AIDS Research and Treatment

AIDS Research and Treatment / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 5019025 | https://doi.org/10.1155/2020/5019025

Sneha Deepak Mallya, Sravan Kumar Reddy T, Asha Kamath, Akhilesh Kumar Pandey, Kavitha Saravu, "Determinants of Metabolic Syndrome and 5-Year Cardiovascular Risk Estimates among HIV-Positive Individuals from an Indian Tertiary Care Hospital", AIDS Research and Treatment, vol. 2020, Article ID 5019025, 8 pages, 2020. https://doi.org/10.1155/2020/5019025

Determinants of Metabolic Syndrome and 5-Year Cardiovascular Risk Estimates among HIV-Positive Individuals from an Indian Tertiary Care Hospital

Academic Editor: Glenda Gray
Received16 Mar 2020
Revised17 Aug 2020
Accepted30 Sep 2020
Published28 Oct 2020

Abstract

Longer survival due to use of antiretroviral therapy (ART) has made human immunodeficiency virus- (HIV-) infected individuals prone to chronic diseases such as diabetes, hypertension, and cardiovascular diseases (CVD). Metabolic syndrome (MS), a constellation of risk factors which increase chances of the cardiovascular disease and diabetes, can increase the morbidity and mortality among this population. Hence, the present study was conducted with the objectives of estimating the prevalence and determinants of MS among ART naïve and ART-treated patients and assess their 5-year CVD risk using the reduced version of Data Collection on Adverse Effects of Anti-HIV Drugs (D : A : D) risk prediction model (D : A : D(R)). This hospital-based cross-sectional study included 182 adults aged ≥ 18 years. MS was defined using the National Cholesterol Education Program-Adult Treatment Panel-3 (NCEP ATP-3) criteria. Univariate and multivariate logistic regressions were done to identify the factors associated with MS. Prevalence of MS was 40.1% (95% confidence interval (CI) = 33.0%–47.2%). About 24.7% of the participants had at least a single criterion for MS. Age >45 years (adjusted odds ratio (AOR) = 2.3; 95% CI = 1.1–4.9, ) and body mass index (BMI) > 23 kg/m2 (AOR = 6.4; 95% CI = 3.1–13.1, ) were positively associated with MS, whereas daily consumption of high sugar items was inversely associated (AOR = 0.2; 95% CI = 0.1–0.5, ). More than 50% of the participants were found to have moderate or high 5-year CVD risk. Observed prevalence of MS among HIV patients was higher than other studies done in India. Considering a sizeable number of participants to be having moderate to high CVD risk, culturally appropriate lifestyle interventions need to be planned.

1. Introduction

Human immunodeficiency virus (HIV) continues to be a major public health issue with 36.7 million people living with HIV globally and 53% of them receiving ART [1]. With the widespread use of ART, the survival rates have greatly improved among HIV-infected individuals [2]. However, there have been reports of long-term ART-related increase in cardiovascular risk among HIV-positive patients [3]. Also, CVD has become an important cause of death among HIV patients [4]. A number of metabolic abnormalities have been detected in HIV patients including dyslipidemia and insulin resistance which can increase the CVD risk [5]. A complex interaction between the host’s advancing age, virus, inflammatory process, and ART use has been described as the underlying mechanism for the increased CVD risk among HIV-infected patients [6].

A quarter of deaths due to noncommunicable diseases were attributed to CVDs in India in the year 2014 [7]. Metabolic syndrome is a constellation of risk factors which can increase an individual’s risk of developing atherosclerotic cardiovascular disease (ASCVD) [8]. Hence, metabolic syndrome as an entity can greatly contribute to enhanced CVD burden. While there exist some studies addressing metabolic syndrome in HIV-infected population among Indians, the estimates vary [911]; a few studies are devoid of data on key cardiovascular risk factors such as tobacco, alcohol, and physical activity. With clear lacunae of such studies from India, this study was planned to estimate the prevalence and identify the factors associated with metabolic syndrome. With widespread availability of ART under the National AIDS (Acquired Immunodeficiency Syndrome) Control Program (NACP) and also the private sector institutions in India, the impact of CVD complications among HIV-infected individuals may undermine the success of health benefits achieved through ART use. Early diagnosis and prevention of these conditions may help to reduce the morbidity and mortality associated with CVD complications.

