Obesity in young South African women living with HIV: A cross-sectional analysis of risk factors for cardiovascular disease

Sherika Hanley; Dhayendre Moodley; Mergan Naidoo

doi:10.1371/journal.pone.0255652

. 2021 Nov 15;16(11):e0255652. doi: 10.1371/journal.pone.0255652

Obesity in young South African women living with HIV: A cross-sectional analysis of risk factors for cardiovascular disease

Sherika Hanley ^1,^*, Dhayendre Moodley ², Mergan Naidoo ³

Editor: Massimiliano Ruscica⁴

PMCID: PMC8592426 PMID: 34780476

Abstract

Background

Young South African women are faced with a dual epidemic of HIV and obesity, placing them at a high risk of developing atherosclerotic cardiovascular disease (CVD). We sought to determine the prevalence of CVD risk factors in a cohort of reproductive-aged South African women living with HIV (WLHIV).

Methods

While the main purpose of an ongoing intervention study is the reduction of cardiovascular disease through the integration of CVD screening and prevention in the HIV management plan for women of reproductive age (ISCHeMiA trial), we present the prevalence of risk factors for CVD in this cohort of young women at baseline. Sociodemographic, conventional CVD risk factors, HIV-related factors and self body image perception were assessed through study questionnaires and standardized clinical and laboratory procedures.

Results

Of the 372 WLHIV enrolled from November 2018 to May 2019, 97% had received efavirenz-based antiretroviral treatment (ART) for at least 1 year and 67.5% (248/367) of women were overweight or obese at the time of enrolment. The prevalence of metabolic syndrome was 17.6% (95%CI 11.6–22.8) at a median age of 35 years (IQR 30.5–40.5). A significant proportion of women had abnormally low levels of high-density lipoprotein (43.2%, 80/185) and elevated levels of high sensitivity C-reactive protein (59.5%, 110/185). Seventy five percent of overweight women with an increased waist circumference reported to be satisfied with their body image.

Conclusions

The high prevalence of metabolic syndrome, obesity and elevated markers of inflammation in young South African WLHIV, underscores the need for a proactive integrated management approach to prevent atherosclerotic cardiovascular disease in low and middle income settings.

Introduction

Globally 19.2 million women and girls are living with HIV, of which 51% reside in South Africa (SA) [1]. Large scale efforts have led to greatly improved access to antiretroviral therapy (ART) in women living with HIV (WLHIV) and a rapid decline in AIDS-related mortality [2]. Conversely, the prevalence of metabolic syndrome and atherosclerotic cardiovascular disease (CVD) has increased in SA and other low and middle income countries (LMICs) as a result of epidemiologic transition and a related increase in conventional risk factors, coupled with HIV and treatment thereof [3–6]. Worldwide the leading cause of death and significant disability is from CVD with highest burden in LMICs highlighting the need for an integrated approach to the prevention and management of chronic communicable and non-communicable diseases (NCDs) [7, 8].

A targeted integrated programme needs to be driven by relevant local evidence of burden of disease however, data on prevalence and incidence of NCDs in WLHIV globally is limited. HIV appears to increase the risk of CVD in women more than it does in men [9]. Cardiovascular disease risk is exacerbated by HIV through direct mechanisms of persistent immune activation and chronic inflammation [10]. Immune activation and inflammation have been shown to be more pronounced in WLHIV than in men, possibly related to sex hormone differences [11]. Women in SA are not only more likely to be affected by the HIV epidemic than their male counterparts but are more prone to develop obesity and the metabolic syndrome compared to men [12]. The obesity epidemic may be further complicated by a self-preference for a higher body mass index in sub-Saharan African women [13].

In this baseline analysis of an ongoing interventional study, we describe HIV-related factors and conventional risk factors for cardiovascular disease in treatment-experienced young South African WLHIV.

Methods

Study design

The ISCHeMiA study (Integration of cardiovascular disease SCreening and prevention in the HIV MAnagement plan for women of reproductive age), is a prospective, quasi-experimental design comparing a primary healthcare intervention plan guided by the WHO Package of Essential Non-communicable Disease interventions for primary health care in low resource settings (WHO PEN) [14] (Intervention Arm), with routine care (Control Arm). Cross-sectional analyses were performed of baseline CVD risk factors in total study population and author-recommended CVD risk factors in the intervention arm. Results of the ongoing intervention are expected to be released in 2022.

Study population

Women aged between 18 and 49 years, who were receiving ART for a minimum of one year from peri-urban primary health care (PHC) clinics in Umlazi and surrounding rural areas who intended to reside within the study catchment area for the three-year duration of the study, were included in the study. Women in the intervention arm were recruited from a cohort of participants co-enrolled in the PEPFAR PROMise Ongoing Treatment Evaluation (PROMOTE) observational study at the Umlazi Clinical Research Site. The PROMOTE study has been implemented to provide long-term follow-up data on safety outcomes of use of combination ART received from standard of care healthcare providers. All 238 women in the PRMOTE study are living with HIV, were between 18–49 years The first 186 interested eligible candidates presenting to the research clinic for their next PROMOTE study visit were co-enrolled into the intervention arm and assessed for CVD risk.

For the control arm, the Tier.Net HIV electronic register was used to select a data base of all women with HIV, aged between 18–49 years and receiving ART for more than 1 year, at the nearest Umlazi Gateway PHC. Scheduled clinic visits at similar time points to the anticipated clinic visits in the intervention group were used to establish a list of potentially eligible women. Following a matched pool of data, the first 186 women fulfilling the inclusion criteria, who attended the clinic for their next appointment and who consent to study participation were enrolled.

Enrolment procedures and data collection at baseline

Written informed consent forms were signed by interested participants in both arms. In the control arm, routinely documented data were extracted from participant medical records. These included demographic characteristics, most recent HIV viral load, CD4 count, ART regimen, duration of ART, height, weight, and blood pressure recordings where available. In the intervention arm, baseline sociodemographic factors, conventional CVD risk factors, HIV-related risk factors, and current self body image acceptance were determined through study questionnaires, physical examination, and laboratory investigations.

Questionnaires and physical examination

Questionnaires were guided by the WHO STEPwise approach to NCD risk factor surveillance (STEPS) and tailored to determining CVD risk using the core sections [15]. The WHO Steps questionnaire diet was categorised as unhealthy or healthy based on response to fruit and vegetable intake (high/ low), high fat and high salt diet. Self-perception of diet (unhealthy or healthy) was also included. Duration of exercise per week (>/< 30 minutes per week) was recorded.

