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Background Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. Methods and findings With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before ([delta]-prepandemic) and during ([delta]-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m.sup.2 /year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period ([delta]-change = [delta]-pandemic-[delta]-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain ([delta]-change [greater than or equal to]1.84 kg/m.sup.2 /year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m.sup.2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m.sup.2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. Conclusions We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning., Author(s): Miriam Samuel 1,*, Robin Y. Park 2, Sophie V. Eastwood 3, Fabiola Eto 1, Caroline E. Morton 1,2, Daniel Stow 1, Sebastian Bacon 2, Amir Mehrkar 2, Jessica Morley [...]

We investigated the relationship between glycemia and cognitive function, brain structure and incident dementia using bidirectional Mendelian randomization (MR). Data were from the UK Biobank (n = ∼500,000). Our exposures were genetic instruments for type 2 diabetes (157 variants) and HbA1c (51 variants) and our outcomes were reaction time (RT), visual memory, hippocampal volume (HV), white matter hyperintensity volume (WMHV), and Alzheimer dementia (AD). We also investigated associations between genetic variants for RT (43 variants) and diabetes and HbA1c We used conventional inverse-variance-weighted (IVW) MR alongside MR sensitivity analyses. Using IVW, genetic liability to type 2 diabetes was not associated with RT (exponentiated β [expβ] = 1.00 [95% CI 1.00; 1.00]), visual memory (expβ = 1.00 [95% CI 0.99; 1.00]), WMHV (expβ = 0.99 [95% CI 0.97; 1.01]), HV (β-coefficient mm3 = -2.30 [95% CI -12.39; 7.78]) or AD (odds ratio [OR] 1.15 [95% CI 0.87; 1.52]). HbA1c was not associated with RT (expβ = 1.00 [95% CI 0.99; 1.02]), visual memory (expβ = 0.99 [95% CI 0.96; 1.02]), WMHV (expβ = 1.03 [95% CI 0.88; 1.22]), HV (β = -21.31 [95% CI -82.96; 40.34]), or risk of AD (OR 1.09 [95% CI 0.42; 2.83]). IVW showed that reaction time was not associated with diabetes risk (OR 0.94 [95% CI 0.54; 1.65]), or with HbA1c (β-coefficient mmol/mol = -0.88 [95% CI = -1.88; 0.13]) after exclusion of a pleiotropic variant. Overall, we observed little evidence of causal association between genetic instruments for type 2 diabetes or peripheral glycemia and some measures of cognition and brain structure in midlife. [ABSTRACT FROM AUTHOR]

Background: In the UK, previous work suggests ethnic inequalities in hypertension management. We studied ethnic differences in hypertension management and their contribution to blood pressure (BP) control., Methods: We conducted a cohort study of antihypertensive-naïve individuals of European, South Asian and African/African Caribbean ethnicity with a new raised BP reading in UK primary care from 2006 to 2019, using the Clinical Practice Research Datalink (CPRD). We studied differences in: BP re-measurement after an initial hypertensive BP, antihypertensive initiation, BP monitoring, antihypertensive intensification, antihypertensive persistence/adherence and BP control one year after antihypertensive initiation. Models adjusted for socio-demographics, BP, comorbidity, healthcare usage and polypharmacy (plus antihypertensive class, BP monitoring, intensification, persistence and adherence for BP control models)., Findings: A total of 731,506 (93.5%), 30,379 (3.9%) and 20,256 (2.6%) people of European, South Asian and African/African Caribbean ethnicity were studied. Hypertension management indicators were similar or more favourable for South Asian than European groups (OR/HR [95% CI] in fully-adjusted models of BP re-measurement: 1.16 [1.09, 1.24]), antihypertensive initiation: 1.49 [1.37, 1.62], BP monitoring: 0.97 [0.94, 1.00] and antihypertensive intensification: 1.10 [1.04, 1.16]). For people of African/African Caribbean ethnicity, BP re-measurement rates were similar to those of European ethnicity (0.98 [0.91, 1.05]), and antihypertensive initiation rates greater (1.48 [1.32, 1.66]), but BP monitoring (0.91 [0.87, 0.95]) and intensification rates lower (0.93 [0.87, 1.00]). Persistence and adherence were lower in South Asian (0.48 [0.45, 0.51] and 0.51 [0.47, 0.56]) and African/African Caribbean (0.38 [0.35, 0.42] and 0.39 [0.36, 0.43]) than European groups. BP control was similar in South Asian and less likely in African/African Caribbean than European groups (0.98 [0.90, 1.06] and 0.81 [0.74, 0.89] in age, gender and BP adjusted models). The latter difference attenuated after adjustment for persistence (0.91 [0.82, 0.99]) or adherence (0.92 [0.83, 1.01]), and was absent for antihypertensive-adherent people (0.99 [0.88, 1.10])., Interpretation: We demonstrate that antihypertensive initiation does not vary by ethnicity, but subsequent BP control was notably lower among people of African/African Caribbean ethnicity, potentially associated with being less likely to remain on regular treatment. A nationwide strategy to understand and address differences in ongoing management of people on antihypertensives is imperative., Funding: Diabetes UK., Competing Interests: SVE is funded by a 10.13039/501100000361Diabetes UK Sir George Alberti research training fellowship (grant number: 17/0005588). RM reports personal fees from 10.13039/100002429Amgen, outside the submitted work. NC has received personal fees from 10.13039/100004325AstraZeneca outside of the submitted work. All other authors have no competing interests to declare., (© 2022 The Author(s).)