2. Materials and Methods

A cross-sectional study was conducted among HIV-positive ART-naïve individuals and those receiving ART at a tertiary care teaching hospital in southern India during March–June 2017. The study included all the HIV-positive ART-treated and ART-naïve adult males and nonpregnant females aged ≥18 years, who visited the hospital during the study period.

A clearance was obtained from the Institutional Ethics Committee of Kasturba Medical College and Kasturba Hospital, Manipal, before the commencement of the study (IEC 26/2017). A written informed consent was obtained from the participants. Details including sociodemographic characteristics and behavioral factors such as diet, tobacco, and alcohol consumption were collected using a structured questionnaire (Supplementary Appendix 1). An enquiry was made regarding frequency of consumption of high salt items (extra table salt, pickle, salted fish, salted nuts, salt in salad, and salted snacks), high sugar items (sweets, soft drinks, jam, cakes, honey, ice cream, and extra table sugar), high fat items (cakes, sweets, ice cream, ghee, dalda, butter, cream, chocolates, fried food items, cookies, red meat, and junk food items), fish, and fruits and vegetables. The frequency of consumption of various food items was classified as daily, 2-3 times per week, at least once a week, and rarely. Current user of tobacco or alcohol was defined as daily or occasional consumption during the current year, and past user was defined as one who has quit for more than a year. A person who used tobacco or alcohol either currently or in the past was considered to have “ever used” any substance. Physical activity was estimated using the International Physical Activity Questionnaire (IPAQ), and the participants were classified as inactive, minimally active, and health-enhancing physical activity (HEPA) [12]. The 5-year cardiovascular risk was estimated among individuals by a Data Collection on Adverse Effects of Anti-HIV Drugs [D : A : D (R)] risk prediction model using a web-based calculator (https://www.chip.dk/Tools-Standards/Clinical-risk-scores). This tool is based on a reduced model and estimates the risk of an individual developing cardiovascular disease (CVD) within the next 5 years. The D : A : D (R) does not include ART as a parameter, and it requires the following information: gender, age, smoking status, diabetes (diagnosis or on antidiabetic treatment), family CVD history, systolic blood pressure (SBP), total cholesterol, high-density lipoprotein (HDL), and cluster of differentiation 4 (CD4) count. The composite CVD outcome includes myocardial infarction, stroke, invasive coronary artery procedure (including coronary artery by-pass or angioplasty and carotid artery endarterectomy), or death from coronary heart disease. This D : A : D (R) model is valid for HIV-infected individuals aged 18–75 years. The 5-year risk of CVD was classified as low (< 1%), moderate (1 to 5%), high (5 to 10%), or very high (>10%) [13].

Anthropometric measures such as weight (kg), height (cm), and waist circumference (cm) were measured. Body mass index (BMI) was computed and classified based on Indian classification [14]. Two blood pressure (BP) readings were taken through a digital sphygmomanometer, in sitting position in the right arm, 10 minutes apart, and an average of two readings was considered. Hypertension was defined as SBP/DBP of ≥140/90 for those aged <60 years and ≥150/90 for individuals aged above 60 years [15]. Diabetes was defined as fasting plasma glucose (FPG) ≥126 mg/dl and or postprandial plasma glucose ≥200 mg/dl [16]. Hypothyroidism was defined as serum triiodothyronine level <0.80 ng/ml or serum thyroxine level <5.1 micrograms/dl and or serum thyroid-stimulating hormone level >4.2 microinternational units/ml [17]. National Cholesterol Education Program-Adult Treatment Panel (NCEP-ATP3) 2005 revision criteria were used to define MS which were as follows: abdominal obesity (waist circumference ≥ 90 cm for Asian men or ≥80 cm for Asian women), fasting triglycerides (TG) ≥ 150 mg/dl or drug treatment for elevated triglycerides, HDL cholesterol (HDL-C) ≤ 40 mg/dl for men or ≤50 mg/dl for women or on drug treatment for reduced HDL-C, systolic/diastolic blood pressure(SBP/DBP) ≥ 130/85 mmHg or receiving drug treatment, and FPG ≥ 100 mg/dl or drug treatment for elevated glucose [18]. Asian cutoffs for abdominal obesity were taken [19, 20]. Participants having three or more of the above criteria were considered to have MS.

