Prevalence and predictors of coronary heart disease among individuals with and without obesity in North Dakota

Emmanuel Angmorteh Mensah; Md Marufuzzaman Khan; Agricola Odoi; Grace Njau; Matthew Schmidt; Jennifer Lord

doi:10.1371/journal.pone.0313283

Abstract

Introduction

Coronary heart disease (CHD) is the most common cardiovascular disease in the United States and heart disease is the leading cause of death in North Dakota. Although obesity is a major risk factor for CHD, some studies have reported an inverse relationship between body mass index (BMI) and CHD outcomes. Understanding the epidemiology of CHD among individuals with and without obesity is essential to guide health planning. Therefore, the objectives of this study were to estimate the prevalence and identify predictors of CHD among North Dakota adults with and without obesity.

Methods

Behavioral Risk Factor Surveillance System data from 2017 to 2021 were provided by the North Dakota Department of Health and Human Services. Weighted prevalence estimates and 95% confidence intervals (CIs) were computed for CHD and potential risk factors among respondents with and without obesity (BMI ≥30 kg/m²). Multivariable logistic regression was used to identify significant predictors of CHD in both groups.

Results

A total of 17,092 respondents were included in the study. Respondents with obesity had a higher prevalence of CHD (4.7%, 95% CI: 4.2–5.4) than those without obesity (3.0%, 95% CI: 2.7–3.4). Predictors of CHD among both groups included age, sex, self-reported general health, high blood pressure, high cholesterol, chronic obstructive pulmonary disease, and diabetes. Having a personal doctor (Odds Ratio [OR] = 1.91, 95% CI: 1.13–3.23) and arthritis (OR = 1.72, 95% CI: 1.34–2.22) were significant predictors of CHD among respondents without obesity, but not among those without obesity.

Conclusion

While the prevalence of CHD was significantly higher among adults with obesity compared to those without obesity, the findings of the stratified analysis indicated that predictors of the condition tended to be similar for the two groups. This study provides useful information to guide health planning and control programs that aim to reduce the burden of CHD in North Dakota.

Figures

Citation: Mensah EA, Khan MM, Odoi A, Njau G, Schmidt M, Lord J (2024) Prevalence and predictors of coronary heart disease among individuals with and without obesity in North Dakota. PLoS ONE 19(11): e0313283. https://doi.org/10.1371/journal.pone.0313283

Editor: Yoshihiro Fukumoto, Kurume University School of Medicine, JAPAN

Received: July 18, 2024; Accepted: October 20, 2024; Published: November 8, 2024

Copyright: © 2024 Mensah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because they belong to a third party (North Dakota Department of Health and Human Services). The authors do not have the legal authority to share the data. However, the data may be requested by contacting the BRFSS Program Director at the North Dakota Department of Health Community Health Section by mail at 600 East Boulevard Ave, Dept. 301, Bismarck, ND 58505, by phone at (701) 328-2367, or by email at brfssnd@nd.gov.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Cardiovascular disease (CVD) is the leading cause of mortality worldwide and presents in many forms, including congenital heart disease, deep vein thrombosis, cerebrovascular disease, coronary heart disease, peripheral arterial disease, and rheumatic heart illnesses [1, 2]. However, coronary heart disease (CHD) is the most common subtype in Europe and North America [3, 4]. The condition occurs due to the accumulation of cholesterol, fat, and calcium deposits in the arteries, leading to arterial constriction, stiffening and decreased oxygen supply to the heart [5]. Coronary heart disease may manifest as chest pain (angina), heart failure, heart attack, and irregular heartbeats (arrhythmia) [5].

Approximately 244.1 million individuals are living with heart disease globally [6]. The global CHD mortality rate was estimated to be 112.37 per 100,000 cases in 2022 [6]. In the United States, approximately 366,000 coronary heart disease deaths occur each year [1], and 1 in 20 adults who are 20 years and older have CHD [7]. Coronary heart disease is a leading cause of morbidity and mortality across all states in the US, including North Dakota [8]. The condition is also associated with a significant financial burden. In 2015, CHD was associated with $89 billion in direct costs in the United States, and this is projected to increase to $215 billion by 2035 [3]. Furthermore, it was estimated that heart disease resulted in $203.3 billion in income losses in 2018 [9]. At the individual level, CHD is associated with poor physical health and diminished functional capacity, and those with CHD experience lengthy periods of sleep, depression, and tiredness [10, 11]. Furthermore, research has linked coronary heart disease to higher hospital readmission rates [12].

Obesity is an important risk factor for CHD, and approximately 80% of CHD patients are either overweight or have obesity [13, 14]. Compared to individuals with normal weight, those with obesity have significantly greater incidence of pre-clinical atherosclerosis [15]. Furthermore, the risk of coronary heart disease is 1.27 times higher for every 5 kg/m² increase in body mass index (BMI) [16]. However, it is worth noting that some studies have reported an inverse relationship between obesity and CHD outcomes, a phenomenon that has been observed for numerous cardiovascular diseases and has been termed the “obesity paradox” [17–20]. Such studies have reported that patients with cardiovascular disease who have a diagnosis of overweight or obesity based on body mass index have a better prognosis than those whose BMI is classified as normal or underweight [19–21]. Several explanations for this paradox have been proposed, including earlier diagnosis of cardiovascular disease and more frequent screening among those with obesity [17, 20], but there is limited research investigating whether characteristics associated with CHD differ for those with and without obesity. Investigating determinants of CHD among individuals with and without obesity is important to better understand the epidemiology of the disease and gather information to guide control programs. This is especially critical for the state of North Dakota because heart disease is the leading cause of deaths in this state, which has an estimated heart disease prevalence of 152.8 per 100,000 [8]. Therefore, the objectives of this study were to estimate the prevalence and identify predictors of coronary heart disease among North Dakota adults with and without obesity.

Methods

Ethics approval

This study was approved by the University of Tennessee Institutional Review Board (Number: UTK IRB-24-08175-XM). The study used secondary data provided to the investigators by the North Dakota Department of Health and Human Services. The need for informed consent was waived by the Institutional Review Board. Study investigators did not have information that could be used to identify individual participants. All data were fully anonymized before being shared with the study investigators.

Study area and design

This retrospective cross-sectional study used survey data from the Behavioral Risk Factor Surveillance System (BRFSS) to investigate socioeconomic, demographic, and lifestyle factors as potential predictors of coronary heart disease (CHD) among individuals with and without obesity in North Dakota. North Dakota is comprised of 53 counties, six of which are designated as urban and ten as semi-rural, with the remainder classified as rural [22]. Urban counties contain a metro area with a population of 50,000 or greater, while those classified as semi-rural contain a town or city with a population between 2,500 and 49,999. Counties that do not contain a town or city with a population of 2,500 or greater are classified as rural [23]. As of 2022, North Dakota had an estimated population of 779,261 residents, 48.6% of whom were female [24]. Persons under the age of 18 represented 23.5% of the population, and 16.7% were aged 65 years and older [25]. The majority of the population was white (86.6%). The next largest racial groups were American Indian or Alaskan Native (5.3%) and Black or African American (3.6%). Other races and ethnic minorities represent a minuscule proportion of North Dakota’s total population [24, 25].

Data sources and variable selection

Behavioral Risk Factor Surveillance System (BRFSS) data from 2017 to 2021 were provided to the investigators by the North Dakota Department of Health and Human Services (NDDOHHS). The BRFSS is a nationwide survey conducted by state health departments with assistance from the Centers for Disease Control and Prevention (CDC) that gathers data on health-related risk behaviors, chronic health conditions and use of preventive services [26]. The study population included adults aged 18 years and older. A respondent with CHD was defined as “a person who has ever been informed by health workers that they have angina or coronary heart disease”. To guide the selection of variables from the dataset for inclusion in the study as potential predictors of CHD, a conceptual model was built (Fig 1). The conceptual model included variables that were available in the BRFSS dataset and were hypothesized to have biological, physical, and/or social relationships with CHD based on evidence from the existing literature [27]. These variables are defined in Table 1. Since some variables of interest were not available during all years of the study period, only records from 2017, 2019 and 2021 were included in the study.

