Adult metabolic syndrome and impaired glucose tolerance are associated with different patterns of BMI gain during infancy: Data from the New Delhi Birth Cohort.

OBJECTIVE: The purpose of this study was to describe patterns of infant, childhood, and adolescent BMI and weight associated with adult metabolic risk factors for cardiovascular disease. RESEARCH DESIGN AND METHODS: We measured waist circumference, blood pressure, glucose, insulin and lipid concentrations, and the prevalence of metabolic syndrome (National Cholesterol Education Program Adult Treatment Panel III definition) in 1,492 men and women aged 26-32 years in Delhi, India, whose weight and height were recorded every 6 months throughout infancy (0-2 years), childhood (2-11 years), and adolescence (11 years-adult).
RESULTS: Men and women with metabolic syndrome (29% overall), any of its component features, or higher (greater than upper quartile) insulin resistance (homeostasis model assessment) had more rapid BMI or weight gain than the rest of the cohort throughout infancy, childhood, and adolescence. Glucose intolerance (impaired glucose tolerance or diabetes) was, like metabolic syndrome, associated with rapid BMI gain in childhood and adolescence but with lower BMI in infancy.
CONCLUSIONS: In this Indian population, patterns of infant BMI and weight gain differed for individuals who developed metabolic syndrome (rapid gain) compared with those who developed glucose intolerance (low infant BMI). Rapid BMI gain during childhood and adolescence was a risk factor for both disorders.

Approximately 10% of urban Indian men and women aged 40–49 years have type 2 diabetes, and a rising prevalence is predicted to produce 80 million diabetic patients in India by 2030 (1–3). Cardiovascular disease is also rising (4). Similar trends, thought to reflect increasing obesity, are occurring in other developing countries undergoing economic transition, and interventions to prevent disease are urgently needed.

Research in high-income countries has shown that factors linked to weight gain in early life contribute to the risk of developing diabetes and cardiovascular disease. Low birth weight (5,6) and accelerated gain in BMI during childhood and adolescence predict increased risk (7,8). The optimal pattern of infant weight gain (the first 1–2 postnatal years) is unclear; studies of adults suggest that low infant weight gain is a risk factor for later disease (7–9), whereas studies of children suggest the opposite (10,11). There are few data from developing countries.

In the New Delhi birth cohort (12,13), children were measured at birth and every 6 months throughout infancy, childhood, and adolescence. We reported earlier that low BMI in infancy and rapid childhood BMI gain were associated with an increased risk of adult diabetes or impaired glucose tolerance (IGT) (12). We have now examined other cardiovascular risk factors and the cluster of risk factors known as the metabolic syndrome.

RESEARCH DESIGN AND METHODS

During 1969–1972, married women living in a 12-km2 area of Delhi (n = 20,755) were followed up (12,13). There were 9,169 pregnancies and 8,181 live births. Trained personnel recorded the babies’ weight and length within 72 h of birth and every 6 months until age 14–21 years. Gaps in funding interrupted measurements in 1972–1973 and 1980–1982. At recruitment, 60% of families had incomes of >50 rupees/month (national average 28 rupees/month) and 15% of parents were illiterate (national average 66%). Nevertheless, 43% of families lived in one room. Hindus were the majority religious group (84%), followed by Sikhs (12%), Christians (2%), Muslims (1%), and Jains (1%).

Current study

In 1998–2002 we retraced 2,584 (32%) of the cohort and 1,583 agreed to participate. Data on schooling, occupation, household possessions, alcohol consumption, tobacco use, physical activity, and family history were obtained by questionnaire (12,13). Weight and height were measured using standardized techniques. Waist circumference was measured using fiberglass tape, in expiration, midway between the lower lateral costal margin and the iliac crest, with the subject standing. Blood pressure was recorded using an automated device (Omron 711) with the subject seated, after 5 minutes of rest (mean of two readings). Plasma glucose concentrations were measured fasting and 120-min after a 75-g glucose load. Glucose and fasting cholesterol and triglyceride concentrations were analyzed by enzymatic methods using Randox kits on a Beckman AutoAnalyzer, and HDL cholesterol was measured using the same method after phosphotungstate precipitation. IGT and diabetes were defined using World Health Organization criteria (14). Metabolic syndrome was defined using National Cholesterol Education Program Adult Treatment Panel III criteria (15,16). Insulin resistance (homeostasis model assessment [HOMA]) was estimated (17). The study was approved by the All India Institute of Medical Sciences research ethics committee, and informed verbal consent was obtained.