2.1. Statistical Analysis

The data were entered and analyzed using the Statistical Package for Social Sciences (SPSS) version 15. Continuous variables were summarized using mean (standard deviation (SD)) or median (interquartile range (IQR)), and categorical variables have been expressed as percentages. The chi-square test and independent-sample t-test were used to find out association between categorical and continuous variables, respectively. The Mann–Whitney test was used to compare mean DAD scores. Univariate and multivariate logistic regressions were done to identify the factors associated with MS and unadjusted and adjusted odds ratio (OR), respectively, with 95% confidence intervals (CI) have been reported. The variables which were significant in univariate analysis were included in the multivariate analysis. A value of <0.05 was considered to be statistically significant.

3. Results

The study included a total of 182 (123 ART-treated and 59 ART-naïve) participants who visited the outpatient facility of the department of medicine or hospitalized as an inpatient during the months of March to June 2017. The recruitment of study participants is shown in Figure 1.

As shown in Table 1, non-nucleoside reverse transcriptase inhibitor- (NNRTI-) based ART regimen was administered to 75.4% of the participants, while 24.6% were on protease inhibitor- (PI-) based regimen.


Drug className of the drugn (%)

NNTRIEfavirenz82 (66.7)
Nevirapine13 (10.6)

NRTITenofovir97 (78.9)
Emtricitabine65 (52.8)
Lamivudine54 (43.9)
Zidovudine26 (21.1)
Abacavir4 (3.3)

PIAtazanavir/ritonavir28 (15.4)
Darunavir/ritonavir2 (1.6)
Lopinavir/ritonavir1 (0.8)

NNRTI, non-nucleoside reverse transcriptase inhibitors; NRTIs, nucleoside reverse transcriptase inhibitors; PI, protease inhibitor.

A quarter of the participants (24.7%) had at least one criterion for MS, and 3.8% of the participants had all the five criteria for MS. The overall prevalence of MS among participants was observed to be 40.1% (95% CI = 33.0%–47.2%) with a higher prevalence among individuals on ART as compared to ART-naïve individuals (43.1% vs 33.9%). However, this difference was not statistically significant (). There was no difference in the prevalence of MS across the two genders (males = 40.4% vs females = 40.3%, ).

The most common abnormal MS component observed was low HDL (68.1%), whilst the least common was BP ≥130/85 or being on antihypertensives (28.6%).

Distribution of the overall morbidities among the participants was diabetes (15.9%), hypertension (20.3%), and hypothyroidism (5.5%). As shown in Table 2, the lipid abnormalities were higher in ART-naïve individuals and the other three components of MS were present to a greater extent among individuals on ART. The above differences with respect to various criteria of metabolic syndrome were found to be statistically significant among ART-naïve and ART-treated patients except for elevated TG levels. Low HDL values in ART naive could be because of them being more sedentary compared to people on ART.


Criteria for MSOn ART (n = 123)ART naïve (n = 59) value
n (%)n (%)

Presence of abdominal obesity52 (42.3)13 (22.0)0.008
Elevated fasting TG or drug treatment for elevated TG56 (45.5)31 (52.5)0.37
Low HDL-C or on drug treatment for reduced HDL-C73 (59.3)51 (86.4)<0.001
Elevated SBP/DBP ≥ 130/85 mmHg or receiving drug treatment42 (34.1)10 (16.9)0.01
Elevated FPG or drug treatment for elevated glucose66 (53.7)20 (33.9)0.01

TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose.

Females had higher prevalence of abdominal obesity (49.3% vs 27.8%, ) and low HDL (79.1% vs 61.7%, ) as compared to males. Males displayed a higher frequency of elevated TG (48.7% vs 46.3%, ), BP (32.2% vs 22.4%, ), and fasting blood sugar (53.9% vs 35.8%, ) against females.

Among the study participants, 63.2% were male. Univariate analysis to find out the association between MS and sociodemographic, disease-related, and lifestyle factors has been shown in Table 3. It was observed that age > 45 years., BMI > 23 kg/m2, duration since HIV infection > 36 months, being on ART > 36 months, and family history of diabetes, hypertension, and CVD were positively associated with MS. Consumption of high sugar and salt items on a daily basis or 2-3 times/week or once a week were inversely associated with MS.


