A Longitudinal Big Data Approach for Precision Health
Open accessAbstract
Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.
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?
Omics data, including transcriptomics, proteomics, and metabolomics, have injected mechanistic insights into predictive modeling for diabetic complications.
“Omics data, including transcriptomics, proteomics, and metabolomics, have injected mechanistic insights into predictive modeling for diabetic complications [158–160].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [160]
The Results (Metabolic Health Profiling; page not provided) report that multi-omics measurements improved prediction of insulin resistance and oral glucose tolerance, with transcriptomic and metabolomic predictors and a protein among the identified molecules. The Discussion links this profiling to insights into glucose dysregulation. The paper does not show predictive modeling of diabetic complications, so that part of the claim is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDeep phenotyping integrates genomic sequencing, blood-based omics, microbiome analysis, and wearable device data to capture each patient's unique biological state over time.
“This strategy integrates genomic sequencing, blood-based omics (e.g., metabolomics and proteomics), microbiome analysis, and wearable device data to capture each patient’s unique biological state over time.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [95]
Taking millions of measurements per individual over time enabled the early detection of potential disease states.
“We found that taking millions of measurements per individual over time enabled the early detection of potential disease states51,87.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [87]
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
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698 · see citing works
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationSecond-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
2013 ACCF/AHA Guideline for the Management of Heart Failure
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., Fonarow, G. C., Geraci, S. A., Horwich, T., Januzzi, J. L., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.019 · Cited by 12,536 · see citing works
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436 · see citing works
Lang, R., Badano, L., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F., Foster, E., Goldstein, S., Kuznetsova, T., et al. (2015) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.10.003 · Cited by 10,093 · see citing works
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581 · see citing works
Graph drawing by force‐directed placement
Fruchterman, T. M. J., & Reingold, E. M. (1991) · Software: Practice and Experience · doi:10.1002/spe.4380211102 · Cited by 6,352 · see citing works
Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M. R., Bolyen, E. K., Knight, R. D., Huttley, G. A., & Caporaso, J. G. (2018) · Microbiome · doi:10.1186/s40168-018-0470-z · Cited by 5,873 · see citing works
Matsuda, M., & DeFronzo, R. A. (1999) · Diabetes Care · doi:10.2337/diacare.22.9.1462 · Cited by 5,621 · see citing works
2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.005 · Cited by 4,945 · see citing works
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437741.48606.98 · Cited by 3,697 · see citing works
Lee, P. H., Macfarlane, D. J., Lam, T., & Stewart, S. M. (2011) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-8-115 · Cited by 3,370 · see citing works
Exact sequence variants should replace operational taxonomic units in marker-gene data analysis
Callahan, B. J., McMurdie, P. J., & Holmes, S. (2017) · The ISME Journal · doi:10.1038/ismej.2017.119 · Cited by 3,253 · see citing works
Ahlqvist, E., Storm, P., Käräjämäki, A., Martinell, M., Dorkhan, M., Carlsson, A., Vikman, P., Prasad, R. B., Aly, D. M., Almgren, P., et al. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30051-2 · Cited by 2,335 · see citing works
Arterial Stiffness and Cardiovascular Events: The Framingham Heart Study
