Higher Proportion of Abnormal Nutritional Status Among Well-Suppressed HIV-Infected Elderly Asians Compared to HIV-Negative Individuals

Abstract

Older adults face physiological, psychological, social, and economic changes, which may impair nutritional status, making the body vulnerable to illness and adverse clinical outcomes. Little is known regarding the nutritional status among elderly people living with HIV (PLHIV). We aimed to study the prevalence of malnutrition and the associated factors in a Thai aging cohort. A cross-sectional study was conducted among PLHIV >50 years of age on long-term antiretroviral therapy and HIV-negative controls, frequency matched by sex and age in Bangkok, Thailand. Nutritional status was assessed by the Mini Nutrition Assessment (MNA) tool. Abnormal nutritional status was defined as MNA score <24 (malnutrition and at risk of malnutrition). Body composition was measured by bioelectrical impedance analysis using Body Composition Analyzer. Demographic and disease-related factors were assessed for their association with abnormal nutrition status using multivariable logistic regression. There were 349 PLHIV and 103 HIV-uninfected controls, with median age 55 years. The majority were male (63%) with median body mass index (BMI) of 23.4 kg/m². PLHIV had lower BMI [median, 23.1 (IQR, 20.8–25.2) vs. 25.3 (22.3–28.7) kg/m², p < .001], lower fat percent [22.8% vs. 26.3%, p < .001] and lower fat mass [14.2 vs. 16.9 kg, p < .001] and higher abnormal nutritional status (18.05% vs. 6.8%, p = .005) than controls. In the multivariate model, older age (adjusted odds ratio [aOR], 1.06, 95% confident interval [CI]: 1.01–1.12, p = .03), positive HIV status (aOR, 2.67, 95% CI: 1.07–6.65, p = .036), diabetes mellitus (aOR, 2.21, 95% CI: 1.003–4.87, p = .049), lower fat mass (aOR, 0.70, 95%CI: 0.57–0.86, p < .001), and lower BMI (aOR, 0.63, 95% CI: 0.51–0.78, p < .001) were independently associated with abnormal nutritional status. PLHIV had higher risks for abnormal nutritional status compared with HIV-uninfected individuals. Regular screening and monitoring of nutritional status among PLHIV may promote better health outcomes.

Background

The longevity of people living with HIV (PLHIV) has dramatically improved due to advances in antiretroviral treatment (ART). PLHIV who initiate ART, and achieve and maintain viral suppression have life expectancy that is similar to HIV-uninfected individuals.^1,2 As a result, the population of PLHIV over the age of 50 years (PLHIV50+) is continually increasing. A recent report estimated that the proportion of PLHIV50+ increased substantially between 2000 and 2016 (8% to 16%) and this population is expected to increase to 21% by 2020.³ A similar trend was found in low-and middle-income countries, including Thailand.⁴

Malnutrition remains prevalent in both developed and developing countries despite ongoing awareness campaigns. Undernutrition can happen to anyone, but older adults are predominantly at risk.^5,6 Older adults face physiological, psychological, social, and economic changes, which may act together or synergistically, to increase their risk of inadequate nutrition.⁷ Aging and nutritional status can influence the outcomes of chronic diseases, and also increase the risk of cardiovascular diseases (CVD).⁸ Emerging data show that both of these factors can also impact viral replication and the immune system in HIV.⁸ Moreover, maintaining optimal nutrition status can improve quality of life and reduce comorbidities and hospitalization, progression of HIV infection, and mortality associated with HIV.^8
–10 Furthermore, good nutrition also helps PLHIV absorb HIV medication.

The Mini Nutrition Assessment (MNA) is a simple, noninvasive, well-validated screening tool for malnutrition in elderly persons, and it is recommended for early detection of malnutrition in older adults, and can detect the risk of malnutrition while albumin levels and body mass index (BMI) are still in the normal range.^11,12

There are limited data describing nutritional status in elderly PLHIV. Identification of malnutrition with a well-validated tool in these populations for the early detection and treatment of malnutrition could improve the overall clinical outcomes among PLHIV50+ receiving long-term suppressive ART. Therefore, this study aimed to assess the prevalence of malnutrition in PLHIV50+ with sustained virological suppression after long-term suppressive ART, and to identify risk factors associated with malnutrition in this population.

