Introduction: This article will discuss some myths about heart and brain aging. The first part of the article will cover the relationship between cardiovascular disease (CVD), high Body Mass Index (BMI), and obesity, the effect of aging on neurodegenerative diseases, the definition of brain age gap (BAG) and its importance in estimation, and the reason why males are more vulnerable than women. The second part of the article will provide an introduction to cerebral small vessel disease (SVD), its definition, significance, and its relationships with brain aging. Furthermore, the article will discuss the importance of magnetic resonance imaging (MRI) in estimating brain intensities.

We may be familiar with how the brain acts in a hit-and-run situation. The driver rushes for a flee with a high heart rate, sweating, and faster blood circulation, all because of the autonomic nervous system, which is regulated by the brain. But little does the public know about the connection between heart health and brain aging [1]. So, this article will discuss the relationship between cardiovascular disease, several lifestyle risk factors, brain aging, and dementia. Moreover, throughout a concise clinical description of the relationship between SVD and brain aging, this article will reveal the mystery of the heart and brain connection.

The relationship between CVD, BMI, and obesity
It is known that CVD is associated with high BMI, obesity, as well as sclerosis [2]. BMI, waist-to-hip ratio (WHR), and body fat percentage (BF%) are well-known CVD risk factors [3]. As people age, and if their lifestyle changes, these risk factors will change too. For example, an increase in the rate of water loss or fatty proportions can be observed more often among older people [4].

BAG and its importance in the estimation
BAG is an excellent estimate of the difference between an individual’s brain age and chronological age. It serves as an indicator of a deviation from healthy aging. For example, older brains have a higher BAG, which may indicate an individual’s brain may be older than the average [6] . It has been associated with dementia-related modifiable risk factors and cognitive functioning [7]. Moreover, some studies have shown that higher BAGs are linked to psychiatric diseases such as schizophrenia and bipolar disorder [8]. BAGs and variations in cortical thickness, surface area, fractional anisotropy, and white and gray matter hyperintensities have been used in different machine learning models [9] .

Figure 1. The amazing heart brain connection. Image source:

The reason why men are more vulnerable to BAG than women
The reason can be explained by understanding the relationship between BMI, WHR, BF%, and the brain’s aging [3] . These three CVD risk factors positively affect BAG. The reason is because of the fat distribution and sex hormones. Males are more likely to have fat distributed in the visceral adipose tissue surrounding the abdominal organs, which causes a higher risk of cardiometabolic disease [10] . The second reason is the sex hormones. The fatty tissue can transform and produce estrogen, manifesting the discrepancy in brain aging between females and males [11].

Figure 2. The elder people exercise for a healthy lifestyle to control weight and improve cognition. Image source:

The definition, significance, and the relationship of cerebral SVD with brain aging
The cerebral SVD will be the main topic of this article’s second part. The brain, kidney, and retina are among the primary cardiac output-receiving organs that are most commonly impacted by SVD. Debilitating diseases including dementia, lacunar infarcts, blindness, or renal failure have SVD as a primary causative factor. Therefore, studying and treating SVD has become a significant primary interest for doctors and researchers.

Cerebral SVD is defined as the pathogenesis of damaged small end arteries, venules, arterioles, and capillaries related to the brain. SVD has previously been associated with vascular risk factors such as blood pressure and obesity [12] . Moreover, evidence has shown that the severity of SVD can predict cognitive function and aging of the brain.

Using MRI to estimate brain intensities
The MRI is necessary for estimating axonal, myelin integrity, grey matter, and white matter hyperintensities. MRI can measure brain vascular damage. These diverse parameters can be used in machine learning models to predict cognition degeneration. For example, some studies have shown that a higher SVD burden in older age groups individuals had relatively faster rates of decline in speech fluency and performance from the mid-to-old transition [13] .

Conclusion: In conclusion, CVD is associated with BMI, WHR, BF%, as well as sclerosis. Additionally, CVD risk factors and aging were connected to cognition impairment. BAG serves as an indicator of a deviation from healthy aging. Machine learning techniques have used BAGs and variations in cortical thickness, surface area, fractional anisotropy, and white and gray matter hyperintensities in their predictive models. Fat distribution and sex hormones have been identified as reasons why men are more vulnerable to BAG than women.

Cerebral SVD is defined as the pathogenesis of damaged small end arteries, venules, arterioles, and capillaries related to the brain. SVD has previously been associated with cognitive function and aging of the brain, and MRI offers an opportunity to measure brain vascular damage [14].

© COPYRIGHT: This article is the property of We Speak Science, a non-profit organisation, co-founded by Dr. Detina Zalli and Dr. Argita Zalli. The article is written by Jenny Zhang, Nanchang University, China


  1. Foundation, B.H. Is the heart connected to the brain? [cited 2022 Sept. 14th]; Available from:
  2. Dwivedi, A.K., et al., Association Between Obesity and Cardiovascular Outcomes: Updated Evidence from Meta-analysis Studies. Curr Cardiol Rep, 2020. 22(4): p. 25.
  3. Subramaniapillai, S., et al., Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort. Hum Brain Mapp, 2022. 43(12): p. 3759-3774.
  4. Malczyk, E., et al., Body composition in healthy older persons: Role of the ratio of extracellular/total body water. Journal of biological regulators and homeostatic agents, 2016. 30: p. 767-772.
  5. Christman, S., et al., Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression. Transl Psychiatry, 2020. 10(1): p. 317.
  6. Chen, C.-L., et al., Multimodal Brain Age Gap as a Mediating Indicator in the Relation between Modifiable Dementia Risk Factors and Cognitive Functioning. bioRxiv, 2020: p. 2020.09.23.309369.
  7. Tønnesen, S., et al., Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study. Biol Psychiatry Cogn Neurosci Neuroimaging, 2020. 5(12): p. 1095-1103.
  8. Patel, R., et al., Inter- and intra-individual variation in brain structural-cognition relationships in aging. Neuroimage, 2022. 257: p. 119254.
  9. Chang, E., M. Varghese, and K. Singer, Gender and Sex Differences in Adipose Tissue. Curr Diab Rep, 2018. 18(9): p. 69.
  10. Meyer, M.R., et al., Obesity, insulin resistance and diabetes: sex differences and role of oestrogen receptors. Acta Physiol (Oxf), 2011. 203(1): p. 259-69.
  11. Li, Q., et al., Cerebral Small Vessel Disease. Cell transplantation, 2018. 27(12): p. 1711-1722.
  12. Jansen, M.G., et al., Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life. J Cereb Blood Flow Metab, 2022. 42(4): p. 600-612.
  13. Poggesi A, Salvadori E, Pantoni L, Pracucci G, Cesari F, Chiti A, et al. Risk and determinants of dementia in patients with mild cognitive impairment and brain subcortical vascular changes: a study of clinical neuroimaging, and biological markers-the VMCI-tuscany study: rationale, design, and methodology. Int J Alzheimers Dis. (2012) 2012:608013. doi: 10.1155/2012/608013