A Longitudinal Comparison of Alternatives to Body Mass Index Z-Scores for Children with Very High Body Mass Indexes.

OBJECTIVE: The current Centers for Disease Control and Prevention (CDC) body mass index (BMI) z-scores are inaccurate for BMIs of ≥97th percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMI-for-age z-score (BMIz), BMI expressed as a percentage of the 95th percentile (%CDC95th percentile), extended BMIz, BMI expressed as a percentage of the median (%median), and %median adjusted for the dispersion of BMIs. STUDY DESIGN: We illustrate the behavior of the metrics among children of different ages and BMIs. We then compared the longitudinal tracking of the BMI metrics in electronic health record data from 1.17 million children in PEDSnet using the intraclass correlation coefficient to determine if 1 metric was superior. RESULTS: Our examples show that using CDC BMIz for high BMIs can result in nonsensical results. All alternative metrics showed higher tracking than CDC BMIz among children with obesity. Of the alternatives, modified BMIz performed poorly among children with severe obesity, and %median performed poorly among children who did not have obesity at their first visit. The highest intraclass correlation coefficients were generally seen for extended BMIz, adjusted %median, and %CDC95th percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results and current familiarity BMI z-scores and percentiles. Both extended BMIz and extended BMI percentiles may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs of <95th percentile and are superior for very high BMIs. Researchers' familiarity with the current CDC z-scores and clinicians with the CDC percentiles may ease the transition to the extended BMI scale.

Longitudinal Trends in Body Mass Index Before and During the COVID-19 Pandemic Among Persons Aged 2-19 Years - United States, 2018-2020.

Obesity is a serious health concern in the United States, affecting more than one in six children (1) and putting their long-term health and quality of life at risk.* During the COVID-19 pandemic, children and adolescents spent more time than usual away from structured school settings, and families who were already disproportionally affected by obesity risk factors might have had additional disruptions in income, food, and other social determinants of health.† As a result, children and adolescents might have experienced circumstances that accelerated weight gain, including increased stress, irregular mealtimes, less access to nutritious foods, increased screen time, and fewer opportunities for physical activity (e.g., no recreational sports) (2,3). CDC used data from IQVIA's Ambulatory Electronic Medical Records database to compare longitudinal trends in body mass index (BMI, kg/m2) among a cohort of 432,302 persons aged 2-19 years before and during the COVID-19 pandemic (January 1, 2018-February 29, 2020 and March 1, 2020-November 30, 2020, respectively). Between the prepandemic and pandemic periods, the rate of BMI increase approximately doubled, from 0.052 (95% confidence interval [CI] = 0.051-0.052 to 0.100 (95% CI = 0.098-0.101) kg/m2/month (ratio = 1.93 [95% CI = 1.90-1.96]). Persons aged 2-19 years with overweight or obesity during the prepandemic period experienced significantly higher rates of BMI increase during the pandemic period than did those with healthy weight. These findings underscore the importance of efforts to prevent excess weight gain during and following the COVID-19 pandemic, as well as during future public health emergencies, including increased access to efforts that promote healthy behaviors. These efforts could include screening by health care providers for BMI, food security, and social determinants of health, increased access to evidence-based pediatric weight management programs and food assistance resources, and state, community, and school resources to facilitate healthy eating, physical activity, and chronic disease prevention.

Body Mass Index and Risk for COVID-19-Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death - United States, March-December 2020.

Obesity* is a recognized risk factor for severe COVID-19 (1,2), possibly related to chronic inflammation that disrupts immune and thrombogenic responses to pathogens (3) as well as to impaired lung function from excess weight (4). Obesity is a common metabolic disease, affecting 42.4% of U.S. adults (5), and is a risk factor for other chronic diseases, including type 2 diabetes, heart disease, and some cancers.† The Advisory Committee on Immunization Practices considers obesity to be a high-risk medical condition for COVID-19 vaccine prioritization (6). Using data from the Premier Healthcare Database Special COVID-19 Release (PHD-SR),§ CDC assessed the association between body mass index (BMI) and risk for severe COVID-19 outcomes (i.e., hospitalization, intensive care unit [ICU] or stepdown unit admission, invasive mechanical ventilation, and death). Among 148,494 adults who received a COVID-19 diagnosis during an emergency department (ED) or inpatient visit at 238 U.S. hospitals during March-December 2020, 28.3% had overweight and 50.8% had obesity. Overweight and obesity were risk factors for invasive mechanical ventilation, and obesity was a risk factor for hospitalization and death, particularly among adults aged <65 years. Risks for hospitalization, ICU admission, and death were lowest among patients with BMIs of 24.2 kg/m2, 25.9 kg/m2, and 23.7 kg/m2, respectively, and then increased sharply with higher BMIs. Risk for invasive mechanical ventilation increased over the full range of BMIs, from 15 kg/m2 to 60 kg/m2. As clinicians develop care plans for COVID-19 patients, they should consider the risk for severe outcomes in patients with higher BMIs, especially for those with severe obesity. These findings highlight the clinical and public health implications of higher BMIs, including the need for intensive COVID-19 illness management as obesity severity increases, promotion of COVID-19 prevention strategies including continued vaccine prioritization (6) and masking, and policies to ensure community access to nutrition and physical activities that promote and support a healthy BMI.

