Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model
Open accessAbstract
Although association studies have unveiled numerous correlations of biochemical markers with age and age-related diseases, we still lack an understanding of their mutual dependencies. To find molecular pathways that underlie age-related diseases as well as their comorbidities, we integrated aging markers from four different high-throughput omics datasets, namely epigenomics, transcriptomics, glycomics and metabolomics, with a comprehensive set of disease phenotypes from 510 participants of the TwinsUK cohort. We used graphical random forests to assess conditional dependencies between omics markers and phenotypes while eliminating mediated associations. Applying this novel approach for multi-omics data integration yields a model consisting of seven modules that represent distinct aspects of aging. These modules are connected by hubs that potentially trigger comorbidities of age-related diseases. As an example, we identified urate as one of these key players mediating the comorbidity of renal disease with body composition and obesity. Body composition variables are in turn associated with inflammatory IgG markers, mediated by the expression of the hormone oxytocin. Thus, oxytocin potentially contributes to the development of chronic low-grade inflammation, which often accompanies obesity. Our multi-omics graphical model demonstrates the interconnectivity of age-related diseases and highlights molecular markers of the aging process that might drive disease comorbidities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Glycomics and epigenomics have only appeared once in multi-omics context, along with mRNA and metabolomics, in Zierer's study of age-related comorbidities using graphical RF.
“On the other hand, glycomics and epigenomics have only appeared once in the multi-omics context (along with mRNA and metabolomics) and used by Zierer (Zierer et al., 2016) for the study of age-related comorbidities using a graphical variant of RF.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zierer et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,769 · see citing works
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603 · see citing works
Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C. P. M., Gustafsson, P., et al. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00034805 · Cited by 15,627 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
<b>mice</b>: Multivariate Imputation by Chained Equations in<i>R</i>
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219 · see citing works
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075 · see citing works
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
European Guidelines for Obesity Management in Adults
Yumuk, V., Tsigos, C., Fried, M., Schindler, K., Busetto, L., Micic, D., & Toplak, H. (2015) · Obesity Facts · doi:10.1159/000442721 · Cited by 3,432 · see citing works
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242 · see citing works
Adipocytokines: mediators linking adipose tissue, inflammation and immunity
Tilg, H., & Moschen, A. R. (2006) · Nature reviews. Immunology · doi:10.1038/nri1937 · Cited by 3,195 · see citing works
Lauritzen, S. L. (1996) · doi:10.1093/oso/9780198522195.001.0001 · Cited by 2,976 · see citing works
Coresh, J., Astor, B. C., Greene, T., Eknoyan, G., & Levey, A. S. (2002) · American Journal of Kidney Diseases · doi:10.1053/ajkd.2003.50007 · Cited by 2,761 · see citing works
Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes
Esser, N., Legrand-Poels, S., Piette, J., Scheen, A. J., & Paquot, N. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.04.006 · Cited by 1,981 · see citing works
Social effects of oxytocin in humans: context and person matter
Bartz, J. A., Zaki, J., Bolger, N., & Ochsner, K. N. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.05.002 · Cited by 1,572 · cited 1× · see citing works
“OXT is a hormone with a broad spectrum of functions, ranging from reproductive functions and control of social behavior 41 to energy metabolism 42 .”
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442 · see citing works
Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., & Wagner, D. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190689 · Cited by 1,243 · see citing works
Chronic kidney disease alters intestinal microbial flora
Vaziri, N. D., Wong, J., Pahl, M., Piceno, Y. M., Yuan, J., DeSantis, T. Z., Ni, Z., Nguyen, T.-H., & Andersen, G. L. (2013) · Kidney International · doi:10.1038/ki.2012.345 · Cited by 1,164 · see citing works
Aging of blood can be tracked by DNA methylation changes at just three CpG sites
Weidner, C. I., Lin, Q., Koch, C., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D., Jöckel, K., Erbel, R., Mühleisen, T., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r24 · Cited by 849 · see citing works
Roflumilast in symptomatic chronic obstructive pulmonary disease: two randomised clinical trials
Calverley, P. M. A., Rabe, K. F., Goehring, U.-M., Kristiansen, S., Fabbri, L. M., & Martínez, F. J. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61255-1 · Cited by 821 · see citing works
Mapping cis- and trans-regulatory effects across multiple tissues in twins
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Grundberg, E., Small, K. S., Hedman, Å. K., Nica, A. C., Buil, A., Keildson, S., Bell, J. T., Yang, T.-P., Meduri, E., et al. (2012) · Nature Genetics · doi:10.1038/ng.2394 · Cited by 729 · see citing works
Self-citationMechanisms by Which Dehydration May Lead to Chronic Kidney Disease
Roncal-Jiménez, C. A., Lanaspa, M. A., Jensen, T., Sánchez‐Lozada, L. G., & Johnson, R. J. (2015) · Annals of Nutrition and Metabolism · doi:10.1159/000381239 · Cited by 696 · see citing works
Uric acid and chronic kidney disease: which is chasing which?