Mitchell, G. F., Hwang, S.-J., Vasan, R. S., Larson, M. G., Pencina, M. J., Hamburg, N. M., Vita, J. A., Levy, D., & Benjamin, E. J. (2010) · Circulation · doi:10.1161/circulationaha.109.886655 · Cited by 2,225 · see citing works
The max-min hill-climbing Bayesian network structure learning algorithm
Tsamardinos, I., Brown, L. E., & Aliferis, C. (2006) · Machine Learning · doi:10.1007/s10994-006-6889-7 · Cited by 1,830 · see citing works
Pharmacogenomics Knowledge for Personalized Medicine
Whirl‐Carrillo, M., McDonagh, E. M., Hebert, J. M., Gong, L., Sangkuhl, K., Thorn, C. F., Altman, R. B., & Klein, T. E. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.96 · Cited by 1,791 · see citing works
Kalia, S. S., Adelman, K., Bale, S. J., Chung, W. K., Eng, C. M., Evans, J. P., Herman, G. E., Hufnagel, S. B., Klein, T. E., Korf, B. R., et al. (2016) · Genetics in Medicine · doi:10.1038/gim.2016.190 · Cited by 1,646 · see citing works
Malik, R., Chauhan, G., Traylor, M., Sargurupremraj, M., Okada, Y., Mishra, A., Rutten‐Jacobs, L. C. A., Giese, A., van der Laan, S. W., Grétarsdóttir, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0058-3 · Cited by 1,566 · see citing works
The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review
Klok, M. D., Jakobsdottir, S., & Drent, M. L. (2007) · Obesity Reviews · doi:10.1111/j.1467-789x.2006.00270.x · Cited by 1,442 · see citing works
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291 · see citing works
Self-citationACG clinical guideline: evaluation of abnormal liver chemistries
Kwo, P. Y., Cohen, S. M., & Lim, J. K. (2017) · American Journal of Gastroenterology · doi:10.1038/ajg.2016.517 · Cited by 1,222 · see citing works
Use of metabolic markers to identify overweight individuals who are insulin resistant
McLaughlin, T., Abbasi, F., Cheal, K., Chu, J., Lamendola, C., & Reaven, G. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-139-10-200311180-00007 · Cited by 1,022 · see citing works
Self-citationCauter, E. V., Mestrez, F., Sturis, J., & Polonsky, K. (1992) · Diabetes · doi:10.2337/diab.41.3.368 · Cited by 938 · see citing works
Berry, C. E., & Hare, J. M. (2003) · The Journal of Physiology · doi:10.1113/jphysiol.2003.055913 · Cited by 925 · see citing works
Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis
Guasch‐Ferré, M., Hruby, A., Toledo, E., Clish, C. B., Martínez‐González, M. Á., Salas‐Salvadó, J., & Hu, F. B. (2016) · Diabetes Care · doi:10.2337/dc15-2251 · Cited by 908 · see citing works
Rolny, C., Mazzone, M., Tugues, S., Laoui, D., Johansson, I., Coulon, C., Squadrito, M. L., Segura, I., Li, X., Knevels, E., et al. (2011) · Cancer Cell · doi:10.1016/j.ccr.2010.11.009 · Cited by 695 · see citing works
McClelland, R. L., Jorgensen, N. W., Budoff, M. J., Blaha, M. J., Post, W. S., Kronmal, R. A., Bild, D. E., Shea, S., Liu, K., Watson, K. E., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.08.035 · Cited by 685 · see citing works
Genetic cause of hyperglycaemia and response to treatment in diabetes
Pearson, E. R., Starkey, B. J., Powell, R. J., Gribble, F. M., Clark, P. M., & Hattersley, A. T. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14571-0 · Cited by 607 · see citing works
Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease
Wollert, K. C., Kempf, T., & Wallentin, L. C. (2016) · Clinical Chemistry · doi:10.1373/clinchem.2016.255174 · Cited by 569 · see citing works
Elevated Risk of Cardiovascular Disease Prior to Clinical Diagnosis of Type 2 Diabetes
Hu, F. B., Stampfer, M. J., Haffner, S. M., Solomon, C. G., Willett, W. C., & Manson, J. E. (2002) · Diabetes Care · doi:10.2337/diacare.25.7.1129 · Cited by 552 · see citing works
(2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.08.014 · Cited by 536 · see citing works
Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Schüssler-Fiorenza Rose, S. M., Perelman, D., Colbert, E., Runge, R., Rego, S., et al. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001402 · Cited by 431 · see citing works
Self-citationA wellness study of 108 individuals using personal, dense, dynamic data clouds
Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3870 · Cited by 427 · see citing works