Height, weight, and blood pressure (BP) measurements were conducted using standard procedures. Waist circumference (WC) was measured by snugly placing a measuring tape in a horizontal plane around the abdomen immediately above the iliac crest at the level of the umbilicus, at the end of expiration. Body mass index (BMI) calculated by weight divided by height squared was classified based on WHO guidelines: overweight (25-<30kg/m²), class 1 obesity (30-<35 kg/m²), class 2 obesity (35-<40 kg/m²) and class 3 obesity (> 40 kg/m²). Hypertension was defined as systolic blood pressure (SBP) ≥140mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg or receiving antihypertensive treatment. Prehypertension was defined as SBP ≥130-139mmHg and/or DBP ≥85-89mmHg.

Laboratory investigations

Laboratory investigations were carried out on overnight fasting samples at two certified laboratories with regular system calibration of validated devices and quality measures in place. High sensitivity CRP was measured via Beckman Coulter AU analyser with detection limits of 0.2–160 mg/L. Per hsCRP latex package insert, CVD relative risk is considered as follows: low < 1mg/L, average 1-3mg/L and high > 3mg/L. Urine microalbumin of <3 mg/mmol is considered normal/mildly increased, 3-29mg/mmol is moderately increased, and >30mg/mmol is severely increased. Fasting glucose levels of < 5.6, ≥ 5.6, 6–6.9, ≥ 7 mmol/L are normal, meets metabolic syndrome criteria, impaired fasting glucose and diagnostic of diabetes mellitus, respectively. Levels in mmol/L of serum total cholesterol (TC) > 5, HDL < 1.2, LDL > 3 and triglyceride > 1.7 are abnormal in adult females.

HIV-1 viral load was measured using COBAS AmpliPrep/COBAS TaqMan HIV-1 test. For the purposes of this study, participants with viral load <200 copies/ml (cp/ml) were considered as virally suppressed. CD4 count was determined by the Becton Dickinson Facscalibur flow cytometer.

Metabolic syndrome and CVD risk assessments

Metabolic syndrome (MetS) was defined by the 2009 Joint Interim Statement (JIS) recommended by the South African Heart Association (SA Heart) and the Lipid and Atherosclerosis Society of Southern Africa (LASSA) and includes the following subcomponents: WC ≥ 80 cm, elevated triglycerides ≥ 1.7 mmol/L, reduced HDL <1.3 mmol/L, elevated SBP ≥ 130 and/or DBP ≥ 85 mmHg or on antihypertensive treatment, and an elevated fasting glucose ≥ 5.6 mmol/L or receiving treatment for diabetes mellitus [16].

Baseline CVD risk assessments were calculated using the Framingham (Fr) 5 and 10 year CVD risk [17], WHO and International Society of Hypertension (WHO/ISH) cardiovascular risk prediction [18], and the Data collection on Adverse Effects of Anti-HIV Drugs Study (DAD) coronary heart disease (CHD) equation [19].

Statistical analysis

Data was captured on Microsoft Excel and analysed using IBM SPSS statistics software, version 25.0 and Epi Info version 7.2.3.1. Conventional CVD risk factors and HIV-related risk factors were represented by means (standard deviation), medians (interquartile ranges) and prevalence. 95% confidence intervals were derived from using Epiinfo 7.0 and were either calculated as a Wilson interval or an Exact interval where appropriate. Wilson 95% Confidence Interval was calculated for Systolic BP, CD4, Age, BMI, ART and ART Duration Groups. Exact 95% Confidence Interval was calculated for all other categories. The relationship between body image perception and BMI was evaluated by Pearson’s chi-square test.

Ethical considerations and approvals

The ISCHeMiA study was conducted in accordance with the ethical standards of University of KwaZulu-Natal (UKZN) Biomedical Research Ethics Committee and with the Helsinki Declaration (1964, amended in 2008). Approvals were obtained from the PEPFAR PROMOTE publication committee, Department of Health KZN and Prince Mshiyeni Memorial Hospital. The trial is registered with Pan African Clinical Trial Registry database, (www.pactr.org), [identification number PACTR201808524461224].

Results

A total of 372 WLHIV (186 in each of the intervention and control arms) were enrolled from November 2018 to May 2019 for whom data were available for a baseline analysis. Demographic, HIV-related and conventional CVD-related data were collected at enrolment and are displayed in Table 1. Data on age, BMI, systolic blood pressure, antiretroviral drug (ARV) regimen and duration, HIV viral load and CD4 count are included for all women enrolled.

Table 1. Prevalence of CVD risk factors determined as per standard of care in total study population (N = 372).

	Categories	N (%; 95%CI)
*Demographic factors*
Mean age in years(SD)	33.5 (6.1)
Age groups (years) N = 372	20–29	113 (30.4; 25.8–35.4)
	30–39	194 (52.2; 46.9–57.3)
	40–49	65 (17.5; 13.8–21.8)
*Traditional CVD risk factors*
BMI Median (IQR)	27.3 (23.2–33.1)
BMI categories (kg/m²) N = 367	<25	139 (37.9; 32.9–43.1)
	25–29	95 (25.9; 21.5–30.7)
	30–39	133 (36.2; 31.4–41.4)
	40–49	20 (5.5; 3.5–8.4)
Systolic blood pressure Mean (SD)	115.7 (15.6)
Systolic blood pressure categories (mmHg) N = 368	<130	315 (85.6; 81.5–88.9)
	130–139	27 (7.3; 4.9–10.6)
	≥140	26 (7.1; 4.8–10.3)
Diastolic blood pressure Mean (SD)	73.3 (11.5)
Diastolic blood pressure categories (mmHg) N = 202	<85	169 (83.7; 77.8–88.5)
	85–89	15 (7.4; 4.2–11.9)
	≥90	18 (8.9; 5.4–13.7)
*HIV-related factors*
ART Regimen (type) N = 372	AZT/3TC+ LPV/r	4 (1.1; 0.4–2.9)
	EFV/FTC/TDF	362 (97.3; 94.9–98.6)
	FTC/TDF + LPV/r	5 (1.3; 0.5–3.3)
	NVP+FTC/TDF	1 (0.3; 0.01–1.7)
Duration of Current ART Regimen (years) Mean(SD)	4.3 (2.2)
Duration of Current ART Regimen (years) Categories	1–4	270 (72.6; 67.7–76.9)
Duration of Current ART Regimen (years) Categories	>4	102 (27.4; 23.0–32.3)
CD4 Count (cells/iu) Median (IQR)	803 (558–1030)
CD4 count (cells/iu) Categories N = 371	<500	68 (18.3; 14.6–22.7)
CD4 count (cells/iu) Categories N = 371	≥500	303 (81.7; 77.3–85.4)
Viral Load (cp/ml) Mean(Range)	768 (0–113 559)
Viral load (cp/ml) Categories N = 368	≥200	18 (4.9; 3.0–7.8)
Viral load (cp/ml) Categories N = 368	<200	350 (95.1; 92.2–96.9)

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Key: SD = Standard deviation, IQR = Interquartile range, CI = confidence interval, AZT = Zidovudine, 3TC = lamivudine, EFV = Efavirenz, FTC = Emtricitabine, TDF = Tenofovir disoproxil fumarate, LPV/r = Lopinavir/Ritonavir, NVP = Nevirapine.

