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Meta-Analysis
. 2015 Jun 3;10(6):e0127550.
doi: 10.1371/journal.pone.0127550. eCollection 2015.

Blood-borne biomarkers of mortality risk: systematic review of cohort studies

Evelyn Barron  1 , Jose Lara  1 , Martin White  2 , John C Mathers  1
Affiliations
Meta-Analysis

Blood-borne biomarkers of mortality risk: systematic review of cohort studies

Evelyn Barron et al. PLoS One. .

Abstract

Background: Lifespan and the proportion of older people in the population are increasing, with far reaching consequences for the social, political and economic landscape. Unless accompanied by an increase in health span, increases in age-related diseases will increase the burden on health care resources. Intervention studies to enhance healthy ageing need appropriate outcome measures, such as blood-borne biomarkers, which are easily obtainable, cost-effective, and widely accepted. To date there have been no systematic reviews of blood-borne biomarkers of mortality.

Aim: To conduct a systematic review to identify available blood-borne biomarkers of mortality that can be used to predict healthy ageing post-retirement.

Methods: Four databases (Medline, Embase, Scopus, Web of Science) were searched. We included prospective cohort studies with a minimum of two years follow up and data available for participants with a mean age of 50 to 75 years at baseline.

Results: From a total of 11,555 studies identified in initial searches, 23 fulfilled the inclusion criteria. Fifty-one blood borne biomarkers potentially predictive of mortality risk were identified. In total, 20 biomarkers were associated with mortality risk. Meta-analyses of mortality risk showed significant associations with C-reactive protein (Hazard ratios for all-cause mortality 1.42, p<0.001; Cancer-mortality 1.62, p<0.009; CVD-mortality 1.31, p = 0.033), N Terminal-pro brain natriuretic peptide (Hazard ratios for all-cause mortality 1.43, p<0.001; CHD-mortality 1.58, p<0.001; CVD-mortality 1.67, p<0.001) and white blood cell count (Hazard ratios for all-cause mortality 1.36, p = 0.001). There was also evidence that brain natriuretic peptide, cholesterol fractions, erythrocyte sedimentation rate, fibrinogen, granulocytes, homocysteine, intercellular adhesion molecule-1, neutrophils, osteoprotegerin, procollagen type III aminoterminal peptide, serum uric acid, soluble urokinase plasminogen activator receptor, tissue inhibitor of metalloproteinases 1 and tumour necrosis factor receptor II may predict mortality risk. There was equivocal evidence for the utility of 14 biomarkers and no association with mortality risk for CD40 ligand, cortisol, dehydroepiandrosterone, ferritin, haemoglobin, interleukin-12, monocyte chemoattractant protein 1, matrix metalloproteinase 9, myelopereoxidase, P-selectin, receptor activator of nuclear factor KappaB ligand, sex hormone binding globulin, testosterone, transferrin, and thyroid stimulating hormone and thyroxine.

Conclusions: Twenty biomarkers should be prioritised as potential predictors of mortality in future studies. More studies using standardised protocols and reporting methods, and which focus on mortality rather than risk of disease or health status as an outcome, are needed.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. PRISMA diagram.
Prisma diagram showing the number of references identified in the search and the number of inclusions and exclusions at each stage.
Fig 2
Fig 2. Forest plot of Hazard ratios for all-cause, cancer, CHD-related, and CVD-related mortality for each 1-SD increase in CRP.
Fig 3
Fig 3. Forest plot of Hazard ratios for all-cause, CHD-related, CVD-related, and Non-CVD-related mortality for each 1-SD increase in NT proBNP.
Fig 4
Fig 4. Forest plot of Hazard ratios for all-cause mortality risk for each 1-SD increase in IL-6.
Fig 5
Fig 5. Forest plot of Hazard ratios for all-cause mortality risk and increases in white blood cell count.
See this image and copyright information in PMC

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