Integrated Multi-Omics: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Qingping Wu.

Advancements in high-throughput technology provide new opportunities for omics research to understand the pathological process of various complex human diseases. The integration of multi-omics technologies can systematically reveal the interactions among aging molecules from a multidimensional perspective.

  • aging
  • aging biomarkers
  • antiaging targets
  • multi-omics
  • aging clock

1. Introduction

In 2019, there were an estimated 702 million people aged ≥65 years according to world population prospects 2019: Highlights, accounting for 9.1% of the world population. The aged population also grows at approximately 3% per year. In addition, human life expectancy rapidly increases, i.e., from 64.2 years in 1990 to 72.6 years in 2019, and is predicted to increase further to 77.1 years in 2050 [1]. Thus, the risk of developing aging-related diseases increases.
Aging is a physiological process in organisms in which multifactorial processes, including genetic factors, external environmental stimuli, and lifestyle factors, determine a progressive decline over time. Environmental factors may have a cumulative and multiple impact on health and longevity. The idea of “healthy lifestyles and environments” comes from the observation of geographical clusters of centenarians around the world, with five identified “longevity hotspots” known as Blue Zones, which are located in Sardinia (Italy), Okinawa (Japan), Loma Linda (California), Nicoya (Costa Rica), and Ikaria (Greece). Thus, their lifestyles and environments are possibly more conducive to longevity than those of the rest of the world. The populations in these areas are characterized by having an active, stress-free lifestyle, strong community bonds, and spirituality. Maybe these are exactly what we in the “non-blue zone” want to learn. It is also subject to regional restrictions such as lifestyle, economic conditions, and geography; they cannot necessarily be broadly extrapolated. However, among tissues and organs, different individuals age at different rates. The aging rate is highly variant, and these specific changes often affect organ functions [2][3]. The aging of the physiological systems and the changes in their functions lead to various chronic diseases and metabolism-related syndromes [4][5].
Therefore, the characterization of aging-related biomarkers is expected to pave the way for the discovery of novel antiaging targets [6]. Understanding the causes of aging and disease and the relationship between the two is important for aging biomarkers that promote the development of geriatrics and clinical translation. To date, there is no accurate independent aging biomarker that can accurately reflect the aging state or aging rate of people. Aging can be characterized by biomarkers [7]. To achieve this goal, studies at the multi-omics level, which integrates epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics data, can provide a more comprehensive overview [8].

2. The Necessity of Distinguishing Chronological Age and Biological Age

Without a method to assess the personal aging rate, determining preventive interventions for aging is impossible. Aging biomarkers show the changes in the molecules, cells, and organs of the human body with age. Ideally, these biomarkers should slow their progression with age or reverse to a young state [9]. Chronological age represents a person’s actual age and is calculated based on the time elapsed in a person's life [10]. Biological age refers to an individual’s overall health status at a certain point in time of physiological age. Generally, the environment, diet, life, and psychological factors should be considered. Biological age has been revealed as a better predictor than chronological age, and its measurement can facilitate the assessment of colonoscopy-related colorectal adenoma risk [11][12].
In aging research, it is common to use chronological age. However, due to the heterogeneity of aging, chronological aging is unpractical. There has been a discrepancy between the predictions of biological age and chronological age. Considering the heterochronism of aging, the measurement of biological age becomes complicated, as it involves the calculation of many target molecules that indicate the dynamics of different processes. The panels of biomarkers can act as an integrated tool for measurements. Generally, special indicators are used to accurately indicate biological age. Klemera and Doubal used chronological age as one of the biomarkers, which is the most popular biomarker [13][14]. The principal component analysis method unites equation construction, correlation analysis, and redundancy analysis [15][16]. The main problem with biological age assessment is that the function of chronological age is unknown among the different available measurement methods. Some people believe that it is a very important biomarker [17], while others consider that the aging rate does not need to be measured by chronological age [18].
Advances in artificial intelligence and statistics provide opportunities to accurately estimate biological age. However, they are not fully effective against heterogeneous populations, and there is no clinical certainty. The application of some aging biomarkers from different sources leads to a reduction in the resolution of most biomarkers. Since the ideal biological age estimation method should be comprehensive and complete, we suggest an integrative approach based on multi-omics technologies for aging biomarkers and novel antiaging targets. This multi-omics method is based on the multi-layered organizational logic of life, thus making the prediction of biological age more accurate.

