Prediction of percent body fat measurements in Americans 8 years and older

Pre-exercise protein intake is associated with reduced time in hypoglycaemia among adolescents with type 1 diabetes

AIM

Secondary analyses were conducted from a randomized trial of an adaptive behavioural intervention to assess the relationship between protein intake (g and g/kg) consumed within 4 h before moderate-to-vigorous physical activity (MVPA) bouts and glycaemia during and following MVPA bouts among adolescents with type 1 diabetes (T1D).

MATERIALS AND METHODS

Adolescents (n = 112) with T1D, 14.5 (13.8, 15.7) years of age and 36.6% overweight/obese, provided measures of glycaemia using continuous glucose monitoring [percentage of time above range (>180 mg/dl), time in range (70-180 mg/dl), time below range (TBR; <70 mg/dl)], self-reported physical activity (previous day physical activity recalls), and 24 h dietary recall data at baseline and 6 months post-intervention. Mixed effects regression models adjusted for design (randomization assignment, study site), demographic, clinical, anthropometric, dietary, physical activity and timing covariates estimated the association between pre-exercise protein intake on percentage of time above range, time in range and TBR during and following MVPA.

RESULTS

Pre-exercise protein intakes of 10-19.9 g and >20 g were associated with an absolute reduction of -4.41% (p = .04) and -4.83% (p = .02) TBR during physical activity compared with those who did not consume protein before MVPA. Similarly, relative protein intakes of 0.125-0.249 g/kg and ≥0.25 g/kg were associated with -5.38% (p = .01) and -4.32% (p = .03) absolute reductions in TBR during physical activity. We did not observe a significant association between protein intake and measures of glycaemia following bouts of MVPA.

CONCLUSIONS

Among adolescents with T1D, a dose of ≥10 g or ≥0.125 g/kg of protein within 4 h before MVPA may promote reduced time in hypoglycaemia during, but not following, physical activity.

Eating behaviors and estimated body fat percentage among adolescents with type 1 diabetes

AIMS

Estimate associations between select eating behaviors and estimated body fat percentage (eBFP) and explore effect modification by sex among adolescents with type 1 diabetes (T1D).

METHODS

This analysis included 257 adolescents (mean age 14.9 ± 1.14 years; 49.8 % female) with baseline hemoglobin A1c (HbA1c) between 8 and 13 % (64 mmol/mol-119 mmol/mol) from a randomized trial designed to improve glycemia. Eating behaviors and eBFP were determined from surveys and validated equations respectively. Linear mixed models were used to estimate associations. Effect modification was assessed via stratified plots, stratified associations, and interaction terms.

RESULTS

Disordered eating, dietary restraint, and eBFP were significantly higher among females while external eating was higher among males. Disordered eating (β: 0.49, 95 %CI: 0.24, 0.73, p = 0.0001) and restraint (β: 1.11, 95 %CI: 0.29, 1.92, p = 0.0081) were positively associated with eBFP while external eating was not (β: -0.19, 95 %CI: -0.470, 0.096, p = 0.20). Interactions with sex were not significant (p-value range: 0.28-0.64).

CONCLUSION

Disordered eating and dietary restraint were positively associated with eBFP, highlighting the potential salience of these eating behaviors to cardiometabolic risk for both female and male adolescents. Prospective studies should investigate whether these eating behaviors predict eBFP longitudinally to inform obesity prevention strategies in T1D.

A High Protein Diet Is Associated with Improved Glycemic Control Following Exercise among Adolescents with Type 1 Diabetes

Nutritional strategies are needed to aid people with type 1 diabetes (T1D) in managing glycemia following exercise. Secondary analyses were conducted from a randomized trial of an adaptive behavioral intervention to assess the relationship between post-exercise and daily protein (g/kg) intake on glycemia following moderate-to-vigorous physical activity (MVPA) among adolescents with T1D. Adolescents (n = 112) with T1D, 14.5 (13.8, 15.7) years of age, and 36.6% overweight or obese, provided measures of glycemia using continuous glucose monitoring (percent time above range [TAR, >180 mg/dL], time-in-range [TIR, 70-180 mg/dL], time-below-range [TBR, <70 mg/dL]), self-reported physical activity (previous day physical activity recalls), and 24 h dietary recall data at baseline and 6 months post-intervention. Mixed effects regression models adjusted for design (randomization assignment, study site), demographic, clinical, anthropometric, dietary, physical activity, and timing covariates estimated the association between post-exercise and daily protein intake on TAR, TIR, and TBR from the cessation of MVPA bouts until the following morning. Daily protein intakes of ≥1.2 g/kg/day were associated with 6.9% (p = 0.03) greater TIR and -8.0% (p = 0.02) less TAR following exercise, however, no association was observed between post-exercise protein intake and post-exercise glycemia. Following current sports nutrition guidelines for daily protein intake may promote improved glycemia following exercise among adolescents with T1D.