Materials and Methods

Study population and design

We conducted a cross-sectional study among an aging cohort comprising PLHIV with virological suppression (HIV-RNA <50 copies/mL), with age- and gender-matched HIV-uninfected participants >50 years of age in Bangkok, Thailand. PLHIV were recruited from the HIV Netherlands Australia Thailand (HIV-NAT) research clinic, and HIV-uninfected controls were recruited from individuals who came for an annual medical checkup at King Chulalongkorn Memorial Hospital from 2016 to 2017. All participants underwent physical examination and anthropometric measurements at the clinic visit. Anthropometric measurement was performed by trained nurses. Body composition was measured by using bioelectrical impedance analysis (BIA) using Body Composition Analyzer (in Body S20, Biospace, Seoul, Korea). Percent body fat and fat mass were recorded. BMI was calculated by using (weight (kg)/height (m²)).

Basic characteristics were collected and recorded, including age, sex, current smoking (yes or no), current alcohol intake (yes or no), comorbidities, including diabetes mellitus (DM), hypertension (HT), hepatitis B and C (HBV and HCV) coinfection, and general medical history. In PLHIV, ART history, nadir, and current CD4 cell count and duration of HIV infection were also collected. DM was defined as having fasting plasma glucose ≥126 mg/dL, or reported the onset of diabetes and the initiation of antidiabetic therapy. HT was defined as systolic blood pressure (SBP) ≥140 mm Hg or diastolic blood pressure (DBP) ≥90 mm Hg or self-reported use of antihypertensive medications.

The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation.¹³ The Fibrosis 4 Score (Fib-4) was calculated by using Sterling's formula: [age (years) × AST (U/L)/(PLT (10⁹/L) × (ALT) ^½(U/L)).¹⁴ HCV coinfection was defined as positive anti-HCV antibody and detectable HCV RNA. HBV coinfection was defined as positive hepatitis B surface antigen.

Laboratory measurements

Blood was drawn from all participants after at least 8 h of fasting for determination of serum albumin level, 25-hydroxyvitamin D (25(OH)D) levels, calcium, phosphate, thyroid-stimulating hormone (TSH), high-sensitivity C-reactive protein (hs-CRP), glucose, lipid panels, serum creatinine, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). CD4 cell count and HIV-RNA for PLHIV were also tested. Whole blood samples were centrifuged at 2500 rpm for 20 min and plasma was stored at −80°C until use.

Definitions of study endpoints

We used the MNA to screen nutrition status of elderly PLHIV and HIV-uninfected controls. The MNA is a screening and assessment tool with reliable scale and simple use for health care professionals to determine the nutrition status in adults 65 years of age and older. The MNA is commonly used to assess nutrition in elderly participants in many countries, and is also widely used by Thai Dieticians in routine clinic and research settings.^15,16

The MNA detects risk of malnutrition before the occurrence of severe changes in weight or serum proteins.¹⁷ The assessment consists of questions derived from anthropometric assessment (weight, height, mid-arm and calf circumference); general assessment (lifestyle, medication use, and mobility); dietary assessment (number of daily meals, food intake); and subjective assessment (self-perception of health, and nutrition status). MNA scores of <17, 17–23.5, and ≥24 are classified as malnourished, at risk of malnutrition, and normal nutrition status, respectively. In our study, we defined abnormal nutritional status by combining those who had malnutrition or were at risk of malnutrition from the MNA tool (scores <24).

Statistical analyses

Demographic and participant characteristics were described as frequencies for categorical variables or median and interquartile range (IQR) for continuous variables. Comparison of characteristics between participants with normal and abnormal nutritional status was made by using Student t-test or Wilcoxon rank-sum test for continuous variables, while Pearson's Chi-square test or Fisher exact test were used for categorical variables as appropriate. Logistic regression models were used to assess which participant characteristics were associated with abnormal nutritional status. Covariates in univariate analysis with p-values <.25 were adjusted for in a multivariate model. Logistic regression analysis results were expressed as odds ratios and 95% confident interval (CI). The linearity of continuous covariate against the logit function was assessed, and in the case of nonlinearity, the covariate was modeled as quartiles. A p-value <.05 was considered statistically significant. All analyses were done in STATA version 15.1 (StataCorp, College Station, TX).

Ethics consideration

The study was reviewed and approved by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. All participants included in the study provided written informed consent.

