Trends and Disparities in Mortality Due to Non-Insulin Dependent Diabetes Mellitus and Acute Myocardial Infarction: A 23-Year Analysis from 1999 to 2022

Trends and Disparities in Mortality Due to Non-Insulin Dependent Diabetes Mellitus and Acute Myocardial Infarction: A 23-Year Analysis from 1999 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends and Disparities in Mortality Due to Non-Insulin Dependent Diabetes Mellitus and Acute Myocardial Infarction: A 23-Year Analysis from 1999 to 2022 Sardar Muhammad Imran Khan, Muneeb Khawar, Allahdad Khan, Muhammad Aizaz Ashraf, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6480876/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Abstract Background Non-insulin dependent diabetes mellitus (NIDDM) and acute myocardial infarction (MI) are critical health challenges that increase mortality, particularly in older adults. This study analyzed trends in AAMRs and disparities in comorbid NIDDM and MI mortality (1999–2022) across demographics, regions, and age groups to identify inequities and guide interventions. Methods Mortality data from CDC death certificates were analyzed. AAMRs per 1000,000 and annual percentage changes (APCs) with 95% confidence intervals (CIs) were calculated using Joinpoint Regression. Results From 1999–2022, 183,221 deaths were attributed to comorbid NIDDM and MI, with men consistently having higher rates than women. American Indians/Alaska Natives showed the highest AAMRs, followed by Non-Hispanic Blacks, while Asian/Pacific Islanders had the lowest. The West (AAMR 118.8) and Midwest (115.0) had the highest rates, while the Northeast had the lowest (70.4). Age-specific trends showed the steepest increases among individuals aged 85+. Non-metropolitan areas saw sharper rises post-2015 (APC 7.4), while metropolitan areas showed a moderate increase (APC 6.1). West Virginia recorded the highest state burden (AAMR 173.6), while Nevada had the lowest (30.1). Conclusion Mortality from comorbid NIDDM and MI has surged, with widening disparities across gender, race, regions, and age groups. Targeted interventions are essential to reduce these inequities and prevent deaths. Diabetes Mellitus Myocardial Infarction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Approximately 38.1 million adults in the United States (14.7% of the adult population) have diabetes mellitus, with an additional 8 million undiagnosed. ( 1 ) Among them, non-insulin dependent diabetes mellitus (NIDDM) has a prevalence of 9%, increasing to 25% in individuals aged 65 and older. ( 2 ) The incidence of newly diagnosed diabetes has nearly doubled in recent years. ( 3 ) Diabetes is a major risk factor for both macrovascular and microvascular complications, including cardiovascular disease, chronic kidney disease, and cerebrovascular disease. ( 4 ) Although overall cardiovascular mortality has declined in recent decades, diabetes remains strongly associated with a 2- to 4-fold increased risk of cardiovascular events and a 3-fold increased risk of cardiovascular-related death. ( 5 , 6 , 7 ) Atherosclerosis, the leading cause of acute myocardial infarction (AMI), is responsible for most fatal cases due to arterial blockage. Addressing modifiable risk factors such as smoking, hypertension, obesity, and dyslipidemia is central to prevention. ( 8 , 9 ) Acute myocardial infarction (AMI) remains a major cause of mortality in the United States. ( 10 ) Among individuals with NIDDM, the co-occurrence of risk factors such as obesity, hypertension, chronic kidney disease, and dyslipidemia, along with disease-specific manifestations like multivessel coronary artery disease, significantly increases AMI-related mortality. ( 11 , 12 ) Advancements in the management of NIDDM and AMI, including newer therapies and updated guidelines, have improved patient outcomes. ( 13 , 14 ) However, understanding demographic and regional trends in NIDDM- and AMI-related mortality remains crucial to assessing the evolving burden of these conditions. Methodology Study design and population: This study used the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database. ( 15 ) The study focused on the mortality associated with comorbid non-insulin dependent diabetes mellitus (NIDDM) and acute myocardial infarction (AMI) in adults between 1999 and 2022. The International Statistical Classification of Diseases and Related Health Problems-10th Revision (ICD-10) codes E11 for NIDDM and I21 for AMI were employed to identify relevant cases. Mortality data linked to these conditions were provided by the Multiple Causes of Death Public Use Death Certificates, specifically targeting comorbid NIDDM and AMI listed as either underlying or contributing cause of death. The study sample consisted of adults aged 55 and older at the time of death. Institutional review board approval was not required, as the study relied on a publicly available government database. The dataset encompassed cause-of-death information from death certificates across all 50 states and the District of Columbia. The study adhered strictly to the STROBE guidelines. ( 16 ) Data extraction: Data on various population characteristics, including population size, demographics, urban-rural classification, census region, age groups, and states, were extracted for this analysis. Demographic data related to gender and overall trends were collected for the period 1999 to 2022, whereas data on race, census region, urban-rural classification, states, and age groups were obtained for the period 1999 to 2020. Race and ethnicity categories included Non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaskan Native, and NH Asian or Pacific Islander. Age groups were categorized as (55–64), (65–74), (75–84), and 85 + years. Urban-rural classification followed the 2013 U.S. urbanization criteria: areas with populations exceeding one million were classified as major metropolitan, those with populations between 50,000 and 999,999 were categorized as medium or small metropolitan, and areas with fewer than 50,000 people were classified as rural. ( 17 ) Regional classifications followed the U.S. Census Bureau's divisions into Northeast, Midwest, South, and West. Statistical analysis: For this analysis, we determined Age-Adjusted Mortality Rates (AAMRs) per 1000,000 individuals, stratified by year, gender, race or ethnicity, state, and urban-rural classification, along with their corresponding 95% confidence intervals (CIs). For age-specific analysis, we computed Crude Mortality Rates (CMRs). The AAMRs and CMRs were calculated by comparing the number of deaths attributable to comorbid NIDDM and AMI to the U.S 2000 standard population. ( 18 ) To assess trends in mortality, we utilized the Joinpoint Regression Program (Joinpoint V 5.0.2, National