Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons
Background:
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings:
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Conclusions:
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Vyšlo v časopise:
Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e32767. doi:10.1371/journal.pmed.1001606
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1001606
Souhrn
Background:
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings:
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Conclusions:
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Zdroje
1. WangTJ, GonaP, LarsonMG, ToflerGH, LevyD, et al. (2006) Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 355: 2631–2639.
2. RobertsLD, GersztenRE (2013) Toward new biomarkers of cardiometabolic diseases. Cell Metab 18: 43–50.
3. NicholsonJK, HolmesE, KinrossJM, DarziAW, TakatsZ, et al. (2012) Metabolic phenotyping in clinical and surgical environments. Nature 491: 384–392.
4. ShahSH, KrausWE, NewgardCB (2012) Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126: 1110–1120.
5. WangTJ, LarsonMG, VasanRS, ChengS, RheeEP, et al. (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17: 448–453.
6. SchulzeA, HarrisAL (2012) How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature 491: 364–373.
7. ClarkeR, EmbersonJR, BreezeE, CasasJP, ParishS, et al. (2008) Biomarkers of inflammation predict both vascular and non-vascular mortality in older men. Eur Heart J 29: 800–809.
8. Emerging Risk Factors Collaboration (2011) SeshasaiSR, KaptogeS, ThompsonA, Di AngelantonioE, et al. (2011) Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 364: 829–841.
9. LangleyRJ, TsalikEL, van VelkinburghJC, GlickmanSW, RiceBJ, et al. (2013) An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 5: 195ra95.
10. LeitsaluL, HallerT, EskoT, TammesooML, AlavereH, et al. (2014) Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int J Epidemiol In press. doi:10.1093/ije/dyt268
11. VartiainenE, LaatikainenT, PeltonenM, JuoleviA, MännistöS, et al. (2010) Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol 39: 504–518.
12. KujalaUM, MäkinenVP, HeinonenI, SoininenP, KangasAJ, et al. (2013) Long-term leisure-time physical activity and serum metabolome. Circulation 127: 340–348.
13. KettunenJ, TukiainenT, SarinAP, Ortega-AlonsoA, TikkanenE, et al. (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44: 269–276.
14. SoininenP, KangasAJ, WürtzP, TukiainenT, TynkkynenT, et al. (2009) High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134: 1781–1785.
15. ThiebautACM, BenichouJ (2004) Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study. Statist Med 23: 3803–3820.
16. D'AgostinoRB, VasanRS, PencinaMJ, WolfPA, CobainM, et al. (2008) General cardiovascular risk profile for use in primary care—the Framingham Heart Study. Circulation 117: 743–753.
17. AntoliniL, NamBH, D'AgosticoRB (2004) Inference on correlated discrimination measures in survival analysis: a nonparametric approach. Commun Statist Theory Meth 33: 2117–2135.
18. PencinaMJ, D'AgostinoRBSr, SteyerbergEW (2010) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30: 11–21.
19. PerkJ, De BackerG, GohlkeH, GrahamI, ReinerZ, et al. (2012) European guidelines on cardiovascular disease prevention in clinical practice (version 2012). Eur Heart J 33: 1635–1701.
20. LeeDS, ParkJ, KayKA, ChristakisNA, OltvaiZN, et al. (2008) The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci U S A 105: 9880–9885.
21. FournierT, Medjoubi-NN, PorquetD (2000) Alpha-1-acid glycoprotein. Biochim Biophys Acta 1482: 157–171.
22. CarriereI, DupuyA, LacrouxA, CristolJ, DelcourtC (2008) Biomarkers of inflammation and malnutrition associated with early death in healthy elderly people. J Am Geriatr Soc 56: 840–846.
23. EngströmG, LindP, HedbladB, StavenowL, JanzonL, et al. (2002) Effects of cholesterol and inflammation-sensitive plasma proteins on incidence of myocardial infarction and stroke in men. Circulation 105: 2632–2637.
24. BrunoR, OlivaresR, BerilleJ, ChaikinP, VivierN, et al. (2003) Alpha-1-acid glycoprotein as an independent predictor for treatment effects and a prognostic factor of survival in patients with non-small cell lung cancer treated with docetaxel. Clin Cancer Res 9: 1077–1082.
25. GoldwasserP, FeldmanJ (1997) Association of serum albumin and mortality risk. J Clin Epidemiol 50: 693–703.
26. PhillipsA, ShaperAG, WhincupPH (1989) Association between serum-albumin and mortality from cardiovascular-disease, cancer, and other causes. Lancet 2: 1434–1436.
27. CortiMC, GuralnikJM, SaliveME, SorkinJD (1994) Serum-albumin level and physical-disability as predictors of mortality in older persons. JAMA 272: 1036–1042.
28. DoR, WillerCJ, SchmidtEM, SenguptaS, GaoC, et al. (2013) Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 45: 1345–1352.
29. NordestgaardBG, BennM, SchnohrP, Tybjaerg-HansenA (2007) Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. JAMA 298: 299–308.
30. VarboA, BennM, Tybjærg-HansenA, NordestgaardBG (2013) Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation. Circulation 128: 1298–1309.
31. FraenklSA, MuserJ, GroellR, ReinhardG, OrgulS, et al. (2011) Plasma citrate levels as a potential biomarker for glaucoma. J Ocul Pharmacol Ther 27: 577–580.
32. QuehenbergerO, DennisEA (2011) The human plasma lipidome. N Engl J Med 365: 1812–1823.
33. LangstedA, FreibergJJ, NordestgaardBG (2008) Fasting and nonfasting lipid levels influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation 118: 2047–2056.
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