VariableCategoryMS present (n = 73)MS absent (n = 109)OR with 95% CI value

Background characteristics
Age in years≤4523 (31.5)56 (51.4)1
>4550 (68.5)53 (48.6)2.2 (1.2–4.2)0.009

GenderMale46 (63.0)69 (63.3)0.9 (0.5–1.8)0.96
Female27 (37.0)40 (36.7)1

Marital statusEver married71 (97.3)98 (89.9)1
Single2 (2.7)11 (10.1)0.2 (0.0–1.1)0.07

EducationUp to 7 years of schooling19 (26.0)30 (27.5)0.9 (0.4–1.8)0.82
>7 years of schooling54 (74.0)79 (72.5)1

OccupationEmployed44 (60.3)78 (71.6)1
Homemaker/unemployed29 (39.7)31 (28.4)1.6 (0.8–3.1)0.11

Average monthly income(Median (IQR))4000 (2500, 7083)4000 (2500, 6667)1.0 (1.0–1.0)0.55
Disease- and treatment-related factors
Duration of HIV infection≤36 months32 (43.8)66 (60.6)1
>36 months41 (56.2)43 (39.4)1.0 (1.0–3.5)0.02

Recent CD4 count in cells/mm3≤50056 (76.7)89 (81.7)0.7 (0.3–1.5)0.41
>50017 (23.3)20 (18.3)1

Treatment statusOn ART53 (72.6)70 (64.2)1.4 (0.7–2.8)0.23
ART naïve20 (27.4)39 (35.8)1

Duration of ART (n = 123)≤36 months13 (24.5)31 (44.3)1
>36 months40 (75.5)39 (55.7)2.4 (1.1–5.3)0.02

ART regimenNNRTI based41 (56.2)50 (45.9)1.5 (0.8–3.1)0.17
PI based12 (16.4)20 (18.3)1.1 (0.4–2.8)0.73
ART naive20 (27.4)39 (35.8)1

Lifestyle factors
BMINormal (≤23 kg/m2)24 (32.9)81 (74.3)1
Overweight and obese (>23 kg/m2)49 (67.1)28 (25.7)5.9 (3.0–11.3)< 0.001

Physical activityInactive35 (32.1)22 (30.1)0.9 (0.2–3.7)0.93
Minimally active68 (62.4)47 (64.4)1.0 (0.2–3.8)0.95
HEPA6 (5.5)4 (5.5)1

Tobacco consumptionEver11 (15.1)21 (19.3)0.7 (0.3–1.6)0.46
Never62 (84.9)88 (80.7)1

Alcohol consumptionEver19 (26.0)30 (27.5)0.9 (0.4–1.8)0.82
Never54 (74.0)79 (72.5)1

High-fat item consumptionDaily8 (11.0)23 (21.1)0.4 (0.1–1.1)0.09
2-3 times/week to once/week.39 (53.4)53 (48.6)0.9 (0.4–1.8)0.83
No/rarely26 (35.6)33 (30.3)1

High-sugar item consumptionDaily24 (32.9)63 (57.8)0.2 (0.09–0.4)< 0.001
2-3 times/week to once/week19 (26.0)30 (27.5)0.3 (0.1–0.7)0.011
No/rarely30 (41.1)16 (14.7)1

High-salt item consumptionDaily29 (39.7)56 (51.4)0.3 (0.1–0.7)0.01
2-3 times/week to once/week21 (28.8)37 (33.7)0.3 (0.1–0.9)0.02
No/rarely23 (31.5)16 (14.7)1

Fish consumptionDaily9 (12.3)7 (6.4)1
2-3 times/week to once/week33 (45.2)36 (33.0)0.7 (0.2–2.1)0.54
No/rarely31 (42.5)66 (60.6)0.3 (0.1–1.0)0.06

Vegetable consumptionDaily71 (97.3)108 (99.1)1
2-3 times/week2 (2.7)1 (0.9)3.0 (0.2–34.1)0.36

Family history of hypertensionPresent25 (34.2)20 (18.3)2.3 (1.6–4.5)0.01
Absent48 (65.8)89 (81.7)1

Family history of DMPresent26 (35.6)27 (24.8)1.6 (0.8–3.2)0.11
Absent47 (64.4)82 (75.2)1

Family history of CVDPresent7 (9.6)2 (1.8)5.6 (1.1–28.1)0.03
Absent66 (90.4)107 (98.2)1

HIV, human immunodeficiency virus; CD4, cluster of differentiation 4; ART, anti-retroviral therapy; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; BMI, body mass index; HEPA, health enhancing physical activity; CVD, cardiovascular disease;

VariableOR with 95% CI value

Age≤45 years10.01
>45 years2.3 (1.1–4.9)

BMINormal (≤23 kg/m2)1<0.001
Overweight and obese (>23 kg/m2)6.4 (3.1–13.1)

High-sugar item consumptionNo/rarely10.001
Daily0.2 (0.1–0.5)

BMI, body mass index.

