Abstract
Background
Diabetes is associated with increased risk of acute myocardial infarction (AMI). The demographic trends, clinical presentation, management, and outcomes of patients with diabetes who are hospitalized with AMI have not been recently reported.
Methods and Results
The ARIC (Atherosclerosis Risk in Communities) study conducted hospital surveillance of AMI in 4 US communities. AMI was classified by physician review using a validated algorithm. Medications and procedures were abstracted from the medical record. From 2000 to 2014, 21 094 weighted hospitalizations for AMI were sampled. The prevalence of diabetes steadily increased, from 35% to 41% to 43% (P‐trend<0.0001) across 2000 to 2004, 2005 to 2009, and 2010 to 2014, respectively. Patients with diabetes were older (61 versus 59 years of age), more often Black (44% versus 31%), and more commonly women (42% versus 34%). The burden of cardiovascular comorbidities was higher with diabetes and increased temporally. Patients with diabetes less often presented with ST‐segment elevation (9% versus 17%) or acute chest pain (72% versus 80%), and had higher mean GRACE (Global Registry of Acute Coronary Syndrome) score (123 versus 109), Thrombolysis in Myocardial Ischemia (TIMI) score (4.3 versus 4.0), and Killip class (1.9 versus 1.5). Patients with diabetes had a lower adjusted probability of receiving aspirin (relative probability, 0.95 [95% CI, 0.91–0.99]), nonaspirin antiplatelets (0.93 [95% CI, 0.86–0.99]), coronary angiography (0.85 [95% CI, 0.78–0.92]), and coronary revascularization (0.85 [95% CI, 0.76–0.92]). Diabetes was associated with a 52% higher hazard of all‐cause 1‐year mortality (hazard ratio, 1.52 [95% CI, 1.23–1.89]).
Conclusions
Diabetes is associated with higher risk of death in patients hospitalized with AMI, highlighting the need for adherence to evidence‐based therapies in this high‐risk population.
Keywords: diabetes, epidemiology, myocardial infarction, outcomes
Subject Categories: Cardiovascular Disease; Diabetes, Type 2; Epidemiology
Nonstandard Abbreviations and Acronyms
- ARIC
Atherosclerosis Risk in Communities
- GRACE
Global Registry of Acute Coronary Syndrome
- SGLT2i
sodium‐glucose cotransporter 2 inhibitor
- TIMI
Thrombolysis in Myocardial Ischemia
Clinical Perspective.
What Is New?
Across this 15‐year surveillance, 4 out of 10 patients who were hospitalized with acute myocardial infarction presented with concomitant diabetes.
Despite carrying more comorbidities, patients with diabetes were less likely to receive guideline‐directed medical therapy, such as antiplatelet agents or coronary revascularization, including coronary artery bypass grafting.
This translated into a higher rate of complications and mortality.
What Are the Clinical Implications?
Acute myocardial infarction continues to remain an important and highly prevalent complication of diabetes.
Effective disease management as well as optimal and timely use of guideline‐directed medical therapies can help curb the excess burden of preventable mortality.
Over 37 million adults in the United States (≈15% of US adults) are reported to have diabetes, with an additional 8 million adults living with undiagnosed diabetes. 1 It is a significant risk factor for macrovascular and microvascular complications including cardiovascular disease, chronic kidney disease, peripheral artery disease, and cerebrovascular disease, among others. 2 Despite a decline in cardiovascular mortality in the past several decades, 3 diabetes continues to remain a key risk factor, with a 2‐ to 4‐fold increased risk of cardiovascular events and a 3‐fold increased risk of cardiovascular mortality. 4 , 5 Acute myocardial infarction (AMI) continues to be the leading cause of morbidity and mortality in patients with diabetes. 6 Factors unique to diabetes increase atherosclerotic plaque formation and thrombosis, predisposing patients to AMI. Autonomic neuropathy may result in atypical symptoms, making timely diagnosis challenging and delaying treatment with evidence‐based therapies. The clinical course of AMI is frequently complicated and carries a higher mortality rate in patients with coexisting diabetes. 7 A large proportion of this increased mortality may be attributable to the clustering of traditional risk factors such as obesity, hypertension, chronic kidney disease, and dyslipidemia in patients with diabetes, as well as unique manifestations of diabetes such as multivessel coronary artery disease and diffuse lesions. 7 , 8
There has been a paradigm shift in the management of diabetes as well as acute coronary syndromes in the past 2 decades with the introduction of newer therapies and updated management guidelines. 9 , 10 Consequently, evaluating the current demographic trends, clinical presentations, management practices, and outcomes of patients with diabetes who are hospitalized with AMI is warranted.
Methods
ARIC Study Data
The community surveillance data collected by the ARIC (Atherosclerosis Risk in Communities) study are publicly available to qualified investigators with an approved article proposal and data use agreement. The data that support the findings of this study are available from the corresponding author upon reasonable request.
ARIC Study Community Surveillance
The ARIC study consists of observational data from multiple populations, which include a prospective cohort population and several hospital‐based community surveillance populations. 11 Our analysis is derived from the AMI community surveillance population, a unique population limited to hospitalized AMI. The ARIC study conducted community surveillance of hospitalized AMI from 1987 to 2014, with a catchment area localized to 4 geographically defined regions of the United States (Forsyth County, NC; Washington County, MD; Jackson, MS; and 8 northwest suburbs of Minneapolis, MN). 11 Informed consent was not required for surveillance, because personal identifiers were removed from the analytic data set. All surveillance protocols were approved by local institutional review boards.
