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Combinations of biomarkers predictive of later life mortality.

ABSTRACT: A wide range of biomarkers, reflecting activity in a number of biological systems (e.g., neuroendocrine, immune, cardiovascular, and metabolic), have been found to prospectively predict disability, morbidity, and mortality outcomes in older adult populations. Levels of these biomarkers, singly or in combination, may serve as an early warning system of risk for future adverse health outcomes. In the current investigation, 13 biomarkers were examined as predictors of mortality occurrence over a 12-year period in a sample of men and women (n = 1,189) 70-79 years of age at enrollment into the study. Biomarkers examined in analyses included markers of neuroendocrine functioning (epinephrine, norepinephrine, cortisol, and dehydroepiandrosterone), immune activity (C-reactive protein, fibrinogen, IL-6, and albumin), cardiovascular functioning (systolic and diastolic blood pressure), and metabolic activity [high-density lipoprotein (HDL) cholesterol, total to HDL cholesterol ratio, and glycosylated hemoglobin]. Recursive partitioning techniques were used to identify a set of pathways, composed of combinations of different biomarkers, that were associated with a high-risk of mortality over the 12-year period. Of the 13 biomarkers examined, almost all entered into one or more high-risk pathways although combinations of neuroendocrine and immune markers appeared frequently in high-risk male pathways, and systolic blood pressure was present in combination with other biomarkers in all high-risk female pathways. These findings illustrate the utility of recursive partitioning techniques in identifying biomarker combinations predictive of mortal outcomes in older adults, as well as the multiplicity of biological pathways to mortality in elderly populations.

SUBMITTER: Gruenewald TL

PROVIDER: S-EPMC1599928 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThere are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either leave out many possible responders, as in the case of PD-L1 testing for first-line metastatic lung cancer, or apply to a small subset of patients, such as the recently approved treatment for microsatellite instability-high or mismatch repair deficiency tumors. In this study, we conducted a survey of the available data on ICI trials with matched genomic or transcriptomic datasets in order to cross-validate the proposed biomarkers, to assess whether their prediction power was confirmed and, mainly, to investigate if their combination was able to generate a better predictive tool.MethodsWe extracted clinical information and sequencing data details from publicly available datasets, along with a list of possible biomarkers obtained from the recent literature. After an operation of data harmonization, we validated the performance of all the biomarkers taken individually. Furthermore, we tested two strategies to combine the best performing biomarkers in order to improve their predictive value.ResultsWhen considered individually, some of the biomarkers, such as the ImmunoPhenoScore, and the IFN-? signature, did not confirm their originally proposed predictive power. The best absolute scoring biomarkers are TIDE, one of the ICB resistance signatures and CTLA4 with a mean AUC?>?0.66. Among the combinations tested, generalized linear models showed the best performance with an AUC of 0.78.ConclusionsWe confirmed that the available biomarkers, taken individually, fail to provide a satisfactory predictive value. Unfortunately, also combination of some of them only provides marginal improvements. Hence, in order to generate a more robust way to predict ICI efficacy it is necessary to analyze and combine additional biomarkers and interrogate a wider set of clinical data.

Project description:ContextDepression is associated with increased mortality, but it is unclear if this relationship is dose-dependent and if it can be modified by treatment with antidepressants.ObjectiveTo determine if (1) the association between depression and mortality is independent of other common potential causes of death in later life, (2) there is a dose-response relationship between increasing severity of depression and mortality rates, and (3) the use of antidepressant drugs reduces mortality rates.MethodsCohort study of 5,276 community-dwelling men aged 68-88 years living in Perth, Australia. We used the Geriatric Depression Scale 15-items (GDS-15) to ascertain the presence and severity of depression. GDS-15 > or = 7 indicates the presence of clinically significant depression. Men were also grouped according to the severity of symptoms: "no symptoms" (GDS-15 = 0), "questionable" (1 < or = GDS-15 < or = 4), "mild to moderate" (5 < or = GDS-15 < or = 9), and "severe" (GDS-15 > or = 10). Participants listed all medications used regularly. We used the Western Australian Data Linkage System to monitor mortality.ResultsThere were 883 deaths between the study assessment and the 30th June 2008 (mean follow-up of participants: 6.0+/-1.1 years). The adjusted mortality hazard (MH) of men with clinically significant depression was 1.98 (95%CI = 1.61-2.43), and increased with the severity of symptoms: 1.39 (95%CI = 1.13-1.71) for questionable, 2.71 (95%CI = 2.13-3.46) for mild/moderate, and 3.32 (95%CI: 2.31-4.78) for severe depression. The use of antidepressants increased MH (HR = 1.31, 95%CI = 1.02-1.68). Compared with men who were not depressed and were not taking antidepressants, MH increased from 1.22 (95%CI = 0.91-1.63) for men with no depression who were using antidepressants to 1.85 (95%CI = 1.47-2.32) for participants who were depressed but were not using antidepressants, and 2.97 (95%CI = 1.94-4.54) for those who were depressed and were using antidepressants. All analyses were adjusted for age, educational attainment, migrant status, physical activity, smoking and alcohol use and the Charlson comorbidity index.ConclusionsThe mortality associated with depression increases with the severity of depressive symptoms and is largely independent of comorbid conditions. The use of antidepressants does not reduce the mortality rates of older men with persistent symptoms of depression.

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Combinations of biomarkers predictive of later life mortality.

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