Risk factors for Overweight/Obesity among people living with HIV on antiretroviral therapy: An ambidirectional cohort study at a tertiary health facility in Zambia

Benson M Hamooya; Lukundo Siame; Matenge Mutalange; Chilala Cheelo; Kingsley Kamvuma; Sepiso K Masenga; Chanda Chitalu; Sadeep Shrestha; Samuel Bosomprah

doi:10.1371/journal.pone.0330777

. 2025 Sep 8;20(9):e0330777. doi: 10.1371/journal.pone.0330777

Risk factors for Overweight/Obesity among people living with HIV on antiretroviral therapy: An ambidirectional cohort study at a tertiary health facility in Zambia

Benson M Hamooya ¹, Lukundo Siame ^1,^2,^3,^*, Matenge Mutalange ¹, Chilala Cheelo ¹, Kingsley Kamvuma ¹, Sepiso K Masenga ¹, Chanda Chitalu ³, Sadeep Shrestha ⁴, Samuel Bosomprah ⁵

Editor: Anne Kapaata⁶

PMCID: PMC12416660 PMID: 40920752

Abstract

Background

Overweight and obesity are major concerns among people living with HIV (PLWH), particularly those on integrase inhibitors, as they elevate the risk of cardiovascular diseases. However, longitudinal data on the burden and risk factors for overweight/obesity in sub-Saharan Africa (SSA) remain limited. This study aimed to estimate the incidence and identify factors associated with overweight and obesity among PLWH who switched to a dolutegravir (DTG)-based ART regimen at Livingstone University Teaching Hospital.

Methods

We enrolled 249 adults aged ≥18 years living with HIV on ART [non-nucleoside/nucleotide reverse transcriptase inhibitor (NNRTI) n = 174, protease inhibitor (PI) n = 21, and DTG n = 54] with a baseline body mass index (BMI) < 25 kg/m² between April 2019 and May 2020 and conducted a single follow-up assessment between December 2022 and June 2023. Participants were followed for a median of 43 months (interquartile range [IQR]: 42, 44). At follow-up, all participants were on a DTG-based regimen for a median time of 23 months (IQR: 19, 40). Demographic, clinical, and laboratory data were collected using a structured questionnaire. The primary outcome was overweight/obesity, defined as BMI ≥ 25 kg/ m². Poisson regression with robust standard errors was used to determine risk factors for being overweight and obesity.

Results

The median age was 44 years (interquartile range (IQR) 36, 51) at baseline, with the majority being female (59.4%, n = 148). Over a total follow-up of 871.5 person-years, 44 incident cases of overweight/obesity occurred, yielding a cumulative incidence of 17.7% (44/249) and an incidence rate of 5.05 per 100 person-years. Factors positively associated with the risk of being overweight/obesity included being married (adjusted incidence rate ratio [aIRR] 2.34; 95% CI 1.24, 4.40), lower baseline CD4 count (aIRR 4.13; 95% CI 1.41, 13.38) and higher waist circumference (WC) values (aIRR 1.07; 95% CI 1.03, 1.11). While older age was associated with a lower risk of overweight/obesity (aIRR 0.97; 95% CI 0.94, 0.99).

Conclusion

The burden of overweight/obesity was high, and it was significantly driven by demographic, anthropometric, and immunological factors among our study participants. The findings suggest the importance of implementing targeted screening and management strategies for overweight and obesity, particularly among married individuals with higher WC values. Studies investigating the underlying mechanisms of excessive weight gain among PLWH on an integrase inhibitor-based regimen in resource-limited settings are warranted.

Introduction

In recent years, overweight and obesity have become a major public health concern [1]. The prevalences are rising particularly in low- and middle-income countries (LMICs), particularly in the southern African region, with estimates suggesting the burden of obesity could soon rival that of infectious diseases [2,3]. Serious diseases like cardiovascular disease, diabetes, chronic kidney disease, and some cancers are associated with excessive weight gain and are leading causes of morbidity and mortality worldwide [2]. With the increased longevity of people living with HIV (PLWH) due to the benefits of antiretroviral therapy (ART), they face challenges of excessive weight gain, which has been associated with a higher risk of metabolic diseases and poorer clinical outcomes compared to HIV-negative individuals [4].

Overweight and obesity in PLWH is a complex interplay of environmental and biological factors [5]. Modern obesogenic environments, characterized by readily available high-calorie foods, smoking, alcohol consumption, sedentary lifestyles, and reduced physical activity, contribute significantly to overweight/obesity [6]. Additionally, HIV-induced inflammation and specific ART, such as those containing integrase inhibitors (INSTIs) like Dolutegravir (DTG), which are widely used in our setting, have been shown to predispose PLWH to significant weight gain [7]. Demographic factors, including age, sex, socioeconomic status, and cultural influences, also play a role in shaping individual susceptibility to overweight/obesity [8,9].

Zambia, with one of the highest HIV prevalence rates globally at 11.0%, is also experiencing a rise in obesity rates, which now affect approximately 20% of the general population, particularly women [2,10]. Despite the increasing prevalence of obesity in our setting, most studies have primarily focused on the general population and factors associated with obesity, while longitudinal studies among PLWH remain limited. This study aimed to fill this gap by investigating the incidence and risk factors associated with overweight and obesity among PLWH on ART at a tertiary hospital in the Southern Province of Zambia.

Methods and materials

Study design and setting

This was an ambidirectional cohort study with one follow-up assessment per participant. The median follow-up time was 43 months (IQR: 42–44), calculated as the time between baseline enrollment (April 2019–May 2020) and the follow-up visit (December 2022–June 2023). This study was conducted at Livingstone University Teaching Hospital ART clinic among adults aged 18 years or older attending routine care and ART. The hospital provides HIV care and treatment to approximately 4,000 individuals and serves as a referral center for specialized medical care in Zambia’s Southern and Western provinces.

Eligibility and sampling method

Adults (≥18 years) on ART at Livingstone University Teaching Hospital were recruited. Eligible participants had a confirmed HIV diagnosis, were on ART for ≥ 6 months and above, had a baseline BMI < 25 kg/m², and consented to participate in the study. Participants with metabolic syndrome (defined according to IDF criteria), pregnant at enrollment, or with terminal illness were excluded.

