Traditional Risk Factors and Subclinical Disease Measures as Predictors of First Myocardial Infarction in Older Adults

Abstract Background Currently used models to predict cardiovascular event risk have limited value. It has been shown repetitively that the addition of single biomarkers has modest impact. Recently we observed that a model consisting of a larger array of plasma proteins performed very well in predicting the presence of vulnerable plaques in primary prevention patients. However, the validation of this protein panel in predicting cardiovascular outcomes remains to be established. Purpose This study investigated the ability of a 384 preselected protein biomarkers to predict acute myocardial infarction, using state-of-the-art machine learning techniques. Secondly, we compared the performance of this multi-protein risk model to traditional risk engines. Methods We selected 822 subjects from the EPIC-Norfolk prospective cohort study, of whom 411 suffered a myocardial infarction during follow-up (median 15 years) compared to 411 controls who remained event-free (median follow-up 20 years). The 384 proteins were measured using proximity extension assay technology. Machine learning algorithms (random forests) were used for the prediction of acute myocardial infarction (ICD code I21–22). Performance of the model was tested against and on top of traditional risk factors for cardiovascular disease (refit Framingham). All performance measurements were averaged over several stability selection routines. Results Prediction of myocardial infarction using a machine-learning model consisting of 50 plasma proteins resulted in a ROC AUC of 0.74±0.14, in comparison to 0.69±0.17 using traditional risk factors (refit Framingham. Combining the proteins and refit Framingham resulted in a ROC AUC of 0.74±0.15. Focussing on events occurring within 3 years after baseline blood withdrawal, the ROC AUC increased to 0.80±0.09 using 50 plasma proteins, as opposed to 0.67±0.22 using refit Framingham (figure). Combining the protein model with refit Framingham resulted in a ROC AUC of 0.82±0.11 for these events. Diagnostic performance events <3yrs Conclusion High-throughput proteomics outperforms traditional risk factors in prediction of acute myocardial infarction. Prediction of myocardial infarction occurring within 3 years after inclusion showed highest performance. Availability of affordable proteomic approaches and developed machine learning pave the path for clinical implementation of these models in cardiovascular risk prediction. Acknowledgement/Funding This study was funded by an ERA-CVD grant (JTC2017) and EU Horizon 2020 grant (REPROGRAM, 667837)

Download Full-text

Mediterranean Diet, Traditional Risk Factors, and the Rate of Cardiovascular Complications after 1 Myocardial Infarction. Final report of the Lyon Diet Heart Study

The Endocrinologist ◽

10.1097/00019616-199907000-00016 ◽

1999 ◽

Vol 9 (4) ◽

pp. 324 ◽

Cited By ~ 3

Author(s):

de Lorger il M ◽

Salen P ◽

Martin J ◽

Monjaud I ◽

Delaye J ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Mediterranean Diet ◽

Cardiovascular Complications ◽

Final Report ◽

Lyon Diet Heart Study ◽

Heart Study ◽

Traditional Risk Factors

Download Full-text

Gender differences in cardiovascular risk assessment in elderly adults in Ecuador: evidence from a national survey

Journal of Investigative Medicine ◽

10.1136/jim-2018-000789 ◽

2018 ◽

Vol 67 (4) ◽

pp. 736-742

Author(s):

Ivan Sisa

Keyword(s):