Hospitalizations were randomly sampled within prespecified strata based on race, sex, ARIC community, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) discharge codes 402, 410–414, 427, 428, and 518.4. Patients were eligible for surveillance if they were 35 to 74 years of age in 1987 to 2004, with an expansion to 35 to 84 years of age in 2005 to 2014. Because the purpose of our analysis was to examine temporal trends, we excluded patients 75 to 84 years of age to avoid temporal confounding by age because of expanded surveillance eligibility in 2005 to 2014. We also limited our analysis to hospitalizations in the recent era, from 2000 to 2014.
AMI Classification
As we have previously described, 12 hospitalized events were adjudicated by ARIC physician review using a standardized algorithm. Inputs for the algorithm included electrocardiography, presence of chest pain, and cardiac biomarkers levels. Details of the standardized algorithm have been published, 13 , 14 and classification criteria remained constant over the study period. We limited our study population to hospitalizations classified as definite or probable AMI. ST‐segment–elevation myocardial infarction and non–ST‐segment–elevation myocardial infarction were classified by standardized analysis and electronic coding of the abstracted electrocardiograms.
Medical History and Comorbidities
Data about medical history and comorbidities were collected from medical records by trained coordinators using a standardized protocol. Diabetes was defined by current use of antidiabetic medications or documented history in the medical record. Chronic kidney disease was defined by an estimated glomerular filtration rate <60 mL/min per 1.73 m2 or receipt of dialysis. For the estimation of glomerular filtration rate, abstracted values of serum creatinine and the Chronic Kidney Disease Epidemiology Collaboration formula were used. History of hypertension, stroke, and prior coronary revascularization by coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI) were classified by documentation in the medical record.
Acute Presentation and Risk Scores
Clinical presentations and in‐hospital complications including acute onset of chest pain, pulmonary edema/heart failure, cardiogenic shock, and ventricular fibrillation/cardiac arrest/asystole were abstracted from the physician notes and admission records using a standardized protocol. The Thrombolysis in Myocardial Ischemia (TIMI) risk score for unstable angina/non–ST‐segment–elevation myocardial infarction and the GRACE (Global Registry of Acute Coronary Syndrome) score were derived from the presenting features at admission and over the course of hospitalization, using clinical data abstracted from the medical record, as previously described. 15 , 16 Because the ARIC study began routine abstraction of creatinine levels in 2004, derivation of the GRACE score was limited to 2004 to 2014.
Medical Management
Medications were recorded if administered during hospitalization or prescribed at hospital discharge. Coronary angiography and invasive procedures (eg, PCI and CABG) were abstracted from the medical record if performed during the AMI hospitalization.
Mortality Outcomes
The primary outcomes of interest were use of guideline‐directed medical therapies during hospitalization, all‐cause death, and cardiovascular death. Cardiovascular death was defined by death attributable to diseases of the circulatory system (ICD, Tenth Revision [ICD‐10] codes I00‐I99). In‐hospital, 28‐day, 1‐year, and 2‐year mortality were collected by the ARIC study, which linked hospitalizations with the National Death Index.
Statistical Analysis
Statistical tests and models accounted for the stratified sampling design of the ARIC surveillance and were weighted by the inverse of the sampling probability. 17 Continuous variables were assessed for normality and compared using the difference in least square means from weighted linear regression. Categorical variables were compared using Rao‐Scott χ2 tests. Temporal trends in the annual prevalence of categorical factors were analyzed by the Cochran‐Armitage test for trend, using logistic regression with year of admission regressed as an ordinal variable. Temporal trends in the yearly averages of continuously measured factors were analyzed by linear regression, regressing on the year of admission. All statistical analyses were performed using SAS 9.4 survey procedures (SAS Institute, Cary, NC).
The relative probabilities of patients with versus without diabetes receiving guideline‐directed AMI medications (aspirin, other antiplatelets, β‐blockers, and lipid‐lowering medications) or undergoing invasive procedures (angiography and revascularization) were derived from multivariable logistic regression, with odds ratios converted into relative risk (RR) and 95% CI. 18 Models were adjusted for age, race, sex, hospital, year of admission, smoking, hypertension, prior myocardial infarction, and history of stroke. As sensitivity analyses, we also stratified the models by race (White or Black), sex (men or women), age category (35–64 years or 65–74 years of age), and AMI status (first‐occurring or recurrent).
Comparisons of short‐term (28‐day) and long‐term (1‐year and 2‐year) all‐cause mortality between patients with and without diabetes were analyzed using multivariable logistic and Cox regression, respectively, adjusted for age, race, sex, hospital, year of admission, smoking, hypertension, prior myocardial infarction, and history of stroke. All Cox regression models met assumptions of proportional hazards across the follow‐up time.
Results
A total of 52 641 eligible hospitalizations were sampled by the ARIC study community surveillance across 2000 to 2014. Of these, 12 414 were deemed to have definite or probable AMI upon standardized physician review. After excluding patients with race other than Black or White, 12 083 remained, and after omitting patients 75 to 84 years of age, 10 007 remained. All had available follow‐up for mortality outcomes, but a small number (N=25) were missing abstractions for diabetes status. The remaining 9982 patients comprised our study population, which corresponded to 21 094 weighted hospitalizations. The presentation of all subsequent results is weighted by the sampling fraction.
