Brain white matter hyperintensities-predicted age reflects neurovascular health in middle-to-old aged subjects

Association between plasma trimethylamine N-oxide and cerebral white matter hyperintensity: a cross-sectional study

Background

Cerebral white matter hyperintensity (WMH) is a pivotal imaging feature of cerebral small vessel disease (CSVD), closely correlated with an elevated risk of ischemic stroke (IS). Trimethylamine N-oxide (TMAO), a metabolite of gut microbiota, is increasingly associated with IS and atherosclerosis. However, the intricate relationship between TMAO and WMH remains ambiguous. This study aimed to study the connection between plasma TMAO and WMH. Furthermore, it assessed the potential of TMAO as a risk evaluation instrument for WMH.

Methods

In this cross-sectional study, we categorized WMH into periventricular WMH (P-WMH) and deep WMH (D-WMH), based on its locations. The severity of WMH was assessed and grouped according to the Fazekas scale. Plasma TMAO levels were quantitatively determined. We established the correlation between plasma TMAO levels and WMH severity using a Logistic regression model. Additionally, we employed ROC curves to evaluate the diagnostic efficacy of plasma TMAO concentration in distinguishing the severity of WMH.

Results

A higher plasma TMAO tertile was significantly linked to a higher Fazekas score, encompassing the overall score, P-WMH score, and D-WMH score (p < 0.001). A logical regression analysis revealed that plasma TMAO levels were independently associated with overall moderate and severe WMH, compared to overall non-mild WMH, in the unadjusted model (OR = 1.373, 95%CI 1.183-1.594 for moderate; OR = 1.384, 95%CI 1.192-1.607 for severe), the adjusted model a (OR = 1.436, 95%CI 1.214-1.669 for moderate; OR = 1.446, 95%CI 1.222-1.711 for severe) and the adjusted model b (OR = 1.490, 95%CI 1.234-1.800 for moderate; OR = 1.494, 95%CI 1.237-1.805 for severe). The analysis also showed an independent correlation between plasma TMAO levels and WMH severity, irrespective of the unadjusted model, adjusted model a, or adjusted model b, when considering P-WMH and D-WMH severity. The ROC indicated that, in overall WMH and P-WMH, the area under curve (AUC) for non-mild and severe WMH were both>0.5, while the AUC for moderate WMH was<0.5. In contrast, in D-WMH, the AUC for non-mild, moderate, and severe WMH were all>0.5.

Conclusion

Plasma TMAO levels exhibited a significant correlation with both overall and region-specific WMH severity. Furthermore, the plasma TMAO levels displayed robust predictive capability for D-WMH.

Brain reserve in midlife is associated with executive function changes across 12 years

We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.

Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization

To explore the causal relationship between age and brain health (cortical atrophy, white matter integrity, white matter hyperintensities and cerebral microbleeds in various brain regions) related multiparameter imaging features using two-sample Mendelian randomization. Age was determined as chronological age of the subject. Cortical volume, white matter micro-integrity, white matter hyperintensity volume and cerebral microbleeds of each brain region were included as phenotypes for brain health. Age and imaging of brain health related genetic data were analysed to determine the causal relationship using inverse-variance weighted model, validated by heterogeneity and horizontal pleiotropy variables. Age is causally related to increased volumes of white matter hyperintensities (β = 0.151). For white matter micro-integrity, fibres of the inferior cerebellar peduncle (axial diffusivity β = -0.128, orientation dispersion index β = 0.173), cerebral peduncle (axial diffusivity β = -0.136), superior fronto-occipital fasciculus (isotropic volume fraction β = 0.163) and fibres within the limbic system were causally deteriorated. We also detected decreased cortical thickness of multiple frontal and temporal regions (P < 0.05). Microbleeds were not related with aging (P > 0.05). Aging is a threat of brain health, leading to cortical atrophy mainly in the frontal lobes, as well as the white matter degeneration especially abnormal hyperintensity and deteriorated white matter integrity around the hippocampus.

