Buergel T, Steinfeldt J, Ruyoga G, Pietzner M, Bizzarri D, Vojinovic D, Upmeier Zu Belzen J, Loock L, Kittner P, Christmann L, Hollmann N, Strangalies H, Braunger JM, Wild B, Chiesa ST, Spranger J, Klostermann F, van den Akker EB, Trompet S, Mooijaart SP, Sattar N, Jukema JW, Lavrijssen B, Kavousi M, Ghanbari M, Ikram MA, Slagboom E, Kivimaki M, Langenberg C, Deanfield J, Eils R, Landmesser U. Metabolomic profiles predict individual multidisease outcomes. Nat Med. 2022 Nov;28(11):2309-2320. Epub 2022 Sep 22 PubMed.

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  1. This is such a heroic analysis effort underscoring the importance of metabolic changes in neurodegenerative dementias, as well as a large number of other major human diseases, in multiple cohorts.

    Branched-chain amino-acid changes are now well-replicated in dementias, plus a number of additional, more novel observations were made.

    The next steps in this research should include examination of metabolic profiles in specific neurodegenerative dementias, and also assessing their relationship with established biomarkers for underlying brain pathologies. An important future research topic will be to determine if the metabolic changes are upstream or downstream of the onset of proteinopathy accumulation and other neurodegenerative brain changes. 

    View all comments by Henrik Zetterberg

  2. Exploring the clinical utility of metabolomics for stratifying risk in cardiometabolic disease is a logical next step in our efforts to combat the rising burden of diseases in the population. Metabolites, which are small molecule intermediates resulting from biological processes, are dynamic biomarkers that have been broadly used in association and prediction studies. They may be useful to inform upon dysregulated processes and provide information on disease pathophysiology and targeted interventions. Buergel et al. coupled metabolomics with machine-learning approaches to simultaneously inform upon multi-disease risk in the UK Biobank population, with replication in four additional cohorts. Results from this work illustrated that metabolite profiles were associated with incident rates of common disease, and were equal to, or outperformed, established clinical predictors of common disease. This demonstrates proof of concept for the clinical utility of metabolomics in disease risk stratification of complex diseases and creates an opportunity for refinement toward broader implementation.

    As the authors note, metabolite coverage on the NMR platform is relatively narrow and lipid-focused. This platform pales in comparison to the thousands of metabolites detected, quantified, and reported in the literature using complementary approaches, e.g., mass spectrometry, and sample types. This is further compounded by the dynamic nature of metabolites, which are subject to rapid changes in response to biologic perturbation. Thus, standardized assessment, e.g., fasting state not often achieved in clinic, would be clinically warranted to optimize informativeness and allow for comparison. Beyond metabolic state, confounders such as diet, medications, environment, etc., need to be considered as these could impact the underlying biology and influence risk models.

    Toward broader implementation, it should be noted that the volunteer study population, while an excellent clinical and research resource, were generally healthier than the U.K. population, e.g., older, decreased CVD risk factors, higher socioeconomic status, etc., and lack diversity in age, race/ethnicity, etc. Moreover, cardiometabolic diseases, such as those included herein, are not mutually exclusive and could bias risk models.

    Moving forward, these observations motivate additional research questions. With a focus on precision medicine, broad generalizability needs to be assessed both in terms of populations and conditions assessed. And, once we identify risk, how can these profiles be used to prioritize, monitor, and target disease interventions with the broad goal of improving metabolic health?

    View all comments by Nicholette Allred

  3. This paper is a very nice example of the power of NMR metabolomics, primarily achieved thanks to the availability of extremely large datasets. This publication highlights the advantages of NMR metabolomics for diagnostics, and shows the potential to increase the power of metabolomics by combining it with readily available clinical measures.

    The authors showed that the addition of quantitative, whole-blood, proton NMR metabolomics to other clinical measures improved risk factor assessment for 23 out of 24 common conditions including metabolic, vascular, respiratory, musculoskeletal, and neurological diseases, as well as cancer. Combining clinical markers with NMR metabolomics data is a very interesting way to boost the power of prediction models, and NMR metabolomics provides sufficient sample size for these analyses. Utilization of a deep, residual, multitask neural network to simultaneously learn predictors for different conditions at the same time, and from the same dataset, is a very interesting approach applied here in a novel way.

    I was very surprised with the low performance of disease-specific metabolic state analysis in risk trajectory assessment for breast cancer, particularly when compared with prostate cancer. Also, high predictor power for Type 2 diabetes, dementia, and heart failure event rate achieved using only age and sex information, with only minor improvement obtained by adding other parameters, including metabolomics, is very interesting and somewhat unexpected.

    Diagnostic metabolomics has been taking its rightful place over the last decade and this work is an excellent addition to many other examples of the power of metabolic profiling for different applications. In this work NMR measurements provided concentrations for 168 metabolic markers that are all used in the development of machine-learning models for risk assessment. Future applications of this particular model for diagnostics would require measurement, assignment, and quantification standardization that can be achieved if appropriate NMR equipment is in place. Perhaps this work will encourage addition of NMR to clinical laboratories of the future.

    View all comments by Miroslava Cuperlovic-Culf

  4. Having more detailed molecular data, which in this case is 150 data points made up of a few small molecules and many lipoprotein particles of different sizes, helps with assessing the risk of developing some diseases such as diabetes. Using this molecular platform for the prediction is similar to using clinical lipids, glucose, fatty acids, ketones, and omegas, so it could be particularly useful for diseases where metabolic syndrome or lipids are known risk predictors. The addition of machine learning for the prediction might exploit additional hidden biochemistry and therefore help the prediction further. But my takeaway was that, for dementia, the results of this particular panel of molecules and lipoproteins were not conclusive. The results of the metabolic state didn’t exceed the risk provided by sex and age, and hazard ratios could not be validated in an additional cohort.

    It surprised me that many of the molecules and lipoproteins, when plotted in box plots, seem to vary in the same manner. This could be biological but also due to the NMR technology not being specific enough, as the authors mentioned in discussing the limitations of the study. And, if measurements are indeed correlated, this would impact the machine-learning modeling and the metabolic state calculation. It would be great to compare these NMR measurements with other technologies to better understand these observations.

    I think of metabolomics as being a snapshot of metabolism and this is very useful to assess health, however, it is early days and this work involved a great number of participants but very few molecules and lipoprotein data. I see the value of measuring metabolic risk, or state, for patients, but for dementia we will have to add brain-derived biomarkers and other omics data to the models to achieve clinically meaningful risk predictions.

    View all comments by Cristina Legido Quigley

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