Download:

Fig 1. Conceptual model depicting potential predictors of coronary heart disease among respondents to the North Dakota Behavioral Risk Factor Surveillance System survey.

https://doi.org/10.1371/journal.pone.0313283.g001

Download:

Table 1. List and definitions of Behavioral Risk Factor Surveillance System variables considered as potential predictors of coronary heart disease among North Dakota adults.

https://doi.org/10.1371/journal.pone.0313283.t001

Data preparation

Respondent age was classified into three categories (18–44, 45–64, and 65 years and older). Since racial and ethnic minorities represent a small fraction of the population in North Dakota, respondent race and ethnicity were classified into one of the following four categories: Non-Hispanic White, Non-Hispanic American Indian (A. Indian)/Alaskan Native, Hispanic, or multiracial or other non-Hispanic. Respondents were considered to have obesity if their body mass index (BMI) was ≥ 30 kg/m². Individuals without obesity included those classified as normal weight (BMI ≥ 18.50 - < 25.00), underweight (BMI < 18.50) or overweight (BMI ≥ 25.00 - < 30.00).

Statistical analysis

Statistical analysis was performed using STATA 17.0 [28]. To account for the complex survey design used for data collection in the BRFSS survey, the sampling weight (_LLCPWT), primary sampling unit (_PSU) and strata (_STSTR) variables were applied when computing frequencies and conducting statistical analyses to ensure that estimates were representative of the North Dakota population [29]. Statistical analyses were conducted separately for respondents with obesity (n = 6,021) and those without obesity (n = 11,071). Unweighted frequencies and weighted percentages and 95% confidence intervals (CIs) of the percentages were computed for all categorical variables.

Multivariable logistic regression was used to identify significant predictors of CHD among North Dakota BRFSS respondents. To identify candidate variables for inclusion in the model-building process, univariable associations between CHD and each of the potential predictor variables from the conceptual model in Fig 1 were first assessed using survey logistic regression. Variables that had significant univariable associations with CHD at a p-value of ≤ 0.2 were considered for inclusion in multivariable models. To prevent problems with multicollinearity in the multivariable models, correlations between all possible pairs of potential predictors were assessed using Spearman correlation tests. Only one of a pair of highly correlated variables (|r_s| ≥ 0.7) was retained for further analysis. However, no pairs of highly correlated variables were identified among the potential predictors.

Multivariable logistic regression models were built using manual backward elimination, specifying a critical p-value of 0.05 for elimination of variables. Confounding was evaluated by assessing changes in regression coefficients following the removal of each variable during the backward elimination process. A change of 20% or more in the magnitude of the regression coefficient(s) of one or more other variables in the model following the removal of a variable indicated potential confounding. Confounding variables were retained in the model regardless of statistical significance. Biologically meaningful two-way interaction terms, based on literature review, were assessed for statistical significance using a critical p-value of 0.05. The goodness of fit of the final models was assessed using the Hosmer-Lemeshow goodness-of-fit test.

Results

Descriptive analyses

A total of 17,092 respondents to the North Dakota BRFSS survey were included in the study, 35.4% of whom had obesity. A significantly higher percentage of respondents with obesity were male (55.7%) compared to those without obesity (51.6%) (Table 2). Among those with obesity, there was a lower percentage of respondents between the ages of 18 and 44 (45.3%) compared to those without obesity (52.2%). The vast majority of respondents in both groups were non-Hispanic White. A higher percentage of respondents without obesity considered themselves to be in good health (89.4%) compared to those with obesity (80.0%). While the percentage of respondents that reported experiencing cost barriers to obtaining medical care was similar between the two groups, a higher percentage of those with obesity reported having a personal doctor (79.3%) compared to those without obesity (71.5%). Respondents with obesity had a higher prevalence of coronary heart disease (4.7%, 95% Confidence Interval [CI]: 4.2–5.4) compared to those without obesity (3.0%, 95% CI: 2.7–3.4).

Download:

Table 2. Sociodemographic characteristics, health behaviors and chronic conditions among BRFSS respondents with and without obesity (BMI ≥ 30 kg/m²) in North Dakota (2017, 2019, and 2021).

https://doi.org/10.1371/journal.pone.0313283.t002

The percentage of respondents who reported that they did not engage in regular physical activity was higher among those with obesity (33.1%) than those without obesity (23.5%). Levels of cigarette smoking and heavy alcohol consumption did not differ substantially between respondents with obesity and those without obesity. While fruit consumption tended to be more frequent among respondents without obesity, vegetable consumption was similar between the two groups. The percentage of respondents who reported eating fruit one or more times per day was 61.8% among those without obesity, compared to 54.3% of those with obesity. The percentage of respondents who reported eating vegetables at least once per day was 78.8% among respondents without obesity and 78.0% among those with obesity.

Comorbidities tended to be more common among those with obesity compared to those without obesity. For instance, 43.0% of respondents with obesity had hypertension and 15.7% had diabetes, compared to 23.8% and 5.6% of respondents without obesity, respectively. High cholesterol (37.3% vs. 28.0%), arthritis (33.1% vs. 21.0%), chronic obstructive pulmonary disease (COPD, 6.2% vs. 4.2%), and asthma (15.3% vs. 11.0%) were also more common among respondents with obesity.

Predictors of coronary heart disease among respondents with and without obesity

A number of variables had significant univariable associations with coronary heart disease among individuals with and without obesity (Table 3). Results of the two final multivariable logistic regression models, one for respondents with obesity and the other for those without, are displayed in Table 4. Predictors of coronary heart disease in both models included sex, age, self-reported general health status, high blood pressure, high cholesterol, COPD, and diabetes (Table 4). Among both groups, the odds of coronary heart disease among females were approximately 50% lower than the odds of CHD among males. Older age was also a significant predictor of coronary heart disease regardless of whether a respondent had obesity. Among those without obesity, the odds of CHD were higher among individuals between the ages of 45 and 64 (Odds Ratio [OR] = 3.16, 95% CI: 1.28–7.80) and those aged 65 and over (OR = 9.49, 95% CI: 3.81–23.6) compared to those between the ages of 18 and 44. Among respondents with obesity, those aged 65 and over also had significantly higher odds of CHD compared to those between the ages of 18 and 44 (OR = 5.42, 95% CI: 2.02–14.5), but the association was not significant for those between the ages of 45 and 64.

Download:

Table 3. Univariable associations between coronary heart disease and selected potential predictors among BRFSS respondents with and without obesity (BMI ≥ 30 kg/m²) in North Dakota (2017, 2019, and 2021).

https://doi.org/10.1371/journal.pone.0313283.t003

Download:

Table 4. Predictors of coronary heart disease among BRFSS respondents with and without obesity (BMI ≥ 30 kg/m²) in North Dakota (2017, 2019, and 2021).

https://doi.org/10.1371/journal.pone.0313283.t004

Compared to those who perceived their general health as good, those who reported fair or poor general health had significantly higher odds of CHD among respondents with obesity (OR = 2.34, 95% CI: 1.66–3.29) as well as those without obesity (OR = 2.98, 95% CI: 2.29–3.89). A number of comorbidities were associated with the odds of CHD. Hypertension (OR = 1.96, 95% CI: 1.48–2.60), high cholesterol (OR = 1.69, 95% CI: 1.29–2.18), and diabetes (OR = 1.58, 95% CI: 1.15–2.18) were associated with significantly higher odds of CHD among individuals without obesity. Similar findings were identified for those with obesity (OR [hypertension] = 3.35, 95% CI: 2.26–4.96); OR [high cholesterol] = 2.27, 95% CI: 1.62–3.17; OR [diabetes] = 1.91, 95% CI: 1.91–2.69). Having COPD was also associated with higher odds of CHD among respondents with obesity (OR = 2.39, 95% CI: 1.42–4.02) as well as those without obesity (OR = 1.97, 95% CI: 1.42–2.73).

