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Trajectory classes of body mass index in a representative elderly community sample.

ABSTRACT: It is unclear whether distinct weight-related trajectory classes, differing in course, demographics, and health characteristics, exist in the elderly population.Data came from the 10-year (1986-1996) Duke Established Populations for Epidemiologic Studies of the Elderly study of 3,861 black (54%) and white (46%) participants aged 65-105 years. Latent-class trajectories of body mass index (BMI: kg/m(2)) based on self-reported weight and height at baseline, 3, 6, and 10 years later were determined using generalized mixture models. Polytomous logistic regression was used to identify baseline demographic and health characteristics that distinguished the trajectories, and 10-year postbaseline data to confirm the findings.We identified three trajectories: normal weight (BMI ~24, 27.6% of the sample), overweight (BMI ~26, 65.1%), and obese (BMI ~31, 7.3%). Demographic characteristics distinguished the three trajectories: highest odds of blacks, women, and less education in the obese trajectory, lowest in the normal-weight trajectory. Obese and overweight differed adversely from normal-weight trajectories, but not significantly from each other on cognitive impairment, hypertension, and diabetes. Depressive symptomatology was more prevalent in the obese; they were also younger. There was no association with cancer or heart disease.Distinct trajectories and course of BMI were present in this older population. Weight loss increased with increase in BMI class. Although demographic characteristics distinguished all trajectory classes, adverse health characteristics distinguished the overweight and obese classes from the normal-weight class, but not from each other. Problems associated with education and health are present at study entry and should be addressed earlier in life.

SUBMITTER: Kuchibhatla MN

PROVIDER: S-EPMC3660114 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Trajectory classes of body mass index in a representative elderly community sample.

Kuchibhatla Maragatha N MN Fillenbaum Gerda G GG Kraus William E WE Cohen Harvey Jay HJ Blazer Dan G DG

The journals of gerontology. Series A, Biological sciences and medical sciences 20121022 6

<h4>Background</h4>It is unclear whether distinct weight-related trajectory classes, differing in course, demographics, and health characteristics, exist in the elderly population.<h4>Methods</h4>Data came from the 10-year (1986-1996) Duke Established Populations for Epidemiologic Studies of the Elderly study of 3,861 black (54%) and white (46%) participants aged 65-105 years. Latent-class trajectories of body mass index (BMI: kg/m(2)) based on self-reported weight and height at baseline, 3, 6, ...[more]

PMID: 23089335

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Project description:What is already known about this subjectRates of childhood obesity have increased since the mid-1970s. Research into behavioural determinants has focused on physical inactivity and unhealthy diets. Cross-sectional studies indicate an association between psychological stress experienced by parents and obesity in pre-adolescents.What this study addsWe provide evidence of a prospective association between parental psychological stress and increased weight gain in pre-adolescents. Family-level support for those experiencing chronic stress might help promote healthy diet and exercise behaviours in children.ObjectiveWe examined the impact of parental psychological stress on body mass index (BMI) in pre-adolescent children over 4 years of follow-up.MethodsWe included 4078 children aged 5-10 years (90% were between 5.5 and 7.5 years) at study entry (2002-2003) in the Children's Health Study, a prospective cohort study in southern California. A multi-level linear model simultaneously examined the effect of parental stress at study entry on the attained BMI at age 10 and the slope of change across annual measures of BMI during follow-up, controlled for the child's age and sex. BMI was calculated based on objective measurements of height and weight by trained technicians following a standardized procedure.ResultsA two standard deviation increase in parental stress at study entry was associated with an increase in predicted BMI attained by age 10 of 0.287 kg m(-2) (95% confidence interval 0.016-0.558; a 2% increase at this age for a participant of average attained BMI). The same increase in parental stress was also associated with an increased trajectory of weight gain over follow-up, with the slope of change in BMI increased by 0.054 kg m(-2) (95% confidence interval 0.007-0.100; a 7% increase in the slope of change for a participant of average BMI trajectory).ConclusionsWe prospectively demonstrated a small effect of parental stress on BMI at age 10 and weight gain earlier in life than reported previously. Interventions to address the burden of childhood obesity should address the role of parental stress in children.

Project description:Background: Limited evidence suggests an association between prenatal exposure to traffic pollution and greater adiposity in childhood, but the time window during which growth may be most affected is not known. Methods: We studied 1,649 children in Project Viva, a Boston-area pre-birth cohort. We used spatiotemporal models to estimate prenatal residential air pollution exposures and geographic information systems to estimate neighborhood traffic density and roadway proximity. We used weight and stature measurements at clinical and research visits to estimate a BMI trajectory for each child with mixed-effects natural cubic spline models. In primary analyses, we examined associations of residential PM2.5 and black carbon (BC) exposures during the third trimester and neighborhood traffic density and home roadway proximity at birth address with (1) estimated BMI at 6 month intervals through 10 years of age, (2) magnitude and timing of BMI peak and rebound, and (3) overall BMI trajectory. In secondary analyses, we examined associations of residential PM2.5 and BC exposures during the first and second trimesters with BMI outcomes. Results: Median (interquartile range; IQR) concentration of residential air pollution during the third trimester was 11.4 (1.7) ?g/m3 for PM2.5 and 0.7 (0.3) ?g/m3 for BC. Participants had a median (IQR) of 13 (7) clinical or research BMI measures from 0 to 10 years of age. None of the traffic pollution exposures were significantly associated with any of the BMI outcomes in covariate-adjusted models, although effect estimates were in the hypothesized direction for neighborhood traffic density and home roadway proximity. For example, greater neighborhood traffic density [median (IQR) 857 (1,452) vehicles/day x km of road within 100 m of residential address at delivery] was associated with a higher BMI throughout childhood, with the strongest associations in early childhood [e.g., per IQR increment natural log-transformed neighborhood traffic density, BMI at 12 months of age was 0.05 (-0.03, 0.13) kg/m2 higher and infancy peak BMI was 0.05 (-0.03, 0.14) kg/m2 higher]. Conclusions: We found no evidence for a persistent effect of prenatal exposure to traffic pollution on BMI trajectory from birth through mid-childhood in a population exposed to modest levels of air pollution.

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.

Dataset Information

Trajectory classes of body mass index in a representative elderly community sample.

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Trajectory classes of body mass index in a representative elderly community sample.

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