The variables which were significant in univariate analysis were included in multivariate analysis. As shown in Table 4, multivariate analysis found that age > 45 years and BMI > 23 kg/m2 were independently associated with MS, whereas daily high-sugar item consumption showed an inverse association.

Figure 2 shows the 5-year CVD risk among the study participants stratified by ART status. The overall median (IQR) D : A : D risk score for the study population was 1.4 (0.6,2.8). Following was the distribution of 5-year CVD risk among all participants: low: 70 (38.7%), moderate: 92 (50.8%), high: 16 (8.8%), and very high: 3 (1.7%). A higher proportion of the participants belonged to moderate and high CVD risk groups in ART-treated category as compared to ART naïve, and this difference was found to be statistically significant (). The median D : A : D score was significantly higher among males as compared to females (1.7 vs 0.8, ). A significant difference was also noted between median D : A : D scores of the two groups, i.e. ART-treated vs ART-naïve individuals (1.7 vs 1.0, ).

4. Discussion

The prevalence of MS in the present study was 40.1%. This is higher when compared to studies done elsewhere in India in which prevalence ranged from 19.8 to 26.6% [911]. Studies conducted outside India have reported a prevalence of MS from the lowest of 15.6% to the highest of 48.3% [2129]. The prevalence of MS in the current HIV-infected participants is higher as compared to uninfected individuals in India in which the prevalence ranged from 18.4 to 30.9% [30, 31]. While most of the studies have used NCEP-ATP 3 criteria, few studies have used International Diabetes Federation (IDF) criteria for assessment of MS. Apart from the criteria used for assessment, this disparity may be attributed to the variation in prevalence of MS components among the patient population studied. Additionally, factors such as the type of study population selected (ART-naïve or ART-treated) and the type and duration of exposure to ART may be contributing factors for this observed difference.

The current study did not observe any statistically significant difference in MS prevalence between ART-treated and ART-naïve individuals. However, many studies have reported prevalence of MS to be higher among patients on ART as compared to their ART-naïve counterparts [9, 11, 29]. Results from a Nigerian study also reported that there was no increase in MS among the ART-treated group [27]. This may be due to the reason that only 0.6% of the study population received PI-based ART regimen, which are known to be associated with metabolic changes. The current study did not observe such association as PI-based regimen (ritonavir and atazanavir) was used among only 31 participants, and hence, did not have adequate power to detect this association.

The current study found a quarter of the participants to be having at least one risk factor for MS. The proportion of participants with at least one abnormal MS component ranged from one-third to almost two-thirds of the study population among various other studies [10, 32]. There was a wide variation in the type of predominant abnormal component among different studies. Similar to the findings of the present study, other studies from India and abroad [10, 11, 22, 25] have reported low HDL as the most common abnormal component of MS among HIV-positive individuals. While the present study found that BP > 130/85 mm Hg or being on antihypertensive medication to be the least common abnormal component of MS, the study from Southeast Nigeria [27] observed it to be the most dominant component. Females in the study population had higher prevalence of low HDL cholesterol and abdominal obesity, and this may be a reflection of real differences pertinent to this population of coastal Karnataka and deserves further investigation.

The overall median 5-year CVD risk observed in the present study was higher than the study findings from Cameroon (0.6% (0.3–1.3)) in which 80% of participants were females [33]. As 63% of study population were males who are traditionally known to have higher CVD risk, the median score is higher in the present study. The study also found that median (IQR) 5-year CVD risk was higher among males in comparison to females (1.4% (0.8–2.7) vs 0.5% (0.3–0.9), ), which is akin to findings of the present study [33]. While in the current study 38.7% of participants had low 5-year CVD risk, a study from Pune found that 63.5% of the study participants (median age = 40 years) had low risk [34]. Another study from Brazil found that two-thirds of the HIV-infected participants (mean age = 36.8 years) to be having low risk (74.2%) [22]. Participants in the both the aforementioned studies were younger as compared to our study population which may be the reason for predominance of the low-risk category.