Overall, our study population was predominantly White (63%) and men (63%), with a mean age of 60 years. The prevalence of diabetes steadily increased over time, from 35% to 41% to 43% across 2000 to 2004, 2005 to 2009, and 2010 to 2014, respectively (P‐trend<0.0001), yielding an aggregated prevalence of 40% across the 15‐year interval. Patients with diabetes were slightly older (61 versus 59 years of age), more often Black (44% versus 31%), and more commonly women (42% versus 34%). When stratified by race and sex, diabetes was most prevalent in Black women (55%), followed by Black men (44%), White women (39%), and White men (39%) (Table 1). The prevalence of diabetes temporally increased across 2000 to 2014, both for Black men (P‐trend=0.0005) and White men (P‐trend=0.0005) but did not appreciably change over time for Black women (P‐trend=0.8) or White women (P‐trend=0.7) (Figure 1).
Table 1.
Demographics and Clinical Characteristics of Patients With and Without Diabetes Admitted With Acute Myocardial Infarction: Atherosclerosis Risk in Communities Study Community Surveillance, 2005 to 2014
| Characteristic | No diabetes | Diabetes | |
|---|---|---|---|
| N=12 626 | N=8468 | P value | |
| Demographics | |||
| Age, y | 59±14 | 61±13 | <0.0001 |
| Black | 3972 (31%) | 3763 (44%) | <0.0001 |
| Women | 4236 (34%) | 3571 (42%) | <0.0001 |
| Health insurance | 7270 (87%) | 5716 (92%) | <0.0001 |
| Presentation | |||
| ST elevation | 2195 (17%) | 804 (9%) | <0.0001 |
| GRACE score | 109±33 | 123±35 | <0.0001 |
| TIMI score | 3.9±1.4 | 4.3±1.7 | <0.0001 |
| Killip class | 1.5±1.3 | 1.9±1.5 | <0.0001 |
| Acute chest pain | 10 042 (80%) | 6122 (72%) | <0.0001 |
| Acute pulmonary edema/heart failure | 2814 (22%) | 3347 (40%) | <0.0001 |
| Cardiogenic shock | 374 (3%) | 336 (4%) | 0.03 |
| Ventricular fibrillation/asystole | 871 (7%) | 723 (9%) | 0.02 |
| Medical history | |||
| Current smoking | 5286 (43%) | 2548 (31%) | <0.0001 |
| Hypertension | 8304 (66%) | 7528 (89%) | <0.0001 |
| Chronic kidney disease | 2913 (32%) | 3631 (54%) | <0.0001 |
| Prior stroke | 904 (7%) | 1443 (17%) | <0.0001 |
| Prior myocardial infarction | 3310 (27%) | 2966 (36%) | <0.0001 |
| Prior coronary revascularization | 3507 (28%) | 3152 (38%) | <0.0001 |
Continuous variables are expressed as mean±SD. GRACE indicates Global Registry of Acute Coronary Events; and TIMI, Thrombolysis in Myocardial Infarction.
Figure 1. Temporal trends in annual prevalence of diabetes among patients hospitalized for acute myocardial infarction, stratified by race and sex.
The Atherosclerosis Risk in Communities study community surveillance, 2000 to 2014.
Across 2000 to 2014, a lower proportion of AMI hospitalizations were identified as ST‐segment–elevation myocardial infarction in patients with diabetes compared with those without diabetes (9% versus 17%). However, patients with diabetes had a higher mean GRACE score (123 versus 109), TIMI score (4.3 versus 3.9), and Killip class (1.9 versus 1.5). Those with diabetes less frequently presented with acute chest pain (72% versus 80%), but acute onset of pulmonary edema/heart failure was more common (40% versus 22%), as was cardiogenic shock (4% versus 3%) and ventricular fibrillation/cardiac arrest (9% versus 7%), when compared with patients without diabetes (Table 1). The prevalence of smoking was lower for patients with versus without diabetes (31% versus 43%), but hypertension (89% versus 66%), chronic kidney disease (54% versus 32%), and history of stroke (17% versus. 7%) were more prevalent with diabetes. Prior myocardial infarction was also more common with diabetes (36% versus 27%), as was prior coronary revascularization (38% versus 28%).
Distributions of race and sex among AMI hospitalizations changed over time, with a steady increase in Black and female patients across 2000 to 2014 (Table 2). Acuity of presentation appeared to worsen slightly over time for patients without diabetes, with a modest increase in mean Killip class and uptick in acute pulmonary edema/heart failure and cardiogenic shock. In contrast, acuity of presentation was largely consistent across 2000 to 2014 for patients with diabetes, but mean TIMI risk score increased slightly, whereas the proportion with ventricular fibrillation/cardiac arrest declined. Hypertension prevalence increased over time, irrespective of diabetes status. Smoking also increased over time, but only for patients with diabetes.
Table 2.