Distance and percentage distance from median BMI as alternatives to BMI z score.

BMI z (BMIz) score based on the Centers for Disease Control and Prevention growth charts is widely used, but it is inaccurate above the 97th percentile. We explored the performance of alternative metrics based on the absolute distance or % distance of a child's BMI from the median BMI for sex and age. We used longitudinal data from 5628 children who were first examined <12 years to compare the tracking of three BMI metrics: distance from median, % distance from median and % distance from median on a log scale. We also explored the effects of adjusting these metrics for age differences in the distribution of BMI. The intraclass correlation coefficient (ICC) was used to compare tracking of the metrics. Metrics based on % distance (whether on the original or log scale) yielded higher ICCs compared with distance from median. The ICCs of the age-adjusted metrics were higher than that of the unadjusted metrics, particularly among children who were (1) overweight or had obesity, (2) younger and (3) followed for >3 years. The ICCs of the age-adjusted metrics were also higher compared with that of BMIz among children who were overweight or obese. Unlike BMIz, these alternative metrics do not have an upper limit and can be used for assessing BMI in all children, even those with very high BMIs. The age-adjusted % from median (on a log or linear scale) works well for all ages, while unadjusted % from median is better limited to older children or short follow-up periods.

A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts.

BACKGROUND: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. AIM: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. SUBJECTS AND METHODS: Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. RESULTS: The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. CONCLUSION: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

State-Specific Prevalence of Obesity Among Children Aged 2-4 Years Enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children - United States, 2010-2016.

Obesity negatively affects children's health because of its associations with cardiovascular disease risk factors, type 2 diabetes, asthma, fatty liver disease, victimization stemming from social stigma and bullying, and poor mental health (e.g., anxiety and depression) (1). Children who have overweight or obesity in early childhood are approximately four times as likely to have overweight or obesity in young adulthood as their normal weight peers (2). Obesity prevalence is especially high among children from low-income families (3). In 2010, the overall upward trend in obesity prevalence turned downward among children aged 2-4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), a program of the U.S. Department of Agriculture (USDA); prevalence decreased significantly in all racial/ethnic groups and in 34 of the 56 WIC state or territory agencies during 2010-2014 (4). A more recent study among young children enrolled in WIC reported that the overall obesity prevalence decreased from 15.9% in 2010 to 13.9% in 2016 and statistically significant decreases were observed in all age, sex, and racial/ethnic subgroups (3). However, this study did not provide obesity trends at the state level. In collaboration with USDA, CDC used data from the WIC Participant and Program Characteristics (WIC PC) to update state-specific trends through 2016. During 2010-2016, modest but statistically significant decreases in obesity prevalence among children aged 2-4 years enrolled in WIC occurred in 41 (73%) of 56 WIC state or territory agencies. Comprehensive approaches that create positive changes to promote healthy eating and physical activity for young children from all income levels,* strengthen nutrition education and breastfeeding support among young children enrolled in WIC, and encourage redemptions of healthy foods in WIC food packages could help maintain or accelerate these declining trends.

Prevalence of Obesity Among Youths by Household Income and Education Level of Head of Household - United States 2011-2014.

Obesity prevalence varies by income and education level, although patterns might differ among adults and youths (1-3). Previous analyses of national data showed that the prevalence of childhood obesity by income and education of household head varied across race/Hispanic origin groups (4). CDC analyzed 2011-2014 data from the National Health and Nutrition Examination Survey (NHANES) to obtain estimates of childhood obesity prevalence by household income (≤130%, >130% to ≤350%, and >350% of the federal poverty level [FPL]) and head of household education level (high school graduate or less, some college, and college graduate). During 2011-2014 the prevalence of obesity among U.S. youths (persons aged 2-19 years) was 17.0%, and was lower in the highest income group (10.9%) than in the other groups (19.9% and 18.9%) and also lower in the highest education group (9.6%) than in the other groups (18.3% and 21.6%). Continued progress is needed to reduce disparities, a goal of Healthy People 2020. The overall Healthy People 2020 target for childhood obesity prevalence is <14.5% (5).