Self-citationIntegrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA
Kamburov, A., Cavill, R., Ebbels, T. M. D., Herwig, R., & Keun, H. C. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr499 · Cited by 403 · see citing works
GM-CSF in inflammation and autoimmunity
Hamilton, J. A. (2002) · Trends in Immunology · doi:10.1016/s1471-4906(02)02260-3 · Cited by 373 · see citing works
Iikuni, N., Lam, Q. K., Lu, L., Matarese, G., & Cava, A. L. (2008) · Current Immunology Reviews · doi:10.2174/157339508784325046 · Cited by 366 · see citing works
Cho, V., Mei, Y., Sanny, A., Chan, S., Enders, A., Bertram, E. M., Tan, A. H., Goodnow, C. C., & Andrews, T. D. (2014) · Genome biology · doi:10.1186/gb-2014-15-1-r26 · Cited by 339 · see citing works
Glucotypes reveal new patterns of glucose dysregulation
Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R., McLaughlin, T., & Snyder, M. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2005143 · Cited by 326 · see citing works
Self-citationInflammation driven by tumour-specific Th1 cells protects against B-cell cancer
Haabeth, O. A. W., Lorvik, K. B., Hammarström, C. L., Donaldson, I. M., Haraldsen, G., Bogen, B., & Corthay, A. (2011) · Nature Communications · doi:10.1038/ncomms1239 · Cited by 315 · see citing works
Bile acid receptors as targets for the treatment of dyslipidemia and cardiovascular disease
Porez, G., Prawitt, J., Gross, B. S., & Staels, B. (2012) · Journal of Lipid Research · doi:10.1194/jlr.r024794 · Cited by 287 · see citing works
Gomez-Arango, L. F., Barrett, H. L., McIntyre, H. D., Callaway, L. K., Morrison, M., & Dekker Nitert, M. (2016) · Diabetes · doi:10.2337/db16-0278 · Cited by 287 · see citing works
Apostolopoulou, M., Gordillo, R., Koliaki, C., Gancheva, S., Jelenik, T., De Filippo, E., Herder, C., Markgraf, D., Jankowiak, F., Esposito, I., et al. (2018) · Diabetes Care · doi:10.2337/dc17-1318 · Cited by 284 · see citing works
Assessment of Pancreatic β-Cell Function: Review of Methods and Clinical Applications
Cersósimo, E., Solis‐Herrera, C., TRAUTMANN, M. E., Malloy, J. L., & Triplitt, C. L. (2014) · Current Diabetes Reviews · doi:10.2174/1573399810666140214093600 · Cited by 280 · see citing works
Carbohydrate Metabolism in Non-Insulin-Dependent Diabetes Mellitus
Dinneen, S., Gerich, J., & Rizza, R. (1992) · New England Journal of Medicine · doi:10.1056/nejm199209033271007 · Cited by 279 · see citing works
Slavich, G. M., & Shields, G. S. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000534 · Cited by 268 · see citing works
Self-citationMcConnell, M. V., Shcherbina, A., Pavlovic, A., Homburger, J. R., Goldfeder, R. L., Waggot, D., Cho, M. K., Rosenberger, M. E., Haskell, W. L., Myers, J., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.4395 · Cited by 239 · see citing works
Contrepois, K., Jiang, L., & Snyder, M. P. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.046508 · Cited by 229 · see citing works
Self-citationIntegrative Personal Omics Profiles during Periods of Weight Gain and Loss
Piening, B., Zhou, W., Contrepois, K., Röst, H. L., Gu, G. J., Mishra, T., Hanson, B., Bautista, E. J., Leopold, S. R., Yeh, C. Y., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.013 · Cited by 210 · see citing works
Self-citationEvaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test
Pei, D., Jones, C. N. O., Bhargava, R., Chen, Y. I., & Reaven, G. M. (1994) · Diabetologia · doi:10.1007/bf00404344 · Cited by 209 · see citing works
Kaminsky, L. A., Imboden, M. T., Arena, R. A., & MYERS, J. N. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.10.003 · Cited by 204 · see citing works
Giese, A., Adams, H. H. H., Chen, W.-M., Bakker, P. I. W. D., Hsu, F., Lee, J.-M., Lin, W.-Y., O'Donnell, M. J., Salomaa, V. V., Sapkota, B. R., et al. (2018) · ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) · doi:10.13025/27851 · Cited by 189 · see citing works
Feature Selection with the R Package MXM : Discovering Statistically Equivalent Feature Subsets
Lagani, V., Athineou, G., Farcomeni, A., et al. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i07 · Cited by 177 · see citing works
ISEC: a program to calculate insulin secretion.