3. Multi-Omics for Aging Clocks

3.1. Epigenetics Aging Clocks

Biological age estimation based on DNA methylation has been accurately discussed [19][20][21]. As an “age estimator”, the epigenetic aging clock is used to estimate the epigenetic (biological) age of DNA. It also demonstrates that age-related diseases are associated with higher biological age relative to the chronological age. This phenomenon is called epigenetic age acceleration [19].
The discovery of some aging clocks can predict age-related pathologies, such as cancers, heart disease, and diabetes[22] . There are other types of epigenetic clocks from whole blood [23], skin [24], and saliva [25]. The aging clock known as DNA methylation GrimAge is an instrument that allows us to view epigenetic acceleration of aging from a new perspective. It can predict the time-to-death and comorbidity count, time-to-cancer, and time-to-coronary heart disease [26]. In a biological aging clock based on DNA methylation, the main indicator of biological age is the methylation of ribosomal DNA exclusively. It can accurately characterize the biological age and show the organism's response to the treatment of aging and effective antiaging interventions [27].

3.2. Transcriptomics Aging Clocks

The positive increase in age reflects faster biological aging. Peters et al. conducted a meta-analysis of 7074 individual peripheral blood samples, in which 11,908 genes were characterized to create age-related predictors. They found that the average absolute error between the chronological age and the predicted age was 7.8 years [28]. Another important study focused on the aging of the transcriptome of skin cells served as a pioneering method for determining the biological age of such datasets [29]. The age was predicted by linear discriminant, and the median absolute error and average absolute error of 4 years and 7.7 years were obtained, respectively [30].
The aging rate varies greatly between individuals and groups and will be significantly affected by factors such as genetics, environment, lifestyle, etc. Due to the data type, transcriptome aging clocks have weaker correlations with the chronological age than DNA methylation aging clocks [31]. To address this, a standardized cohort is needed. In a study of 6465 individual blood samples collected from 17 datasets, the differences in technical performance had a more significant effect on blood expression profiles than disease and age itself [32]. Then, cross-platform normalization methods, normalization through reference genes, distribution transformation, and quantile normalization were used to successfully eliminate the batch processing effects. A deep neural network was utilized as a predictive index to yield an average absolute error of 6.14 years and a Pearson correlation accuracy of 0.91 [32].
In summary, the biological age prediction technology based on transcriptomics has developed rapidly, and its accuracy level has been continuously improved. Thus, transcriptomics aging clocks will catch up with methylation aging clocks in the near future.

3.3. Proteomics Aging Clocks

To help optimize aging clocks and determine the potential novel targets for antiaging interventions, proteomics aging clocks have been systematically reviewed and analyzed. Proteins are studied, because they significantly change their expression levels with age and represent functional products, unlike transcriptome changes, which are not always associated with proteome changes [33][34]. Since previous proteomics studies have used various proteomics techniques, sample sizes and types, and statistical methods, significant differences in the results have been observed. Even when analyzing the same biological sample, the findings can be quite different [35][36].
To achieve these goals, a systematic review of 36 different proteomic analyses was performed, each of which identified proteins that changed significantly with age [37]. There were 32 proteins that have been reported at least five times and 1128 at least twice. Each of these 32 proteins is related to aging and age-related diseases. Furthermore, 1128 common proteins associated with gene regulation, extracellular matrix, and inflammation were analyzed based on bioinformatics enrichment. Finally, a new proteomics aging clock was proposed, which is composed of three or more proteins in the plasma that change with age in different studies. Using a large patient cohort of 3301, the proposed proteomics aging clock was confirmed to accurately predict the age of a person [37].
Another study analyzed 2925 plasma proteins in a cohort of 4263 subjects and developed a new bioinformatics method. This study revealed significant nonlinear changes in the human plasma proteome with age. Changes in the proteome reflect the different biological pathways and reveal the various genome and proteome associations with age-related diseases and phenotypic traits. This new method of studying aging may provide potential novel targets for age-related diseases [38].