Statistical analyses

Data from the whole original cohort was used to derive individual SD scores for BMI and weight at 6 months and birthdays from 1 to 21 years (12). Participants had a mean ± SD of 23 ± 5.5 observations. Interpolated values were used if measurements were made within 6 months (up to 1 year), 1 year (aged 2 years), 1.5 years (aged 3 years), and 2 years (all older ages). Back-transformation provided estimates of measurements at these ages. To measure changes in measurements in early life (e.g., from 2 to 11 years), we regressed SD scores at the end of the interval (11 years) on SD scores at the beginning (2 years) and at all preceding time points (birth, 6 months, and 1 year) and expressed the residuals as SD scores. This method produces uncorrelated variables describing change between specific ages (conditional SD scores). Quadratic terms were included when relationships between measurements at different ages were nonlinear. Associations between size in early life and adult outcomes were examined using regression. Outcomes with skewed distributions (HDL cholesterol and insulin resistance) were log-transformed.

RESULTS

Of the 1,526 subjects attending the clinic, glucose tolerance category was definable for 1,442 and metabolic syndrome for 1,492. Compared with the remainder of the original cohort, more participants were male (58 vs. 52%, P < 0.001), mean birth weight was heavier (2,851 vs. 2,818 g, P = 0.046), and maternal literacy was 6% higher. BMI SD scores differed by −0.10 to 0.06 (mean −0.04) between birth and 21 years and were statistically significant at 11 and 12 years.

The children were short, light, and thin according to an international reference (18), but as adults almost half were overweight or obese (Table 1).

Table 1

Body measurements at birth, 2, 11, and 26–32 years and adult risk factors and components of the metabolic syndrome

	Men	n	Women	n
n	886		640
Birth
Length (SD score)*	−0.44 ± 0.80	779	−0.45 ± 0.81	558
Weight (SD score)*	−1.06 ± 0.71	803	−1.17 ± 0.72	561
At 2 years
Height (SD score)*	−1.54 ± 1.03	840	−1.55 ± 1.04	609
Weight (SD score)*	−2.01 ± 1.19	834	−2.27 ± 1.43	609
BMI (SD score)*	−0.78 ± 1.10	833	−0.85 ± 0.98	604
At 11 years
Height (SD score)*	−1.11 ± 0.84	831	−1.37 ± 1.04	607
Weight (SD score)*	−1.56 ± 1.01	834	−1.87 ± 1.19	608
BMI (SD score)*	−1.23 ± 1.03	830	−1.31 ± 1.04	606
Adult
Age (years)	29.2 ± 1.3	886	29.2 ± 1.4	640
Height (cm)	169.7 ± 6.4	886	154.9 ± 5.7	638
BMI (kg/m2)	24.9 ± 4.3	886	24.6 ± 5.1	638
Waist circumference (cm)	90.2 ± 12.1	886	79.6 ± 12.4	640
Overweight (BMI ≥25) (%)	47.4	886	45.5	638
Obese (BMI ≥30) (%)	9.5	886	13.0	638
Any alcohol intake (%)	56.2	886	1.4	640
Ex-smokers (%)	5.1	886	0.2	640
Current smokers (%)	29.8	886	0.2	640
Systolic blood pressure (mmHg)	118.4 ± 11.4	880	106.6 ± 11.0	631
Diastolic blood pressure (mmHg)	77.9 ± 10.3	880	73.4 ± 9.2	631
Fasting
Glucose (mmol/l)†	5.37 ± 1.21	869	5.28 ± 1.17	623
Insulin (pmol/l)†	34.4 ± 2.62	868	28.8 ± 2.64	623
Insulin resistance (HOMA)†	1.37 ± 2.73	868	1.13 ± 2.75	623
Total cholesterol (mmol/l)†	5.16 ± 1.14	869	4.75 ± 0.94	623
HDL cholesterol (mmol/l)†	1.12 ± 1.30	869	1.24 ± 1.29	621
Triglycerides (mmol/l)†	1.57 ± 1.69	868	1.05 ± 1.51	623
2-h glucose (mmol/l)†	5.93 ± 1.34	848	6.12 ± 1.28	591
Components of the metabolic syndrome
High waist circumference (%): ≥90 cm (men) ≥80 cm (women)	51.5	886	45.5	640
Low HDL cholesterol (%) <1.0 mmol/l (men) <1.3 mmol/l (women)	34.2	869	55.6	621
High triglycerides (%) ≥1.7 mmol/l	41.2	868	10.6	623
High blood pressure (%): systolic ≥130 or diastolic ≥85 mmHg or receiving treatment for hypertension	27.6	880	12.3	632
High fasting glucose (%) ≥5.6 mmol/l	41.3	869	36.6	623
Metabolic syndrome (NCEP-ATPIII) (%)	35.6	869	20.2	623
Diabetes (%)	4.8	849	3.7	593
IGT (%)	11.2	849	10.3	593

