Abstract 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

Editors' Summary Background A biomarker is a biological molecule found in blood, body fluids, or tissues that may signal an abnormal process, a condition, or a disease. The level of a particular biomarker may indicate a patient's risk of disease, or likely response to a treatment. For example, cholesterol levels are measured to assess the risk of heart disease. Most current biomarkers are used to test an individual's risk of developing a specific condition. There are none that accurately assess whether a person is at risk of ill health generally, or likely to die soon from a disease. Early and accurate identification of people who appear healthy but in fact have an underlying serious illness would provide valuable opportunities for preventative treatment. While most tests measure the levels of a specific biomarker, there are some technologies that allow blood samples to be screened for a wide range of biomarkers. These include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. These tools have the potential to be used to screen the general population for a range of different biomarkers. Why Was This Study Done? Identifying new biomarkers that provide insight into the risk of death from all causes could be an important step in linking different diseases and assessing patient risk. The authors in this study screened patient samples using NMR spectroscopy for biomarkers that accurately predict the risk of death particularly amongst the general population, rather than amongst people already known to be ill. What Did the Researchers Do and Find? The researchers studied two large groups of people, one in Estonia and one in Finland. Both countries have set up health registries that collect and store blood samples and health records over many years. The registries include large numbers of people who are representative of the wider population. The researchers first tested blood samples from a representative subset of the Estonian group, testing 9,842 samples in total. They looked at 106 different biomarkers in each sample using NMR spectroscopy. They also looked at the health records of this group and found that 508 people died during the follow-up period after the blood sample was taken, the majority from heart disease, cancer, and other diseases. Using statistical analysis, they looked for any links between the levels of different biomarkers in the blood and people's short-term risk of dying. They found that the levels of four biomarkers—plasma albumin, alpha-1-acid glycoprotein, very-low-density lipoprotein (VLDL) particle size, and citrate—appeared to accurately predict short-term risk of death. They repeated this study with the Finnish group, this time with 7,503 individuals (176 of whom died during the five-year follow-up period after giving a blood sample) and found similar results. The researchers carried out further statistical analyses to take into account other known factors that might have contributed to the risk of life-threatening illness. These included factors such as age, weight, tobacco and alcohol use, cholesterol levels, and pre-existing illness, such as diabetes and cancer. The association between the four biomarkers and short-term risk of death remained the same even when controlling for these other factors. The analysis also showed that combining the test results for all four biomarkers, to produce a biomarker score, provided a more accurate measure of risk than any of the biomarkers individually. This biomarker score also proved to be the strongest predictor of short-term risk of dying in the Estonian group. Individuals with a biomarker score in the top 20% had a risk of dying within five years that was 19 times greater than that of individuals with a score in the bottom 20% (288 versus 15 deaths). What Do These Findings Mean? This study suggests that there are four biomarkers in the blood—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—that can be measured by NMR spectroscopy to assess whether otherwise healthy people are at short-term risk of dying from heart disease, cancer, and other illnesses. However, further validation of these findings is still required, and additional studies should examine the biomarker specificity and associations in settings closer to clinical practice. The combined biomarker score appears to be a more accurate predictor of risk than tests for more commonly known risk factors. Identifying individuals who are at high risk using these biomarkers might help to target preventative medical treatments to those with the greatest need. However, there are several limitations to this study. As an observational study, it provides evidence of only a correlation between a biomarker score and ill health. It does not identify any underlying causes. Other factors, not detectable by NMR spectroscopy, might be the true cause of serious health problems and would provide a more accurate assessment of risk. Nor does this study identify what kinds of treatment might prove successful in reducing the risks. Therefore, more research is needed to determine whether testing for these biomarkers would provide any clinical benefit. There were also some technical limitations to the study. NMR spectroscopy does not detect as many biomarkers as mass spectrometry, which might therefore identify further biomarkers for a more accurate risk assessment. In addition, because both study groups were northern European, it is not yet known whether the results would be the same in other ethnic groups or populations with different lifestyles. In spite of these limitations, the fact that the same four biomarkers are associated with a short-term risk of death from a variety of diseases does suggest that similar underlying mechanisms are taking place. This observation points to some potentially valuable areas of research to understand precisely what's contributing to the increased risk. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001606 The US National Institute of Environmental Health Sciences has information on biomarkers

