Abstract Background Body mass is the primary metabolic compartment related to a vast number of clinical indices and predictions. The extent to which skeletal muscle (SM), a major body mass component, varies between people of the same sex, weight, height, and age is largely unknown. The current study aimed to explore the magnitude of muscularity variation present in adults and to examine if variation in muscularity associates with other body composition and metabolic measures. Methods Muscularity was defined as the difference (residual) between a person's actual and model‐predicted SM mass after controlling for their weight, height, and age. SM prediction models were developed using data from a convenience sample of 492 healthy non‐Hispanic (NH) White adults (ages 18–80 years) who had total body SM and SM surrogate, appendicular lean soft tissue (ALST), measured with magnetic resonance imaging and dual‐energy X‐ray absorptiometry, respectively; residual SM (SMR) and ALST were expressed in kilograms and kilograms per square meter. ALST mass was also evaluated in a population sample of 8623 NH‐White adults in the 1999–2006 National Health and Nutrition Examination Survey. Associations between muscularity and variation in the residual mass of other major organs and tissues and resting energy expenditure were evaluated in the convenience sample. Results The SM, on average, constituted the largest fraction of body weight in men and women up to respective BMIs of 35 and 25 kg/m2. SM in the convenience sample varied widely with a median of 31.2 kg and an SMR inter‐quartile range/min/max of 3.35 kg/−10.1 kg/9.0 kg in men and 21.1 kg and 2.59 kg/−7.2 kg/7.5 kg in women; per cent of body weight as SM at 25th and 75th percentiles for men were 33.1% and 39.6%; corresponding values in women were 24.2% and 30.8%; results were similar for SMR indices and for ALST measures in the convenience and population samples. Greater muscularity in the convenience sample was accompanied by a smaller waist circumference (men/women: P < 0.001/=0.085) and visceral adipose tissue (P = 0.014/0.599), larger liver (P = 0.065/<0.001), kidneys (P = 0.051/<0.009), and bone mineral (P < 0.001/<0.001), and larger magnitude resting energy expenditure (P < 0.001/<0.001) than predicted for the same sex, age, weight, and height. Conclusions Muscle mass is the largest body compartment in most adults without obesity and is widely variable in mass across people of similar body size and age; and high muscularity is accompanied by distinct body composition and metabolic characteristics. This previously unrecognized heterogeneity in muscularity in the general population has important clinical and research implications.

The present study aimed to develop reference values for bioelectrical phase angle in male and female athletes from different sports. Overall, 2224 subjects participated in this study [1658 males (age 26.2 ± 8.9 y) and 566 females (age 26.9 ± 6.6 y)]. Participants were categorized by their sport discipline and sorted into three different sport modalities: endurance, velocity/power, and team sports. Phase angle was directly measured using a foot-to-hand bioimpedance technology at a 50 kHz frequency during the in-season period. Reference percentiles (5th, 15th, 50th, 85th, and 95th) were calculated and stratified by sex, sport discipline and modality using an empirical Bayesian analysis. This method allows for the sharing of information between different groups, creating reference percentiles, even for sports disciplines with few observations. Phase angle differed (men: p < 0.001; women: p = 0.003) among the three sport modalities, where endurance athletes showed a lower value than the other groups (men: vs. velocity/power: p = 0.010, 95% CI = −0.43 to −0.04; vs. team sports: p < 0.001, 95% CI = −0.48 to −0.02; women: vs. velocity/power: p = 0.002, 95% CI = −0.59 to −0.10; vs. team sports: p = 0.015, 95% CI = −0.52 to −0.04). Male athletes showed a higher phase angle than female athletes within each sport modality (endurance: p < 0.01, 95% CI = 0.63 to 1.14; velocity/power: p < 0.01, 95% CI = 0.68 to 1.07; team sports: p < 0.01, 95% CI = 0.98 to 1.23). We derived phase angle reference percentiles for endurance, velocity/power, and team sports athletes. Additionally, we calculated sex-specific references for a total of 22 and 19 sport disciplines for male and female athletes, respectively. This study provides sex- and sport-specific percentiles for phase angle that can track body composition and performance-related parameters in athletes.