Variables in the study

The outcome variable in this study was overweight (BMI, 25–29.9 kg/m2)/obese (BMI, ≥ 30 kg/m2), while independent variables were sociodemographic factors (age, sex, marital status, education level, and work status), behavioral factors (history of smoking, history of alcohol use, physical activity), clinical factors (blood pressure (systolic and diastolic), ART regimen, duration on ART, current duration on ART, waist circumference), and laboratory factors (viral load, CD4 count, lipid fasting profile [low-density lipoprotein (LDL), total cholesterol (TC), high-density lipoprotein (HDL)]).

Data collection

Sociodemographic factors (age, sex, marital status, education level, and employment status), behavioral/ lifestyle (history of smoking, history of alcohol use, physical activity), clinical parameters (ART regimen, blood pressure, height, weight), and laboratory metrics (lipid profile, CD4 count, fasting glucose, viral load) were obtained directly from participants and medical records through a structured questionnaire and a data abstraction form by the trained research assistants at baseline and this was repeated at the follow up period.

Blood pressure was measured using an Omron-HEM-7120 digital equipment from the United States. After a five-minute seated rest period, blood pressure was measured three times at one-minute intervals using an Omron-HEM-7120 digital monitor. The average of the three readings was then calculated to determine each participant’s blood pressure. A height measurement chart, a digital scale, and a tape measure were used to determine height, weight, and waist circumference, respectively.

The ART regimens were retrieved from the electronic medical record (SmartCare) and were classified as follows: A combination of integrase strand transfer inhibitors (INSTIs) that includes dolutegravir (DTG) and tenofovir disoproxil fumarate/lamivudine (TDF/3TC) or tenofovir alafenamide/lamivudine (TAF/3TC). Non-nucleoside reverse transcriptase inhibitor (NNRTI) regimens included either efavirenz (EFV) or nevirapine (NVP) in combination with one of the following nucleoside reverse transcriptase inhibitors (NRTIs): abacavir and lamivudine/emtricitabine (ABC/XTC) or tenofovir disoproxil fumarate and lamivudine/emtricitabine (TDF/XTC). The protease inhibitor (PI) regimens included either lopinavir/ritonavir (LPV/r) or atazanavir/ritonavir (ATV/r) in combination with one of the NRTI combinations: ABC/XTC, zidovudine/XTC (AZT/XTC), or TDF/XTC.

Definitions

Body mass index (BMI) was categorized as underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), or obese (≥30 kg/m²) [11]

A participant was classified as physically active if, during a typical week, they engaged in activities such as carrying or lifting heavy loads, digging, crushing stones, or construction work for at least 10 continuous minutes, or participated in moderate to vigorous-intensity sports, fitness, or recreational activities like running, football, cycling, swimming, or volleyball for at least 10 continuous minutes.

Data analysis

Data were entered into Microsoft Excel 2013 for cleaning. Statistical analysis was conducted using Stata version 15 (Stata Corporation, College Station, TX, USA). Categorical variables were summarized using frequencies and percentages. We used the Shapiro-Wilk test and Q-Q plots for continuous variables to determine the normality of the data; then, medians and interquartile ranges were used to summarize the data if it wasn’t normally distributed. The chi-square test was used to examine the statistical significance between two categorical variables. The Wilcoxon rank sum test was used to determine the statistical significance between the two medians. Robust Poisson logistic regression was employed to calculate the incidence rate ratios (IRR) and 95% confidence intervals (CI) for the associations between overweight/obesity and other study covariates. Poisson regression with robust standard errors is the recommended method for estimating risk ratios (RR) in cohort studies when the incidence of outcome is common (i.e., > 10%), as it provides more accurate estimates compared to logistic regression, which tends to overestimate relative risks or risk ratios [12,13]. The baseline variables included in the final regression model were chosen based on evidence from previous studies and their significance in bivariable analysis. To assess multicollinearity among predictors, the Variance Inflation Factor was used. Statistical significance was defined as p < 0.05.

Ethics

The study received ethical approval from the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee (MUSoMHS-REC- Ref. No: SMHS-MU3-2022-12) valid from 17^th June 2022–17^th June 2023 and data collection was done from 8^th December 2022–17^th January 2023. Participants signed informed consent after being informed of the study’s purpose in a language they could understand. The study data were fully anonymized throughout both collection and processing stages, with all personally identifiable information removed. In line with best practices for research transparency, we strictly followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (S1 File).

Results

Of the 512 participants screened between April 2019 and May 2020, 200 were excluded (124 with metabolic syndrome, 76 overweight/obese). Among the remaining 312, 63 were excluded during follow-up (1 death, 36 non-response, 1 pregnancy, 19 transfers, 6 lost to follow-up), leaving 249 for analysis (Fig 1).

Basic demographic and clinical characteristics at baseline

The study included 249 participants with a median age of 44 years (interquartile range (IQR) 36, 51), and the majority were female (59.4%, n = 148). Most of the participants were not married (53.1%, n = 132). A higher proportion of the participants had secondary education (55.4%, n = 138). Most participants were self-employed (47.4%, n = 118). The median weight of the participant was 57 kg (IQR: 51, 62). At baseline, 69.9% (n = 174) were on NNRTI, 8.4% (n = 21) on PI, and 21.7% (n = 54) on DTG, while at the follow-up period, all participants were switched to a DTG-based regimen (see supplementary Table 1 in S3 File). At baseline, the overall median duration on ART was 102 months (IQR: 60–144) (see Table 1) increasing to 155 months (IQR: 97–189) at follow-up (see supplementary Table 1 in S3 File). The median duration on the current DTG-based regimen at follow-up was 23 months (IQR: 19–40) (see supplementary Table 1 in S3 File). Median values of waist circumference was 76 cm (IQR: 71, 80). Total cholesterol of the participant was 4.4 mmol/L (IQR: 3.5, 5.2) and HDL level was 1.3 mmol/L (IQR: 1.1, 1.7) (see Table 1).

Table 1. Baseline demographic and clinical characteristics of participants. (N = 249).

