Sage Journals: Discover world-class research

Abstract

Aim:

To examine the association of cardiovascular disease risk factors with and their cumulative effect on coronary artery calcium in hard-to-reach asymptomatic patients with diabetes.

Methods:

A total of 2563 community-dwelling asymptomatic subjects from Central Appalachia participated in coronary artery calcium screening at a heart centre. Binary variable was used to indicate that coronary artery calcium was either present or absent. Independent variables consisted of demographic and modifiable risk factors and medical conditions. Descriptive statistics and multinomial logistic regression analyses were conducted.

Results:

In total, 55.8% and 13.7% of study participants had subclinical atherosclerosis (coronary artery calcium ⩾1) and diabetes, respectively. The presence of coronary artery calcium was higher in subjects with diabetes (68.5%) than those without (53.8%). Compared to subjects without diabetes with coronary artery calcium = 0, obesity, hypertension, hypercholesterolaemia and smoking increased the odds of the presence of coronary artery calcium (coronary artery calcium score ⩾1) regardless of diabetes status; however, with larger odds ratios in subjects with diabetes. Compared to subjects without diabetes with coronary artery calcium score = 0, having 3, 4 and ⩾5 risk factors increased the odds of presence of coronary artery calcium in subjects with diabetes by 14.06 (confidence interval = 3.26–62.69), 32.30 (confidence interval = 7.41–140.82) and 47.12 (confidence interval = 10.35–214.66) times, respectively.

Conclusion:

There is a need for awareness about subclinical atherosclerosis in patients with diabetes and more research about coronary artery calcium in subpopulations of patients.

Keywords

Subclinical atherosclerosis diabetes coronary artery calcium cardiovascular diseases Appalachia multiple risk factors coronary artery disease

Introduction

Diabetes^1–3 is a strong risk factor for cardiovascular diseases (CVDs), which is the leading cause of death in the United States.⁴ In patients with diabetes, CVD accounts for 65%–70% of deaths⁵ and 85% of cumulative treatment costs for the initial 5 years following diagnosis.⁶ In 2013, diabetes accounted for over 75,000 deaths in the United States³ with the financial impact for 2012 estimated at US$245 billion in direct and indirect economic costs.^3,7,8 There are nearly 30 million (9.3%) people in the United States diagnosed with diabetes. The prevalence continues to increase^9,10 and is expected to reach one in three adults by 2050.⁷ This increasing prevalence makes diabetes a major public health issue that requires both medical and policy attentions.

Diabetes is widely recognized as an independent risk factor for the development of clinical atherosclerotic CVD and was historically recognized as a cardiovascular risk equivalent,^5,11 although not all patients with diabetes appear to be at equal risk.¹² The prevalence of diabetes in the United States is, likewise, not uniform as major disparities exist across population subgroups and geographic areas.^13,14 Particularly, the Appalachian region is considered as the ‘Diabetes Belt’ due to a disproportionately high burden of the disease.¹³

Of the 644 counties in the ‘Diabetes Belt’, 232 are in Appalachia, including a majority of the Central Appalachian counties.^13,14 Studies have found that residents in distressed Appalachian counties had a 33% higher risk [95% confidence interval (CI), 1.10–1.60] of reporting diabetes than residents of non-Appalachian counties.¹⁵ In addition, people in distressed or at-risk counties (the least economically developed) were diagnosed for diabetes at an earlier age, by 2–3 years, than in non-Appalachian counties.¹⁶ Rural areas, such as Central Appalachia, have a higher diabetes prevalence relative to urban areas and the remainder of the United States as a whole,^13,16 suggesting that research in subclinical atherosclerosis in patients with diabetes in this region is critical to achieve the Healthy People 2020 goals for reducing health disparities.

Coronary artery calcium (CAC) is a measure of subclinical atherosclerosis and has been validated as a predictor of cardiovascular events in asymptomatic patients, including those with diabetes.^17–19 Studies have shown that asymptomatic patients with diabetes have increased prevalence and progression of CAC and a higher incidence of CVD as absolute values increase.¹⁹ Both cross-sectional¹⁷ and longitudinal^20,21 studies have found that CAC scores were higher in men and women with diabetes across all age groups than those without diabetes and that CAC correlates with an increase in mortality for patients with diabetes. As such, it is reasonable to infer that using screening tools, such as CAC to identify subclinical atherosclerosis in regions with a high burden of diabetes, such as Central Appalachia,¹³ may have positive health benefits. However, few studies evaluating the utility of CAC to detect and prevent CVD events in patients with diabetes in the Appalachian region exist,^15,16 providing the basis for this study that assesses subclinical atherosclerosis in Central Appalachian subjects with diabetes.