Johnson, R. J., Nakagawa, T., Jalal, D. I., Sánchez‐Lozada, L. G., Kang, D., & Ritz, E. (2013) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gft029 · Cited by 654 · see citing works
Systems genetics approaches to understand complex traits
Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621 · see citing works
Butte, A. J., Tamayo, P., Slonim, D. K., Golub, T. R., & Kohane, I. S. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.220392197 · Cited by 604 · see citing works
Baulieu, E.-E., Thomas, G., Legrain, S., Lahlou, N., Roger, M., Debuire, B., Faucounau, V., Girard, L., Hervy, M.-P., Latour, F., et al. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.8.4279 · Cited by 577 · see citing works
Predicting disease genes using protein–protein interactions
Oti, M., Snel, ., Huynen, M. A., & Brunner, H. G. (2006) · Journal of Medical Genetics · doi:10.1136/jmg.2006.041376 · Cited by 574 · see citing works
Pučić‐Baković, M., Knežević, A., Vidič, J., Adamczyk, B., Novokmet, M., Polašek, O., Gornik, O., Goreta, S. Š., Wormald, M. R., Redžić, I., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.010090 · Cited by 521 · see citing works
Self-citationMasuo, K., Kawaguchi, H., Mikami, H., Ogihara, T., & Tuck, M. L. (2003) · Hypertension · doi:10.1161/01.hyp.0000091371.53502.d3 · Cited by 516 · see citing works
The implications of human metabolic network topology for disease comorbidity
Lee, D., Park, J., Kay, K. A., Christakis, N. A., Oltvai, Z. N., & Barabási, A.-Ĺ. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802208105 · Cited by 489 · see citing works
Karsten, C. M., Pandey, M. K., Figge, J., Kilchenstein, R., Taylor, P. R., Rosas, M., McDonald, J. U., Orr, S. J., Berger, M., Petzold, D., et al. (2012) · Nature Medicine · doi:10.1038/nm.2862 · Cited by 471 · see citing works
Nutritional methods in the European Prospective Investigation of Cancer in Norfolk
Bingham, S. A., Welch, A. A., McTaggart, A., Mulligan, A. A., Runswick, S. A., Luben, R., Oakes, S., Khaw, K., Wareham, N. J., & Day, N. (2001) · Public Health Nutrition · doi:10.1079/phn2000102 · Cited by 421 · see citing works
Long-term longitudinal measurements of plasma dehydroepiandrosterone sulfate in normal men
Orentreich, N., Brind, J. L., Vogelman, J. H., Andres, R., & Baldwin, H. (1992) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.75.4.1400863 · Cited by 417 · see citing works
Glycans Are a Novel Biomarker of Chronological and Biological Ages
Krištić, J., Vučković, F., Menni, C., Klarić, L., Keser, T., Beceheli, I., Pučić-Baković, M., Novokmet, M., Mangino, M., Thaqi, K., et al. (2014) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glt190 · Cited by 400 · see citing works
Self-citationGrundberg, E., Meduri, E., Sandling, J. K., Hedman, Å. K., Keildson, S., Buil, A., Busche, S., Yuan, W., Nisbet, J., Sekowska, M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.10.004 · Cited by 370 · see citing works
Self-citationGaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data
Krumsiek, J., Suhre, K., Illig, T., Adamski, J., & Theis, F. J. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-21 · Cited by 323 · see citing works
Self-citationVariable Selection with Error Control: Another Look at Stability Selection
Shah, R. D., & Samworth, R. J. (2012) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2011.01034.x · Cited by 301 · see citing works
Metabolomic markers reveal novel pathways of ageing and early development in human populations
Menni, C., Kastenmüller, G., Petersen, A. K., Bell, J. T., Psatha, M., Tsai, P.-C., Gieger, C., Schulz, H., Erte, I., John, S., et al. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt094 · Cited by 282 · see citing works