Variable
Age, years (IQR)	44 (36, 51)
Sex, n (%)
Male	101 (40.6)
Female	148 (59.4)
Marital status, n (%)
Married	117 (46.9)
Unmarried	132 (53.1)
Education level, n (%)
No formal schooling	2 (0.8)
Primary	74 (29.7)
Secondary	138 (55.4)
Tertiary	35 (14.1)
Work status, n (%)
Government employee	18 (7.2)
Non-government	45 (18.1)
Self-employed	118 (47.4)
Unemployed	68 (27.3)
Smoking status, n (%)
No	234 (94.3)
Yes	14 (5.7)
Systolic BP, mmHg (IQR)	118 (108.7, 132)
Diastolic BP, mmHg (IQR)	76 (68, 82)
Weight, kg (IQR)	57 (51, 62)
Height, cm (IQR)	165.5 (160, 172)
Body mass index, n (%)
Underweight	60 (24.1)
Normal	189 (75.9)
Overweight
Obese
ART regimen at baseline, n (%)
NNRTI (EFV/NVP)	174 (69.9)
PI (LPV/r, ATV/r)	21 (8.4)
INSTI (DTG)	54 (21.7)
Baseline NRTI, n (%)
ABC/3TC	7 (2.8)
AZT/3TC	16 (6.4)
TDF/3TC	226 (90.8)
TAF/3TC
Duration on ART, months (IQR)	102 (60, 144)
Current duration on ART, months (IQR)	67 (9, 71)
Viral load, copies/mL	20 (0, 371)
CD4 count, cells/µL (IQR)	510 (365, 772)
Waist circumference, cm (IQR)	76 (71, 80)
Hip circumference, cm (IQR)	89 (84, 94)
LDL cholesterol, mmol/L (IQR)	1.9 (1.3, 2.4)
Total cholesterol, mmol/L (IQR)	4.4 (3.5, 5.2)
HDL cholesterol, mmol/L (IQR)	1.3 (1.1, 1.7)
Physically active, n (%)
No	175 (72.3)
Yes	67 (27.7)

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Note: Values are expressed as median (IQR) for continuous variables and n (%) for categorical variables.

Abbreviations: BP = Blood Pressure; ART = Antiretroviral Therapy; NNRTI = Non-Nucleoside Reverse Transcriptase Inhibitor; PI = Protease Inhibitor; INSTI = Integrase Strand Transfer Inhibitor; NRTI = Nucleoside Reverse Transcriptase Inhibitor; EFV = Efavirenz; NVP = Nevirapine; LPV/r = Lopinavir/ritonavir; ATV/r = Atazanavir/ritonavir; DTG = Dolutegravir; ABC = Abacavir; 3TC = Lamivudine; AZT = Zidovudine; TDF = Tenofovir Disoproxil Fumarate; TAF = Tenofovir Alafenamide; LDL = Low-Density Lipoprotein; HDL = High-Density Lipoprotein; IQR = Interquartile range

Body mass index (BMI) changes from baseline to end of follow-up

Among the 60 participants who were underweight at baseline, 32 (53.3%) remained underweight, 23 (38.3%) transitioned to a normal weight, 3 (5.0%) became overweight, and 2 (3.3%) moved into the obese category. Of the 189 participants with a normal weight at baseline, 11 (5.8%) shifted to underweight, 139 (73.5%) maintained a normal weight, 36 (19.1%) became overweight, and 3 (1.6%) progressed to obesity (see Table 2).

Table 2. Changes in BMI from baseline to end of follow-up.

		BMI at 4 years
	Baseline BMI n (%)	Underweight n (%)	Normal n (%)	Overweight n (%)	Obese n (%)
Underweight	60(24.1)	32(53.3)	23(38.3)	3(5.0)	2(3.3)
Normal	189(75.9)	11(5.8)	139(73.5)	36(19.1)	3(1.6)
Total	249 (100)

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Incidence of overweight/obesity among the study participants

Over a total follow-up of 871.5 person-years (median: 42 months; IQR: 40–44), 44 incident cases of overweight/obesity were recorded, corresponding to a cumulative incidence of 17.7% (44/249), with 15.7% (39/249) classified as overweight and 2.0% (5/249) as obese. This resulted in an incidence rate of 5.05 per 100 person-years.

Relationship of overweight/obesity with other study variables

Participants who were not married had a higher proportion of being overweight or obese compared to married participants (68.2% vs. 31.8%). A higher proportion of individuals who were overweight or obese had a higher BMI at baseline compared to those without (22.9 kg/m2 vs. 20.0 kg/m2, p < 0.001). Median waist circumference was higher in the overweight/obese group compared to individuals who were not overweight or obese (79.5 cm vs. 74 cm, p < 0.001) (see Table 3).

Table 3. Relationship Between Overweight/Obesity and other study variable.

Variable	Overweight/Obese (n = 44)	Not Overweight/Obese (n = 205)	P-value
Age, years	43.5 (37.5, 49.5)	44.0 (36.0, 51.0)	0.985
Sex			0.125
Male	14 (31.8%)	91 (44.4%)
Female	30 (68.2%)	114 (55.6%)
Marital status			0.026
Married	14 (31.8%)	103 (50.2%)
Unmarried	30 (68.2%)	102 (49.8%)
Education level			0.282
No formal schooling	0 (0.0%)	2 (1.0%)
Primary	10 (22.7%)	64 (31.2%)
Secondary	30 (68.2%)	108 (52.7%)
Tertiary	4 (9.1%)	31 (15.1%)
Work status			0.768
Government-employed	3 (6.8%)	15 (7.3%)
Non-government	7 (15.9%)	38 (18.5%)
Self-employed	24 (54.6%)	94 (45.9%)
Unemployed	10 (22.7%)	58 (28.3%)
Smoking			0.285
No	43 (97.7%)	191 (93.6%)
Yes	1 (2.3%)	13 (6.4%)
Alcohol use			0.712
No	20 (47.6%)	102 (50.8%)
Yes	22 (52.4%)	99 (49.2%)
Systolic BP, mmHg	115 (110,131.2)	118 (108.3,133.0)	0.745
Diastolic BP, mmHg	75.7 (69.3, 81.5)	76.3 (68.0,82.0)	0.853
BMI, kg/m²	22.9 (20.8,23.9)	20.0 (18.2,22.2)	<0.001
ART regimen			0.287
NNRTI (EFV/NVP)	35 (79.6%)	139 (67.8%)
PI (LPV/r or ATV/r)	2 (4.5%)	19 (9.3%)
INSTI (DTG)	7 (15.9%)	47 (22.9%)
Baseline NRTI			0.382
ABC/3TC	0 (0.0%)	7 (3.4%)
AZT/3TC	2 (4.6%)	14 (6.8%)
TDF/3TC	42 (95.5%)	184 (89.8%)
Duration on ART, days	27 (0,644)	20 (0, 289.5)	0.241
Waist circumference, cm	79.5 (76.0, 84.0)	74.0 (70.0,79.0)	<0.001
Viral load, copies/mL			0.269
<200	27 (61.4%)	147 (71.7%)
200–1000	8 (18.1%)	21 (10.2%)
≥1000	9 (20.5%)	37 (18.1%)
CD4 count, cells/µL			0.156
<200	6 (13.7%)	13 (6.3%)
200–500	14 (31.8%)	88 (42.9%)
>500	24 (54.6%)	104 (50.7%)
LDL, mmol/L	2.1 (1.3,2.6)	1.9 (1.3, 2.4)	0.317
Total cholesterol, mmol/L	4.5 (3.7,5.4)	4.3 (3.5, 5.1)	0.651
HDL, mmol/L	1.3 (1.1,1.7)	1.3 (1.1, 1.7)	0.656
Physically active			0.537
No	32 (76.2%)	143 (71.5%)
Yes	10 (23.8%)	57 (28.5%)