Data are arithmetic means ± SD or % unless otherwise indicated.

SD scores are based on National Centre for Health Statistics data (18).

Geometric means ± SD. ATPIII, Adult Treatment Panel III; NCEP, National Cholesterol Education Program.

BMI (Table 2) and weight at birth and 2 years were positively related to adult waist circumference and inversely related to 120-min glucose concentrations. Eleven-year BMI was positively related to all outcomes except HDL cholesterol and glucose, and adult BMI was positively related to all outcomes except HDL cholesterol, to which it was inversely related. After adjustment for adult BMI, the associations with earlier BMI reversed, becoming significantly inverse for many outcomes. The associations were little changed after further adjustment for adult lifestyle factors (data not shown).

Table 2

Mean waist circumference, HDL cholesterol and triglyceride concentrations, systolic blood pressure, fasting glucose concentration, prevalence of metabolic syndrome and IGT/DM, insulin resistance, and total cholesterol concentrations according to BMI at birth, age 2 years, age 11 years, and adulthood

	n		Waist circumference (cm)	HDL cholesterol (mmol/l)*	Triglycerides (mmol/l)*	Systolic blood pressure (mmHg)	Fasting glucose (mmol/l)*	120-min glucose (mmol/l)*	Metabolic syndrome (NCEP-ATP III) (%)	IGT/diabetes (%)	Insulin resistance (HOMA)*	Cholesterol (mmol/l)
	Min	Max
Fifths of BMI at birth (kg/m2)
Low	253	266	84.0	1.18	1.35	113.8	5.33	6.16	30.2	15.4	1.27	5.04
	255	268	85.8	1.17	1.33	113.7	5.47	6.06	29.7	16.9	1.35	4.99
	250	268	86.4	1.18	1.34	113.9	5.34	5.92	28.8	13.2	1.33	4.98
	255	268	86.5	1.16	1.33	113.8	5.29	5.97	31.6	12.9	1.22	5.05
High	251	267	86.5	1.16	1.31	112.6	5.26	5.89	24.7	15.5	1.19	4.93
P1			0.02 (+)	0.9	0.4	0.5	0.06	0.03 (−)	0.1	0.4	0.3	0.7
P2			0.1	0.8	0.09	0.05 (−)	0.04 (−)	0.009 (−)	0.002 (−)	0.2	0.004 (−)	0.3
Fifths of BMI at 2 years (kg/m2)
Low	267	286	82.5	1.18	1.31	112.5	5.36	6.31	29.3	19.9	1.22	4.99
	273	288	83.5	1.17	1.23	114.1	5.38	6.15	24.9	17.2	1.17	4.84
	273	288	85.8	1.19	1.42	112.8	5.33	5.93	29.2	12.5	1.25	5.09
	271	288	86.6	1.16	1.38	114.4	5.20	5.76	29.4	12.2	1.26	5.09
High	275	287	90.1	1.15	1.29	113.7	5.39	5.90	32.7	13.5	1.36	4.88
P1			<0.001 (+)	0.3	0.6	0.1	0.6	<0.001 (−)	0.1	0.06	0.1	0.8
P2			0.4	0.9	0.02 (−)	0.05 (−)	0.2	<0.001 (−)	0.01 (−)	0.002 (−)	0.002 (−)	0.02 (−)
Fifths of BMI at 11 years (kg/m2)
Low	267	287	78.2	1.20	1.25	111.8	5.33	6.09	18.2	15.7	1.06	4.91
	272	287	82.4	1.18	1.31	112.6	5.35	5.98	24.8	12.5	1.18	4.94
	271	288	84.7	1.19	1.31	113.5	5.29	5.98	29.1	16.2	1.18	5.03
	268	287	88.7	1.16	1.30	113.6	5.36	5.95	33.0	13.1	1.34	4.91
High	276	287	94.8	1.15	1.44	116.3	5.36	6.04	40.5	18.1	1.62	5.13
P1			<0.001 (+)	0.8	0.002 (+)	<0.001 (+)	0.3	0.8	<0.001 (+)	0.09	<0.001 (+)	0.05 (+)
P2			<0.001 (−)	0.02 (+)	<0.001 (−)	<0.001 (−)	0.3	<0.001 (−)	<0.001 (−)	0.2	<0.001 (−)	<0.001 (−)
Fifths of adult BMI (kg/m2)
Low	278	304	70.6	1.22	1.02	107.9	5.25	5.66	4.5	9.4	0.66	4.53
	288	305	80.3	1.18	1.28	112.1	5.28	5.89	12.7	11.8	0.94	4.93
	297	306	85.8	1.16	1.38	114.0	5.28	6.03	26.4	12.5	1.31	5.12
	292	305	91.3	1.17	1.43	115.5	5.38	6.08	43.7	18.8	1.63	5.12
High	286	304	100.9	1.11	1.57	118.1	5.47	6.38	57.9	23.1	2.36	5.23
P1			<0.001 (+)	<0.001 (−)	<0.001 (+)	<0.001 (+)	0.001 (+)	<0.001 (+)	<0.001 (+)	<0.001 (+)	<0.001 (+)	<0.001 (+)

