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Trends in cardiovascular disease risk factors by BMI category among adults in England, 2003‐2018

This study aimed to estimate trends in cardiovascular disease risk factors by BMI category among adults in England (n = 115,860).

greater chronic disease management may have led to population subgroups at higher risk being screened and tested more frequently (6), potentially improving CVD risk factor profiles through lifestyle advice and/or pharmacological treatment of high levels of BP, cholesterol, and blood glucose.
Monitoring equity in risk factor reduction requires establishing whether any favorable trends at the population level have been achieved equally within high-and low-risk subgroups, including BMI groups (6)(7)(8). Risk factor trends at the population level in England have been investigated previously, including studies that have assessed secular changes at the upper tail of the BMI distribution (9).
Stability or favorable/unfavorable change in risk factor prevalence at the population level potentially mask divergent trends by BMI group. However, to our knowledge, no studies to date have examined trends in both the prevalence and management of CVD risk factors by BMI category in England. Using data from annual repeated cross-sectional surveys of adults spanning 16 years (Health Survey for England [HSE] 2003-2018), we examined change over time in the prevalence of six key CVD risk factors (4): smoking, physical inactivity, harmful alcohol consumption, hypertension, diabetes, and raised total cholesterol, all examined by BMI category.

ME THODS
The HSE is a series of annual surveys designed to measure health and health-related behaviors. Details of the survey methods have been published elsewhere (10). Briefly, new nationally representative samples of people living in private households were drawn annually using multistage stratified probability sampling. All adults (aged 16 years or older) at each selected household were eligible.
Data were collected at two home visits. First, an interviewer administered questionnaires on sociodemographic variables, lifestyle behaviors, general health, and self-reported morbidity and took height and weight measurements. Second, a nurse visited and asked further questions, including questions regarding current prescribed medication, and collected BP as well as additional anthropometric measurements and nonfasting blood samples.

Definitions of key variables
Height and weight measurements were taken by trained interviewers using standardized protocols. Height was measured using portable stadiometers with a sliding head plate, a base plate, and connecting rods marked with a measuring scale. One measurement was taken with the head positioned in the Frankfort plane. Digital scales were used for weight measurement. A single measurement was recorded, and participants who were pregnant, unable to stand, or unsteady on their feet were not weighed. BMI was calculated as weight in kilograms divided by height in meters squared, and the World Health Organization classification was used to group participants into four mutually exclusive categories: underweight (<18.5), normal weight (18.5-24.9), overweight (25.0-29.9), and obesity (≥30.0) (12). Separate estimates are presented for class I obesity (30.0-34.9) and classes II and III combined (hereafter referred to as class II-III obesity: BMI ≥ 35.0).
All risk factors were dichotomized. Self-reported cigarette smoking status was categorized as non-(never and ex-regular smokers) and current smokers. Participants were classified as physically inactive if they achieved <30 minutes of moderate-to-vigorous PA per week. Details of the PA questionnaire used in the HSE series have been described elsewhere (13). This definition of inactivity corresponds with that used by Public Health England and Sport England.

What is already known?
► Previous studies in England have highlighted the associations between obesity and higher risk of cardiovascular disease.

What does this study add?
► Changes in key cardiovascular disease risk factors by BMI category showed heterogeneity. Relative to adults with normal weight, levels of hypertension and raised total cholesterol declined faster among adults with obesity, but levels of total diabetes increased.