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Note: N total observation for the variable, ** variable presented as median (lower quartile, upper quartile),*variable presented as frequency (percentage). Bold p-values indicate statistical significance (p < 0.05). Abbreviations: BMI – body mass index; ART – antiretroviral therapy; NNRTI – non-nucleoside reverse transcriptase inhibitor; PI – protease inhibitor; INSTI – integrase strand transfer inhibitor; NRTI – nucleoside reverse transcriptase inhibitor; EFV – efavirenz; NVP – nevirapine; LPV/r – lopinavir/ritonavir; ATV/r – atazanavir/ritonavir; DTG – dolutegravir; TDF – tenofovir disoproxil fumarate; ABC – abacavir; AZT – zidovudine; 3TC – lamivudine; HDL – high-density lipoprotein; LDL – low-density lipoprotein.

Regression analysis of the factors associated with overweight/obesity.

At bivariable analysis, married participants had a significantly 90% increased risk of overweight/obesity compared to unmarried individuals, incidence rate ratio (IRR) 1.90; 95% confidence interval (CI) 1.06, 3.41. A unit increase in waist circumference was significantly associated with a 6% increased risk of being overweight/obese, IRR 1.06: 95%CI 1.03, 1.09 (see Table 4).

Table 4. Association between baseline sociodemographic, lifestyle and clinical factors and incidence of overweight/obesity.

Variable	Bivariable analysis		Multivariable analysis
Variable	IRR (95%CI)	P-value	IRR (95%CI)	P-value
Age	0.99 (0.98, 1.01)	0.589	0.97 (0.94, 0.99)	0.018
Sex
Male	Ref		Ref
Female	1.57 (0.87, 5.53)	0.134	2.23 (0.90, 5.44)	0.083
Marital status
Unmarried	Ref		Ref
Married	1.90 (1.06, 3.41)	0.032	2.34 (1.24, 4.40)	0.008
Education level
Primary	Ref
Secondary	1.65 (0.85, 3.20)	0.136	Ref
Tertiary	0.87 (0.29, 2.58)	0.8	1.34 (0.69, 2.62)	0.379
Work status			0.48 (0.11,1.99)	0.314
Government employee	Ref		Ref
Non-government	0.93 (0.27, 3.22)	0.913	0.91 (0.24, 3.44)	0.891
Self-employed	1.22 (0.41, 3.65)	0.722	0.86 (0.25, 2.88)	0.801
Unemployed	0.88 (0.27, 2.88)	0.836	0.67 (0.19, 2.34)	0.532
Smoke
No	Ref		Ref
Yes	0.84 (0.28, 2.47)		0.95 (0.13, 7.07)	0.966
Alcohol
No	Ref		Ref
Yes	1.31 (0.75, 2.27)		1.26 (0.69, 2.31)	0.459
ART
NNRTI (EFV & NVP)	Ref		Ref
PI (LPV/r & ATZ/r)	0.47 (0.12, 1.83)	0.279	0.55 (0.18,1.72)	0.312
INSTI (DTG)	0.64 (0.30, 1.37)	0.253	0.70 (0.29, 1.71)	0.431
Median current duration on ART in months	0.99 (0.98, 1.01)	0.871	1.01 (0.99, 1.02)	0.145
waist circumference	1.06 (1.03, 1.09)	<0.001	1.07 (1.03,1.11)	< 0.001
CD4 count, cells/µL
> 500	Ref		Ref
< 200	1.68 (0.79, 3.58)	0.176	4.13 (1.41, 13.38)	0.010
200- 500	0.73 (0.39, 1.34)	0.314	1.07 (0.55, 2.11)	0.826
LDL, mmol/L	1.02 (0.77, 1.33)	0.911	0.95 (0.68, 1.34)	0.788
Total cholesterol, mmol/L	1.2 (0.89, 1.29)	0.453	1.21 (0.78,1.88)	0.404
HDL, mmol/L	1.02 (0.67, 1.55)	0.935	0.98 (0.55, 1.74)	0.945
Lymphocytes, (109 cells/l)	0.98 (0.63, 1.54)	0.931	1.08 (0.63, 1.84)	0.774
Physically Active
No	Ref		Ref
Yes	0.90 (0.52,1.54)	0.697	0.99 (0.54, 1.82)	0.976

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Note: IRR incidence rate ratio, Ref reference group, ART- antiretroviral therapy, NNRTI non-nucleoside/nucleotide reverse transcriptase inhibitor (EFV = efavirenz and NVP = Nevirapine), PI protease inhibitor (LPV/r = lopinavir/ritonavir and ATV/r = atazanavir/ritonavir), INSTI integrase strand transfer inhibitor (DTG = dolutegravir), NRTI nucleotide reverse transcriptase inhibitor, TDF/3TC tenofovir disoproxil fumarate/lamivudine, ABC/3TC abacavir/lamivudine, AZT/3TC zidovudine/lamivudine, LDL-c Low-Density Lipoprotein Cholesterol, HDL High-Density Lipoprotein, bold p < 0.05

At multivariable analysis, a one-year increase in age was significantly associated with a 3% reduced risk of being overweight/obese, IRR 0.97: 95%CI 0.94, 0.99. The married participants had a 2.34 times higher risk of being overweight/obese compared to unmarried individuals. A unit increase in waist circumference was significantly associated with a 7% increased risk of overweight/obesity, IRR 1.07; 95%CI 1.24, 4.40. Individuals with a CD4 count below 200 cells/µL at baseline were 4.13 times more likely to be overweight/obese compared to those with a CD4 count above 500 cells/µL (see Table 4).