Although having a personal doctor (OR = 1.91, 95% CI: 1.13–3.23) and arthritis (OR = 1.72, 95% CI: 1.34–2.22) were both significant predictors of coronary heart disease among respondents without obesity, they were not significantly associated with CHD among those with obesity and were therefore excluded from the final model for this group (Table 4). Prior to removal from the model in the manual backward elimination process, the odds ratios for arthritis (OR = 1.22, 95% CI: 0.88–1.69) and having a personal doctor (OR = 1.56, 95% CI: 0.81–3.03) were not significantly different from 1 (p = 0.227 and p = 0.187, respectively). There was no evidence of confounding or interaction in either of the multivariable models. Results of the Hosmer-Lemeshow goodness-of-fit test for both models did not indicate lack of fit to the data (χ² = 10.81, p = 0.2126 and χ² = 6.99, p = 0.5377).

Discussion

This study investigated the prevalence and predictors of coronary heart disease among adults with and without obesity in North Dakota. The overall prevalence of CHD in North Dakota (3.6%) was similar to that of neighboring states in the Midwest and Western US (South Dakota, Nebraska, Montana and Minnesota), and was slightly lower than the national average (4.4%) in 2022 [30]. North Dakota differs from states with relatively high prevalence of CHD, such as those in the Southeastern US [30], with respect to socioeconomic and demographic characteristics. For instance, North Dakota’s population has a lower proportion of Black or African American residents, higher median household income, and lower levels of poverty [31]. While the prevalence of CHD in North Dakota has exhibited mild fluctuations over the years [30], heart disease remains the leading cause of death in the state [8]. Coronary heart disease prevalence was significantly higher among respondents with obesity compared to those without obesity in this study, consistent with findings of previous research, which has identified obesity as a key risk factor for coronary heart disease [13, 32].

Many of the characteristics associated with the odds of CHD in the current study were shared between those with and without obesity. For instance, the odds of CHD among female respondents were significantly lower than those of male respondents regardless of whether a respondent had obesity. This is consistent with findings of previous research; on average, women are diagnosed with CHD ten years later than men [33]. Although the underlying mechanisms of biological sex-related differences in the occurrence of cardiovascular disease have not been fully characterized [34], the higher prevalence of CHD observed among male respondents in this study may also reflect differences in psychosocial characteristics and/or risk behaviors such as smoking, which is more common among men than women [34, 35]. To some extent, lower prevalence of heart disease in women could also reflect underdiagnosis of the disease [36].

High blood pressure is a well-known risk factor for coronary heart disease, and was associated with increased odds of CHD among survey respondents with and without obesity in this study. Hypertension results in endothelial injury and promotes atherosclerosis, and can directly lead to angina and myocardial ischemia [37], and has consistently been identified as a leading cause of CHD [38–40]. Managing blood pressure with lifestyle changes, medication, and regular monitoring is critical for lowering the risk of CHD and its consequences, such as angina and heart attack [37]. High cholesterol was also associated with significantly higher odds of CHD regardless of whether a respondent had obesity, a finding that is consistent with previous research and highlights the importance of identifying and managing hypercholesterolemia [41, 42]. Indeed, more than 1 in 4 North Dakota adults have been diagnosed with high cholesterol [43]. Elevated cholesterol levels contribute to atherosclerotic plaque development and subsequent arterial blockages, which can result in heart disease [44–46]. The observed association between diabetes and CHD among North Dakota adults is also consistent with previous research, which has shown that both type 1 and type 2 diabetes are related to a significant increase in coronary heart disease risk due to multivessel atherosclerosis [47]. In addition to being a risk factor for CHD, diabetes is associated with an increased risk of mortality among individuals with CHD [48]. Chronic obstructive pulmonary disease was another condition significantly associated with coronary heart disease among North Dakota adults, regardless of whether a respondent had obesity. Epidemiological evidence has previously linked COPD to coronary heart disease risk [47] as well as increased cardiovascular mortality [49].

In general, findings of this study indicated that having one or more other chronic diseases, namely hypertension, high cholesterol, diabetes, and/or COPD, was associated with increased odds of coronary heart disease among North Dakota adults regardless of whether a respondent had obesity. While the magnitudes of association between these conditions and CHD tended to be slightly higher in the model for those with obesity compared to those without obesity, the 95% confidence intervals for their odds ratios overlapped, indicating these differences were not statistically significant. Regardless, the co-occurrence of CHD and other chronic illnesses observed among North Dakota adults in the present study highlights the challenge of managing multiple chronic conditions, and has implications for the control of coronary heart disease as well as other non-communicable diseases [50]. There is a growing body of evidence indicating that the presence of one chronic illness increases the risk of developing others [51]. Furthermore, there is a high prevalence of multimorbidity among U.S. adults, approximately 40% of whom have been diagnosed with multiple chronic conditions [52]. This has primarily been attributed to aging of the population and risk behaviors such as poor dietary habits, lack of exercise, and tobacco use [52]. Control measures that promote prevention, prompt diagnosis and management of high blood pressure, high cholesterol, COPD and diabetes will be important for reducing the burden of CHD in North Dakota.

The observed association between older age and the odds of CHD in this study is also an expected finding, as age is a major known risk factor for cardiovascular diseases [53–55]. Among respondents without obesity, significantly higher odds of CHD were observed among individuals aged 45–64 years and those aged 65 and above compared to those between the ages of 18 and 44. Among those with obesity, individuals aged 65 years and older had higher odds of CHD compared to those between 18 and 44. It is worth noting, however, that although middle age (45–64 years) was associated with higher odds of CHD among those without obesity, it was not a significant predictor of CHD among those with obesity after accounting for other comorbidities and perceived overall health status. Self-reported general health was also associated with coronary heart disease regardless of obesity status, with higher odds of CHD among respondents who perceived their general health to be fair or poor. Due to the cross-sectional design of this study, it is not possible to determine causality, although the observed association may reflect a bidirectional relationship. For instance, perceived health status may have been influenced by the presence of coronary heart disease due to impacts of CHD on physical functioning, quality of life, and overall general health [56–58]. On the other hand, poor general health may precede the development of coronary heart disease, because those who perceive their health as poor may have risk factors for CHD including socioeconomic risk factors (e.g. low levels of income and educational attainment), health behaviors such as smoking, and/or other chronic conditions (e.g. overweight, hypertension, high cholesterol, diabetes and asthma) [59–62].

Interestingly, the current study identified two variables that only had statistically significant associations with coronary heart disease among respondents without obesity: self-reported arthritis and having a personal doctor. Arthritis is a group of chronic inflammatory conditions, and chronic inflammation and the use of analgesic medications (e.g. non-steroidal anti-inflammatory drugs) are both associated with higher risk of cardiovascular disease [63–65]. The two most common types of arthritis among adults in the US are osteoarthritis and rheumatoid arthritis [66]. It is important to note that in the BRFSS, the presence of arthritis is based upon whether a respondent reports a diagnosis of arthritis by a healthcare professional, and there is no distinction between different types of arthritis. In contrast with our findings among those with obesity, previous research reported that osteoarthritis was associated with increased odds of self-reported heart disease after controlling for BMI [67]. Further investigation may be warranted to determine why arthritis was not significantly associated with coronary heart disease among this group of respondents. Among those without obesity, the observed association between arthritis and coronary heart disease could, to some extent, be attributable to decreased physical activity [68], a known risk factor for both arthritis and coronary heart disease. Arthritis itself is an important cause of chronic pain and activity limitations among US adults [69, 70]. It is possible that arthritis is less of an important determinant of physical activity among individuals who have obesity than those without obesity. However, while physical activity was associated with reduced odds of CHD in univariable models, it was not a significant predictor in the final multivariable models after accounting for age, sex, other chronic conditions and perceived general health status. It is also worth noting that many patients with rheumatoid arthritis are affected by loss of lean body mass, and have a higher relative percentage of body fat than individuals of equal weight who do not have arthritis [65, 71, 72]. The difference in body fat percentage between individuals with rheumatoid arthritis and the general population is largest for those in the “healthy weight” and “underweight” ranges for BMI [71]. This may have contributed to the observed association between arthritis and CHD among those without obesity but not those with obesity in the present study. These findings highlight the importance of screening for cardiovascular disease among those with arthritis, including those who may not have other “classical” risk factors such as obesity [73].

