The Ten-year Risk Prediction for Cardiovascular Disease in the National Population (Globorisk) of Malaysian Adults

Globorisk is a novel risk prediction model that predicts cardiovascular disease (CVD) in the national population of all world countries. Using Malaysia's risk factor levels and CVD event rates, we calculated the laboratory-based and oce-based risk scores to predict the 10-year risk for fatal CVD and fatal plus non-fatal CVD for the Malaysian adult population. We analysed data from 8253 participants from the 2015 nationwide Malaysian National Health and Morbidity Survey (NHMS 2015). The average risk for the 10-year fatal and fatal plus non-fatal CVD was calculated, and participants were further grouped into four categories: Low Risk (<10% risk for CVD), High-Risk A ( ≥ 10%), High-Risk B ( ≥ 20%) and High-Risk C ( ≥ 30%). Results were reported for all participants and were then stratied by sex, race, region, and state. The average risks for laboratory-based fatal CVD, laboratory-based fatal plus non-fatal CVD and oce-based fatal plus non-fatal CVD were 0.07 (SD = 0.10), 0.14 (SD = 0.12) and 0.11 (SD = 0.09), respectively. There were substantial differences in terms of the sex-, race- and state-specic Globorisk risk scores obtained.


Introduction
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with nearly 17 million deaths in 2016, 31% of the world's total 1 . The World Health Organisation (WHO)-derived aim is to reduce CVDrelated premature death by 30% 2 . In Malaysia, CVD death accounted for 21.7% of all hospital deaths in 2017 3 .
CVD-related deaths could be reduced by predicting the CVD risk and subsequently mitigating the CVD risk factors. A suitable model of CVD risk prediction and a nationally representative cardiometabolic pro le are needed to measure the number of people at high risk for CVD (e.g., those with a CVD risk greater than 30%). The measurement of CVD risk will help monitor progress towards the global targets for the treatment of non-communicable diseases (NCDs).
Current CVD calculators tested on populations, including Framingham, INTERHEART, SCORE, and WHO/ISH CVD, are helpful for CVD risk prediction [4][5][6][7][8][9] . However, risk predictions developed for a speci c population cannot be used in other populations or the same populations later because the mean levels of CVD predictors vary across populations and over time [10][11][12][13][14] . Globorisk, a newly developed CVD calculator, 15 provides country-speci c CVD risk scores. Globorisk provides models to estimate the 10-year risk of fatal and non-fatal CVD, and the models have been validated and calibrated using data for 182 countries [16][17][18] .
Globorisk is the rst novel, cardiovascular disease risk score that predicts the risk of heart attack or stroke in healthy individuals (those who have not yet had a heart attack or stroke) globally. It uses information on a person's country of residence, age, sex, smoking, diabetes, blood pressure, and cholesterol to predict the chance that they would have a heart attack or stroke in the next ten years (laboratory-based risk prediction). Suppose the individual does not have recent diabetes or cholesterol test. In that case, they can use the o ce-based risk prediction of Globorisk, which is based on body weight and height, to generate body mass index (BMI) and use it to replace total cholesterol and diabetes instead (http://www.globorisk.org/).
There has been no local study that uses Globorisk to calculate the CVD risk among the Malaysian population. However, knowing the average risk for fatal and fatal plus non-fatal CVD and the proportion of the Malaysian population with speci c risk factors for developing a CVD event in the next ten years will help public health workers, epidemiologists, and policymakers in Malaysia guide CVD control and prevention programmes.
The study aimed to provide the overall, the sex-speci c, the ethnic-speci c, the region-speci c, and the state-speci c 10-year risk for CVD among Malaysian adults. Three measures of CVD risk can be calculated using the Globorisk risk prediction model: (1) The laboratory-based 10-year risk of fatal CVD; (2) The laboratory-based 10-year risk of fatal plus non-fatal CVD; and (3) The o ce-based 10-year risk of fatal plus non-non-fatal CVD.

