#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#
preLekára.sk / Journals / Journal of Czech Physicians / 2023 - 5

Biomarkers of aging – current state of knowledge

Česká verzia

Authors: Pavel Borský 1;  Drahomíra Holmannová 1;  Lenka Borská 1;  Zdeněk Fiala 1;  Libor Hruška 2;  Monika Esterková 1;  Helena Párová 3;  Avni Singh 1;  Gabriela Počtová 1;  Otto Kučera 4
Authors place of work: Ústav preventivního lékařství LF UK v Hradci Králové 1;  Klinika onkologie a radioterapie LF UK a FN Hradec Králové 2;  Ústav klinické biochemie a diagnostiky LF UK a FN Hradec Králové 3;  Ústav fyziologie LF UK v Hradci Králové 4
Published in the journal: Čas. Lék. čes. 2023; 162: 194-202
Category: Review Article

Summary

Aging is a process of gradual decline in the functional capacity of the human body that leads to a significant increase in the risk of death over time. Although it is a process universal to all animals, its rate is not the same. Biomarkers of aging aim to better describe the aging process at the level of the individual, organ, tissue, or single cell. They are used to estimate the rate of aging and predict the probability of death. They are good indication of the current state of the organism and are more accurate in predicting a person's susceptibility to disease, its progression and the likelihood of complications and death.

Simple biomarkers measure only one parameter or a narrow group of related parameters that have a known association with age, in human or in a laboratory model. They can be divided into molecular (based on features of aging), functional (describing decreasing functional capacity during aging) and anthropometric (describing structural changes).

Composite biomarkers are the most comprehensive way of measuring biological age. They combine a large amount of data, which they evaluate using algorithms often based on artificial intelligence. The most widely used method for measuring biological age in composite biomarkers is the epigenetic clock.

The aim of this article is to review the many existing markers of aging and describe their relationship to aging.

Keywords:

biomarkers – markers – aging – aging rate – methods

Zdroje
  1. Borský P, Holmannová D, Fiala Z a kol. Fyziologie stárnutí. Časopis lékařů českých 2022; 161: 11–16.
  2. Rando TA, Chang HY. Aging, rejuvenation, and epigenetic reprogramming: resetting the aging clock. Cell 2012; 148: 46–57.
  3. Navarro Negredo P, Yeo RW, Brunet A. Aging and rejuvenation of neural stem cells and their niches. Cell Stem Cell 2020; 27: 202–223.
  4. Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine 2017; 21: 29–36.
  5. Hamczyk MR, Nevado RM, Barettino A et al. Biological versus chronological aging: JACC Focus seminar. J Am Coll Cardiol 2020; 75: 919–930.
  6. Sagers L, Melas-Kyriazi L, Patel CJ, Manrai AK. Prediction of chronological and biological age from laboratory data. Aging (Albany NY) 2020; 12: 7626–7638.
  7. Chen Y, Klein SL, Garibaldi BT et al. Aging in COVID-19: Vulnerability, immunity and intervention. Ageing Res Rev 2021; 65: 101205.
  8. Galkin F, Mamoshina P, Aliper A et al. Biohorology and biomarkers of aging: current state-of-the-art, challenges and opportunities. Ageing Res Rev 2020; 60: 101050.
  9. Bai X. Biomarkers of aging. Adv Exp Med Biol 2018; 1086: 217–234.
  10. Whittemore K, Martínez-Nevado E, Blasco MA. Slower rates of accumulation of DNA damage in leukocytes correlate with longer lifespans across several species of birds and mammals. Aging 2019; 11: 9829–9845.
  11. Borská L, Kremláček J, Andrýs C et al. Systemic inflammation, oxidative damage to nucleic acids, and metabolic syndrome in the pathogenesis of psoriasis. Int J Mol Sci 2017; 18: 2238.
  12. Qiu GH, Zheng X, Fu M et al. The protective function of non-coding DNA in DNA damage accumulation with age and its roles in age-related diseases. Biogerontology 2019; 20: 741–761.
  13. Li W, Vijg J. Measuring genome instability in aging – a mini-review. Gerontology 2012; 58: 129–138.
  14. Hamilton ML, Van Remmen H, Drake JA et al. Does oxidative damage to DNA increase with age? Proc Natl Acad Sci U S A 2001; 98: 10469–10474.
  15. Kubben N, Misteli T. Shared molecular and cellular mechanisms of premature ageing and ageing-associated diseases. Nat Rev Mol Cell Biol 2017; 18: 595–609.
  16. Bodea GO, McKelvey EGZ, Faulkner GJ. Retrotransposon-induced mosaicism in the neural genome. R Soc Open Sci 2018; 8: 180074.
  17. De Cecco M, Ito T, Petrashen AP et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature 2019; 566: 73–78.
  18. Kreiling JA, Jones BC, Wood JG et al. Contribution of retrotransposable elements to aging. In: Human Retrotransposons in Health and Disease. Springer, Cham, 2017: 297–321.
  19. Shay JW. Telomeres and aging. Curr Opin Cell Biol 2018; 52: 1–7.
  20. Lu W, Zhang Y, Liu D et al. Telomeres – structure, function, and regulation. Exp Cell Res 2013; 319: 133–141.
  21. Wang Q, Zhan Y, Pedersen NL et al. Telomere length and all-cause mortality: a meta-analysis. Ageing Res Rev 2018; 48: 11–20.
  22. Lai TP, Zhang N, Noh J et al. A method for measuring the distribution of the shortest telomeres in cells and tissues. Nat Commun 2017; 8: 1356.
  23. Lai TP, Wright WE, Shay JW. Comparison of telomere length measurement methods. Philos Trans R Soc Lond B Biol Sci 2018; 373: 20160451.
  24. Bernardes de Jesus B, Blasco MA. Telomerase at the intersection of cancer and aging. Trends Genet 2013; 29: 513–520.
  25. Iwama H, Ohyashiki K, Ohyashiki JH et al. Telomeric length and telomerase activity vary with age in peripheral blood cells obtained from normal individuals. Hum Genet 1998; 102: 397–402.
  26. Kinser HE, Pincus Z. MicroRNAs as modulators of longevity and the aging process. Hum Genet 2019; 139: 291–308.
  27. Eshkoor SA, Ghodsian N, Akhtari-Zavare M. MicroRNAs influence and longevity. Egypt J Med Hum Genet 2022; 23: 1–16.
  28. Papadopoli D et al. mTOR as a central regulator of lifespan and aging. F1000Res 2019; 8: F1000 Faculty Rev-998.
  29. Xie J, de Souza Alves V, von der Haar T et al. Regulation of the elongation phase of protein synthesis enhances translation accuracy and modulates lifespan. Curr Biol 2019; 29: 737–749. e5.
  30. Zhang W, Hawse J, Huang Q et al. Decreased expression of ribosomal proteins in human age-related cataract. Invest Ophthalmol Vis Sci 2002; 43: 198–204.
  31. Gonskikh Y, Polacek N. Alterations of the translation apparatus during aging and stress response. Mech Ageing Dev 2017; 168: 30–36.
  32. Steffen KK, Dillin A. A ribosomal perspective on proteostasis and aging. Cell Metab 2016; 23: 1004–1012.
  33. Chadwick SR, Lajoie P. Endoplasmic reticulum stress coping mechanisms and lifespan regulation in health and diseases. Front Cell Dev Biol 2019; 7: 263–277.
  34. Brown MK, Naidoo N. The endoplasmic reticulum stress response in aging and age-related diseases. Front Physiol 2012; 3: 263.
  35. Kaganovich D, Kopito R, Frydman J. Misfolded proteins partition between two distinct quality control compartments. Nature 2008; 454: 1088–1095.
  36. Barbosa MC, Grosso RA, Fader CM. Hallmarks of aging: an autophagic perspective. Front Endocrinol 2019; 10: 790.
  37. Sun Y, Li M, Zhao D et al. Lysosome activity is modulated by multiple longevity pathways and is important for lifespan extension in C. elegans. Elife 2020; 9: 1–28.
  38. Njemini R, Bautmans I, Onyema OO et al. Circulating heat shock protein 70 in health, aging and disease. BMC Immunol 2011; 12: 24.
  39. Huang J, Xu J, Pang S et al. Age-related decrease of the LAMP-2 gene expression in human leukocytes. Clin Biochem 2012; 45: 1229–1232.
  40. Loeffler DA. Influence of normal aging on brain autophagy: a complex scenario. Front Aging Neurosci 2019; 11: 49.
  41. Lerner C, Bitto A, Pulliam D et al. Reduced mammalian target of rapamycin activity facilitates mitochondrial retrograde signaling and increases life span in normal human fibroblasts. Aging Cell 2013; 12: 966–977.
  42. Gudelj I, Lauc G, Pezer M. Immunoglobulin G glycosylation in aging and diseases. Cell Immunol 2018; 333: 65–79.
  43. Chan CM, Huang DY, Huang YP et al. Methylglyoxal induces cell death through endoplasmic reticulum stress-associated ROS production and mitochondrial dysfunction. J Cell Mol Med 2016; 20 (9): 1749–1760.
  44. Rojas A, Añazco C, González I, Carcinogenesis PA. Extracellular matrix glycation and receptor for advanced glycation end-products activation: a missing piece in the puzzle of the association between diabetes and. Carcinogenesis 2018; 39: 515–521.
  45. Yumnam S, Subedi L, Kim SY. Glyoxalase system in the progression of skin aging and skin malignancies. Int J Mol Sci 2021; 22: 310.
  46. Chaudhuri J, Bains Y, Guha S et al. The role of advanced glycation end products in aging and metabolic diseases: Bridging association and causality. Cell Metab 2018; 28: 337–352.
  47. Gorisse L, Pietrement C, Vuiblet V et al. Protein carbamylation is a hallmark of aging. Proc Natl Acad Sci U S A 2016; 113: 1191–1196.
  48. Carracedo J, Ramírez-Carracedo R, Martínez de Toda I et al. Protein carbamylation: a marker reflecting increased age-related cell oxidation. Int J Mol Sci 2018; 19: 1495.
  49. Yu X, Wang Y, Kristic J et al. Profiling IgG N-glycans as potential biomarker of chronological and biological ages: a community-based study in a Han Chinese population. Medicine (United States) 2016; 95: e4112.
  50. Krištić J, Vučković F, Menni C et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci 2014; 69: 779–789.
  51. Wang S, Gu K. Insulin-like growth factor 1 inhibits autophagy of human colorectal carcinoma drug-resistant cells via the protein kinase B/mammalian target of rapamycin signaling pathway. Mol Med Rep 2018; 17: 2952–2956.
  52. Milman S, Atzmon G, Huffman DM et al. Low insulin-like growth factor-1 level predicts survival in humans with exceptional longevity. Aging Cell 2014; 13: 769–771.
  53. Yuan R, Tsaih SW, Petkova SB et al. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 2009; 8: 277–287.
  54. Vitale G, Pellegrino G, Vollery M, Hofland LJ. Role of IGF-1 system in the modulation of longevity: Controversies and new insights from a centenarians’ perspective. Front Endocrinol (Lausanne) 2019; 10: 27.
  55. Vitale G, Barbieri M, Kamenetskaya M, Paolisso G. GH/IGF-I/insulin system in centenarians. Mech Ageing Dev 2017; 165: 107–114.
  56. Salminen A, Kaarniranta K. AMP-activated protein kinase (AMPK) controls the aging process via an integrated signaling network. Ageing Res Rev 2013; 11: 230–241.
  57. Fulco M, Cen Y, Zhao P et al. Glucose restriction inhibits skeletal myoblast differentiation by activating SIRT1 through AMPK-mediated regulation of Nampt. Dev Cell 2008; 14: 661–673.
  58. Yuan J, Dong X, Yap J, Hu J. The MAPK and AMPK signalings: Interplay and implication in targeted cancer therapy. J Hematol Oncol 2020; 13: 113.
  59. Amano H, Sahin E. Telomeres and sirtuins: at the end we meet again. Mol Cell Oncol 2019; 6: e1632613.
  60. Anand S, Samuel M, Kumar S, Mathivanan S. Ticket to a bubble ride: Cargo sorting into exosomes and extracellular vesicles. Biochim Biophys Acta Proteins Proteom 2019; 1867: 140203.
  61. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Science 2020; 367: eaau6977.
  62. Mitsuhashi M, Taub DD, Kapogiannis D et al. Aging enhances release of exosomal cytokine mRNAs by A1-42-stimulated macrophages. FASEB Journal 2013; 27: 5141–5150.
  63. Weilner S, Keider V, Winter M et al. Vesicular galectin-3 levels decrease with donor age and contribute to the reduced osteo-inductive potential of human plasma derived extracellular vesicles. Aging 2016; 8: 16–33.
  64. Zhang X, Hubal MJ, Kraus VB. Immune cell extracellular vesicles and their mitochondrial content decline with ageing. Immun Ageing 2020; 17: 1.
  65. Sebastian-Valverde M, Pasinetti GM. The NLRP3 inflammasome as a critical actor in the inflammaging process. Cells 2020; 9: 1552.
  66. Alberro A, Iribarren-Lopez A, Sáenz-Cuesta M et al. Inflammaging markers characteristic of advanced age show similar levels with frailty and dependency. Sci Rep 2021; 11: 4358.
  67. Yiallouris A, Tsioutis C, Agapidaki E et al. Adrenal aging and its implications on stress responsiveness in humans. Front Endocrinol 2019; 10: 54.
  68. Puzianowska-Kuźnicka M, Owczarz M, Wieczorowska-Tobis K et al. Interleukin-6 and C-reactive protein, successful aging, and mortality: the PolSenior study. Immun Ageing 2016; 13: 21.
  69. Calcinotto A, Kohli J, Zagato E et al. Cellular senescence: aging, cancer, and injury. Physiol Rev 2019; 99: 1047–1078.
  70. Olivieri F, Albertini MC, Orciani M et al. DNA damage response (DDR) and senescence: Shuttled inflamma-miRNAs on the stage of inflamm-aging. Oncotarget 2015; 6: 35509–35521.
  71. Idda ML, Mcclusky WG, Lodde V et al. Survey of senescent cell markers with age in human tissues. Aging 2020; 12: 4052–4066.
  72. Liu Y, Sanoff HK, Cho H et al. Expression of p16INK4a in peripheral blood T-cells is a biomarker of human aging. Aging Cell 2009; 8: 439–448.
  73. Sørensen CE, Tritsaris K, Reibel J et al. Elevated p16ink4a expression in human labial salivary glands as a potential correlate of cognitive aging in late midlife. PLoS One 2016; 11: e0152612.
  74. Coppé JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol 2010; 5: 99–118.
  75. Lopes-Paciencia S, Saint-Germain E, Rowell MC et al. The senescence-associated secretory phenotype and its regulation. Cytokine 2019; 117: 15–22.
  76. Spazzafumo L, Mensà E, Matacchione G et al. Age-related modulation of plasmatic beta-galactosidase activity in healthy subjects and in patients affected by T2DM. Oncotarget 2017; 8: 93338–93348.
  77. Piechota M, Sunderland P, Wysocka A et al. Is senescence-associated β-galactosidase a marker of neuronal senescence? Oncotarget 2016; 7: 81099–81109.
  78. Kakimoto Y, Okada C, Kawabe N et al. Myocardial lipofuscin accumulation in ageing and sudden cardiac death. Sci Rep 2019; 9: 3304.
  79. Regmi SG, Rolland SG, Conradt B. Age-dependent changes in mitochondrial morphology and volume are not predictors of lifespan. Aging 2014; 6: 118–130.
  80. Liu YJ, McIntyre RL, Janssens GE, Houtkooper RH. Mitochondrial fission and fusion: a dynamic role in aging and potential target for age-related disease. Mech Ageing Dev 2020; 186: 111212.
  81. Chen G, Kroemer G, Kepp O. Mitophagy: an emerging role in aging and age-associated diseases. Front Cell Dev Biol 2020; 8: 200.
  82. Zapico SC, Ubelaker DH. MtDNA mutations and their role in aging, diseases and forensic sciences. Aging Dis 2013; 4: 364–380.
  83. Trifunovic A, Wredenberg A, Falkenberg M et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 2004; 429: 417–423.
