Intraindividual coexistence of anthropometric undernutrition and “metabolic obesity” in Indian Children: A paradox that needs action

Intraindividual coexistence of anthropometrically dened undernutrition and “metabolic obesity”, characterised by presence of at least one abnormal cardiometabolic risk factor, is rarely investigated in young children and adolescents, particularly in Low-and-Middle-Income-Countries undergoing rapid nutrition transition. Prevalence of biomarkers of metabolic obesity was related to anthropometric and socio-demographic characteristics in 5-19 years old participants from the population-based Comprehensive National Nutrition Survey in India (2016-2018). The biomarkers, serum lipid-prole (total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides), fasting glucose, and glycosylated hemoglobin (HbA1C), and all jointly were analysed in 22567, 23192, 25962 and 19143 participants, respectively. overnutrition along with undernutrition. Nutritional status should be characterized through additional reliable biomarkers, instead of anthropometry alone.


Introduction
In public health settings of most Low-and Middle-Income Countries (LMICs), anthropometry is the sole tool used to diagnose undernutrition. For individuals aged 5-19 years, underweight or thinness is de ned as Body-Mass-Index (BMI)-for-age Z-score below -2, and stunting or chronic undernutrition as height-forage below -2Z of the World Health Organization (WHO) growth reference [1,2]. Acute malnutrition or thinness is believed to be "usually caused by recent and severe weight loss due to extreme deprivation and famine or micronutrient-related malnutrition", while chronic undernutrition "is commonly associated with poverty, poor maternal health and nutrition, and recurring illness" [1].
According to the WHO, "the double burden of malnutrition is characterized by the coexistence of undernutrition along with overweight, obesity or diet-related Non-Communicable-Diseases (NCDs), within individuals, households and populations, and across the life-course" [3]. The Double Burden of Malnutrition (DBM) research has primarily focussed on description, etiology, and identi cation of "double duty actions" for interventions within households and populations, and across the life-course [4][5][6][7][8]. The prevalence and predictors of intraindividual stunting and overweight or obesity in LMICs has received some attention; this burden was a mere 2% in 12-15-year-old adolescents between 2003 and 2013 [9]. At the individual level, DBM has also been conceptualised as the simultaneous development of two of more types of malnutrition, for example, obesity with nutritional anaemia or any vitamin or mineral de ciencies or insu ciencies [3]. This phenomenon has also been documented in population-based surveys, both in adults [10] and children [11]. Sequentially, micronutrient undernutrition probably occurs secondary to obesity, being linked to the intake of poor-quality food, or to the in ammatory nature of adipose tissue.
In 1981, the "metabolically obese normal weight" (MONW) adult phenotype was rst described as individuals in the healthy range of standard body weight (or body-mass-index) who had metabolic abnormalities commonly associated with adult-onset obesity [12]. This adult phenotype, occurs in 5%-45% subjects, including in LMICs, depending on the speci c de nition employed [13]. The MONW phenotype with at least one abnormal cardiometabolic risk factor, is also reported in up to two-thirds of children and adolescents in Iran, China and India [14][15][16]. However, particularly in LMICs undergoing rapid nutrition transition, there is a need to extend this MONW phenotype to anthropometrically undernourished children who could have coexisting metabolic obesity, characterised by the presence of at least one abnormal cardiometabolic risk factor. This is rarely investigated in young children and adolescents, despite our earlier report of paradoxical co-occurrence of metabolic obesity in 9% of thin children and adolescents in a fteen-year-old dataset from urban schools in Delhi [17]. This nding is now con rmed by us in a greater proportion of thin and stunted children from a recent, quality-controlled national survey in India [18].
The Population Council's International Review Board (New York, USA) and ethics committee of Post Graduate Institute of Medical Education and Research (Chandigarh, India) gave ethical approval for the primary survey. Written consent from parent/caregiver for children under 10 years, consent of parent/caregiver as well as assent from adolescents (11-17 years) and written consent of adolescents above 17 years were obtained after due description of the study details in local languages.