All available data was used; minimum (Min) and maximum (Max) numbers of subjects in each row are provided. Fifths of BMI were sex-specific. All values in the table are adjusted for age and sex, except for the binary outcome variables.

Denotes geometric mean. P values are derived from regression analysis, using all variables except binary outcomes as continuous. P1 was adjusted for age and sex. P2 was adjusted for age, sex, and adult BMI. Alongside statistically significant P values (P ≤ 0.05) (+) denotes a positive association and (−) denotes an inverse association. ATPIII, Adult Treatment Panel III; NCEP, National Cholesterol Education Program.

Subjects with high adult waist circumference, triglycerides, blood pressure, and insulin resistance and those with metabolic syndrome had a higher mean BMI than the cohort mean at all ages from birth (Figs. 1 and 2). The pattern for metabolic syndrome (Fig. 2) matched that for overweight/obesity (Fig. 2) but differed from that for IGT/diabetes (Fig. 2), which was associated with high BMI during childhood and adolescence but a low BMI from 1 to 4 years.

Figure 1

Mean ± SD scores for BMI measured at earlier ages in subjects with components of the metabolic syndrome (A–E) and insulin resistance (HOMA) (F) above the upper quartile. High waist circumference (A), low HDL cholesterol concentration (B), high triglyceride concentration (C), high blood pressure (D), high fasting glucose concentration (E), and insulin resistance (HOMA) (F).The rest of the cohort is represented by the zero line in all graphs.

Figure 2

Mean ± SD scores for BMI and weight measured at earlier ages for subjects who developed (A and B) metabolic syndrome, (C and D) overweight or obesity (BMI >25 kg/m2), and (E and F) IGT or diabetes. The rest of the cohort is represented by the zero line in all graphs.

Conditional regression analyses showed (Table 3) that greater BMI gain from birth to 2 years was associated with higher adult waist circumference and systolic blood pressure and lower 120-min glucose concentration. Weight gain in infancy was more strongly related to adult risk factors than BMI gain (Table 4, Fig. 2) and showed additional positive associations with triglycerides, insulin resistance, and metabolic syndrome. Greater BMI/weight gain from 2 to 11 years was associated with higher waist circumference, triglycerides, systolic blood pressure, and insulin resistance and a higher risk of IGT/diabetes and metabolic syndrome. Greater BMI/weight gain between 11 years and adulthood was associated with an increase in all risk factors (lower HDL cholesterol).