Results

Participants' characteristics

A total of 452 participants (349 PLHIV and 103 HIV-uninfected participants) were included in the analysis. Baseline demographics and clinical characteristics of participants stratified by nutrition status are described in Table 1. The majority of participants were male (63%) with a median age of 55 years. The proportion of current smokers (13.5%) and current alcohol drinkers (10.4%) was low. The overall median BMI was 23.4 kg/m². Underweight (BMI <18.5 kg/m²), normal weight (18.5–24.9 kg/m²), and overweight (≥25 kg/m²) was found to be 6.6%, 61.1%, and 32.3%, respectively.

Table 1.

Participants' Characteristics by Nutrition Status

Covariates number (%) or median (IQR)	Normal nutritional status (n = 382)	Abnormal nutritional status (n = 70)	p-value
Sex			.18
Male	245 (64.14)	39 (55.71)
Female	137 (35.86)	31 (44.29)
Age in years (median, IQR)	54.7 (51.9–58.7)	55.6 (53–62)	.03
Current Smoking	49 (12.83)	12 (17.14)	.51
Current drinking	39 (10.21)	8 (11.43)	.84
BMI (kg/m²)	23.9 (21.8–26.2)	20.6 (19–22.2)	<.001
HIV infection	286 (74.87)	63 (90.00)	.006
HBV coinfection	44 (11.55)	5 (7.14)	.40
HCV coinfection	31 (8.14)	5 (7.14)	.78
Diabetes mellitus	60 (15.71)	17 (24.29)	.079
Hypertension	145 (37.96)	34 (48.57)	.095
Body compositions
Waist to hip ratio	0.93 (0.88–0.97)	0.9 (0.85–0.95)	.004
Fat percentage	23.7 (18.5–31.9)	20.1 (14.8–27.1)	.003
Fat mass (kg)	16 (11.6–19.8)	11 (8.2–14.5)	<.001
Mid-arm circumference, cm			<.001
<21 cm	3 (0.79)	15 (21.43)
21–22 cm	20 (5.24)	9 (12.86)
≥22 cm	359 (93.98)	46 (65.71)
Calf circumference, cm			<.001
<31 cm	25 (6.54)	24 (34.29)
≥31 cm	357 (93.46)	46 (65.71)
Metabolic panels
Triglyceride (mg/dL)	149 (100–210)	140 (93–196)	.29
LDL (mg/dL)	127 (103–152)	123 (93–148)	.11
HDL (mg/dL)	48 (40–57)	51 (41–60)	.20
Insulin	6.3 (4.7–9.9)	5.7 (3.7–10.4)	.16
HIV parameters (PLHIV only, n = 349)
Nadir CD4 cell count (cells/mm³)	177 (80–261)	197 (114–237)	.51
Current CD4 cell count (cells/mm³)	616 (489–804)	633 (451–790)	.68
CD4/CD8 ratio	0.96 (0.69–1.29)	1.01 (0.73–1.24)	.50
Duration of HIV (years)	18.2 (14.9–20.9)	19.0 (15.8–20.9)	.72
Duration on ART (years)	16.1 (12.4–19.0)	16.1 (13.9–19.1)	.65
ART regimen			.54
NNRTI-based regimen	156 (54.55)	37 (58.73)
PI-based regimen	90 (31.47)	21 (33.33)
ART exposure
Ever exposed to PI	209 (73.08)	51 (80.95)	.19
Ever exposed to NNRTI	238 (83.22)	55 (87.30)	.42
Ever exposed to Lopinavir	122 (42.66)	27 (42.86)	.98
Ever exposed to Indinavir	81 (28.32)	17 (26.98)	.83
Ever exposed to Stavudine	175 (61.19)	42 (66.67)	.42
hs-CRP levels, mg/dL (median, IQR)	1.3 (0.6–2.8)	1.1 (0.5–2.0)	.05
Hemoglobin, g/dL	14.2 (13–15.2)	13.5 (12.3–14.9)	.039
25 (OH) D, ng/mL	23.2 (18.15–28.1)	25.4 (21.7–29.2)	.09
Albumin, g/dL	4.3 (3.9–4.5)	4.4 (4–4.6)	.17
TSH, mU/L	1.5 (1.03–2.19)	1.52 (0.96–2.23)	.99
Calcium, mg/dL	8.9 (8.5–9.3)	8.8 (8.5–9.2)	.43
Phosphate, mg/dL	3.3 (2.9–3.7)	3.4 (3–3.7)	.74

IQR, interquartile range; ART, antiretroviral therapy; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; TSH, thyroid-stimulating hormone; 25 (OH) D, 25 hydroxyvitamin D.