Infant feeding-related maternity care practices and maternal report of breastfeeding outcomes.

BACKGROUND: Evidence-based maternity practices and policies can improve breastfeeding duration and exclusivity. Maternity facilities report practices through the Maternity Practices in Infant Nutrition and Care (mPINC) survey, but individual outcomes, such as breastfeeding duration and exclusivity, are not collected. METHODS: mPINC data on maternity care practices for 2009 were linked to data from the 2009 Pregnancy Risk Assessment Monitoring System (PRAMS), which collects information on mothers' behaviors and experiences around pregnancy. We calculated total mPINC scores (range 0-100). PRAMS data on any and exclusive breastfeeding at 8 weeks were examined by total mPINC score quartile. RESULTS: Of 15 715 women in our sample, 53.7% were breastfeeding any at 8 weeks, and 29.3% were breastfeeding exclusively. They gave birth at 1016 facilities that had a mean total mPINC score of 65/100 (range 19-99). Care dimension subscores ranged from 41 for facility discharge care to 81 for breastfeeding assistance. In multivariable analysis adjusting for covariates, a positive relationship was found between total mPINC score quartile and both any breastfeeding (quartile 2: odds ratio [OR] 1.40 [95% confidence interval {CI} 1.08-1.83], quartile 3: OR 1.50 [95% CI 1.15-1.96], quartile 4: OR 2.12 [95% CI 1.61-2.78] vs quartile 1) and exclusive breastfeeding (quartile 3: OR 1.41 [95% CI 1.04-1.90], quartile 4: OR 1.89 [95% CI 1.41-2.55] vs quartile 1) at 8 weeks. CONCLUSIONS: These data demonstrate that evidence-based maternity care practices and policies are associated with better breastfeeding outcomes. Maternity facilities may evaluate their practices and policies to ensure they are helping mothers achieve their breastfeeding goals.

Differences in Obesity Prevalence by Demographics and Urbanization in US Children and Adolescents, 2013-2016.

Importance: Differences in childhood obesity by demographics and urbanization have been reported. Objective: To present data on obesity and severe obesity among US youth by demographics and urbanization and to investigate trends by urbanization. Design, Setting, and Participants: Measured weight and height among youth aged 2 to 19 years in the 2001-2016 National Health and Nutrition Examination Surveys, which are serial, cross-sectional, nationally representative surveys of the civilian, noninstitutionalized population. Exposures: Sex, age, race and Hispanic origin, education of household head, and urbanization, as assessed by metropolitan statistical areas (MSAs; large: ≥ 1 million population). Main Outcomes and Measures: Prevalence of obesity (body mass index [BMI] ≥95th percentile of US Centers for Disease Control and Prevention [CDC] growth charts) and severe obesity (BMI ≥120% of 95th percentile) by subgroups in 2013-2016 and trends by urbanization between 2001-2004 and 2013-2016. Results: Complete data on weight, height, and urbanization were available for 6863 children and adolescents (mean age, 11 years; female, 49%). In 2013-2016, the prevalence among youth aged 2 to 19 years was 17.8% (95% CI, 16.1%-19.6%) for obesity and 5.8% (95% CI, 4.8%-6.9%) for severe obesity. Prevalence of obesity in large MSAs (17.1% [95% CI, 14.9%-19.5%]), medium or small MSAs (17.2% [95% CI, 14.5%-20.2%]) and non-MSAs (21.7% [95% CI, 16.1%-28.1%]) were not significantly different from each other (range of pairwise comparisons P = .09-.96). Severe obesity was significantly higher in non-MSAs (9.4% [95% CI, 5.7%-14.4%]) compared with large MSAs (5.1% [95% CI, 4.1%-6.2%]; P = .02). In adjusted analyses, obesity and severe obesity significantly increased with greater age and lower education of household head, and severe obesity increased with lower level of urbanization. Compared with non-Hispanic white youth, obesity and severe obesity prevalence were significantly higher among non-Hispanic black and Hispanic youth. Severe obesity, but not obesity, was significantly lower among non-Hispanic Asian youth than among non-Hispanic white youth. There were no significant linear or quadratic trends in obesity or severe obesity prevalence from 2001-2004 to 2013-2016 for any urbanization category (P range = .07-.83). Conclusions and Relevance: In 2013-2016, there were differences in the prevalence of obesity and severe obesity by age, race and Hispanic origin, and household education, and severe obesity was inversely associated with urbanization. Demographics were not related to the urbanization findings.

