Changes in 10-Year Predicted Cardiovascular Disease Risk for a Multiethnic Semirural Population in South East Asia: Prospective Study

Abstract

Background: Cardiovascular disease (CVD) risk factors tend to cluster and interact multiplicatively and have been incorporated into risk equations such as the Framingham risk score, which can reasonably predict CVD over short- and long-term periods. Beyond risk factor levels at a single time point, recent evidence demonstrated that risk trajectories are differentially related to CVD risk. However, factors associated with suboptimal control or unstable CVD risk trajectories are not yet established.

Objective: This study aims to examine factors associated with CVD risk trajectories in a semirural, multiethnic community-dwelling population.

Methods: Data on demographic, socioeconomic, lifestyle, mental health, and cardiovascular factors were measured at baseline (2013) and during follow-up (2018) of the South East Asia Community Observatory cohort. The 10-year CVD risk change transition was computed. The trajectory patterns identified were improved; remained unchanged in low, moderate, or high CVD risk clusters; and worsened CVD risk trajectories. Multivariable regression analyses were used to examine the association between risk factors and changes in Framingham risk score and predicted CVD risk trajectory patterns with adjustments for concurrent risk factors.

Results: Of the 6599 multiethnic community-dwelling individuals (n=3954, 59.92% female participants and n=2645, 40.08% male participants; mean age 55.3, SD 10.6 years), CVD risk increased over time in 33.37% (n=2202) of the sample population, while 24.38% (n=1609 remained in the high-risk trajectory pattern, which was reflected by the increased prevalence of all major CVD risk factors over the 5-year follow-up. Meanwhile, sex-specific prevalence data indicate that 21.44% (n=567) of male and 41.35% (n=1635) of female participants experienced an increase in CVD risk. However, a stark sex difference was observed in those remaining in the high CVD risk cluster, with 45.1% (n=1193) male participants and 10.52% (n=416) female participants. Regarding specific CVD risk factors, male participants exhibited a higher percentage increase in the prevalence of hypertension, antihypertensive medication use, smoking, and obesity, while female participants showed a higher prevalence of diabetes. Further regression analyses identified that Malay compared to Chinese (P<.001) and Indian (P=.04) ethnicity, nonmarried status (P<.001), full-time employment (P<.001), and depressive symptoms (P=.04) were all significantly associated with increased CVD risk scores. In addition, lower educational levels and frequently having meals from outside were significantly associated to higher odds of both worsening and remaining in high CVD risk trajectories.

Conclusions: Sociodemographics and mental health were found to be differently associated with CVD risk trajectories, warranting future research to disentangle the role of psychosocial disparities in CVD. Our findings carry public health implications, suggesting that the rise in major risk factors along with psychosocial disparities could potentially elevate CVD risk among individuals in underserved settings. More prevention efforts that continuously monitor CVD risk and consider changes in risk factors among vulnerable populations should be emphasized.

Keywords: Framingham risk score; cardiovascular risk trajectory; low- and middle-income countries; population-based study.

Figure 1

Flowchart of participant selection. At baseline, 13,828 individuals aged ≥35 years were included in the Health Survey 2013 of the South East Asia Community Observatory health and demographic surveillance system, established in Segamat, Johor, Malaysia. A subsample of 7462 individuals was reexamined in the Health Survey 2018. The final study population included 6599 participants who completed both the 2013 and 2018 surveys and provided complete data on cardiovascular disease risk factors.

Figure 2

River plot describing transition patterns of low, moderate, or high predicted cardiovascular disease (CVD) risk change from baseline in 2013 to follow-up in 2018 among (A) total population, (B) male population, and (C) female population. Note: The light red lines show the transition of high predicted CVD risk cluster from baseline 2013 branching out to high, moderate or low predicted CVD risk in 2018; the yellow lines indicate the moderate predicted CVD risk cluster from baseline 2013 branching out to high, moderate or low predicted CVD risk in 2018; and the blue lines indicate the low predicted CVD risk cluster from baseline 2013 branching out to high, moderate or low predicted CVD risk in 2018. The percentage (%) values indicate the prevalence of low, moderate, and high CVD risk, based on the 10-year CVD risk classification.

Figure 3

Percentages of predicted cardiovascular disease (CVD) risk trajectories by sex from baseline in 2013 to follow-up in 2018. The distinct trajectory patterns identified were improved; remained unchanged in low, moderate, or high CVD risk clusters; and adverse (worsen) CVD risk trajectories. Notes: improved—the predicted CVD risk had shifted from the high-risk cluster in 2013 to the low-risk cluster in 2018, and adverse (worsen)—the predicted CVD risk had shifted from the low-risk cluster in 2013 to the high-risk cluster in 2018.