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Aims/hypothesis The aim of the study was to examine the association of retinal vessel morphometry with BP, body composition and biochemistry, and to determine whether these associations differ by diabetes status. Methods The UK Biobank ocular assessment included 68,550 participants aged 40-70 years who underwent non-mydriatic retinal photography, BP and body composition measurements, and haematological analysis. A fully automated image analysis program provided measurements of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiometabolic risk factors by diabetes status were examined using multilevel linear regression, to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing for within-person clustering). Results A total of 50,233 participants (a reduction from 68,550) were included in these analyses. Overall, those with diabetes had significantly more tortuous venules and wider arteriolar diameters compared with those without. Associations between venular tortuosity and cardiometabolic risk factors differed according to diabetes status (p interaction 1c, C-reactive protein, white cell count and granulocyte count. For example, a unit rise in white cell count was associated with a 0.18% increase (95% CI 0.05, 0.32%) in venular tortuosity for those without diabetes and a 1.48% increase (95% CI 0.90, 2.07%) among those with diabetes. For arteriolar diameter, significant interactions were evident for systolic BP, diastolic BP, mean arterial pressure (MAP) and LDL-cholesterol. For example, a 10 mmHg rise in systolic BP was associated with a −0.92 μm difference (95% CI −0.96 to −0.88 μm) in arteriolar diameter for those without diabetes, and a −0.58 μm difference (95% CI −0.76 to −0.41 μm) among those with diabetes. No interactions were observed for arteriolar tortuosity or venular diameters. Conclusions/interpretation We provide clear evidence of the modifying effect of diabetes on cardiometabolic risk factor associations with retinal microvascular architecture. These observations suggest the occurrence of preclinical disease processes, and may be a sign of impaired autoregulation due to hyperglycaemia, which has been suggested to play a pivotal role in the development of diabetes-related microvascular complications. Data Availability The data supporting the results reported here are available through the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Graphical abstract

Background Observational studies have shown that higher cereal fiber intake is associated with reduced type 2 diabetes risk. However, it remains uncertain whether this association is causal. Objective This study evaluated the feasibility of an intervention to increase cereal fiber intake in children using breakfast cereals. Methods The study was a 2-arm parallel group randomized controlled trial in 9–10-y-old children, who received free supplies of high-fiber breakfast cereals (g3.5 g/portion) or low-fiber breakfast cereals (l1.0 g/portion) to eat daily for 1 mo with behavioral support to promote adherence. Children provided baseline and 1-mo fasting blood samples, physical measurements, and 24-h dietary recalls. The primary outcome was the group difference in change in plasma total alkylresorcinol (AR) concentration; secondary outcomes were group differences in nutrient intakes and adiposity indices. Analyses (complete case and multiple imputation) were conducted by regressing the final AR concentration on baseline AR in models adjusted for sex, ethnicity, age, and school (random effect). Results Two-hundred seventy-two children were randomly assigned (137 receiving a low-fiber and 135 a high-fiber diet) and 193 (71%) provided fasting blood samples at baseline and follow-up. Among randomized participants, median (IQR) of baseline AR was 43.1 (24.6–85.5) nmol/L and of cereal fiber intake was 4.5 (2.7–6.4) g; 87% of participants reported consuming the cereal on most or all days. Compared with changes in the low-fiber group, the high-fiber group had greater increases in AR (40.7 nmol/L; 95% CI: 21.7, 59.8 nmol/L, P l 0.0001) and in reported cereal fiber intake (2.9g/d; 95% CI: 2.0, 3.7 g; P l 0.0001). There were no appreciable differences in other secondary outcomes. Conclusions We have developed a simple and acceptable nutritional intervention that increases markers of daily cereal fiber intake in children. This intervention could be used to test whether increases in cereal fiber intake in children might reduce insulin resistance. This trial was registered at www.isrctn.com as ISRCTN33260236.

AimsTo analyse the prevalence of visual impairment (VI), compare it to certification of visual impairment (CVI) and analyse VI associations in patients with diabetic retinopathy (DR).MethodsRetrospective cohort study, which included 8007 patients with DR referred from the English diabetic eye screening programme to a tertiary referral eye hospital. Main outcome measure was VI, defined as vision in the best eye of ResultsMean age was 64.5 (SD 13.6) years; 61% of patients were men; and 31% of South Asian ethnicity. There were 68 patients with CVI during the study period, and 84% (272/325) of patients with VI did not have CVI after a mean follow-up of 1.87 (SD ±0.86) years. Older age showed a positive association with VI (OR per decade rise 1.88, 95% CI 1.70 to 2.08; p=1.8×10–34). Men had a lower risk of VI (OR 0.62, 95% CI 0.50 to 0.79, p=6.0×10–5), and less deprivation had a graded inverse association with VI (OR per IMD category increase 0.83, 95% CI 0.74 to 0.93, p value for linear trend 0.002).ConclusionThe majority of people with vision impairment are not registered at the point of care, which could translate to underestimation of diabetes-related VI and all-cause VI at a national level if replicated at other centres. Further work is needed to explore rates of VI and uptake of registration.

AimsWe examine whether inclusion of Artificial Intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-enabled retinal vessel image analysis processed images from 88,052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7,411 EPIC-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C- and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI).ResultsUKB prognostic models were developed on 65,144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5,862 EPIC-Norfolk participants (67.6, 9.1 years respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C- and R2 statistics between 0.75-0.77 and 0.33-0.44 respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS.ConclusionRV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk. (250 words)HIGHLIGHTSWhat is already known on this topicPopulation screening for MI and stroke using risk prediction tools exist but have limited uptake; risk scores for circulator mortality do not exist.What this study addsRisk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence (AI)-enabled retinal vasculometry (RV), age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality.Risk scores for MI and stroke performed similarly to established risk scores.How this study might affect research, practice or policyAI-enabled RV extraction offers a non-invasive prognostic biomarker of vascular health that does not require blood sampling or blood pressure measurement, and potentially has greater community reach to identify individuals at medium-high risk requiring further clinical assessment.SYNOPSIS/PRECISRisk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence enabled retinal vasculometry indices, age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality. Risk scores for MI and stroke performed similarly to established risk scores.