3.4. Metabolomics Aging Clocks

Hertel et al. [39] proposed the use of metabolomics for biological age prediction, called the “metabolomic aging clock”. They based their analysis on urine data obtained through 1H-NMR spectroscopy. The metabolomics aging clock can predict the prognosis of weight loss in bariatric surgery patients and can be applied to other fields of medicine. Similarly, van den Akker et al. [40] developed an innovative, biological age measurement method based on metabolomics and analyzed the 1H-NMR serum metabolomics data. To estimate chronological age, they used a linear model trained with metabolomic variables. Finally, they constructed a score reflective of an individual’s biological age called metaboAge and showed that the excess of metaboAge over chronological age corresponded with a poor cardiometabolic health.

3.5. Microbiomics Aging Clocks

Using the microbiome aging clock to predict biological age is a relatively new analytical method. However, this method has two problems: one is to find people with similar lifestyles, and the other is to normalize the dataset. Under normal circumstances, the structure and composition of the human gut microbiota will decrease with age; however, the elderly occasionally exhibit a microbiota structure similar to that of adults. The gut microbiome is mainly composed of four phyla: Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria [41]. During aging, the relative abundances of Bifidobacterium, Bacteroides, Lactobacillus, Ruminococcus, and Bacillus decrease, whereas those of Streptococcus, Enterobacter, Clostridium, and Escherichia increase [42][43]. The results of studies on aging-related microbial communities are similar to those in microbial communities. In addition to transcriptomics studies, microbiology studies also heavily rely on methodology [44].
In the transcriptional microbiology of aging, the concentration of short-chain fatty acid products in the gut of aging people is low, and it is related to the increase in the number of pathogenic and gas-tolerant bacteria, whose reproduction can lead to malnutrition and age-related diseases [42]. Based on the metagenomic dataset and using deep neural network methods to determine the biological age, including 1673 microbial taxa. An average absolute error of 3.94 years, which is remarkably close to Horvath's [31] [31] 3.4 years, and an R2 value of 0.81 were obtained. This is the first study to establish a quantitative model of gut microflora aging [42].
4.Multi-Omics Approach for the Discovery of Aging Biomarkers

4. Multi-Omics Approach for the Discovery of Aging Biomarkers

Aging is the main risk factor for chronic diseases that limits a healthy lifespan. Therefore, the mechanism of aging is a potential therapeutic target. Age correlation analyses involve large amounts of data obtained from various omics analyses, such as genomics (epigenomics), transcriptomics, proteomics, metabolomics, and microbiomics. We elaborated in detail in the Wu L. et al. [45] literature, 4.1 Aging Genomics, 4.1.1 Aging Epigenomics, 4.1.2 Aging Gene Expression, 4.1.3 Telomere-Based Biomarkers, 4.2 Aging Transcriptomics, 4.2.1Transcriptomics -Based Biomarkers, 4.2.2 MiRNAs, lncRNAs, and circRNAs-Based Biomarkers, 4.3 Aging Proteomics, 4.3.1 Proteomics-Based Biomarkers, 4.3.2 Senescence-Associated Secretory Phenotype-Based Biomarkers, 4.4 Aging Metabolomics, 4.5 Aging Microbiomics, 4.6 Early Biomarkers of AgingThe main advantages of this method include the analysis of all possible data pertaining to a single person or a large group of people, as well as the common and individual characteristics from a multi-dimensional perspective and the identification of aging markers and novel antiaging targets. Machine learning methods based on deep neural networks are the latest and most complex methods for identifying human aging biomarkers. They can utilize any type of omics data to predict age.

5. Integromicsand Systems Biology

To promote the multidimensional analysis of data, advanced omics technology is inseparable from advanced omics analytical tools. At present, large-scale, high-quality, and high-throughput data from various omics methods can be efficiently and independently analyzed. However, separate data analysis and interpretation ignore the correlation and biological interference between different omics levels. Therefore, the integration of single-omics methods is essential for an in-depth understanding of the aging process and its mechanism.

Integromics, the comprehensive analysis of different omics data, and systems biology have provided several breakthroughs in the study of aging and antiaging interventions. Together, they have emerged as a more complex statistical method and combine the experimental data obtained in multiple omics methods with computational models to provide a holistic view of the aging landscape [46]. Considering the complexity and heterogeneity of aging, integromics and systems biology not only provide static maps of molecules but are also used to characterize the mutual changes of molecules over time. This helps determine the optimal time point for aging biomarker measurements and specific antiaging drug treatments. Each omics-level biomarker candidate based on integromics and systems biology has biological relevance. Significant biomarker candidates can be preferentially used as biomarkers of aging in medicine and as new antiaging targets.