Similarly, while having a personal doctor was associated with higher odds of CHD among respondents without obesity, no such association was observed among those with obesity. Respondents with obesity who did not have CHD may receive regular medical care for other conditions, which could explain the observed lack of association between CHD and having a personal doctor in the model for respondents with obesity. Obesity is an important risk factor for a number of chronic illnesses [2], and respondents with obesity had significantly higher prevalence of hypertension, diabetes, and high cholesterol than those without obesity in the current study. In addition, a significantly higher percentage of respondents with obesity reported having a personal doctor compared to those without obesity. Thus, the lack of association between CHD and having a regular source of care among respondents with obesity in the current study may reflect the higher prevalence of comorbidities in this group. In contrast, among those without obesity, who tended to have fewer comorbidities overall, those who have been diagnosed with CHD may be more likely to seek regular medical care. Alternatively, the observed association among those without obesity could suggest that individuals with a regular source of medical care are more likely to receive diagnostic testing for CHD than those without a personal doctor. This would be consistent with the lead time bias proposed to contribute to the obesity paradox, in which individuals with a BMI within the normal range are less likely to receive diagnostic testing for CHD, are diagnosed at a more advanced stage of disease, and therefore have a worse prognosis [17].

Strengths and limitations

To the authors’ knowledge, this is the first study to investigate and compare predictors of coronary heart disease among adults with and without obesity in North Dakota. Stratification of the analysis based upon the presence of obesity permitted the identification of predictors that differed between the two groups of respondents. Strengths of this study included a relatively large sample size, and the quality of BRFSS data with respect to reliability and validity [74]. In addition, sampling methods of the BRFSS survey result in estimates that are representative of the adult population in North Dakota, which is useful to inform health planning efforts in the state. However, this study was not without limitations. Key demographic variables, such as income, educational attainment, marital status and urban-rural location were not available in the dataset. Furthermore, while data from 2017–2021 were available for this study, some variables of interest (arthritis, high blood, pressure, high cholesterol, fruit and vegetable consumption) were not available in the 2018 and 2020 datasets, which were therefore excluded from the study. In addition, BRFSS survey data are self-reported, and can therefore be liable to recall bias and social desirability effects. Furthermore, due to the cross-sectional nature of the BRFSS survey, the temporal relationships between respondent characteristics and the development of CHD are unknown. Therefore, it is important to note that while this study provides evidence of association between CHD and variables identified as significant predictors in the logistic regression model, it does not provide evidence of causality. Despite the above limitations, findings of this study provide important information on the determinants of coronary heart disease among adults with obesity and without obesity in North Dakota.

Conclusions

This study provides information on the frequency and determinants of coronary heart disease among North Dakota adults. While the prevalence of CHD was significantly greater among adults with obesity than among those without obesity, the findings of the stratified analysis indicated that predictors of CHD for the two groups tended to be similar. The study identified a significant sex difference, with males having higher odds of CHD than females. Furthermore, the study highlights the relevance of other chronic conditions (high blood pressure, high cholesterol, chronic obstructive pulmonary disease, and diabetes), perceived general health status, and age as predictors of CHD among individuals regardless of obesity status. While respondents with obesity and without obesity shared many of the same predictors of coronary heart disease, having a personal doctor and having a diagnosis of arthritis were only associated with CHD among respondents without obesity. This study provides useful information to guide health planning and control programs that aim to reduce the burden of coronary heart disease in North Dakota.

Acknowledgments

The authors are grateful to the North Dakota Department of Health and Human Services for providing the Behavioral Risk Factor Surveillance System data used in this study. The authors also gratefully acknowledge Dr. Walid Q. Alali for academic supervision and support of Emmanuel Angmorteh Mensah.