Socioeconomic and demographic characteristics, and anthropometric data of one participant per age group were collected from each household. Wealth index, based on possession of common household items and facilities, was computed as described in National Family Health Survey-4 [19]. Trained female health workers collected all anthropometric data. Height was measured in duplicate on a height board (SECA) to the nearest 0·1 cm; the mean was recorded.
Weight was measured once to 0.01 kg, using a portable digital weighing scale. Quality control included weekly calibration of height board, daily calibration of weighing scale and repeat measurements by monitors in a sub-sample. The periodic inter-and intra-technical error of measurement were within the recommended range [18]. Age-sex standardized z-scores were calculated for height-for-age, weight-for-height and BMI-for-age using the WHO Growth Reference [2].
Biomarkers for metabolic obesity were evaluated only in the 5-19-years-old participants, who comprised the analytic framework. Blood samples, with information on fasting status, were collected in trace-element free tubes, the serum separated and stored frozen until analysis. Biochemical analyses were carried out by a third-party laboratory (SRL Labs, Mumbai, Gurugram and Kolkata, India). Rigorous control and monitoring systems were included in the standard operating procedures for quality assurance of biomarker data. First, an internal quality control sample was used for each batch of 20 survey samples. Second, for external quality assurance, a subset of samples was sent to other participating laboratories monthly for comparison testing. SRL laboratories participated in the BIORAD and US Centre for Disease Control external quality assurance scheme. Third, on a weekly basis, a percentage of samples were split and reanalysed, as detailed in the CNNS report [18].
Blood samples were collected after an overnight fast in 91% of participants, but glucose was estimated in only those who were fasting. Table 1 summarises the estimation methods for the analysed biochemical parameters and their cut-offs for de ning abnormal values [18,[20][21][22][23][24]. Figure 1 depicts the ow chart of the participants analysed for biochemical parameters in relation to anthropometric indices. Dyslipidemia was de ned as abnormality in any of the lipid biomarkers (total cholesterol, LDL, HDL or triglyceride). Dysglycemia was de ned as elevation of either fasting glucose or HbA1C. There are no internationally recognized cut-offs for de ning low total cholesterol, LDL, triglyceride and HbA1c.
The metabolic-syndrome (Met-S) concept identi es a common multiple cardiovascular-risk phenotype. However, in the absence of a de ned etiology, the lack of consensus on de nition [20,25], and the paucity of high-level evidence addressing management in childhood, an expert panel [20] did not consider this as a separate risk entity in childhood and adolescence; instead, a combination of individual metabolic syndrome components was considered a higher risk to trigger prompt intensi cation of therapy. Considering the focus of our analysis, obesity and high blood pressure were not evaluated, but the remaining two components: borderline dyslipidemia (Table 1) and dysglycemia were examined, substituting elevated HbA1C for insulin resistance [20]. Similarly, we Apart from summary proportions, unadjusted and adjusted (for age, sex, place of residence and wealth categories) logistic regression analyses were conducted to evaluate the relation between anthropometric Z-score categories and biomarkers of metabolic obesity.
The prevalence of metabolic obesity biomarkers increased signi cantly at higher BMI-for-age categories, even after adjustment for sociodemographic factors (age, sex, residence location and wealth) ( Table 2 and Figure 2). This positive association was either linear (low HDL and elevated fasting glucose) or quadratic (other biomarkers and various combinations); the upward trend became evident mostly beyond the WHO BMI-for-age median. In contrast, for heightfor-age, different patterns were observed (Table 2 and Figure 3). Height-for-age did not have a signi cant association with elevated fasting glucose or HbA1C and 3-MetS in both crude and adjusted models, high LDL in crude model, and high total cholesterol and low HDL in adjusted model. However, there was a signi cant but gentle 'U' shaped association with high triglyceride, ≥1 metabolic obesity biomarker and 2-MetS, even in the adjusted model.