Differences in Obesity Prevalence by Demographic Characteristics and Urbanization Level Among Adults in the United States, 2013-2016.

Importance: Differences in obesity by sex, age group, race and Hispanic origin among US adults have been reported, but differences by urbanization level have been less studied. Objectives: To provide estimates of obesity by demographic characteristics and urbanization level and to examine trends in obesity prevalence by urbanization level. Design, Setting, and Participants: Serial cross-sectional analysis of measured height and weight among adults aged 20 years or older in the 2001-2016 National Health and Nutrition Examination Survey, a nationally representative survey of the civilian, noninstitutionalized US population. Exposures: Sex, age group, race and Hispanic origin, education level, smoking status, and urbanization level as assessed by metropolitan statistical areas (MSAs; large: ≥1 million population). Main Outcomes and Measures: Prevalence of obesity (body mass index [BMI] ≥30) and severe obesity (BMI ≥40) by subgroups in 2013-2016 and trends by urbanization level between 2001-2004 and 2013-2016. Results: Complete data on weight, height, and urbanization level were available for 10 792 adults (mean age, 48 years; 51% female [weighted]). During 2013-2016, 38.9% (95% CI, 37.0% to 40.7%) of US adults had obesity and 7.6% (95% CI, 6.8% to 8.6%) had severe obesity. Men living in medium or small MSAs had a higher age-adjusted prevalence of obesity compared with men living in large MSAs (42.4% vs 31.8%, respectively; adjusted difference, 9.8 percentage points [95% CI, 5.1 to 14.5 percentage points]); however, the age-adjusted prevalence among men living in non-MSAs was not significantly different compared with men living in large MSAs (38.9% vs 31.8%, respectively; adjusted difference, 4.8 percentage points [95% CI, -2.9 to 12.6 percentage points]). The age-adjusted prevalence of obesity was higher among women living in medium or small MSAs compared with women living in large MSAs (42.5% vs 38.1%, respectively; adjusted difference, 4.3 percentage points [95% CI, 0.2 to 8.5 percentage points]) and among women living in non-MSAs compared with women living in large MSAs (47.2% vs 38.1%, respectively; adjusted difference, 4.7 percentage points [95% CI, 0.2 to 9.3 percentage points]). Similar patterns were seen for severe obesity except that the difference between men living in large MSAs compared with non-MSAs was significant. The age-adjusted prevalence of obesity and severe obesity also varied significantly by age group, race and Hispanic origin, and education level, and these patterns of variation were often different by sex. Between 2001-2004 and 2013-2016, the age-adjusted prevalence of obesity and severe obesity significantly increased among all adults at all urbanization levels. Conclusions and Relevance: In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity and severe obesity in 2013-2016 varied by level of urbanization, with significantly greater prevalence of obesity and severe obesity among adults living in nonmetropolitan statistical areas compared with adults living in large metropolitan statistical areas.

The Limitations of Transforming Very High Body Mass Indexes into z-Scores among 8.7 Million 2- to 4-Year-Old Children.

OBJECTIVE: To examine the associations among several body mass index (BMI) metrics (z-scores, percent of the 95th percentile (%BMIp95) and BMI minus 95th percentile (ΔBMIp95) as calculated in the growth charts from the Centers for Disease Control and Prevention (CDC). It is known that the widely used BMI z-scores (BMIz) and percentiles calculated from the growth charts can differ substantially from those that directly observed in the data for BMIs above the 97th percentile (z = 1.88). STUDY DESIGN: Cross-sectional analyses of 8.7 million 2- to 4-year-old children who were examined from 2008 through 2011 in the CDC's Pediatric Nutrition Surveillance System. RESULTS: Because of the transformation used to calculate z-scores, the theoretical maximum BMIz varied by >3-fold across ages. This results in the conversion of very high BMIs into a narrow range of z-scores that varied by sex and age. Among children with severe obesity, levels of BMIz were only moderately correlated (r ~ 0.5) with %BMIp95 and ΔBMIp95. Among these children with severe obesity, BMIz levels could differ by more than 1 SD among children who had very similar levels of BMI, %BMIp95 and ΔBMIp95 due to differences in age or sex. CONCLUSIONS: The effective upper limit of BMIz values calculated from the CDC growth charts, which varies by sex and age, strongly influences the calculation of z-scores for children with severe obesity. Expressing these very high BMIs relative to the CDC 95th percentile, either as a difference or percentage, would be preferable to using BMI-for-age, particularly when assessing the effectiveness of interventions.