References

1. National Heart L and BI. Know the Difference: Cardiovascular Disease, Heart Disease, Coronary Heart Disease Cardiovascular. Natl Inst Heal. 2021;1–5.
- View Article
- Google Scholar
2. World Health Organization (WHO). Cardiovascular diseases (CVDs). 2021. p. 1–6.
- View Article
- Google Scholar
3. Narvaez Linares NF, Poitras M, Burkauskas J, Nagaratnam K, Burr Z, Labelle PR, et al. Neuropsychological Sequelae of Coronary Heart Disease in Women: A Systematic Review. Neurosci Biobehav Rev. 2021;127(November 2020):837–51. pmid:34062209
- View Article
- PubMed/NCBI
- Google Scholar
4. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics—2020 update a report from the American Heart Association. Vol. 141, Circulation. 2020. 139–596 p. pmid:31992061
- View Article
- PubMed/NCBI
- Google Scholar
5. Hawaii State Department of Health. Coronary Heart Disease Fact Sheet. Chronic Dis Manag Control Branch—Hear Dis Stroke Prev Progr. 2012;123(4):1–8.
- View Article
- Google Scholar
6. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association. Circulation. 2022;145(8):E153–639. pmid:35078371
- View Article
- PubMed/NCBI
- Google Scholar
7. Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart Disease and Stroke Statistics—2023 Update: A Report from the American Heart Association. Vol. 147, Circulation. 2023. 93–621 p. pmid:36695182
- View Article
- PubMed/NCBI
- Google Scholar
8. National Center for Health Statistics. Leading Causes of Death, North Dakota. Centers for Disease Control and Prevention. 2021. p. 3–4.
- View Article
- Google Scholar
9. Weintraub WS. The Economic Burden of Illness. JAMA Netw open. 2023;6(3):e232663. pmid:36912842
- View Article
- PubMed/NCBI
- Google Scholar
10. Olafiranye O, Jean-Louis G, Antwi M, Zizi F, Shaw R, Brimah P, et al. Functional capacity is a better predictor of coronary heart disease than depression or abnormal sleep duration in Black and White Americans. Sleep Med. 2012;13(6):728–31. pmid:22465451
- View Article
- PubMed/NCBI
- Google Scholar
11. Lindeberg SI, Rosvall M, Östergren PO. Exhaustion predicts coronary heart disease independently of symptoms of depression and anxiety in men but not in women. J Psychosom Res. 2012;72(1):17–21. pmid:22200517
- View Article
- PubMed/NCBI
- Google Scholar
12. Wang W, Jiang Y, Lee CH. Independent predictors of physical health in community-dwelling patients with coronary heart disease in Singapore. Health Qual Life Outcomes. 2016;14(1):1–7. pmid:27464673
- View Article
- PubMed/NCBI
- Google Scholar
13. Ades PA, Savage PD. Obesity in coronary heart disease: An unaddressed behavioral risk factor. Prev Med (Baltim). 2017;104:117–9. pmid:28414064
- View Article
- PubMed/NCBI
- Google Scholar
14. Canoy D, Boekholdt SM, Wareham N, Luben R, Welch A, Bingham S, et al. Body fat distribution and risk of coronary heart disease in men and women in the european prospective investigation into cancer and nutrition in norfolk cohort: A population-based prospective study. Circulation. 2007;116(25):2933–43. pmid:18071080
- View Article
- PubMed/NCBI
- Google Scholar
15. Kashima T. Obesity and Cardiovascular Diseases. Vol. 52, IRYO—Japanese Journal of National Medical Services. 1998. 3–7 p.
- View Article
- Google Scholar
16. Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G, et al. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: A pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383(9921):970–83.
- View Article
- Google Scholar
17. De Schutter A, Lavie CJ, Milani R V. The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease-the obesity paradox. Prog Cardiovasc Dis. 2014;56(4):401–8. pmid:24438731
- View Article
- PubMed/NCBI
- Google Scholar
18. Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani R V. Body composition and survival in stable coronary heart disease: Impact of lean mass index and body fat in the “obesity paradox.” J Am Coll Cardiol. 2012;60(15):1374–80. pmid:22958953
- View Article
- PubMed/NCBI
- Google Scholar
19. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Prog Cardiovasc Dis. 2018 Jul 1;61(2):142–50. pmid:29981771
- View Article
- PubMed/NCBI
- Google Scholar
20. Mandviwala T, Khalid U, Deswal A. Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Curr Atheroscler Rep. 2016 May 1;18(5):1–10. pmid:26973130
- View Article
- PubMed/NCBI
- Google Scholar
21. Manoharan MP, Raja R, Jamil A, Csendes D, Gutlapalli SD, Prakash K, et al. Obesity and Coronary Artery Disease: An Updated Systematic Review 2022. Cureus. 2022;14(9). pmid:36299943
- View Article
- PubMed/NCBI
- Google Scholar
22. North Dakota Healthcare Workforce Group. North Dakota County Classification. Center for Rural Health, University of North Dakota School of Medicine and Health Sciences. 2020.
- View Article
- Google Scholar
23. US Census Bureau. 2010 Census Urban and Rural Classification and Urban Area Criteria. 2021.
- View Article
- Google Scholar
24. Center for Social Research. Health and Well-Being in North Dakota, 2022 –A Social Determinants of Health Perspective. North Dakota State Univ. 2023;(109).
- View Article
- Google Scholar
25. United States Census Bureau. QuickFacts: North Dakota. 2023.
26. CDC. Behavioral risk factor surveillance system. Centers for Disease Control and Prevention. USA; 2023.
27. Brady SS, Brubaker L, Fok CS, Gahagan S, Lewis CE, Lewis J, et al. Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms. Health Promot Pract. 2020;21(4):510–24. pmid:31910039
- View Article
- PubMed/NCBI
- Google Scholar
28. StataCorp. Stata statistical analysis software. Version 17. Coll Station TX, USAStataCorp LLC. 2022;
29. CDC. Complex Sampling Weights and Preparing 2021 BRFSS Module Data for Analysis. Behav Risk Factor Surveill Syst. 2022; July(July):1–13.
30. Centers for Disease Control and Prevention. BRFSS prevalence and trends data 2022. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health: Atlanta, GA. 2023. p. 3–4.
31. US Census Bureau. Population estimats; North Dakota and Alabama. QuickFacts. 2023. p. 0–2.
32. Choi S, Kim K, Kim SM, Lee G, Jeong SM, Park SY, et al. Association of obesity or weight change with coronary heart disease among young adults in South Korea. JAMA Intern Med. 2018;178(8):1060–8. pmid:29913019
- View Article
- PubMed/NCBI
- Google Scholar
33. Isiadinso I, Wenger NK. Do We Need a Different Approach to Assess Cardiovascular Risk in Women? US Cardiol Rev. 2017;11(1):5.
- View Article
- Google Scholar
34. Connelly PJ, Azizi Z, Alipour P, Delles C, Pilote L, Raparelli V. The Importance of Gender to Understand Sex Differences in Cardiovascular Disease. Can J Cardiol. 2021;37(5):699–710. pmid:33592281
- View Article
- PubMed/NCBI
- Google Scholar
35. Reitsma MB, Fullman N, Ng M, Salama JS, Abajobir A, Abate KH, et al. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: A systematic analysis from the global burden of disease study 2015. Lancet. 2017;389(10082):1885–906. pmid:28390697
- View Article
- PubMed/NCBI
- Google Scholar
36. Maas AHEM, Appelman YEA. Gender differences in coronary heart disease. Netherlands Hear J. 2010;18(12):598–603. pmid:21301622
- View Article
- PubMed/NCBI
- Google Scholar
37. Olafiranye O, Zizi F, Brimah P, Jean-Louis G, Makaryus AN, McFarlane S, et al. Management of hypertension among patients with coronary heart disease. Int J Hypertens. 2011;2011. pmid:21785704
- View Article
- PubMed/NCBI
- Google Scholar
38. Poulimenos LE, Kallistratos MS, Mancia G, Manolis AJ. HYPERTENSION AND CORONARY HEART DISEASE. Eur Soc Hypertens. 2018;nr. 68(11):1–2.
- View Article
- Google Scholar
39. Cubrilo-Turek M. Hypertension and Coronary Heart Disease. EJIFCC. 2003 Jul;14(2):67–73. pmid:30302078
- View Article
- PubMed/NCBI
- Google Scholar
40. Lawes CM, Hoorn S Vander, Rodgers A. Global burden of blood-pressure-related disease, 2001. Lancet. 2008 May;371(9623):1513–8. pmid:18456100
- View Article
- PubMed/NCBI
- Google Scholar
41. Mortensen MB, Nordestgaard BG. Elevated LDL cholesterol and increased risk of myocardial infarction and atherosclerotic cardiovascular disease in individuals aged 70–100 years: a contemporary primary prevention cohort. Lancet. 2020;396(10263):1644–52. pmid:33186534
- View Article
- PubMed/NCBI
- Google Scholar
