Childhood obesity and risk of the adult metabolic syndrome: a systematic review.

This is an Open Access articleBackground: While many studies have demonstrated positive associations between childhood obesity and adult metabolic risk, important questions remain as to the nature of the relationship. In particular, it is unclear whether the associations reflect the tracking of body mass index (BMI) from childhood to adulthood or an independent level of risk. This systematic review aimed to investigate the relationship between childhood obesity and a range of metabolic risk factors during adult life. Objective: To perform an unbiased systematic review to investigate the association between childhood BMI and risk of developing components of metabolic disease in adulthood, and whether the associations observed are independent of adult BMI. Design: Electronic databases were searched from inception until July 2010 for studies investigating the association between childhood BMI and adult metabolic risk. Two investigators independently reviewed studies for eligibility according to the inclusion/exclusion criteria, extracted the data and assessed study quality using the Newcastle–Ottawa Scale. Results: The search process identified 11 articles that fulfilled the inclusion and exclusion criteria. Although several identified weak positive associations between childhood BMI and adult total cholesterol, low-density lipo protein-cholesterol, triglyceride and insulin concentrations, these associations were ameliorated or inversed when adjusted for adult BMI or body fatness. Of the four papers that considered metabolic syndrome as an end point, none showed evidence of an independent association with childhood obesity. Conclusions: Little evidence was found to support the view that childhood obesity is an independent risk factor for adult blood lipid status, insulin levels, metabolic syndrome or type 2 diabetes. The majority of studies failed to adjust for adult BMI and therefore the associations observed may reflect the tracking of BMI across the lifespan. Interestingly, where adult BMI was adjusted for, the data showed a weak negative association between childhood BMI and metabolic variables, with those at the lower end of the BMI range in childhood, but obese during adulthood at particular risk

Fasting Hyperglycemia Predicts Lower Rates of Weight Gain by Increased Energy Expenditure and Fat Oxidation Rate

Context: Body fat-free mass (FFM), energy expenditure (EE), and respiratory quotient (RQ) are known predictors of daily food intake. Because FFM largely determines EE, it is unclear whether body composition per se or the underlying metabolism drives dietary intake. Objective: The objective of the study was to test whether 24-hour measures of EE and RQ and their components influence ad libitum food intake independently of FFM. Design and Participants: One hundred seven healthy individuals (62 males/45 females, 84 Native Americans/23 whites; age 33 ± 8 y; body mass index 33 ± 8 kg/m2; body fat 31% ± 8%) had 24-hour measures of EE in a whole-room indirect calorimeter during energy balance, followed by 3 days of ad libitum food intake using computerized vending machine systems. Body composition was estimated by dual-energy x-ray absorptiometry. Main Outcome Measures: FFM, 24-hour EE, RQ, spontaneous physical activity, sleeping EE (sleeping metabolic rate), awake and fed thermogenesis, and ad libitum food intake (INTAKE) were measured. Results: Higher 24-hour RQ (P < .001, partial R2 = 16%) and EE (P = .01, partial R2 = 7%), but not FFM (P = .65), were independent predictors of INTAKE. Mediation analysis demonstrated that 24-hour EE is responsible for 80% of the FFM effect on INTAKE (44.5 ± 16.9 kcal ingested per kilogram of FFM, P= .01), whereas the unique effect due to solely FFM was negligible (10.6 ± 23.2, P = .65). Spontaneous physical activity (r = 0.33, P = .001), but not sleeping metabolic rate (P = .71), positively predicted INTAKE, whereas higher awake and fed thermogenesis determined greater INTAKE only in subjects with a body mass index of 29 kg/m2 or less (r = 0.44, P = .01). Conclusions: EE and RQ, rather than FFM, independently determine INTAKE, suggesting that competitive energy-sensing mechanisms driven by the preferential macronutrient oxidation and total energy demands may regulate food intake

Lower “Awake and Fed Thermogenesis” Predicts Future Weight Gain in Subjects with Abdominal Adiposity

