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GeroScience

, Volume 40, Issue 5–6, pp 497–511 | Cite as

Meta-analysis identifies mitochondrial DNA sequence variants associated with walking speed

  • Todd M. ManiniEmail author
  • Thomas W. Buford
  • John A. Kairalla
  • Mary M. McDermott
  • Carlos A. Vaz Fragoso
  • Roger A. Fielding
  • Fang-Chi Hsu
  • Neil Johannsen
  • Stephen Kritchevsky
  • Tamara B. Harris
  • Anne B. Newman
  • Steven R. Cummings
  • Abby C. King
  • Marco Pahor
  • Adam J. Santanasto
  • Gregory J. TranahEmail author
Original Article

Abstract

Declines in walking speed are associated with a variety of poor health outcomes including disability, comorbidity, and mortality. While genetic factors are putative contributors to variability in walking, few genetic loci have been identified for this trait. We examined the role of mitochondrial genomic variation on walking speed by sequencing the entire mitochondrial DNA (mtDNA). Data were meta-analyzed from 1758 Lifestyle Interventions and Independence for Elders (LIFE) Study and replication data from 730 Health, Aging, and Body Composition (HABC) Study participants with baseline walking speed information. Participants were 69+ years old of diverse racial backgrounds (African, European, and other race/ethnic groups) and had a wide range of mean walking speeds [4–6 m (0.78–1.09 m/s) and 400 m (0.83–1.24 m/s)]. Meta-analysis across studies and racial groups showed that m.12705C>T, ND5 variant was significantly associated (p < 0.0001) with walking speed at both short and long distances. Replication and meta-analysis also identified statistically significant walking speed associations (p < 0.0001) between the m.5460.G>A, ND2 and m.309C>CT, HV2 variants at short and long distances, respectively. All results remained statistically significant after multiple comparisons adjustment for 499 mtDNA variants. The m.12705C>T variant can be traced to the beginnings of human global migration and that cells carrying this variant display altered tRNA expression. Significant pooled effects related to stopping during the long-distance walk test were observed across OXPHOS complexes I (p = 0.0017) and III (p = 0.0048). These results suggest that mtDNA-encoded variants are associated with differences in walking speed among older adults, potentially identifying those at risk of developing mobility impairments.

Keywords

Gait speed Aging Mobility Disability Energy 

Notes

Author contributions

TMM and GJT were responsible for the study concept, obtained funding, and created initial draft of manuscript and results; GJT and JAK: statistical analyses; MP: PI of the LIFE study and obtained funding for the LIFE study. Authors MMM, CAF, RAF, FH, NJ, SK, TBH, ABN, SRC, ACK, MP, and AJS were responsible for data collection, critical revisions of content, and approval of final version for publication; GJT and JAK had full access to all of the data (including statistical reports and tables) and can take responsibility for the integrity of the data and the accuracy of the data analysis.

Funding information

This work is primarily supported by R01HL121023. The Lifestyle Interventions and Independence for Elders Study was funded by a National Institutes of Health/National Institute on Aging Cooperative Agreement #UO1 AG22376 and a supplement from the National Heart, Lung, and Blood Institute 3U01AG022376- 05A2S and sponsored in part by the Intramural Research Program, National Institute on Aging, NIH. The research is partially supported by the Claude D. Pepper Older Americans Independence Centers at the University of Florida (1 P30 AG028740), Wake Forest University (1 P30 AG21332), Tufts University (1P30AG031679), University of Pittsburgh (P30 AG024827), and the NIH/NCRR CTSA at Stanford University (UL1 RR025744); Tufts University is also supported by the Boston Rehabilitation Outcomes Center (1R24HD065688-01A1). LIFE investigators are also partially supported by the following: Dr. Carlos Fragoso (Spirometry Reading Center, Yale University) is the recipient of a Career Development Award from the Department of Veterans Affairs. Dr. Roger Fielding (Tufts University) is partially supported by the U.S. Department of Agriculture, under agreement No. 58-1950-0-014. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Dept of Agriculture. This Health, Aging, and Body Composition Study was supported in part by the Intramural Research Program of the NIH, National Institute on Aging, Contracts N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106; National Institutes of Health grants R01-AG028050, R03-AG032498, R01-NR012459, Z01A6000932, and R01-HL121023; and grants from the Research and Education Leadership Committee of the CPMC Foundation and the L. K. Whittier Foundation.