42. Sachdeva A, Cannon CP, Deedwania PC, LaBresh KA, Smith SC, Dai D, et al. Lipid levels in patients hospitalized with coronary artery disease: An analysis of 136,905 hospitalizations in Get With The Guidelines. Am Heart J. 2009;157(1). pmid:19081406
- View Article
- PubMed/NCBI
- Google Scholar
43. America’s Health Ranking analysis of CDC. High Cholesterol in North Dakota. Behavioral Risk Factor Surveillance System, United Health Foundation, AmericasHealthRankings.org. 2021.
44. Linton MF, Yancey PG, Davies SS, Jerome WG, Linton EF, Song WL, et al. The Role of Lipids and Lipoproteins in Atherosclerosis. Vol. 111, Endotext. 2000. 1–109 p.
- View Article
- Google Scholar
45. Feingold KR, Chait A. Approach to patients with elevated low-density lipoprotein cholesterol levels. Best Pract Res Clin Endocrinol Metab. 2023;37(3):101658. pmid:35487874
- View Article
- PubMed/NCBI
- Google Scholar
46. Navar-Boggan AM, Peterson ED, D’Agostino RB, Neely B, Sniderman AD, Pencina MJ. Hyperlipidemia in early adulthood increases long-term risk of coronary heart disease. Circulation. 2015;131(5):451–8. pmid:25623155
- View Article
- PubMed/NCBI
- Google Scholar
47. Wang JJ. Risk of coronary heart disease in people with chronic obstructive pulmonary disease: A meta-analysis. Int J COPD. 2021;16:2939–44. pmid:34737558
- View Article
- PubMed/NCBI
- Google Scholar
48. Lotufo PA, Gaziano JM, Chae CU, Ajani UA, Moreno-John G, Buring JE, et al. Diabetes and all-cause and coronary heart disease mortality among US male physicians. Arch Intern Med. 2001;161(2):242–7. pmid:11176738
- View Article
- PubMed/NCBI
- Google Scholar
49. Hokanson JE. Editorial: Copd and coronary heart disease: Challenges in understanding the natural history of common complex chronic diseases. COPD J Chronic Obstr Pulm Dis. 2009;6(3):149–51.
- View Article
- Google Scholar
50. Ali MK, Narayan KMV, Tandon N. Diabetes & coronary heart disease: Current perspectives. Indian J Med Res. 2010;132(11):584–97.
- View Article
- Google Scholar
51. Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: A systematic review of systematic reviews. Eur J Public Health. 2019;29(1):182–9. pmid:29878097
- View Article
- PubMed/NCBI
- Google Scholar
52. Hoffman D. Commentary on Chronic Disease Prevention in the US in 2022. Ann Bioeth Clin Appl. 2022;5(2).
- View Article
- Google Scholar
53. Rodgers JL, Jones J, Bolleddu SI, Vanthenapalli S, Rodgers LE, Shah K, et al. Cardiovascular risks associated with gender and aging. J Cardiovasc Dev Dis. 2019;6(2). pmid:31035613
- View Article
- PubMed/NCBI
- Google Scholar
54. Alpert JS. New Coronary Heart Disease Risk Factors. Am J Med. 2023;136(4):331–2. pmid:35981648
- View Article
- PubMed/NCBI
- Google Scholar
55. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–47. pmid:9603539
- View Article
- PubMed/NCBI
- Google Scholar
56. Sarkar U, Ali S, Whooley MA. Self-efficacy and health status in patients with coronary heart disease: Findings from the heart and soul study. Psychosom Med. 2007;69(4):306–12. pmid:17510290
- View Article
- PubMed/NCBI
- Google Scholar
57. Greenway SC. Quality of Life in Adults With Congenital Heart Disease: Function Over Form. Can J Cardiol. 2021;37(2):186–7. pmid:32339650
- View Article
- PubMed/NCBI
- Google Scholar
58. Lam CLK, Lauder IJ. The impact of chronic diseases on the health-related quality of life (HRQOL) of Chinese patients in primary care. Fam Pract. 2000;17(2):159–66. pmid:10758080
- View Article
- PubMed/NCBI
- Google Scholar
59. Ko Y, Boo S. Self-perceived health versus actual cardiovascular disease risks. Japan J Nurs Sci. 2016;13(1):65–74. pmid:26103790
- View Article
- PubMed/NCBI
- Google Scholar
60. Gallagher JE, Wilkie AA, Cordner A, Hudgens EE, Ghio AJ, Birch RJ, et al. Factors associated with self-reported health: Implications for screening level community-based health and environmental studies. BMC Public Health. 2016;16(1):1–15. pmid:27460934
- View Article
- PubMed/NCBI
- Google Scholar
61. Snyder K, Hill M, Lee M, Crawford TN, Orlowski M. The Relationships Between Physical Health and Chronic Disease, Stress, and Resource Strain in Head Start Employees. Work Heal Saf. 2020;68(4):190–201. pmid:31735135
- View Article
- PubMed/NCBI
- Google Scholar
62. Axon DR, Jang A, Son L, Pham T. Determining the association of perceived health status among united states older adults with self-reported pain. Aging Heal Res. 2022;2(1):100051.
- View Article
- Google Scholar
63. Fernandes GS, Valdes AM. Cardiovascular disease and osteoarthritis: common pathways and patient outcomes. Eur J Clin Invest [Internet]. 2015 Apr 1;45(4):405–14. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.12413 pmid:25630589
- View Article
- PubMed/NCBI
- Google Scholar
64. Wilson PWF. Evidence of Systemic Inflammation and Estimation of Coronary Artery Disease Risk: A Population Perspective. Am J Med. 2008 Oct 1;121(10):S15–20.
- View Article
- Google Scholar
65. John H, Kitas G. Inflammatory arthritis as a novel risk factor for cardiovascular disease. Eur J Intern Med. 2012 Oct 1;23(7):575–9. pmid:22841864
- View Article
- PubMed/NCBI
- Google Scholar
66. Park J, Mendy A, Vieira ER. Various Types of Arthritis in the United States: Prevalence and Age-Related Trends From 1999 to 2014. Am J Public Health [Internet]. 2018 Feb 1 [cited 2024 Sep 19];108(2):256–8. Available from: https://ajph.aphapublications.org/doi/10.2105/AJPH.2017.304179 pmid:29267054
- View Article
- PubMed/NCBI
- Google Scholar
67. Rahman MM, Kopec JA, Cibere J, Goldsmith CH, Anis AH. The relationship between osteoarthritis and cardiovascular disease in a population health survey: A cross-sectional study. BMJ Open. 2013;3(5):1–8.
- View Article
- Google Scholar
68. Batsis JA, Zbehlik AJ, Barre LK, Bynum JPW, Pidgeon D, Bartels SJ. Impact of obesity on disability, function, and physical activity: Data from the Osteoarthritis Initiative. Scand J Rheumatol. 2015;44(6):495–502. pmid:26083472
- View Article
- PubMed/NCBI
- Google Scholar
69. Theis KA, Murphy LB, Guglielmo D, Boring MA, Okoro CA, Duca LM, et al. Prevalence of Arthritis and Arthritis-Attributable Activity Limitation—United States, 2016–2018. MMWR Morb Mortal Wkly Rep [Internet]. 2021;70(40):1401–7. Available from: https://pubmed.ncbi.nlm.nih.gov/34618800/
- View Article
- Google Scholar
70. Fallon EA, Boring MA, Foster AL, Stowe EW, Lites TD, Odom EL, et al. Prevalence of Diagnosed Arthritis—United States, 2019–2021. MMWR Morb Mortal Wkly Rep [Internet]. 2023 Oct 13 [cited 2024 Sep 19];72(41):1101–7. Available from: https://www.cdc.gov/mmwr/volumes/72/wr/mm7241a1.htm pmid:37824422
- View Article
- PubMed/NCBI
- Google Scholar
71. Challal S, Minichiello E, Boissier MC, Semerano L. Cachexia and adiposity in rheumatoid arthritis. Relevance for disease management and clinical outcomes. Jt Bone Spine. 2016 Mar 1;83(2):127–33. pmid:26184539
- View Article
- PubMed/NCBI
- Google Scholar
72. Stavropoulos-Kalinoglou A, Metsios GS, Koutedakis Y, Nevill AM, Douglas KM, Jamurtas A, et al. Redefining overweight and obesity in rheumatoid arthritis patients. Ann Rheum Dis. 2007 Oct 1;66(10):1316–21. pmid:17289757
- View Article
- PubMed/NCBI
- Google Scholar
73. Aronov A, Kim YJ, Sweiss NJ, Nazir NT. Cardiovascular disease risk evaluation impact in patients with rheumatoid arthritis. Am J Prev Cardiol [Internet]. 2022 Dec 1 [cited 2024 Sep 19];12:100380. Available from: /pmc/articles/PMC9489492/ pmid:36157554
- View Article
- PubMed/NCBI
- Google Scholar
74. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011. BMC Med Res Methodol. 2013;13(1):1–14. pmid:23522349
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. National Heart L and BI. Know the Difference: Cardiovascular Disease, Heart Disease, Coronary Heart Disease Cardiovascular. Natl Inst Heal. 2021;1–5.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. World Health Organization (WHO). Cardiovascular diseases (CVDs). 2021. p. 1–6.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Narvaez Linares NF, Poitras M, Burkauskas J, Nagaratnam K, Burr Z, Labelle PR, et al. Neuropsychological Sequelae of Coronary Heart Disease in Women: A Systematic Review. Neurosci Biobehav Rev. 2021;127(November 2020):837–51. pmid:34062209
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