Awake and fed thermogenesis (AFT) is the energy expenditure (EE) of the nonactive fed condition above the minimum metabolic requirement during sleep and is composed of the thermic effect of food and the cost of being awake. AFT was estimated from whole-room 24-h EE measures in 509 healthy subjects (368 Native Americans and 141 whites) while subjects consumed a eucaloric diet. Follow-up data were available for 290 Native Americans (median follow-up time: 6.6 years). AFT accounted for ~10% of 24-h EE and explained a significant portion of deviations from expected energy requirements. Energy intake was the major determinant of AFT. AFT, normalized as a percentage of intake, was inversely related to age and fasting glucose concentration and showed a nonlinear relationship with waist circumference and BMI. Spline analysis demonstrated that AFT becomes inversely related to BMI at an inflection point of 29 kg/m(2). The residual variance of AFT, after accounting for covariates, predicted future weight change only in subjects with a BMI >29 kg/m(2). AFT may influence daily energy balance, is reduced in obese individuals, and predicts future weight gain in these subjects. Once central adiposity develops, a blunting of AFT may occur that then contributes to further weight gain

Higher daily energy expenditure and respiratory quotient, rather than fat free mass, independently determine greater ad libitum overeating

Context: Body fat-free mass (FFM), energy expenditure (EE), and respiratory quotient (RQ) are known predictors of daily food intake. Because FFM largely determines EE, it is unclear whether body composition per se or the underlying metabolism drives dietary intake. Objective: The objective of the study was to test whether 24-hour measures of EE and RQ and their components influence ad libitum food intake independently of FFM. Design and Participants: One hundred seven healthy individuals (62 males/45 females, 84 Native Americans/23 whites; age 33 +/- 8 y; body mass index 33 +/- 8 kg/m(2); body fat 31% +/- 8%) had 24-hour measures of EE in a whole-room indirect calorimeter during energy balance, followed by 3 days of ad libitum food intake using computerized vending machine systems. Body composition was estimated by dual-energy x-ray absorptiometry. Main Outcome Measures: FFM, 24-hour EE, RQ, spontaneous physical activity, sleeping EE (sleeping metabolic rate), awake and fed thermogenesis, and ad libitum food intake (INTAKE) were measured. Results: Higher 24-hour RQ (P < .001, partial R-2 = 16%) and EE (P = .01, partial R-2 = 7%), but not FFM (P = .65), were independent predictors of INTAKE. Mediation analysis demonstrated that 24-hour EE is responsible for 80% of the FFM effect on INTAKE (44.5 +/- 16.9 kcal ingested per kilogram of FFM, P = .01), whereas the unique effect due to solely FFM was negligible (10.6 +/- 23.2, P = .65). Spontaneous physical activity (r = 0.33, P = .001), but not sleeping metabolic rate (P = .71), positively predicted INTAKE, whereas higher awake and fed thermogenesis determined greater INTAKE only in subjects with a body mass index of 29 kg/m(2) or less (r = 0.44, P = .01). Conclusions: EE and RQ, rather than FFM, independently determine INTAKE, suggesting that competitive energy-sensing mechanisms driven by the preferential macronutrient oxidation and total energy demands may regulate food intake

Energy Expenditure Responses to Fasting and Overfeeding Identify Phenotypes Associated With Weight Change

Because it is unknown whether 24-h energy expenditure (EE) responses to dietary extremes will identify phenotypes associated with weight regulation, the aim of this study was to determine whether such responses to fasting or overfeeding are associated with future weight change. The 24-h EE during energy balance, fasting, and four different overfeeding diets with 200% energy requirements was measured in a metabolic chamber in 37 subjects with normal glucose regulation while they resided on our clinical research unit. Diets were given for 24 h each and included the following: (1) low protein (3%), (2) standard (50% carbohydrate, 20% protein), (3) high fat (60%), and (4) high carbohydrate (75%). Participants returned for follow-up 6 months after the initial measures. The decrease in 24-h EE during fasting and the increase with overfeeding were correlated. A larger reduction in EE during fasting, a smaller EE response to low-protein overfeeding, and a larger response to high-carbohydrate overfeeding all correlated with weight gain. The association of the fasting EE response with weight change was not independent from that of low protein in a multivariate model. We identified the following two independent propensities associated with weight gain: a predilection for conserving energy during caloric and protein deprivation and a profligate response to large amounts of carbohydrates

