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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | Predictive modelling of SNP for myocardial infarction in SMuRFless patients |
| Authors Name: | Amra Elahi , Elamathi Natarajan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_209369
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| Published Paper Id: | IJRTI2601103 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i1.209369 |
| Abstract: | The proportion of patients with myocardial infarction (MI) who have no standard modifiable risk factors such as smoking, hypertension, diabetes and dyslipidaemia has been increasing; this subgroup has been termed ‘SMuRF-less MI’. Although these patients often receive guideline appropriate therapy there are poor short term outcomes; this indicates that current clinical risk stratification methods (i.e., traditional risk factors) are limited for these patients. Recent studies have demonstrated that the genetic predisposition to MI, specifically due to single nucleotide polymorphisms (SNPs), is a significant contributing factor among the SMuRF-less population. This review will explore the utility of SNPs as potential predictive markers for MI development and assess the effectiveness of polygenic risk score and machine learning algorithms for assessing genetic risk. The use of genome-wide approaches that aggregate numerous variants across various genes to better define the level of risk exhibited may identify those patients at high risk of developing MI who had been previously unidentified by conventional risk scoring approaches. However, current SNP-based prediction models show little incremental predictive value beyond existing methods and therefore require more careful variant selection, larger reference populations and more robust modelling techniques. The combination of genetic data with biomarker and clinical data would improve the predictive accuracy of MI risk scores and lead to a more personalised approach to prevention and treatment for patients with SMuRF-less MI. |
| Keywords: | : SMuRF-less myocardial infarction, single nucleotide polymorphisms (SNPs), polygenic risk score (PRS), genetic risk prediction, genome-wide association studies (GWAS), machine learning, cardiovascular risk stratification, precision medicine. |
| Cite Article: | "Predictive modelling of SNP for myocardial infarction in SMuRFless patients", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b15-b19, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601103.pdf |
| Downloads: | 000191 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2601103
Registration ID:209369
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i1.209369
Page No: b15-b19 Country: BANGALORE, KARNATAKA, India Research Area: Biological Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601103 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601103 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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