Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

“Physiological Dysregulation” as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease

22 Jun 2018

Abstract excerpt

Recently suggested novel implementation of the statistical distance measure (DM) for evaluating "physiological dysregulation" (PD) in aging individuals (based on measuring deviations of multiple biomarkers from baseline or normal physiological states) allows reducing high-dimensional biomarker space into a single PD estimate. Here we constructed DM using biomarker profiles from FRAMCOHORT (Framingham Heart Study)...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Frailty in Older Adults

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 29939206
DOI: 10.1093/gerona/gly136

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

“Physiological Dysregulation” as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical DiseasePMID 29939206Read ↗

Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker CompositionSemantic proximity Read ↗

An Objective Metric of Individual Health And Aging For Population SurveysSemantic proximity Read ↗

Blood Omics Models for System-Specific Mortality Risk EstimationSemantic proximity Read ↗

Health and disease phenotyping in old age using a cluster network analysis.Semantic proximity Read ↗

Comprehensive Comparison of Sixteen Markers of Biological Aging: Cross-Sectional and Longitudinal Results from the Berlin Aging Study II (BASE-II)Semantic proximity Read ↗

VAM: A Multimodal Dynamical Foundation Model for Characterizing Human Aging Dynamics and Enabling Virtual Aging PerturbationSemantic proximity Read ↗

Examination of the Dimensions of Biological AgeSemantic proximity Read ↗

Biomarker signatures of agingSemantic proximity Read ↗

Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort StudiesSemantic proximity Read ↗

Unsupervised learning of aging principles from longitudinal dataSemantic proximity Read ↗

Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience.Semantic proximity Read ↗

Systemic inflammation and biological aging in the Health and Retirement StudySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.