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

Abstract

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.

Figure 1

Flow chart showing the number of individuals and BMI measurements recorded in EHRs used for calculating 1-year, 5-year, and 10-year changes in BMI EHR=electronic health record. *Temporal trends in BMI were calculated and compared with HSE data (see appendix pp 6–7). †For people with more than one valid pair of BMI measurements, we chose one pair at random for the calculation of BMI change. ‡We excluded individuals with a follow-up interval since their initial BMI measurement of less than half the time window of interest (ie, <6 months for estimating 1-year BMI changes, 2·5 years for estimating 5-year BMI changes, and 5 years for estimating 10-year BMI changes). §We selected one timepoint with a BMI measurement at random and applied multiple imputation analysis.

Figure 2

Distribution of weight changes at 10 years in men (A) and women (B) by age and BMI category In A, 470 932 men were included in the analysis; an average height of 1·76 m was assumed. In B, 646 392 women were included in the analysis; an average height of 1·62 m was assumed. IDR=interdecile range. p10=10th percentile. p90=90th percentile.

Figure 2

Distribution of weight changes at 10 years in men (A) and women (B) by age and BMI category In A, 470 932 men were included in the analysis; an average height of 1·76 m was assumed. In B, 646 392 women were included in the analysis; an average height of 1·62 m was assumed. IDR=interdecile range. p10=10th percentile. p90=90th percentile.

Figure 3

Estimated age-standardised risk of transitioning to normal weight (A), overweight (B), and obesity (C) according to BMI category at 1, 5, and 10 years of follow-up The risk of transitioning was standardised to the English population based on age structure (using information from Office of National Statistics) and prevalence of BMI categories (using data from the Health Survey for England) between 1998 and 2016. For a detailed description of the methods used for this analysis, see the appendix (pp 20–23).

Figure 4

Absolute risk and odds ratio of transitioning from normal weight to overweight or obesity BMI categories, from overweight to obesity BMI categories, and from class 1 and 2 obesity to class 3 obesity BMI categories at 10 years, according to age, sex, ethnicity, degree of social deprivation, and geographical region Odds ratios were mutually adjusted for BMI (at baseline), age group, sex, IMD quintile, ethnicity, geographical region, use of diuretics, and the prevalence of cardiovascular disease, cancer, diabetes, hypertension, mental health disorders (depression, anxiety, stress, phobia, schizophrenia, bipolar disorder, or affective disorder), and other chronic diseases (HIV, chronic obstructive pulmonary disease, neurological disease [dementia], rheumatological disease [rheumatoid arthritis, gout, or systemic lupus erythematosus], gastrointestinal disease (inflammatory bowel disease), and renal disease [chronic kidney disease or renal failure]). IMD=Index of Multiple Deprivation. *Refers to the number of individuals who transitioned to a higher BMI category; for ethnicity and IMD (the two variables with missing values), the numbers were calculated from the first imputation.

Figure 4

Absolute risk and odds ratio of transitioning from normal weight to overweight or obesity BMI categories, from overweight to obesity BMI categories, and from class 1 and 2 obesity to class 3 obesity BMI categories at 10 years, according to age, sex, ethnicity, degree of social deprivation, and geographical region Odds ratios were mutually adjusted for BMI (at baseline), age group, sex, IMD quintile, ethnicity, geographical region, use of diuretics, and the prevalence of cardiovascular disease, cancer, diabetes, hypertension, mental health disorders (depression, anxiety, stress, phobia, schizophrenia, bipolar disorder, or affective disorder), and other chronic diseases (HIV, chronic obstructive pulmonary disease, neurological disease [dementia], rheumatological disease [rheumatoid arthritis, gout, or systemic lupus erythematosus], gastrointestinal disease (inflammatory bowel disease), and renal disease [chronic kidney disease or renal failure]). IMD=Index of Multiple Deprivation. *Refers to the number of individuals who transitioned to a higher BMI category; for ethnicity and IMD (the two variables with missing values), the numbers were calculated from the first imputation.

Figure 5

Absolute risk of transitioning to a higher BMI category over 10 years, according to baseline BMI category, age, sex, and IMD quintile This analysis included 1 117 324 individuals; across the 900 strata, there are at least 1000 individuals in 458 (51%) strata, and at least 100 individuals in 890 (99%) strata. IMD=Index of Multiple Deprivation.