Self-citationGene expression changes with age in skin, adipose tissue, blood and brain
Glass, D., Viñuela, A., Davies, M. N., Ramasamy, A., Parts, L., Knowles, D. A., Brown, A. A., Hedman, Å. K., Small, K. S., Buil, A., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r75 · Cited by 281 · see citing works
Self-citationMechanisms of the anti-obesity effects of oxytocin in diet-induced obese rats
Deblon, N., Veyrat-Durebex, C., Bourgoin, L., Caillon, A., Bussier, A.-L., Petrosino, S., Piscitelli, F., Legros, J.-J., Geenen, V., Foti, M., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025565 · Cited by 254 · see citing works
The impact of cellular networks on disease comorbidity
Park, J. M., Lee, D., Christakis, N. A., & Barabási, A.-Ĺ. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.16 · Cited by 245 · see citing works
N-glycomic biomarkers of biological aging and longevity: A link with inflammaging
Dall’Olio, F., Vanhooren, V., Chen, C. C., Slagboom, P. E., Wuhrer, M., & Franceschi, C. (2012) · Ageing Research Reviews · doi:10.1016/j.arr.2012.02.002 · Cited by 231 · see citing works
Treatment of obesity and diabetes using oxytocin or analogs in patients and mouse models
Zhang, H., Wu, C., Chen, Q., Chen, X., Xu, Z., Wu, J., & Cai, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061477 · Cited by 224 · see citing works
The influence of the gut microbiome on obesity, metabolic syndrome and gastrointestinal disease
Parekh, P. J., Balart, L. A., & Johnson, D. A. (2015) · Clinical and Translational Gastroenterology · doi:10.1038/ctg.2015.16 · Cited by 219 · see citing works
Oxytocin reduces caloric intake in men
Lawson, E. A., Marengi, D. A., DeSanti, R. L., Holmes, T. M., Schoenfeld, D. A., & Tolley, C. J. (2015) · Obesity · doi:10.1002/oby.21069 · Cited by 208 · see citing works
Szeto, A., Nation, D. A., Mendez, A. J., Domínguez‐Bendala, J., Brooks, L., Schneiderman, N., & McCabe, P. M. (2008) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.90718.2008 · Cited by 207 · see citing works
Variable selection with error control: Another look at stability selection
Shah, R. D., & Samworth, R. J. (2011) · Cited by 203 · see citing works
Early-onset metabolic syndrome in mice lacking the intestinal uric acid transporter SLC2A9
DeBosch, B. J., Kluth, O., Fujiwara, H., Schürmann, A., & Moley, K. H. (2014) · Nature Communications · doi:10.1038/ncomms5642 · Cited by 198 · see citing works
Yang, J., Huang, T., Petralia, F., Long, Q., Zhang, B., Argmann, C., Zhao, Y. B., Mobbs, C. V., Schadt, E. E., Zhu, J., et al. (2015) · Scientific Reports · doi:10.1038/srep15145 · Cited by 198 · see citing works
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A.-L., Baranzini, S., Brunak, S., Chung, K. F., Federoff, H. J., Gavin, A.-C., Meehan, R. R., et al. (2014) · Genome Medicine · doi:10.1186/s13073-014-0082-6 · Cited by 194 · see citing works
Incidence of cardiovascular disease and cancer in advanced age: prospective cohort study
Driver, J. A., Djousse, L., Logroscino, G., Gaziano, J. M., & Kurth, T. (2008) · BMJ · doi:10.1136/bmj.a2467 · Cited by 184 · see citing works
Maeda, K., Mehta, H., Drevets, D., & Coggeshall, K. (2010) · Blood · doi:10.1182/blood-2009-07-230631 · Cited by 164 · see citing works
Close correlation between visceral fat accumulation and uric acid metabolism in healthy men
Takahashi, S., Yamamoto, T., Tsutsumi, Z., Moriwaki, Y., Yamakita, J.-I., & Higashino, K. (1997) · Metabolism · doi:10.1016/s0026-0495(97)90210-9 · Cited by 155 · see citing works
Dehydroepiandrosterone (DHEA) prevents and reverses chronic hypoxic pulmonary hypertension