This study aimed to (1) examine the association of traditional CVD risk factors with CAC status in a hard-to-reach diabetic population and (2) assess the association between the number of traditional CVD risk factors and CAC status in this population. While studies have examined the association between the traditional risk factors and cardiovascular outcomes, such as subclinical atherosclerosis (assessed as CAC),²² little evidence exists on how multiple risk factors assessed collectively predicts CAC in patients with diabetes. Moreover, per our knowledge, no study has evaluated these outcomes in Central Appalachia, although the region constitutes the core of the ‘Diabetes Belt’ in the United States.^13,14 Given the propensity for overt CVD in patients with diabetes,^2,3 identifying the presence of subclinical atherosclerosis could lead to reclassification of patients with diabetes at risk for developing CVD for primary and prevention efforts. Thus, this study has the potential to inform policies and programmes targeting both diabetes and CVD in the Central Appalachian region, which would translate into clinical practice and improve CVD outcomes for residents of this region and other underserved populations.

Methods

Study population

The study population consisted of 3000 asymptomatic individuals from the Central Appalachian region of Southeast Kentucky, Western North Carolina, Northeast Tennessee and Southwest Virginia who had participated in a CAC screening programme in the largest cardiovascular centre in the region between August 2012 and December 2016. The recruitment, enrolment and data collection processes have been published in previous studies.^23–25 In brief, adult patients visiting the cardiovascular centre who were free of any cardiovascular events were assessed for eligibility for the CAC screening. Eligible participants were adults aged 18 years or older who were referred by a physician. Self-referred individuals were also included if they met age eligibility of 45 years for males and 55 years for females. Potential subjects were informed about the study procedure and were assured about the confidentiality of the information collected. The participants completed an informed consent process before participating in the CAC screening. A self-administered questionnaire was used to collect additional information on lifestyle, risk behaviours, history of cardiovascular health and anthropometric measures. Ethical approval for this study was obtained from the institutional review board (IRB) of investigators’ institutions and the cardiovascular centre.

The data for the total population were examined for completeness. Subjects with missing information for either dependent or independent variables (n = 400) were excluded from the final analyses. In addition, non-Whites (n = 34), subjects with extreme recorded values for height (n = 1) and weight (n = 2) were excluded from the final study sample to reduce the effect of outliers. As a result, out of the 3000 initial subjects, the final analyses included 2563 (85.43%) subjects. Presence of diabetes was indicated by a self-reported question, with yes or no response, as to whether or not the subject had been informed by a physician or other healthcare professional that he or she has diabetes. Subjects were also asked whether or not they were currently taking any medication to control their diabetes in a yes/no fashion. Participants who answered yes to either question were assessed as having diabetes.

Assessment of CAC

The dependent variable was CAC score, which was ascertained using a 64-slice multi-detector computed tomography (CT) scanner based on a standard procedure and protocol for identifying and assessing CAC.²⁶ After the procedure, the patient was assigned a CAC score based on the Agatston scale,²⁷ which quantifies the volume of calcification (calculated as the product of the area of calcification per coronary tomographic segment and a factor rated 1 through 4 depending on the maximal X-ray density in that segment) and categorizes CAC based on the severity of risk for coronary artery disease (CAD) as 0 (zero; no plaque), 1–99 (mild plaque), 100–399 (moderate plaque) and ⩾400 (severe plaque). This standard Agatston scale was utilized to describe the extent of CAC in patients with or without diabetes. Due to small sample size for certain cells, presence of CAC was assessed as CAC ⩾1 (vs CAC = 0, indicating its absence) in bivariate and multivariable analyses.

Independent variables

Data on demographics, CVD risk factors and family history of CAD were obtained by self-administered questionnaire. The demographic data included age and sex. While age was assessed as a continuous variable, sex was categorized into male or female. Two modifiable risk factors (smoking, sedentary lifestyle) and three medical conditions (hypertension, hypercholesterolaemia and obesity) were obtained via questionnaire. The participants were asked whether or not they had been diagnosed and/or informed by their physicians if they had hypertension or hypercholesterolaemia and/or were currently using any medications to control these conditions. The participants responded as yes or no and this provided the information on clinical status of hypertension and hypercholesterolaemia, respectively. The participants were further asked about their behaviour related to smoking cigarettes (never smoker, current smoker or former smoker). During analysis, former smokers and current smokers were combined as smokers and the rest were evaluated as nonsmokers. Sedentary lifestyle was assessed by asking the participants whether or not they considered their lifestyle as sedentary; the responses were ‘yes/no’. The participants were also asked if they had a family history of CAD (yes/no). Self-reported height and weight was used to calculate the body mass index (BMI; kg/m²) and those with a BMI ≥30 kg/m² were classified as ‘obese’. Finally, a cumulative risk factor index (RFI) was assessed based on the number of risk factors for CVD (i.e. obesity, hypertension, hypercholesterolaemia, smoking, sedentary lifestyle and family history of CAD); the presence of each calculated as 1 to assign an RFI ranging from 0 to 6. Only five subjects had all six risk factors; hence, participants with five or six risk factors were combined under the same group and were referred to as having ≥5 risk factors in the analysis.

