Cohort Profile: The Singapore Multi-Ethnic Cohort (MEC) study

Non-communicable diseases such as type 2 diabetes (T2D) mellitus, coronary artery disease, stroke and cancers, are major contributors to ill health across the world including Asia. These conditions are multi-factorial in origin, often involving complex gene-environment interactions. Singapore is a multi-ethnic island state and provides a useful model to evaluate determinants of the development of chronic diseases in Asian ethnic groups. Three major Asian ethnic groups are represented in Singapore: Chinese, Malays and Indians. The Singapore Multi-Ethnic Cohort (MEC) allows us to better understand how genes and lifestyle may influence health and diseases differently in persons of Chinese, Malay and Indian ethnicity. As these ethnic groups reside in the same Singapore setting, confounding of ethnic differences by differences between countries is avoided. Through the MEC, we hope to improve preventive and therapeutic measures, as well as provide information to advance public health and health education policies for Asian populations. The MEC is a closed cohort which included a total of 14 465 male and female adults. The cohort was formed by combining two existing population-based studies with measurements in the period 2004 to 2007, the Singapore Prospective Study Program (SP2) and the Singapore Cardiovascular Cohort Study (SCCS2), with additional recruitment of participants from 2007 to 2010.1–3 The combined cohort has a good representation of Chinese, Malay and Indian ethnic groups. The SP2 and SCCS2 recruited 8340 participants from four previous cross-sectional studies: Thyroid and Heart Study 1982–84,4 National Health Survey 1992,5 National University of Singapore Heart Study 1993–956 and National Health Survey 1998.7 All studies involved a random sample of Singapore residents aged 21 years and above, with disproportionate sampling stratified by ethnicity to increase the numbers for ethnic minorities, i.e. Malays and Indians. In addition to the participants from SP2 and SCCS2, a further 6125 Singapore residents were recruited into the MEC study through public outreach and referrals from existing cohort members. Invitation to participate was open to any Singapore citizens or long-term residents of age 21 to 75 years. People with a history of heart disease, stroke, cancer and renal failure were excluded at baseline, as these are the outcomes of interest in this prospective cohort for non-communicable diseases. Recruitment drives were carried out at community events, mosques and temples in addition to household visitation to enrich the proportion of Malays and Indians. Ethics approval for the SP2 was provided by the SingHealth Centralised Institutional Review Board (IRB). The rest of the MEC and the follow-up were approved by the National University of Singapore IRB. Written consent was obtained for registry and medical records linkage, future analysis of stored biological samples and future follow-up. All MEC participants were visited at home to complete an interview, and were subsequently invited to undergo a health screening for clinical assessments and collection of blood and urine samples to be stored for future analysis. Of the 14 465 participants of the MEC study, 77% (N = 11 085) completed both the interviews and health screening, whereas the remaining 23% (N = 3380) only completed the interview-administered questionnaire. The sociodemographic and socioeconomic profiles of participants are presented in Table 1. The age of the participants ranged from 21 to 94 years and the mean age was 46 ± 13 years; 56% of the participants were females, and the ethnic composition was 47% Chinese, 26% Malay and 27% Indian. Most of the participants were married (75%). In terms of highest qualification attained, 30% of the participants received primary or lower education, 35% received secondary education, 6% received vocational training and 29% received post-secondary or higher education. Around 89% of the participants lived in public housing and 11% lived in private housing; this is reflective of the general Singapore resident population where the majority resides in government housing. Demographic profile of participants at baseline (N = 14 465) Educational status: secondary education (‘O’/‘N’ level), vocational training (attended Institute of Technical Education or obtained National Technical Certificate) and post-secondary education (‘A’ level, polytechnic/diploma). Missings marital status (n = 9), educational status (n = 12), monthly household income (n = 3863) and housing type (n = 17). Demographic profile of participants at baseline (N = 14 465) Educational status: secondary education (‘O’/‘N’ level), vocational training (attended Institute of Technical Education or obtained National Technical Certificate) and post-secondary education (‘A’ level, polytechnic/diploma). Missings marital status (n = 9), educational status (n = 12), monthly household income (n = 3863) and housing type (n = 17). Lifestyle, anthropometric measures and biomarkers profiles of study participants, stratified by ethnicity, are presented in Table 2. There were more current smokers in the Malays (24%) than Indians (17%) and Chinese (12%). Self-reported prevalence of T2D was the highest amongst the Indians (16%), followed by Malays (10%) and Chinese (7%). Similarly, Indians had higher HbA1c (6.2 ± 1.3%) than Malays (6.0 ± 1.3%) and Chinese (5.7 ± 0.8%). Prevalence of self-reported hypertension was similar across the three ethnic groups (18–19%, P-value = 0.10), though mean systolic and diastolic blood pressures were higher in Malays and Chinese than Indians. The Chinese had the highest prevalence of self-reported