The association of metabolic obesity biomarkers with socio-demographic characteristics are depicted in Table 3. Total and LDL cholesterol elevation was more prevalent in wealthier subjects, while the poorer sections had higher triglyceride, fasting glucose and HDL abnormalities. With age, high LDL, low HDL and elevated HbA1C were positively related, while elevated triglyceride and all combinations (≥1 obesity biomarker, 2-MetS and 3-Mets) were negatively associated. The inverse association of triglycerides with age was not evident on substituting the lower cut-off de nition for 5-9 years (≥100 mg/dl) with the 10-19 years cut-off (≥130 mg/dl; data not presented). Elevated LDL, total cholesterol and serum triglycerides were more prevalent in girls whereas boys had greater HDL, fasting glucose and HbA1c abnormalities. Elevated total cholesterol was more frequent in urban settings whereas rural participants had greater abnormalities of triglyceride and HbA1C.
The situation with 'borderline high' cut-offs was worse (Supplement Table 3). Borderline abnormalities of total, LDL and HDL cholesterol, serum triglycerides, 2-MetS and 3-MetS were documented in 14%, 11%, 42%, 47%, 29% and 5% of thin children, respectively. A similar or marginally higher prevalence was noted for stunted children and with -1SD cut-offs for both BMI-and height-for age. The prevalence of hypoalbuminemia and low fasting glucose was 2% and 4% for thin and 1% and 3% for stunted children, respectively.
The age strati ed (5-9 and 10-19 years) prevalence of metabolic obesity biomarkers in relation to anthropometric indices is depicted in Supplement Tables 4 and 5. In the 5-9 years age group, the overall prevalence of abnormalities was notably higher for triglycerides (34% vs 17%), but lower for HDL (19% vs 25%) and marginally lower for fasting glucose and HbA1C. Majority of the associations with BMI-for-age were similar to the composite 5-19 years age group. However, for height-for-age, high HBA1C, ≥1 metabolic obesity biomarker and 2-MetS had a signi cant inverse association in the 5-9 years age group, with the highest prevalence in stunted children. In the 10-19 years age group, high LDL and total cholesterol also had a signi cant inverse association with height-forage in the adjusted model, but no signi cant associations were evident for high triglyceride, ≥1 metabolic obesity biomarker and 2-MetS.

Discussion
The paradoxical contrast of metabolic obesity biomarker(s) in over half the surveyed children with conventional anthropometric diagnoses of undernutrition is intriguing, but probably an accurate re ection of the current reality in India. The data emanate from a nationally representative survey with meticulous attention to quality control procedures, especially for anthropometry and biomarkers [18]. In the earlier phase of nutrition transition, almost 15 years ago, similar ndings were documented in a lower proportion (9%) of thin children and adolescents in Delhi schools [17]. The MONW phenotype, de ned as ≥1 metabolic obesity biomarker and BMI-for-age between -2Z and +1Z of WHO reference, occurred in 56% of normal-weight participants. This prevalence resonates with similar reports from national surveys in Iran (41% and 55%  [14][15][16][26][27][28][29]. However, these studies did not speci cally report on the paradoxical co-existence of anthropometric undernutrition (thinness or stunting) and metabolic obesity. Progressively more metabolic abnormalities (0, 1, 2, or 3) are associated with dose-dependent increases in the risk of cardiovascular disease in normal-weight adults [13]. Clustering of two and all three core biochemical abnormalities (2-MetS and 3-MetS), commonly used for de ning metabolic syndrome [25], occurred in 13% and 2% of thin and normal-weight participants, respectively. We could not locate any speci c data for comparison; however, these gures are compatible with similar clustering observed in 10%-19% and 1%-2% of normal-weight adolescents from Asia, albeit with the use of additional criteria of hypertension and abdominal obesity [15,16,26].
The greater prevalence of metabolic obesity biomarkers at higher BMI categories and ages, and their relative preponderance patterning conforms with the current understanding and guidelines [14][15][16]20,21,[26][27][28][29]. Our diagnosis of abnormal biomarkers was aligned with the internationally recommended cut-offs for identi cation of 5-19-year-old children with prediabetes or diabetes, and at risk of developing future cardiovascular disease [20,21]. Although the utility of these cut-offs for accurately predicting the risk of adult disease could be debated, the diagnosis of speci c type(s) of existing metabolic obesity (metabolically unhealthy or metabolic dysfunction/overnutrition or cardiometabolic risk factors) cannot be disputed, particularly when the recommended [20,21] core interventions, such as dietary restrictions and active lifestyle, are directed towards inducing a negative energy balance [30]. The evaluated biomarkers are probably not re ective of short-term changes (few days) in individual metabolic pro les, especially HbA1c, which informs the glycosylation status over 3-4 months. Further, the observed prevalence of abnormal biomarkers cannot be solely attributed to rare inherited metabolic disorders like familial hypercholesterolemia and hypertriglyceridemia.
