Abstract
Telomeres shorten with age and shorter leukocyte telomere length (LTL) has been associated with various age-related diseases. Thus, LTL has been considered a biomarker of biological aging. Dyslipidemia is an established risk factor for most age-related metabolic disorders. However, little is known about the relationship between LTL and dyslipidemia. Lipidomics is a new biochemical technique that can simultaneously identify and quantify hundreds to thousands of small molecular lipid species. In a large population comprising 1843 well-characterized American Indians in the Strong Heart Family Study, we examined the lipidomic profile of biological aging assessed by LTL. Briefly, LTL was quantified by qPCR. Fasting plasma lipids were quantified by untargeted liquid chromatography–mass spectrometry. Lipids associated with LTL were identified by elastic net modeling. Of 1542 molecular lipids identified (518 known, 1024 unknown), 174 lipids (36 knowns) were significantly associated with LTL, independent of chronological age, sex, BMI, hypertension, diabetes status, smoking status, bulk HDL-C, and LDL-C. These findings suggest that altered lipid metabolism is associated with biological aging and provide novel insights that may enhance our understanding of the relationship between dyslipidemia, biological aging, and age-related diseases in American Indians.
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References
Blackburn EH. Telomeres: no end in sight. Cell. 1994;77:621–3.
McEachern MJ, Krauskopf A, Blackburn EH. Telomeres and their control. Annu Rev Genet. 2000;34:331–58.
Chen S, Yeh F, Lin J, Matsuguchi T, Blackburn E, Lee ET, Howard BV, Zhao J. Short leukocyte telomere length is associated with obesity in American Indians: the strong heart family study. Aging. 2014;6:380–9.
Adaikalakoteswari A, Balasubramanyam M, Mohan V. Telomere shortening occurs in Asian Indian Type 2 diabetic patients. Diabet Med. 2005;22:1151–6.
Salpea KD, Talmud PJ, Cooper JA, Maubaret CG, Stephens JW, Abelak K, Humphries SE. Association of telomere length with type 2 diabetes, oxidative stress and UCP2 gene variation. Atherosclerosis. 2010;209:42–50.
Zhao J, Zhu Y, Lin J, Matsuguchi T, Blackburn E, Zhang Y, Cole SA, Best LG, Lee ET, Howard BV. Short leukocyte telomere length predicts risk of diabetes in american indians: the strong heart family study. Diabetes. 2014;63:354–62.
Ellehoj H, Bendix L, Osler M. Leucocyte telomere length and risk of cardiovascular disease in a cohort of 1,397 danish men and women. Cardiology. 2016;133:173–7.
Said MA, Eppinga RN, Hagemeijer Y, Verweij N, van der Harst P. Telomere length and risk of cardiovascular disease and cancer. J Am Coll Cardiol. 2017;70:506–7.
Subedi P, Nembrini S, An Q, Zhu Y, Peng H, Yeh F, Cole SA, Rhoades DA, Lee ET, Zhao J. Telomere length and cancer mortality in American Indians: the Strong Heart Study. Geroscience. 2019;41:351–61.
Wentzensen IM, Mirabello L, Pfeiffer RM, Savage SA. The association of telomere length and cancer: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2011;20:1238–50.
Chen SC, Tseng CH. Dyslipidemia, kidney disease, and cardiovascular disease in diabetic patients. Rev Diabet Stud. 2013;10:88–100.
Liu HH, Li JJ. Aging and dyslipidemia: a review of potential mechanisms. Ageing Res Rev. 2015;19:43–52.
Chen W, Gardner JP, Kimura M, Brimacombe M, Cao X, Srinivasan SR, Berenson GS, Aviv A. Leukocyte telomere length is associated with HDL cholesterol levels: The Bogalusa Heart Study. Atherosclerosis. 2009;205:620–5.