Temporal Trends in Demographics and Clinical Characteristics of Patients With and Without Diabetes Admitted With Acute Myocardial Infarction: Atherosclerosis Risk in Communities Study Community Surveillance, 2005 to 2014
| Characteristic | No diabetes | Diabetes | ||||||
|---|---|---|---|---|---|---|---|---|
| 2000–2004 | 2005–2009 | 2010–2014 | P‐trend* | 2000–2004 | 2005–2009 | 2010–2014 | P‐trend* | |
| N=4141 | N=3863 | N=4622 | N=2246 | N=2694 | N=3528 | |||
| Demographics | ||||||||
| Age, y | 60±12 | 59±16 | 58±15 | 0.0009 | 62±12 | 61±14 | 61±14 | 0.3 |
| Black | 1037 (25%) | 1098 (28%) | 1837 (40%) | <0.0001 | 806 (36%) | 1106 (41%) | 1851 (52%) | <0.0001 |
| Women | 1235 (30%) | 1317 (34%) | 1684 (36%) | 0.0003 | 976 (43%) | 1074 (40%) | 1521 (43%) | 0.9 |
| Health insurance* | … | 3257 (87%) | 4009 (87%) | … | 2405 (91%) | 3298 (93%) | ||
| Presentation | ||||||||
| ST‐elevation | 652 (16%) | 799 (21%) | 744 (16%) | 0.9 | 224 (10%) | 272 (10%) | 297 (8%) | 0.08 |
| GRACE score | 112±33 | 108±33 | 109±33 | 0.8 | 130±39 | 123±34 | 121±34 | 0.4 |
| TIMI score | 3.9±1.2 | 3.9±1.6 | 3.9±1.5 | 0.8 | 4.2±1.4 | 4.2±1.8 | 4.3±1.8 | 0.06 |
| Killip class | 1.5±1.1 | 1.5±1.4 | 1.6±1.5 | 0.005 | 2.0±1.3 | 1.9±1.6 | 1.9±1.7 | 0.2 |
| Acute chest pain | 3303 (80%) | 2990 (77%) | 3748 (81%) | 0.4 | 1715 (76%) | 1864 (69%) | 2544 (72%) | 0.2 |
| Acute pulmonary edema/HF | 852 (21%) | 793 (21%) | 1168 (25%) | 0.008 | 947 (42%) | 1027 (38%) | 1373 (39%) | 0.3 |
| Cardiogenic shock | 86 (2%) | 92 (2%) | 196 (4%) | 0.003 | 95 (4%) | 96 (4%) | 146 (4%) | 0.9 |
| Ventricular fibrillation/asystole | 330 (8%) | 241 (6%) | 301 (7%) | 0.2 | 258 (11%) | 220 (8%) | 245 (7%) | 0.0009 |
| Medical history | ||||||||
| Current smoking | 1779 (44%) | 1544 (41%) | 1963 (43%) | 0.7 | 594 (27%) | 822 (31%) | 1132 (32%) | 0.04 |
| Hypertension | 2563 (62%) | 2465 (65%) | 3277 (71%) | <0.0001 | 1924 (86%) | 2365 (89%) | 3239 (92%) | 0.0002 |
| Chronic kidney disease | 233 (30%) | 1144 (30%) | 1535 (33%) | 0.1 | 272 (57%) | 1447 (54%) | 1913 (54%) | 0.7 |
| Prior stroke | 268 (6%) | 258 (7%) | 379 (8%) | 0.1 | 387 (17%) | 377 (14%) | 678 (19%) | 0.2 |
| Prior myocardial infarction | 1204 (29%) | 942 (25%) | 1164 (26%) | 0.05 | 862 (39%) | 882 (34%) | 1222 (36%) | 0.3 |
| Prior coronary revascularization | 1138 (27%) | 1128 (29%) | 1241 (27%) | 0.7 | 714 (32%) | 1049 (39%) | 1388 (39%) | 0.005 |
Continuous variables are expressed as mean±SD. GRACE indicates Global Registry of Acute Coronary Events; HF, heart failure; and TIMI, Thrombolysis in Myocardial Infarction.
Health insurance status was not collected by the Atherosclerosis Risk in Communities study before 2005.
In the 15‐year aggregate, patients with diabetes were less often administered aspirin (86% versus 90%) and nonaspirin antiplatelet therapies (51% versus 60%), and less frequently underwent invasive angiography (48% versus 64%) or coronary revascularization (33% versus 48% overall, 68% versus 74% among those with angiography) (Figure 2). When examined over time, lipid‐lowering agents were increasingly prescribed for patients with and without diabetes, whereas referrals to angiography steadily declined for both, as did the proportion undergoing coronary revascularization (Table 3). The temporal decline in coronary revascularization was primarily driven by a decrease in CABG procedures for patients with and without diabetes, whereas the proportion undergoing PCI was largely unchanged over time.
Figure 2. Administration of guideline‐directed therapies, comparing patients with and without diabetes who were admitted with acute myocardial infarction.
The Atherosclerosis Risk in Communities study community surveillance, 2000 to 2014.
Table 3.
Temporal Trends in Use of Guideline‐Directed Therapies in Patients With and Without Diabetes Admitted With Acute Myocardial Infarction: Atherosclerosis Risk in Communities Study Community Surveillance, 2005 to 2014
| Therapy | No diabetes | Diabetes | ||||||
|---|---|---|---|---|---|---|---|---|
| 2000–2004 | 2005–2009 | 2010–2014 | P‐trend* | 2000–2004 | 2005–2009 | 2010–2014 | P‐trend* | |
| N=4141 | N=3863 | N=4622 | N=2246 | N=2694 | N=3528 | |||
| Aspirin | 3722 (90%) | 3476 (90%) | 4103 (89%) | 0.4 | 1960 (87%) | 2277 (85%) | 3000 (85%) | 0.3 |
| Nonaspirin antiplatelet | 2578 (62%) | 2417 (63%) | 2519 (55%) | 0.0001 | 1128 (50%) | 1420 (53%) | 1735 (49%) | 0.6 |
| Lipid‐lowering agent | 2755 (67%) | 2779 (72%) | 3427 (74%) | 0.0001 | 1439 (64%) | 1958 (73%) | 2835 (80%) | <0.0001 |
| β‐Blocker | 3507 (85%) | 3433 (89%) | 4017 (87%) | 0.2 | 1792 (80%) | 2344 (87%) | 3080 (87%) | 0.0006 |
| Angiography | 2789 (67%) | 2481 (64%) | 2755 (60%) | 0.0002 | 1179 (52%) | 1295 (48%) | 1547 (44%) | 0.0006 |
| Revascularization (PCI/CABG) | 2175 (53%) | 1926 (50%) | 1992 (43%) | <0.0001 | 825 (37%) | 924 (34%) | 1061 (30%) | 0.0002 |
| PCI | 1717 (41%) | 1692 (44%) | 1770 (38%) | 0.07 | 599 (27%) | 741 (27%) | 873 (25%) | 0.3 |
| CABG | 499 (12%) | 270 (7%) | 268 (6%) | <0.0001 | 249 (11%) | 201 (7%) | 218 (6%) | 0.0001 |
CABG indicates coronary artery bypass grafting; and PCI, percutaneous coronary intervention.