Analysis

Descriptive statistics, including frequency and percentages, were used to describe the study population by the number of risk factors for CVD (0, 1, 2, 3, 4, ⩾5). Differences in sample characteristics, by number of risk factors, were tested using chi-square tests. Bivariate analyses were conducted to compare the prevalence of individual and multiple CVD risk factors by CAC status in subjects with diabetes and those without diabetes. For the regression analyses, a three-category nominal-dependent variable was created as follows: subjects without diabetes and CAC score = 0 (reference), subjects without diabetes and CAC score ⩾1, and subjects with diabetes and CAC score ⩾1. Adjusted multinomial logistic regression models were then constructed to determine the association between individual CVD risk factors and CAC status in patients with and without diabetes. Multinomial logistic regression analyses, adjusting for age and sex, were also carried out to determine the association between number of risk factors and CAC status in subjects with and without diabetes. The level of significance was set at p < 0.05. The data were analysed using SAS software, Version 9.2 (SAS Institute Inc., Cary, NC).

Results

Sample characteristics

A total of 2563 asymptomatic subjects were included in the final analyses. The majority (54.2%) were females and aged 46–74 years (85.7%; Table 1). In total, 35.9% were obese and 49.6% and 56.6% had hypertension and hypercholesterolaemia, respectively. Smokers accounted for 10.1% of the participants and 38.3% reported having a sedentary lifestyle. In total, 65.7% reported a family history of CAD. Of subjects identified as obese, 32.1% had four risk factors (Table 1). For those subjects classified as having hypocholesterolaemia, hypertension, sedentary lifestyle or a family history of CAD, 32.5%, 35.1%, 32.6% and 31.1%, respectively, had three risk factors (Table 1). Of subjects who smoked, 29.5% had three and 29.5% had four risk factors, accounting for 59% of these subjects in these two categories (Table 1). Overall, 13.7% of the subjects had diabetes (Table 2).

Table 1.

Prevalence and distribution of CAC and coronary artery disease risk factors (n = 2563).

Risk factors	Total (n, %)	Number of risk factors (%)						p value
Risk factors	Total (n, %)	0	1	2	3	4	⩾5	p value
Sex								0.0004
Males	1174 (45.8)	6.6	16.5	28.0	26.2	16.8	5.9
Females	1389 (54.2)	3.0	15.4	28.9	28.6	16.6	7.5
Age								0.9308
⩽45	269 (10.5)	5.9	16.0	28.6	25.3	17.1	7.1
46–54	569 (22.2)	4.0	16.3	28.7	26.2	18.6	6.2
55–64	1006 (39.2)	4.9	15.3	28.4	28.2	16.5	6.7
65–74	623 (24.3)	3.9	16.8	28.1	28.1	15.2	7.9
⩾75	96 (3.8)	6.3	13.5	31.2	31.3	14.6	3.1
CAC scores								<0.0001
0	1113 (44.2)	5.5	20.1	30.3	25.1	13.6	5.4
1–99	796 (31.1)	3.8	12.9	27.8	30.5	17.6	7.4
100–399	398 (15.5)	4.0	13.8	26.1	27.9	19.1	9.1
⩾400	236 (9.2)	4.2	9.3	26.3	28.8	24.2	7.2
Risk factors
Obesity								<0.0001
Yes	920 (35.9)	0.0	3.3	16.6	30.4	32.1	17.6
No	1643 (64.1)	7.2	23.0	35.2	25.9	8.0	0.7
Hypercholesterolaemia								<0.0001
Yes	1450 (56.6)	0.0	7.9	24.5	32.5	23.5	11.6
No	1113 (43.4)	10.6	26.3	33.8	21.0	7.8	0.5
Hypertension								<0.0001
Yes	1271 (49.6)	0.0	4.7	20.3	35.1	26.8	13.1
No	1292 (50.4)	9.1	26.9	36.6	20.1	6.8	0.5
Smoking								<0.0001
Yes	258 (10.1)	0.0	3.8	18.6	29.5	29.5	18.6
No	2305 (89.9)	5.1	17.3	29.7	27.3	15.2	5.4
Sedentary lifestyle								<0.0001
Yes	982 (38.3)	0.0	3.3	18.2	32.6	29.9	16.0
No	1581 (61.7)	7.5	23.8	34.9	24.4	8.4	1.0
Family history of CAD								<0.0001
Yes	1685 (65.7)	0.0	9.6	27.8	31.1	21.5	10.0
No	878 (34.3)	13.4	28.1	29.9	20.7	7.3	0.6

CAC: coronary artery calcium; CAD: coronary artery disease.

