Evidence map›Paper›PMID 34481555›Full record

ArticleThe lancet. Diabetes & endocrinology2021

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.

Michail Katsoulis, Alvina G Lai, Karla Diaz-Ordaz, Manuel Gomes, Laura Pasea, Amitava Banerjee, Spiros Denaxas, Kostas Tsilidis, Pagona Lagiou, Gesthimani Misirli and 10 more

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In one paragraph

Article in The lancet. Diabetes & endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Michail KatsoulisInstitute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK. Electronic address: m.katsoulis@ucl.ac.uk.
Alvina G LaiInstitute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
Karla Diaz-OrdazDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Manuel GomesDepartment of Applied Health Research, University College London, London, UK.
Laura PaseaInstitute of Health Informatics, University College London, London, UK.
Amitava BanerjeeInstitute 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.
Spiros DenaxasInstitute 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.
Kostas TsilidisDepartment 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.
Pagona LagiouDepartment 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.
Gesthimani MisirliHellenic Health Foundation, Athens, Greece.
Krishnan BhaskaranDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Goya WannametheeDepartment of Primary Care and Population Health, University College London, London, UK.
Richard DobsonHealth 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.
Rachel L BatterhamCentre 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.
Dimitra-Kleio KipourouDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
R Thomas LumbersInstitute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
Lan WenDepartment of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
Nick WarehamMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
Claudia LangenbergMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health, Charité-University Medicine Berlin, Berlin, Germany.
Harry HemingwayInstitute 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.

Funding

Department of HealthMedical Research Council G0902393Medical Research Council MC_PC_13041Medical Research Council MC_PC_17214Medical Research Council MC_UU_00006/1Medical Research Council MR/K006584/1Medical Research Council MR/S003754/1Wellcome Trust 204841/Z/16/Z
6 · The paper itself

Abstract

backgroundTargeted 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).

methodsIn 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.

findingsWe 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.

interpretationA 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.

fundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

Indexed as

Electronic Health RecordsOverweightAdolescentAdultAgedBody Mass IndexChildChild, PreschoolCohort StudiesEnglandFemaleHumansInfantMaleRisk FactorsYoung Adult

Identifiers

PMID34481555
PMCPMC8440227

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.