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Childhood Obesity in India

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Childhood Obesity in India UNIVERSITA’ DEGLI STUDI DI VERONA MODELLING STUDIES FOR A ‘WHOLE OF SOCIETY (WOS)’ FRAMEWORK TO MONITOR CARDIO-METABOLIC RISK AMONG CHILDREN (6 TO 18 YEARS) FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY (Ph.D) BY MR. RAKESH NEELAKANTA PILLAI, DOCTORAL PROGRAM IN TRANSLATIONAL BIO-MEDICINE, DEPARTMENT OF DIAGNOSTICS AND PUBLIC HEALTH, GRADUATE SCHOOL OF TRANSLATIONAL BIO-MEDICAL SCIENCES, UNIVERSITY OF VERONA, ITALY PERIOD: 2013 TO 2018 SUPERVISED BY PROF. NARENDRA KUMAR ARORA PROF. CRISTIANO CHIAMULERA EXECUTIVE DIRECTOR UNIVERSITÀ DI VERONA, THE INCLEN TRUST INTERNATIONAL VERONA, ITALY NEW DELHI, INDIA 0 UNIVERSITA’ DEGLI STUDI DI VERONA DEPARTMENT OF DIAGNOSTICS AND PUBLIC HEALTH GRADUATE SCHOOL OF TRANSLATIONAL BIOMEDICAL SCIENCES DOCTORAL PROGRAM IN TRANSLATIONAL BIO-MEDICINE WITH THE FINANCIAL CONTRIBUTION OF European Program in Pharmacovigilance and Pharmacoepidemiology (Eu2P) and The INCLEN (International Clinical Epidemiology Network), New Delhi Cycle / year (1° year of attendance): October 2013 TO January 2018 TITLE OF THE DOCTORAL THESIS MODELLING STUDIES FOR A ‘WHOLE OF SOCIETY (WOS)’ FRAMEWORK TO MONITOR CARDIO-METABOLIC RISK AMONG CHILDREN (6 TO 18 YEARS) S.S.D. _Area/06/Med/01/Medical Statistics (Please complete this space with the S.S.D. of your thesis – mandatory information)* Coordinator: Prof./ssa: Cristiano Chiamulera Tutor: Prof./ssa: Narendra Kumar Arora Doctoral Student: Dott./ssa: Rakesh Neelakanta Pillai 1 Declaration of Ownership This thesis is my own work and contains no material which has been accepted for the award of any degree or diploma in any other institution. To the best of my knowledge, the thesis contains no material previously published or written by another person, except where due reference is made in the text of the thesis. By Rakesh Neelakanta Pillai Ph.D Student 2 ABSTRACT In the World Health Assembly (WHA) 2013, India was among the first country to adapt global framework for monitoring non-communicable diseases (NCD) - Government of India (GOI) has set targets to halt the prevalence of diabetes and obesity by 2025. To halt the prevalence of major NCDs it is necessary to protect children from becoming obese or overweight. Childhood obesity is a precursor of adulthood obesity and attendant cardio-metabolic risk. In last 15 years the prevalence of overweight and obesity increased almost four times (4 to 15%). This translates in to approximately 58 million obese and 122 million overweight children in the country. Studies have reported at least one cardiovascular risk factor among 70 per cent of these children. It is frightening to know that, unit percentage rise in its prevalence in India shall add at least another five million children into the cardiovascular risk pool. Body Mass Index (BMI) [Weight (kg)/Height (m2)] is the most widely used definition for monitoring overweight and obesity; among children BMI-for-age based growth curves (centile values) are used. There are number of BMI-for age based guidelines with varying cut offs (like IOTF, WHO, CDC etc.) – in India, the growth curves published Indian Association of Pediatrics (IAP), 2015 is considered as the standard. Despite BMI’s large scale application in clinical and public health programs it has many inherent problems. Firstly, BMI cannot distinguish between fat and fat free mass. Excess body fat is an independent risk factor for cardio vascular and metabolic diseases. In an individual with BMI of 20, body fat may range from 5%-40% whereas for body fat content of 20% BMI may vary from 15-30 points. Validity studies using BMI to identify children with excess adiposity have generally documented low to moderate sensitivities (6-46%). Secondly, BMI is not independent on height of the individuals. BMI may not be a sensitive measure in children at the extremes of the height due to unusual fat distribution or highly developed muscles. BMI preferentially classifies taller children and adolescents as overweight. Finally, the definition of childhood overweight and obesity is arbitrary as it is extrapolated from adult reference data and not based on its association with health outcomes. Considering these variations, there has been a growing concern about using single standard to define overweight and obesity which may be appropriate for many sub-populations in the world. Methods: Overall aim of this study was to develop a monitoring mechanism that correlates with cardio-metabolic risk factors among Indian children aged 6-18 yrs. Primary objective of the study was to relate health outcomes, i.e. measures of cardio-metabolic risk, to body fatness and to 3 measure its distribution. Under this overarching goal specific objectives were finalized as mentioned in section 1.4 (Page no.40). Quantitative data was collected from schools in 3 regions (New Delhi, Shillong and Hyderabad) from a representative sample of 3242 children between 6 to 18 years of age. Detailed assessments were done on; a) anthropometry; b) pubertal staging; c) blood biochemistry (fasting plasma