Accurate assessment of childhood adiposity is important both for individuals and populations. We compared fat mass (FM) predictions from a novel prediction model based on height, weight and demographic factors (height–weight equation) with FM from bioelectrical impedance (BIA) and dual-energy X-ray absorptiometry (DXA), using the deuterium dilution method as a reference standard. FM data from all four methods were available for 174 ALSPAC Study participants, seen 2002–2003, aged 11–12-years. FM predictions from the three approaches were compared to the reference standard using; R2, calibration (slope and intercept) and root mean square error (RMSE). R2 values were high from ‘height–weight equation’ (90%) but lower than from DXA (95%) and BIA (91%). Whilst calibration intercepts from all three approaches were close to the ideal of 0, the calibration slope from the ‘height–weight equation’ (slope = 1.02) was closer to the ideal of 1 than DXA (slope = 0.88) and BIA (slope = 0.87) assessments. The ‘height–weight equation’ provided more accurate individual predictions with a smaller RMSE value (2.6 kg) than BIA (3.1 kg) or DXA (3.4 kg). Predictions from the ‘height–weight equation’ were at least as accurate as DXA and BIA and were based on simpler measurements and open-source equation, emphasising its potential for both individual and population-level FM assessments.

The aim of this study was to assess whether adiposity or body composition relates to microvascular characteristics of the retina, indicative of cardiometabolic function. A fully automated QUARTZ software processed retinal images from 68,550 UK Biobank participants (aged 40-69 years). Differences in retinal vessel diameter and tortuosity with body composition measures from the Tanita analyzer were obtained by using multilevel regression analyses adjusted for age, sex, ethnicity, clinic, smoking, and Townsend deprivation index. Venular tortuosity and diameter increased by approximately 2% (P

Background/aims Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. Methods Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. Results Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. Conclusion The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

Purpose To examine retinal vasculometry associations with different glaucomas in older British people. Design Cross-sectional study. Methods A total of 8,623 European Prospective Investigation into Cancer-Norfolk Eye study participants were examined, who underwent retinal imaging, ocular biometry assessment, and clinical ascertainment of ocular hypertensive or glaucoma status (including glaucoma suspect [GS], high-tension open-angle glaucoma [HTG], and normal-tension glaucoma [NTG]). Automated measures of arteriolar and venular tortuosity, area, and width from retinal images were obtained. MainOutcomeMeasures: Associations between glaucoma and retinal vasculometry outcomes were analyzed using multilevel linear regression, adjusted for age, sex, height, axial length, intraocular and systemic blood pressure, and within-person clustering, to provide absolute differences in width and area, and percentage differences in vessel tortuosity. Presence or absence of within-person-between-eye differences in retinal vasculometry by diagnoses were examined. Results A total of 565,593 vessel segments from 5,947 participants (mean age 67.6 years, SD 7.6 years, 57% women) were included; numbers with HTG, NTG, and GS in at least 1 eye were 87, 82, and 439, respectively. Thinner arterioles (−3.2 μm; 95% confidence interval [CI] −4.4 μm, −1.9 μm) and venules (−2.7 μm; 95% CI −4.9 μm, −0.5 μm) were associated with HTG. Reduced venular area was associated with HTG (−0.2 mm2; 95% CI −0.3 mm2, −0.1 mm2) and NTG (−0.2 mm2; 95% CI −0.3 mm2, −0.0 mm2). Less tortuous retinal arterioles and venules were associated with all glaucomas, but only significantly for GS (−3.9%; 95% CI −7.7%, −0.1% and −4.8%; 95% CI −7.4%, −2.1%, respectively). There was no evidence of within-person-between-eye differences in retinal vasculometry associations by diagnoses. Conclusions Retinal vessel width associations with glaucoma and novel associations with vessel area and tortuosity, together with no evidence of within-person-between-eye differences in retinal vasculometry, suggest a vascular cause of glaucoma., Highlights • Retinal vessel measurements, including (as a first report) vessel tortuosity and area, were associated with high-tension glaucoma and other glaucoma-related outcomes. • Novel analyses showing that within-person-between-eye glaucoma diagnoses, intraocular pressure, and retinal vasculometry were uncorrelated provides further evidence that systemic microvascular changes may cause glaucoma.

The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10−108), 2 loci with arteriolar width (rs12969347, p = 3.30×10−09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa.

Background Adequate sleep duration in childhood has important implications for social, mental, and physical well-being. Recent evidence has demonstrated declining sleep durations in children; therefore effective interventions to improve sleep measures are needed. Despite some trials suggesting that multi-behavioural interventions promoting bed-time routines may be beneficial, the effectiveness of such interventions has not been adequately quantified. Moreover, specific themes and strategies which increase sleep duration, and by how much, have not been formally quantified. Methods We carried out a systematic review and meta-analysis to quantify the effectiveness of sleep interventions to improve sleep duration in children within community settings (PROSPERO ID CRD42019160089). Studies involving infants under 1 year, those using medications, and targeting children with behavioural problems were excluded. We screened 11621 randomised control trials (RCT), after carrying out a search using combined text words and MeSH criteria in CENTRAL, MEDLINE, EMBASE, PSYCHINFO, Web of science, clinicaltrials.gov, and WHO trials databases. Full text screening of 341 studies identified 39 studies for data extraction. All screening was carried out independently by two reviewers using a web-based platform for systematic reviews. Results Studies were categorised by age as follows: 20% in >1–5 years of age, 25% >5–10 years, 33% >10–15 years, 22% >15 years. The mode of delivery of most interventions was face to face (74%). The intervention setting was most commonly school based (54%), with 21% based at home. Intervention duration was less than one month in 49% of studies. Qualitative data to describe each intervention was collected using a framework based on the TIDieR checklist. We are mapping this onto behavioural frameworks (COM-B, theoretical domains framework), to categorise interventions. Our initial analysis, from 9911 participants (4719, intervention, 5192 control) from 33 studies, suggests that mean differences in interventions increase sleep duration by 11 minutes (95% CI 6, 17 mins). However, caution is needed in interpreting this overall effect size (pooled using random effects) given the considerable heterogeneity across studies (I2 = 91%). The influence of study design characteristics in explaining heterogeneity (including age of participants, duration and type of intervention) will be outlined. Conclusion This review furthers our understanding of the most effective interventions to improve sleep duration in children and provides a platform to develop population level health interventions to improve sleep. Given challenges assimilating data, an overarching aim is to develop approaches that could be used to homogenise reporting in future RCTs in order that evidence can be summarised more easily.