Hovorka, R., Soons, P., & Young, M. A. (1996) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(96)01755-5 · Cited by 169 · see citing works
CXCL9: evidence and contradictions for its role in tumor progression
Ding, Q., Lu, P., Xia, Y., Ding, S., Fan, Y., Li, X., Han, P., Liu, J., Tian, D., & Liu, M. (2016) · Cancer Medicine · doi:10.1002/cam4.934 · Cited by 164 · see citing works
Leptin: an essential regulator of lipid metabolism
Reidy, S. P., & Weber, J. (2000) · Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology · doi:10.1016/s1095-6433(00)00159-8 · Cited by 159 · see citing works
Arena, R. A., MYERS, J. N., Aslam, S. S., Varughese, E. B., & Peberdy, M. A. (2003) · CHEST Journal · doi:10.1378/chest.124.2.720 · Cited by 156 · see citing works
White Blood Cells Count and Incidence of Type 2 Diabetes in Young Men
Twig, G., Afek, A. B., Shamiss, A., Derazne, E., Tzur, D., Gordon, B., & Tirosh, A. (2012) · Diabetes Care · doi:10.2337/dc11-2298 · Cited by 145 · see citing works
Turesson, I., Kovalchik, S. A., Pfeiffer, R. M., Kristinsson, S. Y., Goldin, L. R., Drayson, M. T., & Landgren, O. (2013) · Blood · doi:10.1182/blood-2013-05-505487 · Cited by 140 · see citing works
Loss of beta cell function as fasting glucose increases in the non-diabetic range
Godsland, I. F., Jeffs, J. A. R., & Johnston, D. G. (2004) · Diabetologia · doi:10.1007/s00125-004-1454-z · Cited by 131 · see citing works
Protein Biomarkers for Insulin Resistance and Type 2 Diabetes Risk in Two Large Community Cohorts
Nowak, C., Sundström, J., Gustafsson, S., Giedraitis, V., Lind, L., Ingelsson, E., & Fall, T. (2015) · Diabetes · doi:10.2337/db15-0881 · Cited by 118 · see citing works
Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma
Contrepois, K., Mahmoudi, S., Ubhi, B. K., Papsdorf, K., Hornburg, D., Brunet, A., & Snyder, M. P. (2018) · Scientific Reports · doi:10.1038/s41598-018-35807-4 · Cited by 118 · see citing works
Self-citationDetecting and annotating genetic variations using the HugeSeq pipeline
Lam, H. Y. K., Pan, C., Clark, M. J., Lacroute, P., Chen, R., Haraksingh, R., O’Huallachain, M., Gerstein, M., Kidd, J. M., Bustamante, C. D., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2134 · Cited by 114 · see citing works
Self-citationMyers, J., Bader, D. S., Madhavan, R., et al. (2001) · American Heart Journal · doi:10.1067/mhj.2001.118740 · Cited by 114 · see citing works
Adipocytes Secrete Leukotrienes
Mothe‐Satney, I., Filloux, C., Amghar, H., Pons, C., Bourlier, V., Galitzky, J., Grimaldi, P. A., Féral, C. C., Bouloumié, A., OBBERGHEN, E. V., et al. (2012) · Diabetes · doi:10.2337/db11-1455 · Cited by 106 · see citing works
Carotid Intima-Media Thickness Is Associated With Premature Parental Coronary Heart Disease
Wang, T. J., Nam, B., D’Agostino, R. B., Wolf, P. A., Lloyd‐Jones, D. M., MacRae, C. A., Wilson, P. W. F., Polak, J. F., & O’Donnell, C. J. (2003) · Circulation · doi:10.1161/01.cir.0000081764.35431.de · Cited by 104 · see citing works
Perkins, B. A., Caskey, C. T., Brar, P., Dec, E., Karow, D. S., Kahn, A. M., Hou, Y. C., Shah, N., Boeldt, D., Coughlin, E., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706096114 · Cited by 104 · see citing works
The Role of Hepatocyte Growth Factor (HGF) in Insulin Resistance and Diabetes
Oliveira, A. G., Araújo, T. G., Carvalho, B. D. M., Rocha, G. Z., Santos, A., & Saad, M. J. A. (2018) · Frontiers in Endocrinology · doi:10.3389/fendo.2018.00503 · Cited by 103 · see citing works
Additive Prognostic Value of Left Ventricular Systolic Dysfunction in a Population-Based Cohort
Kuznetsova, T., Cauwenberghs, N., Knez, J., Yang, W., Herbots, L., D’hooge, J., Haddad, F., Thijs, L., Voigt, J., & Staessen, J. A. (2016) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.116.004661 · Cited by 98 · see citing works
Self-citationDecoding the Genomics of Abdominal Aortic Aneurysm
Li, J., Pan, C., Zhang, S., Spin, J. M., Deng, A. C., Leung, L. L. K., Dalman, R. L., Tsao, P. S., & Snyder, M. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.021 · Cited by 95 · see citing works
Self-citationRoles of transglutaminases in cardiac and vascular diseases
David, C. S. (2006) · Frontiers in bioscience · doi:10.2741/2253 · Cited by 82 · see citing works
Ma, H., Calderon, T. M., Fallon, J. T., et al. (2002) · Atherosclerosis · doi:10.1016/s0021-9150(02)00062-x · Cited by 80 · see citing works
Go, R. S., Gundrum, J. D., & Neuner, J. M. (2014) · Clinical Lymphoma Myeloma & Leukemia · doi:10.1016/j.clml.2014.09.004 · Cited by 79 · see citing works
Interleukin-2 levels are associated with carotid artery intima-media thickness
Elkind, M. S. V., Rundek, T., Sciacca, R. R., Ramas, R., Chen, H.-J., Boden-Albala, B. M., Rabbani, L. E., & Sacco, R. L. (2005) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2004.11.015 · Cited by 73 · see citing works
Lee, K. K., Cipriano, L. E., Owens, D. K., Go, A. S., & Hlatky, M. A. (2010) · Circulation · doi:10.1161/circulationaha.110.947960 · Cited by 63 · see citing works
The Relationship Between β-Cell Function and Glycated Hemoglobin
Kanat, M., Winnier, D., Norton, L., Arar, N., Jenkinson, C. P., DeFronzo, R. A., & Abdul‐Ghani, M. A. (2011) · Diabetes Care · doi:10.2337/dc10-1352 · Cited by 58 · see citing works
Charbonneau, B., Maurer, M. J., Ansell, S. M., Slager, S. L., Fredericksen, Z. S., Ziesmer, S. C., Macon, W. R., Habermann, T. M., Witzig, T. E., Link, B. K., et al. (2012) · Cytokine · doi:10.1016/j.cyto.2012.08.028 · Cited by 55 · see citing works
Cauwenberghs, N., Knez, J., Thijs, L., Haddad, F., Vanassche, T., Yang, W., Wei, F., Staessen, J. A., & Kuznetsova, T. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.008315 · Cited by 54 · see citing works
Self-citationDouglas, P. S., Cerqueira, M. D., Berman, D. S., et al. (2016) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2016.02.027 · Cited by 45 · see citing works
Hepatocyte Growth Factor Is Positively Associated With Risk of Stroke
Bell, E. J., Larson, N. B., Decker, P. A., et al. (2016) · Stroke · doi:10.1161/strokeaha.116.014172 · Cited by 40 · see citing works
Hall, K. T., Nelson, C. P., Davis, R. B., Buring, J. E., Kirsch, I., Mittleman, M. A., Loscalzo, J., Samani, N. J., Ridker, P. M., Kaptchuk, T. J., et al. (2014) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.114.303845 · Cited by 39 · see citing works
Cross, D. S., McCarty, C. A., Hytopoulos, E., Beggs, M., Nolan, N., Harrington, D. S., Hastie, T. J., Tibshirani, R. J., Tracy, R. P., Psaty, B. M., et al. (2012) · Current Medical Research and Opinion · doi:10.1185/03007995.2012.742878 · Cited by 38 · see citing works
Histidine rich glycoprotein and cancer: a multi-faceted relationship.
Johnson, L. D. S., Goubran, H. A., & Kotb, R. R. (2014) · PubMed · Cited by 37 · see citing works
High-frequency actionable pathogenic exome variants in an average-risk cohortPreprint
Rego, S., Dagan-Rosenfeld, O., Zhou, W., Sailani, M., Limcaoco, P., Colbert, E., Avina, M., Wheeler, J., Craig, C. M., Salins, D., et al. (2018) · Cold Spring Harbor molecular case studies · doi:10.1101/mcs.a003178 · Cited by 30 · see citing works
Self-citationRecruitment of Natural Killer Cells in Advanced Stages of Endogenously Arising B-cell Lymphoma
Przewoznik, M., Hömberg, N., Naujoks, M., et al. (2012) · Journal of Immunotherapy · doi:10.1097/cji.0b013e318247440a · Cited by 26 · see citing works
Moneghetti, K. J., Hock, J., Kaminsky, L. A., Arena, R. A., Lui, G. K., Haddad, F., Wheeler, M. T., Froelicher, V. F., Ashley, E. A., MYERS, J. N., et al. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.02.102 · Cited by 18 · see citing works
Self-citationMechanisms Underlying the Pathogenesis of Isolated Impaired Glucose Tolerance in Humans
Varghese, R. T., Man, C. D., Sharma, A. R., Viegas, I., Barosa, C., Marques, C., Shah, M., Miles, J. M., Rizza, R. A., Jones, J. G., et al. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-1998 · Cited by 16 · see citing works
Role of Cytokine Gene Score in Risk Prediction of Premature Coronary Artery Disease
Omer, W., Naveed, A. K., Khan, O. J., & Khan, D. A. (2016) · Genetic Testing and Molecular Biomarkers · doi:10.1089/gtmb.2016.0108 · Cited by 15 · see citing works
Monocytes from Metabolic Syndrome Subjects Exhibit a Proinflammatory M1 Phenotype
Chen, X., & Devaraj, S. (2014) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2014.0017 · Cited by 13 · see citing works
Smith, D. A. (2016) · Annals of Internal Medicine · doi:10.7326/acpjc-2016-164-6-035 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [1]National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. (National Academies Press (US), 2012). 22536618
- [69]Cohen S Perceived stress in a probability sample of the United States. (Sage Publications, Inc, 1988).
Cited by
Sorted by citations
Chronic inflammation in the etiology of disease across the life span
Furman, D., Campisi, J., Verdin, E., Carrera-Bastos, P., Targ, S., Franceschi, C., Ferrucci, L., Gilroy, D. W., Fasano, A., Miller, G. W., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0675-0 · Cited by 4,915 · see citing works
Self-citationMicrobiome definition re-visited: old concepts and new challenges
Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.-C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00875-0 · Cited by 2,275 · see citing works
The Integrative Human Microbiome Project
The Integrative HMP (iHMP) Research Network Consortium, Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1238-8 · Cited by 1,381 · see citing works
Self-citationLongitudinal multi-omics of host–microbe dynamics in prediabetes
Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., Zhang, M. J., Rao, V., Avina, M., Mishra, T., et al. (2019) · Nature · doi:10.1038/s41586-019-1236-x · Cited by 583 · see citing works
Self-citationMultimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Molecular Choreography of Acute Exercise
Contrepois, K., Wu, S., Moneghetti, K. J., Hornburg, D., Ahadi, S., Tsai, M.-S., Metwally, A. A., Wei, E., Lee-McMullen, B., Quijada, J. V., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.043 · Cited by 502 · see citing works
Self-citationLópez-Otín, C., & Kroemer, G. (2021) · Cell · doi:10.1016/j.cell.2020.11.034 · Cited by 499 · see citing works
Nonlinear dynamics of multi-omics profiles during human aging
Shen, X., Wang, C., Zhou, X., Zhou, W., Hornburg, D., Wu, S., & Snyder, M. P. (2024) · Nature Aging · doi:10.1038/s43587-024-00692-2 · Cited by 500 · see citing works
Self-citationMetabolomic profiles predict individual multidisease outcomes
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406 · see citing works
Personal aging markers and ageotypes revealed by deep longitudinal profiling
Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., & Snyder, M. (2020) · Nature Medicine · doi:10.1038/s41591-019-0719-5 · Cited by 408 · see citing works
Slavich, G. M., & Sacher, J. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05326-9 · Cited by 383 · see citing works
Self-citationPrecision medicine in the era of artificial intelligence: implications in chronic disease management
Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362 · see citing works
Gut microbiome, big data and machine learning to promote precision medicine for cancer
Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0327-3 · Cited by 324 · see citing works
Wearable sensors enable personalized predictions of clinical laboratory measurements
Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schüssler-Fiorenza Rose, S. M., Li, X., Bahmani, A., Delp, S. L., Hastie, T., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01339-0 · Cited by 244 · see citing works
Self-citationA genome-wide atlas of co-essential modules assigns function to uncharacterized genes
Wainberg, M., Kamber, R. A., Balsubramani, A., Meyers, R. M., Sinnott-Armstrong, N., Hornburg, D., Jiang, L., Chan, J., Jian, R., Gu, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00840-z · Cited by 204 · see citing works
Self-citationQuer, G., Gouda, P., Galarnyk, M., Topol, E. J., & Steinhubl, S. R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227709 · Cited by 185 · see citing works
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165 · see citing works
A novel diabetes typology: towards precision diabetology from pathogenesis to treatment
Herder, C., & Roden, M. (2022) · Diabetologia · doi:10.1007/s00125-021-05625-x · Cited by 140 · see citing works
The Future of Precision Medicine in the Cure of Alzheimer’s Disease
Arafah, A., Khatoon, S., Rasool, I., Khan, A., Rather, M. A., Abujabal, K. A., Faqih, Y. A. H., Rashid, H., Rashid, S. M., Bilal Ahmad, S., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11020335 · Cited by 133 · see citing works
