Circulating Metabolome and White Matter Hyperintensities in Women and Men

Author:

Sliz Eeva12ORCID,Shin Jean12ORCID,Ahmad Shahzad34ORCID,Williams Dylan M.56,Frenzel Stefan7ORCID,Gauß FriederikeORCID,Harris Sarah E.8ORCID,Henning Ann-Kristin9,Hernandez Maria Valdes10ORCID,Hu Yi-Han11ORCID,Jiménez Beatriz12ORCID,Sargurupremraj Muralidharan13ORCID,Sudre Carole51415ORCID,Wang Ruiqi16ORCID,Wittfeld Katharina717ORCID,Yang Qiong16ORCID,Wardlaw Joanna M.10ORCID,Völzke Henry18,Vernooij Meike W.31920ORCID,Schott Jonathan M.21,Richards Marcus5,Proitsi Petroula22,Nauck Matthias9,Lewis Matthew R.12,Launer Lenore11ORCID,Hosten Norbert23ORCID,Grabe Hans J.17,Ghanbari Mohsen3ORCID,Deary Ian J.8,Cox Simon R.8,Chaturvedi Nishi5ORCID,Barnes Josephine21,Rotter Jerome I.24ORCID,Debette Stephanie13ORCID,Ikram M. Arfan3ORCID,Fornage Myriam25ORCID,Paus Tomas262728,Seshadri Sudha2930,Pausova Zdenka1227ORCID,

Affiliation:

1. The Hospital for Sick Children (E.S., J.S., Z.P.), University of Toronto, Canada.

2. Departments of Physiology and Nutritional Sciences (E.S., J.S., Z.P.), University of Toronto, Canada.

3. Departments of Epidemiology (S.A., M.W.V., M.G., M.A.I.), Erasmus Medical Centre, Rotterdam, the Netherlands.

4. Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands (S.A.).

5. MRC Unit for Lifelong Health and Ageing at UCL (D.M.W., C.S., M.R., N.C.), University College London, United Kingdom.

6. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (D.M.W.).

7. Department of Psychiatry and Psychotherapy (S.F., K.W., H.J.G.), University Medicine Greifswald, Germany.

8. Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, United Kingdom (S.E.H., I.J.D., S.R.C.).

9. Institute of Clinical Chemistry and Laboratory Medicine (F.G., A.-K.H., M.N.), University Medicine Greifswald, Germany.

10. Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh (M.V.H., J.M.W.), United Kingdom.

11. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD (Y.-H.H., L.L.).

12. National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, United Kingdom (B.J., M.R.L.).

13. Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, University of Bordeaux, France (M.S., S.D.).

14. Centre for Medical Image Computing, Department of Computer Science (C.S.), University College London, United Kingdom.

15. School of Biomedical Engineering & Imaging Sciences (C.S.) King’s College London, United Kingdom.

16. Department of Biostatistics, Boston University, MA (R.W., Q.Y.).

17. Germany Center for Neurodegenerative Diseases (DZNE), partner site Rostock/Greifswald, Greifswald, Germany (K.W., H.J.G.).

18. Institute for Community Medicine (H.V.), University Medicine Greifswald, Germany.

19. Radiology and Nuclear Medicine (M.W.V.), Erasmus Medical Centre, Rotterdam, the Netherlands.

20. Neurology (M.W.V.), Erasmus Medical Centre, Rotterdam, the Netherlands.

21. Dementia Research Centre, UCL Queen Square Institute of Neurology (J.M.S., J.B.), University College London, United Kingdom.

22. Institute of Psychiatry, Psychology and Neuroscience (P.P.), King’s College London, United Kingdom.

23. Institute of Diagnostic Radiology and Neuroradiology (N.H.), University Medicine Greifswald, Germany.

24. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor–UCLA Medical Center, Torrance, CA (J.I.R.).

25. University of Texas Health Science Center at Houston McGovern Medical School (M.F.).

26. Psychology and Psychiatry (T.P.), University of Toronto, Canada.

27. Departments of Psychiatry and Neuroscience and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Canada (T.P., Z.P.).

28. ECOGENE-21, Chicoutimi, Canada (T.P.).

29. The Framingham Heart Study, MA (S.S.).

30. Department of Neurology, Boston University School of Medicine, MA (S.S.).

Abstract

Background: White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. Methods: We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment–stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance–weighted method. Associations with false discovery rate (FDR)–adjusted P values ( P FDR )<0.05 were considered significant. Results: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH ( P FDR <0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men ( P FDR.full.adj =1.40×10 −7 ) and in both the pooled sample ( P FDR.full.adj =1.66×10 4 ) and statin-untreated ( P FDR.full.adj =1.65×10 6 ) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate ( P FDR =0.047). Conclusions: Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine

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