CoChat
Sign Up
Cited by 30

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Predicting the probability of death using proteomics

Eiriksdottir, Thjodbjorg, Ardal, Steinthor, Jonsson, Benedikt A., Lund, Sigrun H., Ivarsdottir, Erna V., Norland, Kristjan, Ferkingstad, Egil, Stefansson, Hreinn, Jonsdottir, Ingileif, Holm, Hilma, Rafnar, Thorunn, Saemundsdottir, Jona, Norddahl, Gudmundur L., Thorgeirsson, Gudmundur, Gudbjartsson, Daniel F., Sulem, Patrick, Thorsteinsdottir, Unnur, Stefansson, Kari, Ulfarsson, Magnus O.·2021·Communications Biology
DOI 10.1038/s42003-021-02289-6PMC PMC8213855
Open this paper in CoChat — free account

Abstract

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s42003-021-02289-6

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Discrimination power of different models for death within 1,2,…,15 years. a AUCs for all participants, N = 6893. b ROC curves for death within 5 years for all participants. c AUCs for participants older than 60, N = 3052. d ROC curves for death within 5 years for participants older than 60.
Fig. 1 Discrimination power of different models for death within 1,2,…,15 years. a AUCs for all participants, N = 6893. b ROC curves for death within 5 years for all participants. c AUCs for participants older than 60, N = 3052. d ROC curves for death within 5 years for participants older than 60.CC BY · PubMed Central
Fig. 2 Survival of 60–80 years old participants. The Kaplan–Meier curves for 2488 participants are split by quantiles of predicted 10-year risk by each model, demonstrating the different survival rates in the different risk groups. The colored areas represent 95% confidence intervals. The red dots show survival after 5 and 10 years.
Fig. 2 Survival of 60–80 years old participants. The Kaplan–Meier curves for 2488 participants are split by quantiles of predicted 10-year risk by each model, demonstrating the different survival rates in the different risk groups. The colored areas represent 95% confidence intervals. The red dots show survival after 5 and 10 years.CC BY · PubMed Central
Fig. 3 Predicted risk of death within 5 years for different causes of death. Participants who died within 5 years are shown as one group and categorized by cause of death and those who were alive after 5 years are shown as one group. The yellow center line represents the median, the box limits are the upper and lower quartiles, the whiskers represent the 1.5× interquartile range, and the black dots are data points outside the whisker range.
Fig. 3 Predicted risk of death within 5 years for different causes of death. Participants who died within 5 years are shown as one group and categorized by cause of death and those who were alive after 5 years are shown as one group. The yellow center line represents the median, the box limits are the upper and lower quartiles, the whiskers represent the 1.5× interquartile range, and the black dots are data points outside the whisker range.CC BY · PubMed Central

