Skip Navigation LinksHome > September 1, 2014 - Volume 67 - Issue > HIV and Metabolic, Body, and Bone Disorders: What We Know F...
JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0000000000000256
Supplement Article

HIV and Metabolic, Body, and Bone Disorders: What We Know From Low- and Middle-Income Countries

Ali, Mohammed K. MBChB, MSc, MBA*; Magee, Matthew J. MPH, PhD*; Dave, Joel A. MBChB, PhD, FCP; Ofotokun, Igho MD, MSc; Tungsiripat, Marisa MD§; Jones, Trammell K. MD; Levitt, Naomi S. MBChB, FCP; Rimland, David MD‡,‖; Armstrong, Wendy S. MD

Free Access
Article Outline
Collapse Box

Author Information

*Rollins School of Public Health, Emory University, Atlanta, GA;

Medical School, University of Cape Town, Cape Town, South Africa;

School of Medicine, Emory University, Atlanta, GA;

§The Cleveland Clinic, Cleveland, OH; and

Veterans Affairs Medical Center, Atlanta, GA.

Correspondence to: Mohammed K. Ali, MBChB, MSc, MBA, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322 (e-mail: mkali@emory.edu).

The authors have no funding or conflicts of interest to disclose.

Collapse Box

Abstract

Abstract: Globally, the HIV epidemic is evolving. Life expectancy for HIV-infected individuals has been extended because of more effective and more widely available antiretroviral therapy. As a result, chronic noncommunicable diseases (NCDs) have become important comorbid conditions. In particular, HIV-infected persons are increasingly at risk of developing metabolic (diabetes, dyslipidemias), body composition (lipodystrophy, overweight/obesity) and bone mineral density abnormalities. We have summarized the published epidemiological and clinical literature regarding these HIV-NCD comorbidities in low- and middle-income countries (LMICs). We found important gaps in knowledge. Specifically, there are few studies that use standardized methods and metrics; consequently, prevalence or incidence data are not comparable. There are very little or no data regarding the effectiveness or cost-effectiveness of clinical monitoring or therapeutic interventions for metabolic disorders in HIV-infected individuals. Also, although NCDs continue to grow in the HIV-negative population of most LMICs, there are few data comparing the incidence of NCD comorbidities between HIV-infected and HIV-negative populations. To address these gaps, we describe potential research and capacity development priorities for the future.

Back to Top | Article Outline

INTRODUCTION

Treatment with effective antiretroviral therapy (ART) has transformed the course of disease for those infected with HIV. In many parts of the world, HIV-infected individuals now experience longer life expectancy than previously, but with that, also long-term exposure to ART toxicities and chronic inflammation. Extended life expectancy, together with increasingly contemporary lifestyles (poor diet quality and sedentary activity patterns), predispose HIV-infected persons to developing chronic noncommunicable diseases (NCDs) like diabetes mellitus (DM), cardiovascular disease (CVD), and cancers.1,2 Although HIV-NCD comorbidities are increasingly common and documented in high-income countries (HICs),3,4 little is known about the epidemiology, treatments, or outcomes of HIV-NCD comorbidities, and the capacity (human, financial, and infrastructural resources)5 needed to address these comorbidities in low- and middle-income countries (LMICs).

In this article, we summarize the published literature on metabolic (DM, dyslipidemias), body composition (lipodystrophy, overweight/obesity), and bone mineral density (BMD) abnormalities among HIV-infected persons with a focus on relevance for LMICs. These conditions are burdensome in their own right, requiring regular monitoring and treatment, but more importantly, they increase the risk for disabling, and often fatal, complications such as heart disease, chronic kidney disease, and fractures. Improving these metabolic parameters has been shown to improve CVD outcomes in longitudinal studies from HICs.6 For each group of disorders, we summarize the available epidemiological and clinical data (Table 1) and identify important data gaps. We conclude by conveying potential research and capacity development opportunities that would help address these HIV-NCD comorbidities in LMICs.

TABLE 1-a Key Studie...
TABLE 1-a Key Studie...
Image Tools
Back to Top | Article Outline

DIABETES AND DYSGLYCEMIA

TABLE 1-b Key Studie...
TABLE 1-b Key Studie...
Image Tools
TABLE 1-c Key Studie...
TABLE 1-c Key Studie...
Image Tools
Back to Top | Article Outline
Epidemiology

The estimated prevalence of DM among HIV-infected people is generally higher in HIC cohorts (range, 1.9%23 to 14.9%24) than in LMICs. Yet, the absolute number of persons living with HIV-DM is greater in LMICs, and given the high prevalence of young HIV-infected people in these countries, their average age is lower. A South African study of HIV-infected persons reported newly diagnosed DM in 3.4% who were ART-naive and in 2.2% who were on ART for ≥6 months.7 A multicountry cohort study from South America showed DM prevalence in HIV-infected persons ranged from 0.8% in Colombia to 6.5% in Brazil.17 Pre-DM, which precedes and predicts DM, is relatively more common, noted among 18.5% and 23.5% of ART-naive and ART patients, respectively, in South Africa7 and 16% of ART patients in Kenya.30 As the global prevalence of DM increases in the coming 15–20 years (from 382 million in 2013, to a projected 592 million in 2035),31 more studies are needed to identify effective DM prevention strategies in HIV-infected persons.

Back to Top | Article Outline
Risk Factors

Whether HIV infection itself increases an individual's risk of developing dysglycemia (pre-DM or DM) remains controversial. Large observational cohort studies from HICs offer the only known data sources to investigate the relationship. The US Multicenter AIDS Cohort Study reported a fourfold higher DM incidence [rate ratio (RR), 4.1; 95% confidence interval (CI): 1.9 to 9.2] among HIV-infected persons using ART compared with HIV-negative persons, adjusting for age and body mass index (BMI).25 Two other cohort studies observed no significant association between HIV and DM risk.26,27 A large cohort study in Denmark comparing DM incidence among HIV-infected and HIV-negative persons reported a higher DM rate among HIV-infected persons being treated with older antiretroviral (ARV) drugs between 1996 and 1998 (RR, 3.2; 95% CI: 1.4 to 7.4), but not during 1999–2009 (RR, 1.0; 95% CI: 0.8 to 1.3).27 Population-based longitudinal measurements of incident dysglycemia from LMICs are needed among persons with and without HIV to improve our understanding of the associations between HIV, ART, and dysglycemia.

Factors associated with incident dysglycemia among HIV-infected persons include traditional risks (eg, physical inactivity, increasing age, waist-to-hip ratio, and obesity) as well as specific HIV and treatment-associated risks (eg, lipoatrophy).32 Currently, an estimated 13% of HIV-positive individuals on ART in sub-Saharan Africa are aged 50 years or older. With continued expansion of ART use, life expectancy and dysglycemia are expected to increase.33 Cumulative exposure to ART (and to specific ARVs) is associated with increased dysglycemia in HIV-infected persons. For example, the Danish cohort reported higher DM incidence (RR, 1.8; 95% CI: 1.2 to 2.8) in those ever exposed to stavudine compared with patients never exposed.27 Data from South Africa also show that exposure to efavirenz was associated with dysglycemia (odds ratio, 1.7; 95% CI: 1.2 to 2.5) after adjusting for age, sex, and CD4 count.7 There are almost no data from LMICs regarding the role of body composition (eg, overweight, obesity, or lipodystrophy), undernutrition, lifestyle factors, concomitant infections (eg, hepatitis C), and common non-ARV medications (eg, statins) to aid our understanding of dysglycemia emergence in HIV-infected persons.