Harte AL, da Silva NF, Miller MA, Cappuccio FP, Kelly A, O'Hare JP, Barnett AH, Al-Daghri NM, Al-Attas O, Alokail M, et al. Telomere length attrition, a marker of biological senescence, is inversely correlated with triglycerides and cholesterol in South Asian males with type 2 diabetes mellitus. Exp Diabetes Res. 2012;2012:895185.
Satoh M, Minami Y, Takahashi Y, Tabuchi T, Itoh T, Nakamura M. Effect of intensive lipid-lowering therapy on telomere erosion in endothelial progenitor cells obtained from patients with coronary artery disease. Clin Sci. 2009;116:827–35.
Gross RW, Han X. Lipidomics at the interface of structure and function in systems biology. Chem Biol. 2011;18:284–91.
Meikle PJ, Christopher MJ. Lipidomics is providing new insight into the metabolic syndrome and its sequelae. Curr Opin Lipidol. 2011;22:210–5.
Surowiec I, Noordam R, Bennett K, Beekman M, Slagboom PE, Lundstedt T, van Heemst D. Metabolomic and lipidomic assessment of the metabolic syndrome in Dutch middle-aged individuals reveals novel biological signatures separating health and disease. Metabolomics. 2019;15:23.
Pietiläinen KH, Sysi-Aho M, Rissanen A, Seppänen-Laakso T, Yki-Järvinen H, Kaprio J, Oresic M. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects--a monozygotic twin study. PLoS One. 2007;2:e218.
Rauschert S, Uhl O, Koletzko B, Kirchberg F, Mori TA, Huang RC, Beilin LJ, Hellmuth C, Oddy WH. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab. 2016;101:871–9.
Lappas M, Mundra PA, Wong G, Huynh K, Jinks D, Georgiou HM, Permezel M, Meikle PJ. The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics. Diabetologia. 2015;58:1436–42.
Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, Menni C, Moayyeri A, Santer P, Rungger G, et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation. 2014;129:1821–31.
van der Spek A, Broer L, Draisma HHM, Pool R, Albrecht E, Beekman M, Mangino M, Raag M, Nyholt DR, Dharuri HK, et al. Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium. Sci Rep. 2019;9:11623.
Zhao J, Zhu Y, Uppal K, Tran VT, Yu T, Lin J, Matsuguchi T, Blackburn E, Jones D, Lee ET, Howard BV. Metabolic profiles of biological aging in American Indians: the Strong Heart Family Study. Aging. 2014;6:176–86.
Zierer J, Kastenmüller G, Suhre K, Gieger C, Codd V, Tsai PC, Bell J, Peters A, Strauch K, Schulz H, et al. Metabolomics profiling reveals novel markers for leukocyte telomere length. Aging. 2016;8:77–94.
Beyene HB, Olshansky G, TS AA, Giles C, Huynh K, Cinel M, Mellett NA, Cadby G, Hung J, Hui J, et al. High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies. PLoS Biol. 2020;18(e3000870).
Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, et al. Plasma lipid profiling in a large population-based cohort. J Lipid Res. 2013;54:2898–908.
Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, Cucchiara AJ, Savage PJ, Howard BV. The Strong Heart Study. A study of cardiovascular disease in American Indians: design and methods. Am J Epidemiol. 1990;132:1141–55.
Peng H, Mete M, Desale S, Fretts AM, Cole SA, Best LG, Lin J, Blackburn E, Lee ET, Howard BV, Zhao J. Leukocyte telomere length and ideal cardiovascular health in American Indians: the Strong Heart Family Study. Eur J Epidemiol. 2017;32:67–75.
Miao G, Zhang Y, Huo Z, Zeng W, Zhu J, Umans JG, Wohlgemuth G, Pedrosa D, DeFelice B, Cole SA, et al. longitudinal plasma lipidome and risk of type 2 diabetes in a large sample of american indians with normal fasting glucose: The Strong Heart Family Study. Diabetes Care. 2021;44:2664–72.
Cajka T, Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics. 2016;12:34.
