TY - JOUR
T1 - Identifying adults at high-risk for change in weight and BMI in England
T2 - a longitudinal, large-scale, population-based cohort study using electronic health records
AU - Katsoulis, Michail
AU - Lai, Alvina G.
AU - Diaz-Ordaz, Karla
AU - Gomes, Manuel
AU - Pasea, Laura
AU - Banerjee, Amitava
AU - Denaxas, Spiros
AU - Tsilidis, Kostas
AU - Lagiou, Pagona
AU - Misirli, Gesthimani
AU - Bhaskaran, Krishnan
AU - Wannamethee, Goya
AU - Dobson, Richard
AU - Batterham, Rachel L.
AU - Kipourou, Dimitra Kleio
AU - Lumbers, R. Thomas
AU - Wen, Lan
AU - Wareham, Nick
AU - Langenberg, Claudia
AU - Hemingway, Harry
N1 - Funding Information:
MK is funded by the British Heart Foundation (FS/18/5/33319). KD-O is supported by the UK Wellcome Trust Institutional Strategic Support Fund?London School of Hygiene & Tropical Medicine Fellowship (204928/Z/16/Z). SD is supported by an Alan Turing Fellowship. AGL is supported by funding from the Wellcome Trust (204841/Z/16/Z), National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre (BRC714/HI/RW/101440), NIHR Great Ormond Street Hospital Biomedical Research Centre (19RX02), and the Health Data Research UK Better Care Catalyst award. HH is an NIHR senior investigator, whose work is supported by (1) Health Data Research UK (LOND1), which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust; (2) the BigData@Heart consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking (116074), which receives support from the EU's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations, and involves a collaboration between 20 academic and industry partners and the European Society of Cardiology; and (3) the NIHR University College London Hospitals Biomedical Research Centre. We thank Bianca DeStavola for her comments on the manuscript, and especially for her suggestions on how to deal with missing values. This study is based, in part, on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation of data and conclusions contained in this study are those of the authors.
Funding Information:
RLB reports consulting fees from Novo Nordisk, ViiV Healthcare, Pfizer, and Gila Therapeutics; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novo Nordisk, ViiV Healthcare, International Medical Press, and Medscape; participation on a data safety monitoring board or advisory board for Novo Nordisk, Pfizer, and ViiV Healthcare; being a committee member of the British Obesity and Metabolic Surgery Society, a trustee for the Association for the Study of Obesity, a scientific chair of the International Federation for the Surgery for Obesity (IFSO) and metabolic disorders European Chapter, a chair of the Royal College of Physicians advisory Committee on Nutrition, Weight and Health, an European Society of Endocrinology clinical committee member, and a trustee of Obesity Empowerment Network UK; being a member of the IFSO scientific committee; being a member of the NICE Weight Management Guideline Development Group; and being a principal investigator on two obesity clinical trials of cagrilintide versus placebo and semaglutide versus placebo (sponsored by Novo Nordisk), and one clinical trial of liraglutide versus placebo (both drugs were provided by Novo Nordisk). AB reports grants from Astra Zeneca. All other authors declare no competing interests.
Funding Information:
MK is funded by the British Heart Foundation (FS/18/5/33319). KD-O is supported by the UK Wellcome Trust Institutional Strategic Support Fund–London School of Hygiene & Tropical Medicine Fellowship (204928/Z/16/Z). SD is supported by an Alan Turing Fellowship. AGL is supported by funding from the Wellcome Trust (204841/Z/16/Z), National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre (BRC714/HI/RW/101440), NIHR Great Ormond Street Hospital Biomedical Research Centre (19RX02), and the Health Data Research UK Better Care Catalyst award. HH is an NIHR senior investigator, whose work is supported by (1) Health Data Research UK (LOND1), which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust; (2) the BigData@Heart consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking (116074), which receives support from the EU's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations, and involves a collaboration between 20 academic and industry partners and the European Society of Cardiology; and (3) the NIHR University College London Hospitals Biomedical Research Centre. We thank Bianca DeStavola for her comments on the manuscript, and especially for her suggestions on how to deal with missing values. This study is based, in part, on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation of data and conclusions contained in this study are those of the authors.
Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/10
Y1 - 2021/10
N2 - Background: Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR). Methods: In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18–74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions. Findings: We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65–74 years), adults in the youngest age group (18–24 years) had the highest OR (4·22 [95% CI 3·86–4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06–5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23–6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18–1·27), for men versus women was 1·12 (1·08–1·16), and for Black individuals versus White individuals was 1·13 (1·04–1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period. Interpretation: A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18–24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care. Funding: The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.
AB - Background: Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR). Methods: In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18–74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions. Findings: We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65–74 years), adults in the youngest age group (18–24 years) had the highest OR (4·22 [95% CI 3·86–4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06–5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23–6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18–1·27), for men versus women was 1·12 (1·08–1·16), and for Black individuals versus White individuals was 1·13 (1·04–1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period. Interpretation: A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18–24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care. Funding: The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.
UR - http://www.scopus.com/inward/record.url?scp=85114820988&partnerID=8YFLogxK
U2 - 10.1016/S2213-8587(21)00207-2
DO - 10.1016/S2213-8587(21)00207-2
M3 - Article
AN - SCOPUS:85114820988
SN - 2213-8587
VL - 9
SP - 681
EP - 694
JO - The Lancet Diabetes and Endocrinology
JF - The Lancet Diabetes and Endocrinology
IS - 10
ER -
