Metabolite Profiles of the Relationship between Body Mass Index (BMI) Milestones and Metabolic Risk during Early Adolescence.
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ABSTRACT:
SUBMITTER: Perng W
PROVIDER: S-EPMC7464362 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Metabolite Profiles of the Relationship between Body Mass Index (BMI) Milestones and Metabolic Risk during Early Adolescence.
Perng Wei W Rahman Mohammad L ML Aris Izzuddin M IM Michelotti Gregory G Sordillo Joanne E JE Chavarro Jorge E JE Oken Emily E Hivert Marie-France MF
Metabolites 20200731 8
Early growth is associated with future metabolic risk; however, little is known of the underlying biological pathways. In this prospective study of 249 boys and 227 girls, we sought to identify sex-specific metabolite profiles that mark the relationship between age and magnitude of the infancy body mass index (BMI) peak, and the childhood BMI rebound with a metabolic syndrome z-score (MetS z-score) during early adolescence (median age 12.8 years). Thirteen consensus metabolite networks were gene ...[more]
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Project description:BackgroundFew studies have examined the independent and combined relationships of body mass index (BMI) peak and rebound with adiposity, insulin resistance and metabolic risk later in life. We used data from Project Viva, a well-characterized birth cohort from Boston with repeated measures of BMI, to help fill this gap.MethodsAmong 1681 children with BMI data from birth to mid childhood, we fitted individual BMI trajectories using mixed-effects models with natural cubic splines and estimated age, and magnitude of BMI, at peak (in infancy) and rebound (in early childhood). We obtained cardiometabolic measures of the children in early adolescence (median 12.9 years) and analysed their associations with the BMI parameters.ResultsAfter adjusting for potential confounders, age and magnitude at infancy BMI peak were associated with greater adolescent adiposity, and earlier adiposity rebound was strongly associated with greater adiposity, insulin resistance and metabolic risk score independently of BMI peak. Children with a normal timing of BMI peak plus early rebound had an adverse cardiometabolic profile, characterized by higher fat mass index {β 2.2 kg/m2 [95% confidence interval (CI) 1.6, 2.9]}, trunk fat mass index [1.1 kg/m2 (0.8, 1.5)], insulin resistance [0.2 units (0.04, 0.4)] and metabolic risk score [0.4 units (0.2, 0.5)] compared with children with a normal BMI peak and a normal rebound pattern. Children without a BMI peak (no decline in BMI after the rise in infancy) also had adverse adolescent metabolic profiles.ConclusionsEarly age at BMI rebound is a strong risk factor for cardiometabolic risk, independent of BMI peak. Children with a normal peak-early rebound pattern, or without any BMI decline following infancy, are at greatest risk of adverse cardiometabolic profile in adolescence. Routine monitoring of BMI may help to identify children who are at greatest risk of developing an adverse cardiometabolic profile in later life and who may be targeted for preventive interventions.
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Project description:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.
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Project description:Objective: Obesity and depression are likely to interact mutually, which makes it unclear whether obesity causes depression or depression leads to obesity, and how the genotypes have a role in obesity and depression. Method: This cross- sectional study was conducted on a sample of 400 individuals from the participants in the third phase of the comprehensive Iranian Multicenter Osteoporosis Study (IMOS). Anthropometric measurements and depression were assessed. PCR-RFLP was used to investigate the NPY polymorphism. Binary logistic regression model was employed to determine depression as the dependent factor and gene polymorphism. Results: The frequency of NPY rs16139 was 6%. No significant association was found between NPY genotypes and depression (p >0.05). Furthermore, the results suggest that those with central obesity had an increased chance of developing depression (P = 0.02). Conclusion: The frequency of NPY polymorphism was 6%. Our study could not find a correlation between rs16139 and depression.
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Project description:OBJECTIVE:To assess associations of pre-, perinatal, and parental factors with age and magnitude at body mass index (BMI) peak and rebound. STUDY DESIGN:Among 1681 children with BMI data from birth to mid-childhood in Project Viva, we fitted individual BMI trajectories using mixed-effect models with natural cubic spline functions and estimated age and magnitude at peak in infancy and rebound in early childhood. We used stepwise multivariable regression to identify predictors of peak and rebound in the 1354 (63.6%) children with estimable trajectory milestones. RESULTS:The mean (SD) of age at BMI peak was 8.4 (2.7) months and at rebound was 59.8 (19.6) months, and the mean (SD) of magnitude at peak was 18.0 (1.4) kg/m2 and at rebound was 15.9 (1.2) kg/m2. Girls had a later age at peak, earlier age at rebound, and lower magnitudes at peak and rebound than boys. Maternal isolated hyperglycemia (vs normoglycemia: ? 0.7 months [95% CI 0.2-1.2]) and pre-eclampsia (vs normal blood pressure: 1.6 months [0.8-2.4]) were associated with a later peak, and impaired glucose tolerance (vs normoglycemia: -0.5?kg/m2 [-0.9, -0.1]) was associated with a lower magnitude at peak. Greater maternal first-trimester weight gain, smoking during pregnancy, no breastfeeding, parental obesity, and no university education were associated with greater BMI at rebound. CONCLUSIONS:We have identified modifiable prenatal and parental predictors of BMI peak in infancy and rebound in childhood. Early-life interventions that address these factors may be effective in changing BMI peak and rebound and potentially preventing later obesity.
