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Nutritional assessment among adult patients with suspected or confirmed active tuberculosis disease in rural India

  • Elaine A. Yu,

    Roles Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing

    Affiliation Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America

  • Julia L. Finkelstein,

    Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America

  • Patsy M. Brannon,

    Roles Writing – original draft, Writing – review & editing

    Affiliation Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America

  • Wesley Bonam,

    Roles Conceptualization, Writing – original draft, Writing – review & editing

    Affiliation Arogyavaram Medical Centre, Madanapalle, Andhra Pradesh, India

  • David G. Russell,

    Roles Writing – original draft, Writing – review & editing

    Affiliation Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America

  • Marshall J. Glesby,

    Roles Writing – original draft, Writing – review & editing

    Affiliation Weill Cornell Medical College, New York, New York, United States of America

  • Saurabh Mehta

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing – review & editing

    Affiliations Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America, Institute for Nutritional Sciences, Global Health, and Technology, Cornell University, Ithaca, New York, United States of America

Nutritional assessment among adult patients with suspected or confirmed active tuberculosis disease in rural India

  • Elaine A. Yu, 
  • Julia L. Finkelstein, 
  • Patsy M. Brannon, 
  • Wesley Bonam, 
  • David G. Russell, 
  • Marshall J. Glesby, 
  • Saurabh Mehta



Our study goal was to evaluate a set of nutritional indicators among adults with confirmed or suspected active tuberculosis disease in southern India, given the limited literature on this topic. Study objectives were to assess the: I) double burden of malnutrition at individual and population levels; II) relative performance of anthropometric indicators (body mass index, waist circumference) in diabetes screening; and III) associations between vitamin D and metabolic abnormalities.


Cross-sectional study.


Hospital in rural southern India.


Among adult patients (n = 834), we measured anthropometry, body composition, and biomarkers (vitamin D, glycated hemoglobin, hemoglobin) of nutritional status. Subsets of participants provided blood and sputum samples.


Among participants, 91.7% had ≥ 1 malnutrition indicator; 34.6% had both undernutrition and overnutrition indicators. Despite the fact that >80% of participants would be considered low-risk in diabetes screening based on low body mass index and waist circumference, approximately one-third had elevated glycated hemoglobin (≥ 5.7%). The lowest quintile of serum 25-hydroxyvitamin D was associated with an increased risk of glycated hemoglobin ≥ 5.7% (adjusted risk ratio 1.61 [95% CI 1.02, 2.56]) compared to the other quintiles, adjusting for age and trunk fat.


Malnutrition and diabetes were prevalent in this patient population; since both can predict poor prognosis of active tuberculosis disease, including treatment outcomes and drug resistance, this emphasizes the importance of dual screening and management of under- and overnutrition-related indicators among patients with suspected or active tuberculosis disease. Further studies are needed to determine clinical implications of vitamin D as a potential modifiable risk factor in metabolic abnormalities, and whether population-specific body mass index and waist circumference cut-offs improve diabetes screening.


Mycobacterium tuberculosis (M. tb) causes the greatest number of deaths worldwide, compared to any other single infectious agent [1]. According to the World Health Organization (WHO), there were 1.7 billion people with latent tuberculosis (TB) infection, 10.0 million incident cases of active TB disease, and 1.2 million TB-related deaths in 2018 [1]. In India, there were nearly 2.7 million incident cases of active TB disease and 449,000 TB-related deaths in 2018 [1]. The burden of disease from active TB disease disproportionately affects low- and middle-income countries [2, 3]. Over 95% of reported TB cases were in 119 low- and middle-income countries [2].”

Active TB disease is associated with malnutrition risk factors, including undernutrition and overnutrition-related clinical sequelae (e.g. diabetes) [27]. Undernutrition and diabetes, respectively, are prevalent among patients with active TB disease, and increase the risk of progressing to active TB disease as well as worse TB treatment outcomes, including greater risk relapse and drug resistance [57]. Putative mechanisms at this complex nexus of undernutrition, diabetes, and active TB disease include that: inadequate nutrients adversely affect cell-mediated immunity, which is necessary in the human host response against M. tb; [8] active TB disease may alter metabolic processes, including increasing energy requirements and loss of appetite [7].

Nutritional screening, assessment, and management are recommended for all patients with TB during diagnosis, treatment, and management of active TB disease [7, 9]. However, there have been a limited number of comprehensive evaluations of nutritional status among patients with active TB disease in many resource-limited settings. Our study goal was to assess a panel of nutritional indicators among a patient population with confirmed or suspected active TB disease. Our three study objectives were based on prior literature. First, we assessed the prevalence of double burden of malnutrition (under- and over-nutrition indicators) at both the individual and population levels. Second, we evaluated the relative performance of anthropometric indicators (body mass index [BMI], waist circumference [WC]) in diabetes screening, compared to HbA1c. Last, we assessed the associations between vitamin D and metabolic abnormalities.

Materials and methods

Ethical conduct of research

The study protocol was designed in accordance with the Declaration of Helsinki principles, and approved by the Institutional Review Board at Cornell University and Institutional Ethics Committee at Arogyavaram Medical Centre. All study participants provided informed written consent to participate prior to data collection. Study participants had minimal risks or harms, largely associated with additional biological sample collection, aside from that associated with their routine clinical care.

Study population

This cross-sectional study included a convenient consecutive sample of patients at a hospital (Arogyavaram Medical Centre) in Andhra Pradesh, India. The clinic is part of a hospital with inpatient and outpatient facilities, which regularly provides services for patients with suspected or confirmed active TB disease. Hospital physicians referred their patients with suspected active TB disease to study staff. Study participants (n = 834) were recruited and sequentially enrolled during their hospital visits between September 2014 and May 2016. The total sample size was based on all participants who were enrolled during the window of data collection dates.