Table 3

Multiple linear and logistic regression analyses using conditional BMI SD scores in earlier life to predict adult outcomes

Risk factors	BMI at birth (SD score)			BMI change*
				Birth–2 years (SD)			2–11 years (SD)			11–adult (SD)
	B	95% CI	P	B	95% CI	P	B	95% CI	P	B	95% CI	P
Waist circumference (SD)	0.07	0.05–0.09	<0.001	0.19	0.17–0.22	<0.001	0.43	0.41–0.46	<0.001	0.74	0.72–0.77	<0.001
HDL cholesterol (SD)	0.00	−0.05–0.05	1.0	−0.01	−0.07–0.05	0.7	−0.01	−0.07–0.05	0.7	−0.14	−0.20–−0.08	<0.001
Triglycerides (SD)	−0.01	−0.06–0.03	0.5	−0.00	−0.06–0.06	1.0	0.06	0.01–0.12	0.03	0.29	0.23–0.35	<0.001
Systolic blood pressure (SD)	−0.03	−0.07–0.02	0.3	0.06	0.01–0.12	0.02	0.11	0.06–0.17	<0.001	0.30	0.24–0.35	<0.001
Fasting glucose (SD)	−0.03	−0.08–0.02	0.2	−0.02	−0.08–0.03	0.4	0.03	−0.03–0.09	0.3	0.08	0.02–0.14	0.006
120-min glucose (SD)	−0.05	−0.09–0.00	0.05	−0.08	−0.14–−0.02	0.01	0.04	−0.02–0.09	0.2	0.19	0.13–0.25	<0.001
Insulin resistance (HOMA) (SD)	−0.01	−0.05–0.04	0.8	0.04	−0.02–0.09	0.2	0.15	0.09–0.20	<0.001	0.45	0.40–0.51	<0.001
Cholesterol (SD)	−0.01	−0.06–0.04	0.7	−0.03	−0.09–0.02	0.2	0.01	−0.04–0.07	0.6	0.19	0.13–0.25	<0.001

BMI changes are calculated as conditional measures; the standardized residuals of a BMI SD score value regressed on earlier SD score values. The continuous outcome variables were normalized so that B (regression coefficient) values for the associations with SD scores at birth and changes in early life indicate the SD change in the outcome per SD change in the predictor. All analyses are adjusted for age, sex, and lifestyle factors: alcohol consumption (four levels from none to heavy), physical activity (continuous measure estimated from reported activity levels, transformed, and expressed as a sex-specific SD score), tobacco use (categorized into never, ex-user, and current user), socioeconomic status in childhood based on father's occupation (ranging from 1 [low class] to 6 [high class]), socioeconomic status in adult life derived from education level, household possessions and occupation (ranging from 1 [low class] to 17 [high class]), and family history of any of high blood pressure, angina, myocardial infarction, stroke, or diabetes in a first-degree relative.

Table 4

Multiple linear and logistic regression analyses using conditional weight SD scores in earlier life to predict adult outcomes

Risk factors	Weight at birth (SD score)			Weight change*
				Birth–2 years (SD)			2–11 years (SD)			11–adult (SD)
	B	95% CI	P	B	95% CI	P	B	95% CI	P	B	95% CI	P
Waist circumference (SD)	0.15	0.13–0.17	<0.001	0.30	0.27–0.32	<0.001	0.41	0.38–0.43	<0.001	0.74	0.72–0.76	<0.001
HDL cholesterol (SD)	−0.03	−0.09–0.02	0.3	−0.03	−0.09–0.03	0.3	−0.03	−0.09–0.03	0.3	−0.14	−0.20–−0.08	<0.001
Triglycerides (SD)	−0.03	−0.09–0.02	0.3	0.09	0.03–0.15	0.004	0.07	0.01–0.12	0.02	0.28	0.22–0.34	<0.001
Systolic blood pressure (SD)	−0.02	−0.07–0.04	0.5	0.11	0.05–0.17	<0.001	0.11	0.06–0.17	<0.001	0.30	0.24–0.35	<0.001
Fasting glucose (SD)	−0.02	−0.08–0.04	0.5	0.04	−0.02–0.10	0.2	0.04	−0.01–0.10	0.1	0.07	0.01–0.13	0.01
120-min glucose (SD)	−0.04	−0.10–0.02	0.2	−0.04	−0.10–0.02	0.2	0.06	−0.00–0.12	0.06	0.17	0.11–0.23	<0.001
Insulin resistance (HOMA) (SD)	0.00	−0.05–0.06	0.9	0.08	0.02–0.13	0.005	0.15	0.10–0.20	<0.001	0.44	0.39–0.49	<0.001
Cholesterol (SD)	−0.04	−0.10–0.02	0.2	0.00	−0.06–0.06	1.0	0.01	−0.05–0.07	0.7	0.16	0.10–0.22	<0.001