Back to Top | Article Outline
Treatment

For the management of dysglycemia in HIV-infected persons, existing HIC guidelines focus on testing for dysglycemia as a baseline assessment before initiating ART and at regular intervals thereafter, or after changing regimens.34 Current treatment recommendations for HIV-infected persons with DM are generally similar to those for HIV-negative persons and include using lifestyle modification and metformin as initial therapy, with the addition of other agents based on glycemic control.34 Whether existing dysglycemia guidelines are relevant (in terms of effectiveness) or feasible (in terms of cost-effectiveness) in LMICs has not been sufficiently assessed.

Back to Top | Article Outline
Knowledge and Implementation Gaps

Several key knowledge gaps remain regarding HIV and dysglycemia. Current observational data are insufficient to infer whether HIV-infected persons truly have a higher incidence of dysglycemia than the general non–HIV-infected population, and whether this risk is mediated by the virus itself, persistent systemic inflammation, or solely some aspect of ARVs (eg, interruption). Regardless of the causal pathways, the coprevalence of HIV-dysglycemia is expected to increase in LMICs, signifying a growing need for clinical capacity to screen, diagnose, and treat HIV-DM. Accurate identification of dysglycemia currently requires laboratory services and typically testing on 2 separate occasions. Point-of-care HbA1c analyzers currently exist and may become the standard of care, but in most LMICs, they are currently cost prohibitive and not validated in persons with chronic infections or anemia. Screening may be more cost-effective through low-cost approaches (eg, risk tests) to identify subgroups at greatest risk, although more data are needed to devise these risk scores. Finally, many unanswered questions remain regarding treatment of HIV-dysglycemia. Comparative effectiveness trials of treatment regimens in this population and studies of drug interactions are needed to compare effectiveness, benefits, and harms of different treatment options.

Back to Top | Article Outline

DYSLIPIDEMIAS

Epidemiology

The risk of CVD events in HIV-infected persons35 has led to an enhanced focus on abnormal serum lipids or dyslipidemia.36 Many individual studies and reviews have summarized the prevalence and risk of dyslipidemias in HIV-infected persons in HICs.37–39 Most studies are cross-sectional, but some have been longitudinal, describing lipid changes after ART regimen initiation; however, none have included HIV-negative comparison groups.40–42 Studies have used multiple ART drug classes and followed participants for varying durations. The relationships between dyslipidemias and ART adherence (specific regimens and cumulative exposure), concurrent CVD risk factors (smoking, exercise), and underlying comorbidities among persons with HIV in LMICs have not been studied.

Dyslipidemia also occurs in ART-naive HIV-infected patients. Increased triglyceride (TG) and reduced total low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol subfractions are common, especially in those with more advanced HIV disease and greater levels of immunosuppression.43–45 In addition, studies have reported more atherogenic lipoprotein composition (eg, higher proportions of small, dense LDL particles) in HIV-infected persons. Most of these were HIC studies, often in populations where obesity and overweight are more common.

The literature from LMICs is scant and subject to methodological concerns similar to those described above. Cross-sectional studies among ART-naive HIV-infected persons in Cameroon, Tanzania, and South Africa generally show normal LDL levels (88–100 mg/dL).12–14 Dyslipidemias (low HDL and high TG) are observed more commonly among patients with longer ART exposure, as shown in a few South American studies.15,17,46 Longitudinal studies of patient lipid levels 6–29 months after ART initiation report variable degrees of elevation of TG and both LDL and HDL cholesterol.16,47 There is variation in the use of fasting and nonfasting lipid testing across studies, which affects the accuracy of TG values. However, nonfasting specimens are more practical to collect from patients who live far from clinics or cannot take time off work repeatedly for blood tests.

Back to Top | Article Outline
Risk Factors

The etiology of dyslipidemias in HIV-infected persons is complex and may be related to HIV infection itself, ART, inflammation, genetic factors, age, obesity, lipodystrophy, DM, and liver disease; all of these relationships remain poorly understood.

Regarding ART and lipid metabolism, there are broad differences in lipid effects among ARV drug classes and even among agents within a class. The pathogenesis of ART-induced dyslipidemia is complex and incompletely understood, although it broadly involves interference with key intracellular processes regulating lipid and glucose metabolism [for protease inhibitors (PIs)], mitochondrial function [for nucleoside reverse-transcriptase inhibitors (NRTIs)], and genetic vulnerability.48,49 Observational data from LMICs regarding ART and lipids have limitations because HIV-infected patients are often on heterogeneous regimens, there are local differences in drug availability, and prescription practices are changing over time. For example, the NRTI stavudine, now rarely prescribed in the United States due to toxicities, has been widely used in LMICs and is the NRTI most likely to lead to elevated TG and total cholesterol. Another widely used agent in LMICs, nevirapine, has beneficial lipid effects, and switch studies (from PI to nonnucleoside reverse-transcriptase inhibitor regimens) have resulted in lipid profile improvements. Many newer agents, including the PIs atazanavir and darunavir, and the integrase inhibitor raltegravir, have lesser or no effects on lipid metabolism but have limited availability in LMICs. Many studies have shown that within and between drug classes, substitutions can lead to improved lipid levels.

Back to Top | Article Outline
Treatment

There have been no randomized clinical trials to establish the optimal treatment of ART-associated dyslipidemia. Treatment guidelines from HICs for HIV-infected patients mirror recommendations for the general HIV-negative population.50 Guidelines51 promote lifestyle modification as initial therapy. If lifestyle modification is insufficient, or if the initial degree of dyslipidemia is severe, lipid-lowering agents are indicated. However, a study auditing treatment regimens reported that HIV-infected patients do not adhere and respond as well to lipid-lowering therapy as HIV-negative patients do.52 To treat elevated LDL cholesterol, pravastatin, atorvastatin, or rosuvastatin are generally recommended to minimize drug interactions with ART.53 For hypertriglyceridemia, fibric acid analogs (gemfibrozil or fenofibrate) are indicated. Niacin and fish oils may offer some additional benefit. In summary, the best treatment approach involves controlling other CVD risks, choosing ARVs with better lipid profiles, and adding lipid-lowering drugs when clinically indicated. Many of these agents are available in more developed LMICs like South Africa, but availability in many other LMICs may be limited.