Bonnet, S., de la Roque, E. D., Bégueret, H., Marthan, R., Fayon, M. J., Santos, P. D. D., Savineau, J., & Baulieu, É.-É. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1633724100 · Cited by 145 · see citing works
Circulating Proteomic Signatures of Chronological Age
Menni, C., Kiddle, S. J., Mangino, M., Viñuela, A., Psatha, M., Steves, C., Sattlecker, M., Buil, A., Newhouse, S., Nelson, S., et al. (2015) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glu121 · Cited by 144 · see citing works
Self-citationIntegration of ‘omics’ data in aging research: from biomarkers to systems biology
Zierer, J., Menni, C., Kastenmüller, G., & Spector, T. D. (2015) · Aging Cell · doi:10.1111/acel.12386 · Cited by 139 · see citing works
Self-citationNakanishi, N., Suzuki, K., & Tatara, K. (2003) · Diabetes Care · doi:10.2337/diacare.26.1.48 · Cited by 120 · see citing works
Dietary Patterns and Heritability of Food Choice in a UK Female Twin Cohort
Teucher, B., Skinner, J. S., Skidmore, P. M. L., Cassidy, A., Fairweather‐Tait, S. J., Hooper, L., Roe, M. A., Foxall, R., Oyston, S. L., Cherkas, L. F., et al. (2007) · Twin Research and Human Genetics · doi:10.1375/twin.10.5.734 · Cited by 114 · see citing works
Self-citationIntegrative network analysis reveals molecular mechanisms of blood pressure regulation
Huan, T., Meng, Q., Saleh, M. A., Norlander, A. E., Joehanes, R., Zhu, J., Chen, B. H., Zhang, B., Johnson, A. D., Ying, S., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145399 · Cited by 104 · see citing works
The new concept of adipose tissue function
Hauner, H. (2004) · Physiology & Behavior · doi:10.1016/j.physbeh.2004.09.016 · Cited by 104 · see citing works
A prospective study of weight change and systemic inflammation over 9 y
Fogarty, A., Glancy, C., Jones, S. G., Lewis, S., McKeever, T. M., & Britton, J. R. (2008) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/87.1.30 · Cited by 98 · see citing works
Glycosylation of Immunoglobulin G: Role of Genetic and Epigenetic Influences
Menni, C., Keser, T., Mangino, M., Bell, J. T., Yet, İ., Trbojević‐Akmačić, I., Vučković, F., Pučić‐Baković, M., Gornik, O., McCarthy, M. I., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082558 · Cited by 97 · see citing works
Self-citationRole of oxytocin in energy metabolism
Chaves, V. E., Tilelli, C. Q., Brito, N. A., & Brito, M. N. (2013) · Peptides · doi:10.1016/j.peptides.2013.04.010 · Cited by 90 · see citing works
Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables
Fellinghauer, B. A. G., Bühlmann, P., Ryffel, M., von Rhein, M., & Reinhardt, J. D. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.02.022 · Cited by 76 · see citing works
Arca, M., Montali, A., Valiante, S., Campagna, F., Pigna, G., Paoletti, V., Antonini, R., Barillà, F., Tanzilli, G., Vestri, A., et al. (2007) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2007.06.049 · Cited by 75 · see citing works
Glycosylation Profile of IgG in Moderate Kidney Dysfunction
Barrios, C. P., Zierer, J., Gudelj, I., Štambuk, J., Ugrina, I., Rodríguez, E., Soler, M. J., Štambuk, T., Šimurina, M., Keser, T., et al. (2015) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015010109 · Cited by 65 · see citing works
Self-citationImmunoglobulin G subclasses and spirometry in patients with chronic obstructive pulmonary disease
O’Keeffe, S. T., G�zel, A., Drury, R., Cullina, M., Greally, J. F., & Finnegan, P. (1991) · European Respiratory Journal · doi:10.1183/09031936.93.04080932 · Cited by 52 · see citing works
Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health
Menni, C., Migaud, M. E., Glastonbury, C. A., Beaumont, M., Nikolaou, A., Small, K. S., Brosnan, M. J., Mohney, R. P., Spector, T. D., & Valdes, A. M. (2016) · Obesity · doi:10.1002/oby.21488 · Cited by 46 · see citing works