How might these results change the direction of research or the focus of clinical practice?
► Policy makers and health care professionals need to address underlying mechanisms for rising diabetes prevalence among adults with obesity.
Participants who had consumed alcohol in the last week were asked questions regarding the amounts of different types of alcohol consumed on the day they drank the most. We defined excess drinking as drinking over the recommended daily limits on the heaviest drinking day (4 and 3 units for men and women, respectively) (11).
At the nurse visit, three BP readings were taken in a seated position at 1-minute intervals using an appropriately sized cuff after 5 minutes of rest, following standardized protocols by Omron digital monitors (Omron HEM-907; Omron Healthcare Co., Ltd., Kyoto, Japan). Participants who had exercised, eaten, drunk alcohol, or smoked in the 30 minutes before measurements were excluded from analyses. The mean of the second and third BP readings were used.
Survey-defined hypertension was defined as systolic BP ≥ 140 mm Hg, diastolic BP ≥ 90 mm Hg, or having reported taking medication prescribed for high BP. We defined and separately analyzed three indicators of hypertension management (diagnosed, treated, and controlled) using the subset of participants classified as hypertensive as the denominator. Diagnosed hypertension was defined as a self-report of having been diagnosed as having high BP by a doctor or nurse. Only those aware of ever having high BP were asked this question; all diagnosed cases were, therefore, aware. Over the study period, two out of three participants with survey-defined hypertension reported ever having high BP. Treated hypertension was defined as a self-report of taking prescribed medication for high BP.
Participants who reported that their doctor had diagnosed them as having diabetes were classified as having doctor-diagnosed diabetes. HbA 1c was measured from EDTA-blood samples and determined by high-performance liquid chromatography (HPLC) using an automated analyzer. Those who did not report diagnosed diabetes were classified as having undiagnosed diabetes if HbA 1c was ≥ 6.5% (prior to HSE 2012) or ≥ 48 mmol/mol (HSE 2012-2018). Total diabetes included both doctor-diagnosed and undiagnosed participants (15). Blood samples were taken for serum total cholesterol. Raised total cholesterol was defined as ≥ 5 mmol/L regardless of lipidlowering medication use, reflecting National Institute for Health and Care Excellence guidelines (16). Adjustments to the measured values of HbA 1c and total cholesterol were applied to account for changes in laboratory equipment.

Statistical analysis
Analyses were limited to participants aged 16 years or older (n = 137,645) with valid height and weight data (n = 115,860). Analytic sample sizes varied by CVD risk factor because information was not available in certain HSE years and because of missing data. All analyses were based on complete cases (those with no missing data in the BMI and relevant CVD risk factor variables), and analytic sample We chose a priori to stratify analyses by sex. Available data from four consecutive annual surveys were aggregated into four nonoverlapping survey periods (2003-2006, 2007-2010, 2011-2014, and 2015-2018) to boost sample sizes and, therefore, increase the precision of estimates. Risk factor prevalence was estimated by time period for all adults (i.e., all BMI groups combined) and by BMI category (normal weight, overweight, obesity, class I obesity, and class II-III obesity); estimates are not presented for the underweight category because of the low prevalence (2%). Estimates were directly age-standardized (pooled HSE data as the standard population). The absolute change in prevalence was computed as the difference between the first and last time periods, expressed in percentage points (PP). Wald tests were used to test the null hypothesis of no change in prevalence between the two estimates. The same procedure was used to compare the difference in prevalence between the first and last time periods in the overweight and obesity categories versus the normal-weight group. Analyses were repeated on adults with survey-defined hypertension (n = 23,216) to estimate the change in prevalence of diagnosed, treated, and controlled hypertension.
Sample sizes were too small to estimate the change in levels of diagnosed diabetes among participants with total diabetes. Therefore, changes in diagnosed and undiagnosed diabetes by BMI category were estimated using all adults as the denominator.
All analyses accounted for the complex survey design, incorporating the appropriate weights that accounted for greater nonparticipation at each successive stage (interview, nurse visit, and blood sample collection) and the geographical clustering of participants in primary sampling units. Statistical significance was set at p < 0.05 for two-tailed tests, with no adjustment for multiple comparisons. HSE data sets, including the most recent survey (17), are available via the UK Data Service (www.ukdat aserv ice.ac.uk) and are subject to an end user license agreement; all reproducible code has been made openly accessible via GitHub (https://github.com/shaun s11/RiskF actor sByBMI.git; GitHub Inc., San Francisco, California). Table 1  CVD risk factor prevalence by survey period and BMI category is shown in Table 2 (men) and Table 3 (women). Figure 1 shows the absolute change in prevalence between the first and last time periods, expressed in PP.       Table 4; Figure 2 shows the change in prevalence between the first and last time periods. The pattern of change (relative to those with normal weight) was similar by BMI for both sexes (p > 0.100), and the proportion of hypertension that was diagnosed remained at a similar level, whereas proportions of hypertension that were treated and controlled improved during the study period.