More hypoglycemia not associated with increasing estimated adiposity in youth with type 1 diabetes

BACKGROUND

Despite the widespread clinical perception that hypoglycemia may drive weight gain in youth with type 1 diabetes (T1D), there is an absence of published evidence supporting this hypothesis.

METHODS

We estimated the body fat percentage (eBFP) of 211 youth (HbA1c 8.0-13.0%, age 13-16) at baseline, 6, and 18 months of the Flexible Lifestyles Empowering Change trial using validated equations. Group-based trajectory modeling assigned adolescents to sex-specific eBFP groups. Using baseline 7-day blinded continuous glucose monitoring data, "more" vs. "less" percent time spent in hypoglycemia was defined by cut-points using sample median split and clinical guidelines. Adjusted logistic regression estimated the odds of membership in an increasing eBFP group comparing youth with more vs. less baseline hypoglycemia.

RESULTS

More time spent in clinical hypoglycemia (defined by median split) was associated with 0.29 the odds of increasing eBFP in females (95% CI: 0.12, 0.69; p = 0.005), and 0.33 the odds of stable/increasing eBFP in males (95% CI: 0.14, 0.78; p = 0.01).

CONCLUSIONS

Hypoglycemia may not be a major driver of weight gain in US youth with T1D and HbA1c ≥8.0. Further studies in different sub-groups are needed to clarify for whom hypoglycemia may drive weight gain and focus future etiological studies and interventions.

IMPACT

We contribute epidemiological evidence that hypoglycemia may not be a major driver of weight gain in US youth with type 1 diabetes and HbA1c ≥8.0% and highlight the need for studies to prospectively test this hypothesis rooted in clinical perception. Future research should examine the relationship between hypoglycemia and adiposity together with psychosocial, behavioral, and other clinical factors among sub-groups of youth with type 1 diabetes (i.e., who meet glycemic targets or experience a frequency/severity of hypoglycemia above a threshold) to further clarify for whom hypoglycemia may drive weight gain and progress etiological understanding of and interventions for healthy weight maintenance.

Equations based on anthropometric measurements for adipose tissue, body fat, or body density prediction in children and adolescents: a scoping review

PURPOSE

Assessing the body composition of children and adolescents is important to monitor their health status. Anthropometric measurements are feasible and less-expensive than other techniques for body composition assessment. This study aimed to systematically map anthropometric equations to predict adipose tissue, body fat, or density in children and adolescents, and to analyze methodological aspects of the development of anthropometric equations using skinfolds.

METHODS

A scoping review was carried out following the PRISMA-ScR criteria. The search was carried out in eight databases. The methodological structure protocol of this scoping review was retrospectively registered in the Open Science Framework ( https://osf.io/35uhc/ ).

RESULTS

We included 78 reports and 593 anthropometric equations. The samples consisted of healthy individuals, people with different diseases or disabilities, and athletes from different sports. Dual-energy X-ray absorptiometry (DXA) was the reference method most commonly used in developing equations. Triceps and subscapular skinfolds were the anthropometric measurements most frequently used as predictors in the equations. Age, stage of sexual maturation, and peak height velocity were used as complementary variables in the equations.

CONCLUSION

Our scoping review identified equations proposed for children and adolescents with a great diversity of characteristics. In many of the reports, important methodological aspects were not addressed, a factor that may be associated with equation bias.

LEVEL IV

Evidence obtained from multiple time series analysis such as case studies. (NB: dramatic results in uncontrolled trials might also be regarded as this type of evidence).

External validation of a prediction model for estimating fat mass in children and adolescents in 19 countries: individual participant data meta-analysis

OBJECTIVE

To evaluate the performance of a UK based prediction model for estimating fat-free mass (and indirectly fat mass) in children and adolescents in non-UK settings.