The impact of genetic variants on BMI increase during childhood versus adulthood

BACKGROUND: Genetic variants that predispose individuals to obesity may have differing influences during childhood versus adulthood, and additive effects of such variants are likely to occur. Our ongoing studies to identify genetic determinants of obesity in American Indians have identified 67 single-nucleotide polymorphisms (SNPs) that reproducibly associate with maximum lifetime non-diabetic body mass index (BMI). This study aimed to identify when, during the lifetime, these variants have their greatest impact on BMI increase. SUBJECTS/METHODS: A total of 5906 Native Americans of predominantly Pima Indian heritage with repeated measures of BMI between the ages of 5 and 45 years were included in this study. The association between each SNP with the rates of BMI increase during childhood (5-19 years) and adulthood (20-45 years) were assessed separately. The significant SNPs were used to calculate a cumulative allelic risk score (ARS) for childhood and adulthood, respectively, to assess the additive effect of these variants within each period of life. RESULTS: The majority of these SNPs (36 of 67) were associated with rate of BMI increase during childhood (P-value range: 0.00004-0.05), whereas only nine SNPs were associated with rate of BMI change during adulthood (P-value range: 0.002-0.02). These 36 SNPs associated with childhood BMI gain likely had a cumulative effect as a higher childhood-ARS associated with rate of BMI change (beta=0.032 kg m(-2) per year per risk allele, 95% confidence interval: 0.027-0.036, P<0.0001), such that at age 19 years, individuals with the highest number of risk alleles had a BMI of 10.2 kg m(-2) greater than subjects with the lowest number of risk alleles. CONCLUSIONS: Overall, our data indicates that genetic polymorphisms associated with lifetime BMI may influence the rate of BMI increase during different periods in the life course. The majority of these polymorphisms have a larger impact on BMI during childhood, providing further evidence that prevention of obesity will need to begin early in life

The effect of differing patterns of childhood body mass index gain on adult physiology in American Indians

OBJECTIVE: Identifying groups of individuals with similar patterns of body mass index (BMI) change during childhood may increase understanding of the relationship between childhood BMI and adult health. METHODS: Discrete classes of BMI z-score change were determined in 1,920 American Indian children with at least four non diabetic health examinations between the ages of 2 and 18 years using latent class trajectory analysis. In subsets of subjects, data were available for melanocortin-4 receptor (MC4R) sequencing; in utero exposure to type 2 diabetes (T2D); or, as adults, oral glucose tolerance tests, onset of T2D, or body composition. RESULTS: Six separate groups were identified. Individuals with a more modern birth year, an MC4R mutation, or in utero exposure to T2D were clustered in the two groups with high increasing and chronic overweight z-scores (P &lt; 0.0001). The z-score classes predicted adult percent fat (P &lt; 0.0001, partial r(2)  = 0.18 adjusted for covariates). There was a greater risk for T2D, independent from adult BMI, in three classes (lean increasing to overweight, high increasing, and chronic overweight z-scores) compared to the two leanest groups (respectively: HRR= 3.2, P = 0.01; 6.0, P = 0.0003; 11.6, P &lt; 0.0001). CONCLUSIONS: Distinct patterns of childhood BMI z-score change associate with adult adiposity and may impact risk of T2D

Energy Expenditure and Hormone Responses in Humans After Overeating High-Fructose Corn Syrup versus Whole-Wheat Foods

OBJECTIVE: This study sought to understand how the dietary source of carbohydrates, either high-fructose corn syrup (HFCS) or complex carbohydrates, affects energy expenditure (EE) measures, appetitive sensations, and hormones during 24 hours of overfeeding. METHODS: Seventeen healthy participants with normal glucose regulation had 24-hour EE measures and fasting blood and 24-hour urine collection during four different 1-day diets, including an energy-balanced diet, fasting, and two 75% carbohydrate diets (5% fat) given at 200% of energy requirements with either HFCS or whole-wheat foods as the carbohydrate source. In eight volunteers, hunger was assessed with visual analog scales the morning after the diets. RESULTS: Compared with energy balance, 24-hour EE increased 12.8% +/- 6.9% with carbohydrate overfeeding (P < 0.0001). No differences in 24-hour EE or macronutrient utilization were observed between the two high-carbohydrate diets; however, sleeping metabolic rate was higher after the HFCS diet (Delta = 35 +/- 48 kcal [146 +/- 200 kJ]; P = 0.01). Insulin, ghrelin, and triglycerides increased the morning after both overfeeding diets. Urinary cortisol concentrations (82.8 +/- 35.9 vs. 107.6 +/- 46.9 nmol/24 h; P = 0.01) and morning-after hunger scores (Delta = 2.4 +/- 2.0 cm; P = 0.01) were higher with HFCS overfeeding. CONCLUSIONS: The dietary carbohydrate source while overeating did not affect 24-hour EE, but HFCS overconsumption may predispose individuals to further overeating due to increased glucocorticoid release and increased hunger the following morning