The present study found that age > 45 years and high BMI to be positively associated with MS. Association between these traditional CVD risk factors and MS have been demonstrated in several studies among HIV-positive individuals [23, 24, 28, 35]. While current study did not find any gender disparity in MS prevalence, Alvarez [24] and Berhane et al. [26] observed a female preponderance.

Studies by Pongthananikorn et al. from India [36] and Tiozzo et al. from United States of America [37] found a positive association between high sugar item consumption and MS; however, the present study found an inverse association between the two. This may be attributed to the fact that individuals with MS had higher frequency of diabetes and hypertension and, hence, may have reduced the consumption of sugar and salt, respectively.

Our study systematically dissected all components of metabolic syndrome in HIV-infected individuals in India. We further estimated their 5-year cardiovascular risk. Our study has highlighted the importance of higher BMI, a modifiable risk factor for MS, which could guide future preventive interventions. However, our study has certain limitations. As this study was carried out in a tertiary referral center, MS prevalence in the study setting may not be generalizable to the general HIV+ population. The information collected on tobacco, alcohol consumption, and dietary factors was minimal, and this might have affected the results. Larger prospective studies are necessary to provide better understanding of the factors associated with MS.

5. Conclusion

Prevalence of MS is high among the HIV-infected study participants. Predictive factors for MS in this study were age and BMI, both being traditional risk factors. None of the HIV-related factors such as duration of infection, exposure to ART, and CD4 count were associated with presence of MS in this population. More than half of the study participants had moderate or high risk of development of CVD in the next five years. It is of utmost importance to develop specific interventions to prevent, diagnose, and treat MS among HIV-infected patients to reduce their long-term morbidity and mortality. Early identification and instituting appropriate management will help to reduce future complications and mortality due to CVD.

Data Availability

The data used to support the findings of this study are available from the corresponding upon request.

Ethical Approval

Ethics committee approval was obtained from the Institutional Ethics Committee of Kasturba Medical College and Kasturba Hospital, Manipal (affiliated to Manipal Academy of Higher Education), India, prior to the commencement of the study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors gratefully acknowledge the support received from the following clinical investigators from the Department of Medicine, Kasturba Medical College, Manipal, for the conduct of the study: Dr. Manjunath Hande, Dr. Sudha Vidyasagar, Dr. Shubha Seshadri, Dr. BA Shastry, Dr. Ram Bhat, Dr. Raviraj Acharya, Dr. Shivshankar, and Dr. Mukhyaprana Prabhu. The authors thank Ms. Shruthi Shetty, Research Assistant, who helped to collect the data for the study. The authors express their sincere gratitude to all the study participants. The authors would like to thank Manipal Centre for Infectious Diseases, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal (MAC ID/SGA/2016/010) for providing seed grant to carry out this study.

Supplementary Materials

Supplementary Appendix 1: it consists of study tool with details pertaining to sociodemographic characteristics, disease-related information, behavioral factors such as diet, tobacco, and alcohol consumption, physical examination, and biochemical investigations. (Supplementary Materials)

