Study design and study population

Details of the RODAM study including the recruitment and sample size estimations have previously been described.^{Reference Agyemang, Beune and Meeks30} In summary, this multicenter cross-sectional study was conducted among Ghanaian adults in rural Ghana, urban Ghana, and Europe (Amsterdam, London, and Berlin) between July 2012 and September 2015 (n = 6,385). For recruitment, in Ghana, census data of 2010 were used to draw rural and urban participants in the Ashanti Region. In Amsterdam, the Municipal Register was used to randomly select Ghanaian migrants who have been invited to be part of the study by postal mail and home visits. In London and Berlin, Ghanaian organizations, church communities, and social unions served as the sampling frame for recruitment. The response rates were 76% in rural Ghana and 74% in urban Ghana. In Amsterdam, 67% replied by response card or after a home visit. Of these, 53% agreed and participated in the study. In London, of those individuals who were invited based on their registration in Ghanaian organizations, 75% agreed and participated in the study. In Berlin, this figure was 68%.

All Ghanaian-born RODAM study participants aged 40 to 70 years, without a history of clinical ASCVD and complete information on leg length and sitting height, were included in the current analysis. Study participants with missing data (systolic blood pressure n = 7; total cholesterol n = 137; low-density lipoprotein [LDL] cholesterol n = 139; high-density lipoprotein [HDL] cholesterol n = 138; body mass index [BMI] n = 2; smoking n = 128; hip circumference n = 9; and waist circumference n = 9) were excluded. This resulted in a final sample size of 3250 (Fig. 1).

Fig. 1. Selection of study participants.

Measurements

Trained study personnel performed all measurements with validated devices according to standardized operating procedures across all study sites. Fasting venous blood samples were collected, manually processed, and immediately aliquoted, and then temporarily stored at the local research location at −20°C. The samples were then transported to the respective local laboratories for registration and storage at −80°C and were subsequently transported according to standardized procedures, to Berlin (Germany) for biochemical analysis to avoid intralaboratory variability. Total cholesterol, HDL cholesterol, and LDL cholesterol were determined using the ABX Pentra 400 chemistry analyzer (HORIBA ABX, Montpellier, France). Type 2 diabetes was defined as fasting glucose ≥7.0 mmol/l or reported current use of medication prescribed to treat diabetes or self-reported diabetes.³¹ Blood pressure (BP) was measured three times using a validated semiautomated device (The Microlife WatchBP home) with appropriate cuffs in a sitting position after at least 5-min rest. The mean of the last two measurements was used in the analyses. All anthropometric measurements were performed twice by the same examiner and the mean of the two measurements was used for analyses. Weight was measured twice in light clothing and without shoes with SECA 877 scales to the nearest 0.1 kg. Height was also measured twice without shoes with a portable stadiometer (SECA 217) to the nearest 0.1 cm. Waist circumference (cm) was measured at the midpoint between the lower rib and the upper margin of the iliac crest using a measuring tape. Hip circumference was taken around the widest portion of the buttocks using a measuring tape.³² BMI was calculated as weight in kilograms divided by height in meters squared. Overweight and obesity were defined as BMI ≥25 to <30 kg/m² and ≥30 kg/m², respectively.³² For sitting height, measurement was taken from the floor to the top of the participant’s head, with the participant sitting upright on the base plate on a flat seat, and with the head in the Frankfort plane position, feet on the floor, and with the thighs unsupported. Sitting height was calculated as the measurement – seat height. Leg length (cm) was calculated as standing height – sitting height.^{Reference Bogin and Varela-Silva24} Relative leg length was estimated as (leg length/height) * 100.

Questionnaire-based interviews were conducted by a trained research assistant or self-administration of a paper questionnaire or digital online version depending on the preference of the participant.^{Reference Agyemang, Beune and Meeks30} Sociodemographic information included age, sex, and educational level. The educational level of the participants and their parents was recorded according to the following categories: never been to school or elementary school, lower vocational schooling or lower secondary schooling, intermediate vocational schooling or intermediate/higher secondary schooling, and higher vocational schooling or university. The use of antihypertensive medication was assessed based on a “Yes” or “No” response to the question “Do you use any antihypertensive medication, including combinations?”. Smoking status was assessed based on either a “Yes,” “No, but I used to smoke” or “No, I’ve never smoked” response to the question “Do you smoke at all?”. Age of migration was categorized as according to <13 years (representing the mean age at menarche among Ghanaian adolescents^{Reference Gumanga and Kwame-Aryee33}), 13–22 years (the maximum age at which most people achieve full growth^{Reference Cameron and Bogin34}) and ≥22 years.