DESIGN

Individual participant data meta-analysis.

SETTING

19 countries.

PARTICIPANTS

5693 children and adolescents (49.7% boys) aged 4 to 15 years with complete data on the predictors included in the UK based model (weight, height, age, sex, and ethnicity) and on the independently assessed outcome measure (fat-free mass determined by deuterium dilution assessment).

MAIN OUTCOME MEASURES

The outcome of the UK based prediction model was natural log transformed fat-free mass (lnFFM). Predictive performance statistics of R², calibration slope, calibration-in-the-large, and root mean square error were assessed in each of the 19 countries and then pooled through random effects meta-analysis. Calibration plots were also derived for each country, including flexible calibration curves.

RESULTS

The model showed good predictive ability in non-UK populations of children and adolescents, providing R² values of >75% in all countries and >90% in 11 of the 19 countries, and with good calibration (ie, agreement) of observed and predicted values. Root mean square error values (on fat-free mass scale) were <4 kg in 17 of the 19 settings. Pooled values (95% confidence intervals) of R², calibration slope, and calibration-in-the-large were 88.7% (85.9% to 91.4%), 0.98 (0.97 to 1.00), and 0.01 (-0.02 to 0.04), respectively. Heterogeneity was evident in the R² and calibration-in-the-large values across settings, but not in the calibration slope. Model performance did not vary markedly between boys and girls, age, ethnicity, and national income groups. To further improve the accuracy of the predictions, the model equation was recalibrated for the intercept in each setting so that country specific equations are available for future use.

CONCLUSION

The UK based prediction model, which is based on readily available measures, provides predictions of childhood fat-free mass, and hence fat mass, in a range of non-UK settings that explain a large proportion of the variability in observed fat-free mass, and exhibit good calibration performance, especially after recalibration of the intercept for each population. The model demonstrates good generalisability in both low-middle income and high income populations of healthy children and adolescents aged 4-15 years.

Association of Insulin Regimen and Estimated Body Fat Over Time among Youths and Young Adults with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

AIMS

To explore how changes in insulin regimen are associated with estimated adiposity over time among youths and young adults with type 1 diabetes and whether any associations differ according to sex.

MATERIALS AND METHODS

Longitudinal data were analyzed from youths and young adults with type 1 diabetes in the SEARCH for Diabetes in Youth study. Participants were classified according to insulin regimen categorized as exclusive pump ("pump only"), exclusive injections ("injections only"), injection-pump transition ("injections-pump"), or pump-injection transition ("pump-injections") for each follow-up visit completed. Estimated body fat percentage (eBFP) was calculated using validated equations. Sex-specific, linear mixed effects models examined the relationship between the insulin regimen group and change in eBFP during follow-up, adjusted for baseline eBFP, baseline insulin regimen, time-varying insulin dose, sociodemographic factors, and baseline HbA1c (≥9.0% vs. <9.0%).

RESULTS

The final sample included 284 females and 304 males, of whom 80% were non-Hispanic white with mean diagnosis age of 12.7 ± 2.4 years. In fully adjusted models for females, exclusive pump use over the study duration was associated with significantly greater increases in eBFP compared to exclusive use of injections (difference in rate of change = 0.023% increase per month, 95%CI = 0.01, 0.04). Injection-to-pump transitions and pump-to-injection transitions were also associated with greater increases in eBFP compared to exclusive use of injections (difference in rate of change = 0.02%, 95%CI = 0.004, 0.03, and 0.02%; 95%CI = 0.0001, 0.04, respectively). There was no relationship between the insulin regimen and eBFP among males.

CONCLUSIONS

Among females with type 1 diabetes, exclusive and partial pump use may have the unintended consequence of increasing adiposity over time compared to exclusive use of injections, independent of insulin dose.