References

  1. Number of people living with HIV and the number of HIV infected individuals receiving antiretroviral therapy. 2018: http://www.who.int/hiv/data/en/.
  2. Antiretroviral Therapy Cohort Collaboration, “Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies,” Lancet, vol. 372, pp. 293–299, 2008. View at: Publisher Site | Google Scholar
  3. F. Islam, J. Wu, J. Jansson, and D. Wilson, “Relative risk of cardiovascular disease among people living with HIV: a systematic review and meta-analysis,” HIV Medicine, vol. 13, no. 8, pp. 453–468, 2012. View at: Publisher Site | Google Scholar
  4. N. Lohse, A.-B. E. Hansen, G. Pedersen et al., “Survival of persons with and without HIV infection in Denmark, 1995-2005,” Annals of Internal Medicine, vol. 146, no. 2, Article ID 17227932, pp. 87–95, 2007. View at: Publisher Site | Google Scholar
  5. S. Grinspoon and A. Carr, “Cardiovascular risk and body-fat abnormalities in HIV-infected adults,” New England Journal of Medicine, vol. 352, no. 1, Article ID 15635112, pp. 48–62, 2005. View at: Publisher Site | Google Scholar
  6. J. N. Kiage, D. C. Heimburger, C. K. Nyirenda et al., “Cardiometabolic risk factors among HIV patients on antiretroviral therapy,” Lipids in Health and Disease, vol. 12, no. 1, p. 50, 2013. View at: Publisher Site | Google Scholar
  7. Burden of non-communicable diseases in India, 2017, http://www.searo.who.int/india/topics/noncommunicable_diseases/ncd_country_profile_2014.pdf?ua=1.
  8. National Cholesterol Education Program (NCEP), “Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) third report of the national cholesterol education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report,” Circulation, vol. 106, pp. 3143–3421, 2002. View at: Google Scholar
  9. D. P. Theengh, P. Yadav, A. K. Jain, and P. Nandy, “Assessment of metabolic syndrome in HIV-infected individuals,” Indian Journal of Sexually Transmitted Diseases and AIDS, vol. 38, no. 2, pp. 152–156, 2017. View at: Publisher Site | Google Scholar
  10. S. Bajaj, A. Bhargava, and S. Tyagi, “Metabolic syndrome in human immunodeficiency virus positive patients,” Indian Journal of Endocrinology and Metabolism, vol. 17, no. 1, pp. 117–120, 2013. View at: Publisher Site | Google Scholar
  11. J. Idiculla, G. D. D’Souza, G. D. Ravindran, G. Singh, and S. Furruqh, “Diabetes mellitus, insulin resistance, and metabolic syndrome in HIV-positive patients in South India,” International Journal of General Medicine, vol. 4, pp. 73–78, 2011. View at: Publisher Site | Google Scholar
  12. International physical activity questionnaire, 2002, http://people.umass.edu/be640/yr2004/resources/Internat-physical-activity-worksheet.pdf.
  13. N. Friis-Møller, L. Ryom, C. Smith et al., “An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons: the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study,” European Journal of Preventive Cardiology, vol. 23, no. 2, pp. 214–223, 2016. View at: Publisher Site | Google Scholar
  14. A. Misra, P. Chowbey, B. M. Makkar et al., “Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management,” Article ID 19582986, 2017. View at: Google Scholar
  15. C. Armstrong, “JNC8 guidelines for the management of hypertension in adults,” American Family Physician, vol. 90, no. 7, pp. 503-504, 2014. View at: Google Scholar
  16. “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 37, pp. S81–S90, 2014. View at: Publisher Site | Google Scholar
  17. Laboratory support for the diagnosis and monitoring of thyroid disease, 2016, http://www.nacb.org/lmpg/thyroid_LMPG_PDF-stm.html.
  18. S. M. Grundy, J. I. Cleeman, S. R. Daniels et al., “Diagnosis and management of the metabolic syndrome,” Circulation, vol. 112, no. 17, pp. 2735–2752, 2005. View at: Publisher Site | Google Scholar
  19. World Health Organization, “Waist circumference and waist-hip ratio. report of who expert consultation,” 2008, http://apps.who.int/iris/bitstream/10665/44583/1/9789241501491_eng.pdf. View at: Google Scholar
  20. P. Z. Zimmet and K. G. M. M. Alberti, “Introduction: globalization and the non-communicable disease epidemic,” Obesity, vol. 14, no. 1, pp. 1–3, 2006. View at: Publisher Site | Google Scholar
  21. H. A. Mbunkah, H. D. Meriki, A. T. Kukwah, O. Nfor, and T. Nkuo-Akenji, “Prevalence of metabolic syndrome in human immunodeficiency virus - infected patients from the South- West region of Cameroon, using the adult treatment panel III criteria,” Diabetology & Metabolic Syndrome, vol. 6, no. 1, p. 92, 2014. View at: Publisher Site | Google Scholar