Compliance with ethical standards

The institutional review board at all field sites approved each study protocol, and written informed consent was obtained from all study participants.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249CrossRefGoogle Scholar
  2. Bar-Am O, Weinreb O, Amit T, Youdim MB (2009) The novel cholinesterase-monoamine oxidase inhibitor and antioxidant, ladostigil, confers neuroprotection in neuroblastoma cells and aged rats. J Mol Neurosci 37:135–145CrossRefGoogle Scholar
  3. Ben-Avraham D, Karasik D, Verghese J, Lunetta KL, Smith JA, Eicher JD, Vered R, Deelen J, Arnold AM, Buchman AS, Tanaka T, Faul JD, Nethander M, Fornage M, Adams HH, Matteini AM, Callisaya ML, Smith AV, Yu L, de Jager PL, Evans DA, Gudnason V, Hofman A, Pattie A, Corley J, Launer LJ, Knopman DS, Parimi N, Turner ST, Bandinelli S, Beekman M, Gutman D, Sharvit L, Mooijaart SP, Liewald DC, Houwing-Duistermaat JJ, Ohlsson C, Moed M, Verlinden VJ, Mellström D, van der Geest JN, Karlsson M, Hernandez D, McWhirter R, Liu Y, Thomson R, Tranah GJ, Uitterlinden AG, Weir DR, Zhao W, Starr JM, Johnson AD, Ikram MA, Bennett DA, Cummings SR, Deary IJ, Harris TB, Kardia SLR, Mosley TH, Srikanth VK, Windham BG, Newman AB, Walston JD, Davies G, Evans DS, Slagboom EP, Ferrucci L, Kiel DP, Murabito JM, Atzmon G (2017) The complex genetics of gait speed: genome-wide meta-analysis approach. Aging 9:209–246.  https://doi.org/10.18632/aging.101151 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Biffi A, Anderson CD, Nalls MA, Rahman R, Sonni A, Cortellini L, Rost NS, Matarin M, Hernandez DG, Plourde A, de Bakker PIW, Ross OA, Greenberg SM, Furie KL, Meschia JF, Singleton AB, Saxena R, Rosand J (2010) Principal-component analysis for assessment of population stratification in mitochondrial medical genetics. Am J Hum Genet 86:904–917CrossRefGoogle Scholar
  5. Blackwell DL, Lucas JW, Clarke TC (2014) Summary health statistics for U.S. adults: national health interview survey, 2012. Vital Health Stat 10:1–161Google Scholar
  6. Brand MD (2010) The sites and topology of mitochondrial superoxide production. Exp Gerontol 45:466–472.  https://doi.org/10.1016/j.exger.2010.01.003 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Cantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22.  https://doi.org/10.1016/j.ajhg.2009.11.017 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Carmelli D, Kelly-Hayes M, Wolf PA, Swan GE, Jack LM, Reed T, Guralnik JM (2000) The contribution of genetic influences to measures of lower-extremity function in older male twins. J Gerontol A Biol Sci Med Sci 55:B49–B53CrossRefGoogle Scholar
  9. Civitarese AE, Carling S, Heilbronn LK, Hulver MH, Ukropcova B, Deutsch WA, Smith SR, Ravussin E, CALERIE Pennington Team (2007) Calorie restriction increases muscle mitochondrial biogenesis in healthy humans. PLoS Med 4:e76CrossRefGoogle Scholar
  10. Cohen T, Levin L, Mishmar D (2016) Ancient out-of-Africa mitochondrial DNA variants associate with distinct mitochondrial gene expression patterns. PLoS Genet 12:e1006407.  https://doi.org/10.1371/journal.pgen.1006407 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Committee on the Future Health Care Workforce for Older Americans IoM (2008) Retooling for an aging America: building the health care workforce. National Academies Press, Washington, DCGoogle Scholar
  12. Cooper GM, Stone EA, Asimenos G, Program NCS, Green ED, Batzoglou S, Sidow A (2005) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 15:901–913.  https://doi.org/10.1101/gr.3577405 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Donati A, Cavallini G, Paradiso C, Vittorini S, Pollera M, Gori Z, Bergamini E (2001) Age-related changes in the regulation of autophagic proteolysis in rat isolated hepatocytes. J Gerontol A Biol Sci Med Sci 56:B288–B293CrossRefGoogle Scholar
  14. Efremov RG, Sazanov LA (2011) Structure of the membrane domain of respiratory complex I. Nature 476:414–420CrossRefGoogle Scholar