6. Conclusions and Prospects

Rapid advances in science and technology have accelerated the arrival of the “omics era”, thereby enabling researchers to collect and integrate data at different molecular levels. The identification of biomarkers of aging and new targets for antiaging interventions is crucial in aging biology and geriatrics. The multi-level information obtained through multi-omics technology contributes to the increased understanding of the mechanisms of aging and provides new opportunities for the diagnosis and treatment of aging and aging-related diseases.

We have summarized the various omics techniques used to characterize aging biomarkers. Each screened biomarker is a promising candidate and can be integrated into an “aging biomarker library” that can serve as a diagnostic and prognostic tool. Here, we mainly categorized them based on the existing biomarkers of aging. We summarized the recent omics methods used to discover biomarkers in genomics, transcriptomics, proteomics, metabolomics, and metagenomics (Figure 1).

Figure 1. Multi-omics-based technologies for characterizing aging clocks and biomarkers. Aging is a comprehensive process affected by multiple factors that is associated with changes at the molecular, cellular, tissue, and organism levels, thus requiring objective analytical research tools. The integrated multi-omics approach is essential to achieve a comprehensive understanding of the biological mechanisms of aging.

Figure 1. Multi-omics-based technologies for characterizing aging clocks and biomarkers. Aging is a comprehensive process affected by multiple factors that is associated with changes at the molecular, cellular, tissue, and organism levels, thus requiring objective analytical research tools. The integrated multi-omics approach is essential to achieve a comprehensive understanding of the biological mechanisms of aging.

In the context of personalized and precision medicine, multi-omics methods have attracted widespread attention, because they can provide an in-depth understanding of the molecular patterns and cover a wide range of characteristics, such as participating in the metabolic, genetic, and signal transduction pathways of complex aging [47]. Therefore, we suggest that a combination of multiple biomarkers for a comprehensive diagnosis and systematic analysis can objectively characterize the aging process (Figure 2).

Figure 2. Schematic diagram of an integrated multi-omics approach to the research and application of aging biomarkers. Genomics, transcriptomics, proteomics, metabolomics, and microbiomics enable the high-throughput quantitative profiling of molecules in biological systems to reveal aging-related changes. Combining single-omics data with integromics and systems biology contributes to an increased understanding of the mechanisms of aging and paves the way for the development and utilization of aging biomarkers and novel antiaging targets.

Figure 2. Schematic diagram of an integrated multi-omics approach to the research and application of aging biomarkers. Genomics, transcriptomics, proteomics, metabolomics, and microbiomics enable the high-throughput quantitative profiling of molecules in biological systems to reveal aging-related changes. Combining single-omics data with integromics and systems biology contributes to an increased understanding of the mechanisms of aging and paves the way for the development and utilization of aging biomarkers and novel antiaging targets.

Advances in computer science, including meta-analysis and artificial intelligence, are expected to remarkably increase the speed and efficiency of aging biomarker research [48]. However, before their application in the clinical setting, candidate biomarkers should be verified. This verification process must include larger sample populations. Despite the large gap between the identification of useful biomarkers and their application in clinical practice, the integrated analysis of multi-omics data is a promising tool to identify new candidate biomarkers that could be developed and used to identify pharmaceutical targets and improve human health during aging, thereby advancing our understanding of the pathophysiology of the complex and dynamic process of aging.

