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BackgroundNew recommendations for the assessment of malnutrition and sarcopenia include body composition, specifically reduced muscle mass. Three-dimensional optical imaging (3DO) is a validated, accessible, and affordable alternative to dual X-ray absorptiometry (DXA).ObjectiveIdentify strengths and weaknesses of 3DO for identification of malnutrition in participants with low body mass index (BMI) and eating disorders.DesignParticipants were enrolled in the cross-sectional Shape Up! Adults and Kids studies of body shape, metabolic risk, and functional assessment and had BMI of

BACKGROUND: The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown. OBJECTIVES: This study aimed to evaluate 3DOs accuracy and precision by subgroups of age, body mass index, and ethnicity. METHODS: A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Students t tests were performed between 3DO and DXA by subgroup to determine significant differences. RESULTS: Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038). CONCLUSIONS: A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).

STUDY OBJECTIVE: Our primary objective was to characterize the degree of dehydration in children with diabetic ketoacidosis (DKA) and identify physical examination and biochemical factors associated with dehydration severity. Secondary objectives included describing relationships between dehydration severity and other clinical outcomes. METHODS: In this cohort study, we analyzed data from 753 children with 811 episodes of DKA in the Pediatric Emergency Care Applied Research Network Fluid Therapies Under Investigation Study, a randomized clinical trial of fluid resuscitation protocols for children with DKA. We used multivariable regression analyses to identify physical examination and biochemical factors associated with dehydration severity, and we described associations between dehydration severity and DKA outcomes. RESULTS: Mean dehydration was 5.7% (SD 3.6%). Mild (0 to

BACKGROUND: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown. OBJECTIVES: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies. METHODS: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis. RESULTS: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. CONCLUSIONS: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).

ObjectivesInvestigating empirical relationships among laboratory measures in children with diabetic ketoacidosis (DKA) can provide insights into physiological alterations occurring during DKA. We determined whether alterations in laboratory measures during DKA conform to theoretical predictions.MethodsWe used Pearson correlation statistics and linear regression to investigate correlations between blood glucose, electrolytes, pH and PCO2 at emergency department presentation in 1,681 pediatric DKA episodes. Among children with repeat DKA episodes, we also assessed correlations between laboratory measures at the first vs. second episode.ResultspH and bicarbonate levels were strongly correlated (r=0.64), however, pH and PCO2 were only loosely correlated (r=0.17). Glucose levels were correlated with indicators of dehydration and kidney function (blood urea nitrogen (BUN), r=0.44; creatinine, r=0.42; glucose-corrected sodium, r=0.32). Among children with repeat DKA episodes, PCO2 levels tended to be similar at the first vs. second episode (r=0.34), although pH levels were only loosely correlated (r=0.19).ConclusionsElevated glucose levels at DKA presentation largely reflect alterations in glomerular filtration rate. pH and PCO2 are weakly correlated suggesting that respiratory responses to acidosis vary among individuals and may be influenced by pulmonary and central nervous system effects of DKA.

UNLABELLED: It is unknown whether febrile infants 29 to 60 days old with positive urinalysis results require routine lumbar punctures for evaluation of bacterial meningitis. OBJECTIVE: To determine the prevalence of bacteremia and/or bacterial meningitis in febrile infants ≤60 days of age with positive urinalysis (UA) results. METHODS: Secondary analysis of a prospective observational study of noncritical febrile infants ≤60 days between 2011 and 2019 conducted in the Pediatric Emergency Care Applied Research Network emergency departments. Participants had temperatures ≥38°C and were evaluated with blood cultures and had UAs available for analysis. We report the prevalence of bacteremia and bacterial meningitis in those with and without positive UA results. RESULTS: Among 7180 infants, 1090 (15.2%) had positive UA results. The risk of bacteremia was higher in those with positive versus negative UA results (63/1090 [5.8%] vs 69/6090 [1.1%], difference 4.7% [3.3% to 6.1%]). There was no difference in the prevalence of bacterial meningitis in infants ≤28 days of age with positive versus negative UA results (∼1% in both groups). However, among 697 infants aged 29 to 60 days with positive UA results, there were no cases of bacterial meningitis in comparison to 9 of 4153 with negative UA results (0.2%, difference -0.2% [-0.4% to -0.1%]). In addition, there were no cases of bacteremia and/or bacterial meningitis in the 148 infants ≤60 days of age with positive UA results who had the Pediatric Emergency Care Applied Research Network low-risk blood thresholds of absolute neutrophil count