The primary cut-offs used by us underestimate the proportion of children warranting active lifestyle interventions to induce an appropriate energy balance and body composition. Considering the recommended 'borderline abnormal' lipid cut-offs for initiating action [20], nearly half (42%-52%) of anthropometrically undernourished (mild/moderate or greater) participants had HDL or triglyceride perturbations, while ~30% had clustering of two core metabolic syndrome components. Conversely, a mere 1%-4% of such children had either hypoalbuminemia or hypoglycemia, the traditionally used biomarkers for clinically relevant, severe and chronic undernutrition. Thus, an overwhelming majority of these children exhibited biomarkers associated with obesity instead of clinically relevant macronutrient inadequacy. These data question the usual narrative of equating undersize in children with undernutrition or hunger, instead of being de ned as a broader surrogate of developmental deprivation that may also include energy and nutrient inadequacy [31].
Similarly, apart from the relatively rare, genetic and primary endocrinal conditions, inappropriate dietary intakes and low physical activity levels may now be more important determinants of these metabolic abnormalities. Reviews on the effects of overfeeding [32] and calorie restriction [33] in humans con rm the etiological role of excess energy intake. A moderate calorie restriction (12%) over two years in healthy normal-weight, young and middle-aged adults, improved multiple cardiometabolic risk factors well below the conventional risk thresholds [34]. Substantial evidence con rms the crucial role of physical activity and cardiorespiratory tness in improving lipid and glucose homeostasis, both in adults and children; consequently, these life style interventions form the core of primary preventive recommendations from various professional organizations [20,[35][36][37][38]. There are scant data speci cally investigating the role of lifestyle interventions in MONW subjects. A recent study in Asian adults, after a diet-induced modest (~5%) weight loss, documented improvements in body composition, lipid pro le and insulin sensitivity [39]. A 2-month life style modi cation trial in 12-16-year olds, comprising aerobic activity classes, diet education and behaviour modi cation, reduced body fat mass and improved lipid pro le and in ammation [40]. Postulated mechanisms from observational evidence in adults and children also include greater relative fat accumulation, especially in the visceral adipose tissue, liver and upper body, inferior aerobic tness, lower skeletal muscle mass and strength, increased screen time and diet quality -lower fruits and vegetables -and higher fructose and glucose intakes [13,41,42]. Mechanisms underlying a similar phenomenon in thin (underweight) subjects have not been investigated.
The aforementioned evidence thus justi es the term "metabolic obesity" for describing unambiguous biochemical aberrations; this will unequivocally alert the policy stakeholders and public about the con icting nutritional signals originating from the thin, short and normal-weight phenotypes. We suggest restricting the terms "metabolic dysfunction" and "metabolically unhealthy" [13] for borderline biochemical abnormalities. Further, we propose the nomenclature "metabolically obese undersized" (MOU) for those who are thin or short.
India is currently undergoing a rapid nutrition transition with attendant escalation of overnutrition related NCDs [43]. Within this backdrop, we hypothesize that at the individual level, thin or stunted or normal-weight children with co-existent metabolic obesity are in positive energy balance. Whether this is a consequence of overfeeding and/or reduced physical activity, among other factors, needs investigation. Lower skeletal muscle mass and strength is likely to be an important mediator or moderator of this phenomenon. Infants, children and adolescents living in India have been characterised by a muscle-thin but adipose body composition compared with those in other countries [44,45]. The National Sample Survey O ce [46] report of dietary consumption patterns in 100,547 households across India, shows that while the carbohydrate intake is high in general, the poor consume an even greater proportion of their energy intake as carbohydrates including free sugars (73% vs 60% in the lowest and highest socioeconomic status quintiles, respectively). However, the proportion of fat intake is greater in wealthier households (15% vs 27%, respectively). Rural-urban comparisons show a slightly higher carbohydrate consumption by about 2-3% in rural settings across all quintiles. High carbohydrate intakes are associated with high de novo lipogenesis [47], and these dietary consumption patterns are consonant with higher HbA1c, fasting glucose and triglyceride abnormalities in the rural setting and the poor, and the converse association for serum cholesterol [32,48].
