Higher body fat leads to increased atrophy of the brain’s gray matter and a greater risk of cognitive decline


It’s the global epidemic that affects two in every five adults, but as obesity continues to expand waistlines worldwide, researchers at the University of South Australia are warning that harmful body fat can also increase the risk of dementia and stroke.

Examining grey brain matter of about 28,000 people, the world first research showed that increased body fat incrementally leads to increased atrophy of grey matter in the brain and consequently higher risk of declining brain health.

Grey matter is an essential part of the brain responsible for execution control, muscular and sensory activity as well as learning, attention, and memory.

Obesity is a major issue worldwide, with numbers nearly tripling since 1975. Data from the World Health Organization shows that more than 1.9 billion adults are overweight, with 650 million being obese. More than 340 million children (aged 5-19) are overweight or obese, with 39 million children under the age of five also falling into this category.

Lead researcher, UniSA’s Dr Anwar Mulugeta, says the findings add to the growing issues associated with being overweight or obese.

“Obesity is a genetically complex condition characterised by the excessive body fat,” Dr Mulugeta says.

“Commonly linked to cardiovascular disease, type 2 diabetes, and chronic inflammation (a marker of dementia), obesity currently costs Australia’s economy about $8.6 billion dollars each year.

While the disease burden of obesity has increased over the past five decades, the complex nature of the disease means that not all obese individuals are metabolically unhealthy, which makes it difficult to pinpoint who is at risk of associated diseases, and who is not.

“Certainly, being overweight generally increases your risk for cardiovascular disease, type 2 diabetes, and low-grade inflammation, but understanding the level of risk is important to better direct supports.

“In this study, we investigated the causal relationships of individuals within three metabolically different obesity types* ­– unfavourable, neutral and favourable – to establish whether specific weight groups were more at risk than others

“Generally, the three obesity subtypes have a characteristic of higher body mass index, yet, each type varies in terms of body fat and visceral fat distribution, with a different risk of cardiometabolic diseases.

“We found that people with higher levels of obesity especially those with metabolically unfavourable and neutral adiposity subtypes had much lower levels of grey brain matter, indicating that these people may have compromised brain function which needed further investigation.

“However, we did not find conclusive evidence to link a specific obesity subtype with dementia or stroke. Instead, our study suggests the possible role of inflammation and metabolic abnormalities and how they can contribute to obesity and grey matter volume reduction.”

The study used Mendelian randomisation to examine the genetic data of up to 336,000 individual records in the UK Biobank, with self-reported information and linked hospital and death register records to connect dementia and stoke.

It found that middle to elderly age groups (37-73) grey brain matter decreased by 0.3 per cent for every extra 1 kg/m2, which is equivalent of an extra 3 kg of weight for person of average height individuals, (173 cm)

Senior investigator, Professor Elina Hyppönen, Director of UniSA’s Australian Centre for Precision Health based at SAHMRI, says maintaining a healthy weight is important for general public health.

“It is increasingly appreciated that obesity is a complex condition, and that especially excess fat which is located around the internal organs have particularly harmful effects on health,” Professor Hyppönen says.

“Here, we used the individuals’ genetic and metabolic profiles to confirm different types of obesity. In practice, our findings very much support the need to look at the type of obesity when assessing the type of likely health impact.

People suffering from obesity are at higher risk for a myriad of diseases and health conditions, including hypertension, type 2 diabetes, stroke, elevated cholesterol, and sleep apnea. Furthermore, epidemiological evidence has established a link between obesity and central nervous system (CNS) degeneration (1–3).

This may reflect neuronal and synaptic degeneration secondary to obesity-induced metabolic disturbances; in turn, these may be contributors to the onset and progression of neurodegenerative diseases including Alzheimer’s and Parkinson’s diseases (1, 4–7).

Moreover, growing evidence indicates that lifestyle factors such as diet and physical activity improve brain structure and function, suggesting novel therapeutic strategies against neurodegenerative disorders (8–10).

Microstructural changes in the gray and white matter of the brain secondary to obesity have been documented by conventional quantitative magnetic resonance imaging (MRI) (11–14). Using diffusion tensor imaging (DTI), magnetization transfer (MT), MR relaxation time mapping, and morphometry, it has been shown that body mass index (BMI) is negatively correlated with tissue integrity in several brain structures (11–13, 15, 16).

However, as recognized by these authors, it is very difficult to interpret these findings in terms of underlying histopathologic changes. Indeed, while sensitive to changes in brain tissue microstructure, conventional MRI techniques are not specific; in addition to axonal degeneration and demyelination, these parameters may also reflect other tissue properties such as macromolecular content, flow, and fiber architecture.

Similar comments apply to the non-specificity of MR spectroscopy studies of the correlation between obesity and myelination (17). Thus, to the best of our knowledge, the specific association between obesity and myelination remains to be established. Furthermore, these previous MRI investigations were conducted on cohorts of limited size and limited age range, both limiting the statistical power of the analysis and providing results that may not be reflective of a wide adult age range.

Several multicomponent MRI relaxometry methods have been introduced for myelin content mapping through measurement of the myelin water fraction (MWF) (18), including BMC-mcDESPOT (19–21) which provides rapid whole-brain MWF maps (19–22). BMC-mcDESPOT has been extensively used in several MWF-based studies to provide quantitative evidence of myelin loss in mild cognitive impairment and dementia (23), to investigate myelination patterns with normative aging (24–26), and to demonstrate an association between myelin content and cerebral blood flow (27).

Production and maintenance of myelin through oligodendrocyte metabolism is critical for saltatory conduction and normal axonal function. Growing evidence is establishing a direct relationship between myelin loss and a number of functional neurological disorders (18, 23, 28), suggesting that the breakdown of the myelin sheath could represent an important feature of early neurodegeneration, including mild cognitive impairment and Alzheimer’s disease (AD) (23, 29, 30).

This notion is further supported by animal studies showing that a high fat diet can trigger myelin damage while obesity inhibits the maturation of the oligodendrocyte cells (9, 31, 32).

In this work, we examined the association between myelin content and obesity in 119 cognitively unimpaired subjects with healthy weight, overweight, or obesity spanning a wide age-range (22 to 94 years). Our main goal was to characterize the association between regional MWF, as a measure of myelin content, and BMI and waist circumference (WC), as measures of obesity, and to develop new insights into the specific effect of obesity on regional myelin integrity.

reference link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009848/

Original Research:
“Unlocking the causal link of metabolically different adiposity subtypes with brain volumes and the risks of dementia and stroke: A Mendelian randomization study” by Anwar Mulugeta et al. Neurobiology of Aging


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