  15. Evangelou E, Kerkhof HJ, Styrkarsdottir U, Ntzani EE, Bos SD, Esko T, Evans DS, Metrustry S, Panoutsopoulou K, Ramos YFM, Thorleifsson G, Tsilidis KK, arcOGEN Consortium, Arden N, Aslam N, Bellamy N, Birrell F, Blanco FJ, Carr A, Chapman K, Day-Williams AG, Deloukas P, Doherty M, Engström G, Helgadottir HT, Hofman A, Ingvarsson T, Jonsson H, Keis A, Keurentjes JC, Kloppenburg M, Lind PA, McCaskie A, Martin NG, Milani L, Montgomery GW, Nelissen RGHH, Nevitt MC, Nilsson PM, Ollier WER, Parimi N, Rai A, Ralston SH, Reed MR, Riancho JA, Rivadeneira F, Rodriguez-Fontenla C, Southam L, Thorsteinsdottir U, Tsezou A, Wallis GA, Wilkinson JM, Gonzalez A, Lane NE, Lohmander LS, Loughlin J, Metspalu A, Uitterlinden AG, Jonsdottir I, Stefansson K, Slagboom PE, Zeggini E, Meulenbelt I, Ioannidis JPA, Spector TD, van Meurs JBJ, Valdes AM (2014) A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip. Ann Rheum Dis 73:2130–2136.  https://doi.org/10.1136/annrheumdis-2012-203114 CrossRefPubMedGoogle Scholar
  16. Feng GF, Zhang J, Feng LM, Shen NX, Li LJ, Zhu YM (2013) Mitochondrial DNA haplogroup associated with sperm motility in the Han population. Asian J Androl 15:630–633.  https://doi.org/10.1038/aja.2013.83 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Fielding RA, Rejeski WJ, Blair S, Church T, Espeland MA, Gill TM, Guralnik JM, Hsu FC, Katula J, King AC, Kritchevsky SB, McDermott MM, Miller ME, Nayfield S, Newman AB, Williamson JD, Bonds D, Romashkan S, Hadley E, Pahor M; LIFE Research Group (2011) The Lifestyle Interventions and Independence for Elders Study: design and methods. J Gerontol A Biol Sci Med Sci 66(11):1226-37.  https://doi.org/10.1093/gerona/glr123
  18. Gogele M et al (2012) Methods for meta-analyses of genome-wide association studies: critical assessment of empirical evidence. Am J Epidemiol 175:739–749.  https://doi.org/10.1093/aje/kwr385 CrossRefPubMedGoogle Scholar
  19. de Grey AD (1997) A proposed refinement of the mitochondrial free radical theory of aging. Bioessays 19:161–166.  https://doi.org/10.1002/bies.950190211 CrossRefPubMedGoogle Scholar
  20. Guarente L (2008) Mitochondria—a nexus for aging, calorie restriction, and sirtuins? Cell 132:171–176CrossRefGoogle Scholar
  21. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB (1995) Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med 332:556–561CrossRefGoogle Scholar
  22. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560.  https://doi.org/10.1136/bmj.327.7414.557 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Ioannidis JP, Patsopoulos NA, Evangelou E (2007) Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2:e841.  https://doi.org/10.1371/journal.pone.0000841 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Katzman SM, Strotmeyer ES, Nalls MA, Zhao Y, Mooney S, Schork N, Newman AB, Harris TB, Yaffe K, Cummings SR, Liu Y, Tranah GJ, for the Health, Aging, and Body Composition Study (2014) Mitochondrial DNA sequence variation associated with peripheral nerve function in the elderly. J Gerontol A Biol Sci Med Sci 70:1400–1408.  https://doi.org/10.1093/gerona/glu175 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Kraft P, Zeggini E, Ioannidis JP (2009) Replication in genome-wide association studies. Stat Sci 24:561–573.  https://doi.org/10.1214/09-STS290 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Lai CS, Wu JC, Ho CT, Pan MH (2017) Chemoprevention of obesity by dietary natural compounds targeting mitochondrial regulation. Mol Nutr Food Res 61.  https://doi.org/10.1002/mnfr.201600721
  27. Lam ET, Bracci PM, Holly EA, Chu C, Poon A, Wan E, White K, Kwok PY, Pawlikowska L, Tranah GJ (2012) Mitochondrial DNA sequence variation and risk of pancreatic cancer. Cancer Res 72:686–695.  https://doi.org/10.1158/0008-5472.CAN-11-1682 CrossRefPubMedGoogle Scholar
  28. Langston JW, Ballard PA Jr (1983) Parkinson’s disease in a chemist working with 1-methyl-4-phenyl-1,2,5,6-tetrahydropyridine. N Engl J Med 309:310PubMedGoogle Scholar
  29. Leitao-Rocha A, Guedes-Dias P, Pinho BR, Oliveira JM (2015) Trends in mitochondrial therapeutics for neurological disease. Curr Med Chem 22:2458–2467CrossRefGoogle Scholar