ImportanceAdequate sleep duration is necessary for many aspects of child health, development, and well-being, yet sleep durations for children are declining, and effective strategies to increase sleep in healthy children remain to be elucidated.ObjectiveTo determine whether nonpharmaceutical interventions to improve sleep duration in healthy children are effective and to identify the key components of these interventions.Data SourcesCENTRAL, MEDLINE, Embase, PsycINFO, Web of Science Core collection, ClinicalTrials.gov, and WHO trials databases were searched from inception to November 15, 2021.Study SelectionRandomized clinical trials of interventions to improve sleep duration in healthy children were independently screened by 2 researchers. A total of 28 478 studies were identified.Data Extraction and SynthesisData were processed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guideline. Random-effects meta-analytic models were used to estimate pooled effect sizes.Main Outcomes and MeasuresDifference in sleep duration, measured in minutes.ResultsA total of 13 539 child participants from 45 randomized clinical trials were included. Of these, 6897 (50.9%) were in the intervention group and 6642 (49.1%) in the control group, and the mean age ranged from 18 months to 19 years. Pooled results indicate that sleep interventions were associated with 10.5 minutes (95% CI, 5.6-15.4) longer nocturnal sleep duration. There was substantial variation between trials. Sources of variation that were not associated with the study effect size included age group, whether the population was identified as having a sleep problem or being at a socioeconomic disadvantage (eg, coming from a low-income family or area), method of assessment of sleep duration (objective vs subjective), location of intervention delivery (home vs school), whether interventions were delivered in person or used parental involvement, whether behavioral theory was used, environmental change, or had greater or lower intensity. Interventions that included earlier bedtimes were associated with a 47-minute sleep extension (95% CI, 18.9-75.0; 3 trials) compared with remaining studies (7.4 minutes; 95% CI, 2.9-11.8; 42 trials) (P = .006 for group difference). Trials of shorter duration (6 months or less) had larger effects.Conclusions and RelevanceInterventions focused on earlier bedtimes may offer a simple, pragmatic, effective way to meaningfully increase sleep duration that could have important benefits for child health.

Purpose To examine associations between retinal vessel morphometry and cardiometabolic risk factors in older British men and women. Design Retinal imaging examination as part of the European Prospective Investigation into Cancer—Norfolk Eye Study. Participants Retinal imaging and clinical assessments were carried out in 7411 participants. Retinal images were analyzed using a fully automated validated computerized system that provides novel measures of vessel morphometry. Methods Associations between cardiometabolic risk factors, chronic disease, and retinal markers were analyzed using multilevel linear regression, adjusted for age, gender, and within-person clustering, to provide percentage differences in tortuosity and absolute differences in width. Main Outcomes Measures Retinal arteriolar and venular tortuosity and width. Results In all, 279 802 arterioles and 285 791 venules from 5947 participants (mean age, 67.6 years; standard deviation [SD], 7.6 years; 57% female) were analyzed. Increased venular tortuosity was associated with higher body mass index (BMI; 2.5%; 95% confidence interval [CI], 1.7%–3.3% per 5 kg/m 2 ), hemoglobin A1c (HbA1c) level (2.2%; 95% CI, 1.0%–3.5% per 1%), and prevalent type 2 diabetes (6.5%; 95% CI, 2.8%–10.4%); wider venules were associated with older age (2.6 μm; 95% CI, 2.2–2.9 μm per decade), higher triglyceride levels (0.6 μm; 95% CI, 0.3–0.9 μm per 1 mmol/l), BMI (0.7 μm; 95% CI, 0.4–1.0 per 5 kg/m 2 ), HbA1c level (0.4 μm; 95% CI, –0.1 to 0.9 per 1%), and being a current smoker (3.0 μm; 95% CI, 1.7–4.3 μm); smoking also was associated with wider arterioles (2.1 μm; 95% CI, 1.3–2.9 μm). Thinner venules were associated with high-density lipoprotein (HDL) (1.4 μm; 95% CI, 0.7–2.2 per 1 mmol/l). Arteriolar tortuosity increased with age (5.4%; 95% CI, 3.8%–7.1% per decade), higher systolic blood pressure (1.2%; 95% CI, 0.5%–1.9% per 10 mmHg), in females (3.8%; 95% CI, 1.4%–6.4%), and in those with prevalent stroke (8.3%; 95% CI, –0.6% to 18%); no association was observed with prevalent myocardial infarction. Narrower arterioles were associated with age (0.8 μm; 95% CI, 0.6–1.0 μm per decade), higher systolic blood pressure (0.5 μm; 95% CI, 0.4–0.6 μm per 10 mmHg), total cholesterol level (0.2 μm; 95% CI, 0.0–0.3 μm per 1 mmol/l), and HDL (1.2 μm; 95% CI, 0.7–1.6 μm per 1 mmol/l). Conclusions Metabolic risk factors showed a graded association with both tortuosity and width of retinal venules, even among people without clinical diabetes, whereas atherosclerotic risk factors correlated more closely with arteriolar width, even excluding those with hypertension and cardiovascular disease. These noninvasive microvasculature measures should be evaluated further as predictors of future cardiometabolic disease.

To examine whether sociodemographic, and ocular factors relate to optical coherence tomography (OCT)-derived foveal curvature (FC) in healthy individuals.We developed a deep learning model to quantify OCT-derived FC from 63,939 participants (age range, 39-70 years). Associations of FC with sociodemographic, and ocular factors were obtained using multilevel regression analysis (to allow for right and left eyes) adjusting for age, sex, ethnicity, height (model 1), visual acuity, spherical equivalent, corneal astigmatism, center point retinal thickness (CPRT), intraocular pressure (model 2), deprivation (Townsend index), higher education, annual income, and birth order (model 3). Fovea curvature was modeled as a z-score.Males had on average steeper FC (0.077; 95% confidence interval [CI] 0.077-0.078) than females (0.068; 95% CI 0.068-0.069). Compared with whites, non-white individuals showed flatter FC, particularly those of black ethnicity. In black males, -0.80 standard deviation (SD) change when compared with whites (95% CI -0.89, -0.71; P 5.2e10-68). In black females, -0.70 SD change when compared with whites (95% CI -0.77, -0.63; p 2.3e10-93). Ocular factors (visual acuity, refractive status, and CPRT) showed a graded inverse association with FC that persisted after adjustment. Macular curvature showed a positive association with FC. Income showed a linear trend increase in males (P for linear trend = 0.005).We demonstrate marked differences in FC with ethnicity on the largest cohort studied for this purpose to date. Ocular factors showed a graded association with FC. Implementation of FC quantification in research and on the clinical setting can enhance the understanding of clinical macular phenotypes in health and disease.