PLHIV50+ had been taking ART for a median of 16.1 (IQR, 12.6–19.1) years. All PLHIV had HIV RNA <50 copies/mL with current CD4 cell count of 620 (IQR, 483–803) cells/mm³; approximately half (55%) were taking NNRTI-based regimens. Body compositions of PLHIV were significantly different from those of HIV-uninfected participants. PLHIV had lower BMI [median, 23.1 (IQR, 20.8–25.2) vs. 25.3 (22.3–28.7) kg/m², p < .001], lower fat percent [median, 22.8% vs. 26.3%, p < .001], and lower fat mass [median, 14.2 vs. 16.9 kg, p < .001] compared with their HIV-uninfected counterparts.

Prevalence of abnormal nutritional status

According to MNA, 382/452 (85%) participants had normal nutrition status, 65 (14%) were at risk of malnutrition, and only 5 (1%) were malnourished. PLHIV had significantly higher abnormal nutritional status (18.1% vs. 6.8%, p = .005), compared with HIV-uninfected individuals. The answers to each question in the MNA by nutritional group are shown in Supplementary Table S1.

Participants with abnormal nutritional status were more likely to be older (median [maximum] age, 55.6 [74] vs. 54.7 [81] years, p = .03), have lower BMI (median, 20.6 vs. 23.9 kg/m², p < .001), lower waist to hip ratio (median, 0.9 vs. 0.93, p = .004), lower fat percentage (median, 20.1% vs. 23.7%, p = .003), lower fat mass (median, 11.0 vs. 16.0 kg, p < .001), and lower hemoglobin (median, 13.5 vs. 14.2 mg/dL, p = .04) than those who were normal nutritional status.

There were no significant differences in the levels of serum vitamin D (25(OH) D) (median, 23.4 ng/mL, p = .09), calcium (median, 8.9 mg/dL, p = .43), albumin (median, 1.5 g/dL, p = .17), and phosphate (median, 3.3 mg/dL, p = .74) between the nutritional status groups.

More details of MNA scores are provided in the Supplementary Table S1. In the questionnaire, we found that a higher proportion of participants at risk for malnutrition had lost weight in the past 3 months (Question B), had presence of neuropsychological problems (Question D), had lower current BMI (question F), were taking more than three prescription drugs per day (question H), were eating less meals (question J), were eating less protein a day (question K), were less likely to think they had no nutritional problems (question O), and had lower mid-arm and calf circumference (questions Q and R).

Association with abnormal nutritional status

In the multivariate logistic regression model, after adjusting for sex, older age (adjusted odds ratio [aOR], 1.06, 95% confident interval [CI]: 1.01–1.12, p = .03), positive HIV status (aOR, 2.67, 95% CI: 1.07–6.65, p = .036), or DM (aOR, 2.21, 95% CI: 1.003–4.87, p = .049) were independently associated with abnormal nutritional status (Table 2). As BMI or fat mass increased, the odds of having abnormal nutrition decreased (fat mass per 1 kg increase, aOR, 0.70, 95%CI: 0.57–0.86, p < .001, and BMI per 1 kg increase, aOR, 0.63, 95% CI: 0.51–0.78, p < .001).

Table 2.

Univariate and Multivariate Analysis for Factors Associated with Malnutrition

Covariates	Univariate		Multivariate
Covariates	OR (95%CI)	p-value	aOR (95%CI)	p-value
Female vs. male	1.4 (0.9–2.4)	.2	1.66 (0.68–4.06)	.27
Age per 1 year increase	1.1 (1.0–1.1)	.001	1.06 (1.01–1.12)	.033
HIV-positive vs. negative	3.0 (1.3–6.8)	.008	2.67 (1.07–6.65)	.036
Current smoking	0.99 (0.6–1.7)	.9
Current alcohol drinking	1.2 (0.7–2.1)	.6
FIB-4 score >1.45	1.8 (1.02–3.2)	.042	0.85 (0.39–1.86)	.69
HBV coinfection	0.6 (0.2–1.5)	.3	0.45 (0.13–1.58)	.21
HCV coinfection	0.9 (0.3–2.3)	.8
Diabetes mellitus	1.7 (0.9–3.2)	.08	2.21 (1.003–4.87)	.049
Hypertension	1.5 (0.9–2.6)	.1
Hs-CRP level (per 1 mg/dL increase)	0.96 (0.9–1.0)	.3
eGFR <60 mL/min (CKD-EPI)	2.01 (0.9–4.5)	.09
Body mass index (per 1 kg/m² increase)	0.79 (0.7–0.9)	<.001	0.63 (0.51–0.78)	<.001
Fat percentage (per 1% increase)	0.95 (0.92–0.98)	.003
Fat mass (per 1 kg increase)	0.90 (0.86–0.95)	<.001	0.70 (0.57–0.86)	<.001
Hemoglobin level (per g/dL increase)	0.8 (0.7–0.9)	.02	0.86 (0.69–1.08)	.20
Waist circumference (per cm increase)	0.94 (0.91–0.97)	<.001
25(OH) D level >20	1.4 (0.8–2.6)	.23	1.47 (0.72–3.02)	.29

Fibrosis-4 (FIB-4) score was calculated by using the equation: Age (years) × AST (U/L)/[Platelet(10⁹/L) × ALT^1/2 (U/L)].

p-values in bold represent significant variables from the multivariate analysis.

Discussion

In this cross-sectional study, we assessed nutritional status in virologically suppressed older PLHIV and HIV-uninfected matched controls to identify nutritional problems using the MNA, designed specifically for older adults. The prevalence of abnormal nutritional status was found to be ∼15% in our study (18% for PLHIV and 7% for HIV negative). The factors independently associated with abnormal nutritional status were older age, lower BMI, lower fat mass, having DM, and being HIV positive.

The prevalence of abnormal nutrition in our study is different from the previous studies in the general population.^{18

–26} Our results showed that 14% of participants were at risk of malnutrition, and only 1% were malnourished, while several other studies demonstrated that the proportion of the aging population suffering from undernutrition varied from 1% to 32% and about 30%–70% of the participants were at risk of malnutrition even in resource-limited settings or resource-adequate setting countries.^{18

–26} Of note, our HIV-negative matched controls also had lower prevalence of abnormal nutritional status compared with previously published studies from the general Thai elderly population.^16,27

In contrast to a cross-sectional study previously conducted in general geriatric outpatients in Thailand,¹⁵ the older adults of the current study were less likely to be malnourished (1% vs. 8%). The discrepancy with our study may be related to participant characteristics, since the former study included participants with more advanced age (the median age 77.5 years), higher comorbidity index, and a greater number with functional dependency. However, our result was comparable with a study from Hong Kong, which reported the prevalence of malnutrition as 1.1%.²⁶ Another explanation may relate to the ability of the tool to capture nutritional status in those at the lower end of the BMI range in different settings, since the median BMI in our normal versus at-risk populations were 23.9 and 20.6 kg/m², respectively.

The MNA assessments included not only weight loss, dietary habits, and body measurements (BMI, mid-arm, and calf circumference) but also neuropsychiatric components. In the current study, participants who were at risk of malnutrition or malnourished had lower scores in those categories than those with normal nutritional status. Hence, it has the potential to capture not only nutrition (which reflects micro- and macronutrient intake) but also the general wellbeing of the elderly.

Our study had also explored factors associated with abnormal nutrition status and found that increasing age was independently associated with malnutrition and at risk of malnutrition. This finding is consistent with several studies that reported an increasing prevalence of malnutrition among study participants with older ages.^28,29 Reduced nutrient and energy intakes may increase the occurrence of undernutrition in this population.³⁰ Poor nutrition status may be related to clinical risk factors other than age, such as underlying diseases and comorbidities.²⁹ Our study also demonstrated that DM was a risk factor for abnormal nutritional status. This is consistent with other studies conducted among elderly populations in Iran and China.^22,31 This finding may be explained by malnutrition-related impaired glucose metabolism. Chronic malnutrition is known to impair both glucose tolerance and pancreatic beta cell function, which could increase the susceptibility of the individual to other genetic and environmental diabetogenic influences.^32,33