Validity and accuracy of body fat prediction equations using anthropometric measurements in children 7 – 10 years old

abstract Children with a deficit of growth because of perinatal malnutrition present specificities in the percentage of body fat (%BF) that could not be detected by previous fat mass-based equations. This study developed and validated predictive equations of the %BF derived from anthropometric variables in children aged 7 to 10 living in Northeast Brazil, using dual-energy x-ray absorptiometry (DXA) as a reference. Body composition data from 58 children were utilized. DXA was used as a reference. A stepwise (forward) multiple regression statistical model was used to develop the new equations. The Bland-Altman analysis (CI: 95%), paired Student's t-test, and the intraclass correlation coefficient (ICC) was used to validate and compare the developed equations. Two new equations were developed for either gender: boys: %BF: 13.642 + (1.527*BMI) + (-0.345*Height) + (0.875*Triceps) + (0.290* Waist Circumference) and girls: %BF: -13.445 + (2.061*Tight). The Bland-Altman analysis showed good agreement, with limits ranging from -1.33 to 1.24% for boys and -3.35 to 4.08% for girls. The paired Student’s t-test showed no difference between %BF-DXA and the two new equations (p> 0.05), and the ICC was 0.948 and 0.915, respectively. DXA-based anthropometric equations provide an accurate and noninvasive method to measure changes in the %BF in children.

Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models

BACKGROUND

Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data.

METHODS

Cross-sectional cohort study of 18 430 adults and children from the US population. Participants were examined with whole-body dual X-ray absorptiometry (DXA) scans, anthropometric assessment, and answered a demographic questionnaire. The primary outcomes were predicted total lean body mass (_predLBM), total body fat mass (_predFM), and trunk fat mass (_predTFM) compared with reference values from DXA scans.

RESULTS

Participants were randomly partitioned into 70% training (12 901) data and 30% validation (5529) data. The prediction model for _predLBM compared with lean body mass measured by DXA (_DXALBM) had a Pearson's correlation coefficient of R = 0.99 with a standard error of estimate (SEE) = 1.88 kg (P < .001). The prediction model for _predFM compared with fat mass measured by DXA (_DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.91 kg (P < .001). The prediction model for _predTFM compared with DXA measured trunk fat mass (_DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.13 kg (P < .001).

CONCLUSIONS

In this study, neural network models based on anthropometric and demographic data could precisely predict body muscle and fat composition. Precise body estimations are relevant in a broad range of clinical diabetes applications, prevention, and epidemiological research.

Non-linear association of anthropometric measurements and pulmonary function

This study examined the association of anthropometric measurements [body mass index (BMI), waist circumference (WC), percentage body fat (PBF), body roundness index (BRI) and A Body Shape Index (ABSI)] with pulmonary function using a United States national cohort. This cross-sectional study included 7346 participants. The association between anthropometric measurements and pulmonary function was assessed by multivariable linear regression. Where there was evidence of non-linearity, we applied a restricted cubic spline to explore the non-linear association. All analyses were weighted to represent the U.S. population and to account for the intricate survey design. After adjusting for age, race, education, smoking, and physical activity, both underweight and obesity were associated with reduced forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC). Furthermore, the associations between BMI and FEV_1, as well as FVC, were reversed U-shape in both males and females. Similar non-linear association shape occurred in WC, PBF, BRI and ABSI. Conclusion: BMI, WC, PBF, BRI, ABSI are non-linearly associated with pulmonary function. Reduced pulmonary function is a risk factor for future all-cause mortality and cardiovascular events; thus, this nonlinearity may explain the U-shape or J-shape association of BMI with overall mortality and cardiovascular events.

Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children

There is a need for accurate, inexpensive and field-friendly methods to assess body composition in children. Bioelectrical impedance analysis (BIA) is a promising approach; however, there have been limited validation and use among young children in resource-poor settings. We aim to develop and validate population-specific prediction equations for estimating total fat mass (FM), fat free-mass (FFM) and percentage body fat (PBF) in Vietnamese children (4-7 years) using reactance and resistance from BIA, anthropometric variables and demographic information. We conducted a cross-sectional survey of 120 children. Body composition was measured using dual-energy X-ray absorptiometry (DXA), BIA and anthropometry. To develop prediction equations, we split all data into development (70 %) and validation datasets (30 %). The model performance was evaluated using predicted residual error sum of squares, root mean squared error (RMSE), mean absolute error (MAE) and R2. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index or resistance and height), low RMSE (FM 0·70, FFM 0·74, PBF 3·10), low MAE (FM 0·55, FFM 0·62, PBF 2·49), high R2 (FM 0·95, FFM 0·92, PBF 0·82) and the least difference between predicted values and actual values from DXA (FM 0·03 kg or 0·01 sd, FFM 0·06 kg or 0·02 sd, PBF 0·27 % or 0·04 sd). In conclusion, we developed the first valid and highly predictive equations to estimate FM, FFM and PBF in Vietnamese children using BIA. These findings have important implications for future research on the double burden of disease and risks associated with overweight and obesity in young children.