More than 80% of the study population were younger than 40 years when enrolled. Two-thirds (67.5%) of the study population had a BMI > 25kg/m² and were classified as being overweight or obese. Elevated SBP and DBP were noted in 14.4% and 16.3% of the study population respectively. The majority of women were on an EFV-based first line ART for more than a year (97.3%), virally suppressed (95.1%), and with a CD4 count > 500 cells/IU (81.7%).

Additional data required by most standard tools for assessing CVD risk are listed in Table 2. Assessing these additional risk factors constitute the WHO PEN intervention package that is applied to the intervention arm only in the ISCHeMiA trial. Review of medical history of the women in the intervention arm revealed a 10.2% prevalence of systemic hypertension and 0.5% Type 2 Diabetes Mellitus (T2DM).

Table 2. Prevalence of additional CVD risk factors investigated in the intervention arm (N = 186).

	Categories	N (%; 95%CI)
*Sociodemographic factors*
Employment N = 186	Yes	94 (50.5; 43.1–57.9)
Employment N = 186	No	92 (49.5; 42.1–56.9)
Parity Median (IQR)	2 (1–3)
Parity N = 186	0–2	55 (30.4; 23.8–37.7)
Parity N = 186	>2	88 (47.3; 39.9–54.8)
*Traditional CVD risk factors-non modifiable*
Family History of CVD	Yes	18 (9.7; 5.8–14.9)
Family History of CVD	No	168 (90.3; 85.1–94.2)
*Modifiable CVD risk factors*
Known with Hypertension N = 186	No	167 (89.8; 84.5–93.7)
Known with Hypertension N = 186	Yes	19 (10.2; 6.3–15.5)
Known with Diabetes N = 186	No	185 (99.5; 97.0–99.9)
Known with Diabetes N = 186	Yes	1 (0.5; 0–2.9)
Fasting Plasma Glucose mmol/L Mean (SD)	4.5 (0.4)
Glucose N = 185	<5.6	181 (97.8; 94.6–99.4)
	5.6–5.9	3 (1.6; 0.3–4.7)
	6–6.9	1 (0.01–2.9)
	≥7	0
Waist Circumference cm Mean (SD)	89.2 (14.9)
Waist Circumference Categories N = 181	<80	55 (30.4; 23.8–37.7)
Waist Circumference Categories N = 181	≥80	126 (69.6; 62.3–76.2)
Total Cholesterol mmol/L Mean(SD)	4.0 (0.8)
Total Cholesterol N = 185	<5	167 (90.3; 85.1–94.1)
Total Cholesterol N = 185	≥5	18 (9.7; 5.9–14.9)
HDL mmol/L Mean(SD)	1.3 (0.3)
HDL mmol/L N = 185	<1.2	80 (43.2%; 35.9–50.7)
HDL mmol/L N = 185	≥1.2	105 (56.8%; 49.3–64.0)
Triglyceride mmol/L Median (IQR)	0.72 (0.57–1.00)
Triglyceride mmol N = 185	<1.7	174 (94.1; 89.6–96.9)
Triglyceride mmol N = 185	≥1.7	11 (5.9; 3.0–10.4)
LDL mmol/L Mean(SD)	2.3 (0.8)
LDL mmol/L = 186	<3	153 (82.3; 76.0–87.5)
LDL mmol/L = 186	≥3	33 (17.7; 12.5–24.0)
hsCRP mg/L Median (IQR)	3.7 (1.4–9.4)
hsCRP mg/L N = 185	<3	75 (40.5; 33.4–47.9)
hsCRP mg/L N = 185	≥3	110 (59.5; 52.0–66.6)
Urine Albumin/Creatinine Ratio mg/mmol Median (IQR)	0.5 (0.35–0.90)
Urine Albumin/Creatinine ratio mg/mmol N = 184	<3	25 (13.6; 8.9–19.4)
Urine Albumin/Creatinine ratio mg/mmol N = 184	≥3	159 (86.4; 80.6–91.1)
Unhealthy Diet	Yes	91 (49.7; 42.8–57.8)
Unhealthy Diet	No	90 (50.3; 42.2–57.4)
Exercise mins/wk Mean (SD)	42 (74.3)
Exercise (>30 mins/wk)	Yes	64 (34.4; 27.6–41.7)
Exercise (>30 mins/wk)	No	122 (65.6; 58.3–72.4)
Alcohol Consumption	Yes	44 (23.7; 17.8–30.4)
Alcohol Consumption	No	142 (76.3; 69.6–82.3)
Ever Smoked	Yes	18 (9.7; 5.6–14.9)
Ever Smoked	No	168 (90.3; 85.1–94.2)

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Key: SD = Standard deviation, IQR = Interquartile range, CI = confidence interval.

Apart from the woman with known T2DM, screening of fasting blood glucose identified one other woman (0.5%) with impaired fasting glucose. Three women (1.6%; 95%CI 0.3–4.7) had fasting glucose levels > 5.6 mmol/L, a subcomponent of MetS. Almost forty five percent (82/183) of women in the intervention arm were obese (≥30kg/m²); and 79/80 (98.8%) of obese women had a high WC (>80 cm). Overall, 79/183 (43.2%) women in the intervention arm had both high BMI >30kg/m² and a high WC.

Other components of MetS noted in participants at baseline included an elevated fasting TC (9.7%), low HDL (43.2%), elevated level of LDL (17.7%), and elevated level of triglycerides (5.9%). A significant proportion of women had elevated inflammatory markers, hsCRP (59.5%). Decreased urine albumin-creatinine ratio was noted in 13.6% of women.