The modeling of violence, including terrorist activity, over space and time is often done using one of two broad classes of statistical models. Typically, the location of an event is modeled as a spatio-temporal point process and the latent structure is either modeled through a latent Gaussian process motivated by a log-Gaussian Cox process or through data dependency similar to a Hawkes process. The former is characterized through dependence in an unobserved latent Gaussian process, while the later assumes a data driven dependence in the data. While both techniques have been used successfully it remains unclear whether the processes are practically different from one another. In this manuscript, we demonstrate that in many situations, the most common statistic to characterize clustering in a process, Ripley's K function, cannot differentiate between the two processes and should not be used.

The project emphasizes an iterative approach, where students collaborate with their instructor throughout the investigative process, providing a research-like experience in an introductory course. A primary goal of introductory statistics courses is to develop statistical thinking. Statistical thinking is a form of quantitative literacy that involves understanding variables can be related in complex ways and appreciating the role of randomness in the data people observe. Students develop this thinking when they collect data in pursuit of topics in which they have a vested interest in the outcome. A course project where all students investigate the same research question using instructor-provided data has important limitations. The project proposal ensures people ask an effective research question. Submit the project proposal in a one-page executive summary format. The proposal consists of three areas: research question, literature review, and study design.

Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.

This study uses 3D body scanner measurements of US Air Force recruits to compare ideal body proportions represented by Leonardo da Vinci’s Vitruvian Man with contemporary body proportions in young adult men and women.

This study uses 3D body scanner measurements of US Air Force recruits to compare ideal body proportions represented by Leonardo da Vinci’s Vitruvian Man with contemporary body proportions in young adult men and women.

Bio‐impedance analysis (BIA) is a common technique used to estimate body composition. BIA measures the electrical impedance in water contained in a subject’s body and uses this measurement to predict body composition. Phase angle is one of the components of body composition and is a major indicator of body fat percentage. Although BIA is less accurate when predicting body composition compared to dual‐energy X‐ray absorptiometry (DXA) or bod pods, it is cheaper, more portable, and does not require a licensed operator. For those reasons, athletes commonly use BIA to estimate body fat composition. Current BIA techniques are shown to be inaccurate for very athletic individuals [1] since it is calibrated based on the general population. Previous studies have calculated phase angle distribution in elite athletes using DXA data, but none have examined BIA. Our research uses empirical Bayesian analysis with BIA data to create reference percentiles for phase angle by sport and gender among elite European athletes.

Substantial evidence shows that weight-related stigma is very pervasive, causes physical and psychological harm to people who experience it, and leads to discrimination in education, employment, and the health-care setting.1–4 To measure public knowledge of the causes and remedies of obesity and to investigate the association between specific beliefs about obesity, weight stigma, and attitudes towards treatment and research, we did a multinational, online, cross-sectional research survey among the general public and health-care professionals: the Attitudes, Stigma and Knowledge (ASK) Study (appendix pp 4–10).

OBJECTIVES:Recent reports on body regional mass scalings to height have advanced understanding differences in adult heights. These studies resulted in conjectures on how regional lengths and circumferences may scale to height. We provide evidence for these conjectures by analyzing a large sample of regional limb, trunk, chest, and head lengths and circumferences in a large sample of US Army basic training recruits.METHODS:Participants consisted of 10 271 males and 2760 females ages 17 to 21 years old who reported for basic training at Fort Jackson, SC. Participants were imaged by a three-dimensional (3D) body scanner for uniform sizing which yielded 159 body measurements of total mass, lengths and circumferences at regional sites of arms, legs, trunk, chest, and head. The allometric model, Body Measur e i = α i H β i was applied to derive scaling exponents which were applied to estimate regional mass scalings.RESULTS:Body mass scaled to height with powers of ∼2.0 (mean β ± SE, 1.98 ± 0.04, 1.93 ± 0.06). Arm and leg lengths scaled to exponents larger than 1.0 and head height and circumferences at regional sites scaled to exponents smaller than 1.0. The leg, arm, and trunk mass scaling exponents were all above 2.0. Head mass scaled to powers smaller than 2.0.CONCLUSIONS:The 3D scanner allowed hundreds of anthropometric measurements to be obtained within seconds. The ensuing analysis revealed that greater height yielded disproportional increases in limb lengths, limb mass and trunk mass. These analyses provide evidence that could not be previously measured that further both biomechanical and metabolic conjectures.

