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The purpose of this study was to examine the associations between sedentary behavior and moderate to vigorous physical activity (MVPA), measured by accelerometry, with body mass index (BMI) and waist circumference in 357 preschool children. Linear mixed models were used adjusting for race/ethnicity, parental education, and preschool. Follow-up analyses were performed using quantile regression. Among boys, MVPA was positively associated with BMI z-score (b = 0.080, p = .04) but not with waist circumference; quantile regression showed that MVPA was positively associated with BMI z-score at the 50th percentile (b = 0.097, p < .05). Among girls, no associations were observed between sedentary behavior and MVPA in relation to mean BMI z-score and mean waist circumference. Quantile regression indicated that, among girls at the 90th waist circumference percentile, a positive association was found with sedentary behavior (b = 0.441, p < .05), and a negative association was observed with MVPA (b = -0.599, p < .05); no associations were found with BMI z-score. In conclusion, MVPA was positively associated with BMI z-score among boys, and MVPA was negatively associated and sedentary behavior was positively associated with waist circumference among girls at the 90th percentile [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine whether moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) were independently associated with body mass index (BMI) and waist circumference (WC) in children and adolescents. Methods: Data from the International Children's Accelerometry Database were used to address our objectives (N = 11,115; 6-18 yr; 51% female). We calculated age- and gender-specific BMI and WC z-scores and used accelerometry to estimate MVPA and total SB. Self-reported television viewing was used as a measure of leisure time SB. Quantile regression was used to analyze the data. Results: MVPA and total SB were associated with lower and higher BMI and WC z-scores, respectively. These associations were strongest at the higher percentiles of the z-score distributions. After including MVPA and total SB in the same model, the MVPA associations remained, but the SB associations were no longer present. For example, each additional hour per day of MVPA was not associated with BMI z-score at the 10th percentile (b = -0.02, P = 0.170) but was associated with lower BMI z-score at the 50th (b = -0.19, P < 0.001) and 90th percentiles (b = -0.41, P < 0.001). More television viewing was associated with higher BMI and WC, and the associations were strongest at the higher percentiles of the z-score distributions, with adjustment for MVPA and total SB. Conclusions: Our observation of stronger associations at the higher percentiles indicates that increasing MVPA and decreasing television viewing at the population-level could shift the upper tails of the BMI and WC frequency distributions to lower values, thereby lowering the number of children and adolescents classified as obese. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine whether baseline sedentary behavior was associated with changes in body mass index (BMI) over 9 yr. Methods: Participants were enrolled into the National Institutes of Health American Association of Retired Persons (NIH-AARP) Diet and Health study in 1995-1996 (median age, 63 yr), and BMI was reported at baseline and 9 yr later (n = 158,436). Sitting time (<3 (referent), 3-4, 5-6, 7-8, or ≥9 h•d-1), television viewing (none, <1, 1-2, 3-4, 5-6, 7-8, or ≥9 h•d-1), and the covariates (age, sex, race, education, smoking, moderate-to-vigorous physical activity, caloric intake, and sleep duration) were reported at baseline. We used longitudinal quantile regression to model changes at the 10th, 25th, 50th, 75th, and 90th BMI percentiles. Results: More sitting at baseline was associated with additional increases in BMI over time, and the association was stronger at the upper BMI percentiles (e.g., <3 (referent) vs 5-6 h•d-1 of sitting additional increases: 50th percentile = 0.41 kg•m-2 and 95% confidence interval (CI) = 0.34-0.48; 90th percentile = 0.85 kg•m-2 and 95% CI = 0.72-0.98). Similar associations were observed between more television viewing at baseline and additional increases in BMI over time (e.g., no television (referent) vs 3-4 h•d-1 of television: 50th percentile = 1.96 kg•m-2 and 95% CI = 1.77-2.15; 90th percentile = 2.11 kg•m-2 and 95% CI = 1.49-2.73). Conclusions: Reducing sedentary behavior could help prevent an increase in BMI in adulthood especially at the upper percentiles of the BMI distribution and thereby reduce the prevalence of obesity. [ABSTRACT FROM AUTHOR]

