The contribution of obesity to the population burden of high metabolic cardiovascular risk among different ethnic groups. The HELIUS study

The contribution of obesity to the population burden of high metabolic cardiovascular risk among... Abstract Background The burden of cardiovascular risk is distributed unequally between ethnic groups. It is uncertain to what extent this is attributable to ethnic differences in general and abdominal obesity. Therefore, we studied the contribution of general and abdominal obesity to metabolic cardiovascular risk among different ethnic groups. Methods We used data of 21 411 participants of Dutch, South-Asian Surinamese, African-Surinamese, Ghanaian, Turkish or Moroccan origin in Healthy Life in an Urban Setting (Amsterdam, the Netherlands). Obesity was defined using body-mass-index (general) or waist-to-height-ratio (abdominal). High metabolic risk was defined as having at least two of the following: triglycerides ≥1.7 mmol/l, fasting glucose ≥5.6 mmol/l, blood pressure ≥130 mmHg systolic and/or ≥85 mmHg diastolic and high-density lipoprotein cholesterol <1.03 mmol/l (men) or <1.29 mmol/l (women). Results Among ethnic minority men, age-adjusted prevalence rates of high metabolic risk ranged from 32 to 59% vs. 33% among Dutch men. Contributions of general obesity to high metabolic risk ranged from 7.1 to 17.8%, vs. 10.1% among Dutch men, whereas contributions of abdominal obesity ranged from 52.1 to 92.3%, vs. 53.9% among Dutch men. Among ethnic minority women, age-adjusted prevalence rates of high metabolic risk ranged from 24 to 35% vs. 12% among Dutch women. Contributions of general obesity ranged from 14.6 to 41.8%, vs. 20% among Dutch women, whereas contributions of abdominal obesity ranged from 68.0 to 92.8%, vs. 72.1% among Dutch women. Conclusions Obesity, especially abdominal obesity, contributes significantly to the prevalence of high metabolic cardiovascular risk. Results suggest that this contribution varies substantially between ethnic groups, which helps explain ethnic differences in cardiovascular risk. Introduction Type-2 diabetes (T2D) and cardiovascular disease (CVD) often affect ethnic minority groups more than European host populations.1–3 For example the age-adjusted odds of T2D was 3–12 times higher among ethnic minority groups in the Netherlands relative to the Dutch.3 Similarly, some ethnic minority groups have shown a particularly high incidence of CVD relative to majority population.1,2 Studies suggest that obesity contributes substantially to the high prevalence of CVD and T2D globally via several mechanisms [e.g. by alterations in lipids, blood pressure (BP) and inflammation].4–7 Estimates of this contribution have varied widely.8–10 For example, studies have estimated that 3–83% of the prevalence of diabetes, and 7–44% of CVD can be attributed to obesity when obesity is defined by body-mass-index (BMI).8–11 Moreover, previous work has suggested that abdominal obesity may be a stronger risk factor for CVD and T2D than general obesity, and may therefore contribute even more to CVD and T2D.12,13 High rates of general and abdominal obesity are found in ethnic minority groups.3,14 Thus, part of the ethnic disparities in the prevalence of CVD and T2D may be attributable to ethnic disparities in obesity. However, the magnitude of the contribution of obesity to cardiovascular risk and, to a lesser extent T2D, is currently unknown among various ethnic minority groups, especially for abdominal obesity. This uncertainty stems from the fact that this contribution does not only depend on ethnic disparities in the prevalence of obesity, but also on disparities in the association between obesity and metabolic risk.15 The aim of this study is to provide more insight in the contribution of obesity to cardiovascular and T2D risk. To this end, we used data from the Healthy Life in an Urban Setting (HELIUS) study among participants of several ethnic groups to estimate the contribution of general and abdominal obesity to metabolic risk factors associated with CVD and T2D. Methods The HELIUS study is a large-scale, multi-ethnic cohort study on health and health care utilization among different ethnic groups living in Amsterdam, the Netherlands. The aims and design of the HELIUS study have been published.16 Briefly, potential participants aged 18–70 years living in Amsterdam were randomly sampled via the municipality register, stratified for ethnicity. Baseline data were obtained in 2011–15 via questionnaires and physical examination. A total of 90 019 subjects were invited. Approximately 55% responded either by response card or after a home visit by an ethnically matched interviewer. Of those, 24 789 agreed to participate, resulting in response rate of 28% (ranging from 21% among Moroccans to 35% among Ghanaians). The study protocols were approved by the AMC Ethical Review Board, and all participants provided written informed consent. Ethnicity Participants’ ethnicity was defined according to the country of birth of the participant as well as that of his/her parents [for a full discussion of the concept of ethnicity in the Netherlands (and this study), see Stronks et al.17]. Specifically, a participant was considered as of non-Dutch ethnic if he/she fulfilled either of the following criteria: (1) he/she was born abroad and has at least one parent born abroad (first generation); or (2) he/she was born in the Netherlands but both his/her parents were born abroad (second generation). Of the Surinamese immigrants in the Netherlands, approximately 80% are either African origin or South-Asian origin. Surinamese subgroups were classified according to self-reported ethnic origin. For the Dutch sample, we invited people who were born in the Netherlands and whose parents were both born in the Netherlands. Anthropometric measures Weight was measured in light clothing using a Seca 877 digital scale to the nearest 0.1 kg. Height was measured without shoes using a portable stadiometer (Seca 217) to the nearest 0.1 cm in upright position. Waist circumference was measured using a flexible tape measure at the level mid-way between the lower rib margin and the iliac crest. All measures were taken in duplicate and the mean was used in the analyses. If the discrepancy between the duplicate measures differed more than 0.5 cm for height, 0.5 kg for weight or 1 cm for waist circumference, a third measurement was taken. The two measures which were most similar were used to calculate the mean. General obesity was based on BMI, calculated as weight in kilograms divided by squared height in meters and defined using WHO cut-off values (BMI ≥ 30 kg/m2) without ethnic-specific cut-off values consistent with current practice in the Netherlands.18 Abdominal obesity was based on weight-to-height ratio (WHtR), calculated as the waist circumference in centimeters divided by height in centimeters. We used the cut-off value proposed by Ashwell et al.12, namely WHtR ≥0.5, as there is no WHO cut-off value for obesity based on WHtR. WHtR was chosen as the abdominal obesity measure because, of the abdominal obesity measures, WHtR may be the most robust across ethnic groups.12 Metabolic risk For this study, we defined high metabolic risk similar to the Adult Treatment Panel III definition of metabolic syndrome [i.e. triglycerides, high-density lipoprotein (HDL) cholesterol, fasting glucose and BP, but not LDL and smoking], but without the abdominal obesity criterion.19 