Data collection

Trained research assistants administered structured interviews to collect sociodemographic and clinical data in the local language, Telugu. Sociodemographic covariates included: age, sex, educational level, monthly household income, and cigarette use. A study physician conducted a complete examination, including blood pressure measurements. Data were collected based on the study protocol, including data management plans (e.g. quality control via skip patterns in electronic data collection forms, data confidentiality).

Subsets of study participants provided sputum and blood samples, based on their hospital visit and the recommendations of their physicians. Trained phlebotomists collected blood samples using standard clinical protocols. From this convenient sample, available blood samples were assayed for serum 25-hydroxyvitamin D (25[OH]D; n = 156) and glycated hemoglobin (HbA1c; n = 236). For active TB disease assessment (n = 363), patients provided a sputum sample at the time of his or her initial hospital visit, and a second sputum sample on the following morning.

Laboratory analyses

Each sputum sample was assessed for active TB disease by the detection of standard acid-fast bacilli (AFB) with Ziehl-Neelsen staining and conventional light microscopy. Blood samples were assayed for HbA1c (%) by high-performance liquid chromatography (D-10; Bio-Rad Laboratories, Hercules, California, United States [US]). Serum 25-hydroxyvitamin D (25[OH]D; nmol/L) was assayed by chemiluminescence immunoassay (LIAISON; DiaSorin Inc., Stillwater, Minnesota, US). We also participated in the D External Quality Assurance Scheme ( program; compared to the National Institute of Standards and Technologies target values, our median percentage difference was -8.8 (IQR -19.7, -4.4). Complete blood counts were assessed by an automated hematology analyzer (BC-2800; Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, People’s Republic of China).


Anthropometric measurements were recorded, based on World Health Organization (WHO) recommendations and other commonly utilized standard methods [10, 11]. Height, WC, mid-upper arm circumference (MUAC), and skinfold thickness were measured to the nearest 0.1 centimeter; weight was assessed to the nearest 0.1 kilogram. Total body and trunk fat (%) were assessed by bioelectrical impedance analysis (BC-418 MA; Tanita Corporation, Tokyo, Japan; 8 electrode).


Anthropometric measurements (BMI, WC) were considered as continuous and categorized variables. BMI was categorized per standard WHO cut-offs (underweight < 18.5 kg/m2; normal weight ≥ 18.5 and < 25.0 kg/m2; overweight ≥ 25.0 and < 30.0 kg/m2; and obese ≥ 30.0 kg/m2) [12]. Additionally, we considered BMI cut-offs for Asian populations (≥ 18.5 to < 23.0 kg/m2; ≥ 23.0 to < 27.5 kg/m2; ≥ 27.5 kg/m2), based on a WHO expert consultation [13]. WC was considered as a continuous variable and tertiles based on distribution in this population. Elevated WC was also defined based on the International Diabetes Federation (IDF) cut-offs (men ≥90 cm, women ≥80 cm) recommended for individuals in South Asia [14]. We calculated limb fat as the sum of body fat (kg) in all four limbs.

Hemoglobin (g/L) was considered as a continuous and categorical variables (including quintiles). Biologically implausible values were considered missing. Anemia and severe anemia were defined by hemoglobin cut-offs, per WHO recommendations (S1 Table) [15]. Hemoglobin was adjusted for smoking by subtracting 0.3 g/L among any individuals who self-reported as currently smoking [15]. We also considered other factors that affect anemia (e.g. pregnancy, residential elevation above sea level, smoking) [15]. However, no study participants self-reported pregnancy; and all study participants resided near the hospital study site, which had an elevation that did not require altitude adjustment [15].

Red blood cell morphology were categorized to assess abnormalities, which reflect different causes of anemia and commonly assist in evaluating different types of anemia [16]. Microcytosis (<80 femtoliters/cell) was defined as mean corpuscular volume (MCV; femtoliters/cell; S1 Table) [16]. Mean corpuscular hemoglobin (MCH; picograms/cell) was categorized as hyper-, normo-, and hypochromia (S1 Table) [16]. Hypochromic microcytic anemia was defined by hemoglobin (men <130 g/L, women <120 g/L) [15] as well as MCV <80 femtoliters/cell and MCH <27 picograms/cell (S1 Table) [16]. Elevated erythrocyte sedimentation rate (ESR; mm/hr) was categorized according to age- and sex-specific cut-off values (S1 Table) [17, 18]. Biologically implausible values were considered missing; additionally, statistical outliers (<2.5th and >97.5th percentiles) were excluded.

HbA1c was categorized by common cut-off values (≥ 6.5%, ≥ 5.7% to < 6.5%, and <5.7%) that are recommended as part of the clinical diagnostic criteria of diabetes and pre-diabetes [19, 20]. Active TB disease was defined as at least one positive AFB sputum smear result. This included patients with one positive AFB result (regardless of first or second sputum sample), as well as two positive AFB results, according to the standard active TB disease diagnostic guidelines in India [21]. Vitamin D status was defined with several cut-off values (25[OH]D <25.0 nmol/L [22], <40.0 nmol/L [23], <50.0 nmol/L [23, 24], <75.0 nmol/L [24]). Additionally, we categorized 25(OH)D (nmol/L) by quintiles.

Educational level was categorized based on self-reported completion of formal coursework (i.e., no formal education or illiterate; primary [grades 1–5], secondary [grades 6–12, including higher secondary], and any higher education [college, graduate, post-graduate]). Self-reported monthly household income was dichotomized as < 5000 Indian rupees (INR) or ≥ 5000 INR) [25]. This cut-off was rounded from the cut-off of 4860 INR, based on the estimated monthly consumption expenditure for a family of five residing in a rural area (2011–2012 prices) and the national poverty line of India (Government of India, Planning Commission, 2014 Report) [25]. Cigarette use was categorized as current, previous, or never.

Statistical analyses

Continuous variables were assessed for normality using the Shapiro-Wilk test statistic. For descriptive statistics, continuous variables were reported as medians (interquartile ranges [IQRs]); categorical variables were reported as percentages. Subgroup comparisons were based on tests for continuous (i.e., Kruskal-Wallis) and categorical variables (i.e., likelihood ratio test).

