OBJECTIVE: Although the tracking of BMI levels from childhood to adulthood has been examined, there is little information on the within-person variability of BMI. METHODS: Longitudinal data from 11,591 schoolchildren, 3,096 of whom were reexamined as adults, were used to explore the tracking and variability of BMI levels. This article focuses on changes in age-adjusted levels of BMI. RESULTS: There was strong tracking of BMI levels. The correlation of adjusted BMI levels was r = 0.88, and 78% of children with severe obesity at one examination had severe obesity at the next examination (mean interval, 2.7 years). Further, an increase in adjusted BMI from +5 kg/m(2) (above the median) to + 10 increased the risk for adult BMI >/= 40 by 2.7-fold. However, BMI levels among children and adolescents were variable. Over a 9- to 15-month interval, the SD of adjusted BMI change was 0.9 kg/m(2) , and 0.7% of children had an absolute change >/= 3.5. This variability was associated with the interval between examinations and with the initial BMI. CONCLUSIONS: Despite the high degree of tracking of BMI, annual changes of 3.5 kg/m(2) or more are plausible. Knowledge of this variability is important when following a child over time.

BACKGROUND: Adequate water intake is critical to physiologic and cognitive functioning. Although water requirements increase with body size, it remains unclear whether weight status modifies the relation between water intake and hydration status. OBJECTIVE: We examined how the association between water intake and urine osmolality, which is a hydration biomarker, varied by weight status. DESIGN: NHANES cross-sectional data (2009-2012) were analyzed in 9601 nonpregnant adults aged ≥20 y who did not have kidney failure. Weight status was categorized with the use of body mass index on the basis of measured height and weight (underweight or normal weight, overweight, and obesity). Urine osmolality was determined with the use of freezing-point depression osmometry. Hypohydration was classified according to the following age-dependent formula: ≥831 mOsm/kg - [3.4 × (age - 20 y)]. Total water intake was determined with the use of a 24-h dietary recall and was dichotomized as adequate or low on the basis of the Institute of Medicine's adequate intake recommendations for men and women (men: ≥3.7 or <3.7 L; nonlactating women: ≥2.7 or <2.7 L; lactating women: ≥3.8 or <3.8 L for adequate or low intakes, respectively). We tested interactions and conducted linear and log-binomial regressions. RESULTS: Total water intake (P = 0.002), urine osmolality (P < 0.001), and hypohydration prevalence (P < 0.001) all increased with higher weight status. Interactions between weight status and water intake status were significant in linear (P = 0.005) and log-binomial (P = 0.015) models, which were then stratified. The prevalence ratio of hypohydration between subjects with adequate water intake and those with low water intake was 0.56 (95% CI: 0.43, 0.73) in adults who were underweight or normal weight, 0.67 (95% CI: 0.57, 0.79) in adults who were overweight, and 0.78 (95% CI: 0.70, 0.88) in adults who were obese. CONCLUSION: On a population level, obesity modifies the association between water intake and hydration status.

OBJECTIVE: Previous research has not investigated the role of prescription medication in sleep-obesity associations despite the fact that 56% of U.S. adults take at least one prescription medication. METHODS: Data from n = 16,622 adults in the National Health and Nutrition Examination Survey (2007-2012) were used to examine how the association between obesity and self-reported sleep duration varied by total number of prescription medications used in the past 30 days and by select classes of prescription medications including anxiolytics/sedatives/hypnotics, antidepressants, sleep aids, anticonvulsants, thyroid agents, and metabolic agents. RESULTS: Logistic regression analyses showed a significant inverse linear association of sleep duration and obesity, regardless of the total number of prescription medications individuals were taking. Each additional hour of sleep was associated with a 10% decrease in the odds of obesity. Results suggest that increased sleep duration is associated with lower odds of having obesity overall, even for long-duration sleepers (≥9 h), and this association does not differ for those taking antidepressants, thyroid agents, metabolic agents, and multiple prescription medications. CONCLUSIONS: The relationship between sleep duration and obesity was similar among all prescription medication users and nonusers. The potential for a nonlinear association between sleep duration and obesity may be important to examine in some specific prescription medication classes.

