Lipodystrophy diagnosis in people living with HIV/AIDS: prediction and validation of sex-specific anthropometric models

The human immunodeficiency virus (HIV) remains one of the most serious global health threats of our time. Global estimates from 2016 found that 36.7 million people are living with HIV. Low- and middle-income countries, primarily in Africa and Latin America, represent 27.5 million (75%) of the total people living with HIV worldwide [1].

A few decades ago, people living with HIV/AIDS (PLWHA) were given a short life expectancy. At the peak of the HIV epidemic between 1990 and 2000, life expectancy for PLWHA dropped to 49.5 years in some low- and middle-income countries; HIV and AIDS (acquired immunodeficiency syndrome) combined, decreased life expectancy by approximately 20 years [2]. The introduction of the combined antiretroviral therapy (cART) in 1996, however, greatly changed this trend. cART is the current standard treatment for HIV [3]. With cART, PLWHA become less susceptible to the development of tumors and opportunistic infections [4]. As a result of cART treatment, today the life expectancy of people living with HIV/AIDS has increased 37 years [5]. Local and international efforts have focused on access to cART for those in need. Currently, around 53% of all people living with HIV have access to treatment [1].

The improvement of the cART therapeutic regimen is one of the greatest public health achievements of the past few decades, and the HIV infection has transitioned into a manageable chronic illness [6]. In more recent years there has been a shift in research focus, from the HIV-related communicable diseases to its long-term impact on non-communicable diseases. This trend is supported by recent studies that have looked into HIV-related cardiovascular disease, kidney- and liver-related diseases, cognitive decline, and osteoporosis [7‐11]. Additional adverse health effects are HIV-related metabolic and body composition alterations. This article focuses on HIV-associated body composition alterations. Such changes include lipohypertrophy, lipoatrophy, or both, and are known as the “lipodystrophy of HIV” [12]. Lipohypertrophy is characterized by an increase in fat mass in the abdomen (visceral), back of the neck, and breast (gynecomastia). Lipoatrophy is characterized by the loss of fat mass in subcutaneous areas of the face, buttocks, arms, and legs [13].

Lipodystrophy is associated with an increased risk for the development of non-communicable diseases among PLWHA. The literature has suggested that PLWHA under cART treatment with lipodystrophy—mainly those with visceral fat accumulation—are at increased risk of developing cardiovascular diseases [14‐17] and diabetes [18]. The association of lipodystrophy with non-communicable disease, especially atherosclerosis, is due to lipodystrophy’s negative effects on lipids and insulin sensitivity, and pro-inflammatory effects on endothelial cells [19, 20]. Thus, an earlier diagnosis and treatment for lipodystrophy can prevent or delay the development of some non-communicable diseases, thereby leading to better overall health and well-being of PLWHA, while reducing costs of treatment and premature deaths [21]. Additional problems associated with lipodystrophy include social and psychological distress that may contribute to discontinuation of cART treatment [22].

Multiple factors are associated with the pathophysiology of lipodystrophy, including cART’s protease inhibitor (PI) that appears to accelerate the rate of development of body composition and metabolic alterations [23, 24]. Other related factors include HIV clinical stage, age at the start of cART, race, and exercise level [25, 26] Since women living with HIV/AIDS experience different metabolic alterations [27] and lipodystrophy characteristics [28] compared to men, sex differences are other important factors to consider when studying lipodystrophy.

The prevalence of lipodystrophy among people living with HIV/AIDS can range from 41 to 70% [29, 30]. This imprecise prevalence is partially due to the absence of a methodological consensus for diagnosing lipodystrophy. Cost and accuracy are major methodological challenges. A clinical evaluation, which is an inexpensive and subjective diagnosis, is commonly used to diagnose lipodystrophy [31]. However, this method fails to accurately assess body composition alterations, especially in early stages. The “gold standard” approach for diagnosis and monitoring of lipodystrophy is imaging methods such as magnetic resonance image, computed tomography, and dual energy X-ray absorptiometry (DXA). DXA is a less costly and more accessible alternative imaging method when compared to the former methods [30, 31]. Additional advantages of DXA include: non-invasive and safe since it involves a minimum amount of radiation. A recommended method to measure body composition [32], DXA uses high and low energy X-ray photons to assess the composition and quantification of fat in regional and whole-body measurements. Using information from regional fat mass from DXA, an index “fat mass ratio” (FMR) was proposed considering data of trunk and leg fat mass to diagnose lipodystrophy [33]. Specific cutoff points for FMR have been developed for specific populations living with HIV/AIDS [33‐35]. The uses of FMR have advanced in the field of body composition assessment among PLWHA. However, although researchers agree that diagnosing lipodystrophy with FMR is the most accurate, DXA is not very accessible [36]. The high operational cost of imaging technologies is an important factor that limits their utilization in clinical settings and in studies with a large number of participants from low- and middle-income countries. This lack of consensus regarding accurate and low-cost methods for diagnosing lipodystrophy remains unresolved in the field. A simplified method for diagnosing lipodystrophy could have a significant impact in low- and middle-income countries where HIV prevalence and incidence are the highest. Also, a feasible method for diagnosing and monitoring lipodystrophy should be available not only in research and hospital settings, but also in local healthcare settings where most patients receive care and medication for HIV.

