Risk Factors for Premature Myocardial Infarction: A Systematic Review and Meta-analysis of 77 Studies

Dugani, Sagar; Hydoub, Yousif M; Ayala, Ana Patricia; Reka, Roger; Nayfeh, Tarek; Ding, Jingyi Francess; McCafferty, Shannon N; Alzuabi, Muayad; Farwati, Medhat; Murad, M. Hassan; Alsheikh‐Ali, Alawi A.; Mora, Samia

doi:10.1016/j.mayocpiqo.2021.03.009

Cited by 25 publications

(16 citation statements)

References 69 publications

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“…At the genetic level, there is a substantial overlap between the genetics of aging and age-associated diseases (AADs) [ 14 ]. For example, some known aging-related targets, such as mTOR, AMPK, IGFR, NF-kB, S6K, TGF-β, AT1, Fgf21, FOXO3a, SIRT1, HIF-1, NRF2, and Klotho, may also impact multiple age-associated diseases [ 1 , 8 , 15 ]. Therefore, given that aging is associated with mechanisms that ultimately lead to age-related comorbidities, drugs that act on targets implicated in aging may potentially reduce the severity of gerolavic (from Greek, géros “old man” and epilavís, “harmful”) diseases and preventing multimorbidity [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Pun

Leung

et al. 2022

Aging

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Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

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Section: Introductionmentioning

confidence: 99%

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Pun

Leung

et al. 2022

Aging

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“…An LCA model was built using 11 known and proposed risk factors for CHD as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol consumption, opium addiction, hypertension, PVD, HLP, history of stroke, and MI. The included risk factors were added to the statistical model based on premature systematic review studies ( 24 , 25 ). The LCA analyses were conducted using the “poLCA” package in R 3.4.2.…”

Section: Methodsmentioning

confidence: 99%

Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Jahangiry

Farhangi

Najafi

et al. 2021

Front. Cardiovasc. Med.

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Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

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“…These rural disparities may be linked to differences in diabetes comorbidities, 25 consumption of fruits and vegetables, 26,27 environmental toxins, 28 suboptimal diabetes management, [29][30][31] health care access (eg, for dilated eye examination), 32,33 and change in treatment targets, 34 which are major challenges in rural counties. For instance, diabetes is a major cardiovascular risk factor, [35][36][37] and despite overall improvement in cardiovascular mortality in rural and nonrural counties, rural-nonrural disparities persist. 8 Addressing systemic ruralnonrural differences may improve outcomes for diabetes and other conditions.…”

Section: Jama Network Open | Diabetes and Endocrinologymentioning

confidence: 99%

Assessment of Disparities in Diabetes Mortality in Adults in US Rural vs Nonrural Counties, 1999-2018

et al. 2022

Self Cite

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ImportanceUS rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown.ObjectiveTo examine rates and trends in diabetes mortality based on county urbanization.Design, Setting, and ParticipantsIn this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022.ExposuresCounty urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West).Main Outcomes and MeasuresAnnual diabetes mortality rate per 100 000 people.ResultsFrom 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1]; P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8]; P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1]; P < .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (−14.0; 95% CI, −17.7 to −10.3) (P < .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) (P < .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0; P < .001).Conclusions and RelevanceIn this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

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Risk Factors for Premature Myocardial Infarction: A Systematic Review and Meta-analysis of 77 Studies

Cited by 25 publications

References 69 publications

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Assessment of Disparities in Diabetes Mortality in Adults in US Rural vs Nonrural Counties, 1999-2018

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