Prevalence of Obesity Among Adults, by Household Income and Education - United States, 2011-2014.

Studies have suggested that obesity prevalence varies by income and educational level, although patterns might differ between high-income and low-income countries (1-3). Previous analyses of U.S. data have shown that the prevalence of obesity varied by income and education, but results were not consistent by sex and race/Hispanic origin (4). Using data from the National Health and Nutrition Examination Survey (NHANES), CDC analyzed obesity prevalence among adults (aged ≥20 years) by three levels of household income, based on percentage (≤130%, >130% to ≤350%, and >350%) of the federal poverty level (FPL) and individual education level (high school graduate or less, some college, and college graduate). During 2011-2014, the age-adjusted prevalence of obesity among adults was lower in the highest income group (31.2%) than the other groups (40.8% [>130% to ≤350%] and 39.0% [≤130%]). The age-adjusted prevalence of obesity among college graduates was lower (27.8%) than among those with some college (40.6%) and those who were high school graduates or less (40.0%). The patterns were not consistent across all sex and racial/Hispanic origin subgroups. Continued progress is needed to achieve the Healthy People 2020 targets of reducing age-adjusted obesity prevalence to <30.5% and reducing disparities (5).

Longitudinal changes in BMI z-scores among 45 414 2-4-year olds with severe obesity.

BACKGROUND: BMI z-scores (BMIz) based on the Centers for Disease Control and Prevention (CDC) growth charts among children do not accurately characterise BMI levels among children with very high BMIs. These limitations may be particularly relevant in longitudinal and intervention studies, as the large changes in the L (normality) and S (dispersion) parameters with age can influence BMIz. AIM: To compare longitudinal changes in BMIz with BMI expressed as a percentage of the 95th percentile (%BMIp95) and a modified z-score calculated as log(BMI/M)/S. SUBJECTS AND METHODS: A total of 45 414 2-4-year-olds with severe obesity (%BMIp95 ≥ 120). RESULTS: Changes in very high BMIz levels differed from the other metrics. Among severely obese 2-year-old girls, for example, the mean BMIz decreased by 0.6 SD between examinations, but there were only small changes in BMIp95 and modified BMIz. Some 2-year-old girls had BMIz decreases of >1 SD, even though they had large increases in BMI, %BMIp95 and modified BMIz. CONCLUSIONS: Among children with severe obesity, BMIz changes may be due to differences in the transformations used to estimate levels of BMIz rather than to changes in body size. The BMIs of these children could be expressed relative to the 95th percentile or as modified z-scores.

Trends in Obesity Among Participants Aged 2-4 Years in the Special Supplemental Nutrition Program for Women, Infants, and Children - United States, 2000-2014.

Childhood obesity is associated with negative health consequences in childhood (1) that continue into adulthood (2), putting adults at risk for type 2 diabetes, cardiovascular disease, and certain cancers (1). Obesity disproportionately affects children from low-income families (3). Through a collaboration with the United States Department of Agriculture (USDA), CDC has begun to use data from the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Participants and Program Characteristics (WIC PC) to replace the Pediatric Nutrition Surveillance System (PedNSS) (4,5) for obesity surveillance among young children from low-income families. CDC examined trends in obesity prevalence during 2000-2014 among WIC participants aged 2-4 years using WIC PC data. Overall obesity prevalence increased from 14.0% in 2000 to 15.5% in 2004 and 15.9% in 2010, and then decreased to 14.5% in 2014. During 2010-2014, the prevalence of obesity decreased significantly overall, among non-Hispanic whites, non-Hispanic blacks, Hispanics, American Indian/Alaska Natives and Asians/Pacific Islanders, and among 34 (61%) of the 56 WIC state agencies in states, the District of Columbia, and U.S. territories. Despite these declines, the obesity prevalence among children aged 2-4 years in WIC remains high compared with the national prevalence of 8.9% among children aged 2-5 years in 2011-2014. Continued initiatives to work with parents and other stakeholders to promote healthy pregnancies, breastfeeding, quality nutrition, and physical activity for young children in multiple settings are needed to ensure healthy child development.

