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Nutritional status of Ugandan women living with HIV/AIDS : anthropometry and body composition assessment

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Queensland University of Technology ePrints Archive
Keywords
  • Hiv/Aids, Anthropometry, Body Composition, Body Fat, Lipodystrophy, Metabolic Syndrome
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Abstract

Human immunodeficiency virus (HIV) that leads to acquired immune deficiency syndrome (AIDs) reduces immune function, resulting in opportunistic infections and later death. Use of antiretroviral therapy (ART) increases chances of survival, however, with some concerns regarding fat re-distribution (lipodystrophy) which may encompass subcutaneous fat loss (lipoatrophy) and/or fat accumulation (lipohypertrophy), in the same individual. This problem has been linked to Antiretroviral drugs (ARVs), majorly, in the class of protease inhibitors (PIs), in addition to older age and being female. An additional concern is that the problem exists together with the metabolic syndrome, even when nutritional status/ body composition, and lipodystrophy/metabolic syndrome are unclear in Uganda where the use of ARVs is on the increase. In line with the literature, the overall aim of the study was to assess physical characteristics of HIV-infected patients using a comprehensive anthropometric protocol and to predict body composition based on these measurements and other standardised techniques. The other aim was to establish the existence of lipodystrophy, the metabolic syndrome, andassociated risk factors. Thus, three studies were conducted on 211 (88 ART-naïve) HIV-infected, 15-49 year-old women, using a cross-sectional approach, together with a qualitative study of secondary information on patient HIV and medication status. In addition, face-to-face interviews were used to extract information concerning morphological experiences and life style. The study revealed that participants were on average 34.1±7.65 years old, had lived 4.63±4.78 years with HIV infection and had spent 2.8±1.9 years receiving ARVs. Only 8.1% of participants were receiving PIs and 26% of those receiving ART had ever changed drug regimen, 15.5% of whom changed drugs due to lipodystrophy. Study 1 hypothesised that the mean nutritional status and predicted percent body fat values of study participants was within acceptable ranges; different for participants receiving ARVs and the HIV-infected ART-naïve participants and that percent body fat estimated by anthropometric measures (BMI and skinfold thickness) and the BIA technique was not different from that predicted by the deuterium oxide dilution technique. Using the Body Mass Index (BMI), 7.1% of patients were underweight (<18.5 kg/m2) and 46.4% were overweight/obese (≥25.0 kg/m2). Based on waist circumference (WC), approximately 40% of the cohort was characterized as centrally obese. Moreover, the deuterium dilution technique showed that there was no between-group difference in the total body water (TBW), fat mass (FM) and fat-free mass (FFM). However, the technique was the only approach to predict a between-group difference in percent body fat (p = .045), but, with a very small effect (0.021). Older age (β = 0.430, se = 0.089, p = .000), time spent receiving ARVs (β = 0.972, se = 0.089, p = .006), time with the infection (β = 0.551, se = 0.089, p = .000) and receiving ARVs (β = 2.940, se = 1.441, p = .043) were independently associated with percent body fat. Older age was the greatest single predictor of body fat. Furthermore, BMI gave better information than weight alone could; in that, mean percentage body fat per unit BMI (N = 192) was significantly higher in patients receiving treatment (1.11±0.31) vs. the exposed group (0.99±0.38, p = .025). For the assessment of obesity, percent fat measures did not greatly alter the accuracy of BMI as a measure for classifying individuals into the broad categories of underweight, normal and overweight. Briefly, Study 1 revealed that there were more overweight/obese participants than in the general Ugandan population, the problem was associated with ART status and that BMI broader classification categories were maintained when compared with the gold standard technique. Study 2 hypothesized that the presence of lipodystrophy in participants receiving ARVs was not different from that of HIV-infected ART-naïve participants. Results showed that 112 (53.1%) patients had experienced at least one morphological alteration including lipohypertrophy (7.6%), lipoatrophy (10.9%), and mixed alterations (34.6%). The majority of these subjects (90%) were receiving ARVs; in fact, all patients receiving PIs reported lipodystrophy. Period spent receiving ARVs (t209 = 6.739, p = .000), being on ART (χ2 = 94.482, p = .000), receiving PIs (Fisher’s exact χ2 = 113.591, p = .000), recent T4 count (CD4 counts) (t207 = 3.694, p = .000), time with HIV (t125 = 1.915, p = .045), as well as older age (t209 = 2.013, p = .045) were independently associated with lipodystrophy. Receiving ARVs was the greatest predictor of lipodystrophy (p = .000). In other analysis, aside from skinfolds at the subscapular (p = .004), there were no differences with the rest of the skinfold sites and the circumferences between participants with lipodystrophy and those without the problem. Similarly, there was no difference in Waist: Hip ratio (WHR) (p = .186) and Waist: Height ratio (WHtR) (p = .257) among participants with lipodystrophy and those without the problem. Further examination showed that none of the 4.1% patients receiving stavudine (d4T) did experience lipoatrophy. However, 17.9% of patients receiving EFV, a non-nucleoside reverse transcriptase inhibitor (NNRTI) had lipoatrophy. Study 2 findings showed that presence of lipodystrophy in participants receiving ARVs was in fact far higher than that of HIV-infected ART-naïve participants. A final hypothesis was that the prevalence of the metabolic syndrome in participants receiving ARVs was not different from that of HIV-infected ART-naïve participants. Moreover, data showed that many patients (69.2%) lived with at least one feature of the metabolic syndrome based on International Diabetic Federation (IDF, 2006) definition. However, there was no single anthropometric predictor of components of the syndrome, thus, the best anthropometric predictor varied as the component varied. The metabolic syndrome was diagnosed in 15.2% of the subjects, lower than commonly reported in this population, and was similar between the medicated and the exposed groups (χ 21 = 0.018, p = .893). Moreover, the syndrome was associated with older age (p = .031) and percent body fat (p = .012). In addition, participants with the syndrome were heavier according to BMI (p = .000), larger at the waist (p = .000) and abdomen (p = .000), and were at central obesity risk even when hip circumference (p = .000) and height (p = .000) were accounted for. In spite of those associations, results showed that the period with disease (p = .13), CD4 counts (p = .836), receiving ART (p = .442) or PIs (p = .678) were not associated with the metabolic syndrome. While the prevalence of the syndrome was highest amongst the older, larger and fatter participants, WC was the best predictor of the metabolic syndrome (p = .001). Another novel finding was that participants with the metabolic syndrome had greater arm muscle circumference (AMC) (p = .000) and arm muscle area (AMA) (p = .000), but the former was most influential. Accordingly, the easiest and cheapest indicator to assess risk in this study sample was WC should routine laboratory services not be feasible. In addition, the final study illustrated that the prevalence of the metabolic syndrome in participants receiving ARVs was not different from that of HIV-infected ART-naïve participants.

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