The participants' characteristics
We performed CVD risk prediction on 8253 individuals aged between 40 and 84 years. The mean age for men was 53.72 years (SD = 9.32) and for women was 52.06 years (SD = 8.32). Ethnically, the sample was predominated by Malay (62%) followed by Chinese (18%), Indian (7.2%) and other ethnicities.
The geographical areas were states, the principal administrative divisions of the country, of which there are 13 states and three federal territories (WP Kuala Lumpur, WP Putrajaya, and Labuan). The proportion of the study population in the state of Selangor was the highest (12%), while Labuan had the lowest (0.2%), which was proportionate to their overall population size. The distribution of people by ethnicity between men and women was fairly equal (Table 1). In terms of cardiovascular risk factors, men had a higher prevalence of smoking (42%) than women (1%). The prevalence of diabetes was equivalent between sexes; 28% for men and 29% for women. The overall mean for systolic blood pressure was 135.65 mm Hg, (SD = 23.74), for total cholesterol 5.41 mmol (SD = 2.82) and BMI 27.33 kg/m 2 (SD = 5.23). These values were higher in women compared with men ( Table  1).
The 10-year CVD risk prediction score at the national level Table 2 shows that the average risk laboratory-based mean (SD) 10-year risk of fatal CVD, the laboratory- Globorisk risk prediction), showed that more men had high CVD risk scores for all Globorisk predicted risk. For example, their 10-year risk of fatal CVD was 30%, the laboratory-based 10-year risk of fatal plus non-fatal CVD was 73%, and the o ce-based ten-year risk of fatal plus non-fatal CVD was 72%.   In Table 4, we present the region-speci c CVD risk based on the Globorisk risk prediction. It shows that the  The states in the Northern region (Perlis, Kedah, Penang and Perak) showed the highest proportion of high CVD risk for all Globorisk scores than the other states (see Figure 2). All related details regarding state-speci c Globorisk risk predictions are available in Table 5.

Discussion
From our analysis, the population-based data for 8253 adults across Malaysia showed variations in the estimated 10-year CVD risk scores. The overall CVD risk for the Malaysian population is higher than Japan, South Korea, Spain, and Denmark 16 . Common comorbidities in diabetes, hypertension, hypercholesterolemia, obesity, and smoking are prevalent in Malaysia 19 . In fact, a substantial increase in those common CVD risk factors was observed in data from the NHMS conducted among Malaysians 18,20 . The increase in these CVD risk factors could be attributed to the sedentary lifestyle pattern common among Malaysians 21-23 .
Men have higher CVD risk scores than women in Malaysia. This result is in line with previous studies that have found similar results 4,7,24 . For the 10-year risk of fatal CVD, our ndings for men and women with a high-risk CVD risk score (See Table 2) were lower than those of South Korea (men: 7.0%, women: 7.0%) and China (men: 33.0%, women: 28.0%) 16 . As for the laboratory-based 10-year risk of fatal and non-fatal CVD, our high-risk CVD scores among men and women were higher than those in South Korea (men: 0.3%, women: 0.5%) and China (men: 10.3%, women: 9.2%) 17 .
The Malaysian sex-speci c high-risk for the o ce-based 10-year risk of fatal plus non-fatal CVD was higher than South Korea (men: 0.1%, women: 0.1%) but was substantially lower than China (men: 8.8%, women: 6.5%) 17 . In general, in all comparisons, the men had higher high-risk CVD risk scores than women; in this study, it was speci cally because men had higher baseline risk factors of CVD compared with women, particularly regarding the smoking rate. In addition, the constellation of smoking with other risk factors increases the CVD risk score and the risk of CVD events in the foreseeable future [25][26][27] . In particular, smoking is associated with increased oxidative stress, thus predisposing individuals to smoke to develop cardiovascular diseases 28,29 .
Meanwhile, the comparison between ethnicities in Malaysia showed that Malays have the highest CVD risk in all the CVD risk groups (see Table 3). This nding was consistent with previous studies showing that the high 10-year CVD risk among Malays was due to higher baseline CVD risk factors, such as diabetes, hypertension, hypercholesterolemia, and smoking 4,5 . The prominent CVD risk factors among Malays were possibly due to a high unawareness of having NCDs and poor health-seeking behavior [30][31][32] . Eating foods high in saturated fat, trans fat, salt, and sugar are also associated with a high risk of CVD. Previous studies conducted among Malaysians showed that Malays had poor eating habits associated with the risk of developing CVD 33,34 .
Regarding the region-speci c analysis, the Northern region, which comprises the states of Penang, Kedah, Perlis, and Perak, have the highest CVD risk scores compared with their counterparts (see Table 4 and Figure 2). The highest proportion of individuals with high CVD risk in the Northern region is due to a high proportion of 4 common CVD risk factors: diabetes, hypertension, hypercholesterolemia, and smoking, as evidenced in the NHMS 2015 and its corresponding cross-sectional study 20,35 .
Estimating 10-year fatal and fatal plus non-fatal laboratory and o ce CVD risk scores reveals a comparable estimate for low and high CVD risk scores. This nding is in line with previous research that showed that 80% of adults were comparably classi ed into low and high CVD risk by laboratory and o ce risk scores 36 . Thus, the o ce Globorisk risk score allows for risk prediction in settings where there is no access to laboratory testing, such as during community screening and home care visits, which subsequently reduces the cost of laboratory testing.
Our study has several strengths and limitations. To the best of our knowledge, this study is the rst of its kind in Malaysia that used the Globorisk risk prediction model to estimate CVD risk. Our study also used large data from the 2015 NHMS, which is representative of the Malaysian adult population. Given that national CVD incidence data are not available for Malaysia, the Globorisk risk prediction estimates fatal and non-fatal CVD rates using national ischaemic heart disease and stroke death rates from the WHO. Globorisk risk prediction also predicts the 10-year CVD risk; however, 10-year risks underestimate lifetime risk and might therefore lead to undertreatment, especially in younger individuals.

