Association between grip strength and diabetes prevalence in black, South Asian, and white European ethnic groups: a cross-sectional study of 418,656 UK Biobank participants

Aims: To quantify the extent to which ethnic differences in muscular strength might account for the substantially higher prevalence of diabetes in black and South-Asian compared with white European adults. Methods: This cross-sectional study used baseline data from the UK Biobank study on 418 656 white European, black and South-Asian participants, aged 40–69 years, who had complete data on diabetes status and hand-grip strength. Associations between hand-grip strength and diabetes were assessed using logistic regression and were adjusted for potential confounding factors. Results: Lower grip strength was associated with higher prevalence of diabetes, independent of confounding factors, across all ethnicities in both men and women. Diabetes prevalence was approximately three- to fourfold higher in South-Asian and two- to threefold higher in black participants compared with white European participants across all levels of grip strength, but grip strength in South-Asian men and women was ~5–6 kg lower than in the other ethnic groups. Thus, the attributable risk for diabetes associated with low grip strength was substantially higher in South-Asian participants (3.9 and 4.2 cases per 100 men and women, respectively) than in white participants (2.0 and 0.6 cases per 100 men and women, respectively). Attributable risk associated with low grip strength was also high in black men (4.3 cases) but not in black women (0.4 cases). Conclusions: Low strength is associated with a disproportionately large number of diabetes cases in South-Asian men and women and in black men. Trials are needed to determine whether interventions to improve strength in these groups could help reduce ethnic inequalities in diabetes prevalence

The association between driving time and unhealthy lifestyles: a cross-sectional, general population study of 386 493 UK Biobank participants

Background: Driving is a common type of sedentary behaviour; an independent risk factor for poor health. The study explores whether driving is also associated with other unhealthy lifestyle factors. Methods: In a cross-sectional study of UK Biobank participants, driving time was treated as an ordinal variable and other lifestyle factors dichotomized into low/high risk based on guidelines. The associations were explored using chi-square tests for trend and binary logistic regression. Results: Of the 386 493 participants who drove, 153 717 (39.8%) drove <1 h/day; 140 140 (36.3%) 1 h/day; 60 973 (15.8%) 2 h/day; and 31 663 (8.2%) ≥3 h/day. Following adjustment for potential confounders, driving ≥3 h/day was associated with being overweight/obese (OR = 1.74, 95% CI: 1.64–1.85), smoking (OR = 1.48, 95% CI: 1.37–1.63), insufficient sleep (1.70, 95% CI: 1.61–1.80), low fruit/vegetable intake (OR = 1.26, 95% CI: 1.18–1.35) and low physical activity (OR = 1.05, 95% CI: 1.00–1.11), with dose relationships for the first three, but was not associated with higher alcohol consumption (OR = 0.94, 95% CI: 0.87–1.02). Conclusions: Sedentary behaviour, such as driving, is known to have an independent association with adverse health outcomes. It may have additional impact mediated through its effect on other aspects of lifestyle. People with long driving times are at higher risk and might benefit from targeted interventions

Associations of dietary protein intake with fat free mass and grip strength: cross-sectional study in 146,816 UK Biobank participants

Adequate dietary protein intake is important for the maintenance of fat-free mass (FFM) and muscle strength: optimal requirements remain unknown. The aim of the current study was to explore the associations of protein intake with FFM and grip strength. We used baseline data from the UK Biobank (146,816 participants aged 40-69 years with data collected 2007-2010 across the UK) to examine the associations of protein intake with FFM and grip strength. Protein intake was positively associated with FFM (men 5.1% [95% CI: 5.0; 5.2] and women 7.7% [95% CI: 7.7; 7.8]) and grip strength (men 0.076 kg/kg [95% CI: 0.074; 0.078] and women 0.074 kg/kg [95% CI: 0.073; 0.076]) per 0.5 grams per kg body mass per day (g/kg/day) increment in protein intake. FFM and grip strength were higher with higher intakes across the full range of intakes, i.e. highest in those reporting consuming > 2.0 g/grams per kg/day independently of socio-demographics, other dietary measures, physical activity and comorbidities. FFM and grip strength were lower with age, but this association did not differ by protein intake categories (P > 0.05). Current recommendation for all adults (40-69 years) for protein intake (0.8 grams per kg body mass per day) may need to be increased to optimise FFM and grip strength

Population-level seasonality in cardiovascular mortality, blood pressure, BMI and inflammatory cells in UK Biobank

Introduction: The risk of mortality from cardiovascular disease (CVD) is higher in wintertime throughout the world, but it is not known if this reflects annual changes in diet or lifestyle, or an endogenous photoperiodic mechanism that is sensitive to changes in daylength. Methods: Phenotypic data on cardiometabolic and lifestyle factors were collected throughout a 4 year time period from 502,642 middle-aged participants in UK Biobank. To assess the impact of seasonal environmental changes on cardiovascular risk factors, we linked these data to the outdoor temperature and day length at the time of assessment. Self-reported information on physical activity, diet and disease status were used to adjust for confounding factors related to health and lifestyle. Results: Mortality related to CVD was higher in winter, as were risk factors for this condition including blood pressure, markers of inflammation and BMI. These seasonal rhythms were significantly related to day length after adjustment for other factors that might affect seasonality including physical activity, diet and outdoor temperature. Conclusions: The risk of CVD may be modulated by day length at temperate latitudes, and the implications of seasonality should be considered in all studies of human cardiometabolic health

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Classification of current anticancer immunotherapies

During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into “passive” and “active” based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches

Tribocorrosion: a new approach of the problems related to combined friction and wear in a corrosive surrounding

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