Back to Top | Article Outline
Knowledge and Implementation Gaps

Despite a number of studies, little is known about dyslipidemia in LMICs. Lipid changes seen in late-stage, untreated HIV infection or after ART initiation are likely similar in LMICs compared with HICs, but this assumption needs to be empirically confirmed. More data are needed to clarify the mechanisms of dyslipidemia, especially the persistence of low HDL in treated patients. Most importantly, longitudinal studies are needed to document CVD event risk attributable to dyslipidemia and other conditions among HIV-infected persons.

The cost-effectiveness and optimal frequency of lipid monitoring in ART patients has not been established in LMICs, and specific therapeutic thresholds and targets for each of the lipid subfractions are not defined. Among individuals without chronically elevated lipids, it remains unclear whether aggressive lipid-lowering therapy should be indicated. There are no data documenting adherence to lipid-lowering therapies, drug interactions, or clinical outcomes after correcting HIV-associated dyslipidemias in LMICs.

Back to Top | Article Outline

BODY COMPOSITION CHANGES

Lipodystrophy

Lipodystrophy refers to changes in body fat distribution encompassing both lipohypertrophy and lipoatrophy. Lipohypertrophy is commonly characterized by increased abdominal visceral fat, dorsocervical fat pad (buffalo hump), and/or breast enlargement, whereas lipoatrophy is characterized by subcutaneous fat wasting of the face, extremities, and/or buttocks. These body composition changes can occur separately or overlap in the same patient; and they have been associated with lower quality of life, depression, and decreased ART adherence.54 Lipodystrophy is also associated with dyslipidemias and insulin resistance.55

Back to Top | Article Outline
Epidemiology

Prevalence reports of lipodystrophy vary widely in HICs with high ART access—from 10% to over 80%56–59 depending on the definitions used.60 Prevalence reports from LMICs similarly range widely from 7% to 65%.8,9,11,61,62 Reports from LMICs are limited by varying definitions (self-report, clinical examination, or objective measurement). One study described its population's lipoatrophy prevalence as 49.5% or 65% depending on whether an objective definition based on measurable characteristics or a broader clinical definition was used.11 Similarly, another study described prevalence as 7% or 49% depending on the definitions used.61 It is important to note that data regarding lipodystrophy from HICs come from individuals with a higher average BMI than are observed among HIV-infected individuals in LMICs.

Back to Top | Article Outline
Risk Factors

ART is commonly noted as a risk factor for lipodystrophy. Lipohypertrophy has been associated with ART, total body fat, older age, and female gender.63 Exposure to the thymidine analog NRTIs, stavudine and zidovudine, has been implicated in lipoatrophy64–67 through inhibition of mitochondrial polymerase gamma.68 Even in studies with widely varying definitions of lipoatrophy, stavudine usage is consistently associated with it.11,61,62,69 In the largest of these studies, the prevalence of lipoatrophy was significantly (P = 0.02) higher: 31.4% in stavudine versus 10.3% in zidovudine-containing regimens.62

Finally, it is important to note that observed fat increases after initiation of ART can also signify a return to health and are not solely indicative of lipohypertrophy. For example, an LMIC study compared 12-month changes in skinfold measurements: arm, hip, thigh, and/or waist circumference; and fat mass (bioelectric impedance) between HIV-infected persons initiating ART and HIV-negative controls. The study found increased measurements in patients using ART, but these were no higher than in HIV-negative controls.10

Back to Top | Article Outline
Treatment

Therapeutic options for lipohypertrophy include exercise,70 surgical interventions,71 and medication, with the goals of decreasing discomfort, lowering cardio-metabolic risk, and cosmetic improvement. Metformin has been shown to be beneficial,72,73 as have recombinant growth hormone and recombinant growth-hormone releasing factor.74 Side effects include dysglycemia, fat loss in those with concomitant lipoatrophy (which has direct cosmetic effects and indirect effects on adherence), and rapid reaccumulation after stopping therapy.75–77

Lipoatrophy treatment options include switching to alternative NRTIs, which can attenuate lipoatrophy modestly,78–81 or changing to regimens with lower risks of lipoatrophy but higher risks of dyslipidemia.82,83 Trials evaluating thiazolidinediones for lipoatrophy have been conflicting84–90 and their use has been limited by concern for adverse effects.91–93 Evaluations of leptin (small studies) and uridine (mixed results) have been inconclusive.94–97 Surgical options (injectable fillers and autologous fat transplantation) are expensive.98

Back to Top | Article Outline
Knowledge and Implementation Gaps

In addition to associations with lower quality of life, depression, and decreased ART adherence, lipoatrophy can be stigmatizing to patients.54 Lipoatrophy seems to be less common in HICs, where thymidine analog usage has decreased substantially, than in LMICs.99,100 However, further studies are needed, as LMIC populations may have very different genetic, environmental, dietary, and socioeconomic exposures whose interaction may affect body composition. The few effective treatment options available are all cost prohibitive worldwide.

Back to Top | Article Outline
Overweight/Obesity

Early in the HIV epidemic, the primary weight-related concern was wasting. Established data from HICs and now-emerging data from LMICs show that overweight/obesity (defined by BMI of 25–29.9 kg/m2 and ≥30 kg/m2, respectively) is becoming more prominent than wasting among HIV-infected individuals.19,20,101 Several recent US studies show that HIV-infected persons on ART gain weight.3,102–106 Among this group, women, African Americans, and those with a higher BMI at ART initiation are at greater risk of becoming overweight/obese.

Pharmacologic treatment with agents like stavudine and didanosine can lead to a paradoxical decline in weight after virologic control is achieved because of the development of lipoatrophy.107 The use of these agents has become uncommon in most HICs. In parallel, overweight and obesity in the HIV-infected population, which signals a return to health, has reached levels observed in the general population. Retrospective data from Brazil20 showed a similar pattern over the course of 7 years after the initiation of ART; the combined prevalence of overweight/obesity among these participants increased from 35.9% to 44.4%, compared with a prevalence of 40.6% in the general adult population. Among 177 individuals in South Africa starting ART, the proportions of participants classified as overweight or obese both increased after 12 months: the overweight from 21% to 36% and the obese from 12% to 22%.21 In this study, despite high rates of overweight and obesity, the majority of participants perceived their weight as “just right,” reinforcing reports that in some cultures, higher weight is a desired state and can be associated with wealth (adequate food) or good health, while low weight can be associated with wasting and sickness and be stigmatizing.21,108

In summary, growth in overweight/obesity in the general population is being mirrored in the HIV-infected population, at least in part because of restored health. More data are needed to understand if and how these body composition changes affect long-term health outcomes (eg, cardiovascular and chronic kidney diseases and mortality).35,109

Back to Top | Article Outline

BMD ABNORMALITIES

Bone disorders are common sequelae of HIV infection. In HICs, close to 50%–60% of HIV-infected adults are osteopenic (low BMD) and an additional 15% are osteoporotic (low BMD and distorted micro-architecture with high fracture risk).110,111

Back to Top | Article Outline
Epidemiology

Available data, although limited, indicate that HIV-induced skeletal abnormalities are equally troubling in LMICs.18,22 For example, a study of 142 HIV-infected Argentinians aged 20–45 years using ART showed an osteopenia prevalence of 59% among the study group, compared with 15% in HIV-negative individuals, and an osteoporosis prevalence of 22% among them, compared with 0% in HIV-negative persons.18,22 Data from HICs suggest that fracture risk is 2–4-fold higher among HIV-infected persons than the general population, and comparable risks are likely in LMICs.112 As ART ameliorates the HIV disease, it paradoxically exacerbates bone loss.111,113 Consequently, there are concerns that in the aging HIV population, the synergistic effects of ART and age-related skeletal damage could precipitate fractures.