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

INTRODUCTION

Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups.

RESEARCH DESIGN AND METHODS

Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively.

RESULTS

The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01).

CONCLUSIONS

There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

Optimum waist circumference-height indices for evaluating adult adiposity: An analytic review

Phenotyping adults for excess adiposity and related health risks usually include three body size measurements: height, weight and waist circumference (WC). Height and weight are now widely used as components of the body shape measure, body mass index (BMI, weight/height² ), with the height power referred to as the scaling factor, α. At present, WC is usually not adjusted for height or is expressed as WC/height in which α = 1. Although other α values have been proposed, a critical review of these shape measures is lacking. Here, we examine classical pathways by which the scaling exponent for height used in BMI was developed and then apply this strategy to identify the optimum WC index characteristic of adult shape. Our analyses explored anthropometric, body composition and clinically-relevant data from US and Korean National Health and Nutrition Surveys. Our findings provide further support for the WC index of WC/height^0.5 as having the strongest associations with adiposity while having the weakest correlations with height across non-Hispanic white and black, Mexican American and Korean men and women. The WC index, defined as WC/height^0.5 , when combined with BMI, can play an important role when phenotyping adults for excess adiposity and associated health risks in research and clinical settings.

Relative Fat Mass as an estimator of whole-body fat percentage among children and adolescents: A cross-sectional study using NHANES

We evaluated the ability of the Relative Fat Mass (RFM) to estimate whole-body fat percentage among children and adolescents who participated in the National Health and Nutrition Examination Survey from 1999 through 2006 (n = 10,390). The RFM equation for adults (64 − (20 × height/waist circumference) + (12 × sex)) may be used for adolescents 15 to 19 years of age. For children and adolescents 8 to 14 years of age, we suggest a modified RFM equation, named as the RFMp (RFM pediatric): 74 − (22 × height/waist circumference) + (5 × sex). In both equations, sex equals 0 for boys and 1 for girls. RFMp was more accurate than BMI to estimate whole-body fat percentage (measured by dual energy X-ray absorptiometry, DXA) among girls (percentage of estimates that were <20% of measured body fat percentage, 88.2% vs. 85.7%; P = 0.027) and boys 8 to 14 years of age (83.4% vs. 71.0%; P < 0.001). RFM was more accurate than BMI among boys 15 to 19 years of age (82.3% vs. 73.9%; P < 0.001) but slightly less accurate among girls (89.0% vs. 92.6%; P = 0.002). Compared with BMI-for-age percentiles, RFMp had lower misclassification error of overweight or obesity (defined as a DXA-measured body fat percentage at the 85^th percentile or higher) among boys 8 to 14 years of age (6.5% vs. 7.9%; P = 0.018) but not girls (RFMp: 8.2%; BMI-for-age: 7.9%; P = 0.681). Misclassification error of overweight or obesity was similar for RFM and BMI-for-age percentiles among girls (RFM: 8.0%; BMI-for-age: 6.6%; P = 0.076) and boys 15 to 19 years of age (RFM: 6.9%; BMI-for-age: 7.8%; P = 0.11). RFMp for children and adolescents 8 to 14 years of age and RFM for adolescents 15 to 19 years of age were useful to estimate whole-body fat percentage and diagnose body fat-defined overweight or obesity.

Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data

OBJECTIVES

To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment.

DESIGN

Individual participant data meta-analysis.

SETTING

Four population based cross sectional studies and a fifth study for external validation, United Kingdom.

PARTICIPANTS

A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass.

MAIN OUTCOME MEASURE

Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model's predictive performance within the four development studies; external validation followed using the fifth dataset.

RESULTS

Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R²: 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R²: 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was -1.29 kg (95% confidence interval -1.62 to -0.96 kg).

CONCLUSION

The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

External Validation of Equations that Use Demographic and Anthropometric Measurements to Predict Percent Body Fat

OBJECTIVE

Numerous equation to predict percent body fat using demographics and anthropometrics have been published but external validation of these equations is limited. The objective of this study was to validate published equations that use anthropometrics for prediction of percent body fat using external data.