Cancer Institute) to calculate the Annual Percent Change (APC) with 95% CIs for both AAMRs and CMRs, allowing us to examine national trends in comorbid NIDDM and AMI related mortality. ( 19 ) The statistical significance of the APC slopes was evaluated using 2-tailed t-tests to determine whether changes in mortality rates were significantly different from zero. When a statistically significant slope (p-value < 0.05) was found, the APC was classified as either increasing or decreasing for that segment of the study period. Results Mortality rates across all demographic groups highlighted significant trends across the study period. From 1999 to 2022, total deaths were recorded at 183,221 for adults (55–85 + years of age) due to NIDDM and AMI. From 1999–2022, men demonstrated a total of 103,658 deaths whereas women had 79,563 deaths during this period (Supplemental table 1 ) .In race-stratified groups from 1999 to 2020, the total recorded deaths were 1,412 among American Indian or Alaska Native individuals, 1,509 among Asian or Pacific Islander individuals, 17,311 among Non-Hispanic Black individuals, 119,430 among Non-Hispanic White individuals, and 14,995 among Hispanic or Latino populations. (Supplemental table 2) .In age-stratified groups from 1999 to 2020, the cumulative deaths were 27,447 among individuals aged (55–64) years, 4,445 among those aged (65–74) years, 51,651 among those aged (75–84) years, and 35,222 among individuals aged (85+) years (Supplemental table 3). The overall trend was consistent from 1999 to 2003 APC 2.3 (95% CI: -1.1 to 9.5) which continued from 2003 to 2014 APC − 2.0 (95% CI: -4.9 to 3.8), and a significant increase from 2014 to 2022 APC 6.0 (95% CI: 4.7 to 7.6) ( Fig. 1 ). Sex disparities: Men consistently displayed higher AAMRs throughout the study period than women, with a stable trend from 1999 to 2003 APC 2.1(95% CI: -1.0 to 9.6). This stabilization of trend continued from 2003 and 2014 APC − 1.4 (95% CI: -4.9 to 8.6), accompanied by a significant increase from 2014 to 2022 APC 6.4 (95% CI: 4.1 to 8.5). Women exhibited a decline from 2005 to 2014 APC − 3.3(95% CI: -6.6 to -2.2), followed by an increase in AAMR from 2014 to 2022 APC 5.3 (95% CI: 4.0 to 6.9) ( Fig. 1 and supplemental table 3) . Racial disparities: American Indian or Alaska Native ethnicity exhibited the highest AAMR throughout the years, showing stable trends in the study period, with an average annual percent change (APC) of -0.9 (95% CI: -1.8 to 0.3) from 1999 to 2020 ( Fig. 2 and supplemental table 4 ) . Asian or Pacific Islander group consistently demonstrated the lowest AAMR, with a consistent trend from 1999 to 2012 with an APC of 0.5(95% CI: -2.2 to 2.2), with the trend increasing markedly from 2012 to 2020 with an APC of 8.2 (95% CI: 6.3 to 12.2). Non-Hispanic Black population showed a consistency in AAMR values between 2005 and 2014 APC − 3.2 (95% CI: 0.2 to 10.1) with a subsequent increase from 2014 to 2020 APC 5.5(95% CI: 3.0 to 10.8) .In a similar fashion, Non-Hispanic White group also showed a relatively stable trend from 2003 to 2014 APC − 2.2 (95% CI: -0.3 to 8.0), accompanied by a significant increase from 2014 to 2020 APC 5.6 (95% CI: 4.2 to 7.8). Hispanic or Latino populations experienced a stable period between 1999 and 2015 APC − 0.2 (95% CI: -1.7 to 1.0), but their AAMR increased notably from 2015 to 2020 with an APC of 9.7(95% CI: 5.8 to 20.5). Age group disparities: For individuals aged 55–64 years, the annual percent change (APC) was recorded at 1.6 (95% CI: -0.4 to 7.8) during 1999–2005, accompanied by a decrease of -2.1(95% CI: -5.9 to -0.6) during 2005–2013, and then a subsequent increase of 6.9 (95% CI: 5.5 to 8.9) during 2013–2020 ( Fig. 3 and supplemental table 5) . In the 65–74 age group, the APC remained stable with APC 4.5 (95% CI: -0.9 to 15.8) from 1999–2002,continuing stability from 2002–2014 with APC − 1.9(95% CI: -6.6 to 1.4).A significant increase of 6.8 (95% CI: 4.6 to 11.0) was observed from 2014–2020. For the 75–84 age group, the APC displayed a consistent trend of 1.4 (95% CI: -1.2 to 6.9) during 1999–2003.This was followed by a decline of -2.1(95% CI: -4.5 to -1.2) during 2003–2014, and a subsequent increase of 6.1(95% CI: 4.4 to 8.4) during 2014–2020. Among individuals aged 85 years and older, the APC increased by 2.5 (95% CI: -0.0 to 8.5) from 1999–2004, accompanied by a decrease of -2.8 (95% CI: -5.7 to -1.9) from 2004–2014.This was followed by an increase again to 5.0 (95% CI: 3.1 to 8.0) from 2014–2020. Census region disparities: In the Northeast, the APC was determined − 0.3 (95% CI: -2.8 to 7.3) during 1999–2003.This decreased by -4.4 (95% CI: -9.1 to -3.6) during 2003–2013, followed by a stable period of APC 0.9 (95% CI: -3.7 to 4.9) during 2013–2018, with a subsequent sharp increase of 16.4 (95% CI: 7.4 to 22.8) during 2018–2020 ( Fig. 4 and supplemental table 6) . In the Midwest, the APC was recorded at 0.7 (95% CI: -0.8 to 3.9) during 1999–2005, with a subsequent drop of -3.6 (95% CI: -5.9 to -2.7) during 2005–2014.Following this, a rise to 6.1 (95% CI: 4.5 to 8.5) was observed from 2014–2020. The South experienced an APC of 5.6(95% CI: 0.4 to 15.3) during 1999–2002.This was accompanied by a decline of -3.0 (95% CI: -4.5 to -2.3) from 2002–2014, and an increase of 6.0(95% CI: 4.2 to 8.7) during 2014–2020. In the West, the APC was determined to be 4.2(95% CI: 3.1 to 8.7) from 1999–2008. The trend remained consistent with APC of -2.4(95% CI: -4.7 to 2.5) during 2008–2011 and subsequently rose to 6.0 (95% CI: 5.1 to 7.9) during 2011–2020. These trends highlight regional disparities, with the Northeast indicating the most dramatic rise in AAMR during recent years. Urban-rural disparities: In non-metropolitan areas, the APC increased by 5.1 (95% CI: 1.4 to 12.5) during 1999–2002, followed by a decline of -2.4(95% CI: -3.2 to -2.0) from 2002–2015 ( Fig. 5 and supplemental table 7 ). A subsequent sharp increase of 7.4 (95% CI: 5.5 to 10.4) was noted during 2015–2020. Metropolitan areas demonstrated a decrease in APC of -0.9 (95% CI: -1.8 to -0.2) during 1999–2014, accompanied by an increase of 6.1(95% CI: 4.0 to 10.0) during 2014–2020. These findings underscore a widening gap between non-metropolitan and metropolitan areas, with non-metropolitan areas consistently exhibiting higher AAMR and more pronounced increases in recent years. State disparities: The state-wise analysis of age-adjusted mortality rates (AAMR) reveals significant geographic disparities. West Virginia reported the highest AAMR at 173.6 per 100,000 population (95% CI: 166.0–181.2), while Nevada had the lowest AAMR at 30.1 (95% CI: 27.0–33.3) ( Fig. 6 and supplemental table 8) . States in the top 90th percentile of AAMR included West Virginia, Ohio (171.8, 95% CI: 168.6–175.0), Tennessee (169.3, 95% CI: 164.9–173.8), Kentucky (130.9, 95% CI: 126.2–135.7), and Indiana (124.1, 95% CI: 120.3–127.9). These states demonstrated AAMR values exceeding 124 per 1000,000 highlighting regions with substantial mortality burdens. On the other hand, states in the lowest 10th percentile, characterized by AAMR values below 40 per 1000,000, included Nevada, Massachusetts (39.6, 95% CI: 37.6–41.7), and Connecticut (39.1, 95% CI: 36.4–41.8). These findings indicate notable regional differences in mortality, with higher rates concentrated in states like West Virginia and Ohio, and lower rates observed in states like Nevada and Massachusetts. Discussion This study examines trends and inequalities in mortality from comorbid non-insulin-dependent diabetic mellitus (NIDDM) and acute myocardial infarction (AMI) across the span of 23 years (1999–2022). The findings highlight major disparities across demographic groupings, geographic locations, and age categories, providing crucial insights for targeted public health initiatives. Many epidemiological studies show that diabetics are more likely to develop cardiac problems such as coronary artery disease, cardiomyopathy, and congestive heart failure. ( 20 ) Diabetes poses a significant risk for both macrovascular and microvascular consequences, such as cardiovascular, renal, peripheral artery, and cerebrovascular illnesses. ( 4 ) Based on 20 years of Framingham cohort monitoring linking eventual cardiovascular events to past signs of diabetes, a twofold to threefold elevated risk of clinical atherosclerotic disease was found. ( 21 ) This study demonstrates a concerning rise in mortality attributed to the comorbidity of non-insulin-dependent diabetes mellitus (NIDDM) and acute myocardial infarction (AMI) between 1999 and 2022. The observed increase, particularly after 2014, warrants further investigation into potential contributing factors. While advancements in diabetes and cardiovascular care may have contributed to a decline earlier in the study period, the subsequent sharp rise suggests that these advances may have been insufficient to offset emerging challenges or worsening disparities. Men had continuously higher age-adjusted mortality rates (AAMRs) than women, which supports the well-documented gender disparity in cardiovascular mortality. The steeper rise in AAMRs for men since 2014 emphasizes the need for gender-specific interventions, such as tailored education and screening programs. Women, albeit displaying comparatively lower rates, suffered a substantial rise throughout the same time, suggesting unrecognized hazards or poor focus of preventative efforts in this demographic. Female patients with AMI are frequently older, have greater rates of diabetes, hypertension, and autoimmune illnesses, have worse Killip class, higher Global Registry of Acute Coronary Events (GRACE) risk scores, lower weight, baseline hemoglobin, and creatinine clearance. Men with AMI frequently have greater rates of smoking, peripheral vascular disease, past MI, and previous percutaneous coronary intervention (PCI) and coronary artery bypass graft surgery (CABG). ( 22 ) American Indians/Alaska Natives had the highest AAMRs, indicating systemic disparities such as restricted access to healthcare, a greater incidence of risk factors, and social determinants of health. Non-Hispanic Blacks and Hispanics have experienced significant increases in mortality in recent years, emphasizing the disproportionate burden on these populations. In contrast, Asian/Pacific Islanders had the lowest AAMRs but experienced a significant increase after 2012, raising concerns about new risk factors or changes in healthcare access within this population. One study showed that in their demographic comparison, more people had Asian or Black ethnicities. More people with diabetes were prone to developing complications like LV dysfunction, presenting with higher Killip class and with pulmonary edema. Participants were less likely to undergo invasive Coronary angiography (ICA) within 72 hours. ( 23 ) West and Midwest showed the highest AAMRs in comparison to the Northeast, which had the lowest, which suggests differences in healthcare provision, access, and population health dynamics. Non-metropolitan areas show a significant rise in mortality rate after 2015, highlighting the healthcare provision challenges and disparities in rural areas. The best way to address these issues is by establishing mobile health units, more telemedicine centers, and investing more in primary care. When we invest in primary care the research has shown that the health outcomes improve, decreased burden on capital with better chronic disease management. This benefits the whole economy and does cost savings. ( 24 ) One of the concerning things in our study was that the mortality burden increased in 85 + age individuals. With advanced age, there does come an increased mortality and morbidity risk, but still, we need to dig deeper in this regard. We need to uncover any underlying etiologies and improve our geriatric healthcare section. For younger age groups, the increase in death rates necessitates a renewed emphasis on early identification and active management of diabetes and cardiovascular risk factors. Strengths of this are due to the extensive temporal scope having large sample size, and robust statistical methodology. These findings can be reproduced easily because of the publicly available data. Moreover, relying solely on death certificates may introduce misclassification bias there should be direct acquisition of data in this regard. Additionally, the study is deficient in examining the socioeconomic factors which play a vital role in inducing disparities. To address the increased mortality burden from comorbid NIDDM and AMI, we require a comprehensive approach that prioritizes early detection and active management of diabetes and cardiovascular hazards, particularly in high-risk populations. Culturally customized outreach activities must target underserved racial and ethnic groups, while rural healthcare infrastructure must be strengthened immediately to bridge access gaps. Geriatric-focused solutions should enhance care for the elderly with multimorbid illnesses, and targeted research is required to understand the causes of these discrepancies, allowing for informed policy changes to address systemic injustices. Declarations Funding Statement: This analysis was conducted without any specific financial support or sponsorship. Open Access funding support is acknowledged from Qatar National Library. Conflict of Interest Disclosure : The authors declare no competing financial interests or personal relationships that could have influenced the findings or conclusions of this analysis. Ethics Approval Statement : This CDC analysis is based solely on publicly available data and did not involve direct research with human participants or animals. Therefore, ethical approval was not required. Patient Consent Statement : This analysis does not include direct patient involvement or identifiable personal data; patient consent was not required. Permission to Reproduce Material from Other Sources : No copyrighted material requiring permission for reproduction was used in this analysis. Author Contribution S.M.I.K. and M.K. conceptualized the study and designed the methodology. S.M.I.K., M.K., and A.K. performed data extraction and statistical analysis. M.A.A. and M.W. prepared figures 1-6 and supplemental tables. J.I. and M.C. wrote the main manuscript text. J.I. coordinated the submission process and served as the corresponding author. All authors (S.M.I.K., M.K., A.K., M.A.A., J.I., M.W., M.C.) reviewed and approved the final manuscript. References Data Sets | CDC Open Technology [Internet]. 