Significance of temporal trends tested by Cochran‐Armitage test for trend.
After adjustments for age, race, sex, year of admission, hospital, smoking, hypertension, prior myocardial infarction and history of stroke, patients with diabetes had a 7% lower probability of receiving nonaspirin antiplatelets than those without diabetes (RR, 0.93 [95% CI, 0.86–0.99]), as well as a 15% lower relative probability of angiography (RR, 0.85 [95% CI, 0.78–0.92]) and coronary revascularization (RR, 0.85 [95% CI, 0.76–0.92]) (Figure 3). These associations were consistent across demographic subgroups in patients with first‐occurring AMI and those with recurring AMI (Table S1).
Figure 3. Adjusted relative probabilities of receiving guideline‐directed therapies, comparing patients with and without diabetes who were admitted with acute myocardial infarction.
The Atherosclerosis Risk in Communities study community surveillance, 2000 to 2014. Models are adjusted for age, race, sex, year of admission, hospital, history of smoking, hypertension, prior myocardial infarction, and stroke.
Overall, there were 1308 all‐cause deaths within 28 days of hospitalization, 2805 deaths within 1 year, and 3256 deaths within 2 years. The crude mortality was higher for patients with diabetes than those without diabetes, whether at 28 days (8% versus 5%, P=0.0004), 1 year (18% versus 10%, P<0.0001), or 2 years (21% versus 12%, P<0.0001) of hospitalization (Figure 4). Approximately half of the deaths were attributable to cardiovascular causes for patients with and without diabetes (51% versus 45%, respectively). After adjustments for age, race, sex, year of admission, hospital, smoking, hypertension, prior myocardial infarction, and history of stroke, patients with diabetes experienced a 29% higher short‐term mortality risk compared with those without diabetes (hazard ratio [HR], 1.29 [95% CI, 0.96–1.77]), a 52% higher 1‐year mortality risk (HR, 1.52 [95% CI, 1.23–1.89]), and a 44% higher 2‐year mortality risk (HR, 1.50; [95% CI, 1.17–1.77]).
Figure 4. Kaplan‐Meier survival curves comparing patients with and without diabetes who were admitted with acute myocardial infarction.
The Atherosclerosis Risk in Communities study community surveillance, 2000 to 2014.
Discussion
In this analysis of >21 000 weighted hospitalizations for AMI, we noted a significant burden of diabetes (≈40%) that increased over the 15‐year interval. This burden was markedly higher than the national prevalence of diabetes (≈10%–15%) over the same time period. 1 The burden of cardiometabolic comorbidities, such as chronic kidney disease, hypertension, and smoking, remained substantially high during this period, with a trend toward higher comorbidities in patient with diabetes. The majority of AMI hospitalizations had a clinical presentation of non–ST‐segment–elevation myocardial infarction, and patients without diabetes were twice as likely to present with ST elevation. Patients with diabetes were disproportionately Black and women, and were less likely to report chest pain at presentation but were much more likely to experience complications of AMI including cardiogenic shock, acute heart failure, and ventricular fibrillation/asystole. This may in part have been driven by a significant underuse of guideline‐directed therapies including aspirin and revascularization in patients with diabetes. Higher complications also translated into higher mortality, with patients with diabetes having a 29% and 52% higher risk of mortality at 28 days and 1 year, respectively.
There has been an exponential increase in the global burden of diabetes from 30 million in 1985 to 382 million in 2014, with current estimates indicating that 1 in 10 adults will be living with diabetes by the year 2035. 19 , 20 It has been associated with a steady increase in morbidity, mortality, and loss of productivity years, with a total economic burden in the United States alone exceeding $174 billion in the year 2007. 21 Cardiovascular disease, especially AMI, remains the leading cause of morbidity and mortality in patients with diabetes. 6 Several hypotheses have been proposed for this increased burden; there is a clustering of risk factors including obesity, hypertension, chronic kidney disease, and hyperlipidemia in patients with diabetes. 22 , 23 In our study, we found a similar trend of higher rates of these risk factors in patients with diabetes, and this trend remained consistent over the study duration. In addition, metabolic abnormalities, including hyperglycemia and hypertriglyceridemia, affect biochemical pathways and epigenetic changes that lead to increased endothelial dysfunction, oxidative stress, inflammation, and platelet reactivity that predispose patients with diabetes to increased thrombotic events. 24 Taken together, these findings emphasize the importance of adherence to cardiovascular preventive therapies for decreasing the burden of AMI in patients with diabetes.