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 41

  1. Glare, P. et al. Predicting survival in patients with advanced disease. Eur. J. Cancer 44, 1146–1156 (2008). · DOI
  2. Hippisley-Cox, J. & Coupland, C. Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study. BMJ 358, j4208 (2017). · DOI
  3. Flegal, K. M., Kit, B. K., Orpana, H. & Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309, 71–82 (2013). · DOI · Paper page
  4. Danaei, G. et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 6, e1000058 (2009). · DOI · Paper page
  5. Puterman, E. et al. Predicting mortality from 57 economic, behavioral, social, and psychological factors. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1918455117 (2020). · DOI
  6. Wang, T. J. et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 355, 2631–2639 (2006). · DOI
  7. Deelen, J. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat. Commun. 10, 3346 (2019). · DOI · Paper page
  8. Cole, J. H. et al. Brain age predicts mortality. Mol. Psychiatry 23, 1385–1392 (2018). · DOI
  9. Ho, J. E. et al. Protein biomarkers of cardiovascular disease and mortality in the community. J. Am. Heart Assoc. 7, e008108 (2018). · DOI
  10. Orwoll, E. S. et al. High-throughput serum proteomics for the identification of protein biomarkers of mortality in older men. Aging Cell 17, e12717 (2018). · DOI
  11. Rohloff, J. C. et al. Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents. Mol. Ther. Nucleic Acids 3, e201 (2014). · DOI · Paper page
  12. Menni, C. et al. Circulating proteomic signatures of chronological age. J. Gerontol. A. Biol. Sci. Med. Sci. 70, 809–816 (2015). · DOI · Paper page
  13. Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). · DOI · Paper page
  14. Sathyan, S. et al. Plasma proteomic profile of age, health span, and all-cause mortality in older adults. Aging Cell 19, e13250 (2020). · DOI
  15. Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541 (2016). · DOI · Paper page
  16. Rafnar, T. et al. The Icelandic Cancer Project—a population-wide approach to studying cancer. Nat. Rev. Cancer 4, 488–492 (2004). · DOI
  17. Ivarsdottir, E. V. et al. Sequence variation at ANAPC1 accounts for 24% of the variability in corneal endothelial cell density. Nat. Commun. 10, 1284 (2019). · DOI
  18. Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta Package. JSS J. Stat. Softw. 36, 1–13 (2010).
  19. Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B Methodol. 58, 267–288 (1996). · DOI · Paper page
  20. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498–D503 (2020).
  21. Ebaid, D., Crewther, S. G., MacCalman, K., Brown, A. & Crewther, D. P. Cognitive processing speed across the lifespan: beyond the influence of motor speed. Front. Aging Neurosci. 9, 62 (2017). · DOI
  22. Reitan, R. M. Validity of the trail making test as an indicator of organic brain damage. Percept. Mot. Skills 8, 271–276 (1958). · DOI
  23. Wiklund, F. E. et al. Macrophage inhibitory cytokine-1 (MIC-1/GDF15): a new marker of all-cause mortality: Serum MIC-1/GDF15 and mortality risk. Aging Cell 9, 1057–1064 (2010). · DOI
  24. Coll, A. P. et al. GDF15 mediates the effects of metformin on body weight and energy balance. Nature 578, 444–448 (2020). · DOI · Paper page
  25. Moon, J. S. et al. Growth differentiation factor 15 protects against the aging-mediated systemic inflammatory response in humans and mice. Aging Cell 19, e13195 (2020). · DOI · Paper page
  26. Lara, J. et al. Towards measurement of the healthy ageing phenotype in lifestyle-based intervention studies. Maturitas 76, 189–199 (2013). · DOI
  27. Nishiwaki, Y. et al. Association of thoracic kyphosis with subjective poor health, functional activity and blood pressure in the community-dwelling elderly. Environ. Health Prev. Med. 12, 246–250 (2007). · DOI
  28. Hynes, R. O. The extracellular matrix: not just pretty fibrils. Science 326, 1216–1219 (2009). · DOI · Paper page
  29. Le Roith, D. Insulin-like growth factors. N. Engl. J. Med. 336, 633–640 (1997). · DOI · Paper page
  30. Helgadottir, A. et al. Genetic variability in the absorption of dietary sterols affects the risk of coronary artery disease. Eur. Heart J. 41, 2618–2628 (2020). · DOI
  31. Brody, E. et al. Life’s simple measures: unlocking the proteome. J. Mol. Biol. 422, 595–606 (2012). · DOI · Paper page
  32. Candia, J. et al. Assessment of variability in the SOMAscan assay. Sci. Rep. 7, 14248 (2017). · DOI
  33. The Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. JAMA 302, 1993–2000 (2009). · DOI · Paper page
  34. Pencina, M. J., D’Agostino, R. B., D’Agostino, R. B. & Vasan, R. S. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157–172 (2008). · DOI · Paper page
  35. Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. JAMA 247, 2543–2546 (1982). · DOI · Paper page
  36. DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988). · DOI · Paper page
  37. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005). · DOI · Paper page
  38. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016). https://doi.org/10.1145/2939672.2939785. · DOI · Paper page
  39. Mi, H. et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 49, D394–D403 (2021). · DOI · Paper page
  40. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
  41. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011). · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Eiriksdottir, T., Ardal, S., Jonsson, B. A., Lund, S. H., Ivarsdottir, E. V., Norland, K., Ferkingstad, E., Stefansson, H., Jonsdottir, I., Holm, H., Rafnar, T., Saemundsdottir, J., Norddahl, G. L., Thorgeirsson, G., Gudbjartsson, D. F., Sulem, P., Thorsteinsdottir, U., Stefansson, K., & Ulfarsson, M. O.
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2021-06-18
Volume
4
Issue
1
ISSN
2399-3642
DOI
10.1038/s42003-021-02289-6
PMC
PMC8213855
Cited by
30
References
10
Language
en
Licence

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