Discussion

This study aimed to explore the incidence, and the risk factors associated with overweight and obesity in PLWH. After a median follow up period of 42 months, the cumulative incidence (17%) of overweight or obese people was high among the study participants. The development of overweight/obesity was positively associated with being married, increasing waist circumference, and having a CD4 count below 200 cells/µl, while older age appeared to have a protective effect.

The cumulative incidence of overweight/obesity in this study was lower than in Tanzania (2018) at 35% [14]. The observed differences may stem primarily from the longer follow-up period in Tanzania, which was 10 years compared to our 4 years, and differences in ART status, as the Tanzanian population was mostly ART-naïve and younger, while ours involved ART-experienced individuals and older. The incidence observed in our setting, primarily among this urban population, is a public health concern due to the higher risk of cardiometabolic conditions among PLWH. This incidence may be caused by urbanization and changes in the built environment, which have reduced opportunities for physical activity [12,13]. Concurrently, a transitional nutritional shift towards highly processed, energy-dense foods has occurred, fueled by their increased availability, affordability, and accessibility due to the rise of supermarkets and aggressive marketing strategies in our setting [16]. Furthermore, the stigma surrounding HIV still prevalent in our setting, which often leads to the perception that being overweight is a sign of good health, this may drive some PLWH to gain weight as part of a ‘return to health,’ potentially contributing to excessive weight gain [2,15,16].

This study suggests that contrary to the generally observed trend of increased obesity with age, a one-year increase in age is associated with a slight reduction in the risk of being overweight or obese. This finding contradicts previous research that has linked aging with a higher likelihood of obesity [9,17]. This finding requires mechanistic studies to understand the relationship being age and obesity among PLWH in our setting.

The incidence of overweight/obesity was significantly higher in married participants compared to unmarried individuals in this current study. This is consistent with a study in Tanzania (2016) [17]. Overweight and obesity are more common among married adults and stable relationship, particularly among married men, because household food distribution normally favors men and men are more inclined to eat out frequently [17–19]. Women, on the other hand, are more inclined to take care of themselves whether or not they are married, but marriage has a greater impact on men [17,20].

In the current study, Individuals with a low CD4 count at baseline have a significantly higher risk of being overweight/obesity compared to those with a higher CD4 count. This study aligns with several studies that have reported similar results, however the reason to this association remains fluid [14,21–23]. The return to health in advanced HIV appears dysregulated by INSTIs, converting immune reconstitution into a driver of excessive adiposity [14,24]. This process occurs in two phases: an initial rapid phase of tissue repair followed by a slower, sustained phase as immune function improves. Individuals with significant LBM deficits at the onset of recovery tend to experience more pronounced weight gain, which can include fat accumulation, ultimately increasing their risk of being overweight and obesity [14,24].

Waist circumference showed a significant correlation with overweight/obesity, consistent with findings from previous studies [25,26]. Waist circumference, combined with BMI, provides a better prediction of health risks compared to BMI alone [27]. It effectively identifies central obesity, which is associated with cardiometabolic risk factors such as hypertension, dyslipidemia, and hyperglycemia, all of which are linked to adverse outcomes [27]. Thus, routine measurement of waist circumference should be incorporated into HIV care, and health practitioners should be trained to properly perform this measurement [27].

In the current study, the DTG-based regimen among this cohort was not associated with an increased risk of obesity. This aligns with the findings of Mounzer et al. (2021) and Guaraldi et al. (2021) [28,29].However, other studies have demonstrated a positive association between DTG use and obesity, particularly among individuals receiving TAF-containing backbones unlike in our studies where all participants had shifted to TDF [20,30–32]. Other reason for this finding in our study could be attributed to the fact related to variations in the impact of DTG on weight gain, which could depend on factors such as sex, baseline BMI, CD4 count, and tuberculosis coinfection [30]. Additionally, all the participants by the end of the follow-up period were on a DTG-based regimen, and there is a high probability that it impacted on the excessive weight gain among our study participants. Nevertheless, further long-term studies are needed to fully understand the risk of a DTG-based regimen with TDF, which is the main treatment therapy in our setting.

The study has some strengths and weaknesses. The study has included only a single follow-up period, making it impossible to capture short-term variability in weight, potentially masking transient weight fluctuations. Future studies with serial measurements could better elucidate the trajectory and timing of weight gain in this population. Information on diet and/or food intake was not included. The reliance on self-reported variables, such as physical activity, alcohol consumption, and smoking, introduces the potential for recall bias. Despite these weaknesses, the study is one of the few longitudinal studies that have explored the incidence of overweight/obesity and risk factors among PLWH in the era of integrase inhibitors. The study provides valuable information concerning the longitudinal burden of overweight/obesity and risk factors among PLWH, information imperative in designing public health actions.

Conclusion

This study found a high incidence of overweight and obesity among PLWH on ART. Being married, low CD4 count, and increased waist circumference were positively associated with overweight/obesity, while age was inversely associated. These findings highlight the need for targeted weight management within HIV care, especially for high-risk groups. Longitudinal studies with serial measurements are needed to clarify the impact and timing of weight gain from integrase inhibitors in this population.

Supporting information

S1 File. STROBE checklist.

(DOCX)

pone.0330777.s001.docx^{(46.5KB, docx)}

S2 File. Dataset.

(XLSX)

pone.0330777.s002.xlsx^{(255.3KB, xlsx)}

S3 File. Supplementary Table.

(DOCX)

pone.0330777.s003.docx^{(18.1KB, docx)}

Acknowledgments

We would like to extend our gratitude to the HAND group, chaired by Professor Masenga and Dr. Hamooya, for their invaluable support, as well as to Mulungushi University for their continued support.

Data Availability

All relevant data are within the manuscript and its Supporting Information files

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0330777.r001

Decision Letter 0

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9 Jun 2025

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Reviewer #1: 1. The authors should use a consistent nosology for people living with HIV (PLWH); there are two nosologies used eg People living with HIV and people with HIV

2. The findings should be stated and then referenced with figues/table and not the other way round.

3. The conclusion did not explicitly answer the objective

Reviewer #2: The methods and statistical analysis and results sections are not clear. It is not clear how long the follow-up period was, and what the interval of follow-up for each participant was. Also, it is not clear what was done at the different follow-up periods. It is not clear what the final sample size is, since the authors indicate they recruited 388 participants and included 249, but after removing all the individuals excluded, the remaining number is less than 190, and not 249. The comparison of the baseline characteristics between baseline and follow-up is quite unusual and should be removed. The tables need to be edited in line with the journal recommendations. In the write-up on 'univariate analysis, which should be bivariate analysis, he needs to include all the variables that were entered in the multivariate analysis. The authors indicate that after checking for normality, they found data not to be normally distributed, why then do they use the chi-square and the poisson regression for non-normally distributed data?

