Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Lu, Tianyuan; Forgetta, Vincenzo; Yu, Oriana Hoi Yun; Mokry, Lauren; Gregory, Madeline; Thanassoulis, George; Greenwood, Celia; Richards, J. Brent

doi:10.1186/s12933-020-0988-9

Cited by 27 publications

(19 citation statements)

References 46 publications

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“…Therefore, while future innovation should go towards a better definition of cardiovascular risk (e.g. using genetic data) [ 31 , 32 ] and the precise definition of subjects who can take the most advantage from more intensive treatments [ 10 , 33 , 34 ], these needs go together with the one urgently advocating the correct application of current knowledge and therapeutic armamentarium. As previously estimated, implementing dyslipidemia guidelines into every day clinical practice would have a major impact on reduction of the occurrence of future cardiovascular events in patients with diabetes [ 1 , 10 ].…”

Section: Discussionmentioning

confidence: 99%

Physicians’ misperceived cardiovascular risk and therapeutic inertia as determinants of low LDL-cholesterol targets achievement in diabetes

Morieri

Lamacchia

Manzato

et al. 2022

Cardiovasc Diabetol

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Background Greater efforts are needed to overcome the worldwide reported low achievement of LDL-c targets. This survey aimed to dissect whether and how the physician-based evaluation of patients with diabetes is associated with the achievement of LDL-c targets. Methods This cross-sectional self-reported survey interviewed physicians working in 67 outpatient services in Italy, collecting records on 2844 patients with diabetes. Each physician reported a median of 47 records (IQR 42–49) and, for each of them, the physician specified its perceived cardiovascular risk, LDL-c targets, and the suggested refinement in lipid-lowering-treatment (LLT). These physician-based evaluations were then compared to recommendations from EAS/EASD guidelines. Results Collected records were mostly from patients with type 2 diabetes (94%), at very-high (72%) or high-cardiovascular risk (27%). Physician-based assessments of cardiovascular risk and of LDL-c targets, as compared to guidelines recommendation, were misclassified in 34.7% of the records. The misperceived assessment was significantly higher among females and those on primary prevention and was associated with 67% lower odds of achieving guidelines-recommended LDL-c targets (OR 0.33, p < 0.0001). Peripheral artery disease, target organ damage and LLT-initiated by primary-care-physicians were all factors associated with therapeutic-inertia (i.e., lower than expected probability of receiving high-intensity LLT). Physician-suggested LLT refinement was inadequate in 24% of overall records and increased to 38% among subjects on primary prevention and with misclassified cardiovascular risk. Conclusions This survey highlights the need to improve the physicians’ misperceived cardiovascular risk and therapeutic inertia in patients with diabetes to successfully implement guidelines recommendations into everyday clinical practice.

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Section: Discussionmentioning

confidence: 99%

Physicians’ misperceived cardiovascular risk and therapeutic inertia as determinants of low LDL-cholesterol targets achievement in diabetes

Morieri

Lamacchia

Manzato

et al. 2022

Cardiovasc Diabetol

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“…Type 2 diabetes is a multifactorial, typical complex disease, which is associated with lifestyle and other environmental factors [30,31]. In this research, a set of ten "real" hub genes, IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2, was integrated into a model to predict type 2 diabetes.…”

Section: Discussionmentioning

confidence: 99%

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Ding

Zhi

et al. 2022

BioMed Research International

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Background. Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes. Methods. Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes. Results. GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset. Conclusion. Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

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“…Further, we found no interaction effect between sex and PRS, and other studies have reported similar performance for CHD polygenic scores in both sexes. 23…”

Section: Discussionmentioning

confidence: 99%

A polygenic risk score for coronary heart disease performs well in individuals aged 70 years and older

Neumann

Riaz²,

Bakshi³

et al. 2021

Preprint

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Background: The use of a polygenic risk score (PRS) to predict coronary heart disease (CHD) events has been demonstrated in the general adult population. However, whether predictive performance extends to older individuals is unclear. Aim: To evaluate the predictive value of a PRS for incident CHD events in a prospective cohort of individuals aged 70 years and older. Methods: We used data from 12,792 genotyped participants of the ASPREE trial, a randomized placebo-controlled trial investigating the effect of daily 100mg aspirin on disability-free survival in healthy older people. Participants had no previous history of diagnosed atherothrombotic cardiovascular events, dementia, or persistent physical disability at enrolment. We calculated a PRS comprising 1.7 million genetic variants (metaGRS). The primary outcome was a composite of incident myocardial infarction or CHD death over 5 years. Results: At baseline, the median population age was 73.9 years and 54.9% were female. In total, 254 incident CHD events occurred. When the PRS was added to conventional risk factors, it was independently associated with CHD (hazard ratio 1.24 [95% confidence interval [CI] 1.08-1.42], p=0.002). The AUC of the conventional model was 70.53 (95%CI 67.00-74.06), and after inclusion of the PRS increased to 71.78 (95%CI 68.32-75.24, p=0.019), demonstrating improved prediction. Reclassification was also improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95%CI 0.15-0.28). Conclusions: A PRS for CHD performs well in older people, suggesting that the clinical utility of genomic risk prediction for CHD extends to this distinct high-risk subgroup.

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Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Cited by 27 publications

References 46 publications

Physicians’ misperceived cardiovascular risk and therapeutic inertia as determinants of low LDL-cholesterol targets achievement in diabetes

Physicians’ misperceived cardiovascular risk and therapeutic inertia as determinants of low LDL-cholesterol targets achievement in diabetes

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

A polygenic risk score for coronary heart disease performs well in individuals aged 70 years and older

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