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Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study.

ABSTRACT: Biological aging results in changes in an organism that accumulate over age in a complex fashion across different regulatory systems, and their cumulative effect manifests in increased physiological dysregulation (PD) and declining robustness and resilience that increase risks of health disorders and death. Several composite measures involving multiple biomarkers that capture complex effects of aging have been proposed. We applied one such approach, the Mahalanobis distance (DM), to baseline measurements of various biomarkers (inflammation, hematological, diabetes-associated, lipids, endocrine, renal) in 3,279 participants from the Long Life Family Study (LLFS) with complete biomarker data. We used DM to estimate the level of PD by summarizing information about multiple deviations of biomarkers from specified "norms" in the reference population (here, LLFS participants younger than 60 years at baseline). An increase in DM was associated with significantly higher mortality risk (hazard ratio per standard deviation of DM: 1.42; 95% confidence interval: [1.3, 1.54]), even after adjustment for a composite measure summarizing 85 health-related deficits (disabilities, diseases, less severe symptoms), age, and other covariates. Such composite measures significantly improved mortality predictions especially in the subsample of participants from families enriched for exceptional longevity (the areas under the receiver operating characteristic curves are 0.88 vs. 0.85, in models with and without the composite measures, p = 2.9 × 10-5). Sensitivity analyses confirmed that our conclusions are not sensitive to different aspects of computational procedures. Our findings provide the first evidence of association of PD with mortality and its predictive performance in a unique sample selected for exceptional familial longevity.

SUBMITTER: Arbeev KG

PROVIDER: S-EPMC7067825 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study.

Arbeev Konstantin G KG Bagley Olivia O Ukraintseva Svetlana V SV Duan Hongzhe H Kulminski Alexander M AM Stallard Eric E Wu Deqing D Christensen Kaare K Feitosa Mary F MF Thyagarajan Bharat B Zmuda Joseph M JM Yashin Anatoliy I AI

Frontiers in public health 20200306

Biological aging results in changes in an organism that accumulate over age in a complex fashion across different regulatory systems, and their cumulative effect manifests in increased physiological dysregulation (PD) and declining robustness and resilience that increase risks of health disorders and death. Several composite measures involving multiple biomarkers that capture complex effects of aging have been proposed. We applied one such approach, the Mahalanobis distance (D<sub>M</sub>), to b ...[more]

PMID: 32211364

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Project description:BackgroundThe Severe Respiratory Insufficiency (SRI) questionnaire is a specific measure of health-related quality of life (HRQoL) in patients treated with long-term mechanical ventilation (LTMV). The aim of the present study was to examine whether SRI sum scores and related subscales are associated with mortality in LTMV patients.MethodsThe study included 112 LTMV patients (non-invasive and invasive) from the Norwegian LTMV registry in Western Norway from 2008 with follow-up in August 2014. SRI data were obtained through a postal questionnaire, whereas mortality data were obtained from the Norwegian Cause of Death Registry. The SRI questionnaire contains 49 items and seven subscales added into a summary score (range 0-100) with higher scores indicating a better HRQoL. The association between the SRI score and mortality was estimated as hazard ratios (HRs) with 95% confidence intervals (95% CI) using Cox regression models and HRs were estimated per one unit change in the SRI score.ResultsOf the 112 participating patients in 2008, 52 (46%) had died by August 2014. The mortality rate was the highest in patients with chronic obstructive pulmonary disease (75%), followed by patients with neuromuscular disease (46%), obesity hypoventilation syndrome (31%) and chest wall disease (25%) (p?<?0.001). Higher SRI sum scores in 2008 were associated with a lower mortality risk after adjustment for age, education, hours a day on LTMV, time since initiation of LTMV, disease category and comorbidity (HR 0.98, 95% CI: 0.96-0.99). In addition, SRI-Physical Functioning (HR 0.98, 95% CI: 0.96-0.99), SRI-Psychological Well-Being (HR 0.98, 95% CI: 0.97-0.99), and SRI-Social Functioning (HR 0.98, 95% CI: 0.97-0.99) remained significant risk factors for mortality after covariate adjustment. In the subgroup analyses of patient with neuromuscular diseases we found significant inverse associations between some of the SRI subscales and mortality.ConclusionsSRI score is associated with mortality in LTMV-treated patients. We propose the use of SRI in the daily clinic with repeated measurements as part of individual follow-up. Randomized clinical trials with interventions aimed to improve HRQoL in LTMV patients should consider the SRI questionnaire as the standard HRQoL measurement.

Project description:Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, DM, that uses statistical distance to assess the degree to which an individual's biomarker profile is normal versus aberrant. However, the sensitivity of DM to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a "normal" profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. DMs calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even DMs calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of DM physiological dysregulation and as an emergent property of a complex system.

Project description:<p>The Long Life Family Study (LLFS) is an international collaborative study of the genetics and familial components of exceptional survival, longevity, and healthy aging. Families were recruited through elderly probands (generally in their 90s) who self-reported on the survival history of their parents and siblings, and on the basis of this information, families which showed clustering of exceptional survival were recruited. [Specifically, a Family Longevity Selection Score (FLOSS) ≥7 was required. The FLOSS measures the average excess Observed lifespan over that Expected based upon lifetables, while adding a bonus term for still-living individuals. Thus FLOSS is a useful tool for scoring and selecting families for inclusion in a research study of exceptional survival (Sebastiani et al., 2009, PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/19910380" target="_blank">19910380</a>)]. Probands resided in the catchment areas of four Field Centers (Boston University, Columbia University, University of Pittsburgh, and University of Southern Denmark). Recruited family members were phenotyped through extensive in-home visits by teams of technicians who traveled all over the USA and Denmark. Blood assays were centrally processed at a Laboratory Core (University of Minnesota) and protocols were standardized, monitored and coordinated through a Data Management Coordinating Center (Washington University). We examined and extensively phenotyped in all major domains of healthy aging, 4,953 individuals in 539 families through comprehensive in-home visits. Of these, 4,815 gave dbGaP sharing permission and had sufficient quantity/quality of DNA for GWAS genotyping. This large collection of families, selected on the basis of clustering for exceptional survival, is a unique resource for the study of human longevity and healthy aging. We estimate that less than 1% of the Framingham Heart Study (FHS) families (a roughly random population family sample) would meet the minimal entrance criteria for exceptional survival required in the LLFS (Sebastiani et al., 2009, PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/19910380" target="_blank">19910380</a>). Thus, the least exceptional LLFS families show more clustering for exceptional longevity than 99% of the FHS families. Although the LLFS pedigrees were selected on the basis of longevity per se in the upper generation (and the generation above that), the children's generation have significantly lower rates of many major diseases and have better healthy aging profiles for many disease phenotypes (Newman et al., 2011, PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/21258136" target="_blank">21258136</a>).</p>

Dataset Information

Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study.

Publications

Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study.

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