Poor Heart Health Predicts Premature Brain Aging

Summary: Worse cardiovascular health at age 36 can predict a higher brain age and associated cognitive problems later in life.

Source: UCL

By estimating people’s brain age from MRI scans using machine learning, a team led by UCL researchers has identified multiple risk factors for a prematurely aging brain.

They found that worse cardiovascular health at age 36 predicted a higher brain age later in life, while men also tended to have older brains than women of the same age, as they report in The Lancet Healthy Longevity.

A higher brain age was associated with slightly worse scores on cognitive tests, and also predicted increased brain shrinkage (atrophy) over the following two years, suggesting it could be an important clinical marker for people at risk of cognitive decline or other brain-related ill health.

Lead author Professor Jonathan Schott (UCL Dementia Research Center, UCL Queen Square Institute of Neurology) said: “We found that despite people in this study all being of very similar real ages, there was a very wide variation in how old the computer model predicted their brains to be.

“We hope this technique could one day be a useful tool for identifying people at risk of accelerated aging, so that they may be offered early, targeted prevention strategies to improve their brain health.”

The researchers applied an established MRI based machine learning model to estimate the brain age of members of the Alzheimer’s Research UK-funded Insight 46 study, led by Professor Schott.

Insight 46 study members are drawn from the Medical Research Council National Survey of Health and Development (NSHD) 1946 British Birth Cohort. As the participants had been a part of the study throughout their lives, the researchers were able to compare their current brain ages to various factors from across the life course.

The participants were all between 69 and 72 years old, but their estimated brain ages ranged from 46 to 93.

The researchers were able to explain roughly one third of the variability in brain age by reviewing various factors from across the life course.

This shows a brain and a heart on a scale
Worse cardiovascular health at age 36 predicted a higher brain age later in life. Image is in the public domain

People with worse cardiovascular health at age 36 or 69 had worse brain health, as did those with increased cerebrovascular disease on MRI (relating to blood flow and blood vessels in the brain). This aligns with a previous study led by Professor Schott finding that high blood pressure at age 36 predicted poorer brain health late in life.

The study did not identify any associations between childhood cognitive function, education level or socioeconomic status, and a prematurely aging brain.

The researchers also found that higher brain age was associated with higher concentration of neurofilament light protein (NfL) in the blood. NfL elevation is thought to arise due to nerve cell damage and is increasingly being recognized as a useful marker of neurodegeneration.

Dr. Sara Imarisio, Head of Research at Alzheimer’s Research UK, said: “The Insight 46 study is helping reveal more about the complex relationship between the different factors influencing people’s brain health throughout their life.

“Using machine learning, researchers in this study have uncovered yet more evidence that poorer heart health in midlife is linked to greater brain shrinkage in later life. We’re incredibly grateful to the dedicated group of individuals who have contributed to research their entire lives making this work possible.”

About this brain aging research news

Author: Chris Lane
Contact: UCL
Contact: Chris Lane – UCL
Image: The image is in the public domain

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Original Research: Open access.
“Life course, genetic, and neuropathological associations with brain age in the British 1946 birth cohort: a population-based study” by Jonathan Schott et al. Lancet Healthy Longevity


Abstract

Life course, genetic, and neuropathological associations with brain age in the British 1946 birth cohort: a population-based study

Background

A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain’s ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age.

Methods

Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18–90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy.

Findings

Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (–1·3 [–2·4 to –0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD.

Interpretation

Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility.

Funding

Alzheimer’s Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer’s Association.

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