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Aging01:26

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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
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Brain age predicts mortality.

J H Cole1, S J Ritchie2,3, M E Bastin2,4

  • 1Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK.

Molecular Psychiatry
|April 26, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a brain age biomarker using neuroimaging. An older brain age predicts increased mortality risk and poorer health, with combined brain and epigenetic age offering complementary health insights.

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Area of Science:

  • Neuroscience
  • Gerontology
  • Biomarker Discovery

Background:

  • Ageing is a significant societal burden, but its effects vary individually.
  • There is a need for reliable biomarkers to identify individuals at higher risk of age-related decline.
  • Current markers may not fully capture the complexity of biological ageing.

Purpose of the Study:

  • To introduce and validate a novel neuroimaging biomarker: brain-predicted age.
  • To assess the association of brain-predicted age with age-associated functional measures and mortality.
  • To explore the predictive value of combining brain-predicted age with other ageing biomarkers.

Main Methods:

  • Machine learning was used to train a model on structural neuroimaging data from a large healthy cohort (N=2001).
  • The model calculated brain-predicted age, which was then tested in the Lothian Birth Cohort 1936 (N=669).
  • Associations between brain-predicted age and grip strength, lung function, walking speed, fluid intelligence, allostatic load, and mortality were analyzed.

Main Results:

  • A brain-predicted age indicating an older brain was significantly associated with poorer physical function (grip strength, lung function, walking speed) and cognitive function (fluid intelligence).
  • Higher brain-predicted age correlated with increased allostatic load and a greater risk of mortality.
  • Combining brain-predicted age with DNA-methylation-predicted age improved mortality risk prediction, unlike combining it with grey matter and cerebrospinal fluid volumes.

Conclusions:

  • Brain-predicted age is a clinically relevant neuroimaging biomarker for biological ageing.
  • Neuroimaging and epigenetics measures of ageing provide complementary information for health outcome prediction.
  • Integrating distinct biological ageing biomarkers enhances the prediction of age-related deterioration and mortality risk.