Thus, we defined high metabolic risk as having at least two of the following four criteria: high triglycerides (≥150 mg/dl), low HDL cholesterol (<40 mg/dl for men, <50 mg/dl for women), high fasting glucose (≥100m g/dl) and a high BP (≥130/85 mmHg). Using medication related to a criterion was considered as a fulfillment of that criterion. BP was measured using a validated automated digital BP device (WatchBP Home; Microlife AG) on the left arm in a seated position after the person had been seated for at least 5 min. BP measurements were conducted in duplicate and the average BP was used for analysis. Fasting blood samples were drawn, and lipids and glucose were determined with by enzymatic colorimetric spectrophotometry and enzymatic spectrophotometric (UV) method respectively (Roche Diagnostics, Japan). Study population Baseline data collected by both questionnaire and physical examination were available among 22 165 participants. We excluded participants with a Javanese Surinamese (n = 233), ‘other/unknown Surinamese’ (n = 267) or unknown/other ethnic background (n = 48) due to small sample sizes. Furthermore, we excluded participants with missing data regarding risk factors used to define high metabolic risk (i.e. triglycerides, HDL, glucose and/or BP, n = 177), as well as participants with missing data regarding anthropometric measures (n = 29). This resulted in a study population of 21 411 participants. Statistical analyses Ethnic groups may differ in the obesity prevalence and associated metabolic risk. To integrate both aspects, we estimated the population attributable fraction (PAF) of obesity to high metabolic risk. We first conducted Poisson regression analyses to determine the prevalence ratio of obesity to high metabolic risk in each subgroup. These analyses were adjusted for age, and were conducted separately for BMI and WHtR. Next, we estimated the PAF of obesity for high metabolic risk via an adjusted PAF algorithm; PAF = P((PR – 1)/PR) × 100, where P is the prevalence of obesity among those with high metabolic risk and PR is the prevalence ratio of obesity on high metabolic risk, adjusted for age (for a detailed discussion regarding the PAF formula, see Rockhill et al.20). We then estimated the prevalence of obesity-related metabolic risk per ethnic group, separately for general obesity and abdominal obesity. To this end, we first estimated the prevalence of high metabolic risk at the mean age of each subgroup via binary logistic regression. Next, we multiplied this prevalence estimate with the previously calculated PAF to determine the obesity-related prevalence of high metabolic risk. Results Ethnic groups differed in mean age (40.9–48.1 years in men and 39.9–46.1 years in women; table 1). In men, the prevalence of BMI-defined general obesity ranged from 10.1% among Dutch men to 28.1% among Turkish men. In women, the prevalence ranged from 10.1% among Dutch women to 44.4% Ghanaian women. The prevalence of WHtR-defined abdominal obesity was higher and ranged from 54.5% to 79.2% among men and 45.1% to 84.4% among women. The prevalence of high metabolic risk was lowest among the Dutch, especially among women. The pattern and prevalence of individual components used to define high metabolic risk also differed between ethnic groups. For a list of additional cardiovascular risk factors per ethnic group, we refer the reader to Supplementary table S1. Table 1 Characteristics (mean (SD) or percentage) of the study population, by ethnicity and sex Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Notes: Data are mean (SD) or percentages. BMI, body mass index; SD, standard deviation; WHtR, waist to height ratio; HDL, high-density lipoprotein cholesterol. Open in new tab Table 1 Characteristics (mean (SD) or percentage) of the study population, by ethnicity and sex Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Notes: Data are mean (SD) or percentages. BMI, body mass index; SD, standard deviation; WHtR, waist to height ratio; HDL, high-density lipoprotein cholesterol. Open in new tab Among men, the contribution of general obesity to high metabolic risk ranged from 7.1% among South-Asian Surinamese to 17.8% among Turkish men, vs. 10.1% among the Dutch (table 2). The contribution of abdominal obesity to high metabolic risk was higher than the contribution of general obesity ranging from 52.1% among African Surinamese to 92.3% among Moroccans, vs. 53.9% among the Dutch. Among women, we observed a more heterogeneous contribution of general obesity to high metabolic risk; among South-Asian Surinamese, African Surinamese and Ghanaian women, this contribution varied between 14.6% and 26.6% vs. 20% among the Dutch, whereas among Turkish and Moroccan women this contribution was substantially higher (41.2% and 41.8%, respectively). The contribution of abdominal obesity to high metabolic risk showed a similar pattern, varying between 68.0% and 73.9% among South-Asian Surinamese, African Surinamese and Ghanaian women vs. 72.1% among the Dutch, whereas this contribution among Turkish and Moroccan women was 82.3% and 92.8%, respectively. We then estimated the potentially achievable health gain in the population in each ethnic group if all participants were to have non-obese levels. Adjusted for age, the prevalence of high metabolic risk varied between 32% and 59% across the ethnic minority men vs. 33% among Dutch men (figure 1, upper panels). If all men were to be non-obese based on general obesity, the prevalence of high metabolic risk would be 28% to 54% among ethnic minority men vs. 30% among the Dutch. If all men were to be non-obese based on abdominal obesity, the prevalence of metabolic risk would be 3% to 27% among ethnic minority men vs. 15% among the Dutch. Figure 1 Open in new tabDownload slide Prevalence of high metabolic risk related and not related to obesity Notes: The prevalence of high metabolic risk by ethnicity and sex, adjusted for age, and split for obesity related and obesity unrelated prevalence based on general obesity (body mass index (BMI) ≥30kg/m2), or abdominal obesity (waist-to-height ratio (WHtR) ≥0.5). DU, Dutch; AS, African Surinamese; GH, Ghanaian; MO, Moroccan; TU, Turkish; SA, South-Asian Surinamese. Figure 1 Open in new tabDownload slide Prevalence of high metabolic risk related and not related to obesity Notes: The prevalence of high metabolic risk by ethnicity and sex, adjusted for age, and split for obesity related and obesity unrelated prevalence based on general obesity (body mass index (BMI) ≥30kg/m2), or abdominal obesity (waist-to-height ratio (WHtR) ≥0.5). DU, Dutch; AS, African Surinamese; GH, Ghanaian; MO, Moroccan; TU, Turkish; SA, South-Asian Surinamese. Table 2 Age adjusted estimated contribution of general obesity or abdominal obesity to high metabolic risk General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 Notes:aGeneral obesity is defined as a body mass index (BMI) ≥30 kg/m2. bAbdominal obesity is defined as a waist-to-height ratio (WHtR) ≥0.5. cPrevalence of obesity among participants with high metabolic risk. dAge-adjusted prevalence ratio (PR) and 95% confidence interval of high metabolic risk between obese and non-obese. ePopulation attributable fraction (PAF), calculated as P((PR-1)/PR) × 100. Open in new tab Table 2 Age adjusted estimated contribution of general obesity or abdominal obesity to high metabolic risk General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 Notes:aGeneral obesity is defined