Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records,The Lancet Diabetes & Endocrinology

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Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records
The Lancet Diabetes & Endocrinology ( IF 44.5 ) Pub Date : 2021-09-02 , DOI: 10.1016/s2213-8587(21)00207-2
Michail Katsoulis ₁ , Alvina G Lai ₁ , Karla Diaz-Ordaz ₂ , Manuel Gomes ₃ , Laura Pasea ₄ , Amitava Banerjee ₅ , Spiros Denaxas ₆ , Kostas Tsilidis ₇ , Pagona Lagiou ₈ , Gesthimani Misirli ₉ , Krishnan Bhaskaran ₁₀ , Goya Wannamethee ₁₁ , Richard Dobson ₁₂ , Rachel L Batterham ₁₃ , Dimitra-Kleio Kipourou ₁₀ , R Thomas Lumbers ₁ , Lan Wen ₁₄ , Nick Wareham ₁₅ , Claudia Langenberg ₁₆ , Harry Hemingway ₁₇

Affiliation

Institute of Health Informatics, University College London, London, UK; Health 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.
Institute of Health Informatics, University College London, London, UK; Health 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, London, UK.
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.
Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health, Charité-University Medicine Berlin, Berlin, Germany.
Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK.

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.

中文翻译：

确定英格兰体重和 BMI 变化的高风险成年人：一项使用电子健康记录的纵向、大规模、基于人群的队列研究

背景

有针对性的肥胖预防政策将受益于体重增加风险最高的人群的识别。成人年龄、性别、种族、地理区域和社会剥夺程度对体重增加的相对重要性尚不清楚。我们旨在使用电子健康记录 (EHR) 识别体重和 BMI 变化的高危人群。

方法

在这项基于人群的纵向队列研究中，我们使用了来自英国 400 家初级保健机构（通过临床实践研究数据链）的关联 EHR 数据，这些数据可通过 CALIBER 程序访问。符合条件的参与者年龄在 18-74 岁之间，在全科诊所注册，并在 1998 年 1 月 1 日至 2016 年 6 月 30 日期间记录了 BMI 和体重测量值，在此期间他们有至少 1 年的合格关联数据跟进时间。我们计算了 1 年、5 年和 10 年 BMI 的纵向变化，并调查了 BMI 类别（体重过轻、正常体重、超重、1 级和 2 级肥胖以及重度肥胖 [级3])，如世界卫生组织所定义。

发现

在我们的研究中，我们纳入了 2 092 260 名符合条件的个体，他们的 BMI 测量值超过 900 万次。在 1 年、5 年和 10 年的随访中，年轻的成年年龄是体重增加的最强危险因素。与最大年龄组（65-74 岁）相比，最小年龄组（18-24 岁）的成年人具有最高的 OR（4·22 [95% CI 3·86-4·62]）和最大的绝对风险（37%对24%）在 10 年内从正常体重转变为超重或肥胖。同样，在基线时超重或肥胖的最年轻年龄组的成年人也最有可能过渡到更高的 BMI 类别；从超重过渡到 1 级和 2 级肥胖的OR 4·60 (4·06–5·22) 和绝对风险（42%对18%），以及 OR 5·87 (5·23–6·59) 和绝对风险（22%对5%）从 1 级和 2 级肥胖转变为 3 级肥胖。其他人口因素与这些转变的相关性始终较弱；例如，生活在社会最贫困地区和最贫困地区的人们从正常体重转变为超重或肥胖的 OR 为 1·23 (1·18–1·27)，男性与女性为 1·12 (1 ·08–1·16)，黑人个体与白人个体的比值为 1·13 (1·04–1·24)。我们提供开放式在线风险计算器，并提供 1 年、5 年和 10 年随访期间的高分辨率肥胖风险图表。