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

Stevens, June; Fs, Ou; Cai, Jun; Heymsfield, Steven B.; Kp, Truesdale

doi:10.1038/ijo.2015.231

Cited by 29 publications

(48 citation statements)

References 12 publications

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“…eBFP is a proxy for body fat, and the predictive equations were derived and validated in the general population. 32 Although we expect minimal differences in the relationship between anthropometric variables and eBFP among young people with type 1 diabetes, it would be ideal to validate equations in this population. We used this measure as an indicator of adiposity in the place of BMI, as BMI is not a precise indicator of the underlying proportion of fat and lean tissue.…”

Section: Discussionmentioning

confidence: 98%

“…Our study has several limitations. eBFP is a proxy for body fat, and the predictive equations were derived and validated in the general population . Although we expect minimal differences in the relationship between anthropometric variables and eBFP among young people with type 1 diabetes, it would be ideal to validate equations in this population.…”

Section: Discussionmentioning

confidence: 99%

“…Equations are sex‐ and race/ethnicity‐specific (White, Black, Mexican American, and “Other”). Participants who identified as Hispanic and also reported that they were Mexican American were modeled with the Mexican American equation; all other Hispanics were modeled with the “other” equation …”

Section: Methodsmentioning

confidence: 99%

“…Here, we set out to address these gaps related to longitudinal trends and predictors in adiposity in youth with type 1 diabetes. Because BMI is limited in its ability to describe changes in fat mass, we used validated predictive equations to estimate body fat percentage . Our objectives were to identify subgroups of youth with type 1 diabetes who follow the same trajectories of body fat percentage and to study how sociodemographic characteristics (ie, race and socioeconomic status) and clinical factors (ie, insulin regimen, insulin dose, and glycemic control) were associated with each subgroup.…”

Section: Introductionmentioning

confidence: 99%

“…Because BMI is limited in its ability to describe changes in fat mass, 31 we used validated predictive equations to estimate body fat percentage. 32 Our objectives were to identify subgroups of youth with type 1 diabetes who follow the same trajectories of body fat percentage and to study how sociodemographic characteristics (ie, race and socioeconomic status) and clinical factors (ie, insulin regimen, insulin dose, and glycemic control) were associated with each subgroup. Given that youths with type 1 diabetes are free-living individuals who interact with the same obesogenic environment and aspects of health inequity as youth without type 1 diabetes, we then tested if there were baseline sociodemographic associations with longitudinal adiposity that operate independent of clinical drivers of weight gain specific to type 1 diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Sociodemographic associations of longitudinal adiposity in youth with type 1 diabetes

Kahkoska

Shay²,

Couch

et al. 2018

Pediatr Diabetes

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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.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sociodemographic associations of longitudinal adiposity in youth with type 1 diabetes

Kahkoska

Shay²,

Couch

et al. 2018

Pediatr Diabetes

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

Bulathsinhala

Hughes

McKinnon

et al. 2017

Journal of Bone and Mineral Research

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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.

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

Reynolds

Stevens

Cai

et al. 2018

Obesity Science & Practice

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SummaryObjectiveNumerous 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.MethodsData 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.ResultsIn 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.ConclusionsOur 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.

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

Cited by 29 publications

References 12 publications

Sociodemographic associations of longitudinal adiposity in youth with type 1 diabetes

Sociodemographic associations of longitudinal adiposity in youth with type 1 diabetes

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

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

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