The following limitations merit consideration. Information on all evaluated biomarkers was not available for every recruited participant; however, this did not bias the prevalence estimates (data not presented). Other important indicators of metabolic obesity (insulin sensitivity, in ammation, blood pressure) and potential explanatory factors (physical activity, body composition and muscle-strength) were either not evaluated in the survey or could not be analysed, pending the release of relevant data.
Urgent research is required on (i) Biological and mechanistic characterization of the MONW and MOU phenotypes, including an evaluation of hepatic and visceral fat distribution. (ii) Optimal public health interventions to address this intraindividual double burden of malnutrition, including focus on dietary quality and exercise plus resistance training that improves body composition without substantial weight loss. (iii) The burden of this phenotype in other geographical regions and its adult health and human capital consequences. (iv) Determining if similar phenotypes exist in the under-ve age group and women planning pregnancies. In this eventuality, it is crucial to evaluate and mitigate the potential risks from consuming energy-dense therapeutic foods in wasted children [49] and dietary supplementation for pregnancies in what are believed to be undernourished populations [50].
The unexpected huge burden of metabolic obesity in Indian children, whether normal or undersized, argues strongly for commensurate investments to address overnutrition along with undernutrition. When this occurs in undersized children, considerable re ection is required on how such children should be fed, since targeting a simple negative energy balance should not be the sine qua non of the remedy. Focusing solely on anthropometry to identify at-risk (overweight/obese) individuals to prevent adult NCDs will miss 90% of those harbouring invisible metabolic threats. Lead national and global stakeholders should therefore urgently determine the optimal strategy to include these phenotypes in programmatic interventions and decide whether, in the current era, nutritional status should be de ned through additional, logistically feasible and reliable biomarker(s) instead of anthropometry alone. This is also desirable from an equity and ethical perspective since poor, illiterate and vulnerable populations generally have undersized or normal-weight children. The continued reliance on undersize metrics sans biomarker(s), to quantify 'hunger' and occasionally 'near starvation' [51,52], contributes to misdirected stigma and the response thereof, primarily an enthusiastic but often blunt approach of food or nutrient(s) supplementation, with a sole focus on the 'left-hand' side of the distribution.
In conclusion, there is a paradoxical contrast of metabolic obesity biomarker(s) in over half of anthropometrically undernourished and normal-weight children and adolescents in India. Almost one-third had clustering of two metabolic dysfunctions warranting immediate, active and appropriate life-style interventions, particularly in the undersized. There is a crucial need for commensurate investments to address overnutrition along with undernutrition, biological characterization of these phenotypes, and consideration for de ning nutritional status through additional reliable biomarker(s) instead of anthropometry alone.

Declarations Data Sharing
The Ministry of Health and Family Welfare (MoHFW), Government of India owns the Comprehensive National Nutrition Survey data. The data used in this paper were released for public use by the MoHFW and United Nations Children Fund, India Country O ce. The dataset is available on request from Dr. P.K.
Agrawal (pkagrawal@unicef.org). The code book and the analytic code can be made available upon reasonable request to the corresponding author(s).
The CNNS was conducted by the Ministry of Health and Family Welfare, Government of India, and the UNICEF, with nancial support from the Mittal Foundation. These secondary analyses and manuscript were not supported by any speci c funding.
Author contributions HSS conceived the idea, guided the analysis and drafted the manuscript. AP conducted all statistical analyses. AVK provided analytic insight for the National Sample Survey O ce dietary consumption data. All authors were involved at every iteration of analyses and drafting, and approved the nal manuscript. All authors had access to raw data.
Compliance with ethical standards