Interestingly, our study revealed that PLHIV were more likely to have abnormal nutrition status than matched HIV-uninfected participants (18% vs. 7%). Our finding is consistent with previous reports, which suggests that compared with health controls, PLHIV are more likely to become malnourished at any point in their illness.^34,35 This is most likely related to the consequences of the infection, which result in increasing energy requirements, imbalanced nutritional intake, poor absorption of nutrients, and changes in the metabolism of nutrients.^28,36
–38 Several studies report that asymptomatic PLHIV appear to have higher energy requirements to maintain body weight and physical activity, compared with the general population.^39

–43

PLHIV participants in our study were at higher risk of malnutrition due to the lower BMI, lower percent fat, and lower fat mass, compared with HIV-negative participants. However, HIV-positive status was still independently associated with abnormal nutrition status after adjusting for BMI and fat mass in the multivariate model. Additionally, PLHIV are more likely to have coinfections, such hepatitis C virus infection and multiple metabolic syndromes such as diabetes and CVDs that may be also related to long-term ART toxicities^44,45; however, little is known regarding the role of nutritional status for the risks of comorbidities among PLHIV. Although higher fat mass and higher BMI showed protective effects against abnormal nutrition accessed by MNA score, one might argue that these could be the risk factors for cardiovascular diseases and other comorbidities (given the association of diabetes with abnormal nutrition in our study).

One of the strengths of our study was using a simple but multidimensional MNA tool to assess nutrition status, and use of commonly available assessments, such as BMI, BIA, and blood biomarkers. The frequency matching design for age and sex between PLHIV and HIV-negative participants was used to ensure our study groups were comparable.

Our study has several limitations that should be acknowledged. First, a cross-sectional study design cannot explore changes in nutritional status over a period of time, and therefore cannot determine the cause and effect relationships between malnutrition and associated factors. Second, our study did not assess levels of micronutrients, which are essential for maintaining proper immunologic function such as zinc, selenium, copper, and iron.^46

–50 Third, subjective and recall bias in the questions in the self-administrative survey could over/underestimate the results, although many factors in the questionnaire are also objective measurements. Finally, our study may not be representative of PLHIV with poorly controlled viral suppression, since participants with HIV viremia were excluded from our analysis. Moreover, our study was conducted among elderly groups in a clinical research center, therefore, our results may be less generalizable to younger PLHIV or other resource-limited settings in Asia. However, our study is likely relevant to PLHIV50+ in long-term HIV care on suppressive ART.

Conclusion

In conclusion, abnormal nutritional status was found in almost one-fifth of adult PLHIV50+ in our study. Malnutrition status affects not only individual health outcomes, but also could be a significant determinant for higher health care costs in an aging population. Our results suggest that nutritional status among PLHIV should be routinely screened, and education and counseling on nutrition should be provided to those who have low BMI and have comorbidities such as DM, to help them maintain adequate nutritional status and prevent adverse clinical outcomes.

Footnotes

Author Contributions

T.A., W.M.H., S.J.K., S.G., A.S., A.A., K.R., P.C., and S.S. contributed to design and concept of the study analysis. S.S., A.S., S.P., and T.U. conducted the MNA survey, data collection, and patient care. T.A., W.M.H., S.J.K., and A.A. contributed to analysis and interpretation of the data. T.A. and W.M.H. performed statistical analysis. T.A. wrote the first draft of the article. W.M.H., A.A., S.J.K. critically reviewed the article. All of the coauthors contributed to interpreting the findings and revised the article. All authors read and approved the final article.

Author Disclosure Statement

K.R. received honoraria or consultation fees from Merck, Roche, Jensen-Cilag, Johnson & Johnson, Mylan, and GPO (governmental pharmaceutical organization, Thailand); participated in a company-sponsored speaker's bureau from Abbott, Gilead, Bristol-Myers Squibb, Merck, Roche, Jensen-Cilag, ViiV Healthcare, and GPO (governmental pharmaceutical organization); received Chulalongkorn Academic Advancement into its 2nd Century Project (CUAASC). A.A. received honoraria or consultation fees from ViiV Healthcare. The rest of the authors declare that they do not have any conflicts of interest.

Funding Information

This study was supported by the Government Research Budget year 2015, grant code GRB-APS-12-58-30-09 and year 2016, and grant code GRB-APS-04-59-30-03.

Supplementary Material

Supplementary Table S1

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