Self-citationRelation between serum triglyceride level, serum urate concentration, and fractional urate excretion
Giacomello, A., Sciascio, N. D., & Quaratino, C. P. (1997) · Metabolism · doi:10.1016/s0026-0495(97)90283-3 · Cited by 28 · see citing works
Nihlén, U., Montnémery, P., Lindholm, L. H., & Löfdahl, C. G. (2001) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/003655101316911378 · Cited by 25 · see citing works
The role of PDE4 in pulmonary inflammation and goblet cell hyperplasia in allergic rats
Tang, H., Chen, J., Xie, Q.-M., Zheng, X., Zhu, Y.-L., Adcock, I. M., & Wang, X. (2006) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2005.12.009 · Cited by 17 · see citing works
Flagged first, then by citations
1 of the 75 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240 · see citing works
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Wilkinson, D. J., Piasecki, M., & Atherton, P. J. (2018) · Ageing Research Reviews · doi:10.1016/j.arr.2018.07.005 · Cited by 885 · see citing works
Integration strategies of multi-omics data for machine learning analysis
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696 · see citing works
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686 · see citing works
Cai, Y., Song, W., Li, J., Jing, Y., Liang, C., Zhang, L., Zhang, X., Zhang, W., Liu, B., An, Y., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2161-3 · Cited by 436 · see citing works
Multi-omics integration in biomedical research – A metabolomics-centric review
Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2020) · Analytica Chimica Acta · doi:10.1016/j.aca.2020.10.038 · Cited by 359 · see citing works
Self-citationMetabolomics: small molecules that matter more
Muthubharathi, B. C., Gowripriya, T., & Balamurugan, K. (2021) · Molecular Omics · doi:10.1039/d0mo00176g · Cited by 214 · see citing works
Pietzner, M., Kaul, A., Henning, A., Kastenmüller, G., Artati, A., Lerch, M. M., Adamski, J., Nauck, M. A., & Friedrich, N. (2017) · BMC Medicine · doi:10.1186/s12916-017-0974-6 · Cited by 152 · see citing works
Self-citationInferring Interaction Networks From Multi-Omics Data
Hawe, J. S., Theis, F. J., & Heinig, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00535 · Cited by 139 · see citing works
Liu, C. H., Abrams, N., Carrick, D. M., Chander, P., Dwyer, J. T., Hamlet, M. R. J., Macchiarini, F., PrabhuDas, M., Shen, G. L., Tandon, P., et al. (2017) · Nature Immunology · doi:10.1038/ni.3828 · Cited by 132 · see citing works
Kwon, J. H., Moon, K. M., & Min, K.-W. (2020) · Healthcare · doi:10.3390/healthcare8040378 · Cited by 116 · see citing works
Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools
Altenbuchinger, M., Weihs, A., Quackenbush, J. F., Grabe, H. J., & Zacharias, H. U. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194418 · Cited by 87 · see citing works
Chu, S. H., Huang, M., Kelly, R., Benedetti, E., Siddiqui, J. K., Zeleznik, O., Pereira, A., Herrington, D., Wheelock, C., Krumsiek, J., et al. (2019) · Metabolites · doi:10.3390/metabo9060117 · Cited by 73 · see citing works
Self-citationHealth and Aging: Unifying Concepts, Scores, Biomarkers and Pathways
Fuellen, G., Jansen, L., Cohen, A. A., Luyten, W. H. M. L., Gogol, M., Simm, A., Saul, N., Cirulli, F., Berry, A., Antal, P., et al. (2018) · Aging and Disease · doi:10.14336/ad.2018.1030 · Cited by 77 · see citing works
Sarno, F., Benincasa, G., List, M., Barabási, A.-L., Baumbach, J., Ciardiello, F., Filetti, S., Glass, K., Loscalzo, J., Marchese, C., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01047-z · Cited by 73 · see citing works
Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