References

  1. World Population Prospects 2019: Highlights . Department of Economic and Social Affairs, Population Division. Retrieved 2022-1-7
  2. Carmona, J.J.; Michan, S.; Biology of healthy aging and longevity. Rev. Investig. Clin.-Clin. Transl. Investig. 2016, 68, 7-16.
  3. Tuttle, C.S.L.; Waaijer, M.E.C.; Slee-Valentijn, M.S.; Stijnen, T.; Westendorp, R.; Maier, A.B.; Cellular senescence and chronological age in various human tissues: A systematic review and meta-analysis. Aging Cell 2020, 19, 1-11, https://doi.org/10.1111/acel.13083.
  4. Denic, A.; Glassock, R.J.; Rule, A.D.; Structural and functional changes with the aging kidney. Adv. Chronic Kidney Dis. 2016, 23, 19-28, https://doi.org/10.1053/j.ackd.2015.08.004.
  5. Kim, I.H.; Kisseleva, T.; Brenner, D.A.; Aging and liver disease. Curr. Opin. Gastroenterol. 2015, 31, 184-191, https://doi.org/10.1097/mog.0000000000000176.
  6. Bernardes de Jesus, B.; Blasco, M.A.; Potential of telomerase activation in extending health span and longevity. Curr. Opin. Cell Biol. 2012, 24, 739-743, https://doi.org/10.1016/j.ceb.2012.09.004.
  7. Gross, A.L.; Carlson, M.C.; Chu, N.M.; McAdams-DeMarco, M.A.; Mungas, D.; Simonsick, E.M.; Derivation of a measure of physiological multisystem dysregulation: Results from WHAS and health ABC. Mech. Ageing Dev. 2020, 188, 111258, https://doi.org/10.1016/j.mad.2020.111258.
  8. Belloni, G.; Cesari, M.; Frailty and intrinsic capacity: Two distinct but related constructs. Front. Med. 2019, 6, 133, https://doi.org/10.3389/fmed.2019.00133.
  9. Zhavoronkov, A.; Buzdin, A.A.; Garazha, A.V.; Borisov, N.M.; Moskalev, A.A.; Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs. Front. Genet. 2014, 5, 49, https://doi.org/10.3389/fgene.2014.00049.
  10. Gott, A.; Andrews, C.; Hormigos, M.L.; Spencer, K.; Bateson, M.; Nettle, D.; Chronological age, biological age, and individual variation in the stress response in the European starling: A follow-up study. PeerJ 2018, 6, e5842, https://doi.org/10.7717/peerj.5842.
  11. Brown, P.J.; Wall, M.M.; Chen, C.; Levine, M.E.; Yaffe, K.; Roose, S.P.; Rutherford B.R.; Rutherford B.R. Biological age, not chronological age, is associated with late-life depression. J. Gerontol. Ser. A-Biol. Sci. Med. Sci. 2018, 73, 1370–1376, https://doi.org/10.1093/gerona/glx162.
  12. Kim, S.J.; Kim, B.J.; Kang, H.; Measurement of biological age may help to assess the risk of colorectal adenoma in screening colonoscopy. World J. Gastroenterol. 2017, 23, 6877–6883, https://doi.org/10.3748/wjg.v23.i37.6877.
  13. Cho, I.H.; Park, K.S.; Lim, C.J.; An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI). Mech. Ageing Dev. 2010, 131, 69–78, https://doi.org/10.1016/j.mad.2009.12.001.
  14. Klemera, P.; Doubal, S.; A new approach to the concept and computation of biological age. Mech. Ageing Dev. 2006, 127, 240–248, https://doi.org/10.1016/j.mad.2005.10.004.
  15. Bai, X.J.; Han, L.L.; Liu, Q.; Shan, H.Y.; Lin, H.L.; Sun, X.F.; Chen, X.M.; Evaluation of biological aging process—A population-based study of healthy people in china. Gerontology 2010, 56, 129–140, https://doi.org/10.1159/000262449.
  16. Zhang, W.G.; Bai, X.J.; Sun, X.F.; Cai, G.Y.; Bai, X.Y.; Zhu, S.Y.; Zhang, M.; Chen, X. M.; Construction of an integral formula of biological age for a healthy Chinese population using principle component analysis. J. Nutr. Health Aging 2014, 18, 137–142, https://doi.org/10.1007/s12603-013-0345-8.
  17. Belsky, D.W.; Caspi, A.; Houts, R.; Cohen, H.J.; Corcoran, D.L.; Danese, A.; Harrington, H.; Israel, S.; Levine, M.E.; Schaefer, J.D.; et al. Quantification of biological aging in young adults. Proc. Natl. Acad. Sci. USA 2015, 112, E4104–E4110, https://doi.org/10.1073/pnas.1506264112.