insulin, fasting plasma glucose, lipid profile and sub-fractions uric acid) using semi-automated analyzer), d) body composition by bio impedance (BIA) (InBody 720, body composition analyzer, Biospace©), e) body composition using DEXA (Hologic QDR 4500A) on selected sub samples, f) socio-economic status (standard of living index), g) media and market exposures, h) food frequency and dietary recalls, and i) physical activity recalls. The results are presented as: Study 1: Assessment of whole-body composition using bioelectrical impedance analysis (BIA) among children 6 to 18 years: Validation with Dual X-Ray Absorptiometry (DEXA) Study 2: Reference values and Percentile curves for cardio-metabolic risk factors among Indian children (6 to 18 years) Study 3: Clustering of Bio-chemical Markers of Cardio-metabolic Risk among Indian Children: An Imperative for Continuous Monitoring of Risk Factors Study 4: A multi-level framework for monitoring cardio-metabolic risk: proximal & distal factors associated with clustering of bio-chemical markers Results: Around 3241 children (1611 boys and 1630 girls) between 6 to <19 years (13 age bands) were recruited from schools in rural and urban settings in three study locations. As per revised national standards (IAP Growth charts, 2015) there was 8.4% overweight and 4.9% obese boys as compared with 10.6% overweight and 3.9% obese girls (Table 6). Body fat estimation: For estimating body fat DEXA measurements were considered as the criterion standard; which was done in a sub-sample of 206 children (105 boys and 101 girls). Body fat from DEXA were compared with direct outputs of bio-impedance (BIA) machine and have shown a mean bias of -18.9% fats among boys and -11.2% fats among girls; highlighting that the existing fat predictions does not represent Indian children and there is a need for new prediction equations for estimating fat. Linear prediction models were developed to predict lean mass and fat mass among girls and boys (developed in sub-sample and were predicted in full 4 sample). Absolute difference (bias) and percentage differences in predicted lean (BIA) from expected lean (DEXA lean) were tested among thin, normo-weight, overweight and obese children (Table 10). In our study, the new BIA prediction equation could precisely predict lean mass among 82.4% (n=84) boys, moderately in 15.7% (n=16) and imprecise in 2% (2) boys. Among girls, the BIA prediction could precisely predict lean mass among 77.6% (n=76) girls, moderately in 21.4% (n=21) and imprecise in 1% (1). Growth curves: Lambda (L), Median (Mu) and Sigma (S) values were estimated for each age bands for boys and girls – templates were developed for manual calculation and compared with LMS chart maker (Pro version). Reference values for 5th, 10th, 25th, 50th, 75th, 85th and 95th centiles were estimated to prepare smoothened centile curves. Reference values and centile curves for various cardio-metabolic risk conditions are presented under two subheads; a) bio-chemical markers [Glucose monitoring (Fasting glucose & HbA1C (Glycated Hemoglobin)); Insulin Resistance (HOMA-IR); blood pressure monitoring (Systolic BP, Diastolic BP and Mean Arterial Pressure) lipid monitoring (Total cholesterol, Low HDL-C, Triglycerides, Apo-B representing non-HDL and Apo-A proteins representing HDL-C) and Uric acid levels] and b) clinical markers [Nutritional status (BMI for age, Weight for age & Height for age); Body Composition (Percentage body fat & Fat mass index) and abdominal obesity (Waist circumference). Clustering of Cardio-metabolic risk: Standard definitions were used for seven different clinical conditions that shall indicate cardio-metabolic risk among children. Clustering of these risk factors were studied among children ≥ 10 years using two models. Overall, there was substantial level of agreement between two models (boys: kappa 0.64, P=0.001) and (girls: kappa 0.63, P=0.001). Clustering of 3+ risk factors are distributed among normo-weight, overweight and obese as per existing BMI-for-age categorization: among normal weights 2.2% boys (19/871) and 5.5% girls (45/818) were having clustering of 3 and more risk factors. Among overweight children, 11% boys (10/90) and 21.7% girls (20/92) were having clustering of 3 and more risk factors as compared to 34% obese boys (14/41) and 37.8% obese girls (14/37) having clustering of risk factors. Moreover existing classification system based on BMI-for-age (at 23rd adult equivalent centile for overweight and 27th adult equivalent centile for obese) could not capture all children with cardio-metabolic risk (less sensitive more false negatives). The current public health system, across the globe, emphasis on obesity (above 27th adult equivalent BMI-for-age in South-east Asia and 30th adult equivalent in other countries); however 5 it shall be too late to intervene as changes in cardio-metabolic profile starts much earlier – at the level of overweight or even lesser. Distribution of fat mass index (FMI) and percentage body fat (PBF) along with waist circumference (indicator of abdominal obesity) were compared in children having risk factors.
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