Key Points Question Is childhood fat mass (FM) more strongly associated with long-term type 2 diabetes (T2D) risk than childhood weight, independent of height? Findings In a population-based cohort study including more than 260 000 schoolchildren in Denmark, there were stronger associations (per kilogram increase) between childhood FM and adult T2D risk, independent of childhood height, than those observed for childhood weight. Meaning Results suggest that weight-based measures, currently used as the basis for childhood obesity assessment in the form of body mass index, were less strongly associated with adult T2D risk than childhood FM., This cohort study uses data from Copenhagen School Health Records Register to investigate the association of childhood fat mass and weight with adult-onset type 2 diabetes., Importance Childhood obesity, defined by cutoffs based on the weight-based marker of body mass index, is associated with adult type 2 diabetes (T2D) risk. Whether childhood fat mass (FM) is the driver of these associations is currently unknown. Objective To quantify and compare height-independent associations between childhood FM and weight with adult T2D risk in a historic Danish cohort. Design, Setting, and Participants This population-based retrospective cohort study included schoolchildren from The Copenhagen School Health Records Register born between January 1930 and December 1985 with follow-up to adulthood through December 31, 2015. Analyses were based on 269 913 schoolchildren aged 10 years with 21 896 established adult T2D cases and 261 192 children aged 13 years with 21 530 established adult T2D cases for whom childhood height and weight measurements, as well as predicted FM, were available. Statistical analyses were performed between April 2019 to August 2020. Exposures Childhood FM and weight at ages 10 and 13 years. Main Outcomes and Measures Diagnoses of T2D were established by linkage to national disease registers for adults aged at least 30 years. Sex-specific Cox regression quantified associations, adjusted for childhood height, which were evaluated within 5 birth-cohort groups. Group-specific results were pooled using random-effects meta-analyses accounting for heterogeneity across group-specific associations. Results This cohort study analyzed data from 269 913 children aged 10 years (135 940 boys [50.4%]) with 21 896 established adult T2D cases and 261 192 children aged 13 years (131 025 boys [50.2%]) with 21 530 established adult T2D cases. After adjusting for childhood height, increases in FM and weight (per kilogram) among boys aged 10 years were associated with elevated T2D risks at age 50 years of 12% (hazard ratio [HR], 1.12; 95% CI, 1.10-1.14) and 7% (HR, 1.07; 95% CI, 1.05-1.09), respectively, and among girls aged 10 years of 15% (HR, 1.15; 95% CI, 1.13-1.17) and 10% (HR, 1.10; 95% CI, 1.08-1.11), respectively. Among children aged 13 years, increases in FM and weight (per kilogram) were associated with increased T2D risks at age 50 years of 10% (HR, 1.10; 95% CI, 1.09-1.10) and 6% (HR, 1.06; 95% CI, 1.05-1.07) for boys, respectively, and of 10% (HR, 1.10; 95% CI, 1.10-1.11) and 7% (HR, 1.07; 95% CI, 1.06-1.08), respectively, for girls. Conclusions and Relevance This cohort study found that a 1-kg increase in childhood FM was more strongly associated with increased adult T2D risk than a 1-kg increase in weight, independent of childhood height. Information on FM, rather than weight-based measures, focuses on a modifiable component of weight that may be associated with adult T2D risk. These findings support the assessment of childhood FM in adiposity surveillance initiatives in an effort to reduce long-term T2D risk.

Background Previous research has reported associations between features of the residential built environment and physical activity but these studies have mainly been cross-sectional, limiting inference. This paper examines whether changes in a range of residential built environment features are associated with changes in measures of physical activity in adults. It also explores whether observed effects are moderated by socio-economic status. Methods Data from the Examining Neighbourhood Activity in Built Living Environments in London (ENABLE London) study were used. A cohort of 1278 adults seeking to move into social, intermediate, and market-rent East Village accommodation was recruited in 2013–2015, and followed up after 2 years. Accelerometer-derived steps (primary outcome), and GIS-derived measures of residential walkability, park proximity and public transport accessibility were obtained both at baseline and follow-up. Daily steps at follow-up were regressed on daily steps at baseline, change in built environment exposures and confounding variables using multilevel linear regression to assess if changes in neighbourhood walkability, park proximity and public transport accessibility were associated with changes in daily steps. We also explored whether observed effects were moderated by housing tenure as a marker of socio-economic status. Results Between baseline and follow-up, participants experienced a 1.4 unit (95%CI 1.2,1.6) increase in neighbourhood walkability; a 270 m (95%CI 232,307) decrease in distance to their nearest park; and a 0.7 point (95% CI 0.6,0.9) increase in accessibility to public transport. A 1 s.d. increase in neighbourhood walkability was associated with an increase of 302 (95%CI 110,494) daily steps. A 1 s.d. increase in accessibility to public transport was not associated with any change in steps overall, but was associated with a decrease in daily steps amongst social housing seekers (− 295 steps (95%CI − 595, 3), and an increase in daily steps for market-rent housing seekers (410 95%CI -191, 1010) (P-value for effect modification = 0.03). Conclusion Targeted changes in the residential built environment may result in increases in physical activity levels. However, the effect of improved accessibility to public transport may not be equitable, showing greater benefit to the more advantaged.

BackgroundNeighbourhood characteristics may affect mental health and well-being, but longitudinal evidence is limited. We examined the effect of relocating to East Village (the former London 2012 Olympic Athletes’ Village), repurposed to encourage healthy active living, on mental health and well-being.Methods1278 adults seeking different housing tenures in East village were recruited and examined during 2013–2015. 877 (69%) were followed-up after 2 years; 50% had moved to East Village. Analysis examined change in objective measures of the built environment, neighbourhood perceptions (scored from low to high; quality −12 to 12, safety −10 to 10 units), self-reported mental health (depression and anxiety) and well-being (life satisfaction, life being worthwhile and happiness) among East Village participants compared with controls who did not move to East Village. Follow-up measures were regressed on baseline for each outcome with group status as a binary variable, adjusted for age, sex, ethnicity, housing tenure and household clustering (random effect).ResultsParticipants who moved to East Village lived closer to their nearest park (528 m, 95% CI 482 to 575 m), in more walkable areas, and had better access to public transport, compared with controls. Living in East Village was associated with marked improvements in neighbourhood perceptions (quality 5.0, 95% CI 4.5 to 5.4 units; safety 3.4, 95% CI 2.9 to 3.9 units), but there was no overall effect on mental health and well-being outcomes.ConclusionDespite large improvements in the built environment, there was no evidence that moving to East Village improved mental health and well-being. Changes in the built environment alone are insufficient to improve mental health and well-being.