Background Body mass index (BMI) represents a normalization of weight to height and is used to classify adiposity. While the capacity of BMI as an adiposity index has been experimentally validated in Caucasians, but there has been little testing Asian populations. Methods To determine whether weight scales to height squared in Asian Indians across the general population and in Asian Indian tribes an allometric analysis on the power law model, W = αH β , where W is weight (kg) and H is height (m) was performed on cross-sectional weight and height data from India ( N = 43,880) collected through the Anthropological Survey of India. The database contained males 18–84 years of age spanning 161 districts of 14 states and including 33 different tribes ( N = 5,549). Models were developed that were unadjusted and adjusted for tribe membership. The Korean National Health and Nutrition Examination Survey (KNHANES) was used to compare to height–weight data from the Anthropological Survey of India and to calculate BMI thresholds for obesity status using a receiver operating characteristic. Results The unadjusted power was β = 2.08 ( s = 0.02). The power for the general population (non-tribal) was β = 2.11 ( s = 0.02). Powers when adjusted for tribe ranged from 1.87 to 2.35 with 24 of the 33 tribes resulting in statistically significant ( p < 0.05) differences in powers from the general population. The coefficients of the adjusted terms ranged from −0.22 to 0.26 and therefore the scaling exponent does not deviate far from 2. Thresholds for BMI classification of overweight in the KNHANES database were BMI = 21 kg/m 2 (AUC = 0.89) for males 18 kg/m 2 (AUC = 0.97) for females. Obesity classification was calculated as BMI = 26 kg/m 2 (AUC = 0.81) and 23 kg/m 2 (AUC = 0.83) for females. Conclusions Our study confirms that weight scales to height squared in Asian Indian males even after adjusting for tribe membership. We also demonstrate that optimal BMI thresholds are lower in a Korean population in comparison to currently used BMI thresholds. These results support the application of BMI in Asian populations with potentially lower thresholds.

Background/objectives Accurately predicting energy requirements form a critical component for initializing dynamic mathematical models of metabolism. The majority of such existing estimates rely on linear regression models that predict total daily energy expenditure (TDEE) from age, gender, height, and body mass, however, there is evidence these predictors obey a power function. Subjects/methods Baseline, free-living TDEE measured by doubly labeled water (DLW) in 20 studies with no overlapping subjects were obtained from the core lab at the University of Chicago and the University of Wisconsin-Madison ( N = 2501 adults, 628 males, 1873 females). Linear regression models of log-transformed equations of the form: TDEE = α _1M^β _1H^β _2 and TDEE = ln( α _1M^β _1 + γ _1SexH^β _2 + γ _2Sex) were developed to determine the values of the exponents of body mass ( M (kg)) and height ( H (cm)) along with a gender effect (Sex). A nonlinear curve fit was performed to develop a power model that also includes age TDEE = α _1M^β _1H^β _2 + α _2Age . Results The power for body mass, β 1 = 0.45 and the power for height was β 2 = 1.52 in the database with both genders combined. Adding gender reduced these to β 1 = 0.43 and β 2 = 1.04. All terms were significant ( p < 0.01) except for height when including gender. The powers for height in the additive gender-specific models were both closer to 1 and the power for body mass was similar across all models ranging between 0.41 and 0.57. Conclusions A nonlinear scaling relationship was found to hold for body mass and needs to be considered when adjusting TDEE for body mass or predicting human energy requirements as a function of body mass especially in individuals with obesity.