It has also been proposed that patients with diabetes may have an atypical presentation with diminished perception of ischemic chest pain, which in part may be because of autonomic neuropathy and prolongation of the anginal perceptual threshold. 25 Furthermore, they may also be less likely to have typical ST elevation, all of which in turn lead to delay in management and worse outcomes. In our study, we found that patients with diabetes were on an average 5% to 10% less likely to report chest pain at presentation. This may partly explain the underuse of invasive angiography and revascularization in patients with diabetes compared with patients without diabetes. Notably, the decreased use of revascularization was predominantly driven by decreasing referrals for CABG over time, whereas rates of PCI largely stayed stable. Our findings are consistent with those from the National Cardiovascular Data ACTION registry that highlight the decreasing and heterogenous trends of CABG use, despite multiple trials establishing the efficacy of CABG over PCI in patients with diabetes and multivessel coronary artery disease. 26 Although this practice may be a reflection of advancements in stent technologies and growing experience of operators in performing complex and high‐risk PCIs, the underuse of guideline‐directed medical management (including medical management and timely revascularization) in patients with diabetes and AMI is a concern and an important finding of the present report. On a related note, because hospitalizations were captured by the ARIC community surveillance irrespective of myocardial infarction type, the underuse of revascularization may in part be reflective of type II myocardial infarction cases.
In this analysis of the ARIC study community surveillance, patients with diabetes and AMI were observed to be predominantly Black and women. In previous reports, a higher prevalence of cardiovascular comorbidities and lower use of guideline‐directed therapies have been noted in Black adults hospitalized with AMI compared with White adults. 27 Adverse social determinants of health, compounded by implicit and explicit biases, have been associated with adverse health outcomes among Black adults. 28 These factors may contribute to challenges attaining lifestyle modifications, such as healthy dietary habits, physical activity, and weight loss. Moreover, there is evidence to suggest that Black adults may have lower trust in their clinicians, influencing adherence to evidence‐based prevention strategies such as lipid‐lowering agents. 29 , 30 Our observations underscore the importance of bridging these disparities by encouraging policy efforts and developing social risk assessment tools that enable health care providers to target socially vulnerable populations.
Our analysis should be interpreted within the context of its limitations. The ARIC study community surveillance includes a large population of Black and White patients hospitalized with AMI but is limited to 4 US communities and may not generalize to all AMI hospitalizations. We were unable to differentiate type 1 from type 2 diabetes, and the analysis was limited to hospitalizations spanning 2000 to 2014. Importantly, however, the upward trend in AMI hospitalizations with concomitant diabetes continued through 2014, the year SGLT2i (sodium‐glucose cotransporter 2 inhibitor) was FDA‐approved for glycemia management. SGLT2i is known to have cardioprotective effects, with the potential to reduce AMI hospitalizations. 31 Additionally, the ARIC data set does not provide information on patients' coronary anatomy. Thus, it is possible that a subset of patients with diabetes was not offered CABG secondary to poor target vessels and high procedural risk. The data set also does not allow us to distinguish between type 1 and type 2 myocardial infarction, a distinction that impacts use of therapies as well as outcomes. The analysis of data from the ARIC study community surveillance also has several important strengths. The ARIC study provides a geographically and racially diverse population of patients hospitalized with adjudicated AMI, with surveillance spanning several decades. Data abstraction from the medical record followed standardized protocols, and classifications remained consistent throughout the surveillance period, allowing an examination of temporal trends.
In conclusion, the burden of diabetes in patients hospitalized with AMI is high and appears to be increasing, particularly for men, while remaining persistently elevated for women and Black patients. Patients with diabetes have a higher burden of comorbidities, are sicker at presentation, and are less likely to report chest pain or have ST‐segment–elevation myocardial infarction. They are less likely to receive guideline‐based AMI therapies and have a higher mortality at 28 days and 1 year. Focused efforts are needed to promote adherence to evidence‐based therapeutic strategies in this high‐risk population.
Sources of Funding
The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I).
Disclosures
Dr. Qamar has received institutional grant support from Novo Nordisk, NorthShore Auxiliary Research Scholar Award, and NorthShore Pilot Grant Award. Dr Vaduganathan has received research grant support or served on advisory boards for American Regent, Amgen, AstraZeneca, Bayer AG, Baxter Healthcare, Boehringer Ingelheim, Cytokinetics, Lexicon Pharmaceuticals, Novartis, Pharmacosmos, Relypsa, Roche Diagnostics, and Sanofi, speaker engagements with Novartis and Roche Diagnostics, and participates on clinical trial committees for studies sponsored by Galmed, Novartis, Bayer AG, Occlutech, and Impulse Dynamics. Dr Bhatt discloses the following relationships: Advisory Board: AngioWave, Bayer, Boehringer Ingelheim, Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma, Regado Biosciences, and Stasys; Board of Directors: AngioWave (stock options), Boston VA Research Institute, Bristol Myers Squibb (stock), DRS.LINQ (stock options), High Enroll (stock), Society of Cardiovascular Patient Care, TobeSoft; Chair: Inaugural Chair, American Heart Association Quality Oversight Committee; Consultant: Broadview Ventures; Data Monitoring Committees: Acesion Pharma, Assistance Publique‐Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Boston Scientific (Chair, PEITHO trial), Cleveland Clinic (including for the ExCEED trial, funded by Edwards), Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo; for the ABILITY‐DM trial, funded by Concept Medical), Novartis, Population Health Research Institute; Rutgers University (for the National Institutes of Heal–funded MINT trial); Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Chair, American College of Cardiolog Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol‐Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE‐DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim; AEGIS‐II executive committee funded by CSL Behring), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), Cowen and Company, Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), K2P (Co‐Chair, interdisciplinary curriculum), Level Ex, Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Oakstone CME (Course Director, Comprehensive Review of Interventional Cardiology), Piper Sandler, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and USA national co‐leader, funded by Bayer), Slack Publications (Chief Medical Editor, Cardiology Today's Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees), Wiley (steering committee); Other: Clinical Cardiology (Deputy Editor), NCDR‐ACTION Registry Steering Committee (Chair), VA CART Research and Publications Committee (Chair); Patent: Sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon; neither he nor Brigham and Women's Hospital receive any income from this patent); Research Funding: Abbott, Acesion Pharma, Afimmune, Aker Biomarine, Amarin, Amgen, AstraZeneca, Bayer, Beren, Boehringer Ingelheim, Boston Scientific, Bristol‐Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Garmin, HLS Therapeutics, Idorsia, Ironwood, Ischemix, Janssen, Javelin, Lexicon, Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Owkin, Pfizer, PhaseBio, PLx Pharma, Recardio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, Synaptic, The Medicines Company, Youngene, 89Bio; Royalties: Elsevier (Editor, Braunwald's Heart Disease); Site Co‐Investigator: Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St. Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte, Vascular Solutions; Trustee: American College of Cardiology; Unfunded Research: FlowCo, Takeda. The remaining authors have no disclosures to report.