Background Interventions to encourage active modes of travel (walking, cycling) may improve physical activity levels, but longitudinal evidence is limited and major change in the built environment / travel infrastructure may be needed. East Village (the former London 2012 Olympic Games Athletes Village) has been repurposed on active design principles with improved walkability, open space and public transport and restrictions on residential car parking. We examined the effect of moving to East Village on adult travel patterns. Methods One thousand two hundred seventy-eight adults (16+ years) seeking to move into social, intermediate, and market-rent East Village accommodation were recruited in 2013–2015, and followed up after 2 years. Individual objective measures of physical activity using accelerometry (ActiGraph GT3X+) and geographic location using GPS travel recorders (QStarz) were time-matched and a validated algorithm assigned four travel modes (walking, cycling, motorised vehicle, train). We examined change in time spent in different travel modes, using multilevel linear regresssion models adjusting for sex, age group, ethnicity, housing group (fixed effects) and household (random effect), comparing those who had moved to East Village at follow-up with those who did not. Results Of 877 adults (69%) followed-up, 578 (66%) provided valid accelerometry and GPS data for at least 1 day (≥540 min) at both time points; half had moved to East Village. Despite no overall effects on physical activity levels, sizeable improvements in walkability and access to public transport in East Village resulted in decreased daily vehicle travel (8.3 mins, 95%CI 2.5,14.0), particularly in the intermediate housing group (9.6 mins, 95%CI 2.2,16.9), and increased underground travel (3.9 mins, 95%CI 1.2,6.5), more so in the market-rent group (11.5 mins, 95%CI 4.4,18.6). However, there were no effects on time spent walking or cycling. Conclusion Designing walkable neighbourhoods near high quality public transport and restrictions on car usage, may offer a community-wide strategy shift to sustainable transport modes by increasing public transport use, and reducing motor vehicle travel.

Supplemental Digital Content is available in the text., To examine the baseline associations of retinal vessel morphometry with blood pressure (BP) and arterial stiffness in United Kingdom Biobank. The United Kingdom Biobank included 68 550 participants aged 40 to 69 years who underwent nonmydriatic retinal imaging, BP, and arterial stiffness index assessment. A fully automated image analysis program (QUARTZ [Quantitative Analysis of Retinal Vessel Topology and Size]) provided measures of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiovascular disease risk factors/outcomes were examined using multilevel linear regression to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing within person clustering), adjusted for age, sex, ethnicity, clinic, body mass index, smoking, and deprivation index. Greater arteriolar tortuosity was associated with higher systolic BP (relative increase, 1.2%; 95% CI, 0.9; 1.4% per 10 mmHg), higher mean arterial pressure, 1.3%; 0.9, 1.7% per 10 mmHg, and higher pulse pressure (PP, 1.8%; 1.4; 2.2% per 10 mmHg). Narrower arterioles were associated with higher systolic BP (−0.9 µm; −0.94, −0.87 µm per 10 mmHg), mean arterial pressure (−1.5 µm; −1.5, −1.5 µm per 10 mmHg), PP (−0.7 µm; −0.8, −0.7 µm per 10 mmHg), and arterial stiffness index (−0.12 µm; −0.14, −0.09 µm per ms/m2). Associations were in the same direction but marginally weaker for venular tortuosity and diameter. This study assessing the retinal microvasculature at scale has shown clear associations between retinal vessel morphometry, BP, and arterial stiffness index. These observations further our understanding of the preclinical disease processes and interplay between microvascular and macrovascular disease.

ObjectivesTo examine the association of sociodemographic characteristics with attendance at diabetic eye screening in a large ethnically diverse urban population.DesignRetrospective cohort study.SettingScreening visits in the North East London Diabetic Eye Screening Programme (NELDESP).Participants84 449 people with diabetes aged 12 years or older registered in the NELDESP and scheduled for screening between 1 April 2017 and 31 March 2018.Main outcome measureAttendance at diabetic eye screening appointments.ResultsThe mean age of people with diabetes was 60 years (SD 14.2 years), 53.4% were men, 41% South Asian, 29% White British and 17% Black; 83.4% attended screening. Black people with diabetes had similar levels of attendance compared with White British people. However, South Asian, Chinese and 'Any other Asian' background ethnicities showed greater odds of attendance compared with White British. When compared with their respective reference group, high levels of deprivation, younger age, longer duration of diabetes and worse visual acuity, were all associated with non-attendance. There was a higher likelihood of attendance per quintile improvement in deprivation (OR, 1.06; 95% CI, 1.03 to 1.08), with increasing age (OR per decade, 1.17; 95% CI, 1.15 to 1.19), with better visual acuity (OR per Bailey-Lovie chart line 1.12; 95% CI, 1.11 to 1.14) and with longer time of NELDESP registration (OR per year, 1.02; 95% CI, 1.01 to 1.03).ConclusionEthnic differences in diabetic eye screening uptake, though small, are evident. Despite preconceptions, a higher likelihood of screening attendance was observed among Asian ethnic groups when compared with the White ethnic group. Poorer socioeconomic profile was associated with higher likelihood of non-attendance for screening. Further work is needed to understand how to target individuals at risk of non-attendance and reduce inequalities.

The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835,914 centreline pixels derived from 100 retinal images selected from the 135,867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image. Morphology of retinal vasculature associated with risk markers of many diseases.Large population based studies help to resolve uncertainties in these associations.Automated system extracts morphometric data from the UK Biobank cohort study.Detection of the vessel type is an important element in such automated systems.Deep learning approach for the automatic classification of arterioles and venules.

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method.

OBJECTIVE: With the increasing prevalence of diabetes, annual screening for diabetic retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aimed to determine whether ARIAS can be safely introduced into DR screening pathways to replace human graders. DESIGN: Observational measurement comparison study of human graders following a national screening program for DR versus ARIAS. PARTICIPANTS: Retinal images from 20 258 consecutive patients attending routine annual diabetic eye screening between June 1, 2012, and November 4, 2013. METHODS: Retinal images were manually graded following a standard national protocol for DR screening and were processed by 3 ARIAS: iGradingM, Retmarker, and EyeArt. Discrepancies between manual grades and ARIAS results were sent to a reading center for arbitration. MAIN OUTCOME MEASURES: Screening performance (sensitivity, false-positive rate) and diagnostic accuracy (95% confidence intervals of screening-performance measures) were determined. Economic analysis estimated the cost per appropriate screening outcome. RESULTS: Sensitivity point estimates (95% confidence intervals) of the ARIAS were as follows: EyeArt 94.7% (94.2%-95.2%) for any retinopathy, 93.8% (92.9%-94.6%) for referable retinopathy (human graded as either ungradable, maculopathy, preproliferative, or proliferative), 99.6% (97.0%-99.9%) for proliferative retinopathy; Retmarker 73.0% (72.0 %-74.0%) for any retinopathy, 85.0% (83.6%-86.2%) for referable retinopathy, 97.9% (94.9%-99.1%) for proliferative retinopathy. iGradingM classified all images as either having disease or being ungradable. EyeArt and Retmarker saved costs compared with manual grading both as a replacement for initial human grading and as a filter prior to primary human grading, although the latter approach was less cost-effective. CONCLUSIONS: Retmarker and EyeArt systems achieved acceptable sensitivity for referable retinopathy when compared with that of human graders and had sufficient specificity to make them cost-effective alternatives to manual grading alone. ARIAS have the potential to reduce costs in developed-world health care economies and to aid delivery of DR screening in developing or remote health care settings.