Back to Top | Article Outline
Risk Factors

The pathophysiology underlying HIV-associated bone abnormalities is incompletely understood.111,114 Common comorbidities with HIV infection such as renal impairment, hypogonadism, and hypovitaminosis-D all predispose to bone catabolism, and this may be perpetuated by lifestyle factors.115 The virus itself contributes to skeletal damage through altered immune B-cell function as well as enhanced bone resorption.116,117 ART-induced bone effects seem to occur through immune reconstitution,116,118 direct suppressive effects on bone cells, and/or alteration of vitamin D metabolism by certain drugs.119,120 Teasing apart the relative contributions of these various mechanisms is currently a focus of intense research that should be extended to individuals from LMICs.

Back to Top | Article Outline
Treatment

Chemotherapies to address osteoporosis can be associated with disabling side effects. Bisphosphonates, although available and affordable in LMICs, cannot be used for more than 5 years due to the risk of atypical femoral fractures.121 Teriparatide, a stimulant of osteoblastic bone formation, is parenteral and carries a rare risk of osteosarcoma. The beneficial effects of vitamin D and/or calcium supplementation on HIV bone loss and the use of bone-neutral ART regimens are not clearly understood and are currently being investigated in both HICs and LIMCs. The utility of denosumab, a long-acting monoclonal antibody that blocks bone resorption, and the benefit of aggressive osteoporosis lifestyle modification remain inadequately evaluated. Antiresorptive prophylaxis has also been proposed to block the bone loss mediated by ART-induced immune reconstitution.118 Although not approved for use in the United States, strontium renelate is widely available in LMICs and could be an effective antiosteoporotic agent for those with HIV, as it increases deposition of new bone by osteoblasts while at the same time reducing osteoclastic bone resorption. However, recent concerns regarding increased CVD risk with the use of strontium renelate suggest that safety needs to be carefully evaluated.122

Back to Top | Article Outline
Knowledge and Implementation Gaps

Many unanswered questions remain regarding HIV and BMD. For example, when should screening for osteoporosis begin, and what tools should be used? Most guidelines emphasize screening at age 65 and do not recognize HIV infection as a risk factor.123,124 Available diagnostic tools also have limitations. Dual energy x-ray absorptiometry (DXA) scan is imperfect, as it averages the density of all bone types without accounting for differences between cortical and trabecular bone losses.125 Also, half of all fragility fractures are missed by DXA scan,126 especially among younger age groups (<55 years), among whom DXA-computed BMD bears no correlation with fracture risk.127 An alternative to DXA is the high-resolution peripheral quantitative computed tomography.125 The use of either DXA or tomography tools in LMICs may be limited by cost, availability, and training. Cost-effective screening tools with better discriminatory properties, particularly for the younger population, would greatly enhance early detection of skeletal decline in HIV infection.

Back to Top | Article Outline

DISCUSSION: GAPS AND RESEARCH PRIORITIES

This review highlights important gaps in our knowledge about HIV-associated metabolic and bone comorbidities in LMICs. In particular, existing LMIC data (Table 1) are derived from heterogeneous populations and report a variety of metrics collected using different measurement tools.

Epidemiological data from LMICs regarding prevalence and incidence of these HIV-NCD comorbidities, their associated clinical end points (eg, myocardial infarction, renal disease, fractures),35,109 and their associated mortality rates are scarce. One such analysis comparing an urban setting in Botswana with urban Tennessee shows that age- and sex-adjusted incidence of CVD events and mortality was actually higher in Botswana.128 However, there is a conspicuous absence of HIV-negative comparison groups to assess the relative risk of metabolic abnormalities and complications among HIV-infected persons (Table 1). Expanding knowledge in these areas may provide significant insights into (1) the prevalence, incidence, and predictors of HIV-NCD comorbidities among different HIV infection phenotype(s) in HIC and LMIC settings, (2) the evolution of these comorbidities in the time course of HIV infection and its treatment, and (3) whether currently recommended treatment thresholds and targets for these metabolic abnormalities are appropriate, effective, and cost-effective for HIV-infected persons (Table 3). There is an urgent need for this research in LMICs as current output is minimal, management guidelines are lacking, and it is unknown which interventions provide greatest benefits, particularly when working with limited resources. Research capacity itself is limited and therefore an emphasis on training should be considered in plans going forward.

Table 2
Table 2
Image Tools
Table 3
Table 3
Image Tools

Health services research will provide a better understanding of clinical practice patterns and how adherence, detection, and monitoring of disease impact morbidity and mortality in LMICs. There are a number of inexpensive point-of-care tools for monitoring metabolic (eg, capillary glucose testing) and body compositional changes (eg, tape measure), but there are no data that demonstrate the value of these tools in practice.

Both epidemiological and health services data need to be incorporated into the larger picture of integrated care in LMICs (Table 3). Using pre-existing HIV clinic infrastructure may be an ideal and efficient opportunity to address these associated comorbidities and enhance population health.129 Implementation research is needed to understand what common benefits could be achieved (eg, approaches to achieving high adherence to combination ARV drug therapies could aid adherence to cardio-metabolic therapies), what capacity is needed to integrate care, and what the potential scalability is of this model. Identifying these processes and resources would help stakeholders assimilate the model into routine practice across LMICs.130–132 In addition, clinical and scientific capacity needs to be developed to address these care and research needs.

Of course, limited resources may be an impediment. Secondary analyses of existing databases of HIV-infected and HIV-negative populations may offer an efficient avenue, although uniformity of metrics used and data collection procedures must be taken into account. High-quality research will have important implications for practice patterns, programs, policies, and ultimately health outcomes related to these disabling HIV-NCD comorbidities. In the words of Dr. Paul Farmer, “… it seems to me that making strategic alliances across national borders to treat HIV among the world's poor is one of the last great hopes of solidarity across a widening divide.”

Back to Top | Article Outline

REFERENCES

1. Jarrett OD, Wanke CA, Ruthazer R, et al.. Metabolic syndrome predicts all-cause mortality in persons with human immunodeficiency virus. AIDS Patient Care STDS. 2013;27:266–271.

2. Young F, Critchley JA, Johnstone LK, et al.. A review of co-morbidity between infectious and chronic disease in Sub Saharan Africa: TB and diabetes mellitus, HIV and metabolic syndrome, and the impact of globalization. Global Health. 2009;5:9.