2018 [cited 2025 Jan 26]. Available from: https://open.cdcgov/data.html Sapra A, Bhandari P. Diabetes. In: StatPearls [Internet]. 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Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470682807.ch21 Kannel WB, McGee DL. Diabetes and cardiovascular disease. The Framingham study. JAMA. 1979;241(19):2035–8. 10.1001/jama.241.19.2035 . Liakos M, Parikh PB. Gender Disparities in Presentation, Management, and Outcomes of Acute Myocardial Infarction. Curr Cardiol Rep. 2018;20(8):64. 10.1007/s11886-018-1006-7 . Published 2018 Jun 16. Cole A, Weight N, Misra S et al. Addressing disparities in the long-term mortality risk in individuals with non-ST segment myocardial infarction (NSTEMI) by diabetes mellitus status: a nationwide cohort study [published correction appears in Diabetologia. 2025;68(3):688. 10.1007/s00125-024-06337-8.] . Diabetologia . 2024;67(12):2711–2725. doi:10.1007/s00125-024-06281-7. Fan N. Investing in Primary Care:: A Work in Progress. Dela J Public Health. 2019;5(5):8–14. 10.32481/djph.2019.12.004 . Published 2019 Dec 18. Additional Declarations No competing interests reported. Supplementary Files DMandMIsupplementarytablesfinalsd.docx Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Chaudhary","email":"","orcid":"","institution":"Wah Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mueed","middleName":"","lastName":"Chaudhary","suffix":""}],"badges":[],"createdAt":"2025-04-18 18:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6480876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6480876/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40200-025-01795-2","type":"published","date":"2025-12-23T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90291945,"identity":"4eb0fa41-3509-4789-86f7-96cd837514c9","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424513,"visible":true,"origin":"","legend":"\u003cp\u003eComorbid NIDDM and AMI-related Age Adjusted Mortality Rates stratified by sex per 1000,000 in the United States from 2000 to 2022\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/993307cdd207aada1f854a8b.jpeg"},{"id":90291942,"identity":"7593f3fa-73de-431f-a855-1b51829ce8e2","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517982,"visible":true,"origin":"","legend":"\u003cp\u003eComorbid NIDDM and AMI -related Age Adjusted Mortality Rates stratified by Race per 1000,000 in the United States from 2000 to 2020\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/42cb134c34971b317f3f799f.jpeg"},{"id":90291944,"identity":"a74ee648-480e-4a31-950b-042c3b976ef1","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":243184,"visible":true,"origin":"","legend":"\u003cp\u003eComorbid NIDDM and AMI Crude Mortality Rates stratified by Age group per 1000,000 in the United States from 2000 to 2020\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/8222e08b72d70a76ab02a390.png"},{"id":90291947,"identity":"86a72a9c-e12c-4d8f-8697-b4bf7ce5e095","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":429455,"visible":true,"origin":"","legend":"\u003cp\u003eComorbid NIDDM and AMI -related Age Adjusted Mortality Rates stratified by Census region per 1000,000 in the United States from 2000 to 2020\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/5c3a5da35feaf5ae7428cafa.jpeg"},{"id":90291951,"identity":"f7845594-d334-4a36-a6b0-291e768d9fbb","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284502,"visible":true,"origin":"","legend":"\u003cp\u003eComorbid NIDDM and AMI -related Age Adjusted Mortality Rates stratified by Urban-Rural classification per 1000,000 in the United States from 2000 to 2020\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/54a0162ad895ce01e82bfc36.jpeg"},{"id":90291949,"identity":"a85d8aa8-8cb5-4499-a1c2-eb4a9b341db6","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":306219,"visible":true,"origin":"","legend":"\u003cp\u003eMap presenting\u003cstrong\u003e \u003c/strong\u003ecomorbid NIDDM and AMI -related Age Adjusted Mortality Rates stratified by States per 1000,000 in the United States from 2000 to 2020\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/2655ef63acdcedc384eebfb2.jpeg"},{"id":99172635,"identity":"a14b8081-a728-4bcb-bf6b-ba3a4b21a873","added_by":"auto","created_at":"2025-12-29 16:11:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2878847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/03176230-f78b-4b09-92cb-8fa61a8813a0.pdf"},{"id":90291941,"identity":"f1527a36-c390-4478-9bc5-f28e3a7ea089","added_by":"auto","created_at":"2025-09-01 07:35:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":40133,"visible":true,"origin":"","legend":"","description":"","filename":"DMandMIsupplementarytablesfinalsd.docx","url":"https://assets-eu.researchsquare.com/files/rs-6480876/v1/12af5218c335f2e32255a2f8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends and Disparities in Mortality Due to Non-Insulin Dependent Diabetes Mellitus and Acute Myocardial Infarction: A 23-Year Analysis from 1999 to 2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 38.1\u0026nbsp;million adults in the United States (14.7% of the adult population) have diabetes mellitus, with an additional 8\u0026nbsp;million undiagnosed. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Among them, non-insulin dependent diabetes mellitus (NIDDM) has a prevalence of 9%, increasing to 25% in individuals aged 65 and older. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The incidence of newly diagnosed diabetes has nearly doubled in recent years. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDiabetes is a major risk factor for both macrovascular and microvascular complications, including cardiovascular disease, chronic kidney disease, and cerebrovascular disease. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Although overall cardiovascular mortality has declined in recent decades, diabetes remains strongly associated with a 2- to 4-fold increased risk of cardiovascular events and a 3-fold increased risk of cardiovascular-related death. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAtherosclerosis, the leading cause of acute myocardial infarction (AMI), is responsible for most fatal cases due to arterial blockage. Addressing modifiable risk factors such as smoking, hypertension, obesity, and dyslipidemia is central to prevention. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAcute myocardial infarction (AMI) remains a major cause of mortality in the United States. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Among individuals with NIDDM, the co-occurrence of risk factors such as obesity, hypertension, chronic kidney disease, and dyslipidemia, along with disease-specific manifestations like multivessel coronary artery disease, significantly increases AMI-related mortality. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e Advancements in the management of NIDDM and AMI, including newer therapies and updated guidelines, have improved patient outcomes. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) However, understanding demographic and regional trends in NIDDM- and AMI-related mortality remains crucial to assessing the evolving burden of these conditions.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population:\u003c/h2\u003e\u003cp\u003eThis study used the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) The study focused on the mortality associated with comorbid non-insulin dependent diabetes mellitus (NIDDM) and acute myocardial infarction (AMI) in adults between 1999 and 2022. The International Statistical Classification of Diseases and Related Health Problems-10th Revision (ICD-10) codes E11 for NIDDM and I21 for AMI were employed to identify relevant cases. Mortality data linked to these conditions were provided by the Multiple Causes of Death Public Use Death Certificates, specifically targeting comorbid NIDDM and AMI listed as either underlying or contributing cause of death. The study sample consisted of adults aged 55 and older at the time of death. Institutional review board approval was not required, as the study relied on a publicly available government database. The dataset encompassed cause-of-death information from death certificates across all 50 states and the District of Columbia. The study adhered strictly to the STROBE guidelines. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData extraction:\u003c/h3\u003e\n\u003cp\u003eData on various population characteristics, including population size, demographics, urban-rural classification, census region, age groups, and states, were extracted for this analysis. Demographic data related to gender and overall trends were collected for the period 1999 to 2022, whereas data on race, census region, urban-rural classification, states, and age groups were obtained for the period 1999 to 2020. Race and ethnicity categories included Non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaskan Native, and NH Asian or Pacific Islander. Age groups were categorized as (55\u0026ndash;64), (65\u0026ndash;74), (75\u0026ndash;84), and 85\u0026thinsp;+\u0026thinsp;years.\u003c/p\u003e\u003cp\u003eUrban-rural classification followed the 2013 U.S. urbanization criteria: areas with populations exceeding one million were classified as major metropolitan, those with populations between 50,000 and 999,999 were categorized as medium or small metropolitan, and areas with fewer than 50,000 people were classified as rural. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Regional classifications followed the U.S. Census Bureau's divisions into Northeast, Midwest, South, and West.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis:\u003c/h2\u003e\u003cp\u003eFor this analysis, we determined Age-Adjusted Mortality Rates (AAMRs) per 1000,000 individuals, stratified by year, gender, race or ethnicity, state, and urban-rural classification, along with their corresponding 95% confidence intervals (CIs). For age-specific analysis, we computed Crude Mortality Rates (CMRs). The AAMRs and CMRs were calculated by comparing the number of deaths attributable to comorbid NIDDM and AMI to the U.S 2000 standard population. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTo assess trends in mortality, we utilized the Joinpoint Regression Program (Joinpoint V 5.0.2, National Cancer Institute) to calculate the Annual Percent Change (APC) with 95% CIs for both AAMRs and CMRs, allowing us to examine national trends in comorbid NIDDM and AMI related mortality. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) The statistical significance of the APC slopes was evaluated using 2-tailed t-tests to determine whether changes in mortality rates were significantly different from zero. When a statistically significant slope (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was found, the APC was classified as either increasing or decreasing for that segment of the study period.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eMortality rates across all demographic groups highlighted significant trends across the study period. From 1999 to 2022, total deaths were recorded at 183,221 for adults (55\u0026ndash;85\u0026thinsp;+\u0026thinsp;years of age) due to NIDDM and AMI. From 1999\u0026ndash;2022, men demonstrated a total of 103,658 deaths whereas women had 79,563 deaths during this period \u003cb\u003e(Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/b\u003e.In race-stratified groups from 1999 to 2020, the total recorded deaths were 1,412 among American Indian or Alaska Native individuals, 1,509 among Asian or Pacific Islander individuals, 17,311 among Non-Hispanic Black individuals, 119,430 among Non-Hispanic White individuals, and 14,995 among Hispanic or Latino populations.\u003cb\u003e(Supplemental table 2)\u003c/b\u003e.In age-stratified groups from 1999 to 2020, the cumulative deaths were 27,447 among individuals aged (55\u0026ndash;64) years, 4,445 among those aged (65\u0026ndash;74) years, 51,651 among those aged (75\u0026ndash;84) years, and 35,222 among individuals aged (85+) years \u003cb\u003e(Supplemental table 3).