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur.

Summary Background The built environment can affect health behaviours, but longitudinal evidence is limited. We aimed to examine the effect of moving into East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village that was repurposed on active design principles, on adult physical activity and adiposity. Methods In this cohort study, we recruited adults seeking new accommodation in East Village and compared physical activity and built environment measures with these data in control participants who had not moved to East Village. At baseline and after 2 years, we objectively measured physical activity with accelerometry and adiposity with body-mass index and bioimpedance, and we assessed objective measures of and participants' perceptions of change in their built environment. We examined the change in physical activity and adiposity between the East Village and control groups, after adjusting for sex, age group, ethnicity, housing tenure, and household (as a random effect). Findings We recruited participants for baseline assessment between Jan 24, 2013, and Jan 7, 2016, and we followed up the cohort after 2 years, between Feb 24, 2015, and Oct 24, 2017. At baseline, 1819 households (one adult per household) consented to initial contact by the study team. 1278 adults (16 years and older) from 1006 (55%) households participated at baseline; of these participants, 877 (69%) adults from 710 (71%) households were assessed after 2 years, of whom 441 (50%) participants from 343 (48%) households had moved to East Village. We found no effect associated with moving to East Village on daily steps, the time spent doing moderate-to-vigorous physical activity (either in total or in 10-min bouts or more), daily sedentary time, body-mass index, or fat mass percentage between participants who had moved to East Village and those in the control group, despite sizeable improvements in walkability and neighbourhood perceptions of crime and quality among the East Village group relative to their original neighbourhood at baseline. Interpretation Despite large improvements in neighbourhood perceptions and walkability, we found no clear evidence that moving to East Village was associated with increased physical activity. Improving the built environment on its own might be insufficient to increase physical activity. Funding National Institute for Health Research and National Prevention Research Initiative.

Objectives To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. Design Individual participant data meta-analysis. Setting Four population based cross sectional studies and a fifth study for external validation, United Kingdom. Participants A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. Main outcome measure Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model’s predictive performance within the four development studies; external validation followed using the fifth dataset. Results Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R 2 : 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R 2 : 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was −1.29 kg (95% confidence interval −1.62 to −0.96 kg). Conclusion The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

Background Vascular risk factors are implicated in the aetiology of polymyalgia rheumatica (PMR) but lack sensitivity as a screening tool to assess risk of future disease or disease progression. Retinal vascular measures from digital images of the retinal fundus provide a direct objective measure of vascular health and have potential value in estimating future disease risk. Objectives The aim of this study was to determine if quantitative changes on retinal vasculometric imaging could be detected prior to the onset of PMR among a large prospective population-based cohort with a view to assessing their clinical utility as a biomarker of disease risk. Methods Known risk factors for vascular disease were obtained at enrolment to the EPIC-Norfolk cohort in 1993, with data available on BMI, blood pressure, smoking status and blood drawn for cholesterol and high-sensitivity C-reactive protein. Eye screening took place between 2004 to 2011. Cases of PMR were identified retrospectively among 8112 volunteers by: (i) free text questionnaire responses at baseline, 18 months, 3, 10 and 13 years; (ii) linkage to hospital electronic discharge summaries containing ICD codes (iii) linkage to key word searches (polymyalgia or rheumatica) of out-patient clinic letters. Case definition conformed to validated methods. Measures of retinal vessel width were generated by an automated software programme (QUARTZ), using Gaussian modelling to determine vessel widths and an ensemble classifier of bagged decision trees to classify vessels into either venules or arterioles with greater than 80% probability. Stepwise Cox modelling was used to investigate vascular risk factors, including vasculometric characteristics, and subsequent development of PMR, within a multivariate adjusted model. Results We identified 71 cases of PMR (median age at diagnosis: 70.5 years, minimum 50.5 years, maximum 87.0 years; mean ESR at diagnosis: 52 mm/hr; 67.6% female) amongst 5130 participants who attended ocular screening and had high quality retinal images acceptable for analysis. There were 50 incident diagnosis of PMR with vascular risk data recorded at enrolment into EPIC-Norfolk prior to their diagnosis of PMR. Results from the univariate Cox modelling revealed positive associations for age, female sex, low-density lipoprotein (LDL) and retinal venular width. There were no associations for arteriolar measures. After adjustment a positive association remained for age and venular dilatation: the hazard ratio for age was 1.14 (95% CI 1.09, 1.18) p Conclusion Retinal venular dilatation is a marker of vascular health and within this data an independent risk factor for subsequent development of PMR. Retinal fundus images are easily captured in the clinical setting. While validation of these findings in other datasets is warranted, they provide a potential biomarker for assessment of PMR risk. Disclosure of Interests None declared

