Are Ethnic and Gender Specific Equations Needed to Derive Fat Free Mass from Bioelectrical Impedance in Children of South Asian, Black African-Caribbean and White European Origin? Results of the Assessment of Body Composition in Children Study

Background Bioelectrical impedance analysis (BIA) is a potentially valuable method for assessing lean mass and body fat levels in children from different ethnic groups. We examined the need for ethnic- and gender-specific equations for estimating fat free mass (FFM) from BIA in children from different ethnic groups and examined their effects on the assessment of ethnic differences in body fat. Methods Cross-sectional study of children aged 8–10 years in London Primary schools including 325 South Asians, 250 black African-Caribbeans and 289 white Europeans with measurements of height, weight and arm-leg impedance (Z; Bodystat 1500). Total body water was estimated from deuterium dilution and converted to FFM. Multilevel models were used to derive three types of equation {A: FFM = linear combination(height+weight+Z); B: FFM = linear combination(height2/Z); C: FFM = linear combination(height2/Z+weight)}. Results Ethnicity and gender were important predictors of FFM and improved model fit in all equations. The models of best fit were ethnicity and gender specific versions of equation A, followed by equation C; these provided accurate assessments of ethnic differences in FFM and FM. In contrast, the use of generic equations led to underestimation of both the negative South Asian-white European FFM difference and the positive black African-Caribbean-white European FFM difference (by 0.53 kg and by 0.73 kg respectively for equation A). The use of generic equations underestimated the positive South Asian-white European difference in fat mass (FM) and overestimated the positive black African-Caribbean-white European difference in FM (by 4.7% and 10.1% respectively for equation A). Consistent results were observed when the equations were applied to a large external data set. Conclusions Ethnic- and gender-specific equations for predicting FFM from BIA provide better estimates of ethnic differences in FFM and FM in children, while generic equations can misrepresent these ethnic differences

The trans-Golgi SNARE syntaxin 6 is recruited to the chlamydial inclusion membrane

Chlamydia trachomatis is an obligate intracellular pathogen that replicates within a parasitophorous vacuole termed an inclusion. The chlamydial inclusion is isolated from the endocytic pathway but fusogenic with Golgi-derived exocytic vesicles containing sphingomyelin and cholesterol. Sphingolipids are incorporated into the chlamydial cell wall and are considered essential for chlamydial development and viability. The mechanisms by which chlamydiae obtain eukaryotic lipids are poorly understood but require chlamydial protein synthesis and presumably modification of the inclusion membrane to initiate this interaction. A polarized cell model of chlamydial infection has demonstrated that chlamydiae preferentially intercept basolaterally directed, sphingomyelin-containing exocytic vesicles. Here we examine the localization and potential function of trans-Golgi and/or basolaterally associated soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins in chlamydia-infected cells. The trans-Golgi SNARE protein syntaxin 6 is recruited to the chlamydial inclusion in a manner that requires chlamydial protein synthesis and is conserved among all chlamydial species examined. The localization of syntaxin 6 to the chlamydial inclusion requires a tyrosine motif or plasma membrane retrieval signal (YGRL). Thus in addition to expression of at least two inclusion membrane proteins that contain SNARE-like motifs, chlamydiae also actively recruit eukaryotic SNARE-family proteins

Physical activity levels in adults and older adults 3–4 years after pedometer-based walking interventions: Long-term follow-up of participants from two randomised controlled trials in UK primary care

Background Physical inactivity is an important cause of noncommunicable diseases. Interventions can increase short-term physical activity (PA), but health benefits require maintenance. Few interventions have evaluated PA objectively beyond 12 months. We followed up two pedometer interventions with positive 12-month effects to examine objective PA levels at 3–4 years. Methods and findings Long-term follow-up of two completed trials: Pedometer And Consultation Evaluation-UP (PACE-UP) 3-arm (postal, nurse support, control) at 3 years and Pedometer Accelerometer Consultation Evaluation-Lift (PACE-Lift) 2-arm (nurse support, control) at 4 years post-baseline. Randomly selected patients from 10 United Kingdom primary care practices were recruited (PACE-UP: 45–75 years, PACE-Lift: 60–75 years). Intervention arms received 12-week walking programmes (pedometer, handbooks, PA diaries) postally (PACE-UP) or with nurse support (PACE-UP, PACE-Lift). Main outcomes were changes in 7-day accelerometer average daily step counts and weekly time in moderate-to-vigorous PA (MVPA) in ≥10-minute bouts in intervention versus control groups, between baseline and 3 years (PACE-UP) and 4 years (PACE-Lift). PACE-UP 3-year follow-up was 67% (681/1,023) (mean age: 59, 64% female), and PACE-Lift 4-year follow-up was 76% (225/298) (mean age: 67, 53% female). PACE-UP 3-year intervention versus control comparisons were as follows: additional steps/day postal +627 (95% CI: 198–1,056), p = 0.004, nurse +670 (95% CI: 237–1,102), p = 0.002; total weekly MVPA in bouts (minutes/week) postal +28 (95% CI: 7–49), p = 0.009, nurse +24 (95% CI: 3–45), p = 0.03. PACE-Lift 4-year intervention versus control comparisons were: +407 (95% CI: −177–992), p = 0.17 steps/day, and +32 (95% CI: 5–60), p = 0.02 minutes/week MVPA in bouts. Neither trial showed sedentary or wear-time differences. Main study limitation was incomplete follow-up; however, results were robust to missing data sensitivity analyses. Conclusions Intervention participants followed up from both trials demonstrated higher levels of objectively measured PA at 3–4 years than controls, similar to previously reported 12-month trial effects. Pedometer interventions, delivered by post or with nurse support, can help address the public health physical inactivity challenge

Bland-Altman plots for equations for deriving fat free mass from bioelectrical impedance analysis in ABCC Study by ethnicity.

<p>Equation A1, fat free mass = height+weight+Z (generic model); Equation A4, fat free mass = height+weight+Z (ethnic- and gender-specific model); Equation C1, fat free mass = height²/Z+weight (generic model); Equation C4, fat free mass = height²/Z+weight (ethnic- and gender-specific model). Abbreviations: FFM, fat free mass; WE, white European; BAC, black African-Caribbean; SA, South Asian; Z, impedance.</p

Coefficients from regression models deriving equations for estimating fat free mass by fitting height, weight and impedance as separate variables in the model and adding interaction terms for ethnicity and gender.

<p>Abbreviations: CI, confidence interval; BAC, Black African-Caribbean; SA, South Asian; HT, height; WT, weight; Z, impedance.</p><p>All models are adjusted for random effect of school.</p

Comparison of ethnic differences in body composition using different equations for deriving fat free mass in CHASE data.

*<p>Percentage differences shown for log transformed variables.</p><p>Abbreviations: CI, confidence interval; HT, height; WT, weight; Z, impedance.</p><p>Adjusted for gender, age quartiles, observer (skinfolds only) and a random effect for school.</p

Coefficients from regression models deriving equations for estimating fat free mass by fitting height²/impedance and weight as variables in the model and adding interaction terms for ethnicity and gender.

<p>Abbreviations: CI, confidence interval; BAC, Black African-Caribbean; SA, South Asian; HT²/Z, height²/impedance; WT, weight.</p><p>All models are adjusted for random effect of school.</p