Estimated 10-year CVD risk

The outcome variable was predicted 10-year ASCVD risk, estimated from the Pooled Cohort Equations (PCE) for African–American men and women.^{Reference Goff, Lloyd-Jones and Bennett29} The equations are derived, using pooled data from ethnically and geographically diverse community-based cohorts, permitting the creation of sex- and ethnic-specific equations for non-Hispanic White American and Black women and men.^{Reference Goff, Lloyd-Jones and Bennett29} The model combines age, sex, total cholesterol, HDL cholesterol, systolic blood pressure, use of antihypertensive medication, diagnosed with diabetes, and smoking to estimate the 10-year absolute risk of ASCVD in people without pre-existing ASCVD.^{Reference Goff, Lloyd-Jones and Bennett29} In their updated clinical practice guidelines for the treatment of blood cholesterol to reduce aASCVD, the American College of Cardiology and American Heart Association recommended the PCE as a novel tool to estimate 10-year ASCVD risk.^{Reference Stone, Robinson and Lichtenstein35} The guidelines provide a strong recommendation (Class I, Level of Evidence: A) for consideration of statin treatment in individuals with a 10-year ASCVD risk ≥ 7.5% and a moderate recommendation (Class IIa, Level of Evidence: B) in individuals with a 10-year ASCVD risk of 5% to <7.5%. CVD risk ≥ 7.5% was considered as an “elevated” risk of CVD based on the prior work by Goff et al.^{Reference Goff, Lloyd-Jones and Bennett29} The distribution of estimated 10-year ASCVD risk between Ghanaian migrant and home populations has been shown in previous analyses.^{Reference Boateng, Agyemang and Beune5, Reference Boateng, Agyemang and Beune36}

Current guidelines for CVD risk reduction have emphasized the need to assess all risk factors as a more effective basis to deliver CVD prevention interventions.^{Reference Cooney, Dudina, D’Agostino and Graham37} Absolute CVD risk estimates, using established risk algorithms, help to determine the probability of a cardiovascular event occurring within a specified time horizon. Most established CVD risk algorithms do not, however, address appropriately the ethnic and socioeconomic differences relating to CVDs.^{Reference Siontis, Tzoulaki, Siontis and Ioannidis38, Reference Kengne, Patel and Colagiuri39} Contextual differences in risk factors among various ethnic groups contribute significantly to differences in CVD,^{Reference Dalton, Bottle, Soljak, Majeed and Millett40} making ethnicity an important parameter when estimating CVD risk. The PCE, developed and validated among African American and European and Asian men and women, has been shown to be comparatively accurate in estimating CVD risk among ethnic and minority populations.^{Reference Muntner, Colantonio and Cushman41}

Statistical analysis

General characteristics, CVD risk factors, and anthropometric characteristics of the study sample are summarized as percentages for categorical variables, mean ± standard deviation (SD) for normally distributed continuous variables, and as median (interquartile range) for non-normally distributed variables. We estimated the mean or prevalence of sociodemographic characteristics and CVD risk factors by sex-specific quintiles of absolute and relative leg lengths and sitting height. Respective P-values for trend were assessed using Pearson χ ² for proportions and Pearson correlations for continuous variables. Prevalence ratios (PR) and 95% confidence intervals (95% CI) were calculated by Poisson regression with robust variance^{Reference Barros and Hirakata42} to assess the associations between the anthropometric markers of early-life exposures (per quintile) and estimated high 10-year risk of ASCVD. In addition, the associations with estimated high 10-year ASCVD risk were calculated per 1 SD increase of the sitting height and leg length. Adjusted regression models were constructed by sex. Early-life exposures might have different effects in male and female biology, and the influence of possible confounders such as age at menarche might also be different for men and women.^{Reference Störmer43, Reference Vafeiadi, Myridakis and Roumeliotaki44} Male and female puberty might differ in terms of behavior, hormonal factors, and changes in body composition. Both men and women put in fat mass. But while women tend to gain more in adiposity, men tend to gain more in muscles and fat free mass. These might create differentials in risks for CVDs.^{Reference Peer, Bradshaw, Laubscher, Steyn and Steyn45–Reference Mongraw-Chaffin, Anderson and Allison47} Model 1 was adjusted for study site (categorical, reference=rural Ghana) and adult and parental education (categorical, reference=no education). For women, model 2 adjusted further for age at menarche (continuous) and model 3 (which is model 2 for men) additionally adjusted for BMI (continuous) and waist and hip circumference (continuous). For leg length and sitting height, a final model (models 3 and 4 for men and women, respectively) was constructed to account for total height (continuous). Variables included in the CVD risk score (age, smoking, total cholesterol, HDL cholesterol, and use of antihypertensives) were not adjusted for to avoid overadjusting. The analyses were further stratified by current residence or study site to explore the contextual differences in early-life exposures and their association with CVD risk. For all statistical tests, a P-value of <0.05 was considered statistically significant. Data were analyzed with STATA 13 (StataCorp LLC).⁴⁸