Table 2.

Prevalence of coronary artery disease risk factors by CAC status in subjects with and without diabetes (n = 2563).

Risk factors	Total (n = 2563)	Subjects without diabetes (n = 2211)		Subjects with diabetes (n = 352)		p value
Risk factors	n (%)	CAC = 0(n = 1022) n (%)	CAC ⩾ 1(n = 1189) n (%)	CAC = 0(n = 111) n (%)	CAC ⩾ 1(n = 241) n (%)	p value
Obesity	920 (35.9)	313 (30.6)	401 (33.7)	68 (61.3)	138 (57.3)	<0.0001
Hypertension	1271 (49.6)	376 (36.8)	640 (53.8)	72 (64.9)	183 (75.9)	<0.0001
Hypercholesterolaemia	1450 (56.6)	502 (49.1)	703 (59.1)	73 (65.8)	172 (71.4)	<0.0001
Smoking	258 (10.1)	89 (8.7)	127 (10.7)	18 (16.2)	24 (10.0)	0.0652
Sedentary lifestyle	982 (38.3)	390 (38.2)	426 (35.8)	45 (40.5)	121 (50.2)	0.0005
Family history of CAD	1685 (65.7)	664 (65.0)	788 (66.3)	79 (71.2)	154 (63.9)	0.5251

CAC: coronary artery calcium; CAD: coronary artery disease.

The presence and extent of CAC in study population

Approximately 55.8% of the participants had CAC score ⩾1 (Tables 1 and 2); the largest proportion constituted of those with mild plaque burden or CAC score = 1–99 on the Agatston Scale.²⁷ Among subjects without diabetes (n = 2211; 86.3%), 53.8% (n = 1189) had a CAC score ⩾1, and in the subjects with diabetes (n = 352; 13.7%), 68.5% (n = 241) had CAC score ⩾1. Except for smoking and family history of CAD, there was difference in the prevalence of CAC for subjects with or without diabetes across the other risk factors (obesity, hypertension, hypercholesterolaemia and sedentary lifestyle). Overall, 95.4% of all the participants had ⩾1 risk factors (Table 3). When comparing subjects with and without diabetes, 95.5% of those with no diabetes and CAC score ⩾1 had one or more risk factors, while 99.2% of subjects with diabetes and CAC score ⩾1 had one or more risk factors.

Table 3.

Prevalence of multiple coronary artery disease risk factors by CAC status in subjects with or without diabetes (n = 2563).

Number of risk factors	Total(n = 2563) n (%)	Subjects without diabetes (n = 2211)		Subjects with diabetes (n = 352)
Number of risk factors	Total(n = 2563) n (%)	CAC = 0(n = 1022)n (%)	CAC ⩾1(n = 1189)n (%)	CAC = 0(n = 111)n (%)	CAC ⩾1(n = 241)n (%)
0	118 (4.6)	59 (5.8)	54 (4.5)	3 (2.7)	2 (0.01)
1	408 (15.9)	221 (21.6)	167 (14.0)	7 (6.3)	13 (5.4)
2	731 (28.5)	325 (31.8)	342 (28.8)	19 (17.1)	45 (18.7)
3	706 (27.5)	250 (24.5)	348 (29.3)	34 (30.6)	74 (30.7)
4	427 (16.7)	122 (11.9)	202 (16.0)	32 (28.8)	71 (29.5)
⩾5	173 (6.8)	45 (4.4)	76 (6.4)	16 (14.4)	36 (14.9)

CAC: coronary artery calcium.

Overall p <0.0001.

Risk factors and cumulative number of risk factors in study population

In the multinomial logistic regression analyses, which controlled for age and sex, we examined the risk factors relative to their association with the presence of CAC in patients with or without diabetes (Table 4) and the number of risk factors present relative to the presence of CAC in patients with or without diabetes (Table 5). Except sedentary lifestyle and family history of CAD, the other risk factors (obesity, hypertension, hypercholesterolaemia and smoking) were associated with increased odds of the presence of CAC in both patients with or without diabetes, but those with diabetes generally had larger odds ratios (ORs). Sedentary lifestyle was only associated with an increased CAC score in the participants with diabetes. Hypertension was associated with the largest odds, almost 4.4-fold, of having subclinical atherosclerotic disease in patients with diabetes. Overall, compared to the healthy subjects, that is, those without diabetes and with CAC = 0, the odds for having CAC score ⩾1 in patients with diabetes were higher for all the examined risk factors, except smoking.

Table 4.

