Association of physical activity and air pollution exposure with the risk of type 2 diabetes: a large population-based prospective cohort study.

BACKGROUND: The interplay between physical activity (PA) and air pollution in relation to type 2 diabetes (T2D) remains largely unknown. Based on a large population-based cohort study, this study aimed to examine whether the benefits of PA with respect to the risk of T2D are moderated by exposure to air pollution. METHODS: UK Biobank participants (n = 359,153) without diabetes at baseline were included. Information on PA was obtained using the International Physical Activity Questionnaire short form. Exposure to air pollution, including PM2.5, PMcoarse (PM2.5-10), PM10, and NO2, was estimated from land use regression models. Cox regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs). RESULTS: During a median of 8.9 years of follow-up, 13,706 T2D events were recorded. Compared with a low PA level, the HRs for the risk of T2D among individuals with moderate and high PA were 0.82 (95% CI, 0.79-0.86) and 0.73 (95% CI, 0.70-0.77), respectively. Compared with low levels of air pollution, the HRs for risk of T2D for high levels of air pollution (PM2.5, PMcoarse, PM10, and NO2) were 1.19 (1.14-1.24), 1.06 (1.02-1.11), 1.13 (1.08-1.18), and 1.19 (1.14-1.24), respectively. There was no effect modification of the associations between PA and T2D by air pollution (all P-interactions > 0.05). The inverse associations between PA and T2D in each air pollution stratum were generally consistent (all P for trend < 0.05). CONCLUSION: A higher PA and lower air pollution level were independently associated with a lower risk of T2D. The beneficial effects of PA on T2D generally remained stable among participants exposed to different levels of air pollution. Further studies are needed to replicate our findings in moderately and severely polluted areas.

Association between supportive attitude and adoptive practice of control strategy against COVID-19 amosng college students in China: a cross-sectional study.

BACKGROUND: We investigated college students' attitude and compliance towards a prevention strategy involving use of non-pharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19). METHODS: We conducted a cross-sectional online survey in four universities in Guangdong Province (China) based on purposive sampling. A self-administered questionnaire was given to College students (CSs) to measure the supportive attitude towards an outbreak control strategy and adoption of NPIs in respondents. RESULTS: A total of 44,446 CSs participated between 31 January and 10 February 2020; 92.7% of respondents supported the outbreak control strategy. The proportion of respondents who avoided public places, wore a facemask, avoid gatherings, and washed hands more frequently than usual was 94.8, 92.8, 91.2 and 86.9%. respectively. A total of 76.5% respondents adopted all four measures. A supportive attitude was associated with NPI adoption. Students who were female, postgraduate, anxious, and not depressed tended to have a higher supportive attitude and higher chance of NPI adoption. CONCLUSIONS: Higher supportiveness towards the disease control strategy for the Chinese public may lead to higher adoption rate of NPIs. Psychosocial factors were related to a supportive attitude and adoption of the NPI. We believe that our findings could aid policymakers to create NPIs to prevent and control emerging infectious diseases such as COVID-19.

Contact Settings and Risk for Transmission in 3410 Close Contacts of Patients With COVID-19 in Guangzhou, China : A Prospective Cohort Study.

BACKGROUND: Risk for transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to close contacts of infected persons has not been well estimated. OBJECTIVE: To evaluate the risk for transmission of SARS-CoV-2 to close contacts in different settings. DESIGN: Prospective cohort study. SETTING: Close contacts of persons infected with SARS-CoV-2 in Guangzhou, China. PARTICIPANTS: 3410 close contacts of 391 index cases were traced between 13 January and 6 March 2020. Data on the setting of the exposure, reverse transcriptase polymerase chain reaction testing, and clinical characteristics of index and secondary cases were collected. MEASUREMENT: Coronavirus disease 2019 (COVID-19) cases were confirmed by guidelines issued by China. Secondary attack rates in different settings were calculated. RESULTS: Among 3410 close contacts, 127 (3.7% [95% CI, 3.1% to 4.4%]) were secondarily infected. Of these 127 persons, 8 (6.3% [CI, 2.1% to 10.5%]) were asymptomatic. Of the 119 symptomatic cases, 20 (16.8%) were defined as mild, 87 (73.1%) as moderate, and 12 (10.1%) as severe or critical. Compared with the household setting (10.3%), the secondary attack rate was lower for exposures in health care settings (1.0%; odds ratio [OR], 0.09 [CI, 0.04 to 0.20]) and on public transportation (0.1%; OR, 0.01 [CI, 0.00 to 0.08]). The secondary attack rate increased with the severity of index cases, from 0.3% (CI, 0.0% to 1.0%) for asymptomatic to 3.3% (CI, 1.8% to 4.8%) for mild, 5.6% (CI, 4.4% to 6.8%) for moderate, and 6.2% (CI, 3.2% to 9.1%) for severe or critical cases. Index cases with expectoration were associated with higher risk for secondary infection (13.6% vs. 3.0% for index cases without expectoration; OR, 4.81 [CI, 3.35 to 6.93]). LIMITATION: There was potential recall bias regarding symptom onset among patients with COVID-19, and the symptoms and severity of index cases were not assessed at the time of exposure to contacts. CONCLUSION: Household contact was the main setting for transmission of SARS-CoV-2, and the risk for transmission of SARS-CoV-2 among close contacts increased with the severity of index cases. PRIMARY FUNDING SOURCE: Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme.

Healthy Sleep Associated With Lower Risk of Hypertension Regardless of Genetic Risk: A Population-Based Cohort Study.

Background: Hypertension is a leading contributor to the global burden of disease and to mortality. The combined effects of sleep factors on the risk of hypertension are unclear. We aimed to evaluate the effect of combined sleep factors on the risk of hypertension and to explore whether this association is independent of genetic risk. Methods: This population-based prospective cohort study included 170,378 participants from the UK Biobank study. We conducted a healthy sleep score based on a combination of major five sleep factors and a genetic risk score based on 118 risk variants. Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: A total of 170,378 participants were included. Compared to participants with a healthy sleep score of 0-1, those with healthy sleep scores of 2 (HR, 0.90; 95% CI, 0.83-0.98), 3 (HR, 0.81; 95% CI, 0.75-0.88), 4 (HR, 0.74; 95% CI, 0.68-0.81), or 5 (HR, 0.67; 95% CI, 0.59-0.77) had increasingly lower risks of hypertension (P for trend <0.001). Participants with high genetic risk and an unfavorable sleep pattern had a 1.80-fold greater risk of hypertension than participants with low genetic risk and a favorable sleep pattern. The association between sleep patterns and hypertension persisted in subgroup analysis, stratified by the genetic risk. Nearly 18.2% of hypertension events in this cohort could be attributed to unfavorable sleep pattern. Conclusions: Favorable sleep pattern was associated with a low risk of hypertension, regardless of genetic risk. These findings highlight the potential of sleep interventions to reduce risk of hypertension across entire populations.