  18. Mitnitski, A.; Howlett, S.E.; Rockwood, K.; Heterogeneity of human aging and its assessment. J. Gerontol. Ser. A-Biol. Sci. Med. Sci. 2017, 72, 877–884, https://doi.org/10.1093/gerona/glw089.
  19. Horvath, S.; Raj, K.; DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 2018, 19, 371–384, https://doi.org/10.1038/s41576-018-0004-3.
  20. Levine, M.E.; Lu, A.T.; Quach, A.; Chen, B.H.; Assimes, T.L.; Bandinelli, S.; Hou, L.; Baccarelli, A.A.; Stewart, J.D.; Li, Y.; et al.et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging-Us 2018, 10, 573–591, https://doi.org/10.18632/aging.101414.
  21. Hannum, G.; Guinney, J.; Zhao, L.; Zhang, L.; Hughes, G.; Sadda, S.; Klotzle, B.; Bibikova, M.; Fan, J.B.; Gao, Y.; et al.et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cel 2013, 49, 359–367, https://doi.org/10.1016/j.molcel.2012.10.016.
  22. Liu, Z.; Leung, D.; Levine, M.; Comparative analysis of epigenetic aging clocks from CpG characteristics to functional associations. bioRxiv 2019, 51, 512483, https://doi.org/10.1101/512483.
  23. Zhang, Y.; Wilson, R.; Heiss, J.; Breitling, L.P.; Saum, K.U.; Schoettker, B.; Holleczek, B.; Waldenberger, M.; Peters, A.; Brenner, H.; et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 2017, 8, 1-11, https://doi.org/10.1038/ncomms14617.
  24. Horvath, S.; Oshima, J.; Martin, G.M.; Lu, A.T.; Quach, A.; Cohen, H.; Felton, S.; Matsuyama, M.; Lowe, D.; Kabacik, S.; et al.et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging-Us 2018, 10, 1758–1775, https://doi.org/10.18632/aging.101508.
  25. Bocklandt, S.; Lin, W.; Sehl, M.E.; Sanchez, F.J.; Sinsheimer, J.S.; Horvath, S.; Vilain, E.; Epigenetic Predictor of Age. PLoS ONE 2011, 6, e14821, https://doi.org/10.1371/journal.pone.0014821.
  26. Lu, A.T.; Quach, A.; Wilson, J.G.; Reiner, A.P.; Aviv, A.; Raj, K.; Hou, L.F.; Baccarelli, A.A.; Li, Y.; Stewart, J.D.; et al.et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging-Us 2019, 11, 303–327, https://doi.org/10.18632/aging.101684.
  27. Wang, M.; Lemos, B.; Ribosomal DNA harbors an evolutionarily conserved clock of biological aging. Genome Res. 2019, 29, 325–333, https://doi.org/10.1101/gr.241745.118.
  28. Peters, M.J.; Joehanes, R.; Pilling, L.C.; Schurmann, C.; Conneely, K.N.; Powell, J.; Reinmaa, E.; Sutphin, G.L.; Zhernakova, A.; Schramm, K.; et al.et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 2015, 6, 1-14, https://doi.org/10.1038/ncomms9570.
  29. Fleischer, J.G.; Schulte, R.; Tsai, H.H.; Tyagi, S.; Ibarra, A.; Shokhirev, M.N.; Huang, L.; Hetzer, M.; Navlakha, S.; Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 2018, 19, 1-18, https://doi.org/10.1186/s13059-018-1599-6.
  30. Putin, E.; Mamoshina, P.; Aliper, A.; Korzinkin, M.; Moskalev, A.; Kolosov, A.; Ostrovskiy, A.; Cantor, C.; Vijg, J.; Zhavoronkov, A.; et al. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging-Us 2016, 8, 1021–1033, https://doi.org/10.18632/aging.100968.
  31. Horvath, S.; DNA methylation age of human tissues and cell types. Genome Biol. 2013, 14, 1-20, https://doi.org/10.1186/gb-2013-14-10-r115.
  32. Mamoshina, P.; Kochetov, K.; Putin, E.; Aliper, A.; Zhavoronkov, A.; Testing for batch effect through age predictors. bioRxiv 2019, 1, 531863, https://doi.org/10.1101/531863.
  33. Bathke, J.; Konzer, A.; Remes, B.; McIntosh, M.; Klug, G.; Comparative analyses of the variation of the transcriptome and proteome of Rhodobacter sphaeroides throughout growth. BMC Genom. 2019, 20, 1-13, https://doi.org/10.1186/s12864-019-5749-3.