We assessed associations between anthropometric indicators and HbA1c with univariate and multivariate linear and binomial regressions. Associations between anthropometric screening indicators (BMI, WC) and HbA1c ≥ 6.5% were assessed by multivariate log-binomial regressions; key covariates (age, sex) were accounted for in these models. The predictive performance of anthropometric screening indicators (i.e., BMI, WC; categorical variables) for elevated HbA1c (≥ 6.5%) were assessed by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic (ROC) curve analysis [26]. Observed area under the ROC curves (AUCs) were compared to AUC for the null hypothesis (0.5), based on the contrast matrices of differences between the areas under the ROC curves (ROCCONTRAST in SAS statistical software) [27]. We also examined the predictive performance of anthropometric screening indicators, considering a cut-off of HbA1c ≥ 5.7% as the outcome of interest.

For the associations between vitamin D and metabolic abnormality outcomes, the selection of potential confounders was based on approaches suggested by Rothman and Greenland [28]. In brief, we identified known or suspected risk factors based on a priori literature review [29, 30]. For each association of interest, we included the confounders in the final adjusted model based on a 10% change-in-estimate criterion [28]. For binary outcomes, binomial regressions were utilized when models converged. Modified Poisson estimates were utilized if binomial models failed to converge [31]. The analytical subsets were as follows (for each respective outcome): WC (n = 150), HbA1c (n = 149), and blood pressure (n = 99); missing-data indicators were utilized for covariates with missingness [28].

Statistical analyses were conducted with SAS statistical software (version 9.4; SAS Institute Incorporated, Cary, North Carolina). All comparisons were two-sided; and considered statistically significant with an α value of 0.05.

Patient involvement

Prior to data collection, patients provided input regarding the structured interview questions; some of their feedback was integrated in the final data collection forms. These patients were individuals who visited and resided near a hospital in rural southern India, and the source population of our study.


Sociodemographic and clinical characteristics

The median age of study participants was 48 years (IQR 35, 60; Table 1), including individuals ranging from 18 to 87 years of age. Nearly three quarters of study participants were male (n = 610; Table 1). Over half of study participants (54.0%) had a self-reported monthly household income less than 5000 INR (Table 1). Among study participants with AFB assessment (n = 363), 24.2% had active TB disease (Table 1).

Table 1. Sociodemographic and clinical characteristics of study participants a.

Overall, median systolic blood pressure was 110 mmHg (IQR 100, 120); 11.1% of study participants had systolic blood pressure ≥ 140 mmHg. Median diastolic blood pressure was 70 mmHg (IQR 60, 80); 13.1% of patients had elevated diastolic blood pressure (≥ 90 mmHg). Eight percent of patients had abnormal blood pressure (either elevated systolic, diastolic, or both).


Per the WHO categories for BMI, 46.8% of men and 40.2% of women were considered underweight (BMI < 18.5 kg/m2; Table 2). Only 9.0% of men and 19.6% of women were considered overweight or obese (BMI ≥ 25.0; Table 2). Based on the alternative WHO BMI cut-offs recommended for Asian populations, [13] 83.1% of men and 69.2% of women were underweight or normal weight (BMI < 23.0 kg/m2; Table 2). Median WC was 70.3 cm (IQR 65.0, 80.1) among men and 66.9 cm (IQR 60.7, 77.5) among women (p<0.01; Table 2). Overall, 87.5% of study participants were below the IDF WC cut-off (men 90.7%, women 78.5%; p<0.01; Table 2). Additional anthropometry and body composition measurements are in S2 Table.

Table 2. Anthropometry among adults with suspected or confirmed active TB disease a.


Median hemoglobin concentrations were 115.0 g/L (IQR 99.0, 129.7; Table 3). Among study participants, 71.9% (n = 600) had anemia and 6.4% had severe anemia (n = 53; Table 3). The prevalence of anemia was 82.1% in women (n = 184), and 68.2% in men (n = 416; Table 3). Elevated ESR was observed in 59.0% of participants (n = 492; Table 3).

Glycemic status

Overall, 12.3% of study participants had HbA1c ≥ 6.5%; and 20.3% of participants had HbA1c ≥ 5.7% and < 6.5% (Table 3). Median HbA1c was 5.4% (IQR 5.0, 5.8; Table 3), and similar between men and women (p>0.05; Table 3).

Double burden of malnutrition

Nearly all (91.7%) study participants had at least one malnutrition indicator, based on BMI, anemia, MUAC, WC (Table 4). Among participants, 66.4% had at least two malnutrition indicators; 27.6% had three or more malnutrition indicators. Among study participants with both undernutrition and overnutrition indicators (34.6%; Table 4), 82.7% had HbA1c ≥ 5.7%, 96.0% had anemia, 45.3% were underweight, and 14.7% were overweight or obese.

Diabetes screening performance of anthropometric indicators

Sensitivity, specificity, PPV and NPV of anthropometric indicators (BMI [standard and alternative WHO categorizations], and WC [IDF cut-offs]) of HbA1c ≥ 6.5% are in Table 5. Considering BMI (standard WHO categories) as an indicator for HbA1c ≥ 6.5%, the AUC was 0.58 among all study participants (p = 0.25), sensitivity was 0.21 (95% CI: 0.06, 0.35), and specificity was 0.92 (95% CI: 0.88, 0.96; Table 5).

Table 5. Comparison of anthropometric (BMI, WC) screening cut-offs for HbA1c ≥ 6.5% a, b.

We also considered the predictive performance of anthropometric indicators for elevated HbA1c, using a cut-off of ≥ 5.7% as the outcome of interest (S3 Table). With BMI (standard WHO categories) as the indicator, the AUCs were: overall 0.49 [p = 0.76], men 0.54 [p = 0.58], women 0.57 [p = 0.17]). The AUC for WC were 0.57 (p = 0.07) among all study participants, 0.53 (p = 0.36) among men, and 0.61 (p = 0.09) among women.

WC (adjusted risk ratio [aRR] 1.03 [95% CI 1.01, 1.06]; continuous variable) was associated with HbA1c ≥ 6.5%, adjusting for age and sex. Similarly, individuals in the highest tertile of WC were positively associated with HbA1c ≥ 6.5% (Tertile 3 vs 1: aRR 2.87 [95% CI 1.19, 6.92]), compared to those in the lowest WC tertile, controlling for age and sex. Higher BMI (≥ 25.0 kg/m2) was positively associated with HbA1c (≥ 6.5%; p<0.01), accounting for age and sex.

Associations between vitamin D and metabolic abnormalities

Median serum 25(OH)D was 51.8 nmol/L (IQR 36.0, 70.0; Table 3). Across quintiles, the median 25(OH)D concentration was 24.9 nmol/L (IQR 19.1, 28.7), 38.8 nmol/L (IQR 35.4, 42.3), 51.7 nmol/L (IQR 49.0, 55.2), 67.5 nmol/L (IQR 63.1, 70.0), and 82.9 nmol/L (IQR 76.9, 102.1).

25(OH)D < 50 nmol/L was associated with HbA1c (%; p = 0.04), adjusting for age and fat free mass (Table 6). The lowest quintile of serum 25(OH)D was associated with an increased risk of HbA1c ≥ 5.7% (aRR 1.61 [95% CI 1.02, 2.56]) compared to the other quintiles, adjusting for age and trunk fat (Table 6). However, the other associations that were assessed between 25(OH)D (continuous, <50 nmol/L) were not associated with HbA1c (continuous, ≥ 6.5% or ≥ 5.7%; p>0.05; Table 6).

Table 6. Serum 25-hydroxyvitamin D and glycated hemoglobin (n = 149).

25(OH)D was not associated with elevated WC (above the IDF cut-off values) in multivariate linear and binomial (or Poisson) regression models (p>0.05; S4 Table). The considered associations between 25(OH)D (continuous, <50 nmol/L) were not associated with blood pressure, including systolic (continuous, elevated), diastolic (continuous, elevated), and abnormally high blood pressure (p>0.05; S5 Table).


Our results showed a high prevalence of malnutrition among a patient population with recently confirmed or suspected active TB disease. Over 90% of participants had at least one malnutrition indicator; one in three participants had both undernutrition and overnutrition indicators. Despite the fact that more than 80% of participants would be considered low-risk in diabetes screening, based on low BMI and WC, approximately one-third had elevated HbA1c (≥ 5.7%). Common cut-off values for anthropometry (overnutrition indicators) had suboptimal predictive performance in detecting elevated HbA1c among an adult outpatient population with a high prevalence of low BMI. Findings suggest the need for population-specific cut-offs for BMI and WC, given that each remained respectively associated with HbA1c. Lastly, vitamin D status and HbA1c were inversely associated.

Undernutrition and elevated HbA1c among patients with suspected or confirmed active tuberculosis disease

The prevalence of underweight (45.0%; BMI <18.5 kg/m2) in our study population was substantially higher than prior South Asia (<25.0%) and global (<12.5%) estimates for adults [32], though other estimates among patients with active TB disease in India have ranged widely. In a study among individuals with active TB disease in Chhattisgarh, over 80% of men and 90% of women had BMI < 18.5 kg/m2 prior to anti-TB treatment [33], which was higher than among our participants. Another study in Karnataka found that 30% of patients with pulmonary or extrapulmonary active TB disease had BMI <18.4 kg/m2 [34]. Our finding that 2.8% of study participants had BMI ≥ 30 kg/m2 was lower than WHO obesity estimates for India (4.9%) and globally (11% men, 15% women) [35].

Based on the WHO classification, the public health significance of anemia in our study population (82.1% women; 68.2% men) is considered severe [15]. A previous global estimate of anemia prevalence was 24.8% [36], which is less than half the proportion observed among our study participants with suspected or confirmed active TB disease. Moreover, in contrast to the global estimate of anemia among women of reproductive age (29.4% (95% CI: 24.5, 35.0) [37], four of every five females in our study had anemia. Previous literature has corroborated the anemia of inflammation, including in active TB disease [38], however nationally representative estimates of anemia prevalence among people with active TB disease are limited, particularly in low- and middle-income countries.

Globally, nearly one of every ten (8.5%) adults has diabetes [39]. In the WHO South-East Asia region, diabetes prevalence among adults is 8.6% [39]. Among our study participants, 12.3% had elevated HbA1c ≥6.5%, despite the high prevalence of people with BMI <25 kg/m2. Our results confirmed a high prevalence of malnutrition among patients with confirmed or suspected active TB disease; over nine of every ten study participants had at least one indicator of malnutrition. Moreover, approximately one-third of study participants had indicators of both undernutrition and overnutrition, suggesting these patients are affected by the double burden of malnutrition at the individual-level.

Relative performance of anthropometric indicators in diabetes screening

Higher BMI (overweight and obesity) and WC are well-established modifiable risk factors of type 2 diabetes mellitus (T2DM) [14, 20, 3942]. The respective associations between BMI and WC with elevated HbA1c have been confirmed in several studies among populations in North America [41, 43] and Asia (India [44, 45], China [46]). One US study among Mexican Americans found an 11 times increased risk of non-insulin dependent diabetes mellitus among those with WC in the highest quartile, compared to the lowest quartile [43]. Despite the high prevalence of low BMI in our study population, BMI and WC similarly were associated with elevated HbA1c.

International and national public health entities, including the WHO [39] and IDF [14], recommend common cut-off values of elevated BMI and WC that identify individuals at risk for T2DM. However, the observation of higher diabetes prevalence among populations with lower mean BMI has instigated the question of whether population-specific cut-off values of anthropometric indicators would be more appropriate for diabetes screening in some populations [13, 4750], such as India.

Previous studies have shown a wide heterogeneity of predictive performance of anthropometric indicators in diabetes and pre-diabetes screening. For example, in one US study among 12,814 adults (African American, white), areas under the ROC curves were similar for BMI (African American men 0.69, white men 0.70; African American women 0.66, white women 0.72) and WC (African American men 0.70, white men 0.70; African American women 0.69, White women 0.73) in predicting diabetes [51].

A growing body of evidence suggests research gaps and limitations in the predictive performance of commonly used anthropometric indicator cut-offs for T2DM screening. First, the heterogeneity of body fat distribution is hypothesized to affect T2DM risk [52], which could cause common cut-points of anthropometric screening indicators for diabetes to perform worse (more false negatives or positives) in some populations. Central obesity as well as visceral fat have been more strongly associated with insulin resistance and T2DM, relative to overall obesity [5355]. Studies have demonstrated that individuals with similar BMI sometimes differ substantially in body fat distribution and percentage [50, 56, 57], metabolic syndrome [58], T2DM [59, 60]. As an example, the predisposition for central fat accumulation among Asian populations has been observed to differ from Caucasians [61], which could explain differential T2DM risk among individuals with the same BMI [13].

Second, studies have begun elucidating the biological basis for these observed patterns. At the cellular level, functional metabolic differences between adipocytes (brown, white, and beige [brown in white]) have been characterized [6264]. Critically, studies have shown metabolically active brown adipocytes associated with improved T2DM indicators and lower BMI [63, 65]; in contrast, white adipocytes were associated with visceral fat, which has been linked to insulin resistance [66].

Successfully addressing the diabetes epidemic requires considering effective screening among populations with different body composition patterns. Overall in our study, common cut-off values for anthropometric screening indicators had suboptimal predictive performance in detecting elevated HbA1c among an adult outpatient population with lower adiposity. Findings suggest the need for population-specific cut-offs for BMI and WC, given that: 1) each remained respectively associated with elevated HbA1c; and 2) standard cut-offs misclassify the HbA1c status of many study participants.

Although our results reveal several research gaps, the importance and challenges of determining appropriate population-specific cut-off values of anthropometric indicators have been acknowledged in previous literature [4750] and by a WHO expert consultation [13]. Future research questions include: What are the appropriate cut-offs across racial and ethnic subgroups, based on representative samples with external validity? How do different fat distributions (including differing body fat percentage and adipocyte type) affect the risk of T2DM incidence and severity? What are the cellular mechanisms involving different adipocyte types that contribute to T2DM development and progression?

Vitamin D as a modifiable risk factor of metabolic indicators

Previous literature has found an inverse association between vitamin D status (25[OH]D concentration) and HbA1c [67, 68], which was consistent with our result. In a nationally representative study among adults in the US, the prevalence of high HbA1c (>6.0%) linearly decreased across vitamin D quintiles (p<0.01) [68]. Separately, systematic reviews have shown vitamin D supplementation was associated with HbA1c in some studies [69, 70]. Although other studies observed null results [7173], many differed widely in methodology, including vitamin D dosage (duration, frequency, dosage).

Our finding that serum 25(OH)D was inversely associated with WC, which has been corroborated by other studies [7476]. At the cellular level, other key findings that support epidemiological findings include the: a) isolation of vitamin D receptor (VDR) as well as hydroxylating enzymes of vitamin D in adipose tissues; b) storage and release of vitamin D in adipocytes [7782]. Many questions remain, in order to elucidate the etiology and mechanisms of 25(OH)D in the context of adiposity and energy homeostasis [83], including the: extent of differences in vitamin D metabolism (e.g. VDR signaling, vitamin D activation:inactivation ratio, interactions with lipid-mediated regulatory processes such as via peroxisome proliferator-activated receptor gamma) across adipocyte types and heterogeneous body composition.

While our study demonstrated a null association between low 25(OH)D concentration and high systolic blood pressure, prior literature has supported an inverse association between 25(OH)D and the renin-angiotensin-aldosterone-system (RAAS), which regulates hypertension [8486]. One hypothesized mechanism is that elevated vitamin D inhibits renin and angiotensin expression, which dampens the RAAS activity and subsequently decreases blood pressure [8588]. Additionally, an overview of systematic reviews of vitamin D supplementation randomized controlled trials found that among nine meta-analyses, two showed protective effects of vitamin D supplements on blood pressure and six had null findings [89].

Strengths and limitations

In our study, there were several strengths, including the: sample size, assessment of multiple BMI and WC categories (based on widely used cut-off values and population distribution [quantiles]), evaluation of microcytosis and hypochromia.

This study had several limitations, including the: cross-sectional study design (with a single timepoint assessment); potential residual confounding; external validity (generalizability of findings, especially among healthy populations); assessment of additional causes of low hemoglobin; biological samples obtained per standard of care (only from participants with a clinical indication and not collected at random or from all participants); and limited biomarker data (such as diagnoses of human immunodeficiency virus [HIV] and no peripheral smears to determine iron deficiency anemia). Active TB disease and diabetes have bi-directional impacts that we were not able to assess, particularly as we only measured HbA1c and not diabetes mellitus [90]. Iron deficiency with and without anemia has been associated with increased HbA1c [91], and this potential interaction needs be evaluated in this study population.


In summary, our findings confirmed that malnutrition and elevated HbA1c were prevalent among this patient population with suspected and confirmed active TB disease in rural India. Dual screening and management of under- and overnutrition-related indicators are needed among patient populations with confirmed and suspected active TB disease, in order to facilitate improved TB control efforts. Further studies are needed to determine any clinical implications of the potential role of vitamin D as a modifiable risk factor in metabolic abnormalities, as well as whether population-specific BMI and WC cut-offs are needed among specific populations (e.g. metabolically unhealthy normal or underweight).

Supporting information

S1 Table. Definitions of anemia and related red blood cell indices.


S2 Table. Additional anthropometric indicators among men and women.


S3 Table. Comparison of anthropometric (BMI, WC) screening cut-offs for HbA1c ≥ 5.7%.


S4 Table. Serum 25-hydroxyvitamin D and waist circumference (n = 150).


S5 Table. Serum 25-hydroxyvitamin D and blood pressure (n = 99).



  1. 1. World Health Organization. Global tuberculosis report 2019. Geneva: World Health Organization; 2019.
  2. 2. World Health Organization. Global tuberculosis report 2018. Geneva: World Health Organization; 2018.
  3. 3. Lonnroth K, Roglic G, Harries AD. Improving tuberculosis prevention and care through addressing the global diabetes epidemic: from evidence to policy and practice. Lancet Diabetes & Endocrinology. 2014;2(9):730–9.
  4. 4. Dooley KE, Chaisson RE. Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infectious Diseases. 2009;9(12):737–46. pmid:19926034
  5. 5. Jeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Medicine. 2008;5(7):e152. pmid:18630984
  6. 6. Odone A, Houben RM, White RG, Lonnroth K. The effect of diabetes and undernutrition trends on reaching 2035 global tuberculosis targets. Lancet Diabetes & Endocrinology. 2014;2(9):754–64.
  7. 7. World Health Organization. Guideline: Nutritional care and support for patients with tuberculosis. Geneva: World Health Organization; 2013.
  8. 8. Cegielski JP, McMurray DN. The relationship between malnutrition and tuberculosis: evidence from studies in humans and experimental animals. International Journal of Tuberculosis and Lung Disease. 2004;8(3):286–98. pmid:15139466
  9. 9. Government of India (Central TB Division) and World Health Organization. Guidance document: Nutritional care and support for patients with tuberculosis in India. New Delhi: World Health Organization; 2017.
  10. 10. Gibson RS. Principles of nutritional assessment. Oxford university press, USA; 2005.
  11. 11. World Health Organization. Physical status: The use of and interpretation of anthropometry. Report of a WHO Expert Committee. Geneva: World Health Organization; 1995.
  12. 12. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. WHO Technical Report Series 894. Geneva: World Health Organization; 2000.
  13. 13. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–63. pmid:14726171
  14. 14. Alberti KG, Zimmet P, Shaw J. International Diabetes Federation: a consensus on Type 2 diabetes prevention. Diabetic Medicine. 2007;24(5):451–63. pmid:17470191
  15. 15. World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva: World Health Organization; 2011.
  16. 16. Kasper DL, Harrison TR. Harrison’s principles of internal medicine. New York: McGraw-Hill Professional; 2005.
  17. 17. Bottiger LE, Svedberg CA. Normal erythrocyte sedimentation rate and age. BMJ. 1967;2(5544):85–7. pmid:6020854
  18. 18. McPherson R, Pincus M. Henry’s clinical diagnosis and management by laboratory methods (21st edition). Elsevier Inc; 2007.
  19. 19. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. Geneva: World Health Organization; 2011.
  20. 20. American Diabetes Association. 2. Classification and diagnosis of diabetes. Diabetes Care. 2017;40(Suppl 1):S11–s24. pmid:27979889
  21. 21. Government of India (Central Tuberculosis Division). Technical and operational guidelines for TB control in India 2016. 2016.
  22. 22. Scientific Advisory Council on Nutrition. Vitamin D and health. Crown; 2016.
  23. 23. Del Valle HB, Yaktine AL, Taylor CL, Ross AC. Dietary reference intakes for calcium and vitamin D: National Academies Press; 2011.
  24. 24. Holick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, et al. Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. Journal of Clinical Endocrinology and Metabolism. 2011;96(7):1911–30. pmid:21646368
  25. 25. Government of India (Planning Commission). Report of the expert group to review the methodology for measurement of poverty. New Delhi: Government of India; 2014.
  26. 26. Greiner M, Pfeiffer D, Smith R. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine. 2000;45(1):23–41.
  27. 27. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. pmid:3203132
  28. 28. Rothman K, Greenland S. Modern epidemiology (2nd ed). Philadelphia: Lippincott Williams & Wilkins; 1998.
  29. 29. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA. 2002;287(3):356–9. pmid:11790215
  30. 30. Holick MF. Vitamin D: physiology, molecular biology, and clinical applications. Springer Science & Business Media; 2010.
  31. 31. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. American Journal of Epidemiology. 2005;162(3):199–200. pmid:15987728
  32. 32. NCD Risk Factor Collaboration. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. pmid:29029897
  33. 33. Bhargava A, Chatterjee M, Jain Y, Chatterjee B, Kataria A, Bhargava M, et al. Nutritional status of adult patients with pulmonary tuberculosis in rural central India and its association with mortality. PLoS One. 2013;8(10):e77979. pmid:24205052
  34. 34. Pande T, Huddart S, Xavier W, Kulavalli S, Chen T, Pai M, et al. Prevalence of diabetes mellitus amongst hospitalized tuberculosis patients at an Indian tertiary care center: A descriptive analysis. PLoS One. 2018;13(7):e0200838. pmid:30021016
  35. 35. World Health Organization. Global status report on noncommunicable diseases 2014. Geneva: World Health Organization; 2014.
  36. 36. de Benoist B, McLean E, Egli I, Cogswell M. Worldwide prevalence of anaemia 1993–2005. WHO global database on anaemia. Geneva: World Health Organization; 2008.