Cajka T, Smilowitz JT, Fiehn O. Validating quantitative untargeted lipidomics across nine liquid chromatography-high-resolution mass spectrometry platforms. Anal Chem. 2017;89:12360–8.
Fan S, Kind T, Cajka T, Hazen SL, Tang WHW, Kaddurah-Daouk R, Irvin MR, Arnett DK, Barupal DK, Fiehn O. Systematic error removal using random forest for normalizing large-scale untargeted lipidomics data. Anal Chem. 2019;91:3590–6.
Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002;30:e47.
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67:301–20.
Benton MC, Sutherland HG, Macartney-Coxson D, Haupt LM, Lea RA, Griffiths LR. Methylome-wide association study of whole blood DNA in the Norfolk Island isolate identifies robust loci associated with age. Aging. 2017;9:753–68.
Højsgaard S, Halekoh U, Yan J. The R Package geepack for Generalized Estimating Equations. J Stat Softw. 2005;15:1–11.
Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Seyama Y, Shaw W, et al. A comprehensive classification system for lipids. J Lipid Res. 2005;46:839–61.
Hulbert AJ. On the importance of fatty acid composition of membranes for aging. J Theor Biol. 2005;234:277–88.
Ford JH. Saturated fatty acid metabolism is key link between cell division, cancer, and senescence in cellular and whole organism aging. Age. 2010;32:231–7.
Fatima S, Hu X, Gong RH, Huang C, Chen M, Wong HLX, Bian Z, Kwan HY. Palmitic acid is an intracellular signaling molecule involved in disease development. Cell Mol Life Sci. 2019;76:2547–57.
Hernández-Cáceres MP, Toledo-Valenzuela L, Díaz-Castro F, Ávalos Y, Burgos P, Narro C, Peña-Oyarzun D, Espinoza-Caicedo J, Cifuentes-Araneda F, Navarro-Aguad F, et al. Palmitic acid reduces the autophagic flux and insulin sensitivity through the activation of the Free Fatty Acid Receptor 1 (FFAR1) in the hypothalamic neuronal cell line N43/5. Front Endocrinol. 2019;10:176.
Joshi-Barve S, Barve SS, Amancherla K, Gobejishvili L, Hill D, Cave M, Hote P, McClain CJ. Palmitic acid induces production of proinflammatory cytokine interleukin-8 from hepatocytes. Hepatology. 2007;46:823–30.
Mozaffarian D. Saturated fatty acids and type 2 diabetes: more evidence to re-invent dietary guidelines. Lancet Diabetes Endocrinol. 2014;2:770–2.
Aguer C, McCoin CS, Knotts TA, Thrush AB, Ono-Moore K, McPherson R, Dent R, Hwang DH, Adams SH, Harper ME. Acylcarnitines: potential implications for skeletal muscle insulin resistance. Faseb J. 2015;29:336–45.
Liepinsh E, Makrecka-Kuka M, Makarova E, Volska K, Svalbe B, Sevostjanovs E, Grinberga S, Kuka J, Dambrova M. Decreased acylcarnitine content improves insulin sensitivity in experimental mice models of insulin resistance. Pharmacol Res. 2016;113:788–95.
Mihalik SJ, Goodpaster BH, Kelley DE, Chace DH, Vockley J, Toledo FG, DeLany JP. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity. 2010;18:1695–700.
Schooneman MG, Vaz FM, Houten SM, Soeters MR. Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes. 2013;62:1–8.
Borodzicz S, Czarzasta K, Kuch M, Cudnoch-Jedrzejewska A. Sphingolipids in cardiovascular diseases and metabolic disorders. Lipids Health Dis. 2015;14:55.
Hannun YA, Obeid LM. Sphingolipids and their metabolism in physiology and disease. Nat Rev Mol Cell Biol. 2018;19:175–91.
Singh P, Li R. Emerging roles for sphingolipids in cellular aging. Curr Genet. 2018;64:761–7.