Lifestyle practice is also demonstrated in Table 2 with almost 50% of the women in the intervention arm reporting an unhealthy diet, and significantly less (34.4%) women reported to exercise more than 30 minutes per week.

Prevalence of Metabolic Syndrome (MetS)

The prevalence of metabolic syndrome at baseline in the intervention arm was 17.6% (95%CI 11.6–22.8) based on meeting 3 or more subcomponents per Joint Interim Statement (JIS). Seven combinations of subcomponents are displayed in (Fig 1). Of the 31 women with MetS, the most common combination at 61% (19/31) was elevated SBP≥ 130 mmHg and/or DBP≥85 mmHg, WC ≥80 cm and reduced HDL <1.3 mmol/L. Elevated WC was present across all women with Mets at a mean WC of 91.2cm. Elevated HDL and elevated BP or known with hypertension occurred in 94% (29/31) and 84% (26/31) respectively. The median age (IQR) of women with MetS was 35 years (IQR 30.5–40.5) and significantly older than women without MetS (31 years IQR 28–36) (P = 0.015), although 23 (74%) of the women with MetS were younger than 40 years.

CVD risk assessment tools

The median 5-year and 10-year CVD risk (%) by Framingham were 0.1 (IQR 0.1 to 0.3) and 0.4 (IQR 0.2 to 1.0) respectively. All women in the intervention arm were considered at low risk (<10%). The mean CVD risk (%) by DAD was 0.2±0.2 and with the exception of one woman with moderate risk (1–5%), all others were considered at low risk. All 173 women assessed by WHO/ISH were also considered at low risk (<10%).

Obesity and self body image

Obesity was significantly more prevalent (40.8% vs 28.3%) among older women (>30 years old) than younger women (25–30 years old) (p = 0.039) (Table 3). When evaluating current satisfaction with self-body image in the intervention arm, 76.1% (140/184) of women reported to be satisfied. There was an association between satisfaction with body image and BMI (p = 0.013). Although, those who were not satisfied with body image were more likely to be in the higher BMI groups, more women who were overweight were satisfied than dissatisfied with their self-body image. Older women (>30 years) who were obese were more likely to be satisfied with their body image when compared to younger obese women (< 30 years) (75.8% vs 59.1%). Among the 79 women with both a high BMI and high WC, 52 (65.8%) were satisfied with their self body image.

Table 3. Obesity and satisfaction with body image by age category.

Age Group	BMI < 25kg/m²	BMI 25<30 kg/m²	BMI ≥ 30 kg/m²	TOTAL n
Age Group	BMI < 25kg/m²	(Overweight)	(Obese)	TOTAL n
< 25 years	11 (52.4%)	5 (23.8%)	5 (23.8%)	21
Body Image Satisfaction	-	-	1/2 (50%)	21
25 < 30 years	49 (46.2%)	27 (25.5%)	30 (28.3%)	106
Body Image Satisfaction	-	14/15 (93.0%)	13/22 (59.1%)	106
≥30 years	79 (32.9%)	63 (26.3%)	98 (40.8%)	240
Body Image Satisfaction	-	22/37 (81.5%)	40/58 (75.8%)	240

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Discussion

In this cross-sectional analysis of baseline characteristics in WLHIV predominantly receiving EFV-based first line ART, we draw attention to the high prevalence of obesity (67.5%) and MetS (17.6%) among WLHIV who were mostly younger than 40. Almost all who were overweight had a high waist circumference (WC), a combination known to be a risk for CVD. Yet, almost all of these women were considered to be low risk for CVD using Framingham, DAD and WHO/ISH tools. A significant proportion of women had decreased HDL-cholesterol levels (43.2%) and an elevated inflammatory marker, hsCRP (59.5%).

The concerning high prevalence of generalized obesity defined by elevated BMI, in our young study population, is comparable to the national prevalence in a similar age category of women independent of HIV [20]. Although this study shows similar rates to their non-HIV counterparts, this is a young cohort of WLHIV who also exhibit other risk factors for CVD associated with premature atherosclerosis [21]. Closely correlating with a high BMI, is an even higher prevalence of elevated WC, suggestive of visceral obesity. Our mean WC in women with MetS was aligned to the estimated optimal WC cut-off point of 92cm to predict the presence of at least two other components of the MetS, determined by Motala and colleagues in SA [22]. The overall mean WC of 89cm in our analysis makes a strong argument for routine WC measurements in monitoring of WLHIV.

South African guidelines on the management of lipid disorders in HIV-infected individuals advocate a full lipid profile at ART initiation followed by subsequent testing when treated with protease inhibitors [23]. However, lipid testing is not routinely practiced having been omitted from national ART guidelines highlighting the gaps between HIV and NCD management guidelines. Our findings are consistent with other studies that have shown that EFV adversely alters TC, LDL cholesterol, and triglycerides levels [24–26]. What stood out was a striking 43% who had low HDL levels, which advocate for routine HDL testing in primary prevention of CVD in WLHIV. Low HDL may possibly be the result of HIV and ART-related structural and functional changes [27], obesity and lifestyle practices, and is currently being explored as a potential biomarker for CVD in HIV [28].

Novel ART may improve lipid levels, and with rising HIV resistance to non-nucleoside reverse transcriptase inhibitors (NNRTI) in SA and other LMIC’s, current first line ART regimens includes dolutegravir (DTG). Although DTG does not appear to cause significant change in lipids [29], findings are suggestive of DTG-associated significant weight gain, more so in women [30]. However, ongoing research is necessary to observe DTG and other integrase inhibitor’s long-term effects on metabolic markers. Transition to DTG in research participants in the ISCheMiA study will shed light on this topic in future analyses.

Although elevated hsCRP was weakly associated with subclinical atherosclerosis in the absence of obesity in a United States multi-ethnic study [31], hsCRP has been shown to add value to models predicting CVD events in PLWH [32]. Furthermore, efavirenz, predominantly used in our cohort, has been associated with a higher increase in hsCRP compared to other ART [33]. The significantly high hsCRP levels in our study warrants long-term monitoring of CVD risk, but disentangling obesity and hsCRP levels remains a challenge. A recent recommendation by WHO is the conduct of a CVD risk assessment in PLWH, which has recently been incorporated into the latest SA HIV management guidelines. This practice is not yet widely implemented. The CARDIA study showed young adults with detectable risk factors at baseline, were up to 3 times as likely to have coronary artery plaque calcification suggesting that intervention when risk factor levels reach management guideline thresholds may be too late in preventing CVD [34].