hypercholesterolaemia (Chinese: 28%; Indians: 23%; Malays: 19%), but mean low-density lipoprotein cholesterol (LDL) was the highest in the Malays (Malays: 3.5 ± 0.9 mmol/l; Indians: 3.4 ± 0.9 mmol/l; Chinese: 3.2 ± 0.8 mmol/l). Lifestyle, anthropometric measures, and biomarkers profiles of study participants at baseline by ethnicity (N = 14 424) Participants were asked if they had been diagnosed with T2D, hypertension or high cholesterol by Western doctors. MET, Metabolic Equivalent of Task. High waist circumference was defined as > 90 cm for men and > 80 cm for women. Missings smoking status (n = 1), T2D (n = 250), hypertension (n = 269), hyperlipidaemia (n = 813), physical activity (n = 1), macronutrients (n = 264). BMI (n = 3389), waist circumference (n = 3393), blood pressure (n = 3379), triglycerides (n = 3423), HDL (n = 3427), LDL (n = 3475) and HbA1c (n = 5643); most missing values for physical and biochemical variables are due to non-participation in the health screening part of the study. Lifestyle, anthropometric measures, and biomarkers profiles of study participants at baseline by ethnicity (N = 14 424) Participants were asked if they had been diagnosed with T2D, hypertension or high cholesterol by Western doctors. MET, Metabolic Equivalent of Task. High waist circumference was defined as > 90 cm for men and > 80 cm for women. Missings smoking status (n = 1), T2D (n = 250), hypertension (n = 269), hyperlipidaemia (n = 813), physical activity (n = 1), macronutrients (n = 264). BMI (n = 3389), waist circumference (n = 3393), blood pressure (n = 3379), triglycerides (n = 3423), HDL (n = 3427), LDL (n = 3475) and HbA1c (n = 5643); most missing values for physical and biochemical variables are due to non-participation in the health screening part of the study. The prevalence of obesity, defined as ≥ 27.5 kg/m2,8 was the highest in Malays (39%), slightly lower in the Indians (35%) and much lower in Chinese (12%). Indians had the highest prevalence of high waist circumference (62%), defined as > 90 cm for men and > 80 cm for women,9 whereas 55% of Malays and 32% of Chinese had high waist circumference. Metabolic equivalents (METs) were obtained from the 2011 Compendium of Physical Activity.10 The median (interquartile range, IQR) weekly leisure-time activity level of moderate and/or vigorous intensity was the highest amongst the Chinese [6 (0–16) MET-h/week], compared with the Malays [5 (0–16) MET-h/week] and Indians [4 (0–14) MET-h/week]. On average, Chinese had a slightly higher protein intake as percentage of total energy intake and lower intake of total fat and carbohydrate than Malays and Indians. Differences between the three ethnic groups were assessed using chi square testing for categorical variables. Comparison of means was done using analysis of variance testing, and comparison of medians was done using the Kruskal-Wallis rank sum test for continuous variables. As shown in Table 2, due to the large number of participants, all differences between ethnic groups had P-values less than 0.001, except hypertension status (P-value = 0.098) and average leisure-time physical activity (P-value = 0.479). Baseline recruitment was done between 2004 and 2010. The first wave of in-person follow-up with the cohort participants began in 2011 and ended in 2016. Of the baseline participants, 28% were not contactable (i.e. contact details changed and no updated information available, or frequently travelling, or access to household denied and unable to contact despite six attempts at household visitation) and 2.5% were confirmed to have been lost to follow-up (i.e. deceased, migrated, declined follow-up, lost mental competence to give consent to continue the research, institutionalized or physically unfit to participate). Of the contactable participants, 60% (N = 6112) agreed to participate in the follow-up survey. Data will also be linked to follow-up data from the Singapore National Registry of Diseases Office on chronic diseases (i.e. acute myocardial infarction, cancers, renal failure and stroke) and mortality; 86% of the baseline participants gave consent for data linkage with medical records and national registries. Table 3 summarizes information collected from the MEC study at baseline and follow-up assessments. The baseline interviews assessed sociodemographic variables, detailed lifestyle behaviours, personal and family medical history and medication use, and health-related quality of life (HRQoL). The health examination included measurements of anthropometric characteristics, peripheral neuropathy, branchial and ankle blood pressures and biomarkers in blood and urine. During the follow-up assessments, we collected information on mental health and cognitive function and conducted electrocardiogram, central blood pressure and hand grip strength measurements in addition to the baseline measurements. More details on these measurements are described below. Summary of variables collected or derived from the cohort Imaging was performed only on a sub-sample of 805 Chinese older participants of the follow-up cohort. Metabolites were measured using a targeted, high-throughput metabolic profile approach using an HPLC online electrospray ionization tandem mass spectrometry.56 Summary of variables collected or derived from the cohort Imaging was performed only on a sub-sample of 805 Chinese older participants of the follow-up cohort. Metabolites were measured using a targeted, high-throughput metabolic profile approach using an HPLC online electrospray ionization tandem mass spectrometry.56 Participants completed an interviewer-administered questionnaire at their home, which took approximately an hour. Questionnaires were available in English, Chinese and