Weight changes are calculated as conditional measures; the standardized residuals of a weight SD score value regressed on earlier SD score values. The continuous outcome variables were normalized so that B (regression coefficient) values for the associations with SD scores at birth and changes in early life indicate the SD change in the outcome per SD change in the predictor. All analyses are adjusted for age, sex, and lifestyle factors: alcohol consumption (four levels from none to heavy), physical activity (continuous measure estimated from reported activity levels, transformed, and expressed as a sex-specific SD score), tobacco use (categorized into never, ex-user, and current user), socioeconomic status in childhood based on father's occupation (ranging from 1 [low class] to 6 [high class]), socioeconomic status in adult life derived from education level, household possessions and occupation (ranging from 1 [low class] to 17 [high class]), and family history of any of high blood pressure, angina, myocardial infarction, stroke, or diabetes in a first-degree relative.

The inverse association between infant BMI gain and adult IGT/diabetes was stronger in subjects with lower birth weight (OR 0.74 [95% CI 0.58–0.95] for subjects weighing <2,850 g [median] compared with 1.05 [0.81–1.36] for subjects weighing ≥2,850 g, Pinteraction = 0.01). There were no significant interactions at other ages or for other outcomes.

Mean ± SD age at adiposity rebound (lowest recorded childhood BMI) was 6.6 ± 1.7 years. Earlier rebound was associated with increased adult metabolic syndrome (P = 0.07) and IGT/diabetes (P = 0.04) and higher waist circumference (P < 0.001), systolic blood pressure (P = 0.052), triglyceride concentration (P = 0.054), and 120-min glucose concentration (P = 0.01). These associations became nonsignificant after adjustment for adult BMI.

CONCLUSIONS

The Delhi cohort represents an affluent, well-educated section of Indian society that has undergone considerable “transition.” As children they were thin, but as young adults almost half were overweight and 29% had metabolic syndrome. Higher levels of all risk factors except IGT/diabetes were associated with BMI or weight above the average for the cohort as a whole (Figs. 1 and 2) and more rapid BMI or weight gain than the cohort average (Tables 3 and 4) from birth onward.

Strengths of the study were that it was population-based and children were measured by trained personnel, with exceptionally frequent follow-up throughout childhood. As with other birth cohorts, there was considerable loss to follow-up and participants are likely to be unrepresentative of the original sample. However, differences in their childhood sizes were small, and in a within-sample analysis, loss to follow-up would introduce bias only if associations between early BMI/weight and later disease differed between those studied and not studied, which seems unlikely given that inclusion was based only on subjects’ availability.

Birth

Studies in high-income countries have shown increased metabolic syndrome in adults of lower birth weight (19). In Delhi, after adjustment for adult BMI, there were inverse associations with BMI at birth for metabolic syndrome and its components (Table 2), but these resulted from positive associations with childhood BMI gain, not from lower BMI at birth (Table 3). The absence of associations between metabolic syndrome and small size at birth in this population may be due to their young age, low mean birth weight, or different newborn body composition (20).

Infancy

Consistent with studies of adults in high-income countries (8,9), greater infant BMI/weight gain was associated with a lower risk of diabetes, especially in lower-birth-weight infants. However, it was associated with an increased risk of metabolic syndrome and its components, which is consistent with recent studies showing higher BMI, blood pressure, and insulin concentrations in children who had greater infant weight gain (10,11,21). Understanding these apparently paradoxical findings is important. Effects may differ among populations according to body composition at birth and fat and lean mass accrual during infancy and may vary for different outcomes according to critical periods of development for different tissues. In developing countries, greater infant weight gain is beneficial for survival, growth, and neurocognitive development (22). However, it may become disadvantageous as obesity-related adult chronic diseases emerge (23). The balance of benefits and risks will become clearer as the cohort ages enough to assess cardiovascular disease and mortality. In an intervention study with relevant adult outcomes, protein-energy supplementation in infancy produced no increase in adult cardiovascular risk factors (24).

Childhood and adolescence

A clear message from our study, consistent with studies in high-income countries, is that rapid BMI gain in childhood and adolescence and earlier adiposity rebound are associated with adult metabolic syndrome and IGT/diabetes. This result probably reflects the known correlation between childhood and adult BMI. Thus, even in underweight children in developing countries, increasing BMI SD scores (“becoming obese relative to oneself”) is a risk factor for later disease. Reinforced by evidence that risk factors in Indian children are already high (25), our study supports efforts to prevent childhood obesity. It also suggests that interventions to control adiposity should be targeted not only to obese children, but also to “normal” weight children with rising BMI SD scores.