The US Food and Drug Administration has a Biomarker Qualification Program to help researchers in identifying and evaluating new biomarkers

Further information on the Estonian Biobank is available

The Computational Medicine Research Team of the University of Oulu and the University of Bristol have a webpage that provides further information on high-throughput biomarker profiling by NMR spectroscopy

Citation: Fischer K, Kettunen J, Würtz P, Haller T, Havulinna AS, Kangas AJ, et al. (2014) 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): e1001606. https://doi.org/10.1371/journal.pmed.1001606 Academic Editor: Cosetta Minelli, Imperial College London, United Kingdom Received: June 20, 2013; Accepted: January 14, 2014; Published: February 25, 2014 Copyright: © 2014 Fischer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Supported by grants from the European Commission Seventh Framework Programme (grants no. 278913, 306031, 313010, ENGAGE HEALTH-F4-2007- 201413, BioSHaRE 261433), Estonian Research Council Grant IUT20-60, the Estonian Research Roadmap through the Estonian Ministry of Education and Research, the Center of Excellence in Genomics (EXCEGEN), the University of Tartu (SP1GVARENG), and the Estonian Science Foundation (ETF9353). This study was also supported by the Academy of Finland (139635, 137870, 250422, 251217, 266199), the Responding to Public Health Challenges Research Programme of the Academy of Finland (129322, 129429), the Academy of Finland Center of Excellence in Complex Disease Genetics (213506, 129680), the Finnish Funding Agency for Technology and Innovation, the European Foundation for the Study of Diabetes, the Jenny and Antti Wihuri Foundation, the Novo Nordisk Foundation, the Sigrid Juselius Foundation, the Finnish Foundation for Cardiovascular Research, UK Medical Research Council, Wellcome Trust UK, and via the Strategic Research Funding from the University of Oulu, Finland, and from the University of Bristol, UK. Competing interests: PW AJK PS and MAK are shareholders of Brainshake Ltd., a startup company offering NMR-based metabolite profiling. All other authors declare that no competing interests exist. Abbreviations: HDL, high-density lipoprotein; HR, hazard ratio; IDI, integrated discrimination improvement; NMR, nuclear magnetic resonance; NRI, net reclassification improvement; SD, standard deviation; VLDL, very-low-density lipoprotein

Introduction Concentrations of metabolites and proteins in the circulation can be indicative of future disease outcomes. The existing molecular biomarkers for all-cause mortality, however, display modest predictive power and risk discrimination [1],[2]. Early and accurate identification of ambulatory persons at high risk of death could assist targeting of preventive therapies. High-throughput profiling technologies for quantification of molecules from blood specimens, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, have emerged as promising tools for identifying biomarkers and clarifying disease etiologies [2]–[4]. Such molecular profiling has primarily been applied to cardiometabolic diseases [3]–[5], yet a deviated circulating biomarker profile reflects systemic abnormalities and could possibly also be predictive of the risk of death from other causes [6]. Biomarkers of inflammation and hyperglycemia are associated with risk of death from cancer and other nonvascular conditions such as respiratory disease and infections, in addition to death from cardiovascular disease [7]–[9]. Novel biomarkers reflecting the risk of death from all causes hold potential to improve risk assessment, and they may further elucidate novel disease connectivities; however, high-throughput profiling of circulating biomarkers for all-cause mortality has not previously been investigated in general population settings. We therefore performed targeted screening of candidate biomarkers by NMR spectroscopy in a large, population-based study with the aim of identifying systemic biomarkers predictive of short-term risk of death from any cause. The findings were validated in an independent cohort and examined for incremental risk discrimination over and above conventional risk factors.