While it is recognised that it would be impossible to accurately estimate risk in all South African subpopulations with a single data set, the Fr was the first choice in our study, having been validated in white and black populations in the USA and are transportable to other culturally diverse populations [17]. Nevertheless, these risk tables are likely to underestimate risk in South African black and Indian patients, and people with HIV, apparent in our study population who had almost 18% prevalence of MetS yet low CVD risk scores. Hence the use of DAD (for HIV), and WHO/ISH which is specific to the South African region. The DAD does not account for low income settings, and WHO/ISH does not incorporate HIV and young adults. Clinical discretion is strongly advised when selecting an appropriate risk assessment tool.

Finally, women in our study population appear to be content with being overweight. Similarly, Malawian women with HIV preferred to be overweight as it was associated with the ability to breast-feed [13]. Unhealthy diet and lack of exercise are targets for our study intervention, however interim analyses suggest that women are not adhering to lifestyle modification advice after 6 months follow-up. Further exploration of body self-image and body-satisfaction is necessary where we are seeing a move in focus of body image from HIV lipodystrophy to obesity.

Health care provider and patient awareness of the heightened CVD risk as well as the potential influence of self body image in WLHIV may facilitate a shift in the typical lifestyle modification advice approach. This change in practice could positively impact on the number of disability-associated life-years (DALYs) related to CVD in PLWH in Sub-Saharan Africa, which currently holds a third of the global annual DALYs [35].

Limitations

Co-enrolment into intervention arm from the ongoing observational PROMOTE study may introduce selection bias, however women receive care from standard of care providers and no prior targeted CVD screening was performed. When comparing the distribution of the CVD risk factors determined as per standard of care presented in Table 1 between the two study arms, only BMI and CD4 differed between control and intervention. The control were higher in BMI and had lower CD4 counts.

Conclusion

Although traditional CVD risk assessments yielded low risk scores in this relatively young cohort of WLHIV, there was a high prevalence of metabolic syndrome, obesity, abnormal HDL levels and elevated markers of inflammation, combined with self-reported unhealthy lifestyle practices and satisfaction with being overweight. These findings, compounded by the additional known and emerging effects of HIV and ART, highlight the need for a pro-active integrated differentiated care approach to the primary prevention of CVD in young WLHIV, preferably initiated at first point of contact at HIV treatment centres. Opportunistic health promotion in ARV clinics may be a good starting point to curb overall NCD incidence in LMICs.

Supporting information

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S2 File

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S3 File

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Acknowledgments

The authors would like to acknowledge the research participants and PROMOTE study team, as well as contributions from research assistants Zinhle Shazi and Nonhlanhla Silindana, statisticians Nonhlanhla Yende-Zuma and Tonya Esterhuizen and the CAPRISA Data Management Centre.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work is based on the research supported in part by the National Research Foundation of South Africa (Grant Number: 117730) awarded to SH. https://www.nrf.ac.za/ Research reported in this publication is also supported by the Fogarty International Center (FIC), NIH Common Fund, Office of Strategic Coordination, Office of the Director (OD/OSC/CF/NIH), Office of AIDS Research, Office of the Director (OAR/NIH), National Institute of Mental Health (NIMH/NIH) of the National Institutes of Health under Award Number D43TW010131. SH is a sub awardee. https://www.fic.nih.gov/Funding/Pages/Fogarty-Funding-Opps.aspx The PROMOTE study is funded by the President’s Emergency Plan for AIDS Relief (PEPFAR) through DAIDS/NIAID/NIH grants to CAPRISA Clinical Trials Unit for AIDS/Tuberculosis Prevention and Treatment, grant # 5UM1AI069469. SH is a site investigator in the PROMOTE study. https://www.hiv.gov/federal-response/pepfar-global-aids/pepfar The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Research Foundation of South Africa and National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0255652.r001

Decision Letter 0

Massimiliano Ruscica

17 Nov 2020

PONE-D-20-27919

Obesity in young South African women living with HIV: a cross-sectional analysis of risk factors for cardiovascular disease

PLOS ONE

Dear Dr. Sherika Hanley,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

As highlighetd by one reviewer, some issues on body size, particularly WC, need clarification and missing data, eg on Lp(a) should be justified.

==============================

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Reviewer #2: Yes

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Reviewer #1: The prevalence of risk factors for CVD, their interrelationship and resultant CVD risk scores were investigated cross-sectionally in a cohort of young South African women undergoing treatment for HIV.

While it is clearly valuable to report on the degree of CVD risk and specific risk factors in particular populations, such as here young women with HIV, it is unclear to me what the results in this relatively small cohort add to our knowledge. Does it contribute anything new concerning prevalence of obesity, other than to confirm that young South African women with HIV show similar rates to their non-HIV counterparts?

A second main point: the account of analyses of the relationships among the various risk factors is poorly structured and difficult to grasp. These were all baseline variables, so the motivation for regression analyses which assign one variable as dependent and others as independent is unclear to me. Since all variables appear to have been dichotomised for these analyses, would in not be simpler to present a matrix of odds ratios between each pair of factors, or alternatively just report those ORs which were significant and state that all others were not significant?

Finally, it is stated that all participants were ‘low risk’ according to Framingham score (please state exactly which score and provide a reference). Indeed, the mean risk is just 0.3%, whereas the threshold for moderate risk lies at 10%. This seems to contradict the high prevalence of obesity and other CVD risk factors, as well as the fairly high prevalence of metabolic syndrome, and needs to be explained.

Minor points:

1. Please explain how the control cohort was matched for age and ART duration with the intervention cohort (line 91).

2. The method of calculating confidence intervals for percentages in Tables 1 and 2 should be given (line 137).

3. More detail on regression methods (including which variables were dependent, and whether stepwise) are needed, if regression is retained in the manuscript (lines 135…).

4. Line 141: sentence unclear – relationship between perception and BMI?

5. Table 2: why are parity mean values integers?

6. Line 199: mean Framingham scores were 0.3 ±0.6 and 0.9±1.2. Are the ± values standard errors or confidence intervals? These ranges include negative values and thus seem to be implausible, maybe due to skewness of the distribution.

7. Selection bias in the intervention cohort (line 346) could be investigated by comparing the distribution of the variables and factors presented in Table 1 between the two cohorts, intervention and control.

8. Fig 1 is difficult to grasp visually; in particular the colour bars belonging to a combination are not always adjacent. Might be easier if the bars (one bar per combination) were labelled directly with the combination of factors, rather than using colours.