Lobato-Delgado, B., Priego, B., & Morillo, D. (2022) · Cancers · doi:10.3390/cancers14133215 · Cited by 68 · see citing works
Westerlund, A. M., Hawe, J. S., Heinig, M., & Schunkert, H. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910291 · Cited by 61 · see citing works
Variability of multi-omics profiles in a population-based child cohort
Gallego-Paüls, M., Hernandéz-Ferrer, C., Bustamante, M., Basagaña, X., Barrera‐Gómez, J., Lau, C. E., Siskos, A., Vives-Usano, M., Ruiz-Arenas, C., Wright, J., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02027-z · Cited by 49 · see citing works
Muscle aging and sarcopenia: The pathology, etiology, and most promising therapeutic targets
Grima-Terrén, M., Campanario, S., Ramírez‐Pardo, I., Cisneros, A., Hong, X., Perdiguero, E., Serrano, A. L., Isern, J., & Muñoz‐Cánoves, P. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101319 · Cited by 49 · see citing works
Menni, C., Migaud, M. E., Kastenmüller, G., Pallister, T., Zierer, J., Peters, A., Mohney, R. P., Spector, T. D., Bagnardi, V., Gieger, C., et al. (2017) · Obesity · doi:10.1002/oby.21922 · Cited by 38 · see citing works
Self-citationGenome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology
Köttgen, A., Raffler, J., Sekula, P., & Kastenmüller, G. (2018) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2018.01.009 · Cited by 37 · see citing works
Self-citationBalzano‐Nogueira, L., Ramirez, R., Zamkovaya, T., Dailey, J. M., Ardissone, A. N., Chamala, S., Serrano‐Quílez, J., Rubio, T., Haller, M. J., CONCANNON, P. J., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02262-w · Cited by 36 · see citing works
Physiological markers and multimorbidity
Ferreira, G. D., Simões, J. A., Senaratna, C. V., Pati, S., Timm, P. F., Batista, S. R., & Nunes, B. P. (2018) · Journal of Comorbidity · doi:10.1177/2235042x18806986 · Cited by 34 · see citing works
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks
Freund, A. (2019) · Cell Systems · doi:10.1016/j.cels.2019.02.005 · Cited by 32 · see citing works
Ogris, C., Hu, Y., Arloth, J., & Müller, N. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-85544-4 · Cited by 26 · see citing works
Cindrić, A., Krištić, J., Kavur, M. M., & Pezer, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-70115-4_17 · Cited by 27 · see citing works
Molecular bases of comorbidities: present and future perspectives
Sánchez-Valle, J., & Valencia, A. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.06.003 · Cited by 24 · see citing works
Fabbri, L., Garlantézec, R., Audouze, K., Bustamante, M., Carracedo, Á., Chatzi, L., González, J. R., Gražulevičienė, R., Keun, H. C., Lau, C. E., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.107856 · Cited by 24 · see citing works
Berlin, R. B., Gruen, R. L., & Best, J. D. (2018) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2018.00112 · Cited by 23 · see citing works
Mechanisms and Advances of Epigenetic Regulation in Cardiovascular Disease
Wang, X., Teng, X., Luo, C., & Kong, L. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2906205 · Cited by 22 · see citing works
Emerging approaches to multiple chronic condition assessment
Suls, J. M., Salive, M. E., Koroukian, S. M., Alemi, F., Silber, J. H., Kastenmüller, G., & Klabunde, C. N. (2022) · Journal of the American Geriatrics Society · doi:10.1111/jgs.17914 · Cited by 19 · see citing works
Self-citationNiedzwiedz, C. L., Katikireddi, S. V., Pell, J. P., & Smıth, D. J. (2019) · Age and Ageing · doi:10.1093/ageing/afz071 · Cited by 18 · see citing works
Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome
Suhre, K., Trbojević‐Akmačić, I., Ugrina, I., Mook-Kanamori, D. O., Spector, T. D., Graumann, J., Lauc, G., & Falchi, M. (2019) · Metabolites · doi:10.3390/metabo9070122 · Cited by 13 · see citing works
Self-citationLee, H.-Y., Jeon, Y., Kim, Y. K., Jang, J. Y., Cho, Y. S., Bhak, J., & Cho, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-91811-1 · Cited by 13 · see citing works
Metabolite and protein associations with general health in the population-based CHRIS study
Hantikainen, E. M., Weichenberger, C. X., Đorđević, N., Hernandes, V. V., Foco, L., Gögele, M., Melotti, R., Pattaro, C., Ralser, M., Amari, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-75627-3 · Cited by 8 · see citing works
Ageotypes: Distinct Biomolecular Trajectories in Human Aging
Piening, B., Lovejoy, J. C., & Earls, J. C. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.02.003 · Cited by 7 · see citing works
Tokarz, J., & Adamski, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-812784-1.00002-5 · Cited by 7 · see citing works
Epigenetic age acceleration in peripheral blood correlates with brain-MRI age acceleration
Ferreira, P. S. B., van Dongen, J., den Braber, A., Boomsma, D. I., de Geus, E. J. C., & Ent, D. V. ‘. (2025) · Brain · doi:10.1093/brain/awaf069 · Cited by 6 · see citing works
Xia, Y., Islam, S. M. S., Li, X., Baten, A., Tan, X., & Wang, W. (2025) · Engineering · doi:10.1016/j.eng.2025.08.016 · Cited by 5 · see citing works
Angarita-Rodríguez, A., Mejía, N. M., González, J., Aristizábal-Pachón, A. F., Hidalgo‐Lanussa, O., Rubio-Mesa, J. J., Barreto, G. E., & Pinzón, A. M. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.896265 · Cited by 5 · see citing works
Seo, D., Do, A. N., Heo, G., Kwon, J., Song, S., Apio, C., Lee, C. M., Timsina, J., Gong, K., Chen, Y., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00971-6 · Cited by 10 · see citing works
Ribosome, Protein Synthesis, and Aging
Manhas, R., & Rath, P. C. (2020) · Springer eBooks · doi:10.1007/978-981-32-9005-1_4 · Cited by 4 · see citing works
Chen, Q., Zhang, L., Huang, D., Zhang, C., Wang, Q., Shen, D., Minjun, X., & Yang, F. (2019) · Chinese Medical Sciences Journal · doi:10.24920/003496 · Cited by 3 · see citing works
Seo, D., Lee, C. M., Apio, C., Heo, G., Timsina, J., Kohlfeld, P., Boada, M., Orellana, A., Fernández, M. V., Ruiz, A., et al. (2024) · medRxiv · doi:10.1101/2024.06.18.24309102 · Cited by 3 · see citing works
Wang, W., Wang, K., Wang, W., Mu, X., Cheng, Z., Zhang, G., Kang, X., & 王勇刚 (2026) · Stem Cell Reviews and Reports · doi:10.1007/s12015-026-11190-w · Cited by 2 · see citing works
Mass Spectrometry for Lipidomics: Methods and Applications – Aging and A lzheimer's Disease
Huynh, K., Beyene, H. B., Wang, T., Giles, C., & Meikle, P. J. (2023) · Wiley eBooks · doi:10.1002/9783527836512.ch25 · Cited by 2 · see citing works
MACHINE LEARNING APPROACHES FOR INTEGRATIVE MULTI-OMICS DATA ANALYSIS OF CANCER: A SYSTEMATIC REVIEW
Hassan, A. M., Naeem, S. M., Eldosoky, M. A. A., & Mabrouk, M. S. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225300044 · Cited by 1 · see citing works
Multiomics Research Strategies in Cancer: A Growing and Innovative Field
Du, Z., Liu, X., Lv, Z., Wang, B., Xia, Y., Al-duais, W. A. M., Yan, L., Zhang, F., & Li, Y. (2026) · MedComm · doi:10.1002/mco2.70644 · Cited by 1 · see citing works
Singh, V., Shirbhate, E., Johariya, V., Kore, R., Veerasamy, R., Tiwari, A. K., & Rajak, H. (2026) · The FASEB Journal · doi:10.1096/fj.202602229r · Cited by 1 · see citing works
Metabolic phenotyping for understanding the gut microbiome and host metabolic interplay
Basson, A. R., & Wijeyesekera, A. (2017) · Emerging Topics in Life Sciences · doi:10.1042/etls20170079 · Cited by 1 · see citing works
Molecular and Epigenetic Clocks of Aging
Raj, S. M., & Lussier, A. A. (2019) · Springer eBooks · doi:10.1007/978-3-319-69892-2_725-2
