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000296215 1001_ $$0P:(DE-He78)7089188e1b7bdb788ba48ba96f21df07$$aXie, Ruijie$$b0$$eFirst author$$udkfz
000296215 245__ $$aImproving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics.
000296215 260__ $$aLondon$$bBioMed Central$$c2025
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000296215 520__ $$aExisting cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients.Data from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Sex-specific LASSO regression with bootstrapping identified significant metabolites. The enhanced model's predictive performance was evaluated using Harrell's C-index.Seven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly (P = 0.037) from 0.660 to 0.678 in the total sample. In external validation with ESTHER, the C-index increase was higher (+ 0.043) and remained statistically significant (P = 0.011).Incorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine.
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000296215 650_7 $$2Other$$aCardiovascular risk
000296215 650_7 $$2Other$$aMetabolomics
000296215 650_7 $$2Other$$aPrediction model
000296215 650_7 $$2Other$$aType 2 diabetes
000296215 650_7 $$2NLM Chemicals$$aBiomarkers
000296215 650_2 $$2MeSH$$aHumans
000296215 650_2 $$2MeSH$$aDiabetes Mellitus, Type 2: diagnosis
000296215 650_2 $$2MeSH$$aDiabetes Mellitus, Type 2: epidemiology
000296215 650_2 $$2MeSH$$aDiabetes Mellitus, Type 2: blood
000296215 650_2 $$2MeSH$$aMale
000296215 650_2 $$2MeSH$$aFemale
000296215 650_2 $$2MeSH$$aMetabolomics
000296215 650_2 $$2MeSH$$aMiddle Aged
000296215 650_2 $$2MeSH$$aCardiovascular Diseases: epidemiology
000296215 650_2 $$2MeSH$$aCardiovascular Diseases: diagnosis
000296215 650_2 $$2MeSH$$aRisk Assessment
000296215 650_2 $$2MeSH$$aAged
000296215 650_2 $$2MeSH$$aPredictive Value of Tests
000296215 650_2 $$2MeSH$$aBiomarkers: blood
000296215 650_2 $$2MeSH$$aTime Factors
000296215 650_2 $$2MeSH$$aHeart Disease Risk Factors
000296215 650_2 $$2MeSH$$aMagnetic Resonance Spectroscopy
000296215 650_2 $$2MeSH$$aPrognosis
000296215 650_2 $$2MeSH$$aReproducibility of Results
000296215 650_2 $$2MeSH$$aGermany: epidemiology
000296215 650_2 $$2MeSH$$aDecision Support Techniques
000296215 650_2 $$2MeSH$$aAdult
000296215 650_2 $$2MeSH$$aSex Factors
000296215 7001_ $$0P:(DE-He78)cfc349d742aee6cc3394ccaa1ef6494f$$aSeum, Teresa$$b1$$udkfz
000296215 7001_ $$0P:(DE-He78)1d6f6305a65e2f7de2c7fbffbae83780$$aSha, Sha$$b2$$udkfz
000296215 7001_ $$0P:(DE-He78)b09508a4c4afe85c57dd131eefa689ea$$aTrares, Kira$$b3$$udkfz
000296215 7001_ $$aHolleczek, Bernd$$b4
000296215 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b5$$udkfz
000296215 7001_ $$0P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aSchöttker, Ben$$b6$$eLast author$$udkfz
000296215 773__ $$0PERI:(DE-600)2093769-6$$a10.1186/s12933-025-02581-3$$gVol. 24, no. 1, p. 18$$n1$$p18$$tCardiovascular diabetology$$v24$$x1475-2840$$y2025
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