Malay. Interviewers provided additional explanation when necessary in other languages common to both the participant and the interviewer. Participants were interviewed about their sociodemographic characteristics, personal and family medical history and lifestyle behaviours such as alcohol consumption, sedentary behaviours, cigarette smoking and exposure to environmental tobacco smoke. In addition, all medications used by the participants were recorded. Dietary intake was assessed by a semi-quantitative 169-item food frequency questionnaire (FFQ) that is also used in the Singapore National Nutrition Surveys.11 The physical activity questionnaire asked about the type, frequency and duration of various activities in the transportation, occupation, leisure time and household domains. Transportation activities included walking and cycling, and occupational activities included light, moderate and vigorous occupational activities. Leisure time activities included 48 specific activities, household activities included 15 specific activities, and options for additional activities not provided in the list were recorded through open-ended questions. Based on the physical activity questionnaire12 and FFQ,11 derived variables such as weekly physical activity level (MET-h/week) and intake of macronutrients, micronutrients and daily servings by food groups were calculated. Both questionnaires were validated in the local population.11,12 Nutrient intakes estimated from the FFQ were reasonably well correlated with estimates from repeated 24-h recalls. In addition, associations between food intakes and plasma fatty acids indicated that sources of several fatty acids were captured well by the FFQ.13 For physical activity, vigorous activity agreed well with accelerometer measurements, whereas modest agreement was observed for moderate-intensity activity. Participants were given the option to fast for the health examination. For those who did, they were told to fast for 8–12 h before their appointment. Fasting status was recorded by phlebotomists who performed venepuncture to collect blood for both biochemical analyses and biobanking. Random spot urine samples, collected mid-stream, were measured semi-quantitatively for protein level and any excess was stored in the biobank. Height was measured without shoes on a portable stadiometer (SECA 200 series, Germany) with the head in the Frankfurt Plane position. Participants were instructed to remove objects from their pockets such as wallets, keys and mobile phones before being measured for weight on SECA digital scales (SECA 700 series, Germany). A stretch-resistant tape was used to measure waist circumference at the mid-point between the last rib and iliac crest, and hip circumference at the greater trochanter of the femur. Systolic and diastolic blood pressures were measured after participants rested for 5 min, using an automated digital monitor to obtain two readings (Dinamap Carescape V100, General Electric). A third reading was performed if the difference between the first two readings was greater than 10 mmHg or 5 mmHg for systolic blood pressure and diastolic blood pressure, respectively. For the minority of the participants whose blood pressure the of the digital a was The was to of peripheral artery disease, as The systolic blood pressure from the artery in the and from the or artery in both were measured using in the at to A was used to the at the the at the artery not be the artery be pressure in the be measured if not be done in the All the systolic blood pressure measurements were repeated was measured using and Participants for with their and closed for the on their was assessed with a where a was to the of the and of both The was from the participants indicated that they the The reading was recorded for both and both was performed by using a on third and and the first and The number of that the participants was recorded for In the cohort follow-up, the health examination included four additional including cognitive function composition from and hand grip a was measured at the and Three measurements were on the participant was electrocardiogram, 10 was performed on participant with head slightly about to For the of the central systolic pressure and measurements were performed on the with the participant and the on a at their hand grip participants were asked to with their at the and to grip the hand as as they with Three readings were recorded for A sub-sample of the follow-up cohort Chinese, aged and more assessments which included and fat and mass and and urine samples were at the collection at and at by a to the and h of sample analyses of the blood samples were performed on the same as The the blood and urine samples on the same except for urine samples that were collected in the which be stored at and the samples were at the of the National University from 2007 to 2007 and to 2016. were performed at the Singapore General for the period 2010. Both are by the of blood samples were on the of collection for and Table as data at provides details and of and between of the blood analyses by the As of the biological are stored in the of the National University and of Table 2 as data at the of data are available for a of the MEC cohort from a of and such as and on the have been to 3 data are also available for of the Interviewers and who performed the health examination were and assessed for before they data was to and in the health examination and collection of As the cohort is multi-ethnic in the and are in at of the local languages Chinese, Chinese questionnaires were for with of the participants on the data All data including consent were for missing data before they were to data by two A third between by