[10] Google Scholar

[ref4] 4. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics—2020 update a report from the American Heart Association. Vol. 141, Circulation. 2020. 139–596 p. pmid:31992061
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref5] 5. Hawaii State Department of Health. Coronary Heart Disease Fact Sheet. Chronic Dis Manag Control Branch—Hear Dis Stroke Prev Progr. 2012;123(4):1–8.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref6] 6. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association. Circulation. 2022;145(8):E153–639. pmid:35078371
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref7] 7. Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart Disease and Stroke Statistics—2023 Update: A Report from the American Heart Association. Vol. 147, Circulation. 2023. 93–621 p. pmid:36695182
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. National Center for Health Statistics. Leading Causes of Death, North Dakota. Centers for Disease Control and Prevention. 2021. p. 3–4.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref9] 9. Weintraub WS. The Economic Burden of Illness. JAMA Netw open. 2023;6(3):e232663. pmid:36912842
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Olafiranye O, Jean-Louis G, Antwi M, Zizi F, Shaw R, Brimah P, et al. Functional capacity is a better predictor of coronary heart disease than depression or abnormal sleep duration in Black and White Americans. Sleep Med. 2012;13(6):728–31. pmid:22465451
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Lindeberg SI, Rosvall M, Östergren PO. Exhaustion predicts coronary heart disease independently of symptoms of depression and anxiety in men but not in women. J Psychosom Res. 2012;72(1):17–21. pmid:22200517
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Wang W, Jiang Y, Lee CH. Independent predictors of physical health in community-dwelling patients with coronary heart disease in Singapore. Health Qual Life Outcomes. 2016;14(1):1–7. pmid:27464673
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Ades PA, Savage PD. Obesity in coronary heart disease: An unaddressed behavioral risk factor. Prev Med (Baltim). 2017;104:117–9. pmid:28414064
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Canoy D, Boekholdt SM, Wareham N, Luben R, Welch A, Bingham S, et al. Body fat distribution and risk of coronary heart disease in men and women in the european prospective investigation into cancer and nutrition in norfolk cohort: A population-based prospective study. Circulation. 2007;116(25):2933–43. pmid:18071080
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Kashima T. Obesity and Cardiovascular Diseases. Vol. 52, IRYO—Japanese Journal of National Medical Services. 1998. 3–7 p.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G, et al. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: A pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383(9921):970–83.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref17] 17. De Schutter A, Lavie CJ, Milani R V. The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease-the obesity paradox. Prog Cardiovasc Dis. 2014;56(4):401–8. pmid:24438731
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref18] 18. Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani R V. Body composition and survival in stable coronary heart disease: Impact of lean mass index and body fat in the “obesity paradox.” J Am Coll Cardiol. 2012;60(15):1374–80. pmid:22958953
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref19] 19. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Prog Cardiovasc Dis. 2018 Jul 1;61(2):142–50. pmid:29981771
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref20] 20. Mandviwala T, Khalid U, Deswal A. Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Curr Atheroscler Rep. 2016 May 1;18(5):1–10. pmid:26973130
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref21] 21. Manoharan MP, Raja R, Jamil A, Csendes D, Gutlapalli SD, Prakash K, et al. Obesity and Coronary Artery Disease: An Updated Systematic Review 2022. Cureus. 2022;14(9). pmid:36299943
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref22] 22. North Dakota Healthcare Workforce Group. North Dakota County Classification. Center for Rural Health, University of North Dakota School of Medicine and Health Sciences. 2020.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref23] 23. US Census Bureau. 2010 Census Urban and Rural Classification and Urban Area Criteria. 2021.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref24] 24. Center for Social Research. Health and Well-Being in North Dakota, 2022 –A Social Determinants of Health Perspective. North Dakota State Univ. 2023;(109).
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref25] 25. United States Census Bureau. QuickFacts: North Dakota. 2023.

[ref26] 26. CDC. Behavioral risk factor surveillance system. Centers for Disease Control and Prevention. USA; 2023.

[ref27] 27. Brady SS, Brubaker L, Fok CS, Gahagan S, Lewis CE, Lewis J, et al. Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms. Health Promot Pract. 2020;21(4):510–24. pmid:31910039
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref28] 28. StataCorp. Stata statistical analysis software. Version 17. Coll Station TX, USAStataCorp LLC. 2022;

[ref29] 29. CDC. Complex Sampling Weights and Preparing 2021 BRFSS Module Data for Analysis. Behav Risk Factor Surveill Syst. 2022; July(July):1–13.

[ref30] 30. Centers for Disease Control and Prevention. BRFSS prevalence and trends data 2022. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health: Atlanta, GA. 2023. p. 3–4.

[ref31] 31. US Census Bureau. Population estimats; North Dakota and Alabama. QuickFacts. 2023. p. 0–2.

[ref32] 32. Choi S, Kim K, Kim SM, Lee G, Jeong SM, Park SY, et al. Association of obesity or weight change with coronary heart disease among young adults in South Korea. JAMA Intern Med. 2018;178(8):1060–8. pmid:29913019
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref33] 33. Isiadinso I, Wenger NK. Do We Need a Different Approach to Assess Cardiovascular Risk in Women? US Cardiol Rev. 2017;11(1):5.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref34] 34. Connelly PJ, Azizi Z, Alipour P, Delles C, Pilote L, Raparelli V. The Importance of Gender to Understand Sex Differences in Cardiovascular Disease. Can J Cardiol. 2021;37(5):699–710. pmid:33592281
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref35] 35. Reitsma MB, Fullman N, Ng M, Salama JS, Abajobir A, Abate KH, et al. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: A systematic analysis from the global burden of disease study 2015. Lancet. 2017;389(10082):1885–906. pmid:28390697
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref36] 36. Maas AHEM, Appelman YEA. Gender differences in coronary heart disease. Netherlands Hear J. 2010;18(12):598–603. pmid:21301622
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref37] 37. Olafiranye O, Zizi F, Brimah P, Jean-Louis G, Makaryus AN, McFarlane S, et al. Management of hypertension among patients with coronary heart disease. Int J Hypertens. 2011;2011. pmid:21785704
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref38] 38. Poulimenos LE, Kallistratos MS, Mancia G, Manolis AJ. HYPERTENSION AND CORONARY HEART DISEASE. Eur Soc Hypertens. 2018;nr. 68(11):1–2.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref39] 39. Cubrilo-Turek M. Hypertension and Coronary Heart Disease. EJIFCC. 2003 Jul;14(2):67–73. pmid:30302078
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref40] 40. Lawes CM, Hoorn S Vander, Rodgers A. Global burden of blood-pressure-related disease, 2001. Lancet. 2008 May;371(9623):1513–8. pmid:18456100
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref41] 41. Mortensen MB, Nordestgaard BG. Elevated LDL cholesterol and increased risk of myocardial infarction and atherosclerotic cardiovascular disease in individuals aged 70–100 years: a contemporary primary prevention cohort. Lancet. 2020;396(10263):1644–52. pmid:33186534
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref42] 42. Sachdeva A, Cannon CP, Deedwania PC, LaBresh KA, Smith SC, Dai D, et al. Lipid levels in patients hospitalized with coronary artery disease: An analysis of 136,905 hospitalizations in Get With The Guidelines. Am Heart J. 2009;157(1). pmid:19081406
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref43] 43. America’s Health Ranking analysis of CDC. High Cholesterol in North Dakota. Behavioral Risk Factor Surveillance System, United Health Foundation, AmericasHealthRankings.org. 2021.