IMPORTANCE: Previous analyses of obesity trends among children and adolescents showed an increase between 1988-1994 and 1999-2000, but no change between 2003-2004 and 2011-2012, except for a significant decline among children aged 2 to 5 years. OBJECTIVES: To provide estimates of obesity and extreme obesity prevalence for children and adolescents for 2011-2014 and investigate trends by age between 1988-1994 and 2013-2014. DESIGN, SETTING, AND PARTICIPANTS: Children and adolescents aged 2 to 19 years with measured weight and height in the 1988-1994 through 2013-2014 National Health and Nutrition Examination Surveys. EXPOSURES: Survey period. MAIN OUTCOMES AND MEASURES: Obesity was defined as a body mass index (BMI) at or above the sex-specific 95th percentile on the US Centers for Disease Control and Prevention (CDC) BMI-for-age growth charts. Extreme obesity was defined as a BMI at or above 120% of the sex-specific 95th percentile on the CDC BMI-for-age growth charts. Detailed estimates are presented for 2011-2014. The analyses of linear and quadratic trends in prevalence were conducted using 9 survey periods. Trend analyses between 2005-2006 and 2013-2014 also were conducted. RESULTS: Measurements from 40,780 children and adolescents (mean age, 11.0 years; 48.8% female) between 1988-1994 and 2013-2014 were analyzed. Among children and adolescents aged 2 to 19 years, the prevalence of obesity in 2011-2014 was 17.0% (95% CI, 15.5%-18.6%) and extreme obesity was 5.8% (95% CI, 4.9%-6.8%). Among children aged 2 to 5 years, obesity increased from 7.2% (95% CI, 5.8%-8.8%) in 1988-1994 to 13.9% (95% CI, 10.7%-17.7%) (P < .001) in 2003-2004 and then decreased to 9.4% (95% CI, 6.8%-12.6%) (P = .03) in 2013-2014. Among children aged 6 to 11 years, obesity increased from 11.3% (95% CI, 9.4%-13.4%) in 1988-1994 to 19.6% (95% CI, 17.1%-22.4%) (P < .001) in 2007-2008, and then did not change (2013-2014: 17.4% [95% CI, 13.8%-21.4%]; P = .44). Obesity increased among adolescents aged 12 to 19 years between 1988-1994 (10.5% [95% CI, 8.8%-12.5%]) and 2013-2014 (20.6% [95% CI, 16.2%-25.6%]; P < .001) as did extreme obesity among children aged 6 to 11 years (3.6% [95% CI, 2.5%-5.0%] in 1988-1994 to 4.3% [95% CI, 3.0%-6.1%] in 2013-2014; P = .02) and adolescents aged 12 to 19 years (2.6% [95% CI, 1.7%-3.9%] in 1988-1994 to 9.1% [95% CI, 7.0%-11.5%] in 2013-2014; P < .001). No significant trends were observed between 2005-2006 and 2013-2014 (P value range, .09-.87). CONCLUSIONS AND RELEVANCE: In this nationally representative study of US children and adolescents aged 2 to 19 years, the prevalence of obesity in 2011-2014 was 17.0% and extreme obesity was 5.8%. Between 1988-1994 and 2013-2014, the prevalence of obesity increased until 2003-2004 and then decreased in children aged 2 to 5 years, increased until 2007-2008 and then leveled off in children aged 6 to 11 years, and increased among adolescents aged 12 to 19 years.

OBJECTIVE: This study assessed the prevalence and consistency of high values of weight, height, and BMI considered to be biologically implausible (BIV) using cut points proposed by WHO among 8.8 million low-income children (13.7 million observations). METHODS: Cross-sectional and longitudinal analyses were performed among 2- to 4-year-olds who were examined from 2008 through 2011. RESULTS: Overall, 2.7% of the body size measurements were classified as BIVs; 95% of these BIVs were very high. Among the subset of children (3.6 million) examined more than once, most of those who initially had a high weight or BMI BIV also had a high BIV at the second examination; odds ratios were >250. Based on several alternative classifications of BIVs, the current cut points likely underestimate the prevalence of obesity by about 1%. CONCLUSIONS: Many of the extremely high values of body size currently flagged as BIVs are unlikely to be errors. Increasing the z-score cut points or using a percentage of the maximum values in the National Health and Nutrition Examination Survey, could improve the balance between removing probable errors and retaining those that are likely correct.