Prevalence of Neuroradiological Abnormalities in First-Episode Psychosis: A Systematic Review and Meta-analysis

Importance

Individuals presenting with first-episode psychosis (FEP) may have a secondary ("organic") etiology to their symptoms that can be identified using neuroimaging. Because failure to detect such cases at an early stage can have serious clinical consequences, it has been suggested that brain magnetic resonance imaging (MRI) should be mandatory for all patients presenting with FEP. However, this remains a controversial issue, partly because the prevalence of clinically relevant MRI abnormalities in this group is unclear.

Objective

To derive a meta-analytic estimate of the prevalence of clinically relevant neuroradiological abnormalities in FEP.

Data Sources

Electronic databases Ovid, MEDLINE, PubMed, Embase, PsychINFO, and Global Health were searched up to July 2021. References and citations of included articles and review articles were also searched.

Study Selection

Magnetic resonance imaging studies of patients with FEP were included if they reported the frequency of intracranial radiological abnormalities.

Data Extraction and Synthesis

Independent extraction was undertaken by 3 researchers and a random-effects meta-analysis of pooled proportions was calculated. Moderators were tested using subgroup and meta-regression analyses. Heterogeneity was evaluated using the I2 index. The robustness of results was evaluated using sensitivity analyses. Publication bias was assessed using funnel plots and Egger tests.

Main Outcomes and Measures

Proportion of patients with a clinically relevant radiological abnormality (defined as a change in clinical management or diagnosis); number of patients needed to scan to detect 1 such abnormality (number needed to assess [NNA]).

Results

Twelve independent studies (13 samples) comprising 1613 patients with FEP were included. Of these patients, 26.4% (95% CI, 16.3%-37.9%; NNA of 4) had an intracranial radiological abnormality, and 5.9% (95% CI, 3.2%-9.0%) had a clinically relevant abnormality, yielding an NNA of 18. There were high degrees of heterogeneity among the studies for these outcomes, 95% to 73%, respectively. The most common type of clinically relevant finding was white matter abnormalities, with a prevalence of 0.9% (95% CI, 0%-2.8%), followed by cysts, with a prevalence of 0.5% (95% CI, 0%-1.4%).

Conclusions and Relevance

This systematic review and meta-analysis found that 5.9% of patients presenting with a first episode of psychosis had a clinically relevant finding on MRI. Because the consequences of not detecting these abnormalities can be serious, these findings support the use of MRI as part of the initial clinical assessment of all patients with FEP.

A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium

China and the world are facing severe population aging and an increasing burden of age-related diseases. Aging of the brain causes major age-related brain diseases, such as neurodegenerative diseases and stroke. Identifying biomarkers for the effective assessment of brain aging and establishing a brain aging assessment system could facilitate the development of brain aging intervention strategies and the effective prevention and treatment of aging-related brain diseases. Thus, experts from the Aging Biomarker Consortium (ABC) have combined the latest research results and practical experience to recommend brain aging biomarkers and form an expert consensus, aiming to provide a basis for assessing the degree of brain aging and conducting brain-aging-related research with the ultimate goal of improving the brain health of elderly individuals in both China and the world.

White matter hyperintensity load is associated with premature brain aging

BACKGROUND

Brain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort.

METHODS

Brain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age).

RESULTS

Brain and chronological age were strongly correlated (r(163)=0.932, p<0.001). There was significant negative correlation between BrainGAP scores and chronological age (r(163)=-0.244, p<0.001) indicating that younger participants had higher BrainGAP (premature brain aging). Chronological age also showed a positive correlation with WMH load (r(163)=0.506, p<0.001) indicating older participants had increased WMH load. Controlling for chronological age, there was a statistically significant relationship between premature brain aging and WMHs load (r(163)=0.216, p=0.003). Each additional year in brain age beyond chronological age corresponded to an additional 1.1mm3 in WMH load.

CONCLUSIONS

WMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy.