Skip to main content

Advertisement

Log in

Biomarkers of mitochondrial dysfunction and inflammaging in older adults and blood pressure variability

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Most physiopathological mechanisms underlying blood pressure variability (BPV) are implicated in aging. Vascular aging is associated with chronic low-grade inflammation occurring in late life, known as “inflammaging” and the hallmark “mitochondrial dysfunction” due to age-related stress. We aimed to determine whether plasma levels of the pleiotropic stress-related mitokine growth/differentiation factor 15 (GDF-15) and two inflammatory biomarkers, interleukin 6 (IL-6) and tumor necrosis factor receptor 1 (TNFR-1), are associated with visit-to-visit BPV in a population of community-dwelling older adults. The study population consisted of 1096 community-dwelling participants [median age 75 (72–78) years; 699 females, 63.7%] aged ≥ 70 years from the MAPT study. Plasma blood sample was collected 12 months after enrolment and BP was assessed up to seven times over a 4-year period. Systolic (SBPV) and diastolic BPV (DBPV) were determined through several indicators taking into account BP change over time, the order of measurements and formulas independent of mean BP levels. Higher values of GDF-15 were significantly associated with increased SBPV (all indicators) after adjustment for relevant covariates [adjusted 1-SD increase in GDF-15: β (SE) = 0.07 (0.04), p < 0.044, for coefficient of variation%]. GDF-15 levels were not associated with DBPV. No significant associations were found between IL-6 and BPV, whereas TNFR1 was only partially related to DBPV. Unlike inflammation biomarkers, higher GDF-15 levels were associated with greater SBPV. Our findings support the age-related process of mitochondrial dysfunction underlying BP instability, suggesting that BPV might be a potential marker of aging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Abramson JL, Lewis C, Murrah NV, et al. Relation of C-reactive protein and tumor necrosis factor-alpha to ambulatory blood pressure variability in healthy adults. Am J Cardiol. 2006;98:649–52. https://doi.org/10.1016/J.AMJCARD.2006.03.045.

    Article  CAS  Google Scholar 

  2. Adela R, Banerjee SK. GDF-15 as a target and biomarker for diabetes and cardiovascular diseases: a translational prospective. J Diabetes Res 2015. https://doi.org/10.1155/2015/490842.

  3. Alpérovitch A, Blachier M, Soumaré A, et al. Blood pressure variability and risk of dementia in an elderly cohort, the Three-City Study. Alzheimer’s Dement. 2014;10:S330–7. https://doi.org/10.1016/j.jalz.2013.05.1777.

    Article  Google Scholar 

  4. Andrieu S, Guyonnet S, Coley N, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017;16:377–89. https://doi.org/10.1016/S1474-4422(17)30040-6.

    Article  CAS  Google Scholar 

  5. Bao M, Song Y, Cai J, et al. Blood pressure variability is associated with hearing and hearing loss: a population-based study in males. Int J Hypertens. 2019. https://doi.org/10.1155/2019/9891025.

  6. Bencivenga L, De Souto Barreto P, Rolland Y, et al. Blood pressure variability: a potential marker of aging. Ageing Res Rev. 2022:101677. https://doi.org/10.1016/J.ARR.2022.101677.

  7. Bilo G, Parati G. Blood pressure variability and kidney disease: another vicious circle? J Hypertens. 2018;36:1019–21. https://doi.org/10.1097/HJH.0000000000001707.

    Article  CAS  Google Scholar 

  8. Brodsky SV, Gealekman O, Chen J, et al. Prevention and reversal of premature endothelial cell senescence and vasculopathy in obesity-induced diabetes by ebselen. Circ Res. 2004;94:377–84. https://doi.org/10.1161/01.RES.0000111802.09964.EF.

    Article  CAS  Google Scholar 

  9. Chowdhury EK, Owen A, Krum H, et al. Systolic blood pressure variability is an important predictor of cardiovascular outcomes in elderly hypertensive patients. J Hypertens. 2014;32:525–33. https://doi.org/10.1097/HJH.0000000000000028.

    Article  CAS  Google Scholar 

  10. Chung HK, Kim JT, Kim HW, et al. (2017a) GDF15 deficiency exacerbates chronic alcohol- and carbon tetrachloride-induced liver injury. Sci Rep. 2017;71(7):1–13. https://doi.org/10.1038/s41598-017-17574-w.

    Article  CAS  Google Scholar 

  11. Chung HK, Ryu D, Kim KS, et al. Growth differentiation factor 15 is a myomitokine governing systemic energy homeostasis. J Cell Biol. 2017;216:149–65. https://doi.org/10.1083/JCB.201607110.

    Article  CAS  Google Scholar 

  12. Conte M, Giuliani C, Chiariello A, et al. GDF15, an emerging key player in human aging. Ageing Res Rev. 2022;75:101569. https://doi.org/10.1016/J.ARR.2022.101569.

    Article  CAS  Google Scholar 

  13. Conte M, Martucci M, Mosconi G, et al. GDF15 plasma level is inversely associated with level of physical activity and correlates with markers of inflammation and muscle weakness. Front Immunol. 2020;11:915. https://doi.org/10.3389/FIMMU.2020.00915/BIBTEX.

    Article  CAS  Google Scholar 

  14. Conte M, Ostan R, Fabbri C, et al. Human aging and longevity are characterized by high levels of mitokines. J Gerontol Ser A. 2019;74:600–7. https://doi.org/10.1093/GERONA/GLY153.

    Article  CAS  Google Scholar 

  15. Coppé JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol. 2010;5:99. https://doi.org/10.1146/ANNUREV-PATHOL-121808-102144.

    Article  Google Scholar 

  16. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:16–31.

    Article  Google Scholar 

  17. Csete M, Doyle J. Bow ties, metabolism and disease. Trends Biotechnol. 2004;22:446–50. https://doi.org/10.1016/J.TIBTECH.2004.07.007.

    Article  CAS  Google Scholar 

  18. Del Giorno R, Balestra L, Heiniger PS, Gabutti L. Blood pressure variability with different measurement methods: reliability and predictors. A proof of concept cross sectional study in elderly hypertensive hospitalized patients. Medicine (Baltimore). 2019;98. https://doi.org/10.1097/MD.0000000000016347.

  19. Donato AJ, Eskurza I, Silver AE, et al. Direct evidence of endothelial oxidative stress with aging in humans: relation to impaired endothelium-dependent dilation and upregulation of nuclear factor-κB. Circ Res. 2007;100:1659–66. https://doi.org/10.1161/01.RES.0000269183.13937.e8.

    Article  CAS  Google Scholar 

  20. Donato AJ, Machin DR, Lesniewski LA. Mechanisms of dysfunction in the aging vasculature and role in age-related disease. Circ Res. 2018;123:825–48. https://doi.org/10.1161/CIRCRESAHA.118.312563.

    Article  CAS  Google Scholar 

  21. Emmerson PJ, Duffin KL, Chintharlapalli S, Wu X. GDF15 and growth control. Front Physiol. 2018;9:1712. https://doi.org/10.3389/FPHYS.2018.01712/BIBTEX.

    Article  Google Scholar 

  22. Ernst ME, Chowdhury EK, Beilin LJ, et al. Long-term blood pressure variability and risk of cardiovascular disease events among community-dwelling elderly. Hypertension. 2020:1945–1952. https://doi.org/10.1161/HYPERTENSIONAHA.120.16209.

  23. Franceschi C, Bonafè M, Valensin S, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci 2000;908:244–54. https://doi.org/10.1111/j.1749-6632.2000.tb06651.x.

  24. Franceschi C, Garagnani P, Parini P, et al. Inflammaging: a new immune–metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14:576–90.

    Article  CAS  Google Scholar 

  25. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822–32. https://doi.org/10.1038/S41591-019-0675-0.

    Article  CAS  Google Scholar 

  26. Giannattasio C, Ferrari AU, Mancia G. Alterations in neural cardiovascular control mechanisms with ageing. J Hyperten Suppl. 1994;12(6):S13–7

  27. Guo Y, Ayers JL, Carter KT, et al. Senescence-associated tissue microenvironment promotes colon cancer formation through the secretory factor GDF15. Aging Cell. 2019;18:e13013. https://doi.org/10.1111/ACEL.13013.

    Article  CAS  Google Scholar 

  28. Guralnik JM, Simonsick EM, Ferrucci L, et al (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49. https://doi.org/10.1093/GERONJ/49.2.M85.

  29. Hartmann A, Hartmann C, Secci R, et al. Ranking biomarkers of aging by citation profiling and effort scoring. Front Genet. 2021;12:686320. https://doi.org/10.3389/fgene.2021.686320

  30. Hashimoto Y, Kaji A, Sakai R, et al. Sarcopenia is associated with blood pressure variability in older patients with type 2 diabetes: a cross-sectional study of the KAMOGAWA-DM cohort study. Geriatr Gerontol Int. 2018;18:1345–9. https://doi.org/10.1111/ggi.13487.