as a body mass index (BMI) ≥30 kg/m2. bAbdominal obesity is defined as a waist-to-height ratio (WHtR) ≥0.5. cPrevalence of obesity among participants with high metabolic risk. dAge-adjusted prevalence ratio (PR) and 95% confidence interval of high metabolic risk between obese and non-obese. ePopulation attributable fraction (PAF), calculated as P((PR-1)/PR) × 100. Open in new tab Among ethnic minority women, the age-adjusted prevalence of high metabolic risk varied between 24% and 35% vs. 12% among the Dutch (figure 1, lower panels). If all ethnic minority women were to be non-obese based on general obesity, the prevalence of high metabolic risk would be 16% to 30% among ethnic minority women vs. 10% among the Dutch. If all ethnic minority women were to be non-obese based on abdominal obesity, the prevalence of metabolic risk would be 2% to 9% vs. 3% among the Dutch). Discussion Key findings Obesity, especially abdominal obesity, contributes substantially to the prevalence of high metabolic risk. Our results suggest that this contribution is generally higher among ethnic minority groups than among the Dutch majority population. Hence, reducing the prevalence of obesity, particularly abdominal obesity, may reduce the prevalence of high metabolic risk among all ethnic groups and reduce some of the metabolic risk differences between ethnic minority groups and the Dutch. Evaluation of potential limitations As with all cohort studies, some selection bias may have occurred due to non-response. The data that was available among non-responders showed only small SES and agree differences between responders and non-responders.12 Although SES and age are known to be related to metabolic health, this non-response data did not include measures regarding CVD risk or adiposity. So selection bias is less likely, but we cannot truly evaluate whether selection bias occurred and, if so, how this bias has affected our results. Due to the cross-sectional design, causal inferences regarding obesity and high metabolic risk should be made with caution. Although it is widely assumed that a causal relation between fat accumulation and metabolic disease exists, a high occurrence of metabolic risk factors may also affect susceptibility for weight gain and obesity.21,22 If so, this may have led to an overestimation of the contribution of obesity to the prevalence of high metabolic risk, and the potential health gain related to weight loss. We used measures of prevalent cardiovascular risk, based on components of the metabolic syndrome, as a proxy for overall cardiovascular risk. Although high metabolic risk can be considered an inferior outcome measure, the association between components of metabolic syndrome and CVD has been well established.23 Nevertheless, this association may differ between ethnic groups, for example due to ethnic disparities in the age-of-onset of these risk factors.3 Thus, it would be of value to determine, in future studies, how the contribution of obesity to metabolic and CVD disease incidence varies between ethnic groups. Obesity was measured using anthropometric measures. More sophisticated measures to determine adiposity mass and distribution (e.g. Dual-Energy X-Ray Absorptiometry) would be preferable because these measures may be more accurate and may better reflect ethnic variations in fat-distributions.24 However, these measures are impractical for both large cohort studies and daily clinical practice. In order to estimate the population contribution of obesity to high metabolic risk, we did not exclude participants with prior CVD. However, our results are similar after exclusion of participants with prior CVD, suggesting that our results are also applicable to a strictly primary prevention setting (Supplementary table S2). Discussion of key findings Although the contribution of general obesity to high metabolic risk was similar between most ethnic groups, the basis of these contributions did differ, with the Dutch showing a relatively low prevalence, but relatively strong association between general obesity and high metabolic risk, whereas the ethnic minority groups showed a weaker association and a higher prevalence of general obesity. This is in accordance with previous studies which also reported higher prevalence rates of general obesity, but weaker associations between general obesity and cardiovascular metabolic disease among ethnic minority groups relative to ethnic majority groups.25,26 Our results suggest that, despite a similar contribution of general obesity to high metabolic risk, a similar absolute reduction in the prevalence of general obesity may not result in a similarly strong reduction of metabolic risk among all ethnic minority groups. Thus, among ethnic minority groups, relatively large reductions in the prevalence of obesity prevalence may be necessary to reduce disparities in high metabolic risk. Due to ethnic differences in fat accumulation, distribution and in the associations between obesity and disease, it has been suggested to apply different obesity thresholds for different ethnic groups, especially for BMI.15,27–29 Controlling for these differences by applying lower, ethnic-specific BMI cut-off values did not strongly affect our results regarding ethnic disparities in the contribution of general obesity to high metabolic risk (results not shown), suggesting that ethnic disparities in fat distribution do not contribute substantially to our results. In our study, general obesity contributed between 7.1% and 26.6% to high metabolic risk for most ethnic groups. This contribution is similar to contributions found in earlier studies among the general population in several countries.8–10 For example, a study from Australia among men and women from the general population found that BMI-defined obesity contributed to 15.7% of all cases of hypertension, 32.4% of all cases of diabetes and 18.8% of all cases of dyslipidemia.10 Turkish and Moroccan women showed a much stronger contribution of general obesity to high metabolic risk (41.1% and 41.8%, respectively) than women in the other ethnic groups. Earlier studies already found a particularly high prevalence of dyslipidemia among Turkish and Moroccans relative to other ethnic groups.30 Our results suggest that obesity contributes substantially to this relatively high prevalence of dyslipidemia and overall metabolic risk. For WHtR-defined obesity, we found that contributions to high metabolic risk varied between 52.1% and 92.8%. Earlier studies have reported lower contributions of abdominal obesity to the prevalence of cardiovascular risk at population level.31,32 For example, an Australian study on type 2 diabetes, low HDL, increased triglycerides and hypertension found that abdominal obesity contributed to 17–38% of these risk factors among men and 30–47% among women.31 Our reported contributions were higher, in part because our participants were from a different ethnic group with a higher prevalence of abdominal obesity. However, this does not explain the higher contribution among the Dutch. Alternatively, these differences may be related to the use of waist circumference to define abdominal obesity in the previous study, as waist circumference may be associated less strongly with metabolic risk factors relative to WHtR.12 Among some ethnic groups (e.g. Dutch, South-Asian Surinamese), the contribution of abdominal obesity to high metabolic risk varied between 50% and 80% whereas among the Moroccan