Trayssac M, Hannun YA, Obeid LM. Role of sphingolipids in senescence: implication in aging and age-related diseases. J Clin Invest. 2018;128:2702–12.
Venable ME, Lee JY, Smyth MJ, Bielawska A, Obeid LM. Role of ceramide in cellular senescence. J Biol Chem. 1995;270:30701–8.
Dbaibo GS, Pushkareva MY, Rachid RA, Alter N, Smyth MJ, Obeid LM, Hannun YA. p53-dependent ceramide response to genotoxic stress. J Clin Invest. 1998;102:329–39.
Meacci E, Vasta V, Neri S, Farnararo M, Bruni P. Activation of phospholipase D in human fibroblasts by ceramide and sphingosine: evaluation of their modulatory role in bradykinin stimulation of phospholipase D. Biochem Biophys Res Commun. 1996;225:392–9.
Lim GB. Sphingolipids are biomarkers of coronary disease. Nat Rev Cardiol. 2020;17:200.
Poss AM, Maschek JA, Cox JE, Hauner BJ, Hopkins PN, Hunt SC, Holland WL, Summers SA, Playdon MC. Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease. J Clin Invest. 2020;130:1363–76.
Taltavull N, Ras R, Mariné S, Romeu M, Giralt M, Méndez L, Medina I, Ramos-Romero S, Torres JL, Nogués MR. Protective effects of fish oil on pre-diabetes: a lipidomic analysis of liver ceramides in rats. Food Funct. 2016;7:3981–8.
Zeng W, Beyene HB, Kuokkanen M, Miao G, Magliano DJ, Umans JG, Franceschini N, Cole SA, Michailidis G, Lee ET, et al. Lipidomic profiling in The Strong Heart Study identified American Indians at risk of chronic kidney disease. Kidney Int. 2022.
Farooqui AA, Horrocks LA, Farooqui T. Glycerophospholipids in brain: their metabolism, incorporation into membranes, functions, and involvement in neurological disorders. Chem Phys Lipids. 2000;106:1–29.
Fonteh AN, Chiang J, Cipolla M, Hale J, Diallo F, Chirino A, Arakaki X, Harrington MG. Alterations in cerebrospinal fluid glycerophospholipids and phospholipase A2 activity in Alzheimer's disease. J Lipid Res. 2013;54:2884–97.
Fonteh AN, Chiang AJ, Arakaki X, Edminster SP, Harrington MG. Accumulation of cerebrospinal fluid glycerophospholipids and sphingolipids in cognitively healthy participants with alzheimer's biomarkers precedes lipolysis in the dementia stage. Front Neurosci. 2020;14:611393.
Palmisano BT, Zhu L, Eckel RH, Stafford JM. Sex differences in lipid and lipoprotein metabolism. Mol Metab. 2018;15:45–55.
Gardner M, Bann D, Wiley L, Cooper R, Hardy R, Nitsch D, Martin-Ruiz C, Shiels P, Sayer AA, Barbieri M, et al. Gender and telomere length: systematic review and meta-analysis. Exp Gerontol. 2014;51:15–27.
Acknowledgements
We thank all participants of the Strong Heart Study (SHS), the Indian Health Service facilities, and participating tribal communities for their extraordinary cooperation and involvement, which has contributed to the success of SHS.
Funding
This study was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01DK091369 and R01DK107532. Pooja Subedi was partially supported by the American Heart Association predoctoral fellowship 20PRE35050001. The Strong Heart Study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers 75N92019D00027, 75N92019D00028, 75N92019D00029, and 75N92019D00030. The study was previously supported by research grants: R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and by cooperative agreements: U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Indian Health Service (IHS).
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Subedi, P., Palma-Gudiel, H., Fiehn, O. et al. Lipidomics profiling of biological aging in American Indians: the Strong Heart Family Study. GeroScience 45, 359–369 (2023). https://doi.org/10.1007/s11357-022-00638-9
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DOI: https://doi.org/10.1007/s11357-022-00638-9