Background Bariatric surgery is known as one of the most effective interventions to manage weight for individuals with obesity. While mean weight loss is typically strong, there exists high variability in these results; not all patients achieve successful weight loss post‐surgery. Follow‐up clinical visits are a known predictor of patient success however little is known about the factors that influence follow‐up persistence. Methods We developed a class of one dimensional 2‐parameter linear discrete dynamical systems to predict the percent of patients that attend each follow up visit. These parameters biologically represent the half‐life in the drop off rate observed in the percent of patients attending follow up visits and their persistence plateau. To test whether the half‐life of persistence differs between surgeries, separate models were developed using data from 7 different types of bariatric surgery performed by the Bariatric Medicine Institute in Utah. Model parameters were determined by regressing between the percent of patients attending follow up visit number n, denoted by the variable, p n,− against the previous follow up visit, (p n−1 ). We then compared the discrete model simulations against the actual persistence data graphically to evaluate the quality of model calibration to observed data and computed the half‐life. Results There were seven types of surgeries (loop to duodenal switch, duodenal switch, sleeve, bypass, duodenal switch to bypass, bandication, and band) where the recursive relationship was found to modeled well by a linear discrete dynamical system p n =ap n‐1 +b. Parameter values specifically the half‐life differed based on surgery type ( Table 1 ). The optimal half‐life was found in band and bandication while the highest drop in persistence was found in bypass surgeries. Conclusions To date, all models that examine persistence of clinic follow‐up visits post‐surgery and predictions of bariatric surgery success are statistical. Our deterministic system provides new mechanistic insights by characterizing persistence by two parameters. Our findings suggest that while gastric bypass has strong mean weight loss results, persistence for clinical follow up visits drops off early in comparison to gastric band. Furthermore, clinicians can estimate our discrete model parameters based on individual demographics to inform optimal surgery types and predict potential patient drop off pre‐surgery. Half‐life of persistence derived from each surgery type Surgery Type Half‐Life Loop DS 0.86587 DS 0.88248 Sleeve 0.80547 Bypass 0.79028 DS to Bypass 0.86417 Bandication 0.97439 Band 0.94337

Poorly performed airway management procedures can lead to a wide variety of adverse events, such as laryngeal trauma, stenosis, cardiac arrest, hypoxemia, or death as in the case of failed airway management or intubation of the esophagus. Current methods for confirming tracheal placement, such as auscultation, direct visualization or capnography, may be subjective, compromised due to clinical presentation or require additional specialized equipment that is not always readily available during the procedure. Consequently, there exists a need for a non-visual detection mechanism for confirming successful airway placement that can give the provider rapid feedback during the procedure. Based upon our previously presented work characterizing the reflectance spectra of tracheal and esophageal tissue, we developed a fiber-optic prototype to detect the unique spectral characteristics of tracheal tissue. Device performance was tested by its ability to differentiate ex vivo samples of tracheal and esophageal tissue. Pig tissue samples were tested with the larynx, trachea and esophagus intact as well as excised and mounted on cork. The device positively detected tracheal tissue 18 out of 19 trials and 1 false positive out of 19 esophageal trials. Our proof of concept device shows great promise as a potential mechanism for rapid user feedback during airway management procedures to confirm tracheal placement. Ongoing studies will investigate device optimizations of the probe for more refined sensing and in vivo testing.