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PLoS One. 2025 Sep 8;20(9):e0330777. doi: 10.1371/journal.pone.0330777.r002

Author response to Decision Letter 1

24 Jun 2025

24 /06/2025

PLOS ONE Journal

Dear Editor,

Ref: Submission of a revised research article for peer review and publication consideration

Reference to the above-mentioned subject. I am writing to submit a revised original research article titled “Risk Factors for Overweight/Obesity among People living with HIV on Antiretroviral Therapy: A cohort study at a Tertiary Health Facility in Zambia”.

Academic Editor comments

Major comments to address

1. Author needs to clearly state the total duration of follow up and what was the interval of follow up per patient because as is it is not clear whether follow up was 2 or 4 years

Response: Thank you very much. We have made it clear now in the abstract

2. For cohort studies, its always good to report the follow-up time in terms of person-years and this information is missing.

Response: Thank you very much. We have now reported the follow-up in terms of person-years

3. Under eligibility and sampling method, the numbers don’t add up, how many people were screened, how many were excluded per reason of exclusion and how many made it to the final analysis? It’s stated that screened 388 and recruited 249….and excludeded most people due to overwight, death, non-response,pregnancy, transfer to another facility and lost to follow up ….from this section it looks like only 49 participants went into the final study study?

Response: Thank you very much. We have now rectified this problem to align with the fin 249 which was followed up.

4. The reasons for exclusion belong to the result section and not materials and methods. Author refers to figure 1, however there is no figure 1 in the write up.

Response: Thank you very much. We have moved the reason for exclusion to the result section, and we have uploaded Figure 1 as a separate file.

5. The statement on measurement of blood pressure is not clear, was the waiting for one minute of five minutes.

Response: Thank you very much. We have now clarified this statement

6. If the Shapiro-wilk test showed that the data were not normally distributed, why did the authors then use chi-square tests and the poisson regression which are for normally distributed data

Response: Thank you very much for the question. The Shapiro-Wilk test was used to see whether the data measured on a ratio/interval scale (quantitative data) followed a normal distribution. The chi-square test was used to determine a statistical relationship between two categorical variables. The Poisson regression with robust standard errors was used to estimate the factors (in which rate ratios where generated) associated with overweight/obesity (binary outcome).

7. Under data analysis, authors mention that “variables included in the model were chosed based on evidence from previous studies” does this mean that there was no bivariate analysis and checking for confounding and interaction terms?

Response: Thank you very much. The final model was based on literature and those variables that were significant in the bivariate analysis, which we have now clarified.

8. Under results authors mention basic demographics and clinical characteristics at baseline and follow-up…for cohort studies should the results not be reported at baseline and end of follow up?

Response: Thank you very much for the suggestion. We have corrected this now as suggested.

9. At what point during follow up were the partcipants switch to DTG? And was this taken into consideration during analysis?

Response: Thank you very much. We collect data on how long each participant was on DTG, and we have indicated the information in the manuscript in terms of the median time participants were on a DTG-based regimen “The median duration on the current DTG-based regimen at follow-up was 23 months (IQR: 19–40)”.

10. The median duration on ART at baseline was 102 (60, 144) months…is this the Interquartile range or minimum maximum time?

Response: Thank you for its interquartile range, as we have defined in the table keys and in the narration section of the results.

11. Edit table 1 according to joural specifications….some cells are empty, and the N seems to be in the wrong columns.

Response: Thank you for your observation. We have edited the table according to journal specifications.

12. Table 1 has p values accompanied by PW. What does this stand for…its not defined at the end of the table.

Response: Thank you for your observation. We have removed this comparison as suggested by Reviewer Two.

13. Under results…there is a title “ BMI changes fron baseline to four years” what does this mean? Authors need to generallt improve on the english for clarity of reading.

Response: Thank you for your observation. We have changed the title to improve clarity.

14. In the abstract, its stated that being married was associated with weight gain , however in the result section, its stated that partipants who were not married had a hiogher proportion of being overweight or obese compared to married participants. This is contradicting.

Response: Thank you for your observation. The apparent contradiction arises from distinguishing the unadjusted descriptive statistics presented in Table 3 from the adjusted regression results in Table 4. Table 3 shows the row proportions, indicating that 68.2% of overweight or obese participants were unmarried. This represents a descriptive snapshot of baseline characteristics and does not account for potential confounders. In contrast, Table 4 presents findings from an adjusted multivariable analysis. After controlling for age, waist circumference, CD4 count, and other relevant covariates, being married was independently associated with a 2.34-fold higher risk of developing overweight or obesity (aIRR 2.34; 95% CI: 1.24–4.40). Therefore, the abstract’s conclusion regarding the association between marriage and overweight/obesity is drawn from this adjusted model, which isolates the effect of marital status while accounting for confounding factors.

15. A higher proportion of individuals who were overweight or obese had a higher BMI at baseline compared to those without (22.9 kg/m2 vs. 20.0 kg/m2, p < 0.001). Median waist circumference was higher in the overweight/obese group compared to individuals who were not overweight or obese (79.5 cm vs. 74 cm, p < 0.001). Is this not obvious?

Response: Thanks for the observation. Waist circumference was included in the model as a measure of central adiposity, complementing BMI. It’s a strong independent association with incident overweight/obesity, which highlights the role of visceral fat in early weight-related risk accumulation. Previous studies have demonstrated their role in the development of overweight and obesity. While this association may seem evident, our aim was to confirm it within our population, which has not been previously studied. (1. Sweatt K, Garvey WT, Martins C. Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward? Curr Obes Rep. 2024;13: 584–595. doi:10.1007/s13679-024-00580-1).

16. Edit table 3 for clarity

Response: Thank you. We have done edits to make the table clearer.

17. It’s written that Table 4 shows the results on the univariable and multivariable regression analyses of fcators associated with overweight…..this should be a bivariate analysis

Response: Thank you for the observation. We have edited this part in our table.