[ref44] 44. Linton MF, Yancey PG, Davies SS, Jerome WG, Linton EF, Song WL, et al. The Role of Lipids and Lipoproteins in Atherosclerosis. Vol. 111, Endotext. 2000. 1–109 p.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref45] 45. Feingold KR, Chait A. Approach to patients with elevated low-density lipoprotein cholesterol levels. Best Pract Res Clin Endocrinol Metab. 2023;37(3):101658. pmid:35487874
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref46] 46. Navar-Boggan AM, Peterson ED, D’Agostino RB, Neely B, Sniderman AD, Pencina MJ. Hyperlipidemia in early adulthood increases long-term risk of coronary heart disease. Circulation. 2015;131(5):451–8. pmid:25623155
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref47] 47. Wang JJ. Risk of coronary heart disease in people with chronic obstructive pulmonary disease: A meta-analysis. Int J COPD. 2021;16:2939–44. pmid:34737558
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref48] 48. Lotufo PA, Gaziano JM, Chae CU, Ajani UA, Moreno-John G, Buring JE, et al. Diabetes and all-cause and coronary heart disease mortality among US male physicians. Arch Intern Med. 2001;161(2):242–7. pmid:11176738
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref49] 49. Hokanson JE. Editorial: Copd and coronary heart disease: Challenges in understanding the natural history of common complex chronic diseases. COPD J Chronic Obstr Pulm Dis. 2009;6(3):149–51.
View Article
Google Scholar

[161] View Article

[162] Google Scholar

[ref50] 50. Ali MK, Narayan KMV, Tandon N. Diabetes & coronary heart disease: Current perspectives. Indian J Med Res. 2010;132(11):584–97.
View Article
Google Scholar

[164] View Article

[165] Google Scholar

[ref51] 51. Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: A systematic review of systematic reviews. Eur J Public Health. 2019;29(1):182–9. pmid:29878097
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref52] 52. Hoffman D. Commentary on Chronic Disease Prevention in the US in 2022. Ann Bioeth Clin Appl. 2022;5(2).
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref53] 53. Rodgers JL, Jones J, Bolleddu SI, Vanthenapalli S, Rodgers LE, Shah K, et al. Cardiovascular risks associated with gender and aging. J Cardiovasc Dev Dis. 2019;6(2). pmid:31035613
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref54] 54. Alpert JS. New Coronary Heart Disease Risk Factors. Am J Med. 2023;136(4):331–2. pmid:35981648
View Article
PubMed/NCBI
Google Scholar

[178] View Article

[179] PubMed/NCBI

[180] Google Scholar

[ref55] 55. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–47. pmid:9603539
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref56] 56. Sarkar U, Ali S, Whooley MA. Self-efficacy and health status in patients with coronary heart disease: Findings from the heart and soul study. Psychosom Med. 2007;69(4):306–12. pmid:17510290
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref57] 57. Greenway SC. Quality of Life in Adults With Congenital Heart Disease: Function Over Form. Can J Cardiol. 2021;37(2):186–7. pmid:32339650
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref58] 58. Lam CLK, Lauder IJ. The impact of chronic diseases on the health-related quality of life (HRQOL) of Chinese patients in primary care. Fam Pract. 2000;17(2):159–66. pmid:10758080
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref59] 59. Ko Y, Boo S. Self-perceived health versus actual cardiovascular disease risks. Japan J Nurs Sci. 2016;13(1):65–74. pmid:26103790
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref60] 60. Gallagher JE, Wilkie AA, Cordner A, Hudgens EE, Ghio AJ, Birch RJ, et al. Factors associated with self-reported health: Implications for screening level community-based health and environmental studies. BMC Public Health. 2016;16(1):1–15. pmid:27460934
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref61] 61. Snyder K, Hill M, Lee M, Crawford TN, Orlowski M. The Relationships Between Physical Health and Chronic Disease, Stress, and Resource Strain in Head Start Employees. Work Heal Saf. 2020;68(4):190–201. pmid:31735135
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref62] 62. Axon DR, Jang A, Son L, Pham T. Determining the association of perceived health status among united states older adults with self-reported pain. Aging Heal Res. 2022;2(1):100051.
View Article
Google Scholar

[210] View Article

[211] Google Scholar

[ref63] 63. Fernandes GS, Valdes AM. Cardiovascular disease and osteoarthritis: common pathways and patient outcomes. Eur J Clin Invest [Internet]. 2015 Apr 1;45(4):405–14. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.12413 pmid:25630589
View Article
PubMed/NCBI
Google Scholar

[213] View Article

[214] PubMed/NCBI

[215] Google Scholar

[ref64] 64. Wilson PWF. Evidence of Systemic Inflammation and Estimation of Coronary Artery Disease Risk: A Population Perspective. Am J Med. 2008 Oct 1;121(10):S15–20.
View Article
Google Scholar

[217] View Article

[218] Google Scholar

[ref65] 65. John H, Kitas G. Inflammatory arthritis as a novel risk factor for cardiovascular disease. Eur J Intern Med. 2012 Oct 1;23(7):575–9. pmid:22841864
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

[ref66] 66. Park J, Mendy A, Vieira ER. Various Types of Arthritis in the United States: Prevalence and Age-Related Trends From 1999 to 2014. Am J Public Health [Internet]. 2018 Feb 1 [cited 2024 Sep 19];108(2):256–8. Available from: https://ajph.aphapublications.org/doi/10.2105/AJPH.2017.304179 pmid:29267054
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref67] 67. Rahman MM, Kopec JA, Cibere J, Goldsmith CH, Anis AH. The relationship between osteoarthritis and cardiovascular disease in a population health survey: A cross-sectional study. BMJ Open. 2013;3(5):1–8.
View Article
Google Scholar

[228] View Article

[229] Google Scholar

[ref68] 68. Batsis JA, Zbehlik AJ, Barre LK, Bynum JPW, Pidgeon D, Bartels SJ. Impact of obesity on disability, function, and physical activity: Data from the Osteoarthritis Initiative. Scand J Rheumatol. 2015;44(6):495–502. pmid:26083472
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref69] 69. Theis KA, Murphy LB, Guglielmo D, Boring MA, Okoro CA, Duca LM, et al. Prevalence of Arthritis and Arthritis-Attributable Activity Limitation—United States, 2016–2018. MMWR Morb Mortal Wkly Rep [Internet]. 2021;70(40):1401–7. Available from: https://pubmed.ncbi.nlm.nih.gov/34618800/
View Article
Google Scholar

[235] View Article

[236] Google Scholar

[ref70] 70. Fallon EA, Boring MA, Foster AL, Stowe EW, Lites TD, Odom EL, et al. Prevalence of Diagnosed Arthritis—United States, 2019–2021. MMWR Morb Mortal Wkly Rep [Internet]. 2023 Oct 13 [cited 2024 Sep 19];72(41):1101–7. Available from: https://www.cdc.gov/mmwr/volumes/72/wr/mm7241a1.htm pmid:37824422
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref71] 71. Challal S, Minichiello E, Boissier MC, Semerano L. Cachexia and adiposity in rheumatoid arthritis. Relevance for disease management and clinical outcomes. Jt Bone Spine. 2016 Mar 1;83(2):127–33. pmid:26184539
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref72] 72. Stavropoulos-Kalinoglou A, Metsios GS, Koutedakis Y, Nevill AM, Douglas KM, Jamurtas A, et al. Redefining overweight and obesity in rheumatoid arthritis patients. Ann Rheum Dis. 2007 Oct 1;66(10):1316–21. pmid:17289757
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref73] 73. Aronov A, Kim YJ, Sweiss NJ, Nazir NT. Cardiovascular disease risk evaluation impact in patients with rheumatoid arthritis. Am J Prev Cardiol [Internet]. 2022 Dec 1 [cited 2024 Sep 19];12:100380. Available from: /pmc/articles/PMC9489492/ pmid:36157554
View Article
PubMed/NCBI
Google Scholar

[250] View Article

[251] PubMed/NCBI

[252] Google Scholar

[ref74] 74. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011. BMC Med Res Methodol. 2013;13(1):1–14. pmid:23522349
View Article
PubMed/NCBI
Google Scholar

[254] View Article

[255] PubMed/NCBI

[256] Google Scholar

Abstract

Introduction

Methods

Results

Conclusion

Figures

Introduction

Methods

Ethics approval

Study area and design

Data sources and variable selection

Data preparation

Statistical analysis

Results

Descriptive analyses

Predictors of coronary heart disease among respondents with and without obesity

Discussion

Strengths and limitations

Conclusions

Acknowledgments

References

Cookie Preference Center

Customize Your Cookie Preference