Interrelationships between BMI, skinfold thicknesses, percent body fat, and cardiovascular disease risk factors among U.S. children and adolescents.

BACKGROUND: Although the estimation of body fatness by Slaughter skinfold thickness equations (PBF(Slaughter)) has been widely used, the accuracy of this method is uncertain. We have previously examined the interrelationships among the body mass index (BMI), PBF(Slaughter), percent body fat from dual energy X-ray absorptiometry (PBF(DXA)) and CVD risk factor levels among children who were examined in the Bogalusa Heart Study and in the Pediatric Rosetta Body Composition Project. The current analyses examine these associations among 7599 8- to 19-year-olds who participated in the (U.S.) National Health and Nutrition Examination Survey from 1999 to 2004. METHODS: We analyzed (1) the agreement between (1) estimates of percent body fat calculated from the Slaughter skinfold thickness equations and from DXA, and (2) the relation of lipid, lipoprotein, and blood pressure levels to BMI, PBF(Slaughter) and PBF(DXA). RESULTS: PBF(Slaughter) was highly correlated (r ~ 0.85) with PBF(DXA). However, among children with a relatively low skinfold thicknesses sum (triceps + subscapular), PBF(Slaughter) underestimated PBF(DXA) by 8 to 9 percentage points. In contrast, PBF(Slaughter) overestimated PBF(DXA) by 10 points among boys with a skinfold thickness sum ≥ 50 mm. After adjustment for sex and age, lipid levels were related similarly to the body mass index, PBF(DXA) and PBF(Slaughter). There were, however, small differences in associations with blood pressure levels: systolic blood pressure was more strongly associated with body mass index, but diastolic blood pressure was more strongly associated with percent body fat. CONCLUSIONS: The Slaughter equations yield biased estimates of body fatness. In general, lipid and blood pressure levels are related similarly to levels of BMI (following adjustment for sex and age), PBF(Slaughter,) and PBF(DXA).

Nanoscale characterization of DNA conformation using dual-color fluorescence axial localization and label-free biosensing.

Quantitative determination of the density and conformation of DNA molecules tethered to the surface can help optimize and understand DNA nanosensors and nanodevices, which use conformational or motional changes of surface-immobilized DNA for detection or actuation. We present an interferometric sensing platform that combines (i) dual-color fluorescence spectroscopy for precise axial co-localization of two fluorophores attached at different nucleotides of surface-immobilized DNA molecules and (ii) independent label-free quantification of biomolecule surface density at the same site. Using this platform, we examined the conformation of DNA molecules immobilized on a three-dimensional polymeric surface and demonstrated simultaneous detection of DNA conformational change and binding in real-time. These results demonstrate that independent quantification of both surface density and molecular nanoscale conformation constitutes a versatile approach for nanoscale solid-biochemical interface investigations and molecular binding assays.

Screening Accuracy of BMI for Adiposity Among 8- to 19-Year-Olds.

BACKGROUND AND OBJECTIVES: Although the limitations of BMI have long been recognized, there are recent concerns that it is not a good screening tool for adiposity. We therefore examined the cross-sectional relation of BMI to adiposity among 6923 8- to 19-year-olds in the National Health and Nutrition Survey from 2011 through 2018. METHODS: Participants were scanned with dual-energy x-ray absorptiometry. Adiposity was expressed as fat mass index (FMI, fat mass kg ÷ m2) and percentage of body fat (%fat). Lean mass was expressed as lean mass index (LMI, lean mass ÷ m2). Regression models and 2 × 2 tables were used to assess the relation of BMI to FMI, %fat, and LMI. RESULTS: Age and BMI accounted (R2) for 90% to 94% of the variability of FMI and LMI in each sex. Associations with %fat were weaker (R2s ∼0.70). We also examined the screening abilities of a BMI ≥ Centers for Disease Control and Prevention 95th percentile for high levels of adiposity and LMI. Cut points were chosen so that prevalences of high values of these variables would be similar to that for high BMI. Of participants with a high BMI, 88% had a high FMI, and 76% had a high %fat. Participants with a high BMI were 29 times more likely to have a high FMI than those with lower BMIs; comparable relative risks were 12 for high %fat and 14 for high LMI. CONCLUSIONS: Despite its limitations, a high BMI is a very good screening tool for identifying children and adolescents with elevated adiposity.