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

Short-term and long-term cost-effectiveness of a pedometer-based exercise intervention in primary care: A within-trial analysis and beyond-trial modelling

Objectives A short-term and long-term cost-effectiveness analysis (CEA) of two pedometer-based walking interventions compared with usual care. Design (A) Short-term CEA: Parallel three-arm cluster randomised trial randomised by household. (B) Long-term CEA: Markov decision model. Setting Seven primary care practices in South London, UK. Participants (A) Short-term CEA: 1023 people (922 households) aged 45-75 years without physical activity (PA) contraindications. (b) Long-term CEA: A cohort of 100 000 people aged 59-88 years. Interventions Pedometers, 12-week walking programmes and PA diaries delivered by post or through three PA consultations with practice nurses. Primary and secondary outcome measures Accelerometer-measured change (baseline to 12 months) in average daily step count and time in 10 min bouts of moderate to vigorous PA (MVPA), and EQ-5D-5L quality-adjusted life-years (QALY). Methods Resource use costs (£2013/2014) from a National Health Service perspective, presented as incremental cost-effectiveness ratios for each outcome over a 1-year and lifetime horizon, with cost-effectiveness acceptability curves and willingness to pay per QALY. Deterministic and probabilistic sensitivity analyses evaluate uncertainty. Results (A) Short-term CEA: At 12 months, incremental cost was £3.61 (£109)/min in ≥10 min MVPA bouts for nurse support compared with control (postal group). At £20 000/QALY, the postal group had a 50% chance of being cost saving compared with control. (B) Long-term CEA: The postal group had more QALYs (+759 QALYs, 95% CI 400 to 1247) and lower costs (-£11 million, 95% CI -12 to -10) than control and nurse groups, resulting in an incremental net monetary benefit of £26 million per 100 000 population. Results were sensitive to reporting serious adverse events, excluding health service use, and including all participant costs. Conclusions Postal delivery of a pedometer intervention in primary care is cost-effective long term and has a 50% chance of being cost-effective, through resource savings, within 1 year. Further research should ascertain maintenance of the higher levels of PA, and its impact on quality of life and health service use. © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY. Published by BMJ

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

Effect of a primary care walking intervention with and without nurse support on physical activity levels in 45- to 75-year-olds: The pedometer and consultation evaluation (PACE-UP) cluster randomised clinical trial

Background Pedometers can increase walking and moderate-to-vigorous physical activity (MVPA) levels, but their effectiveness with or without support has not been rigorously evaluated. We assessed the effectiveness of a pedometer-based walking intervention in predominantly inactive adults, delivered by post or through primary care nurse-supported physical activity (PA) consultations. Methods and Findings A parallel three-arm cluster randomised trial was randomised by household, with 12-mo follow-up, in seven London, United Kingdom, primary care practices. Eleven thousand fifteen randomly selected patients aged 45–75 y without PA contraindications were invited. Five hundred forty-eight self-reporting achieving PA guidelines were excluded. One thousand twenty-three people from 922 households were randomised between 2012–2013 to one of the following groups: usual care (n = 338); postal pedometer intervention (n = 339); and nurse-supported pedometer intervention (n = 346). Of these, 956 participants (93%) provided outcome data (usual care n = 323, postal n = 312, nurse-supported n = 321). Both intervention groups received pedometers, 12-wk walking programmes, and PA diaries. The nurse group was offered three PA consultations. Primary and main secondary outcomes were changes from baseline to 12 mo in average daily step-counts and time in MVPA (in ≥10-min bouts), respectively, measured objectively by accelerometry. Only statisticians were masked to group. Analysis was by intention-to-treat. Average baseline daily step-count was 7,479 (standard deviation [s.d.] 2,671), and average time in MVPA bouts was 94 (s.d. 102) min/wk. At 12 mo, mean steps/d, with s.d. in parentheses, were as follows: control 7,246 (2,671); postal 8,010 (2,922); and nurse support 8,131 (3,228). PA increased in both intervention groups compared with the control group; additional steps/d were 642 for postal (95% CI 329–955) and 677 for nurse support (95% CI 365–989); additional MVPA in bouts (min/wk) were 33 for postal (95% CI 17–49) and 35 for nurse support (95% CI 19–51). There were no significant differences between the two interventions at 12 mo. The 10% (1,023/10,467) recruitment rate was a study limitation. Conclusions A primary care pedometer-based walking intervention in predominantly inactive 45- to 75-y-olds increased step-counts by about one-tenth and time in MVPA in bouts by about one-third. Nurse and postal delivery achieved similar 12-mo PA outcomes. A primary care pedometer intervention delivered by post or with minimal support could help address the public health physical inactivity challenge.The PACE-UP trial was funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) Programme (project number HTA 10/32/02 ISRCTN42122561) and will be published in full in Health Technology Assessment. The funders had no role in study design (beyond the commissioned call outline), data collection and analysis, decision to publish, or preparation of the manuscript

Measuring change in trials of physical activity interventions: a comparison of self-report questionnaire and accelerometry within the PACE-UP trial

Background: Few trials have compared estimates of change in physical activity (PA) levels using self-reported and objective PA measures when evaluating trial outcomes. The PACE-UP trial offered the opportunity to assess this, using the self-administered International Physical Activity Questionnaire (IPAQ) and waist-worn accelerometry. Methods: The PACE-UP trial (N=1023) compared usual care (n=338) with two pedometer-based walking interventions, by post (n=339) or with nurse support (n=346). Participants wore an accelerometer at baseline and 12months and completed IPAQ for the same 7-day periods. Main outcomes were weekly minutes, all in ≥10min bouts as per UK PA guidelines of: i) accelerometer moderate-to-vigorous PA (Acc-MVPA) ii) IPAQ moderate+vigorous PA (IPAQ-MVPA) and iii) IPAQ walking (IPAQ-Walk). For each outcome, 12month values were regressed on baseline to estimate change. Results: Analyses were restricted to 655 (64%) participants who provided data on all outcomes at baseline and 12 months. Both intervention groups significantly increased their accelerometry MVPA minutes/week compared with control: postal group 42 (95% CI 22, 61), nurse group 43 (95% CI 24, 63). IPAQ-Walk minutes/week also increased: postal 57 (95% CI 2, 112), nurse 43 (95% CI -11, 97) but IPAQ-MVPA minutes/week showed non-significant decreases: postal -11 (95% CI -65, 42), nurse -34 (95% CI -87, 19). Conclusions: Our results demonstrate the necessity of using a questionnaire focussing on the activities being altered, as with IPAQ-Walk questions. Even then, the change in PA was estimated with far less precision than with accelerometry. Accelerometry is preferred to self-report measurement, minimising bias and improving precision when assessing effects of a walking intervention

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