    Article  Google Scholar 

  31. Hata Y, Muratani H, Kimura Y, et al. Office blood pressure variability as a predictor of acute myocardial infarction in elderly patients receiving antihypertensive therapy. J Hum Hypertens. 2002;16:141–6. https://doi.org/10.1038/sj.jhh.1001301.

    Article  CAS  Google Scholar 

  32. Havlik RJ, Foley DJ, Sayer B, et al. Variability in midlife systolic blood pressure is related to late-life brain white matter lesions: the Honolulu-Asia aging study. Stroke. 2002;33:26–30. https://doi.org/10.1161/hs0102.101890.

    Article  Google Scholar 

  33. Hekimi S, Lapointe J, Wen Y. Taking a “good” look at free radicals in the aging process. Trends Cell Biol. 2011;21:569–76.

    Article  CAS  Google Scholar 

  34. Irigoyen MC, De Angelis K, dos Santos F, et al. Hypertension, blood pressure variability, and target organ lesion. Rep: Curr. Hypertens; 2016. p. 18.

    Google Scholar 

  35. Johann K, Kleinert M, Klaus S (2021) The role of GDF15 as a myomitokine. Cells 10. https://doi.org/10.3390/CELLS10112990.

  36. Joshipura KJ, Muñoz-Torres FJ, Campos M, et al (2018) Association between within-visit systolic blood pressure variability and development of pre-diabetes and diabetes among overweight/obese individuals. J Hum Hypertens 32. https://doi.org/10.1038/s41371-017-0009-y.

  37. Justice JN, Ferrucci L, Newman AB, et al. (2018) A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup. GeroScience. 2018;405(40):419–36. https://doi.org/10.1007/S11357-018-0042-Y.

    Article  Google Scholar 

  38. Kawai T, Ohishi M, Kamide K, et al. The impact of visit-to-visit variability in blood pressure on renal function. Hypertens Res. 2012;35:239–43. https://doi.org/10.1038/hr.2011.170.

    Article  Google Scholar 

  39. Kennedy BK, Berger SL, Brunet A, et al. Aging: a common driver of chronic diseases and a target for novel interventions. Cell. 2014;159:709. https://doi.org/10.1016/J.CELL.2014.10.039.

    Article  CAS  Google Scholar 

  40. Kim il K, Kang MG, Yoon SJ, et al. Relationship between within-visit blood pressure variability and skeletal muscle mass. J Nutr Heal Aging. 2019;23:79–83. https://doi.org/10.1007/s12603-018-1115-4.

    Article  Google Scholar 

  41. Kim K Il, Lee JH, Chang HJ, et al. Association between blood pressure variability and inflammatory marker in hypertensive patients. Circ J. 2008;72:293–8. https://doi.org/10.1253/circj.72.293.

  42. Kim KH, Lee MS. GDF15 as a central mediator for integrated stress response and a promising therapeutic molecule for metabolic disorders and NASH. Biochim Biophys acta Gen Subj. 2021:1865. https://doi.org/10.1016/J.BBAGEN.2020.129834.

  43. Kostis JB, Sedjro JE, Cabrera J, et al. Visit-to-visit blood pressure variability and cardiovascular death in the systolic hypertension in the elderly program. J Clin Hypertens. 2014;16:34. https://doi.org/10.1111/JCH.12230.

    Article  Google Scholar 

  44. Lambert JR, Whitson RJ, Iczkowski KA, et al. Reduced expression of GDF-15 is associated with atrophic inflammatory lesions of the prostate. Prostate. 2015;75:255–65. https://doi.org/10.1002/PROS.22911.

    Article  CAS  Google Scholar 

  45. Lattanzi S, Vernieri F, Silvestrini M. Blood pressure variability and neurocognitive functioning. J Clin Hypertens. 2018;20:645–7.

    Article  Google Scholar 

  46. Laurent S, Boutouyrie P. Visit-to-visit blood pressure variability: added ‘VALUE’ as a risk marker in low- and high-risk patients. Eur Heart J. 2018;39:2252–4. https://doi.org/10.1093/EURHEARTJ/EHY011.

  47. Li TC, Li CI, Liu CS, et al. Visit-to-visit blood pressure variability and hip fracture risk in older persons. Osteoporos Int. 2019;30:763–70. https://doi.org/10.1007/s00198-019-04899-7.

    Article  Google Scholar 

  48. López-Otín C, Blasco MA, Partridge L, et al. The hallmarks of aging. Cell. 2013;153:1194.

    Article  Google Scholar 

  49. Luan HH, Wang A, Hilliard BK, et al. GDF15 Is an inflammation-induced central mediator of tissue tolerance. Cell. 2019;178:1231-1244.e11. https://doi.org/10.1016/J.CELL.2019.07.033.

    Article  CAS  Google Scholar 

  50. Ma Y, Blacker D, Viswanathan A, et al. Visit-to-visit blood pressure variability, neuropathology, and cognitive decline. Neurology. 2021;96:e2812–23. https://doi.org/10.1212/WNL.0000000000012065.

    Article  Google Scholar 

  51. Mehlum MH, Liestøl K, Kjeldsen SE, et al. Blood pressure variability and risk of cardiovascular events and death in patients with hypertension and different baseline risks. Eur Heart J. 2018;39:2243–51. https://doi.org/10.1093/eurheartj/ehx760.

    Article  CAS  Google Scholar 

  52. Moon JS, Goeminne LJE, Kim JT, et al. Growth differentiation factor 15 protects against the aging-mediated systemic inflammatory response in humans and mice. Aging Cell. 2020;19:e13195. https://doi.org/10.1111/ACEL.13195.

    Article  CAS  Google Scholar 

  53. Ogliari G, Smit RAJ, Westendorp RGJ, et al. Visit-to-visit blood pressure variability and future functional decline in old age. J Hypertens. 2016;34:1544–50. https://doi.org/10.1097/HJH.0000000000000979.

    Article  CAS  Google Scholar 

  54. Paneni F, Diaz Cañestro C, Libby P, et al. The aging cardiovascular system: understanding it at the cellular and clinical levels. J Am Coll Cardiol. 2017;69:1952–67.

    Article  Google Scholar 

  55. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol. 2013;10:143–55.

    Article  Google Scholar 

  56. Parati G, Stergiou GS, Dolan E, Bilo G. Blood pressure variability: clinical relevance and application. J Clin Hypertens. 2018;20:1133–7. https://doi.org/10.1111/JCH.13304.

    Article  Google Scholar 

  57. Poortvliet RKE, Lloyd SM, Ford I, et al. Biological correlates of blood pressure variability in elderly at high risk of cardiovascular disease. Am J Hypertens. 2015;28:469–79. https://doi.org/10.1093/ajh/hpu181.

    Article  CAS  Google Scholar 

  58. Raffin J, Rolland Y, Fischer C, et al. Cross-sectional associations between cortical thickness and physical activity in older adults with spontaneous memory complaints: the MAPT Study. J Sport Heal Sci. 2021. https://doi.org/10.1016/J.JSHS.2021.01.011.

    Article  Google Scholar 

  59. Rothwell PM. Limitations of the usual blood-pressure hypothesis and importance of variability, instability, and episodic hypertension. Lancet. 2010;375:938–48.

    Article  Google Scholar 

  60. Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010;375:895–905. https://doi.org/10.1016/S0140-6736(10)60308-X.

    Article  Google Scholar 

  61. Rouch L, Cestac P, Sallerin B, et al. Visit-to-visit blood pressure variability is associated with cognitive decline and incident dementia. Hypertension. 2020;76:1280–8. https://doi.org/10.1161/HYPERTENSIONAHA.119.14553.

    Article  CAS  Google Scholar 

  62. Rouch L, De Souto BP, Hanon O, et al. Visit-to-visit blood pressure variability and incident frailty in older adults. J Gerontol - Ser A Biol Sci Med Sci. 2021;76:1369–75. https://doi.org/10.1093/gerona/glab112.

    Article  Google Scholar 

  63. Rouch L, Vidal JS, Hoang T, et al. Systolic blood pressure postural changes variability is associated with greater dementia risk. Neurology. 2020;95:e1932–40. https://doi.org/10.1212/WNL.0000000000010420.

    Article  CAS  Google Scholar 

  64. Sabayan B, Wijsman LW, Foster-Dingley JC, et al. Association of visit-to-visit variability in blood pressure with cognitive function in old age: prospective cohort study. BMJ. 2013;347. https://doi.org/10.1136/BMJ.F4600.

  65. Santoro A, Martucci M, Conte M, et al. Inflammaging, hormesis and the rationale for anti-aging strategies. Ageing Res Rev. 2020;64. https://doi.org/10.1016/J.ARR.2020.101142.

  66. Tai C, Sun Y, Dai N, et al. Prognostic significance of visit-to-visit systolic blood pressure variability: a meta-analysis of 77,299 patients. J Clin Hypertens. 2015;17:107–15. https://doi.org/10.1111/jch.12484.