ethnic group (and, to a lesser extent, Turkish women) this contribution was substantially higher. Thus, only for some ethnic groups, (factors related to) abdominal obesity explain almost all of the high metabolic risk. It is unclear why this is the case only for some ethnic groups. Guidelines promote several strategies to reduce the prevalence of obesity at both population and individual level.33,34 In multi-ethnic settings, it may be possible to increase the effectiveness of these strategies by adapting these strategies to the specific ethnic groups and, possibly, initiating such preventive interventions from a younger age than among the ethnic majority group.3,35 This may not only be beneficial due to ethnic differences in the prevalence and determinants of obesity, but also because the preferred interventions to reduce weight may differ between ethnic groups (e.g. for cultural reasons) and these interventions may differ in the effectiveness for reduction of metabolic risk.36 Conclusions Obesity contributes substantially to cardiovascular risk across ethnic groups in the Netherlands. Reducing the prevalence of obesity, in particular abdominal obesity, could potentially reduce both the risk of CVD in all populations and may affect ethnic disparities in cardiovascular risk. Supplementary data Supplementary data are available at EURPUB online. Funding The HELIUS study is conducted by the Academic Medical Center Amsterdam and the Public Health Service of Amsterdam. Both organizations provided core support for HELIUS. The HELIUS study is also funded by the Dutch Heart Foundation, the Netherlands Organization for Health Research and Development (ZonMw), the European Union (FP-7) and the European Fund for the Integration of non-EU immigrants (EIF). The authors are most grateful to the participants of the HELIUS study and the management team, research nurses, interviewers, research assistants and other staff who have taken part in gathering the data of this study. Conflicts of interest: None declared. Key points High metabolic risk is particularly common among Turkish and South-Asian Surinamese. Obesity contributes to high metabolic risk among all ethnic groups. This contribution is much higher for abdominal obesity than general obesity. This contribution is particularly high among Turkish and Moroccans. Reducing obesity may affect ethnic disparities in metabolic risk. References 1 van Oeffelen AA , Agyemang C , Stronks K , et al. Incidence of first acute myocardial infarction over time specific for age, sex, and country of birth . Neth J Med 2014 ; 72 : 20 – 7 . Google Scholar PubMed WorldCat 2 Agyemang C , van Oeffelen AA , Norredam M , et al. Ethnic disparities in ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage incidence in the Netherlands . Stroke 2014 ; 45 : 3236 – 42 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Snijder MB , Agyemang C , Peters RJ , et al. Case finding and medical treatment of type 2 diabetes among different ethnic minority groups: the HELIUS study . J Diabetes Res 2017 ; 2017 : 1 . Google Scholar Crossref Search ADS WorldCat 4 Smith U . Abdominal obesity: a marker of ectopic fat accumulation . J Clin Invest 2015 ; 125 : 1790 – 2 . Google Scholar Crossref Search ADS PubMed WorldCat 5 Kachur S , Lavie CJ , de Schutter A , et al. Obesity and cardiovascular diseases . Minerva Med 2017 ; 108 : 212 – 28 . Google Scholar PubMed WorldCat 6 Saltiel AR , Olefsky JM . Inflammatory mechanisms linking obesity and metabolic disease . J Clin Invest 2017 ; 127 : 1 – 4 . Google Scholar Crossref Search ADS PubMed WorldCat 7 Van Gaal LF , Mertens IL , De Block CE . Mechanisms linking obesity with cardiovascular disease . Nature 2006 ; 444 : 875 – 80 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Jiang Y , Chen Y , Mao Y , et al. The contribution of excess weight to prevalent diabetes in Canadian adults . 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Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis . Obes Rev 2012 ; 13 : 275 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat 13 Cheong KC , Ghazali SM , Hock LK , et al. The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: variations by age, sex and race . Diabetes Metab Syndr 2015 ; 9 : 74 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 14 Modesti PA , Bianchi S , Borghi C , et al. Cardiovascular health in migrants: current status and issues for prevention. A collaborative multidisciplinary task force report . J Cardiovasc Med (Hagerstown) 2014 ; 15 : 683 – 92 . Google Scholar Crossref Search ADS PubMed WorldCat 15 Ntuk UE , Gill JM , Mackay DF , et al. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490, 288 UK biobank participants . Diabetes Care 2014 ; 37 : 2500 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 16 Snijder MB , Galenkamp H , Prins M , et al. Cohort profile: the healthy life in an urban setting (HELIUS) study in Amsterdam, The Netherlands . BMJ Open 2017 ; 7 : e017873. Google Scholar Crossref Search ADS PubMed WorldCat 17 Stronks K , Kulu-Glasgow I , Agyemang C . The utility of ‘country of birth’ for the classification of ethnic groups in health research: the Dutch experience . Ethn Health 2009 ; 14 : 255 – 69 . Google Scholar Crossref Search ADS PubMed WorldCat 18 Cardiovasculair risicomanagement (Tweede herziening), 2012 (1 January 2016, date last accessed). 19 Grundy SM , Brewer HB Jr , Cleeman JI , et al. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition . Arterioscler Thromb Vasc Biol 2004 ; 24 : e13 – 18 . Google Scholar PubMed WorldCat 20 Rockhill B , Newman B , Weinberg C . Use and misuse of population attributable fractions . Am J Public Health 1998 ; 88 : 15 – 19 . Google Scholar Crossref Search ADS PubMed WorldCat 21 Landsberg L , Aronne LJ , Beilin LJ , et al. Obesity-related hypertension: pathogenesis, cardiovascular risk, and treatment–a position paper of the Obesity Society and The American Society of Hypertension . Obesity (Silver Spring) 2013 ; 21 : 8 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 22 Langley-Evans SC . Nutrition in early life and the programming of adult disease: a review . J Hum Nutr Diet 2015 ; 28(Suppl 1) : 1 – 14 . Google Scholar Crossref Search ADS PubMed WorldCat 23 O’Neill S , O’Driscoll L . Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies . Obes Rev 2015 ; 16 : 1 – 12 . Google Scholar Crossref Search ADS PubMed WorldCat 24 Seabolt LA , Welch EB , Silver HJ . Imaging methods for analyzing body composition in human obesity and cardiometabolic disease . Ann N Y Acad Sci 2015 ; 1353 : 41 – 59 . Google Scholar Crossref Search ADS PubMed WorldCat 25 Jackson CL , Wang NY , Yeh HC , et al. Body-mass index and mortality risk in U.S. blacks compared to whites . Obesity (Silver Spring) 2014 ; 22 : 842 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat 26 Taylor HA Jr , Coady SA , Levy D , et al. Relationships of BMI to cardiovascular risk factors differ by ethnicity . Obesity (Silver Spring) 2010 ; 18 : 1638 – 45 . Google Scholar Crossref Search ADS PubMed WorldCat 27 Kohli S , Sniderman AD , Tchernof A , et al. Ethnic-specific differences in abdominal subcutaneous adipose tissue compartments . Obesity (Silver Spring) 2010 ; 18 : 2177 – 83 . Google Scholar Crossref Search ADS PubMed WorldCat 28 Low S , Chin MC , Ma S , et al. Rationale for redefining obesity in Asians . Ann Acad Med Singapore 2009 ; 38 : 66 – 9 . Google Scholar PubMed WorldCat 29 Consultation WHOE . Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies . Lancet 2004 ; 363 : 157 – 63 . Crossref Search ADS PubMed WorldCat 30 Perini W , Snijder MB , Peters RJG , et al. Ethnic disparities in estimated cardiovascular disease risk in Amsterdam, the Netherlands: the HELIUS study . Neth Heart J 2018 ; 26 : 252 – 62 . Google Scholar Crossref Search ADS PubMed WorldCat 31 Cameron AJ , Dunstan DW , Owen N , et al. Health and mortality consequences of abdominal obesity: evidence from the AusDiab study . Med J Aust 2009 ; 191 : 202 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 32 Xue H , Wang C , Li Y , et al. Incidence of type 2 diabetes and number of events attributable to abdominal obesity in China: a cohort study . J Diabetes 2016 ; 8 : 190 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 33 Piepoli MF , Hoes AW , Agewall S , et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR) . Eur Heart J 2016 ; 37 : 2315 – 81 . Google Scholar Crossref Search ADS PubMed WorldCat 34 Jensen MD , Ryan DH , Apovian CM , et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American college of cardiology/American heart association task force on practice guidelines and the obesity society . J Am Coll Cardiol 2014 ; 63 : 2985 – 3023 . Google Scholar Crossref Search ADS PubMed WorldCat 35 Perini W , Snijder MB , Agyemang C , et al. Eligibility for cardiovascular risk screening among different ethnic groups: the HELIUS study . Eur J Prev Cardiol 2019 ; 2047487319866284 . WorldCat 36 Church TS , Blair SN , Cocreham S , et al. Effects of aerobic and resistance training on hemoglobin A1c levels in patients with type 2 diabetes: a randomized controlled trial . JAMA 2010 ; 304 : 2253 – 62 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2020. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

The contribution of obesity to the population burden of high metabolic cardiovascular risk among different ethnic groups. The HELIUS study

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Oxford University Press
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© The Author(s) 2020. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
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1101-1262
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1464-360X
DOI
10.1093/eurpub/ckz190
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Abstract

Abstract Background The burden of cardiovascular risk is distributed unequally between ethnic groups. It is uncertain to what extent this is attributable to ethnic differences in general and abdominal obesity. Therefore, we studied the contribution of general and abdominal obesity to metabolic cardiovascular risk among different ethnic groups. Methods We used data of 21 411 participants of Dutch, South-Asian Surinamese, African-Surinamese, Ghanaian, Turkish or Moroccan origin in Healthy Life in an Urban Setting (Amsterdam, the Netherlands). Obesity was defined using body-mass-index (general) or waist-to-height-ratio (abdominal). High metabolic risk was defined as having at least two of the following: triglycerides ≥1.7 mmol/l, fasting glucose ≥5.6 mmol/l, blood pressure ≥130 mmHg systolic and/or ≥85 mmHg diastolic and high-density lipoprotein cholesterol <1.03 mmol/l (men) or <1.29 mmol/l (women). Results Among ethnic minority men, age-adjusted prevalence rates of high metabolic risk ranged from 32 to 59% vs. 33% among Dutch men. Contributions of general obesity to high metabolic risk ranged from 7.1 to 17.8%, vs. 10.1% among Dutch men, whereas contributions of abdominal obesity ranged from 52.1 to 92.3%, vs. 53.9% among Dutch men. Among ethnic minority women, age-adjusted prevalence rates of high metabolic risk ranged from 24 to 35% vs. 12% among Dutch women. Contributions of general obesity ranged from 14.6 to 41.8%, vs. 20% among Dutch women, whereas contributions of abdominal obesity ranged from 68.0 to 92.8%, vs. 72.1% among Dutch women. Conclusions Obesity, especially abdominal obesity, contributes significantly to the prevalence of high metabolic cardiovascular risk. Results suggest that this contribution varies substantially between ethnic groups, which helps explain ethnic differences in cardiovascular risk. Introduction Type-2 diabetes (T2D) and cardiovascular disease (CVD) often affect ethnic minority groups more than European host populations.1–3 For example the age-adjusted odds of T2D was 3–12 times higher among ethnic minority groups in the Netherlands relative to the Dutch.3 Similarly, some ethnic minority groups have shown a particularly high incidence of CVD relative to majority population.1,2 Studies suggest that obesity contributes substantially to the high prevalence of CVD and T2D globally via several mechanisms [e.g. by alterations in lipids, blood pressure (BP) and inflammation].4–7 Estimates of this contribution have varied widely.8–10 For example, studies have estimated that 3–83% of the prevalence of diabetes, and 7–44% of CVD can be attributed to obesity when obesity is defined by body-mass-index (BMI).8–11 Moreover, previous work has suggested that abdominal obesity may be a stronger risk factor for CVD and T2D than general obesity, and may therefore contribute even more to CVD and T2D.12,13 High rates of general and abdominal obesity are found in ethnic minority groups.3,14 Thus, part of the ethnic disparities in the prevalence of CVD and T2D may be attributable to ethnic disparities in obesity. However, the magnitude of the contribution of obesity to cardiovascular risk and, to a lesser extent T2D, is currently unknown among various ethnic minority groups, especially for abdominal obesity. This uncertainty stems from the fact that this contribution does not only depend on ethnic disparities in the prevalence of obesity, but also on disparities in the association between obesity and metabolic risk.15 The aim of this study is to provide more insight in the contribution of obesity to cardiovascular and T2D risk. To this end, we used data from the Healthy Life in an Urban Setting (HELIUS) study among participants of several ethnic groups to estimate the contribution of general and abdominal obesity to metabolic risk factors associated with CVD and T2D. Methods The HELIUS study is a large-scale, multi-ethnic cohort study on health and health care utilization among different ethnic groups living in Amsterdam, the Netherlands. The aims and design of the HELIUS study have been published.16 Briefly, potential participants aged 18–70 years living in Amsterdam were randomly sampled via the municipality register, stratified for ethnicity. Baseline data were obtained in 2011–15 via questionnaires and physical examination. A total of 90 019 subjects were invited. Approximately 55% responded either by response card or after a home visit by an ethnically matched interviewer. Of those, 24 789 agreed to participate, resulting in response rate of 28% (ranging from 21% among Moroccans to 35% among Ghanaians). The study protocols were approved by the AMC Ethical Review Board, and all participants provided written informed consent. Ethnicity Participants’ ethnicity was defined according to the country of birth of the participant as well as that of his/her parents [for a full discussion of the concept of ethnicity in the Netherlands (and this study), see Stronks et al.17]. Specifically, a participant was considered as of non-Dutch ethnic if he/she fulfilled either of the following criteria: (1) he/she was born abroad and has at least one parent born