Supporting information
Table S1
Acknowledgments
The authors thank the staff and participants of the ARIC study for their important contributions. Drs Jain, Qamar, and Caughey conceptualized the study and wrote the article. Dr Caughey performed the statistical analysis. Drs Arora, Vaduganathan, Bhatt, Matsushita, Khan, and Ashley interpreted the data and revised the article critically.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.122.028923
For Sources of Funding and Disclosures, see page 9.
See Editorial by Abushamat and Nambi.
References
- 1. Prevalence of both diagnosed and undiagnosed diabetes. Centers for Disease Control and Prevention. Accessed January 26, 2022. https://www.cdc.gov/diabetes/data/statistics‐report/diagnosed‐undiagnosed‐diabetes.html
- 2. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321:405–412. doi: 10.1136/bmj.321.7258.405 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Naghavi M, Wang H, Lozano R, Davis A, Liang X, Zhou M, Vollset SE, Ozgoren AA, Abdalla S; GBD 2013 Mortality and Causes of Death Collaborators , et al. Global, regional, and national life expectancy, all‐cause mortality, and cause‐specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015. Lancet. 2016;388:1459–1544. doi: 10.1016/S0140-6736(16)31012-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Fox CS, Coady S, Sorlie PD, Levy D, Meigs JB, D'Agostino RB, Wilson PWF, Savage PJ. Trends in cardiovascular complications of diabetes. JAMA. 2004;292:2495–2499. doi: 10.1001/jama.292.20.2495 [DOI] [PubMed] [Google Scholar]
- 5. Gregg EW, Gu Q, Cheng YJ, Narayan KMV, Cowie CC. Mortality trends in men and women with diabetes, 1971 to 2000. Ann Intern Med. 2007;147:149–155. doi: 10.7326/0003-4819-147-3-200708070-00167 [DOI] [PubMed] [Google Scholar]
- 6. Rao Kondapally Seshasai S, Kaptoge S, Thompson A, DiAngelantonio E, Gao P, Sarwar N, Whincup PH, Mukamal KJ, Gillum RF; Emerging Risk Factors Collaboration , et al. Diabetes mellitus, fasting glucose, and risk of cause‐specific death. N Engl J Med. 2011;364:829–841. doi: 10.1056/NEJMoa1008862 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Martín‐Timón I, Sevillano‐Collantes C, Segura‐Galindo A, del Cañizo‐Gómez FJ. Type 2 diabetes and cardiovascular disease: have all risk factors the same strength? World J Diabetes. 2014;5:444–470. doi: 10.4239/wjd.v5.i4.444 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Krishnan U, Brejt JA, Schulman‐Marcus J, Swaminathan RV, Feldman DN, Goyal P, Wong SC, Minutello RM, Bergman G, Singh H, et al. Temporal trends in the clinical acuity of patients with ST‐segment elevation myocardial infarction. Am J Med. 2018;131:100.e9–100.e20. doi: 10.1016/j.amjmed.2017.06.040 [DOI] [PubMed] [Google Scholar]
- 9. American Diabetes Association . 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes—2021. Diabetes Care. 2020;44:S111–S124. doi: 10.2337/dc21-S009 [DOI] [PubMed] [Google Scholar]
- 10. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli‐Ducci C, Bueno H, Caforio ALP, Crea F, Goudevenos JA; ESC scientific document group , et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST‐segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST‐segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39:119–177. doi: 10.1093/eurheartj/ehx393 [DOI] [PubMed] [Google Scholar]
- 11. Wright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, Mosley TH, Ballantyne CM, Boerwinkle EA, Rosamond WD, et al. The ARIC (Atherosclerosis Risk In Communities) study: JACC focus seminar 3/8. J Am Coll Cardiol. 2021;77:2939–2959. doi: 10.1016/j.jacc.2021.04.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Arora S, Stouffer GA, Kucharska‐Newton AM, Qamar A, Vaduganathan M, Pandey A, Porterfield D, Blankstein R, Rosamond WD, Bhatt DL, et al. Twenty year trends and sex differences in young adults hospitalized with acute myocardial infarction. Circulation. 2019;139:1047–1056. doi: 10.1161/CIRCULATIONAHA.118.037137 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. White AD, Folsom AR, Chambless LE, Sharret AR, Yang K, Conwill D, Higgins M, Williams OD, Tyroler HA. Community surveillance of coronary heart disease in the Atherosclerosis Risk In Communities (ARIC) study: methods and initial two years' experience. J Clin Epidemiol. 1996;49:223–233. doi: 10.1016/0895-4356(95)00041-0 [DOI] [PubMed] [Google Scholar]
- 14. Myerson M, Coady S, Taylor H, Rosamond WD Jr, Goff DC Jr; ARIC Investigators . Declining severity of myocardial infarction from 1987 to 2002: the Atherosclerosis Risk in Communities (ARIC) study. Circulation. 2009;119:503–514. doi: 10.1161/CIRCULATIONAHA.107.693879 [DOI] [PubMed] [Google Scholar]
