Fat metabolism is associated with telomere length in six population-based studies

A. Van Der Spek, H. Karamujić-Čomić, R. Pool, M. Bot, M. Beekman, S. Garmaeva, P. P. Arp, S. Henkelman, J. Liu, A. C. Alves, G. Willemsen, G. Van Grootheest, G. Aubert, M. A. Ikram, M. R. Jarvelin, P. Lansdorp, A. G. Uitterlinden, A. Zhernakova, P. E. Slagboom, B. W. J. H. PenninxD. I. Boomsma, N. Amin, C. M. Van Duijn*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

Abstract

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold Pmeta = 6.5 × 10-4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-CE %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-CE) became significantly associated with LTL (P = 3.6 × 10-4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 × 10-4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.

Original languageEnglish
Pages (from-to)1159-1170
Number of pages12
JournalHuman Molecular Genetics
Volume31
Issue number7
DOIs
Publication statusPublished - 1 Apr 2022

Bibliographical note

Funding Information:
ERF study: The ERF study has received funding from the Centre for Medical Systems Biology (CMSB) and the Netherlands Consortium for Systems Biology (NCSB), both within the framework of the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO). ERF study is also a part of EUROSPAN (European Special Populations Research Network) (FP6 STRP grant number 018947 (LSHG-CT-2006-01947)); European Network of Genomic and Genetic Epidemiology (ENGAGE) from the European Community’s Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413; ‘Quality of Life and Management of the Living Resources’ of fifth Framework Programme (no. QLG2-CT-2002-01254); FP7 project EUROHEADPAIN (nr 602633), the Internationale Stichting Alzheimer Onderzoek (ISAO); the Hersenstichting Nederland (HSN) and the JNPD under the project PERADES (grant number 733051021, Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease using multiple powerful cohorts, focused Epigenetics and Stem cell metabolomics). Metabolomics measurements of ERF has been funded by Biobanking and Biomolecular Resources Research Infrastructure (BBMRI)–NL (184.021.007 and 184.033.111).

Funding Information:
RS: The RS is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII) and the Municipality of Rotterdam. Metabolomics measurements were funded by Biobanking and Biomolecular Resources Research Infrastructure (BBMRI)–NL (184.021.007 and 184.033.111) and the JNPD under the project PERADES (grant number 733051021, Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease using multiple powerful cohorts, focused Epigenetics and Stem cell metabolomics).

Funding Information:
LLDEEP: LifeLines-DEEP project was funded by the Netherlands Heart Foundation (IN-CONTROL CVON grant 2012-03 and IN-CONTROL2 CVON grant 2019-05 to A.Z.), and by Rosalind Franklin Fellowship from the University of Groningen to A.Z. A.Z. holds the Netherlands Organization for Scientific Research (NWO) Vidi grant (NWO-VIDI 016.178.056) and a European Research Council (ERC) starting grant (ERC Starting Grant 715772). S.G. holds the scholarship from the Graduate School of Medical Sciences, University of Groningen.

Funding Information:
NESDA study: The infrastructure for the NESDA study ( www.nesda.nl ) has been funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (ZonMw, grant number 10-000-1002) and by participating universities and 4 mental health care organizations (Amsterdam University Medical Centers (location VUmc), GGZ inGeest, Leiden University Medical Center, University Medical Center Groningen, University of Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Rob Giel Onderzoekcentrum). LTL measurement was supported by an NWO-VICI grant (number 91811602) to prof. Penninx.

Funding Information:
LLS: The LLS has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 259679, the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO) and by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007 and 184.033.111).

Funding Information:
NTR: Funding was obtained from the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 904-61-193,480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192, Biobanking and Biomolecular Resources Research Infrastructure (BBMRI–NL, 184.021.007); the European Community’s Seventh Framework Program (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); the European Science Council (ERC Advanced, 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951). We gratefully acknowledge grant NWO 480-15-001/674: the Netherlands Twin Registry Repository: researching the interplay between genome and environment.

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press. All rights reserved.

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