The mechanisms linking short stature with an increase in cardiovascular and cerebrovascular disease risk remain elusive. This study tested the hypothesis that significant associations are present between height and blood pressure in a representative sample of the US adult population. Participants were 12,988 men and women from a multiethnic sample (age >= 18 years) evaluated in the 1999 to 2006 National Health and Nutrition Examination Survey who were not taking antihypertensive medications and who had complete height, weight,% body fat, and systolic and diastolic arterial blood pressure (SBP and DBP) measurements; mean arterial blood pressure and pulse pressure (MBP and PP) were calculated. Multiple regression models for men and women were developed with each blood pressure as dependent variable and height, age, race/ethnicity, body mass index, % body fat, socioeconomic status, activity level, and smoking history as potential independent variables. Greater height was associated with significantly lower SBP and PP, and higher DBP (all P < .001) in combined race/ethnic-sex group models beginning in the 4th decade. Predicted blood pressure differences between people who are short and tall increased thereafter with greater age except for MBP. Socioeconomic status, activity level, and smoking history did not consistently contribute to blood pressure prediction models. Height-associated blood pressure effects were present in US adults who appeared in the 4th decade and increased in magnitude with greater age thereafter. These observations, in the largest and most diverse population sample evaluated to date, provide support for postulated mechanisms linking adult stature with cardiovascular and cerebrovascular disease risk.

Although recognized for half a century or more, the mechanisms linking stature and coronary artery disease and stroke (hemorrhagic/ischemic) risk remain elusive. Several theories have been proposed, although a definitive mechanism is lacking. Small‐scale, largely experimental, studies support the hypothesis that blood pressure (BP) components (systolic, diastolic, pulse, and mean; SBP, DBP, PP, MBP) are physiologically linked with dynamic vascular system features and height. The aim of the current study was to test the hypothesis that adult stature is associated with a distinct BP pattern consistent with epidemiological cardiovascular and cerebrovascular outcomes by extending experimental observations to a representative sample of the US population. The evaluated sample included 11,602 non‐Hispanic (NH) white, NH black, and Mexican American participants (6,089 male; 5,513 female) in the 1999–2004 National Health and Nutrition Examination Survey (NHANES) who had complete measurements of height, weight, % fat, SBP, and DBP; PP and MBP were calculated as SBP‐DBP and DBP+1/3(SBP‐DBP), respectively. Subjects taking BP medications and a history of high BP were excluded from the analyses. The analysis aim was to establish if height remains a significant BP predictor variable after controlling for age, body mass index (BMI), and % fat in multiple regression models. With 4 BP measures and 6 sex and race/ethnic groups, 24 initial regression models were created. Height was a significant (p<0.05) predictor variable in 14 of these 24 models. Combined sex‐specific models were developed for each BP measure with race/ethnicity as a covariate. Height was a significant (p<0.01) predictor in all 8 of these BP regression models; NH black subjects had higher SBP, DBP, PP, and MBP in the 8 models (p<0.05). The findings were consistent across men and women: greater height was associated with lower SBP and PP and higher DBP and MBP. Greater height in US adults is thus accompanied by a distinct BP pattern across US NH white, NH black, and Mexican American men and women. The observed height‐BP pattern is consistent with earlier small‐scale human experimental studies that provide a mechanistic basis for these effects. These findings suggest height‐associated BP effects may contribute to the greater coronary artery disease and stroke risk in people who are short relative to their tall counterparts.

Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address multiple facets of estimating treatment or exposure effects in observational studies with a large number of covariates. We sought to advance methods to improve analysis of large observational datasets with an end goal of understanding the effect of treatments or exposures on health. First we compared existing methods for propensity score (PS) adjustment, specifically Bayesian propensity scores. This concept had previously been introduced (McCandless et al., 2009) but no rigorous evaluation had been done to evaluate the impact of feedback when fitting the joint likelihood for both the PS and outcome models. We determined that unless specific steps were taken to mitigate the impact of feedback, it has the potential to distort estimates of the treatment effect. Next, we developed a method for accounting for uncertainty in confounding adjustment in the context of multiple exposures. Our method allows us to select confounders based on their association with the joint exposure and the outcome while also accounting for the uncertainty in the confounding adjustment. Finally, we developed two methods to combine heterogenous sources of data for effect estimation, specifically information coming from a primary data source that provides information for treatments, outcomes, and a limited set of measured confounders on a large number of people and smaller supplementary data sources containing a much richer set of covariates. Our methods avoid the need to specify the full joint distribution of all covariates.