  37. 37. World Health Organization. The global prevalence of anaemia in 2011. Geneva: World Health Organization; 2015.
  38. 38. Ganz T. Anemia of inflammation. New England Journal of Medicine. 2019;381(12):1148–57. pmid:31532961
  39. 39. World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.
  40. 40. Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. New England Journal of Medicine. 2001;345(11):790–7. pmid:11556298
  41. 41. Narayan KM, Boyle JP, Thompson TJ, Gregg EW, Williamson DF. Effect of BMI on lifetime risk for diabetes in the U.S. Diabetes Care. 2007;30(6):1562–6. pmid:17372155
  42. 42. Vazquez G, Duval S, Jacobs JDR, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A Meta-Analysis. Epidemiologic Reviews. 2007;29(1):115–28.
  43. 43. Wei M, Gaskill SP, Haffner SM, Stern MP. Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans—a 7-year prospective study. Obesity Research. 1997;5(1):16–23. pmid:9061711
  44. 44. Viswanathan V, Kumpatla S, Aravindalochanan V, Rajan R, Chinnasamy C, Srinivasan R, et al. Prevalence of diabetes and pre-diabetes and associated risk factors among tuberculosis patients in India. PLoS One. 2012;7(7):e41367. pmid:22848473
  45. 45. Mamtani MR, Kulkarni HR. Predictive performance of anthropometric indexes of central obesity for the risk of type 2 diabetes. Archives of Medical Research. 2005;36(5):581–9. pmid:16099342
  46. 46. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. New England Journal of Medicine. 2010;362(12):1090–101. pmid:20335585
  47. 47. Lear SA, Humphries KH, Kohli S, Birmingham CL. The use of BMI and waist circumference as surrogates of body fat differs by ethnicity. Obesity (Silver Spring, Md). 2007;15(11):2817–24.
  48. 48. Carroll JF, Chiapa AL, Rodriquez M, Phelps DR, Cardarelli KM, Vishwanatha JK, et al. Visceral fat, waist circumference, and BMI: impact of race/ethnicity. Obesity (Silver Spring, Md). 2008;16(3):600–7.
  49. 49. Jih J, Mukherjea A, Vittinghoff E, Nguyen TT, Tsoh JY, Fukuoka Y, et al. Using appropriate body mass index cut points for overweight and obesity among Asian Americans. Preventive Medicine. 2014;65:1–6. pmid:24736092
  50. 50. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obesity Reviews. 2002;3(3):141–6. pmid:12164465
  51. 51. Stevens J, Couper D, Pankow J, Folsom AR, Duncan BB, Nieto FJ, et al. Sensitivity and specificity of anthropometrics for the prediction of diabetes in a Biracial Cohort. Obesity Research. 2001;9(11):696–705. pmid:11707536
  52. 52. Nolan CJ, Damm P, Prentki M. Type 2 diabetes across generations: from pathophysiology to prevention and management. Lancet. 2011;378(9786):169–81. pmid:21705072
  53. 53. Lebovitz HE, Banerji MA. Point: Visceral adiposity is causally related to insulin resistance. Diabetes Care. 2005;28(9):2322–5. pmid:16123512
  54. 54. Nyamdorj R, Qiao Q, Lam TH, Tuomilehto J, Ho SY, Pitkaniemi J, et al. BMI compared with central obesity indicators in relation to diabetes and hypertension in Asians. Obesity (Silver Spring, Md). 2008;16(7):1622–35.
  55. 55. Montague CT, O’Rahilly S. The perils of portliness: causes and consequences of visceral adiposity. Diabetes. 2000;49(6):883–8. pmid:10866038
  56. 56. Deurenberg-Yap M, Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutrition Reviews. 2003;61(5 Pt 2):S80–7. pmid:12828197
  57. 57. Wang J, Thornton JC, Russell M, Burastero S, Heymsfield S, Pierson RN, Jr. Asians have lower body mass index (BMI) but higher percent body fat than do whites: comparisons of anthropometric measurements. American Journal of Clinical Nutrition. 1994;60(1):23–8. pmid:8017333
  58. 58. Palaniappan LP, Wong EC, Shin JJ, Fortmann SP, Lauderdale DS. Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index. International Journal of Obesity. 2011;35(3):393–400. pmid:20680014
  59. 59. Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisited. Diabetes. 1998;47(5):699–713. pmid:9588440
  60. 60. Yoon KH, Lee JH, Kim JW, Cho JH, Choi YH, Ko SH, et al. Epidemic obesity and type 2 diabetes in Asia. Lancet. 2006;368(9548):1681–8. pmid:17098087
  61. 61. Lim U, Ernst T, Buchthal SD, Latch M, Albright CL, Wilkens LR, et al. Asian women have greater abdominal and visceral adiposity than Caucasian women with similar body mass index. Nutrition & Diabetes. 2011;1:e6.
  62. 62. Giralt M, Villarroya F. White, brown, beige/brite: different adipose cells for different functions? Endocrinology. 2013;154(9):2992–3000. pmid:23782940
  63. 63. Bartelt A, Heeren J. Adipose tissue browning and metabolic health. Nature Reviews Endocrinology. 2014;10(1):24–36. pmid:24146030
  64. 64. Harms M, Seale P. Brown and beige fat: development, function and therapeutic potential. Nature Medicine. 2013;19(10):1252–63. pmid:24100998
  65. 65. Cypess AM, Lehman S, Williams G, Tal I, Rodman D, Goldfine AB, et al. Identification and importance of brown adipose tissue in adult humans. New England Journal of Medicine. 2009;360(15):1509–17. pmid:19357406
  66. 66. Bjorndal B, Burri L, Staalesen V, Skorve J, Berge RK. Different adipose depots: their role in the development of metabolic syndrome and mitochondrial response to hypolipidemic agents. Journal of Obesity. 2011;2011:490650. pmid:21403826
  67. 67. Kositsawat J, Freeman VL, Gerber BS, Geraci S. Association of A1C levels with vitamin D status in U.S. adults: data from the National Health and Nutrition Examination Survey. Diabetes Care. 2010;33(6):1236–8. pmid:20215453
