Additional file 7: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Adjusted risk ratios (RRs) describing the associations of household income with body mass index (estimated using a multivariable linear regression model, n = 51,361), obesity (estimated using a binomial logistic regression model, n = 51,361), and frequent consumption of processed meat (estimated using a binomial logistic regression model, n = 51,090) in the Greater London UK Biobank sample. (DOCX 22 kb

Additional file 4: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Associations of quartiles of fast-food outlet proportion with body fat percentage (estimated using a multivariable linear regression model, n = 50,766) in the Greater London UK Biobank sample. (DOCX 20 kb

Additional file 5: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Adjusted risk ratios (RRs) describing the associations of quartiles of fast-food outlet proportion with body mass index (estimated using a multivariable linear regression model, n = 51,361), obesity (estimated using a binomial logistic regression model n = 51,361), and frequent consumption of processed meat (estimated using a binomial logistic regression model, n = 51,090) in the Greater London UK Biobank sample. (DOCX 22 kb

Additional file 1: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Flow diagram for UK Biobank sample restriction, for body weight-, processed meat consumption- and percentage body fat-based analyses reported in this study. (DOCX 37 kb

Additional file 3: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Characteristics of participants in the UK Biobank sample, UK (n = 51,361), overall and stratified by household income. (DOCX 24 kb

Additional file 6: of Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

Associations of household income with body fat percentage (estimated using a multivariable linear regression model, n = 50,766) in the Greater London UK Biobank sample. (DOCX 20 kb

supplementary_material – Supplemental material for Effects of green space on walking: Does size, shape and density matter?

Supplemental material, supplementary_material for Effects of green space on walking: Does size, shape and density matter? by Xiaohu Zhang, Scott Melbourne, Chinmoy Sarkar, Alain Chiaradia and Chris Webster in Urban Studies</p

Association between Residential Greenness and Allostatic Load: A Cohort Study

The association between residential greenness and allostatic load (AL), a marker of composite physiological burden and predictor of chronic disease, remains understudied. This study comprised 212,600 UK Biobank participants recruited over 2007 and 2010 at the baseline. Residential greenness was modeled as the normalized difference vegetation index (NDVI) from high spatial resolution (0.50 m) color infrared imagery and measured within a 0.5 km radial catchment. AL was measured as a composite index from 13 biomarkers comprising three physiological systems (metabolic, cardiovascular, and inflammatory systems) and two organ systems (liver and kidney). Multilevel mixed-effects generalized linear models with a random intercept for UK Biobank assessment centers were employed to examine the association between residential greenness and AL. Each interquartile range (IQR = 0.24) increment in NDVI greenness was associated with lower AL (beta (β) = −0.28, 95% confidence interval (CI) = −0.55, −0.01). Consistently, relative to the lowest NDVI greenness quintile, participants in the highest quintile had lower AL (β = −0.64, 95% CI = −1.02, −0.26). The proportion of the association between greenness and AL mediated by the physical activity was 3.2%. In conclusion, residential greenness was protectively associated with AL, a composite marker of wear and tear and general health

Impaired autophagy flux is associated with neuronal cell death after traumatic brain injury

<p>Dysregulation of autophagy contributes to neuronal cell death in several neurodegenerative and lysosomal storage diseases. Markers of autophagy are also increased after traumatic brain injury (TBI), but its mechanisms and function are not known. Following controlled cortical impact (CCI) brain injury in <i>GFP-Lc3</i> (green fluorescent protein-LC3) transgenic mice, we observed accumulation of autophagosomes in ipsilateral cortex and hippocampus between 1 and 7 d. This accumulation was not due to increased initiation of autophagy but rather to a decrease in clearance of autophagosomes, as reflected by accumulation of the autophagic substrate SQSTM1/p62 (sequestosome 1). This was confirmed by <i>ex vivo</i> studies, which demonstrated impaired autophagic flux in brain slices from injured as compared to control animals. Increased SQSTM1 peaked at d 1–3 but resolved by d 7, suggesting that the defect in autophagy flux is temporary. The early impairment of autophagy is at least in part caused by lysosomal dysfunction, as evidenced by lower protein levels and enzymatic activity of CTSD (cathepsin D). Furthermore, immediately after injury both autophagosomes and SQSTM1 accumulated predominantly in neurons. This was accompanied by appearance of SQSTM1 and ubiquitin-positive puncta in the affected cells, suggesting that, similar to the situation observed in neurodegenerative diseases, impaired autophagy may contribute to neuronal injury. Consistently, GFP-LC3 and SQSTM1 colocalized with markers of both caspase-dependent and caspase-independent cell death in neuronal cells proximal to the injury site. Taken together, our data indicated for the first time that autophagic clearance is impaired early after TBI due to lysosomal dysfunction, and correlates with neuronal cell death.</p

Supporting information figures.

Fig A: An illustration showing the attributes of housing exposures in the developed HKHED database; livable floor area, building units per block and neighborhood residential density. km: kilometer. Fig B: Flowchart of the selection of participants for cross-sectional analyses at baseline and wave 2. Fig C: Flowchart of the selection of participants for longitudinal analyses on linked data across 2 waves. Fig D: Density plot showing the distribution of propensity scores of the incident hypertension model in the control group (participants who did not change their residential address between the 2 waves) marked as 0, and the treatment group (participants who changed residence to lower liveable floor area) marked as 1 after matching. HKHED, Hong Kong Housing Environment Database. (DOCX)</p