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall trend was consistent from 1999 to 2003 APC 2.3 (95% CI: -1.1 to 9.5) which continued from 2003 to 2014 APC \u0026minus;\u0026thinsp;2.0 (95% CI: -4.9 to 3.8), and a significant increase from 2014 to 2022 APC 6.0 (95% CI: 4.7 to 7.6) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSex disparities:\u003c/h3\u003e\n\u003cp\u003eMen consistently displayed higher AAMRs throughout the study period than women, with a stable trend from 1999 to 2003 APC 2.1(95% CI: -1.0 to 9.6). This stabilization of trend continued from 2003 and 2014 APC \u0026minus;\u0026thinsp;1.4 (95% CI: -4.9 to 8.6), accompanied by a significant increase from 2014 to 2022 APC 6.4 (95% CI: 4.1 to 8.5). Women exhibited a decline from 2005 to 2014 APC \u0026minus;\u0026thinsp;3.3(95% CI: -6.6 to -2.2), followed by an increase in AAMR from 2014 to 2022 APC 5.3 (95% CI: 4.0 to 6.9) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand supplemental table 3)\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRacial disparities:\u003c/h2\u003e\u003cp\u003eAmerican Indian or Alaska Native ethnicity exhibited the highest AAMR throughout the years, showing stable trends in the study period, with an average annual percent change (APC) of -0.9 (95% CI: -1.8 to 0.3) from 1999 to 2020 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand supplemental table 4 )\u003c/b\u003e. Asian or Pacific Islander group consistently demonstrated the lowest AAMR, with a consistent trend from 1999 to 2012 with an APC of 0.5(95% CI: -2.2 to 2.2), with the trend increasing markedly from 2012 to 2020 with an APC of 8.2 (95% CI: 6.3 to 12.2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNon-Hispanic Black population showed a consistency in AAMR values between 2005 and 2014 APC \u0026minus;\u0026thinsp;3.2 (95% CI: 0.2 to 10.1) with a subsequent increase from 2014 to 2020 APC 5.5(95% CI: 3.0 to 10.8) .In a similar fashion, Non-Hispanic White group also showed a relatively stable trend from 2003 to 2014 APC \u0026minus;\u0026thinsp;2.2 (95% CI: -0.3 to 8.0), accompanied by a significant increase from 2014 to 2020 APC 5.6 (95% CI: 4.2 to 7.8). Hispanic or Latino populations experienced a stable period between 1999 and 2015 APC \u0026minus;\u0026thinsp;0.2 (95% CI: -1.7 to 1.0), but their AAMR increased notably from 2015 to 2020 with an APC of 9.7(95% CI: 5.8 to 20.5).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAge group disparities:\u003c/h3\u003e\n\u003cp\u003eFor individuals aged 55\u0026ndash;64 years, the annual percent change (APC) was recorded at 1.6 (95% CI: -0.4 to 7.8) during 1999\u0026ndash;2005, accompanied by a decrease of -2.1(95% CI: -5.9 to -0.6) during 2005\u0026ndash;2013, and then a subsequent increase of 6.9 (95% CI: 5.5 to 8.9) during 2013\u0026ndash;2020 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand supplemental table 5)\u003c/b\u003e. In the 65\u0026ndash;74 age group, the APC remained stable with APC 4.5 (95% CI: -0.9 to 15.8) from 1999\u0026ndash;2002,continuing stability from 2002\u0026ndash;2014 with APC \u0026minus;\u0026thinsp;1.9(95% CI: -6.6 to 1.4).A significant increase of 6.8 (95% CI: 4.6 to 11.0) was observed from 2014\u0026ndash;2020. For the 75\u0026ndash;84 age group, the APC displayed a consistent trend of 1.4 (95% CI: -1.2 to 6.9) during 1999\u0026ndash;2003.This was followed by a decline of -2.1(95% CI: -4.5 to -1.2) during 2003\u0026ndash;2014, and a subsequent increase of 6.1(95% CI: 4.4 to 8.4) during 2014\u0026ndash;2020. Among individuals aged 85 years and older, the APC increased by 2.5 (95% CI: -0.0 to 8.5) from 1999\u0026ndash;2004, accompanied by a decrease of -2.8 (95% CI: -5.7 to -1.9) from 2004\u0026ndash;2014.This was followed by an increase again to 5.0 (95% CI: 3.1 to 8.0) from 2014\u0026ndash;2020.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCensus region disparities:\u003c/h3\u003e\n\u003cp\u003eIn the Northeast, the APC was determined \u0026minus;\u0026thinsp;0.3 (95% CI: -2.8 to 7.3) during 1999\u0026ndash;2003.This decreased by -4.4 (95% CI: -9.1 to -3.6) during 2003\u0026ndash;2013, followed by a stable period of APC 0.9 (95% CI: -3.7 to 4.9) during 2013\u0026ndash;2018, with a subsequent sharp increase of 16.4 (95% CI: 7.4 to 22.8) during 2018\u0026ndash;2020 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003eand supplemental table 6)\u003c/b\u003e. In the Midwest, the APC was recorded at 0.7 (95% CI: -0.8 to 3.9) during 1999\u0026ndash;2005, with a subsequent drop of -3.6 (95% CI: -5.9 to -2.7) during 2005\u0026ndash;2014.Following this, a rise to 6.1 (95% CI: 4.5 to 8.5) was observed from 2014\u0026ndash;2020. The South experienced an APC of 5.6(95% CI: 0.4 to 15.3) during 1999\u0026ndash;2002.This was accompanied by a decline of -3.0 (95% CI: -4.5 to -2.3) from 2002\u0026ndash;2014, and an increase of 6.0(95% CI: 4.2 to 8.7) during 2014\u0026ndash;2020. In the West, the APC was determined to be 4.2(95% CI: 3.1 to 8.7) from 1999\u0026ndash;2008. The trend remained consistent with APC of -2.4(95% CI: -4.7 to 2.5) during 2008\u0026ndash;2011 and subsequently rose to 6.0 (95% CI: 5.1 to 7.9) during 2011\u0026ndash;2020. These trends highlight regional disparities, with the Northeast indicating the most dramatic rise in AAMR during recent years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eUrban-rural disparities:\u003c/h2\u003e\u003cp\u003eIn non-metropolitan areas, the APC increased by 5.1 (95% CI: 1.4 to 12.5) during 1999\u0026ndash;2002, followed by a decline of -2.4(95% CI: -3.2 to -2.0) from 2002\u0026ndash;2015 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eand supplemental table 7\u003c/b\u003e). A subsequent sharp increase of 7.4 (95% CI: 5.5 to 10.4) was noted during 2015\u0026ndash;2020. Metropolitan areas demonstrated a decrease in APC of -0.9 (95% CI: -1.8 to -0.2) during 1999\u0026ndash;2014, accompanied by an increase of 6.1(95% CI: 4.0 to 10.0) during 2014\u0026ndash;2020. These findings underscore a widening gap between non-metropolitan and metropolitan areas, with non-metropolitan areas consistently exhibiting higher AAMR and more pronounced increases in recent years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eState disparities:\u003c/h2\u003e\u003cp\u003eThe state-wise analysis of age-adjusted mortality rates (AAMR) reveals significant geographic disparities. West Virginia reported the highest AAMR at 173.6 per 100,000 population (95% CI: 166.0\u0026ndash;181.2), while Nevada had the lowest AAMR at 30.1 (95% CI: 27.0\u0026ndash;33.3) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003eand supplemental table 8)\u003c/b\u003e. States in the top 90th percentile of AAMR included West Virginia, Ohio (171.8, 95% CI: 168.6\u0026ndash;175.0), Tennessee (169.3, 95% CI: 164.9\u0026ndash;173.8), Kentucky (130.9, 95% CI: 126.2\u0026ndash;135.7), and Indiana (124.1, 95% CI: 120.3\u0026ndash;127.9). These states demonstrated AAMR values exceeding 124 per 1000,000 highlighting regions with substantial mortality burdens.