    Article  Google Scholar 

  67. Tatasciore A, Zimarino M, Renda G, et al. Awake blood pressure variability, inflammatory markers and target organ damage in newly diagnosed hypertension. Hypertens Res. 2008;31:2137–46. https://doi.org/10.1291/hypres.31.2137.

    Article  CAS  Google Scholar 

  68. Taylor HL, Jacobs DR, Schucker B, et al. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis. 1978;31:741–55. https://doi.org/10.1016/0021-9681(78)90058-9.

    Article  CAS  Google Scholar 

  69. Ungvari Z, Tarantini S, Donato AJ, et al. Mechanisms of vascular aging. Circ Res. 2018;123:849–67. https://doi.org/10.1161/CIRCRESAHA.118.311378.

    Article  CAS  Google Scholar 

  70. Van Epps P, Oswald D, Higgins PA, et al. Frailty has a stronger association with inflammation than age in older veterans. Immun Ageing. 2016;13. https://doi.org/10.1186/S12979-016-0082-Z.

  71. van Middelaar T, Richard E, Moll van Charante EP, et al. Visit-to-visit blood pressure variability and progression of white matter hyperintensities among older people with hypertension. J Am Med Dir Assoc. 2019;20:1175-1177.e1. https://doi.org/10.1016/j.jamda.2019.04.003.

    Article  Google Scholar 

  72. Varadhan R, Yao W, Matteini A, et al. Simple biologically informed inflammatory index of two serum cytokines predicts 10 year all-cause mortality in older adults. J Gerontol A Biol Sci Med Sci. 2014;69:165–73. https://doi.org/10.1093/GERONA/GLT023.

    Article  CAS  Google Scholar 

  73. Vellas B, Carrie I, Gillette-Guyonnet S, et al. MAPT Study: a multidomain approach for preventing Alzheimer’s disease: design and baseline data. J Prev Alzheimer’s Dis. 2014;1:13.

    CAS  Google Scholar 

  74. Yatsuga S, Fujita Y, Ishii A, et al. Growth differentiation factor 15 as a useful biomarker for mitochondrial disorders. Ann Neurol. 2015;78:814–23. https://doi.org/10.1002/ANA.24506.

    Article  CAS  Google Scholar 

  75. Yoo JE, Yoon JW, Park HE, et al. Blood pressure variability and the risk of fracture: a nationwide cohort study. J Clin Endocrinol Metab. 2021. https://doi.org/10.1210/CLINEM/DGAB856.

Download references

Acknowledgements

Dr. Leonardo Bencivenga has been supported by the research grant provided by the Cardiopath PhD program, the research grant provided by the FDIME and the STAR PLUS Research Grant provided by University of Naples Federico II and Compagnia di San Paolo.

Funding

The present work was performed in the context of the INSPIRE Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856).

Author information

Authors and Affiliations

Authors

Consortia

Contributions

Study concept and design: LB, YR, PDSB, LR. Acquisition, analysis and interpretation of data: LB, MS, LR. They had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drafting of the manuscript: LB, PDSB, LR. Critical revision of the manuscript for important intellectual content: YR, LM, BV, PDSB. All authors approved the final version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Leonardo Bencivenga.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

MAPT/DSA group

MAPT Study Group. Principal investigator: Bruno Vellas (Toulouse); coordination: Sophie Guyonnet; project leader: Isabelle Carrié; CRA: Lauréane Brigitte; investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, and Hélène Villars; psychologists: Emeline Combrouze, Carole Badufle, and Audrey Zueras; methodology, statistical analysis, and data management: Sandrine Andrieu, Christelle Cantet, and Christophe Morin; multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), and Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle and Sylvie Belleville, Brigitte Gilbert, and Francine Fontaine from the University of Montreal. Co-investigators in associated centers: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristel Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI Group: Carole Dufouil (Bordeaux); Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET Scans Group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics Group: Laurent Molinier, Hélène Derumeaux, and Nadège Costa (Toulouse). Biological Sample Collection: Bertrand Perret, Claire Vinel, and Sylvie Caspar-Bauguil (Toulouse). Safety Management: Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, and Nicola Coley.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 18.4 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bencivenga, L., Strumia, M., Rolland, Y. et al. Biomarkers of mitochondrial dysfunction and inflammaging in older adults and blood pressure variability. GeroScience 45, 797–809 (2023). https://doi.org/10.1007/s11357-022-00697-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-022-00697-y

Keywords

Navigation