Poor Cardiovascular Health a Predictor for Premature Brain Aging
By Seema Gupta, MD, MSPH
Clinical Assistant Professor, Department of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV
SYNOPSIS: Worse cardiovascular health at age 36 years can predict worse brain aging and associated cognitive problems later in life.
SOURCE: Wagen AZ, Coath W, Keshavan A, et al. Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: A population-based study. Lancet Healthy Longev 2022;3:e607-e616.
As the U.S. population ages, clinicians often are required to make difficult and complex decisions regarding the treatment of patients for whom chronological age alone frequently is a poor indicator of physiological and functional status. As neurodegenerative diseases, including Alzheimer’s disease, progress, the brain seems to be particularly sensitive after onset. Progression often is exponential as one ages.
Neuroanatomically, brain aging is characterized primarily by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement.1 Pathophysiologically, brain aging is associated with neuronal shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions.
Because there is so much interindividual variance in brain aging from disease-related factors, it may be significant to incorporate mechanisms of decline caused by both disease and typical aging when evaluating patients. Such clinical decision tools could not only encompass mechanisms of decline caused by both disease processes and biological aging, but even may be able to detect them early and intervene in those individuals whose brain age is declining faster than predicted.
Wagen et al studied the Medical Research Council National Survey of Health and Development (NSHD) 1946 British Birth Cohort, consisting of 5,362 participants. Since the participants had been a part of the study throughout their lives, the researchers compared current brain ages to various factors from across the life course. All participants were between age 69 and 72 years, but their estimated brain ages ranged from age 46 to 94 years. Using MRI data from a previously defined portion of this cohort, Wagen et al derived brain-predicted age from an established machine-learning model. In the model, researchers used the MRI data and substracted it from chronological age (at time of assessment), resulting in the brain-predicted age difference (brain-PAD). Then, the authors tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy.
Participants with the worst brain health also exhibited worse cardiovascular health at age 36 years or age 69 years, as well as those with worse cerebrovascular disease on MRI. Investigators found worse cardiovascular health at age 36 years predicted a more advanced brain age later in life. Generally, men tended to exhibit older brains than women of the same age.
Researchers also found older brain age was associated with higher concentrations of neurofilament light protein (NfL) in the blood. NfL elevation is thought to arise because of nerve cell damage. This protein has become recognized as a useful biomarker for several neurodegenerative disorders.2 Wagen et al concluded brain-PAD relates to both general and disease-specific contributions to age-related brain changes.
COMMENTARY
Life expectancy has risen steadily thanks to innovations in medicine and better living standards. Cognitive strength is critical for healthy aging, with a significant effect on tasks of independent living, such as medication adherence, financial management, or nutrition.3 Yet a reliable cross-sectional metric is unavailable as a clinical tool that can assist in quantifying the complex relationship between the different factors influencing brain health throughout life. This clinical decision instrument could be valuable for clinical practice as a diagnostic and predictive tool.
Wagen et al estimated brain age from MRI scans using machine learning and compared it to biological age. While this technique needs further validation, this approach one day could serve as a useful tool for identifying patients at risk of accelerated aging. In turn, clinicians could offer early, targeted prevention tactics to improve brain health. It also could identify patients with other comorbidities, such as cardiovascular disease, who may be aging more rapidly than expected, before the onset of clinical manifestations.4 This is another example where artificial intelligence and machine learning could significantly improve patient care in the future.
REFERENCES
1. Blinkouskaya Y, Caçoilo A, Gollamudi T, et al. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021;200:111575.
2. Preische O, Schultz SA, Apel A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med 2019;25:277-283.
3. Oschwald J, Guye S, Liem F, et al. Brain structure and cognitive ability in healthy aging: A review on longitudinal correlated change. Rev Neurosci 2019;31:1-57.
4. Belsky DW, Caspi A, Arseneault L, et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. Elife 2020;9:e54870.
Worse cardiovascular health at age 36 years can predict worse brain aging and associated cognitive problems later in life.
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