Sex differences in Alzheimer’s and Parkinson’s diseases

Determining sex differences in disease phenotypes is the gateway to precision medicine, said Dr Maria Teresa Ferretti, co-founder of the Women’s Brain Project, Switzerland, and co-chair of a symposium at ADPD2022 in which new evidence was presented on sex differences in an Alzheimer’s disease biomarker and aging-related sex differences in brain connectivity.

Sex-dependent changes in biomarkers

A biomarker of microglial activation and inflammation increases in men over time

The effect of sex on changes in biomarkers over time in cerebrospinal fluid (CSF) samples have been investigated by Dr Claudia Cicognola, Lund University, Sweden.

Samples were collected at baseline and then every 2 years for 8–10 years from 801 cognitively unimpaired individuals and 265 individuals with mild cognitive impairment from the longitudinal Swedish BioFINDER cohort,1 explained Dr Cicognola.

A significant increase in soluble Triggering Receptor Expressed on Myeloid cells 2 (sTREM2), which is a biomarker of microglial activation and inflammation, was observed in men compared with women independently of amyloid-beta status.1

 

Aging-related sex differences in brain connectivity

Sex differences in normal aging brain could help explain different prevalences of neurodegenerative diseases in men and women

Age is a major risk factor for many diseases including AD and Parkinson’s disease, but not everyone ages in the same way, said Dr Mite Mijalkov, Karolinska Institute, Sweden, and sex plays a role in this variability in aging.2

Understanding sex differences in normal aging brain could help to explain the different prevalences of neurodegenerative diseases in men and women, but studies of functional brain connectivity have not been large enough to provide appropriate data, explained Dr Mijalkov.

To address this lack of information, Dr Mijalkov and his colleagues evaluated the resting-state functional magnetic resonance images (MRI) of 19,975 women and 17,568 men aged 47 to 79 years in the UK Brain Biobank cohort.3

A novel multilayer analysis of brain connectivity captures the interaction between positive and negative connections

They assessed functional brain connectivity for each participant using the negative and positive correlations between 21 nodes corresponding to the resting-state functional MRI networks using classical single-layer connectivity measures — that is, average whole-brain connectivity, average negative connectivity, average positive connectivity, and number of negative correlations.3

The positive and negative correlations of each functional network were separated and analysed as independent layers with evaluation of:

  • Global efficiency, which indicates how well the nodes within networks integrate with each other
  • Clustering coefficient, which indicates specialization within the networks3

Women have higher average positive connectivity than men because they have fewer negative connections

To increase the power of the analysis, the team then designed a novel multilayer approach assuming that all nodes in one layer are connected to all nodes in the other layer and evaluated:

  • Multilayer global efficiency to compare differences in global efficiency due to intra- and interlayer connections
  • Multilayer clustering coefficient to compare the clustering coefficients between the two layers3

Results from all analyses revealed that:

  • Women have higher average positive connectivity because they have fewer negative connections than men, but the sex difference dissipates with increasing age in terms of positive connectivity
  • Men have higher multilayer measures compared with women3

Multilayer measures that capture the interaction between both positive and negative connections can therefore provide crucial new information on the impact of sex on age-related changes in brain network topology,3 concluded Dr Mijalkov.

Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.

References
  1. Cicognola C, et al. Effects of APOE genotype, age and sex on cerebrospinal fluid biomarkers measured with NeuroToolKit in the Longitudinal Swedish Biofinder Cohort. Alzheim Dementia 2021;17:e055153.
  2. Niccoli T, Partridge L. Ageing as a risk factor for disease. Current Biology 2012;22:R741–52.
  3. Mijalkov M, et al. Sex differences in functional network topology over the course of aging in 37543 UK Biobank participants. medRxiv 2022.03.08.22272089; doi: https://doi.org/10.1101/2022.03.08.22272089
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