abroad (first generation); or (2) he/she was born in the Netherlands but both his/her parents were born abroad (second generation). Of the Surinamese immigrants in the Netherlands, approximately 80% are either African origin or South-Asian origin. Surinamese subgroups were classified according to self-reported ethnic origin. For the Dutch sample, we invited people who were born in the Netherlands and whose parents were both born in the Netherlands. Anthropometric measures Weight was measured in light clothing using a Seca 877 digital scale to the nearest 0.1 kg. Height was measured without shoes using a portable stadiometer (Seca 217) to the nearest 0.1 cm in upright position. Waist circumference was measured using a flexible tape measure at the level mid-way between the lower rib margin and the iliac crest. All measures were taken in duplicate and the mean was used in the analyses. If the discrepancy between the duplicate measures differed more than 0.5 cm for height, 0.5 kg for weight or 1 cm for waist circumference, a third measurement was taken. The two measures which were most similar were used to calculate the mean. General obesity was based on BMI, calculated as weight in kilograms divided by squared height in meters and defined using WHO cut-off values (BMI ≥ 30 kg/m2) without ethnic-specific cut-off values consistent with current practice in the Netherlands.18 Abdominal obesity was based on weight-to-height ratio (WHtR), calculated as the waist circumference in centimeters divided by height in centimeters. We used the cut-off value proposed by Ashwell et al.12, namely WHtR ≥0.5, as there is no WHO cut-off value for obesity based on WHtR. WHtR was chosen as the abdominal obesity measure because, of the abdominal obesity measures, WHtR may be the most robust across ethnic groups.12 Metabolic risk For this study, we defined high metabolic risk similar to the Adult Treatment Panel III definition of metabolic syndrome [i.e. triglycerides, high-density lipoprotein (HDL) cholesterol, fasting glucose and BP, but not LDL and smoking], but without the abdominal obesity criterion.19 Thus, we defined high metabolic risk as having at least two of the following four criteria: high triglycerides (≥150 mg/dl), low HDL cholesterol (<40 mg/dl for men, <50 mg/dl for women), high fasting glucose (≥100m g/dl) and a high BP (≥130/85 mmHg). Using medication related to a criterion was considered as a fulfillment of that criterion. BP was measured using a validated automated digital BP device (WatchBP Home; Microlife AG) on the left arm in a seated position after the person had been seated for at least 5 min. BP measurements were conducted in duplicate and the average BP was used for analysis. Fasting blood samples were drawn, and lipids and glucose were determined with by enzymatic colorimetric spectrophotometry and enzymatic spectrophotometric (UV) method respectively (Roche Diagnostics, Japan). Study population Baseline data collected by both questionnaire and physical examination were available among 22 165 participants. We excluded participants with a Javanese Surinamese (n = 233), ‘other/unknown Surinamese’ (n = 267) or unknown/other ethnic background (n = 48) due to small sample sizes. Furthermore, we excluded participants with missing data regarding risk factors used to define high metabolic risk (i.e. triglycerides, HDL, glucose and/or BP, n = 177), as well as participants with missing data regarding anthropometric measures (n = 29). This resulted in a study population of 21 411 participants. Statistical analyses Ethnic groups may differ in the obesity prevalence and associated metabolic risk. To integrate both aspects, we estimated the population attributable fraction (PAF) of obesity to high metabolic risk. We first conducted Poisson regression analyses to determine the prevalence ratio of obesity to high metabolic risk in each subgroup. These analyses were adjusted for age, and were conducted separately for BMI and WHtR. Next, we estimated the PAF of obesity for high metabolic risk via an adjusted PAF algorithm; PAF = P((PR – 1)/PR) × 100, where P is the prevalence of obesity among those with high metabolic risk and PR is the prevalence ratio of obesity on high metabolic risk, adjusted for age (for a detailed discussion regarding the PAF formula, see Rockhill et al.20). We then estimated the prevalence of obesity-related metabolic risk per ethnic group, separately for general obesity and abdominal obesity. To this end, we first estimated the prevalence of high metabolic risk at the mean age of each subgroup via binary logistic regression. Next, we multiplied this prevalence estimate with the previously calculated PAF to determine the obesity-related prevalence of high metabolic risk. Results Ethnic groups differed in mean age (40.9–48.1 years in men and 39.9–46.1 years in women; table 1). In men, the prevalence of BMI-defined general obesity ranged from 10.1% among Dutch men to 28.1% among Turkish men. In women, the prevalence ranged from 10.1% among Dutch women to 44.4% Ghanaian women. The prevalence of WHtR-defined abdominal obesity was higher and ranged from 54.5% to 79.2% among men and 45.1% to 84.4% among women. The prevalence of high metabolic risk was lowest among the Dutch, especially among women. The pattern and prevalence of individual components used to define high metabolic risk also differed between ethnic groups. For a list of additional cardiovascular risk factors per ethnic group, we refer the reader to Supplementary table S1. Table 1 Characteristics (mean (SD) or percentage) of the study population, by ethnicity and sex Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Notes: Data are mean (SD) or percentages. BMI, body mass index; SD, standard deviation; WHtR, waist to height ratio; HDL, high-density lipoprotein cholesterol. Open in new tab Table 1 Characteristics (mean (SD) or percentage) of the study population, by ethnicity and sex Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Dutch South-Asian Surinamese African Surinamese Ghanaian Turkish Moroccan Men  N 2075 1362 1594 897 1618 1504  Age (years) 46.9 (13.8) 44.8 (13.6) 48.1 (12.9) 46.9 (11.5) 40.9 (12.1) 42.1 (12.7)  BMI (kg/m2) 25.2 (3.8) 25.83 (4.17) 26.28 (4.14) 26.72 (3.76) 27.85 (4.37) 26.69 (4.01)  Waist-to-height ratio 0.51 (0.07) 0.55 (0.07) 0.52 (0.07) 0.54 (0.07) 0.56 (0.07) 0.54 (0.07)  BMI obesity (%) 10.1 13.7 17.2 17.4 28.1 19.2  WHtR obesity (%) 54.5 74.7 60.6 71.5 79.2 74.2  High metabolic risk (%) 38.2 57.0 39.1 38.9 48.9 40.7   High triglycerides (%) 23.7 40.2 18.1 15.5 35.4 21.4   Low HDL cholesterol (%) 20.9 44.6 21.1 17.8 42.0 31.9   High blood pressure (%) 51.5 58.1 64.9 74.0 49.0 44.1   High glucose (%) 36.2 50.9 35.9 36.1 37.5 41.3 Women  N 2452 1663 2496 1418 1957 2375  Age (years) 45.6 (14.2) 46.1 (13.2) 47.8 (12.3) 43.4 (10.7) 39.9 (12.1) 39.4 (12.9)  BMI (kg/m2) 24.4 (4.5) 26.7 (5.3) 28.8 (5.9) 29.6 (5.3) 29.1 (6.5) 28.1 (5.8)  Waist-to-height ratio 0.50 (0.08) 0.57 (0.09) 0.57 (0.09) 0.58 (0.08) 0.58 (0.08) 0.57 (0.10)  BMI obesity (%) 10.1 23.4 37.7 44.4 40.8 35.2  WHtR obesity (%) 45.1 77.8 75.9 84.4 75.6 73.6  High metabolic risk (%) 18.6 43.1 34.1 28.9 30.5 25.6   High triglycerides (%) 11.7 25.3 12.4 7.3 19.0 11.3   Low HDL cholesterol (%) 18.6 47.1 30.9 21.8 42.5 38.7   High blood pressure (%) 28.4 46.1 57.7 62.2 30.2 24.5   High glucose (%) 16.5 34.8 27.1 23.7 20.9 24.1 Notes: Data are mean (SD) or percentages. BMI, body mass index; SD, standard deviation; WHtR, waist