  34. Haider, S.; Pal, R.; Integrated analysis of transcriptomic and proteomic data. Curr. Genom. 2013, 14, 91–110, https://doi.org/10.2174/1389202911314020003.
  35. Tin, A.; Yu, B.; Ma, J.; Masushita, K.; Daya, N.; Hoogeveen, R.C.; Ballantyne, C.M.; Couper, D.; Rebholz, C.M.; Grams, M.E.; et al.et al. Reproducibility and variability of protein analytes measured using a multiplexed modified aptamer assay. J. Appl. Lab. Med. 2019, 4, 30–39, https://doi.org/10.1373/jalm.2018.027086.
  36. Tanaka, T.; Biancotto, A.; Moaddel, R.; Moore, A.Z.; Gonzalez-Freire, M.; Aon, M.A.; Candia, J.; Zhang, P.B.; Cheung, F.; Fantoni, G.; et al.et al. Plasma proteomic signature of age in healthy humans. Aging Cell 2018, 17, e12799, https://doi.org/10.1111/acel.12799.
  37. Johnson, A.A.; Shokhirev, M.N.; Wyss-Coray, T.; Lehallier, B.; Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res. Rev. 2020, 60, 101070, https://doi.org/10.1016/j.arr.2020.101070.
  38. Lehallier, B.; Gate, D.; Schaum, N.; Nanasi, T.; Lee, S.E.; Yousef, H.; Losada, P.M.; Berdnik, D.; Keller, A.; Verghese, J.; et al.et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 2019, 25, 1843–1850, https://doi.org/10.1038/s41591-019-0673-2.
  39. Hertel, J.; Friedrich, N.; Wittfeld, K.; Pietzner, M.; Budde, K.; Van der Auwera, S.; Lohmann, T.; Teumer, A.; Voelzke, H.; Nauck, M.; et al.et al. Measuring biological age via metabonomics: The metabolic age score. J. Proteome Res. 2016, 15, 400–410, https://doi.org/10.1021/acs.jproteome.5b00561.
  40. van den Akker, E.B.; Trompet, S.; Wolf, J.J.H.B.; Beekman, M.; Suchiman, H.E.D.; Deelen, J.; Predicting biological age based on the BBMRI-NL 1H-NMR metabolomics repository. bioRxiv 2019, 1, 632919, https://doi.org/10.1101/632919.
  41. Choi, J.; Hur, T.Y.; Hong, Y.; Influence of altered gut microbiota composition on aging and aging-related diseases. J. Lifestyle Med. 2018, 8, 1-7, https://doi.org/10.15280/jlm.2018.8.1.1.
  42. Galkin, F.; Mamoshina, P.; Aliper, A.; Putin, E.; Moskalev, V.; Gladyshev, V.N.; Zhavoronkov, A.; Human gut microbiome aging clock based on taxonomic profiling and deep learning. Iscience 2020, 23, 101199, https://doi.org/10.1016/j.isci.2020.101199.
  43. Claesson, M.J.; Jeffery, I.B.; Conde, S.; Power, S.E.; O'Connor, E.M.; Cusack, S.; Harris, H.M.B.; Coakley, M.; Lakshminarayanan, B.; O'Sullivan, O.; et al.et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 2012, 488, 178–184, https://doi.org/10.1038/nature11319.
  44. Woodmansey, E.J.; McMurdo, M.E.T.; Macfarlane, G.T.; Macfarlane, S.; Comparison of compositions and metabolic activities of fecal microbiotas in young adults and in antibiotic-treated and non-antibiotic-treated elderly subjects. Appl. Environ. Microbiol. 2004, 70, 6113–6122, https://doi.org/10.1128/aem.70.10.6113-6122.2004.
  45. Wu, L.; Xie, X.Q.; Liang, T.T.; Ma, J.; Yang, L.S.; Yang, J.; Li, L.Y.; Xi, Y.; Li, H.X.; Zhang, J.M.; et al. Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets. Biomolecules 2022, 12, 39, https://doi.org/10.3390/biom12010039.
  46. Cisek, K.; Krochmal, M.; Klein, J.; Mischak, H.; The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease. Nephrol. Dial. Transplant. 2016, 31, 2003–2011, https://doi.org/10.1093/ndt/gfv364.
  47. Solovev, I.; Shaposhnikov, M.; Moskalev, A.; Multi-omics approaches to human biological age estimation. Mech. Ageing Dev. 2020, 185, 111192, https://doi.org/10.1016/j.mad.2019.111192.
  48. Balasubramanian, P.; Howell, P.R.; Anderson, R.M.; Aging and caloric restriction research: A biological perspective with translational potential. EBioMedicine 2017, 21, 37–44, https://doi.org/10.1016/j.ebiom.2017.06.015.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback