Paul Kittner’s research while affiliated with Charité Universitätsmedizin Berlin and other places

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Publications (2)


Study overview
a, To learn metabolomic states from circulating blood metabolites, the eligible UK Biobank population (with NMR blood metabolomics and valid consent) was split into training, validation and test sets with 22-fold nested cross-validation based on the assigned UK Biobank assessment center. b, For each of the 22 partitions, the metabolomic state model was trained on the 168 metabolomic markers to predict metabolomic risk against 24 common disease endpoints. Subsequently, for each endpoint, CPH models were developed on the metabolomic state in combination with sets of commonly available clinical predictors to model disease risk. Predictions of the CPH model on the test set were aggregated for downstream analysis. c, The metabolomic state model was externally validated in four independent cohorts—the Whitehall II cohort and three from the BBMRI-NL consortium: the Rotterdam Study, the Leiden Longevity Study and the PROSPER cohort. d, In this study we consider clinical predictors from scores commonly applied in primary prevention. We additionally integrate variables into a comprehensive predictor set (PANEL) to investigate overlapping information with the metabolomic state. FH, family history.
Metabolomic state is associated with ORs and stratifies survival
a, Observed event frequency for incident disease plotted against metabolomic state percentiles over the entire study population for all 24 endpoints. b, Cumulative event rates over the observation time for all assessed endpoints, stratified by metabolomic state quantiles (light blue, bottom 10%; blue, median 10%; dark blue, top 10%), with 95% CIs indicated. PAD, peripheral artery disease.
Predictive value of the metabolomic state is endpoint dependent
a, Comparison of discriminative performance of CPH models trained on the metabolomic state only (MET), the three clinical predictor sets (Age+Sex, ASCVD and PANEL) and the sets’ combinations with the metabolomic state. Horizontal dashed lines indicate the median performance of the three clinical predictor sets. b, Differences in discriminative performance between the Age+Sex baseline (dashed line), metabolomic state only (blue) and the combination of Age+Sex and metabolomic state (green). c, Differences in discriminative performance between ASCVD predictors (dashed line), the combination of Age+Sex and the metabolomic state (green) and the combination of metabolomic state and ASCVD predictors (red). d, Difference in discriminative performance between comprehensive PANEL predictors (dashed line), ASCVD + MET (red) and PANEL + MET (black). a–d, Statistical measures were derived from n = 117.981 individuals; those with previous events were excluded (Supplementary Table 1). Data are presented as median (center of error bar) and 95% CI (line of error bar) determined by bootstrapping of with 1,000 iterations. b–d, The x-axis range differs across panels; vertical grid lines indicate differences of 0.02 C-index.
Model calibration and additive predictive value of the metabolomic state translate to potential clinical utility
a–c, Calibration curves for CPH models, including baseline parameter sets Age+Sex, ASCVD and PANEL, as well as their combinations with the metabolomic state (Age+Sex + MET) for the endpoints T2D (a), dementia (b) and heart failure (c). d–f, Endpoint-specific net benefit curves standardized by endpoint prevalence, where horizontal solid gray lines indicate ‘treat none’ and vertical solid gray lines indicate ‘treat all’; T2D (d), dementia (e) and heart failure (f). The standardized net benefits of sets Age+Sex, ASCVD and PANEL are compared with Age+Sex + MET and additional non-laboratory predictors of PANEL (PANELnoLaboratory). Green and blue color-filled areas indicate the added benefit of the combination of the metabolomic state and Age+Sex and PANELnoLaboratory, respectively. g–i, Standardized net benefit curves comparing the performance of PANEL + MET against baselines Age+Sex, ASCVD and PANEL; T2D (g), dementia (h) and heart failure (i). Decision curves were derived from n = 111,745 (T2D), n = 117,245 (dementia) and n = 113,636 (heart failure) individuals.
Analysis of the metabolomic state informs on metabolite profiles associated with disease risk
a, Heatmap showing the importance of metabolites in regard to the estimated metabolomic states, represented by absolute global SHAP value estimates per endpoint for the 75 globally most important metabolites. Endpoints are sorted by the discriminative performance of the metabolomic state (left to right; Fig. 3a). b, Global metabolite attributions for T2D; individual attributions are aggregated by percentiles and each dot indicates one percentile. The more distant a dot from the circular baseline, the stronger the absolute attribution for that percentile. Deviations toward the center and periphery represent negative and positive contributions, respectively, to the metabolomic state. Colors indicate the metabolite’s mean plasma value. c, Global metabolite attributions for all-cause dementia. IDL, intermediate-density lipoprotein.
Metabolomic profiles predict individual multidisease outcomes
  • Article
  • Full-text available

September 2022

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486 Reads

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202 Citations

Nature Medicine

Thore Buergel

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Jakob Steinfeldt

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Greg Ruyoga

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[...]