METHODS

Data were from the Visceral Fat, Metabolic Rate, and Coronary Heart Disease Risk I (VIM I) Study and the Fels Longitudinal Study (Fels). VIM I was conducted in a subset of subjects from the CARDIA study and included black and white adults 28-40 years (n = 392). Fels consisted of white participants 8-88 years (n = 1,044). Percent body fat assessed by dual X-ray absorptiometry (DXA) in these two studies was compared to results calculated using 13 equations from Stevens et al. and nine other published equations.

RESULTS

In general, the Stevens equations performed better than equations from other studies. For example, equation "I" in women in VIM I, Fels adults, and Fels youth, R2 estimates were 0.765, 0.757 and 0.789, respectively. In men the estimates were 0.702 in VIM I, 0.822 in Fels adults and 0.905 in Fels youth. None of the results from the nine published equations showed R2 this high in corresponding groups.

CONCLUSIONS

Our results indicate that several of the Stevens equations have external validity superior to that of nine other published equations among varying age groups, genders and races.

Sociodemographic associations of longitudinal adiposity in youth with type 1 diabetes

Excess adiposity is common in youth with type 1 diabetes, yet little is known about the sociodemographic factors that predict longitudinal trajectories of body fat. We analyzed data from 363 females and 379 males with type 1 diabetes over ~9 years of follow-up (mean baseline age 12.8 ± 2.3 years in females, 13.2 ± 2.4 years in males). Estimated body fat percentage (eBFP) was calculated with validated sex- and race/ethnicity-specific equations. Group-based modeling identified three eBFP trajectories for each sex. All female trajectories showed gradual increases, while male trajectories showed gradual decreases (<5% in eBFP) that plateaued around 7 years of diabetes duration. Female trajectories showed differences in baseline eBFP: Group F1 (38.0%), mean eBFP 27.8 ± 3.0%: Group F2 (47.9%), mean eBFP 33.9 ± 3.0%: and Group F3 (14.1%), mean eBFP 41.7 ± 4.1%. Male trajectories also showed differences in baseline eBFP: Group M1 (57.2%), mean eBFP 22.0 ± 3.0%: Group M2 (30.9%), mean eBFP 33.9 ± 3.0%: and Group M3 (12.9%), mean eBFP 36.1 ± 3.7%. In multinomial models, adjusted for clinical factors (eg, insulin regimen, insulin dose, and hemoglobin A1c), females who reported a single-parent household (adjusted odds ratio [aOR] = 3.34, 95% confidence interval [CI]: 1.49, 7.47), parental education of less than a college degree (aOR = 3.79, 95% CI: 1.60, 9.60), and a lack of private health insurance (aOR = 3.74, 95% CI: 1.45, 9.60), and a household income of less than $75 000 per year (aOR = 3.13, 95% CI: 1.27, 7.70) were approximately three to four times more likely to be in the highest eBFP trajectory group relative to the lowest eBFP trajectory group. Males who reported a household income of <$75 000/year were almost twice as likely to be in the Group M3 than the Group M1 in the unadjusted model only (aOR = 1.79, 95% CI: 0.91, 4.01 vs unadjusted OR: 2.48, 95% CI: 1.22, 5.06). Lower socioeconomic status may be associated with excess body fat throughout adolescence in type 1 diabetes, particularly among females.

Relative fat mass (RFM) as a new estimator of whole-body fat percentage ─ A cross-sectional study in American adult individuals

High whole-body fat percentage is independently associated with increased mortality. We aimed to identify a simple anthropometric linear equation that is more accurate than the body mass index (BMI) to estimate whole-body fat percentage among adult individuals. National Health and Nutrition Examination Survey (NHANES) 1999–2004 data (n = 12,581) were used for model development and NHANES 2005–2006 data (n = 3,456) were used for model validation. From the 365 anthropometric indices generated, the final selected equation was as follows: 64 − (20 × height/waist circumference) + (12 × sex), named as the relative fat mass (RFM); sex = 0 for men and 1 for women. In the validation dataset, compared with BMI, RFM better predicted whole-body fat percentage, measured by dual energy X-ray absorptiometry (DXA), among women and men. RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men. RFM reduced total obesity misclassification among all women and all men and, overall, among Mexican-Americans, European-Americans and African-Americans. In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European or African ethnicity.

Development and validation of anthropometric equations to estimate body composition in adult women

Objective

To develop anthropometric equations to predict body fat percentage (BF%).