  84. Bowman A, Birch-Machin MA. Age-dependent decrease of mitochondrial complex II activity in human skin fibroblasts. J Invest Dermatol 2016; 136 (5): 912–919.
  85. Greaves LC, Barron MJ, Plusa S et al. Defects in multiple complexes of the respiratory chain are present in ageing human colonic crypts. Exp Gerontol 2010; 45 (7–8): 573–579.
  86. Conley KE, Jubrias SA, Esselman PC. Oxidative capacity and ageing in human muscle. Journal of Physiology 2000; 526 (1): 203–210.
  87. Yaku K, Okabe K, Nakagawa T. NAD metabolism: implications in aging and longevity. Ageing Res Rev 2018; 47: 1–17.
  88. Massudi H, Grant R, Braidy N et al. Age-associated changes in oxidative stress and NAD+ metabolism in human tissue. PLoS One 2012; 7 (7): e42357.
  89. Zhu XH, Lu M, Lee BY et al. In vivo NAD assay reveals the intracellular NAD contents and redox state in healthy human brain and their age dependences. Proc Natl Acad Sci U S A 2015; 112 (9): 2876–2881.
  90. Fang EF, Lautrup S, Hou Y et al. NAD+ in aging: Molecular mechanisms and translational implications. Trends Mol Med 2017; 23: 899–916.
  91. Pence B, Yarbro J. Growth differentiation factor 15 is correlated to markers of immunosenescence in monocytes. Innov Aging 2019; 3 (Supplement_1): S103–S103.
  92. Doerstling S, Hedberg P, Öhrvik J et al. Growth differentiation factor 15 in a community-based sample: age-dependent reference limits and prognostic impact. Ups J Med Sci 2018; 123 (2): 86–93.
  93. Łos MJ, Skubis A, Ghavami S. Stem cells – definition and types of stem cells, stemness and differentiation. In: Los MJ, Hudecki A, Wiechec E. Stem cells and biomaterials for regenerative medicine. Elsevier, 2018.
  94. Schultz MB, Sinclair DA. When stem cells grow old: Phenotypes and mechanisms of stem cell aging. Development (Cambridge) 2016; 143: 3–14.
  95. Ahmed ASI, Sheng MH, Wasnik S et al. Effect of aging on stem cells. World J Exp Med 2017; 7(1): 1.
  96. Trombetti A, Reid KF, Hars M et al. Age-associated declines in muscle mass, strength, power, and physical performance: Impact on fear of falling and quality of life. Osteoporos Int 2016; 27 (2): 463.
  97. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol 2001; 37 (1): 153–156.
  98. Harada C, Natelson Love M, Triebel K. Normal cognitive aging. Clin Geriatr Med 2013; 29 (4): 737–752.
  99. Vaz Fragoso CA, McAvay G, Van Ness PH et al. Phenotype of normal spirometry in an aging population. Am J Respir Crit Care Med 2015; 192 (7): 817–825.
  100. Löllgen H, Leyk D. Exercise testing in sports medicine. Dtsch Arztebl Int 2018; 115 (24): 409.
  101. Tosato M, Marzetti E, Cesari M et al. Measurement of muscle mass in sarcopenia: From imaging to biochemical markers. Aging Clin Exp Res 2017; 29 (1): 19–27.
  102. Jafari Nasabian P, Inglis JE, Reilly W et al. Aging human body: Changes in bone, muscle and body fat with consequent changes in nutrient intake. J Endocrin 2017; 234 (1): R37–R51.
  103. Rivero-Segura NA, Bello-Chavolla OY, Barrera-Vázquez OS et al. Promising biomarkers of human aging: In search of a multi-omics panel to understand the aging process from a multidimensional perspective. Ageing Res Rev 2020; 64: 101164.
  104. Zhavoronkov A, Li R, Ma C, Mamoshina P. Deep biomarkers of aging and longevity: From research to applications. Aging (Albany NY) 2019; 11 (22): 10771.
  105. Jiang S, Guo Y. Epigenetic clock: DNA methylation in aging. Stem Cells Int 2020; 2020: 1047896.
  106. Horvath S, Garagnani P, Bacalini MG et al. Accelerated epigenetic aging in Down syndrome. Aging Cell 2015; 14 (3): 491.
  107. Horvath S, Erhart W, Brosch M et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A 2014; 111 (43): 15538–15543.
  108. Levine ME, Lu AT, Bennett DA, Horvath S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY) 2015; 7 (12): 1198.
  109. Marioni R, Shah S, McRae A et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol 2015; 44: 1388–1396.
  110. Lu AT, Quach A, Wilson JG et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 2019; 11: 303.
  111. Levine ME, Lu AT, Quach A et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 2018; 10: 573–591.
  112. Zhang Y, Wilson R, Heiss J et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun 2017; 8: 14617.