Ensuring that biomedical AI benefits diverse populations
Zou, J., & Schiebinger, L. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103358 · Cited by 124 · see citing works
Liu, Y., Vu, V., & Sweeney, G. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00842 · Cited by 69 · see citing works
Muse, E. D., & Topol, E. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.002 · Cited by 50 · see citing works
Review of multi-omics data resources and integrative analysis for human brain disorders
Dong, X., Liu, C., & Dozmorov, M. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab024 · Cited by 47 · see citing works
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Steenkamp, D. W., Cheney, M. C., Ju, Z., Rodbard, D., & Wolpert, H. A. (2023) · Journal of the Endocrine Society · doi:10.1210/jendso/bvad038 · Cited by 7 · see citing works
Xie, X., Wu, C., Huang, Z., Zhou, Y., Huang, J., Dao, F., Yan, D., Deng, K., Lyu, H., Ma, C., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag083 · Cited by 4 · see citing works
“Omics data, including transcriptomics, proteomics, and metabolomics, have injected mechanistic insights into predictive modeling for diabetic complications [158–160].”
Self-citation
- Authors
- Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., Rego, S., Perelman, D., Ahadi, S., Sailani, M. R., Zhou, Y., Leopold, S. R., Chen, J., Ashland, M., Christle, J. W., Avina, M., Limcaoco, P., Ruiz, C., Tan, M., Butte, A. J., Weinstock, G. M., Slavich, G. M., Sodergren, E., McLaughlin, T. L., Haddad, F., & Snyder, M. P.
- Affiliations
- Stanford Medicine (US)
- The University of Melbourne (AU)
- University of California, Berkeley (US)
- The Jackson Laboratory for Genomic Medicine (US)
- University of California, San Francisco (US)
- Stanford University (US)
- University of California, Los Angeles (US)
- Corresponding author
- Snyder, Michael P.
- ORCID
- Schüssler-Fiorenza Rose, Sophia Miryam
- Contrepois, Kévin
- Moneghetti, Kegan J.
- Zhou, Wenyu
- Mishra, Tejaswini
- Mataraso, Samson
- Ganz, Ariel B.
- Dunn, Jessilyn
- Hornburg, Daniel
- Rego, Shannon
- Perelman, Dalia
- Ahadi, Sara
- Sailani, M. Reza
- Zhou, Yanjiao
- Leopold, Shana R.
- Chen, Jieming
- Ashland, Melanie
- Christle, Jeffrey W.
- Ruiz, Camilo
- Tan, Marilyn
- Butte, Atul J.
- Weinstock, George M.
- Slavich, George M.
- Sodergren, Erica
- McLaughlin, Tracey L.
- Haddad, Francois
- Snyder, Michael P.
- Venue
- Nature Medicine
- Published
- 2019-05
- Volume
- 25
- Issue
- 5
- Pages
- 792-804
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC6713274
- OpenAlex
- W2944477354
- Cited by
- 494
- References
- 95
- Citations by year
- 2026: 51 · 2025: 40 · 2024: 53 · 2023: 61 · 2022: 108 · 2021: 78 · 2020: 70 · 2019: 30 · 2017: 1
- FWCI
- 30.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001085, UL1TR003142, 1s10od020141-01)
- National Institute of Mental Health (K08MH103443)
- National Cancer Institute (P30CA124435)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK110186, U54DK102556, P30DK116074)
- National Institute of Environmental Health Sciences (K08ES028825)
- National Institute of Dental and Craniofacial Research (U54DE023789)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- precision health, type 2 diabetes, personalized medicine, wearable monitoring, multi-omics integration, microbiome, metabolomics, transcriptomics, proteomics, immunomics, cardiovascular risk, cancer risk, behavior change, diet and exercise
- MeSH terms
- Gastrointestinal Microbiome, Big Data, Adult, Aged, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Female, Humans, Insulin Resistance, Longitudinal Studies, Male, Middle Aged, Models, Biological, Risk Factors, Cohort Studies, Metabolome, Precision Medicine, Transcriptome, Exome
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