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Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

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Figure 1: Selection and characteristics of study population (A) Individuals in the UK Biobank population who withdrew consent, with missing information about their sex or with earlier records of incident myocardial infarction or stroke or lipid-lowering treatment at baseline were excluded. The remaining set was split into training, validation, and test sets in 22-fold nested cross-validation based on the assigned UK Biobank assessment centre. (B) Distribution of observation times for the derived study population. The median observation time was 11·7 years (IQR 11·0-12·3). (C) Kaplan-Meier estimates for the disease-free survival function stratified by sex. (D) Numbers at risk in 5-year intervals stratified by sex.
appendix pp 11, 15). Although we observed improvements in discriminative performance for the Cox model after addition of the PGSs as well, the NeuralCVD model remained superior in C­index (COX plus PGS 0·002, 95% CI 0·002-0·003; COX plus PGS*age 0·002, 0·002-0·003) and NRI (COX plus PGS 0·0424, 95% CI 0·0383-0·0464; COX plus PGS*age 0·0359,
Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort

February 2022

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255 Reads

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33 Citations

The Lancet Digital Health

Background In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, towards clinical application, the added value over clinical predictors later in life is crucial. Currently, this genotype–phenotype relationship and implications for overall cardiovascular risk are unclear. Methods In this study, we developed and validated a neural network-based risk model (NeuralCVD) integrating polygenic and clinical predictors in 395 713 cardiovascular disease-free participants from the UK Biobank cohort. The primary outcome was the first record of a major adverse cardiac event (MACE) within 10 years. We compared the NeuralCVD model with both established clinical scores (SCORE, ASCVD, and QRISK3 recalibrated to the UK Biobank cohort) and a linear Cox-Model, assessing risk discrimination, net reclassification, and calibration over 22 spatially distinct recruitment centres. Findings The NeuralCVD score was well calibrated and improved on the best clinical baseline, QRISK3 (ΔConcordance index [C-index] 0·01, 95% CI 0·009–0·011; net reclassification improvement (NRI) 0·0488, 95% CI 0·0442–0·0534) and a Cox model (ΔC-index 0·003, 95% CI 0·002–0·004; NRI 0·0469, 95% CI 0·0429–0·0511) in risk discrimination and net reclassification. After adding polygenic scores we found further improvements on population level (ΔC-index 0·006, 95% CI 0·005–0·007; NRI 0·0116, 95% CI 0·0066–0·0159). Additionally, we identified an interaction of genetic information with the pre-existing clinical phenotype, not captured by conventional models. Additional high polygenic risk increased overall risk most in individuals with low to intermediate clinical risk, and age younger than 50 years. Interpretation Our results demonstrated that the NeuralCVD score can estimate cardiovascular risk trajectories for primary prevention. NeuralCVD learns the transition of predictive information from genotype to phenotype and identifies individuals with high genetic predisposition before developing a severe clinical phenotype. This finding could improve the reprioritisation of otherwise low-risk individuals with a high genetic cardiovascular predisposition for preventive interventions. Funding Charité–Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative.

Citations (2)


... Omics sciences represent a very promising instrument to perform the analysis of patients and their biological characteristics within the dynamic context of disease evolution, thus enabling the molecular characterization of a disease onset and evolution, and providing insight into individual susceptibility to drug treatments [5][6][7][8][9][10]. Given these premises, metabolomics and lipoproteomics present themselves as compelling approaches for investigating alterations of multiple biochemical networks throughout the entire course of AD [11][12][13][14][15][16][17][18]. ...

Reference:

Studying Alzheimer’s disease through an integrative serum metabolomic and lipoproteomic approach
Metabolomic profiles predict individual multidisease outcomes

Nature Medicine

... Importantly, our approach, based on routine health records, shows large discriminative improvements for the majority of diseases compared with conventionally tested biomarkers [55][56][57] and can be generalized across diverse health systems, populations, and ethnicities. However, we also see that including the medical history over age and sex deteriorated the performance for a subset of 0.7% (UK Biobank) and 5.5% (All Of Us cohort), respectively. ...

Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort

The Lancet Digital Health