3. Kim DJ, Westfall AO, Chamot E, et al.. Multimorbidity patterns in HIV-infected patients: the role of obesity in chronic disease clustering. J Acquir Immune Defic Syndr. 2012;61:600–605.

4. Triant VA, Lee H, Hadigan C, et al.. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J Clin Endocrinol Metab. 2007;92:2506–2512.

5. Ali MK, Rabadan-Diehl C, Flanigan J, et al.. Systems and capacity to address noncommunicable diseases in low- and middle-income countries. Sci Transl Med. 2013;5:181cm184.

6. Data Collection on Adverse Events of Anti-HIV Drugs Study Group. Changes over time in risk factors for cardiovascular disease and use of lipid-lowering drugs in HIV-infected individuals and impact on myocardial infarction. Clin Infect Dis. 2008;46:1101–1110.

7. Dave JA, Lambert EV, Badri M, et al.. Effect of nonnucleoside reverse transcriptase inhibitor-based antiretroviral therapy on dysglycemia and insulin sensitivity in South African HIV-infected patients. J Acquir Immune Defic Syndr. 2011;57:284–289.

8. Zannou DM, Denoeud L, Lacombe K, et al.. Incidence of lipodystrophy and metabolic disorders in patients starting non-nucleoside reverse transcriptase inhibitors in Benin. Antivir Ther. 2009;14:371–380.

9. Puttawong S, Prasithsirikul W, Vadcharavivad S. Prevalence of lipodystrophy in Thai-HIV infected patients. J Med Assoc Thai. 2004;87:605–611.

10. Thompson V, Medard B, Taseera K, et al.. Regional anthropometry changes in antiretroviral-naive persons initiating a zidovudine-containing regimen in Mbarara, Uganda. AIDS Res Hum Retroviruses. 2011;27:785–791.

11. Mercier S, Gueye NF, Cournil A, et al.. Lipodystrophy and metabolic disorders in HIV-1-infected adults on 4- to 9-year antiretroviral therapy in Senegal: a case-control study. J Acquir Immune Defic Syndr. 2009;51:224–230.

12. Armstrong C, Liu E, Okuma J, et al.. Dyslipidemia in an HIV-positive antiretroviral treatment-naive population in Dar es Salaam, Tanzania. J Acquir Immune Defic Syndr. 2011;57:141–145.

13. Nguemaim NF, Mbuagbaw J, Nkoa T, et al.. Serum lipid profile in highly active antiretroviral therapy-naive HIV-infected patients in Cameroon: a case-control study. HIV Med. 2010;11:353–359.

14. Fourie CM, Van Rooyen JM, Kruger A, et al.. Lipid abnormalities in a never-treated HIV-1 subtype C-infected African population. Lipids. 2010;45:73–80.

15. Albuquerque EM, de Faria EC, Oliveira HC, et al.. High frequency of Fredrickson's phenotypes IV and IIb in Brazilians infected by human immunodeficiency virus. BMC Infect Dis. 2005;5:47.

16. Ceccato MG, Bonolo PF, Souza Neto AI, et al.. Antiretroviral therapy-associated dyslipidemia in patients from a reference center in Brazil. Braz J Med Biol Res. 2011;44:1177–1183.

17. Cahn P, Leite O, Rosales A, et al.. Metabolic profile and cardiovascular risk factors among Latin American HIV-infected patients receiving HAART. Braz J Infect Dis. 2010;14:158–166.

18. Cournil A, Eymard-Duvernay S, Diouf A, et al.. Reduced quantitative ultrasound bone mineral density in HIV-infected patients on antiretroviral therapy in Senegal. PLoS One. 2012;7:e31726.

19. Kroll AF, Sprinz E, Leal SC, et al.. Prevalence of obesity and cardiovascular risk in patients with HIV/AIDS in Porto Alegre, Brazil. Arq Bras Endocrinol Metabol. 2012;56:137–141.

20. Maia Leite LH, Mattos Marinho, Sampaio ABD. Progression to overweight, obesity and associated factors after antiretroviral therapy initiation among Brazilian persons with HIV/AIDS. Nutr Hosp. 2010;25:635–640.

21. Hurley E, Coutsoudis A, Giddy J, et al.. Weight evolution and perceptions of adults living with HIV following initiation of antiretroviral therapy in a South African urban setting. S Afr Med J. 2011;101:645–650.

22. Bruera D, Luna N, David DO, et al.. Decreased bone mineral density in HIV-infected patients is independent of antiretroviral therapy. AIDS. 2003;17:1917–1923.

23. Ledergerber B, Furrer H, Rickenbach M, et al.. Factors associated with the incidence of type 2 diabetes mellitus in HIV-infected participants in the Swiss HIV Cohort Study. Clin Infect Dis. 2007;45:111–119.

24. Butt AA, McGinnis K, Rodriguez-Barradas MC, et al.. HIV infection and the risk of diabetes mellitus. AIDS. 2009;23:1227–1234.

25. Brown TT, Cole SR, Li X, et al.. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med. 2005;165:1179–1184.

26. Tien PC, Schneider MF, Cole SR, et al.. Antiretroviral therapy exposure and incidence of diabetes mellitus in the Women's Interagency HIV Study. AIDS. 2007;21:1739–1745.

27. Rasmussen LD, Mathiesen ER, Kronborg G, et al.. Risk of diabetes mellitus in persons with and without HIV: a Danish nationwide population-based cohort study. PLoS One. 2012;7:e44575.

28. Lo YC, Chen MY, Sheng WH, et al.. Risk factors for incident diabetes mellitus among HIV-infected patients receiving combination antiretroviral therapy in Taiwan: a case-control study. HIV Med. 2009;10:302–309.

29. De Wit S, Sabin CA, Weber R, et al.. Incidence and risk factors for newonset diabetes in HIV-infected patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Diabetes Care. 2008;31:1224–1229.

30. Mutimura E, Stewart A, Rheeder P, et al.. Metabolic function and the prevalence of lipodystrophy in a population of HIV-infected African subjects receiving highly active antiretroviral therapy. J Acquir Immune Defic Syndr. 2007;46:451–455.

31. International Diabetes Federation. IDF Diabetes Atlas. 6th ed. Brussels, Belgium: 2013.

32. Capeau J, Bouteloup V, Katlama C, et al.. Ten-year diabetes incidence in 1046 HIV-infected patients started on a combination antiretroviral treatment. AIDS. 2012;26:303–314.

33. Mills EJ, Bakanda C, Birungi J, et al.. Life expectancy of persons receiving combination antiretroviral therapy in low-income countries: a cohort analysis from Uganda. Ann Intern Med. 2011;155:209–216.

34. US Department of Veterans Affairs. US Department of Veterans Affairs: Primary Care of Veterans With HIV, Diabetes. 2011.

35. Bloomfield G, Khazanie P, Morris A, et al.. HIV and non-communicable cardiovascular and pulmonary diseases in low- and middle-income countries in the ART era: what we know and best directions for future research. J Acquir Immune Defic Syndr. 2014;67(suppl 1):S40–S53.

