Examining the role of systemic inflammation as a mediator of the glycaemia-brain volume associations in women
fatih, N.; Chaturvedi, N.; Garfield, V.; Sudre, C.; Silverwood, R. J.; Cash, D.; Malone, I. B.; Barnes, J.; Richards, M.; Schott, J.; Hughes, A. D.; James, S.-N.
Show abstract
Previous studies have found that diabetes and its mechanistic factors (e.g. glycaemia) are associated with poorer cognitive and brain health. There is also growing evidence of sex differences in how diabetes manifests itself and impacts the brain. The mechanisms through which this association manifests itself are still poorly understood, but the possible role of inflammation has been proposed. This study aims to explore whether the relationship between mid-life glycaemia and brain volumes in later-life in women is mediated by systemic inflammation. The sample consisted of female participants from the National Survey of Health and Development (NSHD) who underwent neuroimaging as part of the Insight 46 sub-study. Path analysis models were then constructed between glycaemic markers (age 60-64) and brain health outcomes (age 69-71) with adjustments for social and metabolic confounders (age 60-64). Although glycaemia was mostly associated with a higher systemic inflammatory state in two of the three markers (e.g., HbA1c and interleukin-6: {beta} = 0.05 [0.02. 0.01], p = 0.001 and glycoprotein A: {beta} = 0.02 [=-0.01. 0.02], p = 0.001), we did not find a relationship between inflammation and our brain volume markers [whole brain, grey matter and white matter] (e.g. interleukin-6 and whole brain volume: {beta} = -3.1 [=-7.7. 1.5], p = 0.2; interleukin-6 and grey matter: {beta} = -0.3 [=-1.8. 1.2], p = 0.7), thus no mediated effect between the glycaemic markers and outcomes via the pathway of systematic inflammation. This raises the possibility alternative mechanistic pathway, to inflammation, playing a role in the relationship between hyperglycaemia and brain health outcomes.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Association between inflammation and cognition: triangulation of evidence using a population-based cohort and Mendelian randomization analyses 94%
- Multimodal imaging suggests potential immune-vascular contributions to altered regional brain perfusion and oxygen metabolism in Post-COVID-19 Syndrome 93%
- Biopsychosocial Correlates of Resting and Stress-Reactive Salivary GDF15: Preliminary Findings 92%
Similar papers in this journal
- Mapping Pathways to Neuronal Atrophy in Healthy, Mid-aged Adults: From Chronic Stress to Systemic Inflammation to Neurodegeneration? 95%
- A Physiometric Investigation of Inflammatory Composites: Comparison of A Priori Aggregates, Empirically-identified Factors, and Individual Proteins 93%
- Inflammation biomarkers and neurobehavioral performance in rural adolescents 93%
Similar papers in this journal
- White matter hyperintensities are common in midlife and already associated with cognitive decline 94%
- Right fronto-parietal networks mediate the neurocognitive benefits of enriched environments. 93%
- The PREVENT Dementia programme: Baseline demographic, lifestyle, imaging and cognitive data from a midlife cohort study investigating risk factors for dementia 93%
Similar papers in this journal
- Psychological Distress and Metabolomic Markers: A Systematic Review 91%
- Structural Brain Correlates of Cognitive Function in Schizophrenia: A Meta-Analysis 91%
- Interpersonal early adversity demonstrates dissimilarity from early socioeconomic disadvantage in the course of human brain development: A meta-analysis 90%
Similar papers in this journal
- Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities 95%
- Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults 94%
- Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.