  68. 68. Zhao G, Ford ES, Li C. Associations of serum concentrations of 25-hydroxyvitamin D and parathyroid hormone with surrogate markers of insulin resistance among U.S. adults without physician-diagnosed diabetes: NHANES, 2003–2006. Diabetes Care. 2010;33(2):344–7. pmid:19846799
  69. 69. Poolsup N, Suksomboon N, Plordplong N. Effect of vitamin D supplementation on insulin resistance and glycaemic control in prediabetes: a systematic review and meta-analysis. Diabetic Medicine. 2016;33(3):290–9. pmid:26308752
  70. 70. Zuk A, Fitzpatrick T, Rosella LC. Effect of vitamin D3 supplementation on inflammatory markers and glycemic measures among overweight or obese Adults: a systematic review of randomized controlled trials. PLoS One. 2016;11(4):e0154215. pmid:27116227
  71. 71. Haroon NN, Anton A, John J, Mittal M. Effect of vitamin D supplementation on glycemic control in patients with type 2 diabetes: a systematic review of interventional studies. Journal of Diabetes & Metabolic Disorders. 2015;14(1):3.
  72. 72. Seida JC, Mitri J, Colmers IN, Majumdar SR, Davidson MB, Edwards AL, et al. Effect of vitamin D(3) supplementation on improving glucose homeostasis and preventing diabetes: a systematic review and meta-analysis. Journal of Clinical Endocrinology and Metabolism. 2014;99(10):3551–60. pmid:25062463
  73. 73. Mitri J, Muraru MD, Pittas AG. Vitamin D and type 2 diabetes: a systematic review. European Journal of Clinical Nutrition. 2011;65(9):1005–15. pmid:21731035
  74. 74. Snijder MB, van Dam RM, Visser M, Deeg DJH, Dekker JM, Bouter LM, et al. Adiposity in relation to vitamin D status and parathyroid hormone levels: a population-based study in older men and women. Journal of Clinical Endocrinology & Metabolism. 2005;90(7):4119–23.
  75. 75. Cheng S, Massaro JM, Fox CS, Larson MG, Keyes MJ, McCabe EL, et al. Adiposity, cardiometabolic risk, and vitamin D status: the Framingham Heart Study. Diabetes. 2010;59(1):242–8. pmid:19833894
  76. 76. Chacko SA, Song Y, Manson JE, Van Horn L, Eaton C, Martin LW, et al. Serum 25-hydroxyvitamin D concentrations in relation to cardiometabolic risk factors and metabolic syndrome in postmenopausal women. American Journal of Clinical Nutrition. 2011;94(1):209–17. pmid:21613558
  77. 77. Chang E, Kim Y. Vitamin D decreases adipocyte lipid storage and increases NAD-SIRT1 pathway in 3T3-L1 adipocytes. Nutrition. 2016;32(6):702–8. pmid:26899162
  78. 78. Abbas MA. Physiological functions of Vitamin D in adipose tissue. Journal of Steroid Biochemistry and Molecular Biology. 2017;165(Pt B):369–81. pmid:27520301
  79. 79. Blumberg JM, Tzameli I, Astapova I, Lam FS, Flier JS, Hollenberg AN. Complex role of the vitamin D receptor and its ligand in adipogenesis in 3T3-L1 cells. Journal of Biological Chemistry. 2006;281(16):11205–13. pmid:16467308
  80. 80. Kong J, Li YC. Molecular mechanism of 1,25-dihydroxyvitamin D3 inhibition of adipogenesis in 3T3-L1 cells. American Journal of Physiology Endocrinology and Metabolism. 2006;290(5):E916–24. pmid:16368784
  81. 81. Lee H, Bae S, Yoon Y. Anti-adipogenic effects of 1,25-dihydroxyvitamin D3 are mediated by the maintenance of the wingless-type MMTV integration site/beta-catenin pathway. International journal of Molecular Medicine. 2012;30(5):1219–24. pmid:22922938
  82. 82. Wamberg L, Christiansen T, Paulsen SK, Fisker S, Rask P, Rejnmark L, et al. Expression of vitamin D-metabolizing enzymes in human adipose tissue—the effect of obesity and diet-induced weight loss. International Journal of Obesity (2005). 2013;37(5):651–7.
  83. 83. Bouillon R, Carmeliet G, Lieben L, Watanabe M, Perino A, Auwerx J, et al. Vitamin D and energy homeostasis: of mice and men. Nature Reviews Endocrinology. 2014;10(2):79–87. pmid:24247221
  84. 84. Forman JP, Williams JS, Fisher ND. Plasma 25-hydroxyvitamin D and regulation of the renin-angiotensin system in humans. Hypertension. 2010;55(5):1283–8. pmid:20351344
  85. 85. Doorenbos CRC, van den Born J, Navis G, de Borst MH. Possible renoprotection by vitamin D in chronic renal disease: beyond mineral metabolism. Nature Reviews Nephrology. 2009;5(12):691–700. pmid:19859070
  86. 86. Plum LA, DeLuca HF. Vitamin D, disease and therapeutic opportunities. Nature Reviews Drug Discovery. 2010;9(12):941–55. pmid:21119732
  87. 87. Li YC, Qiao G, Uskokovic M, Xiang W, Zheng W, Kong J. Vitamin D: a negative endocrine regulator of the renin-angiotensin system and blood pressure. Journal of Steroid Biochemistry and Molecular Biology. 2004;89-90(1–5):387–92. pmid:15225806
  88. 88. Grubler MR, Gaksch M, Kienreich K, Verheyen N, Schmid J, BW OH, et al. Effects of vitamin D supplementation on plasma aldosterone and renin-a randomized placebo-controlled trial. Journal of Clinical Hypertension. 2016;18(7):608–13. pmid:27098193
  89. 89. Rejnmark L, Bislev LS, Cashman KD, Eiriksdottir G, Gaksch M, Grubler M, et al. Non-skeletal health effects of vitamin D supplementation: A systematic review on findings from meta-analyses summarizing trial data. PLoS One. 2017;12(7):e0180512. pmid:28686645
  90. 90. World Health Organization. Tuberculosis & diabetes. Geneva: World Health Organization; 2016.
  91. 91. English E, Idris I, Smith G, Dhatariya K, Kilpatrick ES, John WG. The effect of anaemia and abnormalities of erythrocyte indices on HbA1c analysis: a systematic review. Diabetologia. 2015;58(7):1409–21. pmid:25994072