Predictive models using anthropometric methods such as skinfold thickness and body circumferences are typically used with the general population to measure body composition [37, 38] and could be valuable tools for the diagnosis and monitoring of lipodystrophy in PLWHA. Anthropometric methods have been validated in various populations, including in low- and middle-income countries, where they have proven to be easy-to-use at a low operational cost. However, there is no current literature that has explored the development of accurate and simplified anthropometric predictive models to diagnose lipodystrophy in PLWHA. Our study proposes to fill this gap in the literature by developing anthropometric models to predict and subsequently validate lipodystrophy diagnoses for men and women living with HIV/AIDS. Findings from this study may contribute to advancing global health and epidemiological research on the long-term impact of HIV treatment on non-communicable diseases.

Average age of the participants was 46.2 ± 9.6 years, time of diagnosis of HIV 115.3 ± 80 months (9.6 years), and exposure to cART 87.4 ± 63.1 months (7.3 years). Formal education was 8.3 ± 3.5 years, 28% were smokers (n = 30), 24% consumed alcoholic drinks (n = 25), and 3% used illicit drugs (n = 4).

Results of the descriptive analysis by sex (Table 1) showed homogeneity of participants among age (p = 0.966), diagnosis of HIV (p = 0.387), and exposure to cART treatment (p = 0.306). We observed similar proportions (%) of self-reported race/ethnicity between men and women: White (69.2 / 53.6), Black (7.7 / 12.2), Asian (6.2 / 12.2), and Brown (16.9 / 22.0), respectively. Men used cART with-protease inhibitor (PI) (47.7%) and without-PI (52.3%), and women used cART with-PI (36.6%) and without-PI (63.4%). Men had on average three more years of formal education than women (p = 0.011).

For the anthropometric measurements, there were no statistical differences between sexes in most body circumferences (BC): left arm extended and contracted (p = 0.255 and p = 0.359, respectively) right arm extended and contracted (p = 0.526 and p = 0.335), left medial calf (p = 0.214), right thigh corrected (p = 0.977), right medial calf (p = 0.238), abdomen (p = 0.409), and ratio of waist/medial calf (p = 0.071). Regarding DXA, we observed differences in body composition among sex for all variables, where men have a higher body weight (kg) and FMR values, (p = 0.003, p < 0.001, respectively), and lower total trunk fat (kg) (p < 0.001) and total leg fat (kg) (p < 0.001).

The main goal of this study was to predict and validate methods to diagnose lipodystrophy among people living with HIV/AIDS through anthropometric measurements and using DXA as a reference method. Prediction models were validated for both men and women. Our predictive models advance the field of HIV/AIDS studies by proposing a method for the diagnosis of lipodystrophy that is objective, accurate, low cost, and easy to use. In public health, especially in low- and middle-income countries, our predictive models should lead to improvements in the understanding of the prevalence, incidence, risk factors, pathogenesis, prevention, and treatment of lipodystrophy. In addition, the feasible identification of body composition alterations of lipodystrophy will contribute to interventions from health professionals that lead to better health outcomes on the overall well-being of people living with HIV/AIDS. To date, only France [33], Portugal [34], and Brazil [35, 44] have developed cutoff points for lipodystrophy diagnosis using fat mass ratio by DXA. Therefore, researchers and other health professionals using our predictive models could adopt the most convenient cutoff point for fat mass ratio by DXA as previously cited, or establish a cutoff point for our models to diagnose and monitor lipodystrophy among people living with HIV/AIDS.

Our study is important not only in the context of Brazil—place of residence of study participants—but to other countries with limited resources available to develop such studies, including Eastern and Southern African countries, the regions hit hardest by HIV/AIDS. To ensure global dissemination and accessibility, lipodystrophy diagnosis based on our predictive models can be found in an excel file in the following link (http://​www2.​eerp.​usp.​br/​pos/​arquivos/​Routine_​Models_​Men_​and_​Women.​xls).

Lipodystrophy is the second most common adverse health effect experienced by people living with HIV/AIDS undergoing cART treatment [45]. For this reason, special attention has been given to changes in body composition when treating HIV/AIDS patients. The development of the predictive models for lipodystrophy proposed by this study include important key variables such as age, time of diagnosis to HIV and exposure to cART, and type of cART treatment. These variables were controlled for both sexes to ensure true interpretations on each predictive model. Overall, the accuracy of the models was high as indicated by the adjusted R² values found in both adjusted and non-adjusted predictive models in men and women.

Our results support the assumption that the best predictive model for men is ‘adjusted model 6,’ where the following anthropometric variables were included: ratio of skinfold thickness of subscapular to medial calf, skinfold thickness of thigh, and waist circumference. A recent study published by Alencastro et al. (2017) [36] compared self-reported signs of lipodystrophy (lipohypertrophy and lipoatrophy) with skinfold thickness and body circumferences among 815 people living with HIV/AIDS. The authors observed several associations in self-reported signs of lipodystrophy with objective measurements mainly of the increase of fat in the back of the neck and waist, and the decrease of fat in limbs. Those areas match with the variables included in our ‘adjusted model 6’ (for men), supporting their use to diagnose lipodystrophy. Moreover, the aforementioned risk factors are associated with the development of atherosclerotic cardiovascular disease, [25, 26] reinforcing the importance of an early and accurate diagnosis of lipodystrophy to prevent non-communicable diseases.

In addition, ‘adjusted model 6’ included important variables such as “education,” “time of diagnosis to HIV,” and “type of cART,” which are important social and physiological determinants for body composition alterations. Our findings on the associations between these factors and the development of lipodystrophy in people living with HIV/AIDS are supported by two systematic reviews [46, 47]. The reviews report evidence that low education level is associated with several adverse health outcomes among people living with HIV/AIDS, including low health knowledge and an increase in the incidence of chronic illness. The inclusion of the “education” variable in the model proposed by the current study underscores the association of high education level and healthy lifestyle with the prevention of lipodystrophy. A cohort study explored the effect of HIV infection in metabolic parameters in 419 antiretroviral-naïve HIV infected patients. The results showed—in the absence of cART treatment—that HIV disease influences lipid values and glucose homeostasis [48]. Time of diagnosis to HIV is the most influential variable because it expresses the effect of the virus’ influence on body composition and metabolic alterations over time. Two large studies provided evidence of the influence and type of cART treatment that causes the development of lipodystrophy. Martinez et al. (2001) [49] enrolled 494 people living with HIV/AIDS and, using clinical observation, investigated the risk factors for lipodystrophy after a three-year follow-up period and found that cART treatment including protease inhibitors is associated with the development of body composition alterations in lipodystrophy. In a cross-sectional study, Miller et al. (2003) [50] used DXA to evaluate 1348 people living with HIV/AIDS in different regions of Australia to establish the prevalence and risk factors associated with lipodystrophy. They found people living with HIV/AIDS and under cART treatment with protease inhibitors have the highest prevalence and are at the greatest risk of developing lipodystrophy. The literature above is in agreement with our findings. Our study developed predictive models for lipodystrophy diagnosis considering the influence of cART treatment by creating a dummy variable for protease inhibitors.

For women, we believe that the best predictive model is ‘non-adjusted model 5’. For this model, only three anthropometric variables were included: skinfold thickness of thigh, skinfold thickness of subscapular, and body circumference of breast. Zulfa et al. (2016) [51] used anthropometric measurements and DXA to describe changes in body fat distribution in a 24-month longitudinal study enrolling 132 women with HIV/AIDS. They observed that women showed a significant decrease in leg fat (%) from 45.2 to 42.1 and an increase in trunk fat (kg) from 7.6 to 11.7 after the follow-up. In addition, in a longitudinal study enrolling 922 people living with HIV/AIDS, Scherzer et al. (2011) [52] determined the association for all causes of mortality by measuring total and regional adipose tissue levels using imaging methods with a 5-year follow-up. After adjusting for multiple variables they found that an increase in trunk fat corresponds to an absolute risk of one death per 100 people living with HIV/AIDS over 5 years. These findings support the importance of the anthropometric variables included in our ‘non-adjusted model 5’ for women, due to the representativeness of lipodystrophy characteristics and association with non-communicable diseases and risk of death. Moreover, this model included variables such as “time of exposure to cART” and “race (Asian),” which reflect the influence of the time of treatment and genetic background on the development of lipodystrophy. The studies cited above suggest that protease inhibitors are important in the development of body composition alterations in people living with HIV/AIDS, and that the length of exposure to cART treatment is an influential factor for considering the severity of lipodystrophy [49, 50]. Regarding the association of race and lipodystrophy, there is no consensus on whether race has an influence on body composition alterations. In their review, Gripson and Carr (2005) [53] agree that lipodystrophy has multifaceted origins, but they do not show evidence of a distinct racial influence. Also, two studies found different associations regarding race. In a case-controlled study, Filippini et al. (2006) [54] assessed body composition to identify racial factors related to lipodystrophy’s prevalence in Sub-Saharan Black Africans and White Italians living with HIV/AIDS. They observed that lipodystrophy is more prevalent in White Italians (65.2%) than in Sub-Saharan Black Africans (4.4%). Andany et al. (2011) [55] investigated the influence of race on lipodystrophy characteristics in a cross-sectional study enrolling 778 people living with HIV/AIDS. After a multi-variable regression analysis was conducted, black women were found to be most vulnerable to lipodystrophy, mainly lipohypertrophy. We did not find in the literature a specific association with the Asian race for the development of lipodystrophy. In addition, in our model “race (Asian)^” is a dummy variable. Dichotomizing the variable as presence of “race (Asian)” or no presence provided us with accurate predictive power.

This study represents a first step towards proposing an objective, accurate, and low operational cost tool for lipodystrophy diagnosis and monitoring in men and women living with HIV/AIDS. We believe that the greatest challenge in the future is developing accessible predictive models separately for lipoatrophy and lipohypertrophy. This next step will enable better comprehension of the pathways that contribute to losses and gains in fat mass, and promotion of specific treatments (pharmacologic or otherwise) for each. Also, further analysis is needed to establish whether the predictive models for lipodystrophy differ between people living with HIV/AIDS in treatment for opportunistic disease, for those who are over 69 years old, and for those who are younger. The use of our findings in future studies of HIV/AIDS will allow for more reliable estimates of lipodystrophy prevalence, incidence, and cause, and more effective prevention and treatment strategies.

Findings should be interpreted with caution. Due to the amount of independent variables to estimate fat mass ratio and the separation of the sample into men and women, there are limitations regarding sample size of this study. We have chosen to separate men and women due to the similar number of people living with HIV/AIDS for both sexes in our total sample size. In addition, the body composition differences among sexes is established in the literature and confirmed in our results. In accordance with statistical assumptions, we reduced the number of independent variables through the principal component analysis. However, after we conducted stepwise linear regression adopting the reduced number of independent variables, we did not have predictive models with accurate predictive power for lipodystrophy diagnosis in men and women. In this case we assumed the inclusion of all additional predictor variables, anthropometric ratios, and all anthropometric variables.

The power of prediction based on the adjusted R² values for our best predictive model for men (77) and women (78) is limited, but is representative of the complex factors which influence the high variability in body composition alterations in people living with HIV/AIDS. While the accuracy and significance could be greater, a prediction of 80% for lipodystrophy diagnosis can be achieved using an accessible and low operational cost tool that represents a great advancement in the health care services available to people living with HIV/AIDS.

In our study we did not exclude people living with HIV/AIDS with diabetes mellitus or other chronic metabolic diseases. These conditions are often experienced by people living with HIV/AIDS under cART treatment. Even though some chronic metabolic diseases may influence body composition alterations, our goal was to study the most typical group of people living with HIV/AIDS. However, we understand that the presence of diabetes mellitus or other chronic metabolic diseases could have influenced our predictive models.

Accurate and sex-specific predictive models using anthropometric measurements were proposed to diagnose lipodystrophy in people living with HIV/AIDS. We believe this information will advance the public health field by assisting health professionals in diagnosing and monitoring lipodystrophy at a low cost, and thus will contribute to decreasing the adverse health effects of lipodystrophy in people living with HIV/AIDS.