1.Lipid Abnormalities in Type 2 Diabetes Mellitus Patients with Overt Nephropathy.
Sabitha PALAZHY ; Vijay VISWANATHAN
Diabetes & Metabolism Journal 2017;41(2):128-134
BACKGROUND: Diabetic nephropathy is a major complication of diabetes and an established risk factor for cardiovascular events. Lipid abnormalities occur in patients with diabetic nephropathy, which further increase their risk for cardiovascular events. We compared the degree of dyslipidemia among type 2 diabetes mellitus (T2DM) subjects with and without nephropathy and analyzed the factors associated with nephropathy among them. METHODS: In this retrospective study, T2DM patients with overt nephropathy were enrolled in the study group (n=89) and without nephropathy were enrolled in the control group (n=92). Both groups were matched for age and duration of diabetes. Data on total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), urea and creatinine were collected from the case sheets. TG/HDL-C ratio, a surrogate marker for small, dense, LDL particles (sdLDL) and estimated glomerular filtration rate (eGFR) were calculated using equations. Multivariate analysis was done to determine the factors associated with eGFR. RESULTS: Dyslipidemia was present among 56.52% of control subjects and 75.28% of nephropathy subjects (P=0.012). The percentage of subjects with atherogenic dyslipidemia (high TG+low HDL-C+sdLDL) was 14.13 among controls and 14.61 among nephropathy subjects. Though serum creatinine was not significantly different, mean eGFR value was significantly lower among nephropathy patients (P=0.002). Upon multivariate analysis, it was found that TC (P=0.007) and HDL-C (P=0.06) were associated with eGFR among our study subjects. CONCLUSION: Our results show that dyslipidemia was highly prevalent among subjects with nephropathy. Regular screening for dyslipidemia may be beneficial in controlling the risk for adverse events among diabetic nephropathy patients.
Biomarkers
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Cholesterol
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Cholesterol, HDL
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Cholesterol, LDL
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Creatinine
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Diabetes Mellitus, Type 2*
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Diabetic Nephropathies
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Dyslipidemias
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Glomerular Filtration Rate
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Humans
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Mass Screening
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Multivariate Analysis
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Retrospective Studies
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Risk Factors
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Triglycerides
;
Urea
2.Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
Narendra N KHANNA ; Manasvi SINGH ; Mahesh MAINDARKAR ; Ashish KUMAR ; Amer M. JOHRI ; Laura MENTELLA ; John R LAIRD ; Kosmas I. PARASKEVAS ; Zoltan RUZSA ; Narpinder SINGH ; Mannudeep K. KALRA ; Jose Fernandes E. FERNANDES ; Seemant CHATURVEDI ; Andrew NICOLAIDES ; Vijay RATHORE ; Inder SINGH ; Jagjit S. TEJI ; Mostafa AL-MAINI ; Esma R. ISENOVIC ; Vijay VISWANATHAN ; Puneet KHANNA ; Mostafa M. FOUDA ; Luca SABA ; Jasjit S. SURI
Journal of Korean Medical Science 2023;38(46):e395-
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established.It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction.Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.