  22. H. Dohou, D. Shm, H. L. Codjo et al., “Prevalence and factors associated with metabolic syndrome in people living with HIV in parakou in 2016,” Journal of Lipid and Atherosclerosis, vol. 1, no. 1, p. 1005, 2017. View at: Google Scholar
  23. J. A. Leal, M. A. Fausto, and M. Carnetro, “Anthropometric risk factors for metabolic syndrome in HIV patients,” Medical Express (Sao Paulo, Online), vol. 3, no. 4, Article ID M160405, 2016. View at: Publisher Site | Google Scholar
  24. C. Alvarez, R. Salazar, J. Galindez et al., “Metabolic syndrome in HIV-infected patients receiving antiretroviral therapy in Latin America,” The Brazilian Journal of Infectious Diseases, vol. 14, no. 3, pp. 256–263, 2010. View at: Publisher Site | Google Scholar
  25. M. W. Nery, C. M. Martelli, E. A. Silveira et al., “Cardiovascular risk assessment: a comparison of the Framingham, PROCAM, and DAD equations in HIV-infected persons,” Scientific World Journal, vol. 2013, Article ID 969281, 9 pages, 2013. View at: Publisher Site | Google Scholar
  26. T. Berhane, A. Yami, F. Alemseged et al., “Prevalence of lipodystrophy and metabolic syndrome among HIV positive individuals on HighlyActive Anti-Retroviral treatment in Jimma, South West Ethiopia,” Pan African Medical Journal, vol. 13, no. 1, p. 43, 2012. View at: Google Scholar
  27. K. Uwanuruochi, V. Uwanuruochi, F. Michael et al., “Assessment of Metabolic syndrome among adult human immunodeficiency virus/acquired immunodeficiency syndrome patients in a tertiary health facility in Southeast Nigeria,” Journal of HIV and Human Reproduction, vol. 3, no. 2, pp. 41–46, 2015. View at: Publisher Site | Google Scholar
  28. M. F. Villamar, A. C. Albuja, and N. I. C. Salas, “Metabolic syndrome among HIV infected outpatients from a hospital in Quiti, Ecuador: a cross sectional study,” Revista Panamericana de Infectología, vol. 13, no. 2, pp. 12–18, 2011. View at: Google Scholar
  29. C. Obirikorang, L. Quaye, J. Osei-Yeboah, E. Odame, and I. Asare, “Prevalence of metabolic syndrome among HIV-infected patients in Ghana: a cross-sectional study,” Nigerian Medical Journal, vol. 57, no. 2, pp. 86–90, 2016. View at: Publisher Site | Google Scholar
  30. M. Deepa, S. Farooq, M. Datta, R. Deepa, and V. Mohan, “Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: the Chennai urban rural epidemiology study (CURES-34),” Diabetes/Metabolism Research and Reviews, vol. 23, no. 2, pp. 127–134, 2007. View at: Publisher Site | Google Scholar
  31. R. Gupta, P. C. Deedwania, A. Gupta, S. Rastogi, R. B. Panwar, and K. Kothari, “Prevalence of metabolic syndrome in an Indian urban population,” International Journal of Cardiology, vol. 97, no. 2, pp. 257–261, 2004. View at: Publisher Site | Google Scholar
  32. C. Jericó, H. Knobel, M. Montero et al., “Metabolic syndrome among HIV-infected patients: prevalence, characteristics, and related factors,” Diabetes Care, vol. 28, no. 1, pp. 132–137, 2005. View at: Publisher Site | Google Scholar
  33. S. R. Noumegni, J. J. Bigna, V. J. A.M. E. Nkegoum et al., “Relationship between estimated cardiovascular disease risk and insulin resistance in a black African population living with HIV: a cross-sectional study from Cameroon,” BMJ Open, vol. 7, no. 8, Article ID e016835, 2017. View at: Publisher Site | Google Scholar
  34. I. P. Marbaniang, D. Kadam, R. Suman et al., “Cardiovascular risk in an HIV-infected population in India,” Heart Asia, vol. 9, no. 2, Article ID e010893, pp. 1–6, 2017. View at: Publisher Site | Google Scholar
  35. S. Krishnan, J. T. Schouten, B. Atkinson et al., “Metabolic syndrome before and after initiation of antiretroviral therapy in treatment-naive HIV-infected individuals,” JAIDS Journal of Acquired Immune Deficiency Syndromes, vol. 61, no. 3, pp. 381–389, 2012. View at: Publisher Site | Google Scholar
  36. S. Pongthananikorn, K. Jantarathaneewat, P. Somnikha, S. Jaturapullarp, and K. Meksawan, “Prevalence and risk factors of metabolic syndrome in HIV-infected patients receiving the highly active antiretroviral therapy,” Topics in Clinical Nutrition, vol. 33, no. 1, pp. 41–49, 2018. View at: Publisher Site | Google Scholar
  37. E. Tiozzo, J. Konefal, S. Adwan et al., “A cross-sectional assessment of metabolic syndrome in HIV-infected people of low socio-economic status receiving antiretroviral therapy,” Diabetology & Metabolic Syndrome, vol. 7, p. 15, 2015. View at: Publisher Site | Google Scholar

Copyright © 2020 Sneha Deepak Mallya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views247
Downloads235
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.