18. In the multivariable analyis, one year increase in age was significantly associated with a 3% reduced risk of being overweight…where does weight come from given it is not included in the bivariate analysis?

Response: Thank you. The main outcome of the study was overweight/obesity, and hence the interpretation “one-year increase in age was significantly associated with a 3% reduced risk of being overweight/obese”. Age was included in the final model (multivariable analsysis) based on the previous literature as opposed to its significance at bivariable analysis. Hope we were able to get your question. We sincerely appreciate your comment.

19. CD4 counts were also not included in the univariate analysis yet it’s mentioned under the same section on multivariate analysis

Response: Thank you. We did not interpret CD4 in bivariable analysis because it was not statistically significant; however, in the multivariable analysis, it became significant, and we had to interpret it. CD4 was included in the final model (multivariable analysis) based on the previous literature, as opposed to its significance at bivariable analysis

20. Table 4 the upper limit of 95% CI is very close to the null value and so this seems to be a chance finding.

Response: Thank you. We appreciate your insightful comment regarding the upper limit of the 95% confidence interval approaching the null value, which could also indicate a weak association. However, we defined statistical significance as a p-value <0.05 and/or a 95% confidence interval that does not include 1 in the regression model.

Review Comments to the Author

Reviewer #1: 1. The authors should use a consistent nosology for people living with HIV (PLWH); there are two nosologies used eg People living with HIV and people with HIV

Response: Thank you. We have endeavored to use on nosology for people living with HIV throughout the manuscript

2. The findings should be stated and then referenced with figues/table and not the other way round.

Response: Thank you for the observation. We have changed throughout the manuscript.

3. The conclusion did not explicitly answer the objective

Response: Thank you for the observation. We have changed this conclusion to answer the objective

Response: Thank you for the observation. We have now clarified what the follow-up period was and the median follow-up period for the participants “This was a longitudinal cohort study with one follow-up assessment per participant. The median follow-up time was 43 months (IQR: 42–44), calculated as the time between baseline enrollment (April 2019–May 2020) and the follow-up visit (December 2022–June 2023)”. We had only two points to collect data (baseline and follow-up period), and we have explicitly said what was done at the two time periods.

It is not clear what the final sample size is, since the authors indicate they recruited 388 participants and included 249, but after removing all the individuals excluded, the remaining number is less than 190, and not 249.

Response: Thank you for the observation. We have now corrected this and justified how we arrived at 249, see Figure 1 and the explanation in the first part of the results section.

The comparison of the baseline characteristics between baseline and follow-up is quite unusual and should be removed.

Response: Thank you for the observation. We have now removed the comparison from Table 1.

The tables need to be edited in line with the journal recommendations.

Response: Thank you. We have edited the tables to reflect journal recommendations

In the write-up on 'univariate analysis, which should be bivariate analysis, he needs to include all the variables that were entered in the multivariate analysis.

Response: Thank you. In the write-up up we only narrated significant variables in bivariable and multivariable analysis, and the selection of the model was based on literature and significance in bivariable analysis.

The authors indicate that after checking for normality, they found data not to be normally distributed, why then do they use the chi-square and the poisson regression for non-normally distributed data?

Response: Thank you for this important methodological question. Chi-square tests were used appropriately for categorical variables (e.g., marital status, education level), which do not require normality. Assumptions of independent observations and adequate expected cell sizes (≥5 in ≥80% of cells) were satisfied. Normality checks (Shapiro-Wilk) were applied only to continuous variables. For these, we used non-parametric Wilcoxon signed-rank tests where appropriate (Table 3). Poisson regression with robust standard errors was used to estimate factors associated with incident overweight/obesity cases. As a generalized linear model, it does not assume normality. We applied robust standard errors to address overdispersion, consistent with recommendations for binary outcomes with >10% incidence. Logistic regression tends to overestimate risk ratios for common outcomes; Poisson regression with robust errors yields more accurate estimates. Continuous predictors (e.g., waist circumference) were included without requiring normality, as the log-link function accommodates skewed distributions.

We would like to thank the reviewers for taking the time to make suggestions that have improved our manuscript. We have extensively revised the manuscript and addressed all concerns and suggestions. We now hope the current manuscript is acceptable for publication.

Please address all correspondence to lukundosiame23@gmail.com . We look forward to hearing from you at your earliest convenience.

Please do not hesitate to contact me should you have further questions.

Yours sincerely,

Dr. Lukundo Siame, Bsc., MBcHB.

Junior Residence Medical Livingstone University Teaching Hospital

Attachment

Submitted filename: Response to Reviewers (1).docx

pone.0330777.s005.docx^{(34.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0330777.r003

Decision Letter 1

Anne Kapaata

17 Jul 2025

Dear Dr. Siame,

Please submit your revised manuscript by Aug 31 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Anne Kapaata

Academic Editor

PLOS ONE

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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.

Additional Editor Comments:

Clarifications needed before final approval to publish

While the Author has addressed most queries given in the first rebuttal, it is still unclear with regards to:

1. This appears to be a retrospective cohort study given that data was collected at start and end point. Most ART providing health facilities prescribe 3 monthly ART and clients have to return to the facility every three months for refill. Why did the authors not collect data at least every 6 months for the 3-4 year period and only collect data at baseline and end point?.

2. The total follow-up time for each participants appears to be 3-4 years (between 2019/2020 to 2022-2023), and each participant was followed only once, meaning you collected data from each participant at baseline and at the end of data collection only. We know that weight changes rapidly over short periods of time, and many factors explain these changes. Making conclusions about changes in weight over a four year period may not be very accurate and liable to a lot of biases and confounding.

3. Authors mention in the manuscript that they excluded 63 participants after enrolment due to death, pregnancy, loss to follow-up among others. Please note that in a cohort study, once people are enrolled into the study, they contribute some person time to the follow-up and so should not be excluded especially in the baseline and survival analysis. At what point did these people fall out of the study and how much follow-up time did they contribute to the study. Did any of them have the outcome of interest at the time of dropping out of the study?

4. Table one should not have the follow-up data. It should only contain baseline demographics and should be on 312 participants that were enrolled into the study. Table one still has empty cells in the follow-up section. This is not acceptable.