to height ratio; HDL, high-density lipoprotein cholesterol. Open in new tab Among men, the contribution of general obesity to high metabolic risk ranged from 7.1% among South-Asian Surinamese to 17.8% among Turkish men, vs. 10.1% among the Dutch (table 2). The contribution of abdominal obesity to high metabolic risk was higher than the contribution of general obesity ranging from 52.1% among African Surinamese to 92.3% among Moroccans, vs. 53.9% among the Dutch. Among women, we observed a more heterogeneous contribution of general obesity to high metabolic risk; among South-Asian Surinamese, African Surinamese and Ghanaian women, this contribution varied between 14.6% and 26.6% vs. 20% among the Dutch, whereas among Turkish and Moroccan women this contribution was substantially higher (41.2% and 41.8%, respectively). The contribution of abdominal obesity to high metabolic risk showed a similar pattern, varying between 68.0% and 73.9% among South-Asian Surinamese, African Surinamese and Ghanaian women vs. 72.1% among the Dutch, whereas this contribution among Turkish and Moroccan women was 82.3% and 92.8%, respectively. We then estimated the potentially achievable health gain in the population in each ethnic group if all participants were to have non-obese levels. Adjusted for age, the prevalence of high metabolic risk varied between 32% and 59% across the ethnic minority men vs. 33% among Dutch men (figure 1, upper panels). If all men were to be non-obese based on general obesity, the prevalence of high metabolic risk would be 28% to 54% among ethnic minority men vs. 30% among the Dutch. If all men were to be non-obese based on abdominal obesity, the prevalence of metabolic risk would be 3% to 27% among ethnic minority men vs. 15% among the Dutch. Figure 1 Open in new tabDownload slide Prevalence of high metabolic risk related and not related to obesity Notes: The prevalence of high metabolic risk by ethnicity and sex, adjusted for age, and split for obesity related and obesity unrelated prevalence based on general obesity (body mass index (BMI) ≥30kg/m2), or abdominal obesity (waist-to-height ratio (WHtR) ≥0.5). DU, Dutch; AS, African Surinamese; GH, Ghanaian; MO, Moroccan; TU, Turkish; SA, South-Asian Surinamese. Figure 1 Open in new tabDownload slide Prevalence of high metabolic risk related and not related to obesity Notes: The prevalence of high metabolic risk by ethnicity and sex, adjusted for age, and split for obesity related and obesity unrelated prevalence based on general obesity (body mass index (BMI) ≥30kg/m2), or abdominal obesity (waist-to-height ratio (WHtR) ≥0.5). DU, Dutch; AS, African Surinamese; GH, Ghanaian; MO, Moroccan; TU, Turkish; SA, South-Asian Surinamese. Table 2 Age adjusted estimated contribution of general obesity or abdominal obesity to high metabolic risk General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 Notes:aGeneral obesity is defined as a body mass index (BMI) ≥30 kg/m2. bAbdominal obesity is defined as a waist-to-height ratio (WHtR) ≥0.5. cPrevalence of obesity among participants with high metabolic risk. dAge-adjusted prevalence ratio (PR) and 95% confidence interval of high metabolic risk between obese and non-obese. ePopulation attributable fraction (PAF), calculated as P((PR-1)/PR) × 100. Open in new tab Table 2 Age adjusted estimated contribution of general obesity or abdominal obesity to high metabolic risk General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 General obesity (BMI)a Abdominal obesity (WHtR)b Pc PRd PAFe Pc PRd PAFe Men  Dutch 21.2 1.9 (1.6; 2.3) 10.1 82.2 2.9 (2.4; 3.5) 53.9  South-Asian Surinamese 19.9 1.6 (1.3; 1.9) 7.1 90.6 2.4 (1.9; 3.2) 53.8  African Surinamese 28.9 1.9 (1.6; 2.2) 13.5 83.8 2.6 (2.1; 3.3) 52.1  Ghanaian 28.7 1.7 (1.4; 2.2) 12.1 92.0 3.5 (2.3; 5.2) 65.4  Turkish 42.9 1.7 (1.5; 2.0) 17.8 95.1 3.8 (2.7; 5.3) 70.2  Moroccan 31.9 1.7 (1.5; 2.1) 13.5 92.3 3.0 (2.2; 4.1) 92.3 Women  Dutch 31.4 2.8 (2.3; 3.4) 20.0 88.1 5.5 (4.1; 7.4) 72.1  South-Asian Surinamese 37.1 1.7 (1.4; 1.9) 14.6 96.1 4.3 (2.9; 6.4) 73.9  African Surinamese 57.9 1.8 (1.6; 2.1) 26.6 94.5 3.6 (2.6; 4.8) 68.0  Ghanaian 62.1 1.6 (1.3; 2.0) 23.8 97.6 3.9 (2.0; 7.3) 72.3  Turkish 71.3 2.4 (2.0; 2.9) 41.2 97.3 6.5 (3.9; 10.8) 82.3  Moroccan 69.7 2.5 (2.1; 3.0) 41.8 99.0 16.0 (7.1; 36.0) 92.8 Notes:aGeneral obesity is defined as a body mass index (BMI) ≥30 kg/m2. bAbdominal obesity is defined as a waist-to-height ratio (WHtR) ≥0.5. cPrevalence of obesity among participants with high metabolic risk. dAge-adjusted prevalence ratio (PR) and 95% confidence interval of high metabolic risk between obese and non-obese. ePopulation attributable fraction (PAF), calculated as P((PR-1)/PR) × 100. Open in new tab Among ethnic minority women, the age-adjusted prevalence of high metabolic risk varied between 24% and 35% vs. 12% among the Dutch (figure 1, lower panels). If all ethnic minority women were to be non-obese based on general obesity, the prevalence of high metabolic risk would be 16% to 30% among ethnic minority women vs. 10% among the Dutch. If all ethnic minority women were to be non-obese based on abdominal obesity, the prevalence of metabolic risk would be 2% to 9% vs. 3% among the Dutch). Discussion Key findings Obesity, especially abdominal obesity, contributes substantially to the prevalence of high metabolic risk. Our results suggest that this contribution is generally higher among ethnic minority groups than among the Dutch majority population. Hence, reducing the prevalence of obesity, particularly abdominal obesity, may reduce the prevalence of high metabolic risk among all ethnic groups and reduce some of the metabolic risk differences between ethnic minority groups and the Dutch. Evaluation of potential limitations As with all cohort studies, some selection bias may have occurred due to non-response. The data that was available among non-responders showed only small SES and agree differences between responders and non-responders.12 Although SES and age are known to be related to metabolic health, this non-response data did not include measures regarding CVD risk or adiposity. So selection bias is less likely, but we cannot truly evaluate whether selection bias occurred and, if so, how this bias has affected our results. Due to the cross-sectional design, causal inferences regarding obesity and high metabolic risk should be made with caution. Although it is widely assumed that a causal relation between fat accumulation and metabolic disease exists, a high occurrence of metabolic risk factors may also affect susceptibility for weight gain and obesity.21,22 If so, this may have led to an overestimation of the contribution of obesity to the prevalence of high metabolic risk, and the potential health gain related to weight loss. We used measures of prevalent cardiovascular risk, based on components of the metabolic syndrome, as a proxy for overall cardiovascular risk. Although high metabolic risk can be considered an inferior outcome measure, the association between components of metabolic syndrome and CVD has been well established.23 Nevertheless, this association may differ between ethnic groups, for example due to ethnic disparities in the age-of-onset of these risk factors.3 Thus, it would be of value to determine, in future studies, how the contribution of obesity to metabolic and CVD disease incidence varies between ethnic groups. Obesity was measured using anthropometric measures. More