Multinomial logistic regression analyses for the association between coronary artery disease risk factors and CAC status in asymptomatic subjects with or without diabetes (n = 2563).^a

Risk factors	Subjects without diabetes		Subjects with diabetes
Risk factors	CAC = 0 (n = 1022)OR (95% CI)	CAC ⩾1 (n = 1189)OR (95% CI)	CAC ⩾1 (n = 241)OR (95% CI)
Obesity (ref: no obesity)	Referent^b	1.29 (1.05–1.58) <0.0133	3.62 (2.66–4.93) <0.0001
Hypertension (ref: no hypertension)	Referent^b	1.63 (1.35–1.97) <0.0001	4.38 (3.14–6.10) <0.0001
Hypercholesterolaemia (ref: no hypercholesterolaemia)	Referent^b	1.59 (1.32–1.93) <0.0001	2.70 (1.96–3.71) <0.0001
Smoking (ref: no smoking)	Referent^b	2.21 (1.59–3.01) <0.0001	2.13 (1.29–3.54) <0.0034
Sedentary lifestyle (ref: no sedentary lifestyle)	Referent^b	1.09 (0.90–1.33)	1.98 (1.47–2.67) <0.0001
Family history of CAD (ref: no family history of CAD)	Referent^b	1.30 (1.00–1.68)	1.42 (0.96–2.10)

CI: confidence interval; OR: odds ratio; CAC: coronary artery calcium; CAD: coronary artery disease.

Adjusted for sex and age.

Referent category refers to absence of risk factor and CAC score of 0 in subjects without diabetes.

Table 5.

Multinomial logistic regression analyses for the association between number of coronary artery disease risk factors and CAC status in asymptomatic subjects with or without diabetes (n = 2563).^a

Number of risk factors	Subjects without diabetes^b	Subjects with diabetes
Number of risk factors	CAC ⩾1 (n = 1189)OR (95% CI)	CAC ⩾1 (n = 241)OR (95% CI)
1	1.03 (0.63–1.69)	2.19 (0.47–10.26)
2	1.68 (1.05–2.68) <0.2026	6.10 (1.40–26.56) <0.1487
3	2.37 (1.48–3.81) <0.0211	14.06 (3.26–60.69) <0.0029
4	3.19 (1.93–5.28) <0.0001	32.30 (7.41–140.82) <0.0001
⩾5	3.50 (1.92–6.35) <0.0006	47.12 (10.35–214.66) <0.0001
RFI	1.38 (1.27–1.49) <0.0001	2.21 (1.94–2.52) <0.0001

CI: confidence interval; OR: odds ratio; CAC: coronary artery calcium; CAD: coronary artery disease; RFI: risk factor index.

Adjusted for sex and age.

Referent category for the outcome was group with CAC score of 0 in subjects without diabetes.

Finally, Table 5 shows that as the number of risk factors increases, so does the odds for subclinical atherosclerotic disease, as defined by a CAC score ⩾1, in patients with diabetes. Compared to the reference group, the risk for subclinical atherosclerosis increased incrementally as the presence of two risk factors was associated with a greater than sixfold increased odds for high CAC score, while having five or more risk factors increased the odds by more than 47-fold. Generally, each increase in the number of likelihood factors was associated with an increased odds of having a CAC score ⩾1 in patients with and without diabetes.

Discussion

Although diabetes mellitus is an independent risk factor for developing atherosclerotic CVD,^28,29 studies involving CAC in patients with diabetes in the ‘Diabetes Belt’ of the Central Appalachian region of the United States^13,14 are sparse. In order to close this gap in CVD research, we conducted this study to examine the presence and the extent of CAC in asymptomatic patients with and without diabetes in this population. Overall, it was found that 55.8% of the total study population had a CAC score ⩾1, and in subjects with diabetes (13.7%), 68.5% had a CAC score ⩾1. While these findings are consistent with similar studies in this population,^23,25,30 the proportion of patients with diabetes is higher than both the national average and the averages of the states comprising the Central Appalachia region (Kentucky, North Carolina, Ohio, Tennessee, Virginia and West Virginia). Thus, this study affirms the assertion that disparities in diabetes and CVD exist in this region and that using traditional risk factors and CAC scoring, health outcomes may be targeted for improvement in the Central Appalachian region and similar populations in the United States.³¹

Six standard risk factors were examined in this analysis: three medical conditions (obesity, hypertension and hypocholesterolaemia); two behavioural risk factors (smoking and sedentary lifestyle); and one genetic factor (family history of CAD). While one in three of the study participants were obese, one in two had hypertension and hypocholesterolaemia. In terms of the behavioural factors, 1 in 10 were current smokers, while 1 in 3 reported a sedentary lifestyle. Approximately two in three of the study participants reported having a family history of CAD, mirroring the pervasiveness of CVD in the region. With the exception of smoking, the prevalence of all other CVD risk factors examined in this study population, when compared to the states comprising the Central Appalachian region and those of the entire United States, was higher.³² The proportion of these risk factors was greater among patients with diabetes than those without. Specifically, as an example, the prevalence of obesity was 59% and 39%, respectively, among subjects with and without diabetes. Consistent with a previous study,²³ 95.4% of the study population reported having ⩾1 risk factors, which is substantially higher than the national rate.³

