48:272–80.
Baker JV, Sharma S, Achhra AC, et al.; INSIGHT START Study Group. Changes in cardiovascular disease risk factors with immediate versus deferred antiretroviral therapy initiation among HIV-positive participants in the START trial. J Am Heart Assoc. 2017;6(5):e004987. doi: 10.1161/JAHA.116.004987. PMID: 28533305; PMCID: PMC5524070.
The RESPOND Study Group. Incidence of dyslipidemia in people with HIV who are treated with integrase inhibitors versus other antiretroviral agents. AIDS. 2021;35(6):869–82. doi: 10.1097/QAD.0000000000002811. PMID: 33443370.
Alencastro PR, Fuchs SC, Wolff FH, et al. Independent predictors of metabolic syndrome in HIV-infected patients. AIDS Patient Care STDS. 2011;25:627–34.
Akl LD, Valadares ALR, Moraes MJ, et al. Metabolic syndrome in HIV-infected middle-aged women on antiretroviral therapy: prevalence and associated factors. Braz J Infect Dis. 2017;21(3):263–9. doi: 10.1016/j.bjid.2017.02.003. PMID: 28284656; PMCID: PMC9427828.
Biron A, Bobin-Dubigeon C, Volteau C, et al. Metabolic syndrome in French HIV-infected patients: prevalence and predictive factors after 3 years of antiretroviral therapy. AIDS Res Hum Retroviruses. 2012;28:1672–8.
Abdela AA, Yifter H, Reja A, et al. Prevalence and risk factors of metabolic syndrome in Ethiopia: describing an emerging outbreak in HIV clinics of sub-Saharan Africa—a cross-sectional study. BMJ Open. 2023;13.
Ang LW, Ng OT, Boudville IC, et al. An observational study of the prevalence of metabolic syndrome in treatment-experienced people living with HIV in Singapore. PLoS One. 2021;16(6):e0252320. doi: 10.1371/journal.pone.0252320. PMID: 34077481; PMCID: PMC8171957.
Strategies for Management of Antiretroviral Therapy (SMART) Study Group; El-Sadr WM, Lundgren J, Neaton JD, et al. CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med. 2006;355(22):2283–96. doi: 10.1056/NEJMoa062360. PMID: 17135583.
Calza L, Colangeli V, Magistrelli E, et al. Prevalence of metabolic syndrome in HIV-infected patients naïve to antiretroviral therapy or receiving a first-line treatment. HIV Clin Trials. 2017;18:110–7.
Ranzenigo M, Diotallevi S, Lolatto R, et al. Prevalence and incidence of metabolic syndrome in people with HIV receiving cabotegravir and rilpivirine. J Acquir Immune Defic Syndr. 2025;100(2):e2–e3. doi: 10.1097/QAI.0000000000003719. PMID: 40539800.
Myers J, Kokkinos P, Nyelin E, et al. Physical activity, cardiorespiratory fitness, and the metabolic syndrome. Nutrients. 2019;11(7):1652. doi: 10.3390/nu11071652. PMID: 31331009; PMCID: PMC6683051.
Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365(9468):1415–28. doi: 10.1016/S0140-6736(05)66378-7. PMID: 15836891.
Ying X, Yang S, Li S, et al. Prevalences of metabolic syndrome and its sex-specific association with socioeconomic status in rural China: a cross-sectional study. BMC Public Health. 2021;21:1–8.
Ye Q, Wang Z, Deng T, et al. Association of socioeconomic status with metabolic syndrome and its components among adult populations: a community-based cross-sectional study.

No competing interests reported.

Download PDF

Reviews received at journal
24 Nov, 2025
Reviewers agreed at journal
09 Nov, 2025
Reviewers invited by journal
06 Nov, 2025
Submission checks completed at journal
31 Oct, 2025
First submitted to journal
30 Oct, 2025

You are reading this latest preprint version

Beyond HIV: Lifestyle as a Key Driver of Metabolic Syndrome in Midlife Women with HIV in the INSTIs era

Status:

Version 1

Abstract

Introduction

Methods

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1