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Your search keyword '"Sophie V. Eastwood"' showing total 2 results
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1. Estimation of CT-derived abdominal visceral and subcutaneous adipose tissue depots from anthropometry in Europeans, South Asians and African Caribbeans

Author

Sophie V Eastwood, Therese Tillin, Andrew Wright, John Heasman, Joseph Willis, Ian F Godsland, Nita Forouhi, Peter Whincup, Alun D Hughes, and Nishi Chaturvedi

Subjects
Male, Models, Anatomic, Subcutaneous Fat, lcsh:Medicine, Black People, Intra-Abdominal Fat, White People, Sex Factors, Asian People, Risk Factors, Humans, lcsh:Science, Adiposity, Aged, Anthropometry, lcsh:R, nutritional and metabolic diseases, Reproducibility of Results, Middle Aged, Cross-Sectional Studies, Body Composition, lcsh:Q, Female, Tomography, X-Ray Computed, Follow-Up Studies, Research Article
Abstract

South Asians and African Caribbeans experience more cardiometabolic disease than Europeans. Risk factors include visceral (VAT) and subcutaneous abdominal (SAT) adipose tissue, which vary with ethnicity and are difficult to quantify using anthropometry.\ud Objective\ud \ud We developed and cross-validated ethnicity and gender-specific equations using anthropometrics to predict VAT and SAT.Design\ud \ud 669 Europeans, 514 South Asians and 227 African Caribbeans (70±7 years) underwent anthropometric measurement and abdominal CT scanning. South Asian and African Caribbean participants were first-generation migrants living in London. Prediction equations were derived for CT-measured VAT and SAT using stepwise regression, then cross-validated by comparing actual and predicted means.\ud Results\ud \ud South Asians had more and African Caribbeans less VAT than Europeans. For basic VAT prediction equations (age and waist circumference), model fit was better in men (R2 range 0.59-0.71) than women (range 0.35-0.59). Expanded equations (+ weight, height, hip and thigh circumference) improved fit for South Asian and African Caribbean women (R2 0.35 to 0.55, and 0.43 to 0.56 respectively). For basic SAT equations, R2 was 0.69-0.77, and for expanded equations it was 0.72-0.86. Cross-validation showed differences between actual and estimated VAT of

Published
2013

2. Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank.

Author

Sophie V Eastwood, Rohini Mathur, Mark Atkinson, Sinead Brophy, Cathie Sudlow, Robin Flaig, Simon de Lusignan, Naomi Allen, and Nishi Chaturvedi

Subjects
Medicine, Science
Abstract

OBJECTIVES:UK Biobank is a UK-wide cohort of 502,655 people aged 40-69, recruited from National Health Service registrants between 2006-10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request. METHODS:We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank. RESULTS AND SIGNIFICANCE:For prevalent diabetes, 0.001% and 0.002% of people classified as "diabetes unlikely" in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as "probable" type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.

Published
2016
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