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, states in the lowest 10th percentile, characterized by AAMR values below 40 per 1000,000, included Nevada, Massachusetts (39.6, 95% CI: 37.6\u0026ndash;41.7), and Connecticut (39.1, 95% CI: 36.4\u0026ndash;41.8). These findings indicate notable regional differences in mortality, with higher rates concentrated in states like West Virginia and Ohio, and lower rates observed in states like Nevada and Massachusetts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines trends and inequalities in mortality from comorbid non-insulin-dependent diabetic mellitus (NIDDM) and acute myocardial infarction (AMI) across the span of 23 years (1999\u0026ndash;2022). The findings highlight major disparities across demographic groupings, geographic locations, and age categories, providing crucial insights for targeted public health initiatives.\u003c/p\u003e\u003cp\u003eMany epidemiological studies show that diabetics are more likely to develop cardiac problems such as coronary artery disease, cardiomyopathy, and congestive heart failure. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Diabetes poses a significant risk for both macrovascular and microvascular consequences, such as cardiovascular, renal, peripheral artery, and cerebrovascular illnesses. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Based on 20 years of Framingham cohort monitoring linking eventual cardiovascular events to past signs of diabetes, a twofold to threefold elevated risk of clinical atherosclerotic disease was found. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis study demonstrates a concerning rise in mortality attributed to the comorbidity of non-insulin-dependent diabetes mellitus (NIDDM) and acute myocardial infarction (AMI) between 1999 and 2022. The observed increase, particularly after 2014, warrants further investigation into potential contributing factors. While advancements in diabetes and cardiovascular care may have contributed to a decline earlier in the study period, the subsequent sharp rise suggests that these advances may have been insufficient to offset emerging challenges or worsening disparities.\u003c/p\u003e\u003cp\u003eMen had continuously higher age-adjusted mortality rates (AAMRs) than women, which supports the well-documented gender disparity in cardiovascular mortality. The steeper rise in AAMRs for men since 2014 emphasizes the need for gender-specific interventions, such as tailored education and screening programs. Women, albeit displaying comparatively lower rates, suffered a substantial rise throughout the same time, suggesting unrecognized hazards or poor focus of preventative efforts in this demographic.\u003c/p\u003e\u003cp\u003eFemale patients with AMI are frequently older, have greater rates of diabetes, hypertension, and autoimmune illnesses, have worse Killip class, higher Global Registry of Acute Coronary Events (GRACE) risk scores, lower weight, baseline hemoglobin, and creatinine clearance. Men with AMI frequently have greater rates of smoking, peripheral vascular disease, past MI, and previous percutaneous coronary intervention (PCI) and coronary artery bypass graft surgery (CABG). (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAmerican Indians/Alaska Natives had the highest AAMRs, indicating systemic disparities such as restricted access to healthcare, a greater incidence of risk factors, and social determinants of health. Non-Hispanic Blacks and Hispanics have experienced significant increases in mortality in recent years, emphasizing the disproportionate burden on these populations. In contrast, Asian/Pacific Islanders had the lowest AAMRs but experienced a significant increase after 2012, raising concerns about new risk factors or changes in healthcare access within this population.\u003c/p\u003e\u003cp\u003eOne study showed that in their demographic comparison, more people had Asian or Black ethnicities. More people with diabetes were prone to developing complications like LV dysfunction, presenting with higher Killip class and with pulmonary edema. Participants were less likely to undergo invasive Coronary angiography (ICA) within 72 hours. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWest and Midwest showed the highest AAMRs in comparison to the Northeast, which had the lowest, which suggests differences in healthcare provision, access, and population health dynamics. Non-metropolitan areas show a significant rise in mortality rate after 2015, highlighting the healthcare provision challenges and disparities in rural areas. The best way to address these issues is by establishing mobile health units, more telemedicine centers, and investing more in primary care. When we invest in primary care the research has shown that the health outcomes improve, decreased burden on capital with better chronic disease management. This benefits the whole economy and does cost savings. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eOne of the concerning things in our study was that the mortality burden increased in 85\u0026thinsp;+\u0026thinsp;age individuals. With advanced age, there does come an increased mortality and morbidity risk, but still, we need to dig deeper in this regard. We need to uncover any underlying etiologies and improve our geriatric healthcare section. For younger age groups, the increase in death rates necessitates a renewed emphasis on early identification and active management of diabetes and cardiovascular risk factors.\u003c/p\u003e\u003cp\u003eStrengths of this are due to the extensive temporal scope having large sample size, and robust statistical methodology. These findings can be reproduced easily because of the publicly available data. Moreover, relying solely on death certificates may introduce misclassification bias there should be direct acquisition of data in this regard. Additionally, the study is deficient in examining the socioeconomic factors which play a vital role in inducing disparities.\u003c/p\u003e\u003cp\u003eTo address the increased mortality burden from comorbid NIDDM and AMI, we require a comprehensive approach that prioritizes early detection and active management of diabetes and cardiovascular hazards, particularly in high-risk populations. Culturally customized outreach activities must target underserved racial and ethnic groups, while rural healthcare infrastructure must be strengthened immediately to bridge access gaps. Geriatric-focused solutions should enhance care for the elderly with multimorbid illnesses, and targeted research is required to understand the causes of these discrepancies, allowing for informed policy changes to address systemic injustices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement:\u003c/h2\u003e\u003cp\u003eThis analysis was conducted without any specific financial support or sponsorship. Open Access funding support is acknowledged from Qatar National Library.