- 15. Arora S, Matsushita K, Qamar A, Stacey RB, Caughey MC. Early versus late percutaneous revascularization in patients hospitalized with non ST‐segment elevation myocardial infarction: the atherosclerosis risk in communities (ARIC) surveillance study. Catheter Cardiovasc Interv. 2017;91:253–259. doi: 10.1002/ccd.27156 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Chunawala ZS, Hall ME, Arora S, Dai X, Menon V, Smith SC, Matsushita K, Caughey MC. Prognostic value of shock index in patients admitted with non‐ST‐segment elevation myocardial infarction: the ARIC study community surveillance. Eur Heart J Acute Cardiovasc Care. 2021;10:869–877. doi: 10.1093/ehjacc/zuab050 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Mansournia MA, Altman DG. Inverse probability weighting. BMJ. 2016;352:1–2. doi: 10.1136/bmj.i189 [DOI] [PubMed] [Google Scholar]
- 18. Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. J Am Med Assoc. 1998;280:1690–1691. doi: 10.1001/jama.280.19.1690 [DOI] [PubMed] [Google Scholar]
- 19. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27:1047–1053. doi: 10.2337/diacare.27.5.1047 [DOI] [PubMed] [Google Scholar]
- 20. Aguirre F, Brown A, Cho NH, Dahlquist G, Dodd S, Dunning T, Hirst M, Hwang C, Magliano D, Patterson C, et al. IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013. [Google Scholar]
- 21. American Diabetes Association . Economic costs of diabetes in the U.S. in 2007. Diabetes Care. 2008;31:596–615. doi: 10.2337/dc08-9017 [DOI] [PubMed] [Google Scholar]
- 22. de Burgos‐Lunar C, Jiménez‐García R, Salinero‐Fort MA, Gómez‐Campelo P, Gil Á, Abánades‐Herranz JC, Cárdenas‐Valladolid J, del Cura‐González I. Trends in hypertension prevalence, awareness, treatment and control in an adult type 2 diabetes Spanish population between 2003 and 2009. PLOS ONE. 2014;9:e86713. doi: 10.1371/journal.pone.0086713 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wu B, Bell K, Stanford A, Kern DM, Tunceli O, Vupputuri S, Kalsekar I, Willey V. Understanding CKD among patients with T2DM: prevalence, temporal trends, and treatment patterns—NHANES 2007–2012. BMJ Open Diabetes Res Care. 2016;4:e000154. doi: 10.1136/bmjdrc-2015-000154 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Schneider DJ. Factors contributing to increased platelet reactivity in people with diabetes. Diabetes Care. 2009;32:525–527. doi: 10.2337/dc08-1865 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Scognamiglio R, Negut C, Ramondo A, Tiengo A, Avogaro A. Detection of coronary artery disease in asymptomatic patients with type 2 diabetes mellitus. J Am Coll Cardiol. 2006;47:65–71. doi: 10.1016/j.jacc.2005.10.008 [DOI] [PubMed] [Google Scholar]
- 26. Pandey A, McGuire DK, de Lemos JA, Das SR, Berry JD, Brilakis ES, Banerjee S, Marso SP, Barsness GW, Simon DN, et al. Revascularization trends in patients with diabetes mellitus and multivessel coronary artery disease presenting with non–ST elevation myocardial infarction. Circulation. 2016;9:197–205. doi: 10.1161/CIRCOUTCOMES.115.002084 [DOI] [PubMed] [Google Scholar]
- 27. Arora S, Stouffer G, Kucharska‐Newton A, Vaduganathan M, Qamar A, Matsushita K, Kolte D, Reynolds H, Bangalore S, Rosamond W, et al. Fifteen‐year trends in management and outcomes of non‐ST‐segment‐elevation myocardial infarction among black and white patients: the ARIC community surveillance study, 2000‐2014. J Am Heart Assoc. 2018;7:e010203. doi: 10.1161/JAHA.118.010203 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Nayak A, Hicks AJ, Morris AA. Understanding the complexity of heart failure risk and treatment in African Americans. Circ Heart Fail. 2020;13:e007264. doi: 10.1161/CIRCHEARTFAILURE.120.007264 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Morris AA, Ko Y, Hutcheson SH, Quyyumi A. Race/ethnic and sex differences in the association of atherosclerotic cardiovascular disease risk and healthy lifestyle behaviors. J Am Heart Assoc. 2018;7:e008250. doi: 10.1161/JAHA.117.008250 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Nanna MG, Navar AM, Zakroysky P, Xiang Q, Goldberg AC, Robinson J, Roger VL, Virani SS, Wilson PWF, Elassal J, et al. Association of patient perceptions of cardiovascular risk and beliefs on statin drugs with racial differences in statin use. JAMA Cardiol. 2018;3:739–748. doi: 10.1001/jamacardio.2018.1511 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Harrington J, Udell JA, Jones WS, Anker SD, Bhatt DL, Petrie MC, Vedin O, Sumin M, Zwiener I, Hernandez AF, et al. Empagliflozin in patients post myocardial infarction rationale and design of the EMPACT‐MI trial. Am Heart J. 2022;253:86–98. doi: 10.1016/j.ahj.2022.05.010 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1