with the The data were and the of data data were and in only to performed further data of the data by for between variables and data before the data for analysis. information of participants and study records were in that only the access for the of follow-up. large multi-ethnic cohort has provided us an to differences in health and behaviours between major Asian ethnic groups a and access to health in The prevalence of T2D and other outcomes is with the in and and Asian in the MEC study, we and evaluate metabolic biomarkers and their to outcomes between the three ethnic groups. For Indians had the followed by Malays and The difference between Chinese and Malays be by differences in mass the greater in Indians only be by higher waist circumference and In addition, BMI had a greater influence on protein and in Chinese than the other major Asian ethnic These that ethnicity the between and and involved in the of T2D and diseases and may to greater in T2D and in Chinese than other ethnic groups as a of follow-up in the cohort allows us to the of in their of long-term and we if the current T2D on peripheral and chronic disease, we that both in to plasma at the existing for T2D of For chronic disease, these associations after for other These that current for T2D may have for with in an Asian prospective study in weight and waist circumference the follow-up period and their associations with and socioeconomic who the most weight were to be of higher socioeconomic status and ethnic minority and had the BMI at The data provided for to be at a age and with specific for the minority ethnic and higher groups. linkage with national the of with heart in the cohort. that not only Indians have a higher for T2D than Chinese and but also the of in Indians with T2D was higher than the other In study, we that metabolic with or without central the same for in on to that when using the of metabolic which central as an at of in this cohort be than if central was as an as defined by the Heart and detailed validated questionnaires on intakes and physical activity, we associations of and physical activity with health outcomes including and how these associations across ethnic groups. observed that a of and vigorous physical activity was with lower the between and to be by and than lower physical activity. to was with less whereas high of and was with greater and the 2 and validated in that between ethnic Chinese had higher physical health than Malays and whereas Malays higher mental health than The same study also that the of stroke and conditions on may by ethnicity. studies have further the between and T2D and and that the of with T2D was by and not T2D with peripheral in being with the in These the of T2D to or the of In with T2D, hypertension and was to be with lower whereas was which may the not medical these lifestyle lifestyle and health as to with chronic diseases for preventive health study by Malay ethnicity, lower educational lower family and and physical activity as to in health the competence of preventive health as of the to improve the health screening of these groups and the in the population that with chronic the ethnic groups represented in MEC across these and of with various outcomes and For a common (10%) at was to be with T2D in whereas the is in other have and to in and and and with blood anthropometric and blood In addition to metabolic MEC participants have been to cancer of a cohort to as in 80 on the MEC data have been to Data have also been for such as the as the of Metabolic of Diseases and the Cohort in on associations and and in and A major strength of the MEC study is that three major ethnic groups in who are at of major health The interviewer-administered questionnaire a of health medical history and quality of life with missing The physical activity and FFQ used in the MEC study were validated in samples of the local population with similar The MEC study also included a detailed physical biochemical analyses of blood samples and of blood and spot urine A of the MEC cohort was the the follow-up be by follow-up using linkage to that have good of the Singapore the of Health for information on a for data and/or data are available at Singapore is a multi-ethnic island state and provides a useful model to evaluate determinants of the development of chronic diseases in Asian ethnic groups. Baseline recruitment was done between 2004 and 2010. The cohort 14 465 male and female participants aged 21 years and above, with 47% Chinese, 26% Malay and 27% Indian ethnicity. The first follow-up began in 2011 and ended in 28% of the baseline participants were not contactable and 2.5% were confirmed to have been lost to follow-up. Of the participants that were 60% agreed to participate in the follow-up survey. lifestyle behaviours, personal and family medical medication and health-related quality of life information was collected at of anthropometric characteristics, peripheral neuropathy, and ankle blood pressure and biomarkers in blood and urine were also During the follow-up, additional information on mental health and cognitive function was collected and electrocardiogram, central blood pressure and hand grip strength measurements were 86% of all baseline participants gave consent for data linkage, follow-up through medical records and national registries. the of Health for information on a for data and/or to the analysis and of data and the to of the study, of the study and the and the study. and the are to the Singapore residents who data and samples for this study and to the community who had recruitment to the and data for their on this study. The MEC study was by the National and of National University of Singapore and National University Health of