PLoS One. 2025 Sep 8;20(9):e0330777. doi: 10.1371/journal.pone.0330777.r004

Author response to Decision Letter 2

24 Jul 2025

23/07/2025

PLOS ONE Journal

Dear Editor,

Ref: Submission of a revised research article for peer review and publication consideration

Reference to the above-mentioned subject. I am writing to submit a revised original research article titled " Risk Factors for Overweight/Obesity among People Living with HIV on Antiretroviral Therapy: A Cohort Study at a Tertiary Health Facility in Zambia.

We would like to thank the reviewers for taking the time to make suggestions that have improved our manuscript. We have revised the manuscript and addressed all concerns and suggestions. We now hope the current manuscript is acceptable for publication. Below are the point-by-point responses to all comments and suggestions.

Journal Requirements:

Response: thank you, noted.

Response: thank you, all reviewed .

Additional Editor Comments:

Clarifications needed before final approval to publish

While the Author has addressed most queries given in the first rebuttal, it is still unclear with regards to:

1. This appears to be a retrospective cohort study given that data was collected at start and end point.

Response: Thank you for your observation. Indeed, there is a retrospective element to the study. Initial data were collected approximately three years ago, after which the same cohort was recalled, and key study variables were physically collected. Given that some data—such as ART regimen and duration on ART—were obtained from medical records (SmartCare), while other data—such as weight, height, and lipid profiles—were collected directly from participants, we believe this qualifies as an ambidirectional cohort study, which takes into account the retrospective and prospective nature of the data. The manuscript has been revised accordingly.

Most ART providing health facilities prescribe 3 monthly ART and clients have to return to the facility every three months for refill. Why did the authors not collect data at least every 6 months for the 3–4-year period and only collect data at baseline and end point?.

Response: Thank you very much for your concern. While we would have preferred to collect data at six-month intervals as suggested, limited funding constrained us to only two data collection points. In our setting, medical records often have significant gaps, and without a properly designed study, there is a high risk of underpowered results and substantial missing data. Hence we could only recall the participants after securing research funds. Nevertheless, we have acknowledged this limitation in the Discussion section of the manuscript. “The study has some strengths and weaknesses. The study has included only a single follow-up period, making it impossible to determine the exact point when individuals met the criteria for overweight or obesity”

2. The total follow-up time for each participants appears to be 3-4 years (between 2019/2020 to 2022-2023), and each participant was followed only once, meaning you collected data from each participant at baseline and at the end of data collection only. We know that weight changes rapidly over short periods of time, and many factors explain these changes. Making conclusions about changes in weight over a four-year period may not be very accurate and liable to a lot of biases and confounding.

Response: thank you very much. We have revised and modified the discussion and conclusion of this manuscript to be more cautious study.

Response: Thank you for the observation. Given the study design we used where we had only one follow-up period, we were unable to conduct survival analysis. We did not collect data on the exact time each of the events happened for all the excluded participants; our interest was to analyze data for all the participants who had a measurement on the outcome of interest at follow-up period. Therefore, we had to exclude anyone without the measurement on the outcome of interest. We have included a recommendation in the conclusion “. Longitudinal studies with serial measurements are needed to clarify the impact and timing of weight gain from integrase inhibitors in this population”

Response: thank you very much. we have redone Table to only show the baseline characteristics and a supplementary for endeline characteristics of the participant. Regarding including 312 participants; our design in another way is a pre-post study design and our aim was seeing what outcome each participant will end up having at follow-up given the baseline characteristics. Therefore, excluded everyone without the measurement on the outcome of interest (We did not collect data on the exact time each of the events happened for all the excluded participants).

We have revised the manuscript and addressed all concerns raised. We want to thank you all again for the tremendous work and time that you committed to reviewing and correcting our work. Our manuscript is much improved, and we are very grateful.

Please address all correspondence to lukundosiame23@gmail.com . We look forward to hearing from you at your earliest convenience.

Please do not hesitate to contact me should you have further questions.

Yours sincerely,

Dr. Lukundo Siame, Bsc., MBcHB.

Junior Residence Medical Livingstone University Teaching Hospital

Attachment

Submitted filename: response to review.docx

pone.0330777.s006.docx^{(20.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0330777.r005

Decision Letter 2

Anne Kapaata

6 Aug 2025

Risk Factors for Overweight/Obesity among People living with HIV on Antiretroviral Therapy: An ambidirectional cohort study at a Tertiary Health Facility in Zambia.

PONE-D-25-22448R2

Dear Dr. Lukundo Siame

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Anne Kapaata (PhD)

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0330777.r006

Acceptance letter

Anne Kapaata

PONE-D-25-22448R2

PLOS ONE

Dear Dr. Siame,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. STROBE checklist.

(DOCX)

pone.0330777.s001.docx^{(46.5KB, docx)}

S2 File. Dataset.

(XLSX)

pone.0330777.s002.xlsx^{(255.3KB, xlsx)}

S3 File. Supplementary Table.

(DOCX)

pone.0330777.s003.docx^{(18.1KB, docx)}

Attachment

Submitted filename: Response to Reviewers (1).docx

pone.0330777.s005.docx^{(34.3KB, docx)}

Attachment

Submitted filename: response to review.docx

pone.0330777.s006.docx^{(20.7KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files

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PERMALINK

Risk factors for Overweight/Obesity among people living with HIV on antiretroviral therapy: An ambidirectional cohort study at a tertiary health facility in Zambia

Benson M Hamooya

Lukundo Siame

Matenge Mutalange

Chilala Cheelo

Kingsley Kamvuma

Sepiso K Masenga

Chanda Chitalu

Sadeep Shrestha

Samuel Bosomprah

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods and materials

Study design and setting

Eligibility and sampling method

Variables in the study

Data collection

Definitions

Data analysis

Ethics

Results

Fig 1. Flowchart of screened and eligible participants.

Basic demographic and clinical characteristics at baseline

Table 1. Baseline demographic and clinical characteristics of participants. (N = 249).

Body mass index (BMI) changes from baseline to end of follow-up

Table 2. Changes in BMI from baseline to end of follow-up.

Incidence of overweight/obesity among the study participants

Relationship of overweight/obesity with other study variables

Table 3. Relationship Between Overweight/Obesity and other study variable.

Regression analysis of the factors associated with overweight/obesity.

Table 4. Association between baseline sociodemographic, lifestyle and clinical factors and incidence of overweight/obesity.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Anne Kapaata

Roles

Author response to Decision Letter 1

Decision Letter 1

Anne Kapaata

Roles

Author response to Decision Letter 2

Decision Letter 2

Anne Kapaata

Roles

Acceptance letter

Anne Kapaata

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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