Methods

In 151 women (aged 18-59) body weight, height, eight- skinfold thickness (STs), six- circumferences (CIs), and BF% by hydrodensitometry were measured. Subjects data were randomly divided in two groups, equation-building group (n= 106) and validation group (n= 45). The equation-building group was used to run linear regression models using anthropometric measurements as predictors to find the best prediction equations of the BF%. The validation group was used to compare the performance of the new equations with those of Durnin-Womersley, Jackson-Pollock and Ramirez-Torun.

Results

There were two preferred equations: Equation 1= 11.76 + (0.324 x tricipital ST) + (0.133 x calf ST) + (0.347 x abdomen CI) + (0.068 x age) - (0.135 x height) and Equation 2= 11.37 + (0.404 x tricipital ST) + (0.153 x axilar ST) + (0.264 x abdomen CI) + (0.069 x age) - (0.099 x height). There were no significant differences in BF% obtained by hydrodensitometry (31.5 ±5.3) and Equation 1 (31.0 ±4.0) and Equation 2 (31.2 ±4.0). The BF% estimated by Durning-Womersley (35.8 ±4.0), Jackson-Pollock (26.5 ±5.4) and Ramirez-Torun (32.6 ±4.8) differed from hydrodensitometry (p <0.05). The interclass correlation coefficient (ICC) was high between hydrodensitometry and Equation 1 (ICC= 0.77), Equation 2 (ICC= 0.76), and Ramirez-Torun equation (ICC= 0.75). The ICC was low between hydrodensitometry and Durnin-Womersley (ICC= 0.51) and Jackson-Pollock (ICC= 0.53) equations.

Conclusion

The new Equations-1 and 2, performed better than the commonly used anthropometric equations to predict BF% in adult women.

The Role of Age and Excess Body Mass Index in Progression to Type 1 Diabetes in At-Risk Adults

BACKGROUND

Given the global rise in both type 1 diabetes incidence and obesity, the role of body mass index (BMI) on type 1 diabetes pathophysiology has gained great interest. Sustained excess BMI in pediatric participants of the TrialNet Pathway to Prevention (PTP) cohort increased risk for progression to type 1 diabetes, but the effects of age and obesity in adults remain largely unknown.

OBJECTIVE

To determine the effect of age and sustained obesity on the risk for type 1 diabetes in adult participants in the TrialNet PTP cohort (i.e., nondiabetic autoantibody-positive relatives of patients with type 1 diabetes).

RESEARCH DESIGN AND METHODS

Longitudinally accumulated BMI >25 kg/m2 was calculated to generate a cumulative excess BMI (ceBMI) for each participant, with ceBMI values ≥0 kg/m2 and ≥5 kg/m2 representing sustained overweight or obese status, respectively. Recursive partitioning analysis yielded sex- and age-specific thresholds for ceBMI that confer the greatest risk for type 1 diabetes progression.

RESULTS

In this cohort of 665 adults (age 20 to 50 years; median follow-up, 3.9 years), 49 participants developed type 1 diabetes. Age was an independent protective factor for type 1 diabetes progression (hazard ratio, 0.95; P = 0.008), with a threshold of >35 years that reduced risk for type 1 diabetes. In men age >35 years and women age <35 years, sustained obesity (ceBMI ≥5 kg/m2) increased the risk for type 1 diabetes.

CONCLUSIONS

Age is an important factor for type 1 diabetes progression in adults and influences the impact of elevated BMI, indicating an interplay of excess weight, age, and sex in adult type 1 diabetes pathophysiology.

Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006

Quantification of lean body mass and fat mass can provide important insight into epidemiological research. However, there is no consensus on generalisable anthropometric prediction equations to validly estimate body composition. We aimed to develop and validate practical anthropometric prediction equations for lean body mass, fat mass and percent fat in adults (men, n 7531; women, n 6534) from the National Health and Nutrition Examination Survey 1999-2006. Using a prediction sample, we predicted each of dual-energy X-ray absorptiometry (DXA)-measured lean body mass, fat mass and percent fat based on different combinations of anthropometric measures. The proposed equations were validated using a validation sample and obesity-related biomarkers. The practical equation including age, race, height, weight and waist circumference had high predictive ability for lean body mass (men: R 2=0·91, standard error of estimate (SEE)=2·6 kg; women: R 2=0·85, SEE=2·4 kg) and fat mass (men: R 2=0·90, SEE=2·6 kg; women: R 2=0·93, SEE=2·4 kg). Waist circumference was a strong predictor in men only. Addition of other circumference and skinfold measures slightly improved the prediction model. For percent fat, R 2 were generally lower but the trend in variation explained was similar. Our validation tests showed robust and consistent results with no evidence of substantial bias. Additional validation using biomarkers demonstrated comparable abilities to predict obesity-related biomarkers between direct DXA measurements and predicted scores. Moreover, predicted fat mass and percent fat had significantly stronger associations with obesity-related biomarkers than BMI did. Our findings suggest the potential application of the proposed equations in various epidemiological settings.