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConflict of Interest Disclosure\u003c/b\u003e: The authors declare no competing financial interests or personal relationships that could have influenced the findings or conclusions of this analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthics Approval Statement\u003c/b\u003e: This CDC analysis is based solely on publicly available data and did not involve direct research with human participants or animals. Therefore, ethical approval was not required.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Consent Statement\u003c/b\u003e: This analysis does not include direct patient involvement or identifiable personal data; patient consent was not required.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePermission to Reproduce Material from Other Sources\u003c/b\u003e: No copyrighted material requiring permission for reproduction was used in this analysis.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.M.I.K. and M.K. conceptualized the study and designed the methodology. S.M.I.K., M.K., and A.K. performed data extraction and statistical analysis. M.A.A. and M.W. prepared figures 1-6 and supplemental tables. J.I. and M.C. wrote the main manuscript text. J.I. coordinated the submission process and served as the corresponding author. All authors (S.M.I.K., M.K., A.K., M.A.A., J.I., M.W., M.C.) reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eData Sets | CDC Open Technology [Internet]. 2018 [cited 2025 Jan 26]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://open.cdcgov/data.html\u003c/span\u003e\u003cspan address=\"https://open.cdcgov/data.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSapra A, Bhandari P. Diabetes. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Jan 26]. 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Lancet. 2007;370(9596):1453\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(07)61602-X7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(07)61602-X7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIngram DD, Franco SJ. 2013 NCHS Urban-Rural Classification Scheme for Counties. Vital Health Stat 2. 2014;(166):1\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep. 1998;47(3):1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoinpoint Regression Program [Internet]. [cited 2025 Jan 26]. 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Diabetes and cardiovascular disease. The Framingham study. JAMA. 1979;241(19):2035\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.241.19.2035\u003c/span\u003e\u003cspan address=\"10.1001/jama.241.19.2035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiakos M, Parikh PB. Gender Disparities in Presentation, Management, and Outcomes of Acute Myocardial Infarction. Curr Cardiol Rep. 2018;20(8):64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11886-018-1006-7\u003c/span\u003e\u003cspan address=\"10.1007/s11886-018-1006-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2018 Jun 16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCole A, Weight N, Misra S et al. Addressing disparities in the long-term mortality risk in individuals with non-ST segment myocardial infarction (NSTEMI) by diabetes mellitus status: a nationwide cohort study [published correction appears in Diabetologia. 2025;68(3):688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-024-06337-8.]\u003c/span\u003e\u003cspan address=\"10.1007/s00125-024-06337-8.]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eDiabetologia\u003c/em\u003e. 2024;67(12):2711\u0026ndash;2725. doi:10.1007/s00125-024-06281-7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan N. Investing in Primary Care:: A Work in Progress. Dela J Public Health. 2019;5(5):8\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32481/djph.2019.12.004\u003c/span\u003e\u003cspan address=\"10.32481/djph.2019.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2019 Dec 18.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes Mellitus, Myocardial Infarction","lastPublishedDoi":"10.21203/rs.3.rs-6480876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6480876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNon-insulin dependent diabetes mellitus (NIDDM) and acute myocardial infarction (MI) are critical health challenges that increase mortality, particularly in older adults. This study analyzed trends in AAMRs and disparities in comorbid NIDDM and MI mortality (1999\u0026ndash;2022) across demographics, regions, and age groups to identify inequities and guide interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMortality data from CDC death certificates were analyzed. AAMRs per 1000,000 and annual percentage changes (APCs) with 95% confidence intervals (CIs) were calculated using Joinpoint Regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 1999\u0026ndash;2022, 183,221 deaths were attributed to comorbid NIDDM and MI, with men consistently having higher rates than women. American Indians/Alaska Natives showed the highest AAMRs, followed by Non-Hispanic Blacks, while Asian/Pacific Islanders had the lowest. The West (AAMR 118.8) and Midwest (115.0) had the highest rates, while the Northeast had the lowest (70.4). Age-specific trends showed the steepest increases among individuals aged 85+. Non-metropolitan areas saw sharper rises post-2015 (APC 7.4), while metropolitan areas showed a moderate increase (APC 6.1). West Virginia recorded the highest state burden (AAMR 173.6), while Nevada had the lowest (30.1).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMortality from comorbid NIDDM and MI has surged, with widening disparities across gender, race, regions, and age groups. Targeted interventions are essential to reduce these inequities and prevent deaths.\u003c/p\u003e","manuscriptTitle":"Trends and Disparities in Mortality Due to Non-Insulin Dependent Diabetes Mellitus and Acute Myocardial Infarction: A 23-Year Analysis from 1999 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 07:35:29","doi":"10.21203/rs.3.rs-6480876/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed20600b-4190-41a6-baa2-5154608943dc","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:07:51+00:00","versionOfRecord":{"articleIdentity":"rs-6480876","link":"https://doi.org/10.1007/s40200-025-01795-2","journal":{"identity":"journal-of-diabetes-and-metabolic-disorders","isVorOnly":false,"title":"Journal of Diabetes \u0026 Metabolic Disorders"},"publishedOn":"2025-12-23 15:57:10","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-09-01 07:35:29","video":"","vorDoi":"10.1007/s40200-025-01795-2","vorDoiUrl":"https://doi.org/10.1007/s40200-025-01795-2","workflowStages":[]},"version":"v1","identity":"rs-6480876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6480876","identity":"rs-6480876","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}