Discussion Four circulating biomarkers—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—were predictive of the short-term risk of death from any cause in two general population cohorts. All four biomarkers were not only associated with cardiovascular mortality, but were also indicators of the risk of cancer death and other nonvascular causes of mortality. In combination, the biomarkers improved risk discrimination and reclassification over and above conventional risk factors and may potentially aid the identification of high-risk individuals in need of medical intervention. Although the clinical implications remain unclear in terms of disease specificity and treatment strategies, these findings illustrate the utility of population-level molecular profiling for biomarker discovery, and suggest systemic reflections of the risk for death across disparate disease causes [7],[20]. The four biomarkers associated with all-cause mortality among ambulatory people are implicated in various pathophysiological mechanisms including inflammation, fluid imbalance, lipoprotein metabolism, and metabolic homeostasis. The acute phase protein alpha-1-acid glycoprotein (also known as orosomucoid) is elevated in response to infection and inflammation [21]. Plasma levels of alpha-1-acid glycoprotein have been associated with all-cause mortality in elderly persons, as well as cardiovascular mortality and prognosis of certain cancers [22]–[24]. Here, alpha-1-acid glycoprotein was the strongest multivariate predictor of the risk of death from all causes. Once added to the prediction model, alpha-1-acid glycoprotein additionally influenced the association of several VLDL lipid measures with all-cause mortality (Figure 2). The association of alpha-1-acid glycoprotein with mortality was only slightly attenuated when C-reactive protein, a widely used marker of low-grade inflammation, was included in the prediction model (Figure S5). The functional role of alpha-1-acid glycoprotein remains poorly understood; however, these findings support the notion of acute phase proteins being reflective of the risk of death from vascular and nonvascular disease, as well as cancer [7]. Plasma albumin, as available from a routine blood test, is a marker of liver and kidney function, nutritional status, and inflammation [25]. Low circulating albumin levels are associated with increased mortality from vascular, nonvascular, and cancer causes, both in apparently healthy persons and acutely ill patients [7],[25],[26]. The strong inverse association of albumin with short-term risk of death may therefore be considered as a positive control in the biomarker discovery. Although hypoalbuminemia has been linked with susceptibility to various diseases and can be used as a marker of frailty in older people [27], the general population variation in albumin levels is not routinely used for risk assessment among asymptomatic persons. Triglyceride-mediated lipoprotein metabolism is recognized as a risk factor for cardiovascular disease, particularly in the non-fasting state [28],[29]. VLDL particles are the starting point of the hepatic lipoprotein cascade, and the average size of VLDL particles may be an overall indicator of triglyceride metabolism. In this study, VLDL particle size was inversely associated with risk of death, and the association became stronger when alpha-1-acid glycoprotein was included in the multivariate model (Figures 2C and S2). This might indicate a combined effect of perturbed triglyceride metabolism and low-grade inflammation, as has been supported by genetic evidence [30]. Although postprandial triglyceride levels have been linked with all-cause mortality [29], measures of VLDL and triglyceride metabolism have not previously been associated with cancer mortality or death from other nonvascular causes. Citrate is an intermediate in the Krebs cycle and thus central to energy metabolism. Circulating citrate levels are tightly regulated, since citrate acts as a chelator to modulate calcium, magnesium, and zinc ion concentrations, and thereby exhibits anticoagulating properties [31]. However, citrate has not been previously implicated as a biomarker for mortality in general population settings. Increased citrate was associated with increased risk of cardiovascular death as well as cancer death among participants in the Estonian Biobank cohort; however, the most consistent associations were observed for deaths from other causes (Figure 3C). A recent molecular profiling study indicated citric acid cycle deviations, including elevated citrate levels, as being predictive of death from sepsis in hospital settings [9]. The mechanisms underlying how citrate is associated with short-term risk of death among ambulatory people nonetheless remain elusive. Out of all available risk factors, the biomarker summary score was the strongest predictor of all-cause mortality in the Estonian Biobank cohort. The biomarker score stratified the short-term risk of death: persons with a very high biomarker score were associated with substantially higher mortality rates than those with average levels of the biomarker score, indicating prominent reflections of frailty in the systemic biomarker profile (Figure 5). Importantly, all hazard estimates were similar in analyses limited to individuals without prevalent diabetes, cardiovascular disease, or cancer (Figure S4). If these findings are further validated, it might be envisioned that NMR-based biomarker profiling of non-fasting blood specimens could be helpful for identifying asymptomatic people at high risk to be referred for more detailed screening procedures. Additional studies are, however, still required to elucidate the disease specificity and etiological underpinnings of the biomarker associations, as well as inform potential treatment strategies. For these reasons, the risk prediction model for all-cause mortality (Tables 2 and 3) should serve only as an illustration of the potential to enhance risk discrimination; evaluation of the predictive utility of the biomarkers in settings closer to clinical practice are called for to clarify implications for public health intervention. Although the associations of the four biomarkers were largely unaffected by potential confounders (Figures 3 and S5), it is still plausible that subclinical or overt disease processes may underpin the biomarker reflections of the short-term risk of death. Co-morbidities such as respiratory, renal, and liver disease could partly mediate the biomarker associations; additional studies are warranted to address the effects of low-grade inflammation, infection, and prevalent disease on the biomarker concentrations. Importantly, the strong associations do not imply causal influences of the biomarkers on the risk of death. Notwithstanding, the biomarker associations across cardiovascular, nonvascular, and cancer mortality open a host of pathophysiological questions, and highlight latent systemic connectivities across seemingly dissimilar causes of death. Some limitations of our study should be considered. The molecular coverage available from NMR spectroscopy is limited compared to that afforded by mass spectrometry, which holds further promise for risk assessment and elucidation of disease pathways [2],[32]. Both plasma and serum samples were non-fasting, and generalization to fasting biomarker concentrations requires further studies. Albumin and lipoprotein levels are, however, only weakly associated with fasting duration [33]; all results were similar when adjusting for time since last meal. The risk of all-cause mortality is not customarily assessed in general practice, and no established risk categories exist to guide treatment; nonetheless, progress towards enhanced risk prediction accuracy may enable applications for targeted prevention. This study was conducted in two independent cohorts of northern European individuals; further evaluation of the biomarkers in other lifestyle environments and ethnic groups is warranted. In summary, high-throughput molecular profiling by NMR spectroscopy highlighted four circulating biomarkers—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—predictive of the short-term risk of death from all causes. The biomarker associations were replicated in an independent population and were consistent when limiting analyses to persons free of apparent disease. All four biomarkers were predictive of death from cancer and nonvascular causes in addition to cardiovascular mortality, and may therefore indicate novel relationships between systemic biomarkers and diverse morbidities. Incorporating the biomarkers into risk prediction scores led to improved discrimination and reclassification of 5-y mortality in the validation cohort. Further investigations are required to clarify the utility of these circulating biomarkers for guiding screening and targeted prevention based on the molecular profile of an individual.