Objective: There is considerable interest in whether the built environment influences health behaviours, but longitudinal evidence is limited. We examined the effect of moving into East Village (the former London 2012 Olympic Games Athletes Village, repurposed on active design principles), on levels of physical activity (PA) and adiposity among adults. Design: A natural experiment. Participants: A cohort of 1278 adults seeking to move into social, intermediate, and market-rent East Village accommodation was recruited in 2013-2015, and followed up after two years. Intervention: Moving to East Village. Main outcome: Change in objectively measured daily steps (ActiGraph GT3X+). Methods: Change in environmental exposures associated with physical activity were assessed using Geographic Information System derived measures. Individual objective measures of PA using accelerometry, body mass index (BMI) and bioelectrical impedance (fat mass %) were obtained, as were perceptions of change in the built environment. We examined change in levels of PA and adiposity, using multilevel models adjusting for sex, age group, ethnic group, housing sector (fixed effects) and household (random effect), comparing those who moved to East Village (intervention group) with those who did not (control group). Effects of housing sector as an effect modifier were also examined. Results: 877 adults (69%) were followed-up, of whom 50% had moved to East Village. Moving to East Village was associated with only a small non-significant increase in mean daily steps (154, 95% CI -231, 539), despite sizeable improvements in walkability and neighbourhood perceptions of crime and quality. There were no appreciable effects on time spent in moderate-to-vigorous PA (MVPA) or sedentary time, BMI or percentage fat mass, overall or by housing sector. Conclusion: Despite strong evidence of large improvements in neighbourhood perceptions and walkability there was no clear evidence that moving to East Village was associated with increased physical activity. Improving the physical activity environment on its own may be insufficient to increase population physical activity. Funding: This research is being supported by project grants from the Medical Research Council National Prevention Research Initiative (MR/J000345/1) and National Institute for Health Research (12/211/69). Diabetes and obesity prevention research at St George’s, University of London is supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London. BR is supported by a St George’s, University of London, PhD Studentship. AE is funded by the UK Medical Research Council as part of the Neighbourhoods and Communities Programme (MC_UU_12017-10). BG-C is supported by an NHMRC Principal Research Fellowship (#1107672). ARC and ASP are supported by NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. Declaration of Interest: We declare that we have no competing interests. Ethical Approval: Full ethical approval was obtained from the relevant Multi-Centre Research Ethics Committee (REC Reference 12/LO/1031). All participants provided written informed consent.

Causes and effects of differences in image quality with respect to retinal fundus camera images are described. Examples of applications including diabetic retinopathy screening and epidemiology studies are given that demonstrate how assessment of image quality is linked closely to the clinical application in which the images are used. An overview of work in the field is given which covers image quality assessment algorithms, datasets and evaluation metrics. Finally, a selection of image quality assessment algorithms are summarized to illustrate different approaches such as image structure clustering and deep learning.

Background: Studies have found associations between features of the residential built environment and physical activity but these have been mainly cross-sectional. In this paper we explore whether longitudinal changes in environmental exposures are associated with changes in physical activity in adults. Methods: Data from the Examining Neighbourhood Activity in Built Living Environments in London (ENABLE London) study were used. Daily steps at follow-up were regressed on daily steps at baseline, change in built environment exposures and confounding variables using multilevel linear regression to assess if neighbourhood walkability, park proximity and public transport accessibility were associated with changes in accelerometer-derived daily steps. We also explored whether observed effects were moderated by housing tenure as a marker of socio-economic status. Findings: Between baseline and follow-up, participants experienced a 1·4 unit (95%CI 1·2,1·6) increase in neighbourhood walkability; a 270m (95%CI 232,307) decrease in distance to their nearest park; and a 0·7 point (95%CI 0·6,0·9) increase in accessibility to public transport. A 1 s.d. increase in neighbourhood walkability was associated with an increase of 302 (95%CI 110,494) daily steps. A 1 s.d. increase in accessibility to public transport was not associated with any change in steps overall, but was associated with a decrease in daily steps amongst social housing seekers (-295 steps (95%CI -595, 3), and an increase in daily steps for market-rent housing seekers (410 95%CI -191, 1010) (P-value for effect modification=0·03). Interpretation: Targeted changes in the residential built environment may result in increases in physical activity levels. However, the effect of improved accessibility to public transport may not be equitable, showing greater benefit to the more advantaged. Funding Statement: This research is being supported by project grants from the UK National Prevention Research Initiative (MR/J000345/1) and the UK National Institute for Health Research (NIHR; 12/211/69). Diabetes and obesity prevention research at St George’s, University of London is supported by the NIHR Collaboration for Leadership in Applied Health Research and Care, South London. CMN is supported by the Wellcome Trust Institutional Strategic Support Fund (204809/Z/16/Z). BR was supported by a PhD studentship from St George’s, University of London. ARC and ASP are supported by the NIHR Biomedical Research Centre at University Hospitals Bristol National Health Service Foundation Trust and the University of Bristol. AE is funded by the Medical Research Council as part of the Neighbourhoods and Communities Programme (MC_UU_12017–10). BG-C is supported by a National Health and Medical Research Council Principal Research Fellowship (1107672). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Full ethical approval was obtained from the relevant Multi-Centre Research Ethics Committee (REC Reference 12/LO/1031). All participants provided written informed consent.

The aim of this review was to quantify the global variation in childhood myopia prevalence over time taking account of demographic and study design factors. A systematic review identified population-based surveys with estimates of childhood myopia prevalence published by February 2015. Multilevel binomial logistic regression of log odds of myopia was used to examine the association with age, gender, urban versus rural setting and survey year, among populations of different ethnic origins, adjusting for study design factors. 143 published articles (42 countries, 374 349 subjects aged 1- 18 years, 74 847 myopia cases) were included. Increase in myopia prevalence with age varied by ethnicity. East Asians showed the highest prevalence, reaching 69% (95% credible intervals (CrI) 61% to 77%) at 15 years of age (86% among Singaporean-Chinese). Blacks in Africa had the lowest prevalence; 5.5% at 15 years (95% CrI 3% to 9%). Time trends in myopia prevalence over the last decade were small in whites, increased by 23% in East Asians, with a weaker increase among South Asians. Children from urban environments have 2.6 times the odds of myopia compared with those from rural environments. In whites and East Asians sex differences emerge at about 9 years of age; by late adolescence girls are twice as likely as boys to be myopic. Marked ethnic differences in age-specific prevalence of myopia exist. Rapid increases in myopia prevalence over time, particularly in East Asians, combined with a universally higher risk of myopia in urban settings, suggest that environmental factors play an important role in myopia development, which may offer scope for prevention.

Background: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. Methods: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for seven days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402749 points), and 10 participants from a separate study (STAMP-2, 210936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. Results: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals’ travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). Conclusion: We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.

Background There has been increasing interest in whether the built environmentinfluences health behaviours, but robust longitudinal evidence is limited. We assessed the effect of moving into East Village (the former London 2012 Olympic Games Athletes Village), a neighbourhood built on active design principles, on levels of physical activity (PA) and adiposity among adults. Methods A cohort of 1278 adults (16+) seeking to move into social, intermediate (a mixture of shared ownership, shared equity, affordable rent), and market-rent East Village accommodation were recruited in 2013–2015, and followed up after two years. Objective measures of PA using accelerometry (ActiGraph GT3X+), body mass index (BMI) and bioelectrical impedance (fat mass %) were made. We examined change in levels of PA and adiposity, using multilevel models adjusting for sex, age group, ethnicity, housing sector (fixed effects) and household (random effect), comparing those who moved to East Village (intervention group) with those who lived outside East Village (control group). Effects by housing sector and weekdays versus weekends for PA were also examined. Results 877 adults (69%) were followed-up, half had moved to East Village. Moving to East Village was associated with a small increase in daily steps (151, 95% CI −233, 534), more so in the intermediate sector (399, 95% CI −211, 1009) than in the social and market-rent sectors, but effects were not statistically significant. There were no differences in time spent in moderate-to-vigorous PA (MVPA) or any appreciable weekday versus weekend effects. There was no evidence of differences in time spent in light PA or sedentary time both overall and by housing sector with the exception of the market-rent group in whom moving to East Village was associated with a decrease in light PA (−13.0 mins, 95% CI −24.7,–1.2 mins). There were no effects of moving to East Village on BMI or fat mass% overall or by housing sector. Conclusion At two-year follow-up, moving to East Village, a neighbourhood designed for healthy active living, did not have beneficial, consistent effects on objectively measured physical activity or adiposity of public health importance.