Risk Factors ◽

Older Adults ◽

Cardiovascular Risk ◽

Rural Areas ◽

Risk Model ◽

Smoking Status ◽

Population Based ◽

Cvd Risk ◽

Serum Triglycerides ◽

Traditional Risk Factors

The present study aimed to predict the risk of developing cardiovascular disease (CVD) over a 5-year period and how it might vary by sex in an ethnically diverse population of older adults. We used a novel CVD risk model built and validated in older adults named the Systematic Coronary Risk Evaluation in Older Persons (SCORE OP). A population-based study analyzed a total of 1307 older adults. Analyses were done by various risk categories and sex. Of the study population, 54% were female with a mean age of 75±7.1 years. According to the SCORE OP model, individuals were classified as having low (9.8%), moderate (48.1%), and high or very high risk (42.1%) of CVD-related mortality. Individuals at higher risk of CVD were more likely to be male compared with females, 53.9% vs 31.8%, respectively (p<0.01). Males were more likely to be younger, living in rural areas, had higher levels of schooling, and with the exception of smoking status and serum triglycerides, had lower values of traditional risk factors than females. In addition, males were less likely to require blood pressure-lowering therapy and statin drugs than females. This gender inequality could be driven by sociocultural determinants and a risk factor paradox in which lower levels of the cardiovascular risk factors are associated with an increase rather than a reduction in mortality. These data can be used to tailor primary prevention strategies such as lifestyle counseling and therapeutic measures in order to improve male elderly health, especially in low-resource settings.

Download Full-text

Comparison of Traditional Risk Factors, Angiographic Findings, and In-Hospital Mortality between Smoking and Nonsmoking Turkish Men and Women With Acute Myocardial Infarction

Clinical Cardiology ◽

10.1002/clc.20716 ◽

2010 ◽

Vol 33 (6) ◽

pp. E49-E54 ◽

Cited By ~ 5

Author(s):

Nazif Aygul ◽

Kurtulus Ozdemir ◽

Adnan Abaci ◽

Meryem Ulku Aygul ◽

Mehmet Akif Duzenli ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Hospital Mortality ◽

Men And Women ◽

Traditional Risk Factors ◽

Angiographic Findings

Download Full-text

Incident myocardial infarction associated with major types of arthritis in the general population: a systematic review and meta-analysis

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2016-210275 ◽

2017 ◽

Vol 76 (8) ◽

pp. 1396-1404 ◽

Cited By ~ 52

Author(s):

Orit Schieir ◽

Cedomir Tosevski ◽

Richard H Glazier ◽

Sheilah Hogg-Johnson ◽

Elizabeth M Badley

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Meta Analysis ◽

Population Based ◽

Case Control Studies ◽

Increased Risk ◽

Traditional Risk Factors ◽

Meta Analyses ◽

Review Criteria ◽

Age And Sex

ObjectiveTo synthesise, quantify and compare risks for incident myocardial infarction (MI) across five major types of arthritis in population-based studies.MethodsA systematic search was performed in MEDLINE, EMBASE and CINAHL databases with additional manual/hand searches for population-based cohort or case-control studies published in English of French between January 1980 and January 2015 with a measure of effect and variance for associations between incident MI and five major types of arthritis: rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), gout or osteoarthritis (OA), adjusted for at least age and sex. All search screening, data abstraction quality appraisals were performed independently by two reviewers. Where appropriate, random-effects meta-analysis was used to pool results from studies with a minimum of 10 events.ResultsWe identified a total of 4, 285 articles; 27 met review criteria and 25 criteria for meta-analyses. In studies adjusting for age and sex, MI risk was significantly increased in RA (pooled relative risk (RR): 1.69, 95% CI 1.50 to 1.90), gout (pooled RR: 1.47, 95% CI 1.24 to 1.73), PsA (pooled RR: 1.41, 95% CI 1.17 to 1.69), OA (pooled RR: 1.31, 95% CI 1.01 to 1.71) and tended towards increased risk in AS (pooled RR: 1.24, 95% CI 0.93 to 1.65). Traditional risk factors were more prevalent in all types of arthritis. MI risk was attenuated for each type of arthritis in studies adjusting for traditional risk factors and remained significantly increased in RA, PsA and gout.ConclusionsMI risk was consistently increased in multiple types of arthritis in population-based studies, and was partially explained by a higher prevalence of traditional risk factors in all types of arthritis. Findings support more integrated cardiovascular (CV) prevention strategies for arthritis populations that target both reducing inflammation and enhancing management of traditional CV risk factors.

Download Full-text