Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic … – Springer

Background and Objective

Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.

Methods

We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.

Results

We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.

Conclusion

Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

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Authors and Affiliations

1. Division of Preventive Medicine, Department of Medicine, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

   Azam Yazdani

2. Beckman Coulter, Brea, USA

   Raul Mendez-Giraldez

3. Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, USA

   Akram Yazdani

4. Department of Medicine, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, USA

   Rui-Sheng Wang, Jessica Lasky-Su, Samia Mora & Daniel I. Chasman

5. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55902, USA

   Daniel J. Schaid

6. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA

   Sek Won Kong

7. School of Mathematics, University of Science and Technology of Iran, Tehran, Iran

   M. Reza Hadi

8. Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, USA

   Ahmad Samiei

9. Gamelectronic, Tehran, Iran

   Esmat Samiei

10. Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden

    Clemens Wittenbecher

11. Broad Institute of MIT and Harvard, Cambridge, USA

    Clary B. Clish

12. Department of Anesthesia, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Jochen D. Muehlschlegel

13. ReGenera R&D International for Aging Intervention and Vitality & Longevity Medical Science Commission, Femtec, Milano, Italy

    Francesco Marotta

14. The Division of Cardiovascular Medicine, Department of Medicine, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Joseph Loscalzo

15. Department of Biostatistics, Boston University, Boston, MA, 02118, USA

    Martin G. Larson

16. Baylor College of Medicine, Houston, USA

    Sarah H. Elsea

17. Harvard Data Science Initiative, The Broad Institute, Harvard Medical School, Boston, USA

    Azam Yazdani

Contributions

AY1 structured the manuscript, analyzed the data, wrote the manuscript, and made the figures; RMG helped to structure the manuscript, provided feedback on the findings, helped to improve the discussion section, edited, and commented on the manuscript; AY2 was involved in discussions for the analysis and structuring the manuscript. She also edited and commented on the manuscript; RW read and commented on the manuscript, and helped connect the metabolomics findings to proteins; DJS were involved in the discussions for the analysis, read and commented on the manuscript; SWK read and commented on the manuscript and helped to improve the structure of the manuscript; MRH, AS, and ES were involved in Bayesian network analysis, in addition AS commented on the figures to improve them; CW, JLS, JDM, and FM read and commented on the manuscript; CBC provided information about metabolomics and annotations, and read and commented on the manuscript; JL provided feedback on the findings, read and commented on the manuscript, and contributed to improving the discussion section; SM and DIC read and commented on the manuscript and helped structure the manuscript; MGL and SHE were involved in all aspects of the manuscripts including the discussions about analysis, writing, and structuring the manuscript.

Corresponding author

Correspondence to Azam Yazdani.

Conflict of interest

The authors declare no conflict of interest.

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