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Project description:ImportancePast studies have showed associations between antibiotic exposure and child weight outcomes. Few, however, have documented alterations to body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) trajectory milestone patterns during childhood after early-life antibiotic exposure.ObjectiveTo examine the association of antibiotic use during the first 48 months of life with BMI trajectory milestones during childhood in a large cohort of children.Design, setting, and participantsThis retrospective cohort study used electronic health record data from 26 institutions participating in the National Patient-Centered Clinical Research Network from January 1, 2009, to December 31, 2016. Participant inclusion required at least 1 valid set of same-day height and weight measurements at each of the following age periods: 0 to 5, 6 to 11, 12 to 23, 24 to 59, and 60 to 131 months (183 444 children). Data were analyzed from June 1, 2019, to June 30, 2020.ExposuresAntibiotic use at 0 to 5, 6 to 11, 12 to 23, 24 to 35, and 36 to 47 months of age.Main outcomes and measuresAge and magnitude of BMI peak and BMI rebound.ResultsOf 183 444 children in the study (mean age, 3.3 years [range, 0-10.9 years]; 95 228 [51.9%] were boys; 80 043 [43.6%] were White individuals), 78.1% received any antibiotic, 51.0% had at least 1 episode of broad-spectrum antibiotic exposure, and 65.0% had at least 1 episode of narrow-spectrum antibiotic exposure at any time before 48 months of age. Exposure to any antibiotics at 0 to 5 months of age (vs no exposure) was associated with later age (β coefficient, 0.05 months [95% CI, 0.02-0.08 months]) and higher BMI (β coefficient, 0.09 [95% CI, 0.07-0.11]) at peak. Exposure to any antibiotics at 0 to 47 months of age (vs no exposure) was associated with an earlier age (-0.60 months [95% CI, -0.81 to -0.39 months]) and higher BMI at rebound (β coefficient, 0.02 [95% CI, 0.01-0.03]). These associations were strongest for children with at least 4 episodes of antibiotic exposure. Effect estimates for associations with age at BMI rebound were larger for those exposed to antibiotics at 24 to 35 months of age (β coefficient, -0.63 [95% CI, -0.83 to -0.43] months) or 36 to 47 (β coefficient, -0.52 [95% CI, -0.72 to -0.31] months) than for those exposed at 0 to 5 months of age (β coefficient, 0.26 [95% CI, 0.01-0.51] months) or 6 to 11 (β coefficient, 0.00 [95% CI, -0.20 to 0.20] months).Conclusions and relevanceIn this cohort study, antibiotic exposure was associated with statistically significant, but small, differences in BMI trajectory milestones in infancy and early childhood. The small risk of an altered BMI trajectory milestone pattern associated with early-life antibiotic exposure is unlikely to be a key factor during prescription decisions for children.
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Project description:BackgroundA high body mass index (BMI) is a major risk factor for several chronic diseases, but the biology underlying these associations is not well-understood. Dyslipidemia, inflammation, and elevated levels of growth factors and sex steroid hormones explain some of the increased disease risk, but other metabolic factors not yet identified may also play a role.DesignIn order to discover novel metabolic biomarkers of BMI, we used non-targeted metabolomics to assay 317 metabolites in blood samples from 947 participants and examined the cross-sectional associations between metabolite levels and BMI. Participants were from three studies in the United States and China. Height, weight, and potential confounders were ascertained by questionnaire (US studies) or direct measurement (Chinese study). Metabolite levels were measured using liquid-phase chromatography and gas chromatography coupled with mass spectrometry. We evaluated study-specific associations using linear regression, adjusted for age, gender, and smoking, and we estimated combined associations using random effects meta-analysis.ResultsThe meta-analysis revealed 37 metabolites significantly associated with BMI, including 19 lipids, 12 amino acids, and 6 others, at the Bonferroni significance threshold (p<0.00016). Eighteen of these associations had not been previously reported, including histidine, an amino acid neurotransmitter, and butyrylcarnitine, a lipid marker of whole-body fatty acid oxidation. Heterogeneity by study was minimal (all Pheterogeneity >0.05). In total, 110 metabolites were associated with BMI at the p<0.05 level.ConclusionThese findings establish a baseline for the BMI metabolome, and suggest new targets for researchers attempting to clarify mechanistic links between high BMIs and disease risk.