  113. Mamoshina P, Kochetov K, Putin E et al. Population specific biomarkers of human aging: a big data study using South Korean, Canadian, and Eastern European patient populations. J Gerontol A Biol Sci Med Sci 2018; 73: 1482–1490.
  114. Mamoshina P, Kochetov K, Cortese F et al. Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers. Sci Rep 2019; 9: 142.
  115. Zhavoronkov A, Li R, Ma C, Mamoshina P. Deep biomarkers of aging and longevity: From research to applications. Aging 2019; 11: 10771–10780.
  116. Yeung A, Desai AD, Peng C et al. Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging. Biomed Opt Express 2018; 9: 6038–6052.
  117. Navega D, Coelho J d’Oliveira et al. DXAGE: a new method for age at death estimation based on femoral bone mineral density and artificial neural networks. J Forensic Sci 2018; 63: 497–503.
  118. Sajedi H, Pardakhti N. Age prediction based on brain MRI image: a survey. J Med Syst 2019; 43: 279.
  119. Høgestøl EA, Kaufmann T, Nygaard GO et al. Cross-sectional and longitudinal MRI brain scans reveal accelerated brain aging in multiple sclerosis. Front Neurol 2019; 10: 450.
  120. McGue M, Vaupel J, Holm N, Harvald B. Longevity is moderately heritable in a sample of Danish twins born 1870-1880. J Gerontol 1993; 48: B237–B244.
  121. Sebastiani P, Perls TT. The genetics of extreme longevity: Lessons from the New England centenarian study. Front Genet 2012; 3: 277.
  122. Mayer P. Inheritance of longevity evinces no secular trend among members of six New England families born 1650-1874. Am J Hum Biol 1991; 3: 49–58.
  123. Gomez-Verjan JC, Vazquez-Martinez ER, Rivero-Segura NA, Medina-Campos RH. The RNA world of human ageing. Hum Genet 2018; 137: 865–879.
  124. Kukurba KR, Montgomery SB. RNA sequencing and analysis. Cold Spring Harb Protoc 2015; 2015: 951–969.
  125. Marttila S, Jylhävä J, Nevalainen T et al. Transcriptional analysis reveals gender-specific changes in the aging of the human immune system. PLoS One 2013; 8: 66229.
  126. Kochunov P, Charlesworth J, Winkler A et al. Transcriptomics of cortical gray matter thickness decline during normal aging. Neuroimage 2013; 82: 273–283.
  127. Peters MJ, Joehanes R, Pilling LC et al. The transcriptional landscape of age in human peripheral blood. Nat Commun 2015; 6: 8570.
  128. Santos-Lozano A, Valenzuela PL, Llavero F et al. Successful aging: Insights from proteome analyses of healthy centenarians. Aging 2020; 12: 3502–3515.
  129. Ignjatovic V, Lai C, Summerhayes R et al. Age-related differences in plasma proteins: How plasma proteins change from neonates to adults. PLoS One 2011; 6: e17213.
  130. Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud 2015; 1: a000588.
  131. Kohler IA, Verhoeven A, Derks R, Giera M. Analytical pitfalls and challenges in clinical metabolomics. Bioanalysis 2016; 8: 1509–1532.
  132. Novotny MV, Soini HA, Mechref Y. Biochemical individuality reflected in chromatographic, electrophoretic and mass-spectrometric profiles. J Chromatogr B Analyt Technol Biomed Life Sci 2008; 866: 26–47.
  133. Chong J, Wishart DS, Xia J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr Protoc Bioinformatics 2019; 68: e86.
Štítky
Addictology Allergology and clinical immunology Angiology Audiology Clinical biochemistry Dermatology & STDs Paediatric gastroenterology Paediatric surgery Paediatric cardiology Paediatric neurology Paediatric ENT Paediatric psychiatry Paediatric rheumatology Diabetology Pharmacy Vascular surgery Pain management Dental Hygienist
Článek ÚVODEM
Článek Assisted reproduction – principles, procedures, and their effectiveness
Článek Change in the prevalence of overweight/obesity in Czech seven-year-old children between 2008 and 2019
Článek Prophylactic application of closed-incision NPWT in aseptic surgery – a review of literature
Článek Coloscopy, or colonoscopy?
Článek Edward Donnall Thomas (1920–2012)
Článek The Balkan Wars in prof. Přecechtěl’s memories
Článek Ceny předsednictva ČLS JEP za nejlepší vědecké publikace za rok 2022
Článek PŘEDNÁŠKOVÉ VEČERY SPOLKU ČESKÝCH LÉKAŘŮ V PRAZE (LISTOPAD – PROSINEC 2023)
Článok vyšiel v časopise

Journal of Czech Physicians

Číslo 5
2023 Číslo 5
Stáhnout celé číslo v PDF
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#