Coronary artery disease is one of the most common cardiovascular diseases worldwide. Despite its high prevalence, CAD has not been well observed with the consideration of environmental (particulate matter, humidity, temperature, air pressure, sunlight) and behavioral factors (alcohol and smoking) in Turkiye. Our study aimed to explore the correlation of the factors with CAD using limited data. Using the result of this observation, we aimed to see if our findings aligned with previous studies from Turkiye and other countries around the world.
Some of the environmental factors that are considered are environmental markers such as air quality (Particulate Matter-10) and climatic conditions (humidity, temperature variations), which were found to be significantly associated with CAD mortality. In terms of particulate matter, the findings of this study showed similarity with Brook et al.’s studies. Their study had experimental evidence showing biological mechanisms where particulate matter can cause cardiovascular diseases. Through different pathways and cascade of inflammatory responses, some cardiovascular events can be initiated [14].
Some behavioral factors were also considered. Behavioral factors like alcohol consumption and smoking, as shown in Table 1, also emerged as significant predictors of CAD mortality. In their studies, Salehi et al. found out that smoking was associated with the severity of CAD. The increase in dose of smoking and duration would lead to an increase in the likelihood of occlusion in coronary arteries [15]. One finding in this study showed very similar results, a strong positive correlation of smoking with CAD.
In another study conducted by Song et al. discovered that an excess consumption of alcohol led to a worse prognosis for coronary artery disease [16]. Their research findings support this study findings which show of a positive correlation of CAD with excess alcohol consumption in Turkiye.
The leading strength of this study is its comprehensive analysis of various influential environmental and behavioral predictors, and their association with CAD mortality in Turkiye. The use of province-level data (Fig. 3) allows for a nuanced understanding of the different levels of CAD burden, illustrated as a strong spatial autocorrelation where provinces in different regions have similar CAD mortality burden.
Map of Turkiye with the CAD Mortality Trajectores to illustrate the spatial autocorrelation
However, the study has several limitations. The reliance on secondary data sources and its overall observational nature may hinder the accuracy of some measurements, and preclude causal inferences. In addition, environmental impact occurs potentially slowly over time and having the outcome and predictor coming from the same time frame does not provide associatiations beyond correlation hypotheses, not necessarily indicating any form of causal association.
Secondly, even as an ecological data, our data has depth only at the province level, not at smaller geographical definitions like counties, zip-codes, etc., which limits the capturing of true variation of both the exposure and outcome. Such higher level of aggregation naturally limits the statistical power as well.
Not but not least, the study does not account for potential genetic predispositions, which could play a role in CAD mortality outcomes. Lack of data at the province-level on obesity prevalence, level of physical activity, and dietary patterns is another restriction preventing the examination of their relation with the incidince of CAD.
Future research should focus on longitudinal studies to understand the causal pathways of these associations better. Investigating the genetic factors and their interplay with environmental and lifestyle factors (i.e., epigenetics) could also provide deeper insights into CAD etiology and prevention strategies.
In conclusion, this study presents a comprehensive analysis regarding the influence of the geographical and predictor-based variations on coronary artery disease (CAD) mortality rates in Turkiye. The significant associations between environmental factors, behavioral patterns, and CAD mortality emphasize the need for multifaceted public health strategies. While the study highlights essential regional disparities in CAD outcomes, it also points to universal risk factors such as air pollution and lifestyle choices. Having a good understanding of each of the environmental and behavioral factors at the regional level where geographical units such as provinces, cities, towns etc. that are closer to each other are likely to have similar exposure and health outcome profiles may succesfully lead to targeted interventions that take into account both the unique regional characteristics and the shared risk factors. Ultimately, this study contributes to a growing body of evidence that can inform public health authorities, researchers, and individuals so that more effective and timely healty policies for disease prevention and disease intervention can be developed and life style changes can be implemented such as increasing physical exercise and decreasing smoking and alcohol consumption. The conclusions derived from this study extend beyond Turkiye to include other regions with similar demographic and environmental profiles and provide the hypotheses to be tested in a well-designed prostective cohort studies.
link