ObjectivesThe neighbourhood environment is increasingly shown to be an important correlate of health. We assessed associations between housing tenure, neighbourhood perceptions, sociodemographic factors and levels of physical activity (PA) and adiposity among adults seeking housing in East Village (formerly London 2012 Olympic/Paralympic Games Athletes’ Village).SettingCross-sectional analysis of adults seeking social, intermediate and market-rent housing in East Village.Participants1278 participants took part in the study (58% female). Complete data on adiposity (body mass index (BMI) and fat mass %) were available for 1240 participants (97%); of these, a subset of 1107 participants (89%) met the inclusion criteria for analyses of accelerometer-based measurements of PA. We examined associations between housing sector sought, neighbourhood perceptions (covariates) and PA and adiposity (dependent variables) adjusted for household clustering, sex, age group, ethnic group and limiting long-standing illness.ResultsParticipants seeking social housing had the fewest daily steps (8304, 95% CI 7959 to 8648) and highest BMI (26.0 kg/m2, 95% CI 25.5kg/m2to 26.5 kg/m2) compared with those seeking intermediate (daily steps 9417, 95% CI 9106 to 9731; BMI 24.8 kg/m2, 95% CI 24.4 kg/m2to 25.2 kg/m2) or market-rent housing (daily steps 9313, 95% CI 8858 to 9768; BMI 24.6 kg/m2, 95% CI 24.0 kg/m2to 25.2 kg/m2). Those seeking social housing had lower levels of PA (by 19%–42%) at weekends versus weekdays, compared with other housing groups. Positive perceptions of neighbourhood quality were associated with higher steps and lower BMI, with differences between social and intermediate groups reduced by ~10% following adjustment, equivalent to a reduction of 111 for steps and 0.5 kg/m2for BMI.ConclusionsThe social housing group undertook less PA than other housing sectors, with weekend PA offering the greatest scope for increasing PA and tackling adiposity in this group. Perceptions of neighbourhood quality were associated with PA and adiposity and reduced differences in steps and BMI between housing sectors. Interventions to encourage PA at weekends and improve neighbourhood quality, especially among the most disadvantaged, may provide scope to reduce inequalities in health behaviour.

Body mass index (BMI) overweight/obesity thresholds in South Asian (SA) adults, at equivalent type 2 diabetes risk are lower than for white Europeans (WE). We aimed to define adjusted overweight/obesity thresholds for UK SA children based on equivalent insulin resistance (HOMA-IR) to WE children. In 1138 WE and 1292 SA children 9.0-10.9y, multi-level regression models quantified associations between BMI and HOMA-IR, by ethnic group. HOMA-IR levels for WE children were calculated at established overweight/obesity thresholds (9.5y,10.5y), based on UK90 BMI cut-offs. Quantified associations in SA children were then used to estimate adjusted SA weight status thresholds at the calculated HOMA-IR levels. At 9.5y, current WE BMI overweight and obesity thresholds were 19.2kg/m2, 21.3kg/m2 (boys) and 20.0kg/m2, 22.5kg/m2 (girls). At equivalent HOMA-IR, SA overweight and obesity thresholds were lower by 2.9kg/m2 (95%CI: 2.5–3.3kg/m2) and 3.2kg/m2 (95%CI: 2.7–3.6kg/m2) in boys and 3.0kg/m2 (95%CI: 2.6–3.4kg/m2) and 3.3kg/m2 (95%CI: 2.8–3.8kg/m2) in girls respectively. At these lower thresholds, overweight/obesity prevalences in SA children were approximately doubled (boys:61%,girls:56%). Patterns at 10.5y were similar. In conclusion, SA adjusted overweight/obesity thresholds based on equivalent IR, were markedly lower than BMI thresholds for WE children and defined more than half of SA children as overweight/obese.

Purpose: Breastfeeding may influence early visual development. We examined whether an intervention to promote increased duration and exclusivity of breastfeeding improves visual outcomes at 16 years of age.\ud \ud Methods: Follow-up of a cluster-randomized trial in 31 Belarusian maternity hospitals/polyclinics randomized to receive a breastfeeding promotion intervention, or usual care, where 46% vs. 3% were exclusively breastfed at 3 months respectively. Low vision in either eye was defined as unaided logMAR vision of ≥0.3 or worse (equivalent to Snellen 20/40) and was used as the primary outcome. Open-field autorefraction in a subset (n = 963) suggested that 84% of those with low vision were myopic. Primary analysis was based on modified intention-to-treat, accounting for clustering within hospitals/clinics. Observational analyses also examined the effect of breastfeeding duration and exclusivity, as well as other sociodemographic and environmental determinants of low vision.\ud \ud Results: A total of 13,392 of 17,046 (79%) participants were followed up at 16 years. Low vision prevalence was 19.6% (95% confidence interval [CI]: 17.5, 22.0%) in the experimental group versus 21.6% (19.5, 23.8%) in the control group. Cluster-adjusted odds ratio (OR) of low vision associated with the intervention was 0.92 (95% CI: 0.73, 1.16); 0.88 (95% CI: 0.74, 1.05) after adjustment for parental and early life factors. In observational analyses, breastfeeding duration and exclusivity had no significant effect on low vision. However, maternal age at birth (OR: 1.13, 95% CI: 1.07, 1.14/5-year increase) and urban versus rural residence were associated with increased risk of low vision. Lower parental education, number of older siblings was associated with a lower risk of low vision; boys had lower risk compared with girls (0.64, 95% CI: 0.59,0.70).\ud \ud Conclusions: Exclusive breastfeeding promotion had no significant effect on visual outcomes in this study, but other environmental factors showed strong associations. (ClinicalTrials.gov number, NCT01561612.)