Conclusion
The 10-year risk for fatal and fatal plus non-fatal CVD based on the Globorisk risk prediction model shows substantial differences in the average CVD risk and CVD risk categories for sex, ethnicity, region,  Participants were eligible if older than 40 years of age in 2015 and with no prior history of major cardiovascular diseases (ischaemic heart disease or stroke). We excluded participants if their data were incomplete for the calculation of the 10-year CVD risk.
We excluded data from 10,142 male and 11,065 female participants because they were younger than 40 years or had missing data for all the Globorisk risk prediction model (age, systolic blood pressure, total cholesterol level, history of diabetes mellitus, and smoking status). Ultimately, 4083 men and 4170 women were eligible for inclusion in the study because they met the Globorisk risk prediction model ( Figure 1).
The Globorisk risk prediction model to calculate 10-year CVD risk The Globorisk risk prediction model is based on an analysis of baseline CVD risk factors. It produces a population's cardiovascular disease risk in a speci c year (linked to each year in which the data were collected). The model has a set of coe cients, usually hazard ratios (speci c to a population), each of which quanti es the risk factor's proportional impact on the risk of cardiovascular disease. For Globorisk risk prediction to calculate country-speci c fatal and non-fatal CVD, it needs data on CVD event rates in each 5-year age group and by sex, and the mean risk factor levels for the country. After replacing the values of sex, age, smoking status, systolic blood pressure, diabetes status, and total cholesterol level in the Globorisk risk prediction, it will return three primary outcomes: (1) the laboratorybased mean 10-year risk of fatal CVD; (2) the laboratory-based mean 10-year risk of fatal plus non-fatal CVD; and (3) the o ce-based mean 10-year risk of fatal plus non-fatal CVD 16,17 . The fatal plus non-fatal Globorisk risk scores are the probability of future fatal or non-fatal CVD events relative to the fatal CVD events in the initial recalibration process for each country, respectively.
The laboratory-based 10-year risk of fatal CVD This score is the 10-year risk for fatal cardiovascular disease only. Although fatal and non-fatal cardiovascular disease are important for clinical and public health applications, national data for average death rates are more reliable than those for disease incidence, even in high-income countries 16 . The laboratory-based 10-year risk of fatal and non-fatal CVD Globorisk risk score is calculated from 6 variables: sex, age, smoking status, systolic blood pressure, diabetes status and total cholesterol level 16,17 .
The laboratory-based 10-year risk of fatal plus non-fatal CVD The laboratory-based 10-year risk of fatal and non-fatal CVD Globorisk risk score is calculated from same 6 variables: sex, age, smoking status, systolic blood pressure, diabetes status and total cholesterol level 17 . This calculation allows for the estimation of speci c CVD risk using readily available population-wide survey data in most middle-and high-income countries.
The o ce-based 10-year risk of fatal plus non-non-fatal CVD The o ce-based 10-year risk of fatal and non-non-fatal CVD Globorisk risk score is calculated from 5 variables: sex, age, smoking status, systolic blood pressure and BMI (diabetes status and total cholesterol are replaced with BMI) 17 . BMI has a strong association with diabetes status and total cholesterol and acts as a proxy to increased body weight, blood glucose and serum cholesterol 17,39 . The modi cation estimates CVD risk score in an economically poor resource setting in which laboratory facilities are limited.

Statistical methods
Categorical variables are presented using frequencies (n) and percentages (%). Meanwhile, continuous variables are presented using means (SD) for normally distributed data and medians (interquartile range) for skewed data. We calculated the CVD risk scores for each eligible participant. CVD risk scores are typically classi ed into various categories. For this paper, we divided them into 4 categories: a low risk for future CVD (if the CVD risk score was <10%) and 3 high-risk CVD categories (if CVD risk is equal to or greater than 10%, 20% and 30%) 2,40,41 . An analysis was performed for overall risk and by sex, race, region, and state in Malaysia. All the statistical analyses were performed using R software version 3.6.1 42 and the gtsummary, summarytools and ggplot2 packages [43][44][45] in RStudio IDE.
Declarations Figure 1 Study participant ow chart