| アブストラクト | 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. |
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| 投稿者 | Katsoulis, Michail; Lai, Alvina G; Diaz-Ordaz, Karla; Gomes, Manuel; Pasea, Laura; Banerjee, Amitava; Denaxas, Spiros; Tsilidis, Kostas; Lagiou, Pagona; Misirli, Gesthimani; Bhaskaran, Krishnan; Wannamethee, Goya; Dobson, Richard; Batterham, Rachel L; Kipourou, Dimitra-Kleio; Lumbers, R Thomas; Wen, Lan; Wareham, Nick; Langenberg, Claudia; Hemingway, Harry |
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| 組織名 | Institute of Health Informatics, University College London, London, UK; Health;Data Research UK, University College London, London, UK. Electronic address:;m.katsoulis@ucl.ac.uk.;Data Research UK, University College London, London, UK.;Department of Medical Statistics, London School of Hygiene and Tropical Medicine,;London, UK.;Department of Applied Health Research, University College London, London, UK.;Institute of Health Informatics, University College London, London, UK.;Institute of Health Informatics, University College London, London, UK;;University College London Hospitals NHS Trust, London, UK; Barts Health NHS;Trust, The Royal London Hospital, London, UK.;Data Research UK, University College London, London, UK; Alan Turing Institute,;London, UK; National Institute of Health Research, University College London;Hospitals Biomedical Research Centre, London, UK.;Department of Epidemiology and Biostatistics, School of Public Health, Imperial;College London, London, UK; Department of Hygiene and Epidemiology, University of;Ioannina School of Medicine, Ioannina, Greece.;Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine,;National and Kapodistrian University of Athens, Athens, Greece; Department of;Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.;Hellenic Health Foundation, Athens, Greece.;Department of Non-Communicable Disease Epidemiology, London School of Hygiene and;Tropical Medicine, London, UK.;Department of Primary Care and Population Health, University College London,;Health Data Research UK, University College London, London, UK; Institute of;Health Informatics, University College London, London, UK; Department of;Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and;Neuroscience, King's College London, London, UK.;Centre for Obesity Research, University College London, London, UK; National;Institute of Health Research, University College London Hospitals Biomedical;Research Centre, London, UK; University College London Hospitals Bariatric Centre;for Weight Management and Metabolic Surgery, London, UK.;National and Kapodistrian University of Athens, Athens, Greece.;MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine,;Cambridge, UK.;Cambridge, UK; Computational Medicine, Berlin Institute of Health,;Charite-University Medicine Berlin, Berlin, Germany.;Data Research UK, University College London, London, UK; National Institute of;Health Research, University College London Hospitals Biomedical Research Centre, |
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