36. Giannarelli C, Klein RS, Badimon JJ. Cardiovascular implications of HIV-induced dyslipidemia. Atherosclerosis. 2011;219:384–389.

37. Oh J, Hegele RA. HIV-associated dyslipidaemia: pathogenesis and treatment. Lancet Infect Dis. 2007;7:787–796.

38. Estrada V, Portilla J. Dyslipidemia related to antiretroviral therapy. AIDS Rev. 2011;13:49–56.

39. Dube MP, Cadden JJ. Lipid metabolism in treated HIV Infection. Best Pract Res Clin Endocrinol Metab. 2011;25:429–442.

40. Fontas E, van Leth F, Sabin CA, et al.. Lipid profiles in HIV-infected patients receiving combination antiretroviral therapy: are different antiretroviral drugs associated with different lipid profiles? J Infect Dis. 2004;189:1056–1074.

41. Young J, Weber R, Rickenbach M, et al.. Lipid profiles for antiretroviral-naive patients starting PI- and NNRTI-based therapy in the Swiss HIV cohort study. Antivir Ther. 2005;10:585–591.

42. Riddler SA, Li X, Chu H, et al.. Longitudinal changes in serum lipids among HIV-infected men on highly active antiretroviral therapy. HIV Med. 2007;8:280–287.

43. Constans J, Pellegrin JL, Peuchant E, et al.. Plasma lipids in HIV-infected patients: a prospective study in 95 patients. Eur J Clin Invest. 1994;24:416–420.

44. Rose H, Woolley I, Hoy J, et al.. HIV infection and high-density lipoprotein: the effect of the disease vs the effect of treatment. Metabolism. 2006;55:90–95.

45. Penzak SR, Chuck SK. Hyperlipidemia associated with HIV protease inhibitor use: pathophysiology, prevalence, risk factors and treatment. Scand J Infect Dis. 2000;32:111–123.

46. Signorini DJ, Monteiro MC, Signorini DH, et al.. Prevalence and determinant factors to lipid abnormalities among HIV-infected patients: a cross-sectional study of 812 patients. Arq Bras Endocrinol Metabol. 2010;54:583.

47. Lazzaretti RK, Kuhmmer R, Sprinz E, et al.. Dietary intervention prevents dyslipidemia associated with highly active antiretroviral therapy in human immunodeficiency virus type 1-infected individuals: a randomized trial. J Am Coll Cardiol. 2012;59:979–988.

48. Tarr PE, Rotger M, Telenti A. Dyslipidemia in HIV-infected individuals: from pharmacogenetics to pharmacogenomics. Pharmacogenomics. 2010;11:587–594.

49. Egana-Gorrono L, Martinez E, Cormand B, et al.. Impact of genetic factors on dyslipidemia in HIV-infected patients starting antiretroviral therapy. AIDS. 2013;27:529–538.

50. Dube MP, Stein JH, Aberg JA, et al.. Guidelines for the evaluation and management of dyslipidemia in human immunodeficiency virus (HIV)-infected adults receiving antiretroviral therapy: recommendations of the HIV Medical Association of the Infectious Disease Society of America and the Adult AIDS Clinical Trials Group. Clin Infect Dis. 2003;37:613–627.

51. Stone NJ, Robinson JG, Lichtenstein AH, et al.. 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(Suppl 25):S1–S45.

52. Silverberg MJ, Leyden W, Hurley L, et al.. Response to newly prescribed lipid-lowering therapy in patients with and without HIV infection. Ann Intern Med. 2009;150:301–313.

53. Singh S, Willig JH, Mugavero MJ, et al.. Comparative effectiveness and toxicity of statins among HIV-infected patients. Clin Infect Dis. 2011;52:387–395.

54. Dukers NH, Stolte IG, Albrecht N, et al.. The impact of experiencing lipodystrophy on the sexual behaviour and well-being among HIV-infected homosexual men. AIDS. 2001;15:812–813.

55. Carr A, Samaras K, Burton S, et al.. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS. 1998;12:F51–F58.

56. Mallon PW, Miller J, Cooper DA, et al.. Prospective evaluation of the effects of antiretroviral therapy on body composition in HIV-1-infected men starting therapy. AIDS. 2003;17:971–979.

57. Leitz G, Robinson P. The development of lipodystrophy on a protease inhibitor-sparing highly active antiretroviral therapy regimen. AIDS. 2000;14:468–469.

58. Lo JC, Mulligan K, Tai VW, et al.. Body shape changes in HIV-infected patients. J Acquir Immune Defic Syndr Hum Retrovirol. 1998;19:307–308.

59. Heath KV, Hogg RS, Chan KJ, et al.. Lipodystrophy-associated morphological, cholesterol and triglyceride abnormalities in a population-based HIV/AIDS treatment database. AIDS. 2001;15:231–239.

60. Carter VM, Hoy JF, Bailey M, et al.. The prevalence of lipodystrophy in an ambulant HIV-infected population: it all depends on the definition. HIV Med. 2001;2:174–180.

61. Cournil A, Coudray M, Kouanfack C, et al.. Reduced dose of stavudine and lipoatrophy in HIV-infected patients in Cameroon. Antivir Ther. 2010;15:1039–1043.

62. van Griensven J, De Naeyer L, Mushi T, et al.. High prevalence of lipoatrophy among patients on stavudine-containing first-line antiretroviral therapy regimens in Rwanda. Trans R Soc Trop Med Hyg. 2007;101:793–798.

63. Brown TT, Chu H, Wang Z, et al.. Longitudinal increases in waist circumference are associated with HIV-serostatus, independent of antiretroviral therapy. AIDS. 2007;21:1731–1738.

64. Dube MP, Parker RA, Tebas P, et al.. Glucose metabolism, lipid, and body fat changes in antiretroviral-naive subjects randomized to nelfinavir or efavirenz plus dual nucleosides. AIDS. 2005;19:1807–1818.

65. Lichtenstein KA, Ward DJ, Moorman AC, et al.. Clinical assessment of HIV-associated lipodystrophy in an ambulatory population. AIDS. 2001;15:1389–1398.

66. Joly V, Flandre P, Meiffredy V, et al.. Increased risk of lipoatrophy under stavudine in HIV-1-infected patients: results of a substudy from a comparative trial. AIDS. 2002;16:2447–2454.

67. Mallal SA, John M, Moore CB, et al.. Contribution of nucleoside analogue reverse transcriptase inhibitors to subcutaneous fat wasting in patients with HIV infection. AIDS. 2000;14:1309–1316.

68. Hammond E, McKinnon E, Nolan D. Human immunodeficiency virus treatment-induced adipose tissue pathology and lipoatrophy: prevalence and metabolic consequences. Clin Infect Dis. 2010;51:591–599.

69. Kinabo GD, Sprengers M, Msuya LJ, et al.. Prevalence of lipodystrophy in HIV-infected children in Tanzania on highly active antiretroviral therapy. Pediatr Infect Dis J. 32:39–44.

