Because of high population density and rapid urbanization, different human life stages have distinct growth experiences, leading to different lifestyles and age-spatial separation in the same neighborhood environment, particularly in smaller Chinese cities. The relationship of environment to physical activity may differ from western or larger Chinese cities. This study examined the associations of walking duration to the neighborhood environment and other factors, and explored the nuances of walking behavior for different life stages of adults in a smaller Chinese city, Yuncheng. An interviewer-administered questionnaire survey (n = 173) and face-to-face interviews (n = 19) were conducted in August 2017. Descriptive analysis and multiple linear regression were performed to describe walking motivations, sociodemographic characteristics, neighborhood environments, and their impacts on walking duration across three life stages. The quantitative findings were followed by interviews to validate and interpret them. Our results showed no positive associations of land-use mix (LUM) and residential density on walking duration, and even inverse associations of LUM-recreation and LUM-education for specific life stages were identified. Younger people’s walking behavior was more related to consumption amenities distinct from those of older people. Our findings suggest that using walkable neighborhood policies (e.g., high residential density and land-use mix) to increase physical activity might be ineffective in smaller Chinese cities.

Recent studies indicate the accepted concept of using land-use mix (LUM) to promote physical activity is ineffective and even counteractive in the Chinese context. Before considering LUM as a whole, different amenity types need to be respectively analyzed in relation to various functions and demands. This study aims to examine the specific associations between food-related amenities and perceived daily walking duration (WD) in small Chinese cities. Two interviewer-administered questionnaire surveys (n = 354) were conducted in Yuncheng and Suihua between 2017 and 2018. Logistic regression models were used to examine the associations of WD with seven different categories of food outlet at three levels of walking distance. The associations were further explored by food environment diversity and through two age groups. With the exception of café/tea house, the other six food outlets were positively associated with WD. After adjusting for socioeconomic variables, the associations of grocery store and supermarket weakened. Higher levels of food environment diversity were associated with a longer WD. Among the age groups, food outlets were more associated with older adults’ WD. This novel quantitative study suggests that increasing the number and heterogeneity of food-related amenities (including mobile street vendors) within a neighborhood can enhance physical activity in small Chinese cities.

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models’ calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models’ prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Physical activity (PA) is known to be a protective lifestyle factor against several non-communicable diseases while its impact on infectious diseases, including Coronavirus Disease 2019 (COVID-19) is not as clear. We performed univariate and multivariate logistic regression to identify associations between body mass index (BMI) and both objectively and subjectively measured PA collected prospectively and COVID-19 related outcomes (Overall COVID-19, inpatient COVID-19, outpatient COVID-19, and COVID-19 death) in the UK Biobank (UKBB) cohort. Subsequently, we tested causality by using two-sample Mendelian randomisation (MR) analysis. In the multivariable model, the increased acceleration vector magnitude PA (AMPA) was associated with a decreased probability of overall and outpatient COVID-19. No association was found between self-reported moderate-to-vigorous PA (MVPA) or BMI and COVID-19 related outcomes. Although no causal association was found by MR analyses, this may be due to limited power and we conclude policies to encourage and facilitate exercise at a population level during the pandemic should be considered.

A growing body of evidence shows that poor vitamin D status has been associated with an increased susceptibility to viral and bacterial respiratory infections. In this study, we aimed to examine the association between vitamin D and COVID-19 risk and outcomes, and to explore potential causal effects. We used logistic regression to identify associations between different vitamin D variables (25-hydroxyvitamin D concentration (25-OHD), ambient UVB and genetically-predicted 25-OHD concentrations) and COVID-19 (risk of infection, hospitalisation and death) in 495,780 participants from UK Biobank. We subsequently performed a Mendelian Randomisation (MR) study to test if there was any causal effect. In total, 1,746 COVID-19 cases and 399 COVID-19 deaths occurred between March and June 2020. We found significant inverse associations between COVID-19 infection and 25-OHD in univariable models, but these associations were non-significant after adjustment for confounders. Ambient UVB was strongly and inversely associated with hospitalization and death. Although the main MR analysis showed that genetically-predicted vitamin D levels were not causally associated with COVID-19 risk, MR sensitivity analysis using weighted mode method indicated a potential causal effect (OR=0.72, 95% CI:0.53-0.98; P=0.041). In conclusion, our study found suggestive evidence of association between vitamin D and the risk or severity of COVID-19 but further studies are needed.

Summary: Background: Stratifying the risk of colorectal cancer (CRC) based on polygenic risk scores (PRSs) within populations has the potential to optimize screening and develop targeted prevention strategies. Methods: A meta-analysis of eleven genome-wide association studies (GWAS), comprising 16 871 cases and 26 328 controls, was performed to capture CRC susceptibility variants. Genetic models with several candidate PRSs were generated from Scottish CRC case–control studies (6478 cases and 11 043 controls) for prediction of overall and site-specific CRC. Model performance was validated in UK Biobank (4800 cases and 20 287 controls). The 10-year absolute risk of CRC was estimated by modelling PRS with age and sex using the CRC incidence and mortality rates in the UK population. Findings: A weighted PRS including 116 CRC SNPs (wPRS ₁₁₆ ) showed the strongest performance. Deconstructing the PRS into multiple genetic risk regional scores or inclusion of additional SNPs that did not reach genome-wide significance did not provide any further improvement on predictive performance. The odds ratio (OR) for CRC risk per SD of wPRS ₁₁₆ in Scottish dataset was 1·46 (95%CI: 1·41-1·50, c-statistics: 0·603). Consistent estimates were observed in UK Biobank (OR=1·49, 95%CI: 1·44-1·54, c-statistics: 0·610) and showed no substantial heterogeneity among tumor sites. Compared to the middle quintile, those in the highest 1% of PRSs had 3·25-fold higher risk and those in the lowest 1% had 0·32-fold lower risk of developing CRC. Modelling PRS with age and sex in the general UK population allows the identification of a high-risk group with 10-year absolute risk ≥5%. Interpretation: By optimizing wPRS ₁₁₆ , we show that genetic factors increase predictive performance but this increment is equivalent to the extraction of only one-tenth of the genetic susceptibility. When employing genetic risk profiling in population settings it provides a degree of risk discrimination that could, in principle, be integrated into population-based screening programs.