Total diabetes, including diagnosed and undiagnosed
Estimates of total diabetes, including diagnosed and undiagnosed (elevated HbA 1c ), are shown in Table 5 ( Figure 3 shows the change in prevalence between the first and last time periods

Comparisons with other studies
Among adults with overweight and obesity, our findings of 1) de-

Population-level trends in CVD risk factor prevalence
In agreement with the population-level trends presented herein, previous studies using HSE data have shown decreases in current cigarette smoking (19), hypertension (19), and raised total cholesterol (19), as well as stability in PA (13,20) and increases in total diabetes (19) and obesity (5). Multiple policies have been enacted in England over the study period that have likely affected these trends.

0.200
Estimates age-standardized to the pooled HSE data; ---denotes comparable data not available.  (24). Whole-population based strategies included tobacco control policies such as smoke-free legislation, higher taxation, a higher minimum age of sale of cigarettes, licensing of nicotine replacement therapy for harm reduction, and introduction of plain packaging (25).
Despite these policies, the population-level trends in CVD risk factors show both favorable and unfavorable changes. The decline in current cigarette smoking prevalence has been linked to the tobacco control policies listed above (26). The recent fall in survey-defined hypertension is typically attributed to decreased salt intake in foods (24) and improved detection, treatment, and control of high BP, although levels of management remain suboptimal (27). Although levels of glycemic control have improved across all social groups (28), the rise in total diabetes reflects both improved case ascertainment and increases in incidence associated with rising obesity (through numerous pathophysiological mechanisms that increase the risk of type II diabetes among adults with obesity) (29). Longer survival for those with diabetes is also a potential contributory factor (30). The increased uptake and efficacy of lipid-lowering medications such as statins for the primary prevention of CVD within UK primary care during the study period (31) may be a key driver of the reduction in raised total cholesterol prevalence.

TA B L E 4 (Continued)
survey period (2003)(2004)(2005)(2006), and the marginal increase in inactivity prevalence among women with overweight, may, to some extent, reflect influences of living in obesogenic environments (32): i.e., neighborhoods with higher socioeconomic deprivation, geographic barriers to PA, and lower air quality that may influence adiposity levels over and above other individual characteristics (33). Reverse causality, i.e., a reluctance of smokers to tolerate post-cessation weight gain (34) and the fact that higher adiposity in itself is a risk factor for smoking (35) which makes it more difficult to quit, is also a potential contributory factor for the slower decline in current smoking levels among men with obesity.

Strengths and limitations
The main strengths of our study include the use of repeated nationally representative health examination surveys that objectively measured anthropometry, BP, cholesterol, and HbA1c using standardized protocols over the 16-year study period, thereby eliminating self-report bias for these factors (36). Stratifying results by sex enabled us to identify differences in the patterns of change over time; our use of direct standardization removed the potential confounding influence of age.

ACK N OWLED G M ENTS
The authors thank our colleagues at NatCen Social Research, the interviewers and nurses, and the participants in the Health Survey for England series.
The Health Survey for England was funded by National Health Service (NHS) Digital. NHS Digital is the trading name of the Health and Social Care Information Centre. The authors are funded to conduct the annual Health Survey for England, but this specific study was not funded. NHS Digital had no role in the analysis, interpretation of data, decision to publish, or preparation of the manuscript for this specific study.

CO N FLI C T O F I NTE R E S T
The authors declared no conflict of interest.

AUTH O R CO NTR I B UTI O N S
SS and JSM conceptualized the study. SS was responsible for generating the data sets, conducting the analyses, interpreting the results, and drafting the manuscript. SS, LNF, and JSM critically revised the manuscript. All authors have read and approved the manuscript. Nottingham REC 15/EE/0299. This study is a secondary analysis of previously collected data; therefore, additional ethical approval was not required.

DATA AVA I L A B I L I T Y S TAT E M E N T
The HSE data sets generated and analyzed during the current study are available via the UK Data Service (UKDS, https://ukdat aserv ice. ac.uk/) and are subject to their end user license agreement. Statistical code to enable replication of our results (using the data sets deposited at the UKDS) has been made openly accessible via GitHub