OBJECTIVE: This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics. METHODS: A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model. RESULTS: A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p

BackgroundThe accurate assessment of total body and regional body circumferences, volumes, and compositions are critical to monitor physical activity and dietary interventions, as well as accurate disease classifications including obesity, metabolic syndrome, sarcopenia, and lymphedema. We assessed body composition and anthropometry estimates provided by a commercial 3-dimensional optical (3DO) imaging system compared to criterion measures.MethodsParticipants of the Shape Up! Adults study were recruited for similar sized stratifications by sex, age (18-40, 40-60, >60 years), BMI (under, normal, overweight, obese), and across five ethnicities (non-Hispanic [NH] Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander). All participants received manual anthropometry assessments, duplicate whole-body 3DO (Styku S100), and dual-energy X-ray absorptiometry (DXA) scans. 3DO estimates provided by the manufacturer for anthropometry and body composition were compared to the criterion measures using concordance correlation coefficient (CCC) and Bland-Altman analysis. Test-retest precision was assessed by root mean square error (RMSE) and coefficient of variation.ResultsA total of 188 (102 female) participants were included. The overall fat free mass (FFM) as measured by DXA (54.1 ± 15.2 kg) and 3DO (55.3 ± 15.0 kg) showed a small mean difference of 1.2 ± 3.4 kg (95% limits of agreement -7.0 to +5.6) and the CCC was 0.97 (95% CI: 0.96-0.98). The CCC for FM was 0.95 (95% CI: 0.94-0.97) and the mean difference of 1.3 ± 3.4 kg (95% CI: -5.5 to +8.1) reflected the difference in FFM measures. 3DO anthropometry and body composition measurements showed high test-retest precision for whole body volume (1.1 L), fat mass (0.41 kg), percent fat (0.60%), arm and leg volumes, (0.11 and 0.21 L, respectively), and waist and hip circumferences (all

ObjectiveFluid replacement to correct dehydration, acidosis, and electrolyte abnormalities is the cornerstone of treatment for diabetic ketoacidosis (DKA), but little is known about optimal fluid infusion rates and electrolyte content. The objective of this study was to evaluate whether different fluid protocols affect the rate of normalization of biochemical derangements during DKA treatment.Research design and methodsThe current analysis involved moderate or severe DKA episodes (n = 714) in children age

The study evaluated safe sex attitudes and intentions from a comprehensive, peer-led sexual health program for teenage students enrolled in three rural colleges in Mississippi. The methods consisted of pretest and posttest survey items regarding attitudes and intentions toward safe sex from 149 participants and a peer educator focus group on curriculum delivery and participation. Intentions and motivations for contraception and sexual risk awareness significantly improved at posttest for all participants. The focus group suggested that peer-led approaches are critical for engaging students, fostering participation, and addressing students' sexual health needs in culturally sensitive ways.

The Brascamp–Lieb inequalities are a generalization of the Hölder, Loomis–Whitney, Young, and Finner inequalities that have found many applications in harmonic analysis and elsewhere. In this paper, we introduce an "adjoint" version of these inequalities, which can be viewed as an Lp$L^p$ version of the entropic Brascamp–Lieb inequalities of Carlen and Cordero–Erausquin. As applications, we reprove a log‐convexity property of the Gowers uniformity norms, and establish some reverse Lp$L^p$ inequalities for various tomographic transforms. We conclude with some open questions. [ABSTRACT FROM AUTHOR]