Nationally representative equations that include resistance and reactance for the prediction of percent body fat in Americans

Background/Objectives

Resistance and reactance collected by bioelectrical impedance (BIA) can be used in equations to estimate percent body fat at relatively low cost and subject burden. To our knowledge no such equations have been developed in a nationally representative sample.

Subjects/Methods

Dual-energy X-ray absorptiometry (DXA) assessed percent body fat from the 1999–2004 NHANES was the criterion method for development of sex-specific percent body fat equations using up to 6,467 males or 4,888 females 8 to 49 years of age. Candidate variables were studied in multiple mathematical forms and interactions using the Least Absolute Shrinkage and Selection Operator (LASSO). Models were fit in 2/3′s of the data and validated in 1/3 of the data selected at random. Final coefficients, R² values and root mean square error (RMSE) were estimated in the full data set.

Results

Models that included age, ethnicity, height, weight, BMI and BIA assessments (resistance, reactance and height²/resistance) had R² values of 0.831 in men and 0.864 in women in the full data set. RMSE measurements were between 2 and 3 body fat percentage points, and all equations showed low bias across groups formed by age, race/ethnicity or body mass index category. The addition of triceps skinfold and waist circumference increased the R² to 0.905 in males and 0.883 in females. Adding other anthropometrics (plus menses in females) had little impact on performance. Reactance and resistance alone (in multiple mathematical forms) performed poorly with R² ~ 0.2.

Conclusions

Equations that included BIA assessments along with demographic and anthropometric variables provided percent body fat assessments that had high generalizability, strong predictive ability and low bias.

Risk of Stress Fracture Varies by Race/Ethnic Origin in a Cohort Study of 1.3 Million US Army Soldiers

Stress fractures (SF) are common and costly injuries in military personnel. Risk for SF has been shown to vary with race/ethnicity. Previous studies report increased SF risk in white and Hispanic Soldiers compared with black Soldiers. However, these studies did not account for the large ethnic diversity in the US military. We aimed to identify differences in SF risk among racial/ethnic groups within the US Army. A retrospective cohort study was conducted using data from the Total Army Injury and Health Outcomes Database from 2001 until 2011. SF diagnoses were identified from ICD-9 codes. We used Cox-proportional hazard models to calculate time to SF by racial/ethnic group after adjusting for age, education, and body mass index. We performed a sex-stratified analysis to determine whether the ethnic variation in SF risk depends on sex. We identified 21,549 SF cases in 1,299,332 Soldiers (more than 5,228,525 person-years of risk), revealing an overall incidence rate of 4.12 per 1000 person-years (7.47 and 2.05 per 1000 person-years in women and men, respectively). Using non-Hispanic blacks as the referent group, non-Hispanic white women had the highest risk of SF, with a 92% higher risk of SF than non-Hispanic black women (1.92 [1.81-2.03]), followed by American Indian/Native Alaskan women (1.72 [1.44-1.79]), Hispanic women (1.65 [1.53-1.79]), and Asian women (1.32 [1.16-1.49]). Similarly, non-Hispanic white men had the highest risk of SF, with a 59% higher risk of SF than non-Hispanic black men (1.59 [1.50-1.68]), followed by Hispanic men (1.19 [1.10-1.29]). When examining the total US Army population, we found substantial differences in the risk of stress fracture among racial/ethnic groups, with non-Hispanic white Soldiers at greatest risk and Hispanic, American Indian/Native Alaskan, and Asian Soldiers at an intermediate risk. Additional studies are needed to determine the factors underlying these race- and ethnic-related differences in stress fracture risk. © 2017 American Society for Bone and Mineral Research.