sophisticated measures to determine adiposity mass and distribution (e.g. Dual-Energy X-Ray Absorptiometry) would be preferable because these measures may be more accurate and may better reflect ethnic variations in fat-distributions.24 However, these measures are impractical for both large cohort studies and daily clinical practice. In order to estimate the population contribution of obesity to high metabolic risk, we did not exclude participants with prior CVD. However, our results are similar after exclusion of participants with prior CVD, suggesting that our results are also applicable to a strictly primary prevention setting (Supplementary table S2). Discussion of key findings Although the contribution of general obesity to high metabolic risk was similar between most ethnic groups, the basis of these contributions did differ, with the Dutch showing a relatively low prevalence, but relatively strong association between general obesity and high metabolic risk, whereas the ethnic minority groups showed a weaker association and a higher prevalence of general obesity. This is in accordance with previous studies which also reported higher prevalence rates of general obesity, but weaker associations between general obesity and cardiovascular metabolic disease among ethnic minority groups relative to ethnic majority groups.25,26 Our results suggest that, despite a similar contribution of general obesity to high metabolic risk, a similar absolute reduction in the prevalence of general obesity may not result in a similarly strong reduction of metabolic risk among all ethnic minority groups. Thus, among ethnic minority groups, relatively large reductions in the prevalence of obesity prevalence may be necessary to reduce disparities in high metabolic risk. Due to ethnic differences in fat accumulation, distribution and in the associations between obesity and disease, it has been suggested to apply different obesity thresholds for different ethnic groups, especially for BMI.15,27–29 Controlling for these differences by applying lower, ethnic-specific BMI cut-off values did not strongly affect our results regarding ethnic disparities in the contribution of general obesity to high metabolic risk (results not shown), suggesting that ethnic disparities in fat distribution do not contribute substantially to our results. In our study, general obesity contributed between 7.1% and 26.6% to high metabolic risk for most ethnic groups. This contribution is similar to contributions found in earlier studies among the general population in several countries.8–10 For example, a study from Australia among men and women from the general population found that BMI-defined obesity contributed to 15.7% of all cases of hypertension, 32.4% of all cases of diabetes and 18.8% of all cases of dyslipidemia.10 Turkish and Moroccan women showed a much stronger contribution of general obesity to high metabolic risk (41.1% and 41.8%, respectively) than women in the other ethnic groups. Earlier studies already found a particularly high prevalence of dyslipidemia among Turkish and Moroccans relative to other ethnic groups.30 Our results suggest that obesity contributes substantially to this relatively high prevalence of dyslipidemia and overall metabolic risk. For WHtR-defined obesity, we found that contributions to high metabolic risk varied between 52.1% and 92.8%. Earlier studies have reported lower contributions of abdominal obesity to the prevalence of cardiovascular risk at population level.31,32 For example, an Australian study on type 2 diabetes, low HDL, increased triglycerides and hypertension found that abdominal obesity contributed to 17–38% of these risk factors among men and 30–47% among women.31 Our reported contributions were higher, in part because our participants were from a different ethnic group with a higher prevalence of abdominal obesity. However, this does not explain the higher contribution among the Dutch. Alternatively, these differences may be related to the use of waist circumference to define abdominal obesity in the previous study, as waist circumference may be associated less strongly with metabolic risk factors relative to WHtR.12 Among some ethnic groups (e.g. Dutch, South-Asian Surinamese), the contribution of abdominal obesity to high metabolic risk varied between 50% and 80% whereas among the Moroccan ethnic group (and, to a lesser extent, Turkish women) this contribution was substantially higher. Thus, only for some ethnic groups, (factors related to) abdominal obesity explain almost all of the high metabolic risk. It is unclear why this is the case only for some ethnic groups. Guidelines promote several strategies to reduce the prevalence of obesity at both population and individual level.33,34 In multi-ethnic settings, it may be possible to increase the effectiveness of these strategies by adapting these strategies to the specific ethnic groups and, possibly, initiating such preventive interventions from a younger age than among the ethnic majority group.3,35 This may not only be beneficial due to ethnic differences in the prevalence and determinants of obesity, but also because the preferred interventions to reduce weight may differ between ethnic groups (e.g. for cultural reasons) and these interventions may differ in the effectiveness for reduction of metabolic risk.36 Conclusions Obesity contributes substantially to cardiovascular risk across ethnic groups in the Netherlands. Reducing the prevalence of obesity, in particular abdominal obesity, could potentially reduce both the risk of CVD in all populations and may affect ethnic disparities in cardiovascular risk. Supplementary data Supplementary data are available at EURPUB online. Funding The HELIUS study is conducted by the Academic Medical Center Amsterdam and the Public Health Service of Amsterdam. Both organizations provided core support for HELIUS. The HELIUS study is also funded by the Dutch Heart Foundation, the Netherlands Organization for Health Research and Development (ZonMw), the European Union (FP-7) and the European Fund for the Integration of non-EU immigrants (EIF). The authors are most grateful to the participants of the HELIUS study and the management team, research nurses, interviewers, research assistants and other staff who have taken part in gathering the data of this study. Conflicts of interest: None declared. Key points High metabolic risk is particularly common among Turkish and South-Asian Surinamese. Obesity contributes to high metabolic risk among all ethnic groups. This contribution is much higher for abdominal obesity than general obesity. This contribution is particularly high among Turkish and Moroccans. Reducing obesity may affect ethnic disparities in metabolic risk. References 1 van Oeffelen AA , Agyemang C , Stronks K , et al. Incidence of first acute myocardial infarction over time specific for age, sex, and country of birth . Neth J Med 2014 ; 72 : 20 – 7 . Google Scholar PubMed WorldCat 2 Agyemang C , van Oeffelen AA , Norredam M , et al. Ethnic disparities in ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage incidence in the Netherlands . Stroke 2014 ; 45 : 3236 – 42 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Snijder MB , Agyemang C , Peters RJ , et al. 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Journal

The European Journal of Public HealthOxford University Press

Published: Aug 13, 18

References

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