BMI-for-age graphs with severe obesity percentile curves: Tools for plotting cross-sectional and longitudinal youth BMI data

Abstract Background Severe obesity is an important and distinct weight status classification that is associated with disease risk and is increasing in prevalence among youth. The ability to graphically present population weight status data, ranging from underweight through severe obesity class 3, is novel and applicable to epidemiologic research, intervention studies, case reports, and clinical care. Methods The aim was to create body mass index (BMI) graphing tools to generate sex-specific BMI-for-age graphs that include severe obesity percentile curves. We used the Centers for Disease Control and Prevention youth reference data sets and weight status criteria to generate the percentile curves. The statistical software environments SAS and R were used to create two different graphing options. Results This article provides graphing tools for creating sex-specific BMI-for-age graphs for males and females ages 2 to <20 years. The novel aspects of these graphing tools are an expanded BMI range to accommodate BMI values ˃35 kg/m2, inclusion of percentile curves for severe obesity classes 2 and 3, the ability to plot individual data for thousands of children and adolescents on a single graph, and the ability to generate cross-sectional and longitudinal graphs. Conclusions These new BMI graphing tools will enable investigators, public health professionals, and clinicians to view and present youth weight status data in novel and meaningful ways

Human total, basal and activity energy expenditures are independent of ambient environmental temperature

Lower ambient temperature (

Energy compensation and adiposity in humans

Understanding the impacts of activity on energy balance is crucial. Increasing levels of activity may bring diminishing returns in energy expenditure because of compensatory responses in non-activity energy expenditures

Physical activity and fat-free mass during growth and in later life

BACKGROUND: Physical activity may be a way to increase and maintain fat-free mass (FFM) in later life, similar to the prevention of fractures by increasing peak bone mass. OBJECTIVES: A study is presented of the association between FFM and physical activity in relation to age. METHODS: In a cross-sectional study, FFM was analyzed in relation to physical activity in a large participant group as compiled in the International Atomic Energy Agency Doubly Labeled Water database. The database included 2000 participants, age 3-96 y, with measurements of total energy expenditure (TEE) and resting energy expenditure (REE) to allow calculation of physical activity level (PAL = TEE/REE), and calculation of FFM from isotope dilution. RESULTS: PAL was a main determinant of body composition at all ages. Models with age, fat mass (FM), and PAL explained 76% and 85% of the variation in FFM in females and males \u3c 18 y old, and 32% and 47% of the variation in FFM in females and males ≥ 18 y old, respectively. In participants \u3c 18 y old, mean FM-adjusted FFM was 1.7 kg (95% CI: 0.1, 3.2 kg) and 3.4 kg (95% CI: 1.0, 5.6 kg) higher in a very active participant with PAL = 2.0 than in a sedentary participant with PAL = 1.5, for females and males, respectively. At age 18 y, height and FM-adjusted FFM was 3.6 kg (95% CI: 2.8, 4.4 kg) and 4.4 kg (95% CI: 3.2, 5.7 kg) higher, and at age 80 y 0.7 kg (95% CI: -0.2, 1.7 kg) and 1.0 kg (95% CI: -0.1, 2.1 kg) higher, in a participant with PAL = 2.0 than in a participant with PAL = 1.5, for females and males, respectively. CONCLUSIONS: If these associations are causal, they suggest physical activity is a major determinant of body composition as reflected in peak FFM, and that a physically active lifestyle can only partly protect against loss of FFM in aging adults

Calorie restriction improves lipid-related emerging cardiometabolic risk factors in healthy adults without obesity: Distinct influences of BMI and sex from CALERIE™ a multicentre, phase 2, randomised controlled trial