9. Fig. 2: might be more useful to calculate percentages per BMI category rather than per satisfaction group.

Reviewer #2: The paper by Hanley et al provides a potentially important contribution on the cardiovascular risk in young South African women with AIDS and overweight.

The Authors give a lot of information in the Supplements, most of which (budget, change in protocol etc) are of no interest to readers and should be left out.

There are instead a few problems in the text/Methods.

Introduction

The mechanism of raised CV risk by immune activation is one of the many. The paper by SA Authors is just an opinion paper and the issue should be better supported or left out.

There is repeated emphasis on self (not selff) preference for overweight in these individuals, but the supporting criteria are very weak (is it just a simple question: do you like yourself: yes or no?) and really has little meaning for non SA readers.

In the Methods one does not really understand how recruitment occurs. From what I understand ART is the major criterion. What are the controls? Nearest place?

Waist circumference is measured at the iliac crest? This is certainly wrong. It is either midway between the iliac crest and the lowest rib or at the umbilical level. The figures I see are way too low for the type of measurement they carried out.

p.7 probably Becton Dickinson

MetSyn is by criteria used in low income nations, particularly for WC. Since most participants had WC above 80 cm, the Authors should either justify this or recalculate data based on European/US parameters.

In my view using the Framingham risk prediction makes little sense (essentially all participants were at very low levels). Please restrict yourselves to the DAD equation.

In the Results it is inappropriate to use brand names for medicines: some have become generic. TRUVADA is emtricitabine/tenofovir etc.

Coming to the body variables, the Authors should list the WHt ratio a very selective and sensitive parameter for the MetSyn (Pavanello et al. , Influence of body variables in the development of metabolic syndrome-A long term follow-up study PLoS One. 2018 Feb 12;13(2):e0192751) . They have the data, the advantage is that this is a single marker. The color figures are horrible, Fig. 1 could be more understandable with clear markers, Fig.2 on self-image, is worthless and should be left out.

I have already pointed out the problems of WC. Assessment of risk by the FRS should be left out, whereas some more information should be provided on DAD.

What is Lipogram? Essentialy you measure lipids: cholesterol,TG, HDL-C, no data on Lp(a)?

Finally the self-body image must be left out.

In the Discussion, overly lengthy, they present a summary of findings, of no value, and indicate that a WC of 91 cm is probably the real threshold for MetSyn. ART is of little concern except probably for dolutegravir. If so, why using this drug?

The small presence of albuminuria is not worth discussing, whereas the lipid abnormalities are examined very briefly. Why low HDL-C in so many individuals? Again, these are the issues that merit discussion, not self-body image. Discussion should be cut to not more than one half.

**********

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Reviewer #1: Yes: Jeremy Franklin

Reviewer #2: No

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PLoS One. 2021 Nov 15;16(11):e0255652. doi: 10.1371/journal.pone.0255652.r002

Author response to Decision Letter 0

18 Feb 2021

Response to Reviewers

ACADEMIC EDITOR comments:

As highlighted by one reviewer, some issues on body size, particularly WC, need clarification and missing data, eg on Lp(a) should be justified.

Thank you for the opportunity to revise my manuscript.

The WC and missing Lp(a) has been clarified below.

The method of WC measurement has been described: Waist circumference (WC) was measured by snugly placing a measuring tape in a horizontal plane around the abdomen immediately above the iliac crest at the level of the umbilicus, at the end of expiration.

According to the South African Dyslipidaemia Guidelines, additional Lp(a) measurement may be considered for reclassification of subjects falling on a borderline between moderate and high risk. Lp(a) screening can also be considered for selected individuals at high CVD risk, including those with premature CVD, FH, a family history of premature CVD and/or elevated Lp(a), recurrent CVD despite optimal lipid-lowering treatment, and risk ≥15% on the 10-year Framingham risk tables. The author’s intention was to assess baseline CVD risk and determine additional tests to be conducted during the course of the study.

Reviewer #1:

1. The prevalence of risk factors for CVD, their interrelationship and resultant CVD risk scores were investigated cross-sectionally in a cohort of young South African women undergoing treatment for HIV.

Thank you. We recognise that the mere prevalence of obesity does not add to the readers’ knowledge. We do however want to remind the reader that the high prevalence of obesity in this fairly young cohort of WLHV together with other risk factors for CVD associated with premature mortality, and in keeping with the National Non-Communicable Disease Strategic Plan for South Africa, we should focus attention on primary prevention and management of these risk factors as part of our integrated plan to care for WLHIV.

2. A second main point: the account of analyses of the relationships among the various risk factors is poorly structured and difficult to grasp. These were all baseline variables, so the motivation for regression analyses which assign one variable as dependent and others as independent is unclear to me. Since all variables appear to have been dichotomised for these analyses, would in not be simpler to present a matrix of odds ratios between each pair of factors, or alternatively just report those ORs which were significant and state that all others were not significant?

Thank you for raising this point. We have corrected the analyses.

3. Finally, it is stated that all participants were ‘low risk’ according to Framingham score (please state exactly which score and provide a reference). Indeed, the mean risk is just 0.3%, whereas the threshold for moderate risk lies at 10%. This seems to contradict the high prevalence of obesity and other CVD risk factors, as well as the fairly high prevalence of metabolic syndrome, and needs to be explained.

Centre of Excellence for Health, Immunity and Infections (CHIP), University of Copenhagen Risk assessment tool system (RATS)-The Framingham algorithm estimates the risk of developing a cardiovascular disease within the next 5 years (modified to be compared with the D:A:D CVD 5 year risk score) and next 10 years (original Framingham risk score). The Framingham model is valid for individuals aged 30 to 75. Required information: Gender, age, smoking status, diabetes (diagnosis or on antidiabetic treatment), systolic BP, antihypertensive treatment, total cholesterol, HDL.

Reference: D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008 Feb 12;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579. Epub 2008 Jan 22. PMID: 18212285.

The low risk scores generated by Framingham highlight the limitations of current CVD risk assessment tools in young people with HIV. The young age of the study population, and the exclusion of obesity and HIV risk factors, have resulted in low scoring.

4. Minor points:

1. Please explain how the control cohort was matched for age and ART duration with the intervention cohort (line 91).

Method of selection was via convenience sampling.