In the examination of the associations between the CVD risk factors and CAC, we identified significant independent associations between obesity, hypertension, hypercholesterolaemia, smoking, and sedentary lifestyle and subclinical atherosclerosis in patients with diabetes. These results are consistent with those in previous studies involving populations elsewhere in the United States.^20,33,34 In a further analysis to delineate the associations between the number of risk factors and the presence of CAC, it was found that compared to health subjects with a CAC score = 0, having ⩾1 risk factors increased the likelihood of a CAC score ⩾1 in both subjects with and without diabetes. This was exhibited as a dose response with substantially higher ORs among subjects with diabetes. The subjects with diabetes had a much greater likelihood for subclinical atherosclerosis as having two risk factors was significantly associated with more than sixfold increased odds of having CAC score ⩾1, while having ⩾5 risk factors increased the odds for CAC score ⩾1 by more than 47-fold.

The dose–response relationship in subjects with diabetes, coupled with data from previous studies, lends credence to future exploration in this clinical area. Prior studies have demonstrated an association with the number of CVD risk factors along with CAC on all-cause mortality.³⁵ In addition, the number of risk factors is associated with the risk for CAC in asymptomatic patients.²³ Thus, this study provides evidence to support research involving CAC in populations with specific health conditions, namely, diabetes. Perhaps such an approach will provide information for early detection and treatment of CVD in high-risk population subgroups, ultimately helping to address the persistent CVD disparities in the United States.

These findings support the claim that the Central Appalachian region is not only disproportionately burdened with CVD outcomes such as subclinical atherosclerosis, that is, CAC, but also the risk factors for CVD, supporting and reinforcing previous findings from earlier studies.^23,25,30 Furthermore, these findings highlight the necessity for research to understand why individuals are predisposed to a disproportionately high prevalence of these CVD risk factors while engaging in individual- and population-based interventional programmes to address these risk factors, and in turn the overall health, of this and similar regions.

The higher proportions of these risk factors in subjects with diabetes suggest that more aggressive efforts to address CVD risk is warranted in this high-risk subset of patients. Given the increased prevalence of diabetes in this and similar regions, not only are additional programmes needed but these programmes should focus not just on diabetes but in the overall reduction of risk factors for CVD, the primary threat to this vulnerable population.² Furthermore, consistent with American College of Cardiology and American Heart Association guidelines, CAC screening could potentially be incorporated into the standard care of patients with diabetes in the region, with a Class IIA recommendation.^19,22,33 In fact, some leading experts feel this is an underutilized resource that could greatly enhance the ability to identify high-risk patients, such as those requiring statin therapy for primary prevention, which is directly translatable to our high-risk population.^36,37

Moreover, previous studies identified that the rate of adherence to medications is low in patients with diabetes.³⁸ Not only does CAC provide objective motivation for patients to adhere to prescribed medical treatments, but it also provides a visual representation of the level of disease in their coronary arteries. Future research in this vein would follow best practice recommendations for strategies to improve medication adherence among patients with diabetes and could be significantly useful for a region with higher levels of poor health literacy, such as Central Appalachia. This is supported by previous studies that indicate the protective effect of CAC visualization in lifestyle modification.^3,39 Medication adherence among patients with diabetes is critically important because it not only improves blood glucose, a measure many patients focus on today, but also improves the long-term CVD health outcomes and saves healthcare costs.⁴⁰ In the United States alone, poor medication adherence contributes to 33%–69% of medication-related hospital admissions and nearly US$100 billion in annual costs.⁴⁰

The results of this study should be interpreted with recognition of certain limitations. First, cross-sectional data were utilised for this study, which precludes claims of causation. Second, several measures were self-reported, including weight and height; hence, they were subject to recall and social desirability biases. For example, asking study subjects to report whether they consider their lifestyle as sedentary could lead to a major bias due to under-reporting. Third, the study examined only individual risk factors; however, previous studies suggest that factors such as social determinants and neighbourhood or environmental characteristics affect CVD outcomes, including subclinical atherosclerosis.^30,41 Fourth, CAC score was collapsed into two categories which may lead to loss of information. However, categorizing CAC status as a binary variable has been successfully done in multiple studies^{23,25,30,42,43} and facilitates interpretation of results. Nevertheless, the study adopts a unique approach that examined the effects of traditional risk factors and the cumulative number of these risk factors present on the outcome of subclinical atherosclerosis in subjects with and without diabetes, as assessed by CAC scoring. With further research, this could provide guidance for not only early detection and treatment of CVD but also addressing the CVD disparities in population subgroups.