70. Thoni GJ, Fedou C, Brun JF, et al.. Reduction of fat accumulation and lipid disorders by individualized light aerobic training in human immunodeficiency virus infected patients with lipodystrophy and/or dyslipidemia. Diabetes Metab. 2002;28:397–404.

71. Connolly N, Manders E, Riddler S. Suction-assisted lipectomy for lipodystrophy. AIDS Res Hum Retroviruses. 2004;20:813–815.

72. Hadigan C, Corcoran C, Basgoz N, et al.. Metformin in the treatment of HIV lipodystrophy syndrome: a randomized controlled trial. JAMA. 2000;284:472–477.

73. Kohli R, Shevitz A, Gorbach S, et al.. A randomized placebo-controlled trial of metformin for the treatment of HIV lipodystrophy. HIV Med. 2007;8:420–426.

74. Falutz J, Potvin D, Mamputu JC, et al.. Effects of tesamorelin, a growth hormone-releasing factor, in HIV-infected patients with abdominal fat accumulation: a randomized placebo-controlled trial with a safety extension. J Acquir Immune Defic Syndr. 53:311–322.

75. Grunfeld C, Thompson M, Brown SJ, et al.. Recombinant human growth hormone to treat HIV-associated adipose redistribution syndrome: 12 week induction and 24-week maintenance therapy. J Acquir Immune Defic Syndr. 2007;45:286–297.

76. Lo J, You SM, Canavan B, et al.. Low-dose physiological growth hormone in patients with HIV and abdominal fat accumulation: a randomized controlled trial. JAMA. 2008;300:509–519.

77. Lo J, You SM, Liebau J, et al.. Effects of low-dose growth hormone withdrawal in patients with HIV. JAMA. 304:272–274.

78. Carr A, Workman C, Smith DE, et al.. Abacavir substitution for nucleoside analogs in patients with HIV lipoatrophy: a randomized trial. JAMA. 2002;288:207–215.

79. McComsey GA, Paulsen DM, Lonergan JT, et al.. Improvements in lipoatrophy, mitochondrial DNA levels and fat apoptosis after replacing stavudine with abacavir or zidovudine. AIDS. 2005;19:15–23.

80. Martin A, Smith DE, Carr A, et al.. Reversibility of lipoatrophy in HIV-infected patients 2 years after switching from a thymidine analogue to abacavir: the MITOX Extension Study. AIDS. 2004;18:1029–1036.

81. McComsey GA, Ward DJ, Hessenthaler SM, et al.. Improvement in lipoatrophy associated with highly active antiretroviral therapy in human immunodeficiency virus-infected patients switched from stavudine to abacavir or zidovudine: the results of the TARHEEL study. Clin Infect Dis. 2004;38:263–270.

82. Tebas P, Zhang J, Hafner R, et al.. Peripheral and visceral fat changes following a treatment switch to a non-thymidine analogue or a nucleoside-sparing regimen in HIV-infected subjects with peripheral lipoatrophy: results of ACTG A5110. J Antimicrob Chemother. 2009;63:998–1005.

83. Tebas P, Zhang J, Yarasheski K, et al.. Switching to a protease inhibitor-containing, nucleoside-sparing regimen (lopinavir/ritonavir plus efavirenz) increases limb fat but raises serum lipid levels: results of a prospective randomized trial (AIDS clinical trial group 5125s). J Acquir Immune Defic Syndr. 2007;45:193–200.

84. Carr A, Workman C, Carey D, et al.. No effect of rosiglitazone for treatment of HIV-1 lipoatrophy: randomised, double-blind, placebo-controlled trial. Lancet. 2004;363:429–438.

85. Cavalcanti RB, Raboud J, Shen S, et al.. A randomized, placebo-controlled trial of rosiglitazone for HIV-related lipoatrophy. J Infect Dis. 2007;195:1754–1761.

86. Hadigan C, Yawetz S, Thomas A, et al.. Metabolic effects of rosiglitazone in HIV lipodystrophy: a randomized, controlled trial. Ann Intern Med. 2004;140:786–794.

87. Sutinen J, Hakkinen AM, Westerbacka J, et al.. Rosiglitazone in the treatment of HAART-associated lipodystrophy–a randomized double-blind placebo-controlled study. Antivir Ther. 2003;8:199–207.

88. Tungsiripat M, Bejjani DE, Rizk N, et al.. Rosiglitazone improves lipoatrophy in patients receiving thymidine-sparing regimens. AIDS. 2010;24:1291–1298.

89. Slama L, Lanoy E, Valantin MA, et al.. Effect of pioglitazone on HIV-1-related lipodystrophy: a randomized double-blind placebo-controlled trial (ANRS 113). Antivir Ther. 2008;13:67–76.

90. Calmy A, Hirschel B, Hans D, et al.. Glitazones in lipodystrophy syndrome induced by highly active antiretroviral therapy. AIDS. 2003;17:770–772.

91. Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med. 2007;356:2457–2471.

92. Lincoff AM, Wolski K, Nicholls SJ, et al.. Pioglitazone and risk of cardiovascular events in patients with type 2 diabetes mellitus: a meta-analysis of randomized trials. JAMA. 2007;298:1180–1188.

93. Colmers IN, Bowker SL, Majumdar SR, et al.. Use of thiazolidinediones and the risk of bladder cancer among people with type 2 diabetes: a meta-analysis. CMAJ. 184:E675–E683.

94. Walker UA, Auclair M, Lebrecht D, et al.. Uridine abrogates the adverse effects of antiretroviral pyrimidine analogues on adipose cell functions. Antivir Ther. 2006;11:25–34.

95. Lee JH, Chan JL, Sourlas E, et al.. Recombinant methionyl human leptin therapy in replacement doses improves insulin resistance and metabolic profile in patients with lipoatrophy and metabolic syndrome induced by the highly active antiretroviral therapy. J Clin Endocrinol Metab. 2006;91:2605–2611.

96. Mulligan K, Khatami H, Schwarz JM, et al.. The effects of recombinant human leptin on visceral fat, dyslipidemia, and insulin resistance in patients with human immunodeficiency virus-associated lipoatrophy and hypoleptinemia. J Clin Endocrinol Metab. 2009;94:1137–1144.

97. Sutinen J, Walker UA, Sevastianova K, et al.. Uridine supplementation for the treatment of antiretroviral therapy-associated lipoatrophy: a randomized, double-blind, placebo-controlled trial. Antivir Ther. 2007;12:97–105.

98. Guaraldi G, Fontdevila J, Christensen LH, et al.. Surgical correction of HIV-associated facial lipoatrophy. AIDS. 25:1–12.

99. Phan V, Thai S, Choun K, et al.. Incidence of treatment-limiting toxicity with stavudine-based antiretroviral therapy in Cambodia: a retrospective cohort study. PLoS One. 7:e30647.