Intervention group: All 238 women enrolled into the parent PEPFAR PROMise Ongoing Treatment Evaluation (PROMOTE) observational study at the CAPRISA Research Clinic in Umlazi, are living with HIV and are between 18 and 49 years of age, and on ART for more than 1 year. This study has been implemented to provide long-term follow-up data on safety outcomes of widespread use of combination antiretrovirals (cART) among an already well-characterized cohort of HIV infected mothers and their children who previously enrolled in the multi-site PROMISE study. Women were briefed at their next PROMOTE study visit and the first 186 interested candidates meeting all eligibility criteria were co-enrolled into the Intervention arm of the ISCheMiA study.

Control group: The Tier data base was used to select a data base of all women with HIV aged between 18-49 years, receiving ART for more than 1 year at the nearest Umlazi Gateway PHC. Scheduled clinic visits at similar time points to the anticipated clinic visits in the intervention group were used to establish a list of potentially eligible women. Following a matched pool of data, the first 186 women fulfilling the inclusion criteria, who attended the clinic for their next appointment and who consent to study participation were enrolled.

2. The method of calculating confidence intervals for percentages in Tables 1 and 2 should be given (line 137).

Using Epiinfo 7.0, calculation of the confidence interval is based on a mathematical relation between the binomial distribution and F distribution (Fisher & Yates, 1963; Zar, 1996, p. 524).

3. More detail on regression methods (including which variables were dependent, and whether stepwise) are needed, if regression is retained in the manuscript (lines 135…).

Regression analyses has been removed.

4. Line 141: sentence unclear – relationship between perception and BMI?

The sentence has been restructured.

5. Table 2: why are parity mean values integers?

Parity is corrected to median and IQR.

Since the Framingham scores are very skew, median and IQr has been reported: 5 yr median = 0.1 (IQR 0.1 to 0.3) and 10 year median = 0.4 (IQR 0.2 to 1.0)

Only BMI and CD4 differed between control and intervention. The control were higher in BMI and had lower CD4 counts.

Figure 1 has been revised.

9. Fig. 2: might be more useful to calculate percentages per BMI category rather than per satisfaction group.

Figure 2 has been replaced by Table 3.

Reviewer #2: The paper by Hanley et al provides a potentially important contribution on the cardiovascular risk in young South African women with AIDS and overweight.

Thank you

1.The Authors give a lot of information in the Supplements, most of which (budget, change in protocol etc) are of no interest to readers and should be left out.

Information left out

2.There are instead a few problems in the text/Methods.

Introduction

The mechanism of raised CV risk by immune activation is one of the many. The paper by SA Authors is just an opinion paper and the issue should be better supported or left out.

Further reference added in support

3.There is repeated emphasis on self (not selff) preference for overweight in these individuals, but the supporting criteria are very weak (is it just a simple question: do you like yourself: yes or no?) and really has little meaning for non SA readers.

The author acknowledges the weak data collection tool for the image analyses (It was a simple yes or no question as to whether participants were currently satisfied with their current body image). The authors have been working on an improved collection tool for future qualitative analysis. The authors do believe that there are multifactorial causes for obesity in young women and would like to further explore, create interest and expand on the typical lifestyle modification advice that clinicians have been using for years.

4.In the Methods one does not really understand how recruitment occurs. From what I understand ART is the major criterion. What are the controls? Nearest place?

Method of selection was via convenience sampling.

Control group: The Tier data base was used to select a data base of all women with HIV aged between 18-49 years, receiving ART for more than 1 year at the nearest PHC, Umlazi Gateway PHC. Scheduled clinic visits at similar time points to the anticipated clinic visits in the intervention group were used to establish a list of potentially eligible women. Following a matched pool of data, the first 186 women fulfilling the inclusion criteria, who attended the clinic for their next appointment and who consent to study participation were enrolled.

5.Waist circumference is measured at the iliac crest? This is certainly wrong. It is either midway between the iliac crest and the lowest rib or at the umbilical level. The figures I see are way too low for the type of measurement they carried out.

Waist circumference (WC) was measured by snugly placing a measuring tape in a horizontal plane around the abdomen immediately above the iliac crest at the level of the umbilicus, at the end of expiration

5. p.7 probably Becton Dickinson

Thank you, spelling error correction from Dickenson to Dickinson

6. MetSyn is by criteria used in low income nations, particularly for WC. Since most participants had WC above 80 cm, the Authors should either justify this or recalculate data based on European/US parameters.

The authors acknowledges the vast ethnic diversity in South Africa, however for consistency opted to restrict MetSyn diagnostic parameters to South African guidelines. The South African Heart Association (SA Heart) and the Lipid and Atherosclerosis Society of Southern Africa (LASSA) have recommended the JIS guidelines. This has been further clarified in the method section of the manuscript. Long-term prospective studies are required to reach more reliable waist circumference cut points for different ethnic groups, particularly for women.

7. In my view using the Framingham risk prediction makes little sense (essentially all participants were at very low levels). Please restrict yourselves to the DAD equation.

While it is recognised that it would be impossible to accurately estimate risk in all South African subpopulations with a single data set, the Adult Treatment Panel (ATP) III Framingham risk tables which provide an estimate of the 10-year risk of CHD, have been validated in white and black populations in the USA and are transportable to other culturally diverse populations. Consequently, we considered this approach to be more appropriate for South Africa. Nevertheless, these risk tables are likely to underestimate risk in South African black and Indian patients, and patients with HIV. Hence the use of DAD (for HIV), and WHO/ISH which is specific to the South African region. The DAD could not be the single means of assessing CVD risk as it has been developed in high income settings and do not account for low income settings

8. In the Results it is inappropriate to use brand names for medicines: some have become generic. TRUVADA is emtricitabine/tenofovir etc.

Thank you for pointing this out. All brand names in the results table have been removed.

9.Coming to the body variables, the Authors should list the WHt ratio a very selective and sensitive parameter for the MetSyn (Pavanello et al. , Influence of body variables in the development of metabolic syndrome-A long term follow-up study PLoS One. 2018 Feb 12;13(2):e0192751) . They have the data, the advantage is that this is a single marker. The color figures are horrible, Fig. 1 could be more understandable with clear markers, Fig.2 on self-image, is worthless and should be left out.

Thank you for this useful recommendation. As mentioned above, for consistency the authors have opted to restrict MetSyn diagnostic parameters to South African recommendations.

Figure 1 has been revised. Figure 2 has been replaced by Table 3.

10. I have already pointed out the problems of WC. Assessment of risk by the FRS should be left out, whereas some more information should be provided on DAD.

Explanation is provided above.