Skeletal muscle health in aging
Grevendonk, L. (2021) · doi:10.26481/dis.20211006lg
Molecular and Epigenetic Clocks of Aging
Raj, S. M., & Lussier, A. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-22009-9_725
Elucidating Diversity in Obesity-Related Phenotypes Using Longitudinal and Multi-omic Approaches
Piening, B., Dowdell, A. K., & Snyder, M. P. (2022) · Springer eBooks · doi:10.1007/978-3-030-92196-5_2
Molecular and Epigenetic Clocks of Aging
Raj, S. M., & Lussier, A. A. (2019) · Springer eBooks · doi:10.1007/978-3-319-69892-2_725-1
Additional file 2 of Variability of multi-omics profiles in a population-based child cohort
Gallego-Paüls, M., Hernandéz-Ferrer, C., Bustamante, M., Basagaña, X., Barrera‐Gómez, J., Lau, C. E., Siskos, A., Vives-Usano, M., Ruiz-Arenas, C., Wright, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15033205
Hantikainen, E. M., Weichenberger, C. X., Đorđević, N., Hernandes, V. V., Foco, L., Gögele, M., Melotti, R., Pattaro, C., Ralser, M., Amari, F., et al. (2024) · medRxiv · doi:10.1101/2024.07.15.24310410
Aspects of Biological Age Assessment and Its Significance in Ophthalmology
Yusef, Y., Guseynov, Y. A., & Durzhinskaya, M. H. (2024) · Ophthalmology in Russia · doi:10.18008/1816-5095-2024-4-844-849
Kar, D., & Roy, S. (2025) · Anthropological Review · doi:10.18778/1898-6773.88.2.05
Balzano-Nogueira, L., Ramirez, R., Zamkovaya, T., Dailey, J., Ardissone, A. N., Chamala, S., Serrano‐Quílez, J., Rubio, T., Haller, M. J., CONCANNON, P. J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13627229
Additional file 1 of Variability of multi-omics profiles in a population-based child cohort
Gallego-Paüls, M., Hernández-Ferrer, C., Bustamante, M., Basagaña, X., Barrera-Gómez, J., Lau, C., Siskos, A. P., Vives-Usano, M., Ruiz-Arenas, C., Wright, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15033202
Balzano-Nogueira, L., Ramirez, R., Zamkovaya, T., Dailey, J., Ardissone, A. N., Chamala, S., Serrano‐Quílez, J., Rubio, T., Haller, M. J., CONCANNON, P. J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13627223
Pradeepkiran, J. A., Munikumar, M., More, P., Pradhan, D. P., Maddineni, J., Kosanam, M., Srichurnam, S., Malempati, T., Thota, A., & Sainath, S. B. (2026) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2026.141192
- Authors
- Zierer, J., Pallister, T., Tsai, P.-C., Krumsiek, J., Bell, J. T., Lauc, G., Spector, T. D., Menni, C., & Kastenmüller, G.
- Affiliations
- King's College London (GB)
- Helmholtz Munich (DE)
- Corresponding author
- Kastenmüller, Gabi
- ORCID
- Venue
- Scientific Reports
- Published
- 2016-11-25
- Volume
- 6
- Issue
- 1
- Pages
- 37646-37646
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5122881
- OpenAlex
- W2553767656
- Cited by
- 63
- References
- 75
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 3 · 2022: 5 · 2021: 15 · 2020: 5 · 2019: 9 · 2018: 6 · 2017: 5
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, cc-by-nc)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- Funders
- Wellcome Trust (FP7/2007–2013)
- National Institute for Health and Care Research (FP7/2007–2013, NF-SI-0514-10027)
- King's College London
- Medical Research Council (FP7/20072013, MR/M004422/1, MR/M016560/1)
- Economic and Social Research Council (ES/N000404/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- age-related diseases, comorbidities, aging, multi-omics integration, conditional dependence, epigenomics, transcriptomics, glycomics, metabolomics, uric acid, kidney disease, body composition, obesity, IgG, oxytocin, low-grade inflammation
- MeSH terms
- Aging, Humans, Models, Biological, Comorbidity, Proteomics, Metabolomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