100. Jouquet G, Bygrave H, Kranzer K, et al.. Cost and cost-effectiveness of switching from d4T or AZT to a TDF-based first-line regimen in a resource-limited setting in rural Lesotho. J Acquir Immune Defic Syndr. 58:e68–74.

101. Mandina Ndona M, Longo-Mbenza B, Wumba R, et al.. Nadir CD4+, religion, antiretroviral therapy, incidence of type 2 diabetes mellitus, and increasing rates of obesity among black Africans with HIV disease. Int J Gen Med. 2012;5:983–990.

102. Amorosa V, Synnestvedt M, Gross R, et al.. A tale of 2 epidemics: the intersection between obesity and HIV infection in Philadelphia. J Acquir Immune Defic Syndr. 2005;39:557–561.

103. Crum-Cianflone N, Roediger MP, Eberly L, et al.. Increasing rates of obesity among HIV-infected persons during the HIV epidemic. PLoS One. 2010;5:e10106.

104. Lakey W, Yang LY, Yancy W, et al.. Short communication: from wasting to obesity: initial antiretroviral therapy and weight gain in HIV-infected persons. AIDS Res Hum Retroviruses. 2013;29:435–440.

105. Tate T, Willig AL, Willig JH, et al.. HIV infection and obesity: where did all the wasting go? Antivir Ther. 2012;17:1281–1289.

106. Crum-Cianflone N, Tejidor R, Medina S, et al.. Obesity among patients with HIV: the latest epidemic. AIDS Patient Care STDS. 2008;22:925–930.

107. van Griensven J, Zachariah R, Mugabo J, et al.. Weight loss after the first year of stavudine-containing antiretroviral therapy and its association with lipoatrophy, virological failure, adherence and CD4 counts at primary health care level in Kigali, Rwanda. Trans R Soc Trop Med Hyg. 2010;104:751–757.

108. Airhihenbuwa CO, Ford CL, Iwelunmor JI. Why culture matters in health interventions: lessons from HIV/AIDS stigma and NCDs. Health Educ Behav. 2013.

109. Kalyesubula R, Wearne N, Semitala F, et al.. HIV-associated renal and genitourinary comorbidities in Africa. J Acquir Immune Defic Syndr. 2014;67(suppl 1):S68–S78.

110. Ofotokun I, McIntosh E, Weitzmann MN. HIV: inflammation and bone. Curr HIV/AIDS Rep. 2012;9:16–25.

111. Walker Harris V, Brown TT. Bone loss in the HIV-infected patient: evidence, clinical implications, and treatment strategies. J Infect Dis. 2012 205(suppl 3):S391–S398.

112. Triant VA, Brown TT, Lee H, et al.. Fracture prevalence among human immunodeficiency virus (HIV)-infected versus non-HIV-infected patients in a large U.S. healthcare system. J Clin Endocrinol Metab. 2008;93:3499–3504.

113. Brown TT, McComsey GA, King MS, et al.. Loss of bone mineral density after antiretroviral therapy initiation, independent of antiretroviral regimen. J Acquir Immune Defic Syndr. 2009;51:554–561.

114. Cotter AG, Mallon PW. Therapeutic options for low bone mineral density in HIV-infected subjects. Curr HIV/AIDS Rep. 2012;9:148–159.

115. Bolland M, Grey A. HIV and low bone density: responsible party, or guilty by association? IBMS BoneKEy. 2011;8:7–15.

116. Titanji KVA, Sheth A, Lennox J, et al.. B cell dysregulation promotes HIV-induced bone loss. Paper presented at: 20th Conference on Retroviruses and Opportunistic Infections; Atlanta, GA.

117. Vikulina T, Fan X, Yamaguchi M, et al.. Alterations in the immuno-skeletal interface drive bone destruction in HIV-1 transgenic rats. Proc Natl Acad Sci U S A. 2010;107:13848–13853.

118. Ofotokun IWN, Vunnava A, Sheth A, et al.. HAART-induced immune reconstitution: a driving force behind bone resorption in HIV/AIDS. Paper presented at: 18th Conference on Retroviruses and Opportunistic Infections; Boston, MA.

119. Childs K, Welz T, Samarawickrama A, et al.. Effects of vitamin D deficiency and combination antiretroviral therapy on bone in HIV-positive patients. AIDS. 2012;26:253–262.

120. Gibellini D, Borderi M, de Crignis E, et al.. Analysis of the effects of specific protease inhibitors on OPG/RANKL regulation in an osteoblast-like cell line. New Microbiol. 2010;33:109–115.

121. Fleming BB, Greenfield S, Engelgau MM, et al.. The Diabetes Quality Improvement Project. Diabetes Care. 2001;24:1815–1820.

122. Medicines and Healthcare Products Regulatory Agency. Strontium ranelate (Protelos): risk of serious cardiac disorders-restricted indications, new contraindications, and warnings. 2013.

123. US Preventive Services Task Force. Screening for osteoporosis: U.S. preventive services task force recommendation statement. Ann Intern Med. 2011;154:356–364.

124. Watts NB, Lewiecki EM, Miller PD, et al.. National Osteoporosis Foundation 2008 Clinician's Guide to Prevention and Treatment of Osteoporosis and the World Health Organization Fracture Risk Assessment Tool (FRAX): what they mean to the bone densitometrist and bone technologist. J Clin Densitom. 2008;11:473–477.

125. Zebaze R, Ghasem-Zadeh A, Mbala A, et al.. A new method of segmentation of compact-appearing, transitional and trabecular compartments and quantification of cortical porosity from high resolution peripheral quantitative computed tomographic images. Bone. 2013;54:8–20.

126. Nguyen ND, Eisman JA, Center JR, et al.. Risk factors for fracture in nonosteoporotic men and women. J Clin Endocrinol Metab. 2007;92:955–962.

127. Hui SL, Slemenda CW, Johnston CC Jr. Age and bone mass as predictors of fracture in a prospective study. J Clin Invest. 1988;81:1804–1809.

128. Wester CW, Koethe JR, Shepherd BE, et al.. Non-AIDS-defining events among HIV-1-infected adults receiving combination antiretroviral therapy in resource-replete versus resource-limited urban setting. AIDS. 2011;25:1471–1479.

129. Gounder CR, Chaisson RE. A diagonal approach to building primary healthcare systems in resource-limited settings: women-centred integration of HIV/AIDS, tuberculosis, malaria, MCH and NCD initiatives. Trop Med Int Health. 2012.

130. Damschroder L, Aron D, Keith R, et al.. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.

131. Klein K, Sorra J. The challenge of innovation implementation. Acad Manage Rev. 1996;21:1055–1080.

132. De Wit S, Sabin CA, Weber R, et al.. Incidence and risk factors for new-onset diabetes in HIV-infected patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Diabetes Care. 2008;31:1224–1229.

Keywords:

diabetes and dysglycemia; dyslipidemia; overweight and obesity; lipodystrophy; bone mineral density abnormalities; low- and middle-income countries

© 2014 by Lippincott Williams & Wilkins

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.