BACKGROUND: For many cardiovascular risk factors there is no lower limit to which further reduction will result in decreased disease risk; this includes values within ranges considered normal for healthy adults. This seems to be true for new emerging metabolic risk factors identified by innovative technological advances. Further, there seems to be ever evolving evidence of differential responses to lifestyle interventions by sex and body compositions in the normal range. In this secondary analysis, we had the opportunity to test these principles for newly identified molecular biomarkers of cardiometabolic risk in a young (21-50 years), normal weight healthy population undergoing calorie restriction for two years. METHODS: The Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) was a 24-month, multicenter, randomized controlled trial (May 2007-November 2012) in healthy, adults without obesity to evaluate the potential for calorie restriction (CR) to promote anti-aging adaptations, including those associated with disease risk. 218 participants (age 37.9 ± 7.2 years and body mass index (BMI) 25.1 ± 1.7 kg/m FINDINGS: Averaging 11.9% CR, the CR group had reductions at 12 and 24 months in the cardiovascular disease risk markers, apolipoprotein B and GlycA, and risks for insulin resistance and type 2 diabetes-Lipoprotein Insulin Resistance Index and Diabetes Risk Index (all INTERPRETATION: In normal to slightly overweight adults without overt risk factors or disease, 12 months of ∼12% CR improved newly identified risk markers for atherosclerotic cardiovascular disease, insulin resistance and type 2 diabetes. These markers suggest that CR improves risks by reducing inflammation and BCAAs and shifting lipoproteins from atherogenic to cholesterol transporting. Additionally, these improvements are greater for men and for those with greater BMIs indicating sex and BMI-influences merit attention in future investigations of lifestyle-mediated improvements in disease risk factors. FUNDING: The CALERIE™ trial design and implementation were supported by a National Institutes of Health (NIH) U-grant provided to four institutions, the three intervention sites and a coordinating center (U01 AG022132, U01 AG020478, U01 AG020487 U01 AG020480). For this secondary analysis including sample acquisition and processing, data analysis and interpretation, additional funding was provided by the NIH to authors as follows: R01 AG054840 (MO, VBK); R33 AG070455 (KMH, DCP, MB, SBR, CKM, LMR, SKD, CFP, CJR, WEK); P30 DK072476 (CKM, LMR); and U54 GM104940 (CKM, LMR)

Validation of a Parkinson disease predictive model in a population-based study

Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66–90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010–2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p<0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6–17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%–84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%–83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples

Immunosuppressants and risk of Parkinson disease

We performed a population-based case-control study of United States Medicare beneficiaries age 60-90 in 2009 with prescription data (48,295 incident Parkinson disease cases and 52,324 controls) to examine the risk of Parkinson disease in relation to use of immunosuppressants. Inosine monophosphate dehydrogenase inhibitors (relative risk = 0.64; 95% confidence interval 0.51-0.79) and corticosteroids (relative risk = 0.80; 95% confidence interval 0.77-0.83) were both associated with a lower risk of Parkinson disease. Inverse associations for both remained after applying a 12-month exposure lag. Overall, this study provides evidence that use of corticosteroids and inosine monophosphate dehydrogenase inhibitors might lower the risk of Parkinson disease

Anthropometric discriminators of type 2 diabetes among White and Black American adults

BACKGROUND: The aim of the present study was to determine the best anthropometric discriminators of type 2 diabetes mellitus (T2DM) among White and Black males and females in a large US sample. METHODS: We used Atherosclerosis Risk in Communities study baseline data (1987–89) from 15 242 participants (1827 with T2DM) aged 45–65 years. Anthropometric measures included a body shape index (ABSI), body adiposity index (BAI), body mass index, waist circumference (WC), waist:height ratio (WHtR), and waist:hip ratio (WHR). All anthropometric measures were standardized to Z-scores. Using logistic regression, odds ratios for T2DM were adjusted for age, physical activity, and family history of T2DM. The Akaike information criterion and receiver operating characteristic C-statistic were used to select the best-fit models. RESULTS: Body mass index, WC, WHtR, and WHR were comparable discriminators of T2DM among White and Black males, and were superior to ABSI and BAI in predicting T2DM (P < 0.0001). Waist circumference, WHtR, and WHR were the best discriminators among White females, whereas WHR was the best discriminator among Black females. The ABSI was the poorest discriminator of T2DM for all race–gender groups except Black females. Anthropometric values distinguishing T2DM cases from non-cases were lower for Black than White adults. CONCLUSIONS: Anthropometric measures that included WC, either alone or relative to height (WHtR) or hip circumference (WHR), were the strongest discriminators of T2DM across race–gender groups. Body mass index was a comparable discriminator to WC, WHtR, and WHR among males, but not females