11. What is Lipogram? Essentialy you measure lipids: cholesterol,TG, HDL-C, no data on Lp(a)?

Finally the self-body image must be left out.

Thank you for raising this. The term lipogram has been replaced by lipid profile.

A lipogram and lipid profile is used interchangeably in South Africa, and refers to a standard set of tests: total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides. According to the South African Dyslipidaemia Guidelines, additional Lp(a) measurement may be considered for reclassification of subjects falling on a borderline between moderate and high risk. Lp(a) screening can also be considered for selected individuals at high CVD risk, including those with premature CVD, FH, a family history of premature CVD and/or elevated Lp(a), recurrent CVD despite optimal lipid-lowering treatment, and risk ≥15% on the 10-year Framingham risk tables. The author’s intention was to assess baseline CVD risk and determine additional tests to be conducted during the course of the study.

12. In the Discussion, overly lengthy, they present a summary of findings, of no value, and indicate that a WC of 91 cm is probably the real threshold for MetSyn. ART is of little concern except probably for dolutegravir. If so, why using this drug?

Discussion has been revised/shortened.

13. The small presence of albuminuria is not worth discussing, whereas the lipid abnormalities are examined very briefly. Why low HDL-C in so many individuals? Again, these are the issues that merit discussion, not self-body image. Discussion should be cut to not more than one half.

Discussion on albuminuria and other sections have been left out/reduced.

Use of Dolutegravir and Low HDL elaborated on.

The brief discussion on self image may allow for exploration into different strategies when providing lifestyle modification advice to women

________________________________________

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PLoS One. doi: 10.1371/journal.pone.0255652.r003

Decision Letter 1

Massimiliano Ruscica

15 Mar 2021

PONE-D-20-27919R1

Obesity in young South African women living with HIV: a cross-sectional analysis of risk factors for cardiovascular disease

PLOS ONE

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: The problematic regression analyses have been omitted from the revised version. Although I believe they could have been replaced by more meaningful methods, this is not essential to the manuscript.

The authors have responded appropriately to the comments I made; only the following still need to be attended to:

1. Please add details of matching in the control group, as requested by both reviewers. The text in the reply to reviewers would form a good basis.

2. State the method used to calculate confidence intervals for proportions. The statement in the reply about Fisher and Yates and the F distributiion is rather too vague.

3. Minor point 7: on the topic of selection bias, under 'Limitations' is would be informative to briefly describe the comparison of characteristics in the intervention and control groups.

4. Finally, the special relevance of the findings for women with HIV, as opposed to other young women, including the consequences for health care, should perhaps be more clearly stated in the discussion.

Reviewer #2: You have done an adequate job. The differences with other populations are there, but i believe you cannot do anything about it

**********

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Reviewer #2: No

PLoS One. 2021 Nov 15;16(11):e0255652. doi: 10.1371/journal.pone.0255652.r004

Author response to Decision Letter 1

5 Apr 2021

Thank you for the opportunity to submit a revised version of my manuscript entitled “Obesity in young South African women living with HIV: a cross-sectional analysis of risk factors for cardiovascular disease”.

Please see responses to each point raised by the academic editor and reviewer below.

Journal Requirements:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: Reference list has been reviewed and is complete and correct. No papers have been retracted.

The most recent UNAIDS data sheet has been referenced in the introduction section.

New reference no. 35. Shah ASV, Stelzle D, Lee KK, et al. Global Burden of Atherosclerotic Cardiovascular Disease in People Living With HIV: Systematic Review and Meta-Analysis. Circulation. 2018;138(11):1100-1112.

Reviewer #1:

Query 1. Please add details of matching in the control group, as requested by both reviewers. The text in the reply to reviewers would form a good basis.

Response 1. Thank you. The details of matching in the control group have been included in the text of the manuscript.

Query 2. State the method used to calculate confidence intervals for proportions. The statement in the reply about Fisher and Yates and the F distribution is rather too vague.

Response 2. 95% confidence intervals were derived from using Epiinfo 7.0 and were either calculated as a Wilson interval or an Exact interval where appropriate. Wilson 95% Confidence Interval was calculated for Systolic BP, CD4, Age, BMI, ART and ART Duration Groups. Exact 95% Confidence Interval was calculated for all other categories. This has been added in the text under statistical analyses.

Query 3. Minor point 7: on the topic of selection bias, under 'Limitations' it would be informative to briefly describe the comparison of characteristics in the intervention and control groups.

Response 3. Thank you. This information has been added under the Limitations section.

Query 4. Finally, the special relevance of the findings for women with HIV, as opposed to other young women, including the consequences for health care, should perhaps be more clearly stated in the discussion.

Response 4. The author has made additional reference to disability-associated life-years from CVD in persons with HIV in the discussion without significantly altering the length of the discussion, and has placed emphasis on women with HIV in the conclusion.

PLoS One. doi: 10.1371/journal.pone.0255652.r005

Decision Letter 2

Massimiliano Ruscica

22 Jul 2021

Obesity in young South African women living with HIV: a cross-sectional analysis of risk factors for cardiovascular disease

PONE-D-20-27919R2

Dear Dr. Hanley,

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PLoS One. doi: 10.1371/journal.pone.0255652.r006

Acceptance letter

Massimiliano Ruscica

5 Nov 2021

PONE-D-20-27919R2

Obesity in young South African women living with HIV: a cross-sectional analysis of risk factors for cardiovascular disease

Dear Dr. Hanley:

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PERMALINK

Obesity in young South African women living with HIV: A cross-sectional analysis of risk factors for cardiovascular disease

Sherika Hanley

Dhayendre Moodley

Mergan Naidoo

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design

Study population

Enrolment procedures and data collection at baseline

Questionnaires and physical examination

Laboratory investigations

Metabolic syndrome and CVD risk assessments

Statistical analysis

Ethical considerations and approvals

Results

Table 1. Prevalence of CVD risk factors determined as per standard of care in total study population (N = 372).

Table 2. Prevalence of additional CVD risk factors investigated in the intervention arm (N = 186).

Prevalence of Metabolic Syndrome (MetS)

Fig 1. Number of women in the various categories of metabolic syndrome.

CVD risk assessment tools

Obesity and self body image

Table 3. Obesity and satisfaction with body image by age category.

Discussion

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Massimiliano Ruscica

Roles

Author response to Decision Letter 0

Decision Letter 1

Massimiliano Ruscica

Roles

Author response to Decision Letter 1

Decision Letter 2

Massimiliano Ruscica

Roles

Acceptance letter

Massimiliano Ruscica

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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