Conclusion

Diabetes is a major risk factor for CVD and this study examined the presence and extent of subclinical atherosclerosis (assessed as CAC) in asymptomatic subjects with diabetes in the Central Appalachian region, part of the ‘Diabetes Belt’ of the United States. In total, 13.7% of the total population comprises patients with diabetes. While the prevalence of subclinical atherosclerosis was 55.8% in the total population; it was substantially higher at 68.5% among subjects with diabetes. All risk factors examined were associated with an increased likelihood of subclinical atherosclerotic disease in this asymptomatic population with diabetes, except family history of CAD. In addition, the presence of subclinical atherosclerotic disease in these subjects with diabetes positively correlated with the number of CVD risk factors. While this study affirms disparities in CVD for residents of Central Appalachia, it also suggests the need for awareness about subclinical atherosclerosis (i.e. CAC) in patients with diabetes and more research about CAC in subpopulations of patients.

Footnotes

Acknowledgements

We would like to thank the Colleges of Public Health, Medicine, and Pharmacy at East Tennessee State University (ETSU) for supporting the ETSU Cardiovascular Research Group on this project. We would also like to thank Wellmont CVA Heart Institute (now Ballad Health) for providing the researchers with the electronic medical record data. We thank the Office of Research and Sponsored Program and the ETSU Office of Equity and Diversity for providing the funds to support data cleaning and management and research assistants. Finally, we would like to say special thanks to all colleagues and graduate assistants who reviewed different versions of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Hadii M Mamudu

References

Fox

Golden

Anderson

et al . Update on prevention of cardiovascular disease in adults with type 2 diabetes mellitus in light of recent evidence: a scientific statement from the American Heart Association and the American Diabetes Association. Circulation 2015; 132: 691–718.

American Diabetes Association (ADA). Standards of medical care in diabetes – 2017. Diabetes Care 2017; 40: S1–S135.

Benjamin

Blaha

Chiuve

et al . Heart disease and stroke statistics – 2017 update: a report from the American Heart Association. Circulation 2017; 135: e146–e603.

Polonsky

Greenland

CVD screening in low-risk, asymptomatic adults: clinical trials needed. Nat Rev Cardiol 2012; 9: 599–604.

Goraya

Leibson

Palumbo

et al . Coronary atherosclerosis in diabetes mellitus: a population-based autopsy study. J Am Coll Cardiol 2002; 40: 946–953.

Caro

Ward

O’Brien

JA.

Lifetime costs of complications resulting from type 2 diabetes in the U.S.

Diabetes Care 2002; 25: 476–481.

American Diabetes Association (ADA). Fast facts, http://professional.diabetes.org/content/fast-facts-data-and-statistics-about-diabetes/?loc=dorg_statistics (2015, accessed 6 June 2016).

American Diabetes Association (ADA). Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013; 36: 1033–1046.

Mazzone

Current concepts and controversies in the pathogenesis, prevention, and treatment of the macrovascular complications of diabetes. J Lab Clin Med 2000; 135: 437–443.

10.

Grundy

Benjamin

Burke

et al . Diabetes and cardiovascular disease: a statement for healthcare professionals from the American Heart Association. Circulation 1999; 100: 1134–1146.

11.

Diabetes mellitus: a major risk factor for cardiovascular disease. A joint editorial statement by the American Diabetes Association; The National Heart, Lung, and Blood Institute; The Juvenile Diabetes Foundation International; The National Institute of Diabetes and Digestive and Kidney Diseases; and The American Heart Association. Circulation 1999; 100: 1132–1133.

12.

Bulugahapitiya

Siyambalapitiya

Sithole

et al . Is diabetes a coronary risk equivalent? Systematic review and meta-analysis. Diabet Med 2009; 26: 142–148.

13.

Barker

Kirtland

Gregg

et al . Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med 2011; 40: 434–439.

14.

Gregg

Kirtland

Cadwell

et al . Estimated county-level prevalence of diabetes and obesity – United States, 2007. Morb Mortal Wkly Rep 2009; 58: 1259–1263.

15.

Barker

Crespo

Gerzoff

et al . Residence in a distressed county in Appalachia as a risk factor for diabetes, behavioral risk factor surveillance system, 2006-2007. Prev Chronic Dis 2010; 7: A104.

16.

Barker

Gerzoff

Crespo

et al . Age at diagnosis of diabetes in Appalachia. Popul Health Metr 2011; 9: 54.

17.

Hoff

Quinn

Sevrukov

et al . The prevalence of coronary artery calcium among diabetic individuals without known coronary artery disease. J Am Coll Cardiol 2003; 41: 1008–1012.

18.

Wong

Nelson

Granston

et al . Metabolic syndrome, diabetes, and incidence and progression of coronary calcium: the multiethnic study of atherosclerosis study. JACC Cardiovasc Imaging 2012; 5: 358–366.