Acknowledgments Funding: This research was supported by grants from the European Commission Seventh Framework Programme (grants no. 278913, 306031, 313010, ENGAGE HEALTH-F4-2007-201413, BioSHaRE 261433), Estonian Research Council Grant IUT20-60, the Estonian Research Roadmap through the Estonian Ministry of Education and Research, the Center of Excellence in Genomics (EXCEGEN), the University of Tartu (SP1GVARENG), and the Estonian Science Foundation (ETF9353). This study was also supported by the Academy of Finland (139635, 137870, 250422, 251217, 266199), the Responding to Public Health Challenges Research Programme of the Academy of Finland (129322, 129429), the Academy of Finland Center of Excellence in Complex Disease Genetics (213506, 129680), the European Foundation for the Study of Diabetes, the Jenny and Antti Wihuri Foundation, the Sigrid Juselius Foundation, the Finnish Foundation for Cardiovascular Research, and Strategic Research Funding from the University of Oulu, Finland.

Author Contributions Conceived and designed the experiments: KF JK PW VS MAK MP AM. Performed the experiments: AJK PS MAK. Analyzed the data: KF JK PW. Contributed reagents/materials/analysis tools: TH ASH AJK TE RM SR. Wrote the first draft of the manuscript: KF JK PW. Contributed to the writing of the manuscript: KF JK PW TH ASH VS. ICMJE criteria for authorship read and met: KF JK PW TH ASH AJK PS TE MLT RM SS AP SR VS MAK MP AM. Agree with manuscript results and conclusions: KF JK PW TH ASH AJK PS TE MLT RM SS AP SR VS MAK MP AM. Enrolled patients: MLT SS AP VS MP AM.