  30. Li GM (2003) DNA mismatch repair and cancer. Front Biosci 8:d997–1017Google Scholar
  31. Li B, Leal SM (2009) Discovery of rare variants via sequencing: implications for the design of complex trait association studies. PLoS Genet 5:e1000481.  https://doi.org/10.1371/journal.pgen.1000481 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Li M, Schonberg A, Schaefer M, Schroeder R, Nasidze I, Stoneking M (2010) Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes. Am J Hum Genet 87:237–249CrossRefGoogle Scholar
  33. Menshikova EV, Ritov VB, Fairfull L, Ferrell RE, Kelley DE, Goodpaster BH (2006) Effects of exercise on mitochondrial content and function in aging human skeletal muscle. J Gerontol A Biol Sci Med Sci 61:534–540CrossRefGoogle Scholar
  34. Merriwether DA, Clark AG, Ballinger SW, Schurr TG, Soodyall H, Jenkins T, Sherry ST, Wallace DC (1991) The structure of human mitochondrial DNA variation. J Mol Evol 33:543–555CrossRefGoogle Scholar
  35. MITOMAP (2011) A Human Mitochondrial Genome Database. http://www.mitomap.org
  36. Moilanen JS, Finnila S, Majamaa K (2003) Lineage-specific selection in human mtDNA: lack of polymorphisms in a segment of MTND5 gene in haplogroup. J Mol Biol Evol 20:2132–2142.  https://doi.org/10.1093/molbev/msg230 CrossRefGoogle Scholar
  37. Newman AB, Simonsick EM, Naydeck BL, Boudreau RM, Kritchevsky SB, Nevitt MC, Pahor M, Satterfield S, Brach JS, Studenski SA, Harris TB (2006) Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. Jama 295:2018–2026CrossRefGoogle Scholar
  38. Ng PC, Henikoff S (2006) Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet 7:61–80CrossRefGoogle Scholar
  39. Nguyen TT, Pahl R, Schafer H (2009) Optimal robust two-stage designs for genome-wide association studies. Ann Hum Genet 73:638–651.  https://doi.org/10.1111/j.1469-1809.2009.00544.x CrossRefPubMedGoogle Scholar
  40. Ortega-Alonso A et al (2006) A twin study on the heritability of walking ability among older women. J Gerontol Ser A Biol Med Sci 61:1082–1085CrossRefGoogle Scholar
  41. van Oven M, Kayser M (2009) Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum Mutat 30:E386–E394.  https://doi.org/10.1002/humu.20921 CrossRefPubMedGoogle Scholar
  42. Perera S, Mody SH, Woodman RC, Studenski SA (2006) Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc 54:743–749.  https://doi.org/10.1111/j.1532-5415.2006.00701.x CrossRefPubMedGoogle Scholar
  43. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A (2010) Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20:110–121CrossRefGoogle Scholar
  44. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909.  https://doi.org/10.1038/ng1847 CrossRefGoogle Scholar
  45. Ramsay RR, Singer TP (1986) Energy-dependent uptake of N-methyl-4-phenylpyridinium, the neurotoxic metabolite of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, by mitochondria. J Biol Chem 261:7585–7587PubMedGoogle Scholar
  46. Schork NJ, Murray SS, Frazer KA, Topol EJ (2009) Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 19:212–219.  https://doi.org/10.1016/j.gde.2009.04.010 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two-stage genome-wide association studies. Genet Epidemiol 31:776–788.  https://doi.org/10.1002/gepi.20240 CrossRefPubMedGoogle Scholar
  48. Statistics FIFoA-R (2008) Older Americans 2008: key indicators of well-being. U.S. Government Printing Office, Washington, DCGoogle Scholar
  49. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor EB, Nevitt M, Visser M, Kritchevsky S, Badinelli S, Harris T, Newman AB, Cauley J, Ferrucci L, Guralnik J (2011) Gait speed and survival in older adults. JAMA 305:50–58.  https://doi.org/10.1001/jama.2010.1923 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Suissa S, Wang Z, Poole J, Wittkopp S, Feder J, Shutt TE, Wallace DC, Shadel GS, Mishmar D (2009) Ancient mtDNA genetic variants modulate mtDNA transcription and replication. PLoS Genet 5:e1000474CrossRefGoogle Scholar
