Multi-Omic Prediction Of Incident Type 2 Diabetes

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We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up

Longitudinal multi-omics of host–microbe dynamics in prediabetes

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical

Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the

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Multi-omic prediction of incident type 2 diabetes. Browse All of QMRO Communities & Collections By Issue Date Authors Titles Subjects This Collection By Issue Date Authors Titles Subjects

Multi-omic prediction of incident type 2 diabetes. Carrasco-Zanini J, Pietzner M, Wheeler E, Kerrison ND, Langenberg C, Wareham NJ.Diabetologia. 2024 Jan;67 (1):102-112.

Multi-omic prediction of incident type 2 diabetes,Diabetologia
Multi-omic prediction of incident type 2 diabetes
A framework towards digital twins for type 2 diabetes
EPIC-Norfolk Publications Database

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA

Diabetes in China: epidemiology, pathophysiology and multi-omics

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an

proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. Methods We developed sparse interpretable prediction models in a

Incident type 2 diabetes cases were on average older, more likely to be men and presented with higher BMI and HbA 1c levels compared with cohort control participants. Comparison between the predictive performance of sparse omics

Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ±

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration J Transl Med. 2022 Dec 16;20(1) :604. doi We further predict the co

Predicting type 2 diabetes via machine learning integration of multiple
Multi-omic prediction of incident type 2 diabetes.
The Cambridge Diabetes Risk Score
Large-Scale Proteomics Improve Risk Prediction for Type 2 Diabetes
Diabetes in China: epidemiology, pathophysiology and multi-omics

proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. Methods We developed sparse interpretable prediction models in a

Aims/hypothesis The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as

Multi-omic prediction of incident type 2 diabetes. Authors: Carrasco-Zanini, J Pietzner, M Wheeler, E Kerrison, ND Langenberg, C Wareham, NJ: Keywords: Biomarkers Genomics Metabolomics

Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ±

Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA in the

Methods: Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic

The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective

A Multi-omic Integrative Scheme Characterizes Tissues of Action at Loci ...

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proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. Methods We developed sparse interpretable prediction models in a

Proteins are promising predictors of type 2 diabetes because of their direct involvement in disease pathways ().High-throughput proteomic technologies have identified

Previous studies assessing contributions of a type 2 diabetes PS for prediction of type 2 diabetes incidence have mostly been conducted in European-ancestry populations [1,

Core Tip: The prospects of multi-omics in the study of the mechanisms of type 2 diabetes mellitus (T2DM)-related intestinal flora perturbation and plasma dyslipidemia are tremendous.The use

In the past two decades, the study of multi-omics on T2DM-related intestinal flora perturbation and plasma dyslipidemia has shown tremendous potential and is expected to achieve major

Aims/hypothesis The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the

Jia Liu, Lu Wang, Yun Qian, Qian Shen, Man Yang, Yunqiu Dong, Hai Chen, Zhijie Yang, Yaqi Liu, Xuan Cui, Hongxia Ma, Guangfu Jin, Metabolic and Genetic Markers

In this Review, the authors summarize epidemiological trends of type 2 diabetes in China, discuss unique risk factors contributing to diabetes risk in the Chinese population and

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of

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