INTRODUCTION: Although decline in muscle mass and quality and resulting declines in muscle strength are associated with aging, more research is needed in general populations to assess the utility of handgrip strength as an indicator of muscle strength and cardiovascular disease risk. METHODS: Data from 4,221 participants aged ≥20 years in the 2011-2012 cycle of National Health and Nutrition Examination Survey were analyzed during 2014-2015. Standing isometric relative handgrip strength (calculated as maximal absolute handgrip strength from both hands divided by BMI) was used to predict cardiovascular biomarkers, including blood pressure (measured systolic and diastolic blood pressure); serum lipids (total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides); and plasma insulin and glucose. RESULTS: Results from regression analyses showed that higher relative grip strength was significantly associated with lower systolic blood pressure, triglycerides, and plasma insulin and glucose, and higher high-density lipoprotein cholesterol in male and female participants (p<0.05 for all). Secondary descriptive analyses found that absolute handgrip strength increased significantly with increasing weight status, but relative handgrip strength decreased significantly with increasing weight status. CONCLUSIONS: Results suggest that increased relative handgrip strength may be associated with a better profile of cardiovascular health biomarkers among U.S. adults. Relative grip strength, which both adjusts for the confounding of mass and assesses concomitant health risks of increased body size and low muscle strength, may be a useful public health measure of muscle strength.

BACKGROUND: The WHO cutoffs to classify biologically implausible values (BIVs) for weight, height, and weight-for-height in children and adolescents are widely used in data cleaning. OBJECTIVES: We assess 1) the prevalence of these BIVs, 2) whether they were consistent with information on waist circumference, arm circumference, and leg lengths, and 3) the effect of their exclusion on the estimated prevalence of obesity in 2- to 19-y-olds in the NHANES, which is a study in which extreme values were verified when recorded. DESIGN: We conducted cross-sectional analyses in 26,480 children and adolescents in the NHANES from 1999-2000 through 2011-2012. RESULTS: The overall prevalence for a BIV for any body-size measure was 0.9% (n = 277), and almost all BIVs were due to extremely high, rather than low, values. Of 186 subjects who had a high BIV for weight or body mass index (BMI), all but one subject had both arm and waist circumferences that were greater than the sex- and age-specific 95th percentiles; 75% of subjects had circumferences greater than the 99th percentile. Of 63 subjects with a high height BIV, 75% of them had a leg length that was greater than the 95th percentile. The exclusion of children and adolescents with a BIV reduced the overall prevalence of obesity by approximately 0.5 percentage points and by 1.7% in non-Hispanic blacks. CONCLUSIONS: Most of the extremely high values of weight, height, and BMI flagged as BIVs in the NHANES are very likely correct. The increase of z score cutoffs or the use of an alternative method to detect possible errors could improve the balance between removing incorrect values and retaining extremely high, but accurate, values in other data sets.

As more epidemiologic data on childhood obesity become available, researchers are faced with decisions regarding how to determine biologically implausible values (BIVs) in height, weight, and body mass index. The purpose of the current study was 1) to track how often large, epidemiologic studies address BIVs, 2) to review BIV identification methods, and 3) to apply those methods to a large data set of youth to determine the effects on obesity and BIV prevalence estimates. Studies with large samples of anthropometric data (n > 1,000) were reviewed to track whether and how BIVs were defined. Identified methods were then applied to a longitudinal sample of 13,662 students (65% African American, 52% male) in 55 urban, low-income schools that enroll students from kindergarten through eighth grade (ages 5-13 years) in Philadelphia, Pennsylvania, during 2011-2012. Using measured weight and height at baseline and 1-year follow-up, we compared descriptive statistics, weight status prevalence, and BIV prevalence estimates. Eleven different BIV methods were identified. When these methods were applied to a large data set, severe obesity and BIV prevalence ranged from 7.2% to 8.6% and from 0.04% to 1.68%, respectively. Approximately 41% of large epidemiologic studies did not address BIV identification, and existing identification methods varied considerably. Increased standardization of the identification and treatment of BIVs may aid in the comparability of study results and accurate monitoring of obesity trends.

The purposes of this study were to use the National Health and Nutrition Examination Study (NHANES 2011-12) data to determine nationally representative combined-hand grip strength ranges and percentile information by sex and age group, examine trends in strength across age by sex, and to determine the relative proportion of children and adults falling into established Health Benefit Zones (HBZ). Results indicate that mean strength was greater among males than females, increased linearly for children and in a quadratic fashion among adults for both sexes. Grip strength peaked in the 30 -39 year age group for both men (216.4lbs) and women (136.5lbs) with subsequent age groups showing gradual decline, ps < .0001. Relative and absolute increase in grip strength was greater for males than for females, but relative decrease from peak strength was less among women than men. Although absolute strength was greater among men than women, HBZ data indicated that a higher percentage of males than females overall and at each age group fell into the Needs Improvement zone, with differences particularly pronounced during adolescence and older adulthood. These data provide the first nationally representative population estimates of combined-hand grip strength and percentile information from childhood through senescence, and suggest consideration of HBZ information in conjunction with grip strength to improve surveillance data interpretation and intervention planning.