  51. Takakusaki K (2013) Neurophysiology of gait: from the spinal cord to the frontal lobe. Mov Disord 28:1483–1491.  https://doi.org/10.1002/mds.25669 CrossRefPubMedGoogle Scholar
  52. Tang S, Huang T (2010) Characterization of mitochondrial DNA heteroplasmy using a parallel sequencing system. Biotechniques 48:287–296CrossRefGoogle Scholar
  53. Tarnopolsky MA, Simon DK, Roy BD, Chorneyko K, Lowther SA, Johns DR, Sandhu JK, Li Y, Sikorska M (2004) Attenuation of free radical production and paracrystalline inclusions by creatine supplementation in a patient with a novel cytochrome b mutation. Muscle Nerve 29:537–547CrossRefGoogle Scholar
  54. Tranah GJ (2011) Mitochondrial-nuclear epistasis: implications for human aging and longevity. Ageing Res Rev 10:238–252.  https://doi.org/10.1016/j.arr.2010.06.003 CrossRefPubMedGoogle Scholar
  55. Tranah GJ, Manini TM, Lohman KK, Nalls MA, Kritchevsky S, Newman AB, Harris TB, Miljkovic I, Biffi A, Cummings SR, Liu Y (2011) Mitochondrial DNA variation in human metabolic rate and energy expenditure. Mitochondrion 11:855–861.  https://doi.org/10.1016/j.mito.2011.04.005 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Tranah GJ, Lam ET, Katzman SM, Nalls MA, Zhao Y, Evans DS, Yokoyama JS, Pawlikowska L, Kwok PY, Mooney S, Kritchevsky S, Goodpaster BH, Newman AB, Harris TB, Manini TM, Cummings SR, Health, Aging and Body Composition Study (2012) Mitochondrial DNA sequence variation is associated with free-living activity energy expenditure in the elderly. Biochim Biophys Acta 1817:1691–1700.  https://doi.org/10.1016/j.bbabio.2012.05.012 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Tranah GJ, Yaffe K, Katzman SM, Lam ET, Pawlikowska L, Kwok PY, Schork NJ, Manini TM, Kritchevsky S, Thomas F, Newman AB, Harris TB, Coleman AL, Gorin MB, Helzner EP, Rowbotham MC, Browner WS, Cummings SR, for the Health, Aging and Body Composition Study (2015) Mitochondrial DNA Heteroplasmy associations with neurosensory and mobility function in elderly adults. J Gerontol A Biol Sci Med Sci 70:1418–1424.  https://doi.org/10.1093/gerona/glv097 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, Harris TB (2002) Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the health, aging and body composition study. J Am Geriatr Soc 50:897–904CrossRefGoogle Scholar
  59. Wallace DC (2010) Colloquium paper: bioenergetics, the origins of complexity, and the ascent of man. Proc Natl Acad Sci U S A 107 Suppl 2:8947–8953CrossRefGoogle Scholar
  60. Wallace DC, Fan W, Procaccio V (2010) Mitochondrial energetics and therapeutics. Annu Rev Pathol 5:297–348.  https://doi.org/10.1146/annurev.pathol.4.110807.092314 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Weinreb O, Amit T, Bar-Am O, Yogev-Falach M, Youdim MB (2008) The neuroprotective mechanism of action of the multimodal drug ladostigil. Front Biosci 13:5131–5137CrossRefGoogle Scholar
  62. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics (Oxford, England) 26:2190–2191CrossRefGoogle Scholar
  63. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X (2010) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93CrossRefGoogle Scholar
  64. Yang Y, Shou Z, Zhang P, He Q, Xiao H, Xu Y, Li C, Chen J (2008) Mitochondrial DNA haplogroup R predicts survival advantage in severe sepsis in the Han population. Genet Med 10:187–192.  https://doi.org/10.1097/GIM.0b013e318163c343 CrossRefPubMedGoogle Scholar
  65. Yang Y, Zhang P, Lv R, He Q, Zhu Y, Yang X, Chen J (2011) Mitochondrial DNA haplogroup R in the Han population and recovery from septic encephalopathy. Intensive Care Med 37:1613–1619.  https://doi.org/10.1007/s00134-011-2319-9 CrossRefPubMedGoogle Scholar
  66. Youdim MB, Buccafusco JJ (2005) Multi-functional drugs for various CNS targets in the treatment of neurodegenerative disorders. Trends Pharmacol Sci 26:27–35CrossRefGoogle Scholar
  67. Zeggini E, Ioannidis JP (2009) Meta-analysis in genome-wide association studies. Pharmacogenomics 10:191–201.  https://doi.org/10.2217/14622416.10.2.191 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© American Aging Association 2018

