June 7, 2026Diabetes

2247-P: Uncertainty-Calibrated Prediction of Cardiovascular–Kidney–Liver–Metabolic Disease Using Clinical Biomarkers and Plasma Proteomics

Q: What does this research mean for the field?

Integrating clinical biomarkers and plasma proteomics using conformal inference provides accurate, uncertainty-calibrated risk prediction for cardiovascular-kidney-liver-metabolic (CKLM) disease. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

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Why the study?

Does integrating clinical biomarkers and plasma proteomics improve uncertainty-calibrated risk prediction for cardiovascular-kidney-liver-metabolic disease?

Population

49,312 UK Biobank participants with proteomic profiling

Design

Cohort

Follow-up

median 12.3-year

Authors

MXManrong Xu LGLuqin Gan WCWENTAO CAO

Discussion

Member takes

Overview

Integrating clinical and proteomic data with machine learning provides accurate, uncertainty-calibrated risk estimates for cardiovascular-kidney-liver-metabolic disease.

Key Points

The study aims to develop a risk prediction framework for cardiovascular-kidney-liver-metabolic disease using clinical biomarkers and plasma proteomics, focusing on uncertainty calibration.
Analyzed 49,312 UK Biobank participants with proteomic profiling
Used an 80/20 stratified split and cross-validated random forest to select predictors
Employed J+aB conformal inference for uncertainty-calibrated risk prediction and Cox models for hazard ratios.
9,787 participants developed CKLM over a median follow-up of 12.3 years.
Final model achieved AUC of 0.78 and average precision of 0.52, with consistent performance across demographics.
Key clinical predictors included HbA1c and cystatin C; top proteomic predictors included GDF15 and HAVCR1.

Structured PICO

Does integrating clinical biomarkers and plasma proteomics improve uncertainty-calibrated risk prediction for cardiovascular-kidney-liver-metabolic disease?

Population

49,312 UK Biobank participants with proteomic profiling

Intervention

Risk prediction framework integrating clinical biomarkers and large-scale plasma proteomics using machine learning with conformal inference

Outcome

First occurrence of chronic kidney disease, cardiovascular disease, type 2 diabetes, or metabolic dysfunction-associated steatotic liver diseasecomposite

Integrating clinical and proteomic data with machine learning provides accurate, uncertainty-calibrated risk estimates for cardiovascular-kidney-liver-metabolic disease.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a250bca7def13d035e1bd10 https://doi.org/10.2337/db26-2247-p

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June 7, 2026Diabetes

2247-P: Uncertainty-Calibrated Prediction of Cardiovascular–Kidney–Liver–Metabolic Disease Using Clinical Biomarkers and Plasma Proteomics

View Full Paper

Ask AI

Bookmark

Why the study?

Does integrating clinical biomarkers and plasma proteomics improve uncertainty-calibrated risk prediction for cardiovascular-kidney-liver-metabolic disease?

Population

49,312 UK Biobank participants with proteomic profiling

Design

Cohort

Follow-up

median 12.3-year

Authors

MXManrong Xu LGLuqin Gan WCWENTAO CAO

Discussion

Member takes

Overview

Integrating clinical and proteomic data with machine learning provides accurate, uncertainty-calibrated risk estimates for cardiovascular-kidney-liver-metabolic disease.

Key Points

The study aims to develop a risk prediction framework for cardiovascular-kidney-liver-metabolic disease using clinical biomarkers and plasma proteomics, focusing on uncertainty calibration.
Analyzed 49,312 UK Biobank participants with proteomic profiling
Used an 80/20 stratified split and cross-validated random forest to select predictors
Employed J+aB conformal inference for uncertainty-calibrated risk prediction and Cox models for hazard ratios.
9,787 participants developed CKLM over a median follow-up of 12.3 years.
Final model achieved AUC of 0.78 and average precision of 0.52, with consistent performance across demographics.
Key clinical predictors included HbA1c and cystatin C; top proteomic predictors included GDF15 and HAVCR1.

Structured PICO

Does integrating clinical biomarkers and plasma proteomics improve uncertainty-calibrated risk prediction for cardiovascular-kidney-liver-metabolic disease?

Population

49,312 UK Biobank participants with proteomic profiling

Intervention

Risk prediction framework integrating clinical biomarkers and large-scale plasma proteomics using machine learning with conformal inference

Outcome

First occurrence of chronic kidney disease, cardiovascular disease, type 2 diabetes, or metabolic dysfunction-associated steatotic liver diseasecomposite

Integrating clinical and proteomic data with machine learning provides accurate, uncertainty-calibrated risk estimates for cardiovascular-kidney-liver-metabolic disease.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a250bca7def13d035e1bd10 https://doi.org/10.2337/db26-2247-p

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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