Christian 2008 {published data only} Christian JG, Bessesen DH, Byers TE, Christian KK, Goldstein MG, Bock BC. Clinic-based support to help overweight patients with type 2 diabetes increase physical activity and lose weight. Archives of Internal Medicine 2008; 168(2):141-6. Glasgow 1997 {published data only} Glasgow RE, La Chance PA, Toobert DJ, Brown J, Hampson SE, Riddle MC. Long-term effects and costs of brief behavioural dietary intervention for patients with diabetes delivered from the medical office. Patient Education and Counselling 1997;32(3):175-84. Glasgow 2003 {published data only} Glasgow RE, Boles SM, Mckay HG, Feil EG, Barrera M. The D-Net diabetes self-management program: longterm implementation, outcomes and generalization results.

Background We investigated which clinical and sociodemographic characteristics were associated with unhealthy patterns of weight gain amongst adults living in England during the pandemic. Methods With the approval of NHS England we conducted an observational cohort study of Body Mass Index (BMI) changes between March 2015 and March 2022 using the OpenSAFELY–TPP platform. We estimated individual rates of weight gain before and during the pandemic, and identified individuals with rapid weight gain (>0.5kg/m2/year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period and defined extreme-accelerators as the ten percent of individuals with the greatest increase (>1.84kg/m2/year). We estimated associations with these outcomes using multivariate logistic regression. Findings We extracted data on 17,742,365 adults (50.1% female, 76.1% White British). Median BMI increased from 27.8kg/m2[IQR:24.3 to 32.1] in 2019 (March 2019 to February 2020) to 28.0kg/m2[24.4 to 32.6] in 2021. Rapid pandemic weight gain (n=3,214,155) was associated with female sex (male vs female: aOR 0.76 [95%CI:0.76 to 0.76]); younger age (50 to 59 years vs 18 to 29 years: aOR 0.60 [0.60 to 0.61]); White British ethnicity (Black Caribbean vs White British: aOR 0.91 [0.89 to 0.94]); deprivation (least–deprived–IMD–quintile vs most–deprived:aOR 0.77 [0.77 to 0.78]); and long-term conditions, of which mental health conditions had the greatest effect (e.g. depression (aOR 1.18[1.17 to 1.18])). Similar characteristics increased risk of extreme acceleration (n=2,768,695). Interpretation We found female sex, younger age, deprivation and mental health conditions increased risk of unhealthy patterns of pandemic weight gain. This highlights the need to incorporate sociodemographic, physical, and mental health characteristics when formulating post-pandemic research, policies, and interventions targeting BMI. Funding NIHR

BackgroundAtherosclerotic cardiovascular disease (ASCVD) risk differs by ethnicity. In comparison with Europeans (EA) South Asian (SA) people in UK experience higher risk of coronary heart disease (CHD) and stroke, while African Caribbean people have a lower risk of CHD but a higher risk of stroke.AimTo compare carotid atherosclerosis in EA, SA, and AC participants in the Southall and Brent Revisited (SABRE) study and establish if any differences were explained by ASCVD risk factors.MethodsCardiovascular risk factors were measured, and carotid ultrasound was performed in 985 individuals (438 EA, 325 SA, 228 AC). Carotid artery plaques and intima-media thickness (cIMT) were measured. Associations of carotid atherosclerosis with ethnicity were investigated using generalised linear models (GLMs), with and without adjustment for non-modifiable (age, sex) and modifiable risk factors (education, diabetes, hypertension, total cholesterol, HDL-C, alcohol consumption, current smoking).ResultsPrevalence of any plaque was similar in EA and SA, but lower in AC (16, 16, and 6%, respectively; p < 0.001). In those with plaque, total plaque area, numbers of plaques, plaque class, or greyscale median did not differ by ethnicity; adjustment for risk factors had minimal effects. cIMT was higher in AC than the other ethnic groups after adjustment for age and sex, adjustment for risk factors attenuated this difference.ConclusionPrevalence of carotid artery atherosclerotic plaques varies by ethnicity, independent of risk factors. Lower plaque prevalence in in AC is consistent with their lower risk of CHD but not their higher risk of stroke. Higher cIMT in AC may be explained by risk factors. The similarity of plaque burden in SA and EA despite established differences in ASCVD risk casts some doubt on the utility of carotid ultrasound as a means of assessing risk across these ethnic groups.

Aims: To investigate the relationship between glycaemia and cognitive function, brain structure and incident dementia using bidirectional Mendelian randomisation (MR). Methods: UK Biobank (n~500,000) individuals, aged 40-69 years at baseline. Our exposures were genetic instruments for type-2 diabetes (163 variants) and HbA1c (52 variants) and our outcomes were reaction time (RT - milliseconds), visual memory (number of incorrect responses), hippocampal and white matter hyperintensity volumes (both mm3), Alzheimer's disease (AD). To study potential bidirectional effects, we then investigated the associations between genetic variants for RT (43 variants) and clinical type-2 diabetes and measured HbA1c. We used conventional inverse-variance weighted (IVW) MR, alongside standard MR sensitivity analyses. Results: Using IVW, genetic liability to type-2 diabetes was not associated with reaction time (exponentiatedβ=1.00, 95%CI=1.00; 1.00), visual memory (expβ=1.00, 95%CI=0.99; 1.00), white matter hyperintensity volume (expβ=0.98, 95%CI=0.93; 1.03), hippocampal volume (coefficient mm3=0.00, 95%CI=-0.01; 0.01) or risk of AD (OR 0.97, 95%CI=0.89; 1.06). HbA1c was not associated with reaction time (expβ=1.01, 95%CI=1.00; 1.01), white matter hyperintensity volume (expβ=0.88, 95%CI=0.73; 1.07), hippocampal volume (coefficient=-0.02, 95%CI=-0.10; 0.06), risk of AD (OR 0.94, 95%CI=0.47; 1.86), but HbA1c was associated with visual memory (expβ=1.06, 95%CI=1.05; 1.07) using a weighted median approach. IVW showed no evidence that reaction time was associated with diabetes (OR 0.96, 95%CI=0.63; 1.46) or HbA1c (coefficient=-0.08, 95%CI=-0.57; 0.42). MR-Egger intercept p-values indicated no major issues with unbalanced horizontal pleiotropy (all p>0.05). Conclusions: Overall, we observed little evidence of causal associations between glycaemia and cognition, structural brain and dementia phenotypes.