Authors and Affiliations

  • Todd M. Manini
    • 1
    Email author
  • Thomas W. Buford
    • 2
  • John A. Kairalla
    • 3
  • Mary M. McDermott
    • 4
  • Carlos A. Vaz Fragoso
    • 5
  • Roger A. Fielding
    • 6
  • Fang-Chi Hsu
    • 7
  • Neil Johannsen
    • 8
  • Stephen Kritchevsky
    • 9
  • Tamara B. Harris
    • 10
  • Anne B. Newman
    • 11
  • Steven R. Cummings
    • 12
  • Abby C. King
    • 13
  • Marco Pahor
    • 1
  • Adam J. Santanasto
    • 11
  • Gregory J. Tranah
    • 12
    Email author
  1. 1.Department of Aging and Geriatric ResearchUniversity of FloridaGainesvilleUSA
  2. 2.Department of MedicineUniversity of Alabama at BirminghamBirminghamUSA
  3. 3.Department of BiostatisticsUniversity of FloridaGainesvilleUSA
  4. 4.General Internal Medicine and Geriatrics and Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoUSA
  5. 5.Department of Internal MedicineYale University School of MedicineNew HavenUSA
  6. 6.Gerald J. and Dorothy R. Friedman School of Nutrition Science and PolicyTufts UniversityBostonUSA
  7. 7.The Department of Biostatistical Sciences, Division of Public Health SciencesWake Forest School of MedicineWinston-SalemUSA
  8. 8.Preventive Medicine DepartmentPennington Biomedical Research CenterBaton RougeUSA
  9. 9.Sticht Center on AgingWake Forest School of MedicineWinston-SalemUSA
  10. 10.Intramural Research Program, Laboratory of Epidemiology and Population SciencesNational Institute on AgingBethesdaUSA
  11. 11.Department of EpidemiologyUniversity of PittsburghPittsburghUSA
  12. 12.California Pacific Medical Center Research InstituteSan FranciscoUSA
  13. 13.Department of Health Research and Policy – EpidemiologyStanford University School of MedicineStanfordUSA

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