19.

Blankstein

Gupta

Rana

et al . The implication of coronary artery calcium testing for cardiovascular disease prevention and diabetes. Endocrinol Metab 2017; 32: 47–57.

20.

Raggi

Shaw

Berman

et al . Prognostic value of coronary artery calcium screening in subjects with and without diabetes. J Am Coll Cardiol 2004; 43: 1663–1669.

21.

Kramer

Zinman

Gross

et al . Coronary artery calcium score prediction of all cause mortality and cardiovascular events in people with type 2 diabetes: systematic review and meta-analysis. BMJ 2013; 346: f1654.

22.

Hecht

Narula

Coronary artery calcium scanning in asymptomatic patients with diabetes mellitus: a paradigm shift. J Diabetes 2012; 4: 342–350.

23.

Mamudu

Paul

Wang

et al . The effects of multiple coronary artery disease risk factors on subclinical atherosclerosis in a rural population in the United States. Prev Med 2016; 88: 140–146.

24.

Mamudu

Paul

Veeranki

et al . Subclinical atherosclerosis and relationship with risk factors of coronary artery disease in a rural population. Am J Med Sci 2015; 350: 257–262.

25.

Mamudu

Paul

Wang

et al . Association between multiple modifiable risk factors of cardiovascular disease and hypertension among asymptomatic patients in central Appalachia. South Med J 2017; 110: 90–96.

26.

Rumberger

Brundage

Rader

et al . Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons. Mayo Clin Proc 1999; 74: 243–252.

27.

Agatston

Janowitz

Hildner

et al . Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990; 15: 827–832.

28.

Kannel

McGee

DL.

Diabetes and cardiovascular disease. The Framingham Study. JAMA 1979; 241: 2035–2038.

29.

Pan

Cedres

Liu

et al . Relationship of clinical diabetes and asymptomatic hyperglycemia to risk of coronary heart disease mortality in men and women. Am J Epidemiol 1986; 123: 504–516.

30.

Mamudu

Jones

Paul

et al . Geographic and individual correlates of subclinical atherosclerosis in an asymptomatic rural Appalachian population. Am J Med Sci, 2018;355(2):140-148.

31.

Centers for Disease Control and Prevention (CDC). Disparities in premature deaths from heart disease – 50 states and the district of Columbia, 2001. Morb Mortal Wkly Rep 2004; 53: 121–125.

32.

Centers for Disease Control and Prevention (CDC). Diabetes report card 2014. http://www.cdc.gov/diabetes/pdfs/library/diabetesreportcard2014.pdf (2015, accessed 20 January 2016).

33.

Rana

Gransar

Shaw

et al . Coronary artery calcium score and risk classification for mortality prediction for individuals with and without diabetes. J Am Coll Cardiol 2011; 57: E883.

34.

Yusuf

Hawken

Ounpuu

et al . Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364: 937–952.

35.

Nasir

Rubin

Blaha

et al . Interplay of coronary artery calcification and traditional risk factors for the prediction of all-cause mortality in asymptomatic individuals. Circ Cardiovasc Imaging 2012; 5: 467–473.

36.

Nasir

Bittencourt

Blaha

et al.

Implications of coronary artery calcium testing among statin candidates according to American College of Cardiology/American Heart Association Cholesterol Management Guidelines: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2015; 66: 1657–1668.

37.

Pursnani

Massaro

D’Agostino

RB Sr

et al . Guideline-based statin eligibility, coronary artery calcification, and cardiovascular events. JAMA 2015; 314: 134–141.

38.

Cramer

JA.

A systematic review of adherence with medications for diabetes. Diabetes Care 2004; 27: 1218–1224.

39.

Mamudu

Paul

Veeranki

et al . The effects of coronary artery calcium screening on behavioral modification, risk perception, and medication adherence among asymptomatic adults: a systematic review. Atherosclerosis 2014; 236: 338–350.

40.

Osterberg

Blaschke

Adherence to medication. N Engl J Med 2005; 353: 487–497.

41.

Havranek

Mujahid

Barr

et al . Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation 2015; 132: 873–898.

42.

Blaha

Budoff

Shaw

et al . Absence of coronary artery calcification and all-cause mortality. JACC Cardiovasc Imaging 2009; 2: 692–700.

43.

Raggi

Cooil

Callister

TQ.

Use of electron beam tomography data to develop models for prediction of hard coronary events. Am Heart J 2001; 141: 375–382.

Diabetes,subclinical atherosclerosis and multiple cardiovascular risk factors in hard-to-reach asymptomatic patients

Abstract

Aim:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Study population

Assessment of CAC

Independent variables

Analysis

Results

Sample characteristics

The presence and extent of CAC in study population

Risk factors and cumulative number of risk factors in study population

Discussion

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

References