Objective: To determine whether previously identified adult obesity susceptibility loci were associated uniformly with childhood BMI across the BMI distribution. Design and Methods: Children were recruited through the Children's Hospital of Philadelphia ( n = 7,225). Associations between the following loci and BMI were assessed using quantile regression: FTO (rs3751812), MC4R (rs12970134), TMEM18 (rs2867125), BDNF (rs6265), TNNI3K (rs1514175), NRXN3 (rs10146997), SEC16B (rs10913469), and GNPDA2 (rs13130484). BMI z-score (age and gender adjusted) was modeled as the dependent variable, and genotype risk score (sum of risk alleles carried at the 8 loci) was modeled as the independent variable. Results: Each additional increase in genotype risk score was associated with an increase in BMI z-score at the 5th, 15th, 25th, 50th, 75th, 85th, and 95th BMI z-score percentiles by 0.04 (±0.02, P = 0.08), 0.07 (±0.01, P = 9.58 × 10−7), 0.07 (±0.01, P = 1.10 × 10−8), 0.09 (±0.01, P = 3.13 × 10−22), 0.11 (±0.01, P = 1.35 × 10−25), 0.11 (±0.01, P = 1.98 × 10−20), and 0.06 (±0.01, P = 2.44 × 10−6), respectively. Each additional increase in genotype risk score was associated with an increase in mean BMI z-score by 0.08 (±0.01, P = 4.27 × 10−20). Conclusion: Obesity risk alleles were more strongly associated with increases in BMI z-score at the upper tail compared to the lower tail of the distribution. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Sleep promotion is not specifically recommended as a target for hypertension management. We examined associations of sleep duration and timing with blood pressure parameters in patients referred to pediatric nephrology clinic for elevated blood pressure evaluation. METHODS: This is a retrospective study of initial ambulatory blood pressure monitoring data and self-report sleep data collected from patients referred to nephrology clinic for the evaluation of elevated blood pressure. Linear and logistic regression modeling determined associations between sleep exposures (duration and timing) and continuous and dichotomous blood pressure outcomes, respectively, adjusted for age, sex, body mass index, and weekday versus weekend status. RESULTS: The study sample included 539 patients with mean age 14.6 years and 56% meeting hypertension criteria. Sleep duration averaged 9.1 hours per night. Average timing of sleep onset and offset were 11:06 PM and 8:18 AM, respectively. Longer sleep duration was associated with better daytime blood pressure parameters (eg, every extra hour of sleep duration was associated with a reduced odds of wake hypertension [odds ratio, 0.88; 95% CI, 0.79-0.99]). Later sleep onset was associated with worse daytime blood pressure parameters (eg, each additional hour of later sleep onset was associated with higher wake systolic blood pressure index [mean wake blood pressure/95th percentile]) (β = 0.07; 95% CI, 0.02-0.13). Associations were consistent across sex, age, body mass index, and weekday status. CONCLUSIONS: Longer sleep duration and earlier sleep onset were associated with lower blood pressure. This suggests that sleep optimization may be an important target for intervention in hypertension management. [ABSTRACT FROM AUTHOR]

BACKGROUND: Weight-for-length (WFL) is currently used to assess adiposity under 2 years. We assessed WFL- versus BMI-based estimates of adiposity in healthy infants in determining risk for early obesity. METHODS: Anthropometrics were extracted from electronic medical records for well-child visits for 73 949 full-term infants from a large pediatric network. World Health Organization WFL and BMI z scores (WFL-z and BMI-z, respectively) were calculated up to age 24 months. Correlation analyses assessed the agreement between WFL-z and BMI-z and within-subject tracking over time. Logistic regression determined odds of obesity at 2 years on the basis of adiposity classification at 2 months. RESULTS: Agreement between WFL-z and BMI-z increased from birth to 6 months and remained high thereafter. BMI-z at 2 months was more consistent with measurements at older ages than WFL-z at 2 months. Infants with high BMI (≥85th percentile) and reference WFL (5th-85th percentiles) at 2 months had greater odds of obesity at 2 years than those with high WFL (≥85th percentile) and reference BMI (5th-85th percentiles; odds ratio, 5.49 vs 1.40; P < .001). At 2 months, BMI had a higher positive predictive value than WFL for obesity at 2 years using cut-points of either the 85th percentile (31% vs 23%) or 97.7th percentile (47% vs 29%). CONCLUSIONS: High BMI in early infancy is more strongly associated with early childhood obesity than high WFL. Forty-seven percent of infants with BMI ≥97.7th percentile at 2 months (versus 29% of infants with WFL ≥97.7th percentile at 2 months) were obese at 2 years. Epidemiologic studies focused on assessing childhood obesity risk should consider using BMI in early infancy. [ABSTRACT FROM AUTHOR]