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Project description:Background: This study aimed to identify patterns of body mass index (BMI) changes in adolescence and to assess whether delivery mode (Cesarean and vaginal delivery) was associated with BMI trajectories. Methods: This study was conducted among 569 adolescents aged 10-15 years that resided in the city of Zibo, China. The height and weight of each participant were repeatedly measured at 10, 11, 12, 13, 14 and 15 years. Group based trajectory modeling (GBTM) was used to estimate BMI change trajectories, and multinomial logistic regression was conducted to evaluate the independent association of delivery mode and BMI trajectory classes. Results: Of the 569 participants, 407 (71.5%) were vaginal deliveries and 162 (28.5%) were Cesarean deliveries. Five distinct long-term BMI trajectories were identified: "persistent healthy weight" (57.5%), "persistent underweight" (6.5%), "obesity to healthy weight" (7.8%), "progressive overweight" (10.6%), "progressive obesity" (17.6%). Adjusted multinomial logistic models revealed a twofold increase in risks between ages 10-15 years of "progressive obesity" trajectory (OR = 2.50, 95% CI: 1.42, 4.41) for children born through Cesarean section compared with vaginal birth. Conclusions: Five distinct long-term BMI trajectories were identified during adolescence in our research, and we confirmed that Cesarean birth was significantly increased the risk of "progressive obesity" trajectory but not the "obesity to healthy weight" trajectory.
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Project description:BACKGROUND:Body Mass Index (BMI), like most human phenotypes, is substantially heritable. However, BMI is not normally distributed; the skew appears to be structural, and increases as a function of age. Moreover, twin correlations for BMI commonly violate the assumptions of the most common variety of the classical twin model, with the MZ twin correlation greater than twice the DZ correlation. This study aimed to decompose twin correlations for BMI using more general skew-t distributions. METHODS:Same sex MZ and DZ twin pairs (N = 7,086) from the community-based Washington State Twin Registry were included. We used latent profile analysis (LPA) to decompose twin correlations for BMI into multiple mixture distributions. LPA was performed using the default normal mixture distribution and the skew-t mixture distribution. Similar analyses were performed for height as a comparison. Our analyses are then replicated in an independent dataset. RESULTS:A two-class solution under the skew-t mixture distribution fits the BMI distribution for both genders. The first class consists of a relatively normally distributed, highly heritable BMI with a mean in the normal range. The second class is a positively skewed BMI in the overweight and obese range, with lower twin correlations. In contrast, height is normally distributed, highly heritable, and is well-fit by a single latent class. Results in the replication dataset were highly similar. CONCLUSIONS:Our findings suggest that two distinct processes underlie the skew of the BMI distribution. The contrast between height and weight is in accord with subjective psychological experience: both are under obvious genetic influence, but BMI is also subject to behavioral control, whereas height is not.
| S-EPMC5874062 | biostudies-literature
Project description:ObjectiveHigh body mass index (BMI) is an important predictor of mortality but estimating underlying causality is hampered by confounding and pre-existing disease. Here, we use information from the offspring to approximate parental BMIs, with an aim to avoid biased estimation of mortality risk caused by reverse causality.MethodsThe analyses were based on information on 9674 offspring-mother and 9096 offspring-father pairs obtained from the 1958 British birth cohort. Parental BMI-mortality associations were analysed using conventional methods and using offspring BMI as a proxy, or instrument, for their parents' BMI.ResultsIn the conventional analysis, associations between parental BMI and all-cause mortality were U-shaped (Pcurvature < 0.001), while offspring BMI had linear associations with parental mortality (Ptrend < 0.001, Pcurvature > 0.46). Curvature was particularly pronounced for mortality from respiratory diseases and from lung cancer. Instrumental variable analyses suggested a positive association between BMI and mortality from all causes [mothers: HR per SD of BMI 1.43 (95% CI 1.21-1.69), fathers: HR 1.17 (1.00-1.36)] and from coronary heart disease [mothers: HR 1.65 (1.15-2.36), fathers: HR 1.51 (1.17-1.97)]. These were larger than HR from the equivalent conventional analyses, despite some attenuation by adjustment for social indicators and smoking.ConclusionsAnalyses using offspring BMI as a proxy for parental BMI suggest that the apparent adverse consequences of low BMI are considerably overestimated and adverse consequences of overweight are underestimated in conventional epidemiological studies.
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