Factors associated with brain ageing - a systematic review

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (N = 13,394, 5,900 males; mean age: 65.82 ± 8.89 years) are included for DNN training, testing, validation, and saliency map generation to estimate BA. To study saliency robustness to the presence of anatomic deviations from normality, saliency maps are also generated for adults with mild traumatic brain injury (mTBI, N = 214, 135 males; mean age: 55.3 ± 9.9 years). We assess saliency methods' capacities to capture known anatomic features of brain aging and compare them to a surrogate ground truth whose anatomic saliency is known a priori. Anatomic aging features are identified most reliably by the integrated gradients method, which outperforms all others through its ability to localize relevant anatomic features. Gradient Shapley additive explanations, input × gradient, and masked gradient perform less consistently but still highlight ubiquitous neuroanatomic features of aging (ventricle dilation, hippocampal atrophy, sulcal widening). Saliency methods involving gradient saliency, guided backpropagation, and guided gradient-weight class attribution mapping localize saliency outside the brain, which is undesirable. Our research suggests the relative tradeoffs of saliency methods to interpret DNN findings during BA estimation in typical aging and after mTBI.

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study

BACKGROUND AND AIMS

The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.

DESIGN

This was a cross-sectional study.

SETTING

This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.

PARTICIPANTS

This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.

MEASUREMENTS

This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.

FINDINGS

Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.

CONCLUSIONS

People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Chronic tinnitus is associated with aging but not dementia

AIM

Aging is related to deterioration of bodily and neural functions, leading to various disorders and symptoms, including the development of dementia, hearing loss, or tinnitus. Understanding how these phenomena are intertwined and how aging affects those is crucial for prevention and the future development of interventions.

METHODS

We utilized the UK Biobank which includes a total of 502,382 participants between 40 and 70 years old. We used logistic regression models and cox proportional hazard models and compared hazard ratios.

RESULTS

The odds of reporting tinnitus in the older age group (i.e., older than 58 years) were increased by 53.6 % and a one decibel increase in the speech-reception thresholds enhanced the odds for tinnitus by 13.0 %. For our second analysis regarding hearing loss, the risk of dementia increased by 14.0 % with an increase by one decibel in the speech-reception threshold score. In terms of aging, each additional year increased the risk by 17.3 %. Tinnitus alone showed a significant influence with a hazard ratio of 52.1 %, however, when adding hearing loss, age and various covariates, the effect vanished.

CONCLUSION

Findings confirm that tinnitus is indeed related to aging, but presumably independent of the aging processes accompanying the development of dementia. This highlights the urge to further investigate the impact of aging on neural processes that are relevant for alterations in the auditory systems (e.g., leading to the development of tinnitus or hearing loss) as well as for increased vulnerability in terms of neurodegenerative diseases.

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults ( N = 13,394 , 5,900 males; mean age: 65.82 ± 8.89 years) are included for DNN training, testing, validation, and saliency map generation to estimate BA. To study saliency robustness to the presence of anatomic deviations from normality, saliency maps are also generated for adults with mild traumatic brain injury (mTBI, N = 214 , 135 males; mean age: 55.3 ± 9.9 years). We assess saliency methods' capacities to capture known anatomic features of brain aging and compare them to a surrogate ground truth whose anatomic saliency is known a priori. Anatomic aging features are identified most reliably by the integrated gradients method, which outperforms all others through its ability to localize relevant anatomic features. Gradient Shapley additive explanations, input × gradient, and masked gradient perform less consistently but still highlight ubiquitous neuroanatomic features of aging (ventricle dilation, hippocampal atrophy, sulcal widening). Saliency methods involving gradient saliency, guided backpropagation, and guided gradient-weight class attribution mapping localize saliency outside the brain, which is undesirable. Our research suggests the relative tradeoffs of saliency methods to interpret DNN findings during BA estimation in typical aging and after mTBI.

A perspective on brain-age estimation and its clinical promise

Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.

Single-nucleus transcriptomic profiling of human orbitofrontal cortex reveals convergent effects of aging and psychiatric disease

Aging is a complex biological process and represents the largest risk factor for neurodegenerative disorders. The risk for neurodegenerative disorders is also increased in individuals with psychiatric disorders. Here, we characterized age-related transcriptomic changes in the brain by profiling ~800,000 nuclei from the orbitofrontal cortex from 87 individuals with and without psychiatric diagnoses and replicated findings in an independent cohort with 32 individuals. Aging affects all cell types, with LAMP5+LHX6+ interneurons, a cell-type abundant in primates, by far the most affected. Disrupted synaptic transmission emerged as a convergently affected pathway in aged tissue. Age-related transcriptomic changes overlapped with changes observed in Alzheimer's disease across multiple cell types. We find evidence for accelerated transcriptomic aging in individuals with psychiatric disorders and demonstrate a converging signature of aging and psychopathology across multiple cell types. Our findings shed light on cell-type-specific effects and biological pathways underlying age-related changes and their convergence with effects driven by psychiatric diagnosis.

Causal relationships between neuropsychiatric disorders and nonalcoholic fatty liver disease: A bidirectional Mendelian randomization study

BACKGROUND

Increasing evidences suggest that nonalcoholic fatty liver disease (NAFLD) is associated with neuropsychiatric disorders. Nevertheless, whether there were causal associations between them remained vague. A causal association between neuropsychiatric disorders and NAFLD was investigated in this study.

METHODS

We assessed the published genome-wide association study summary statistics for NAFLD, seven mental disorder-related diseases and six central nervous system dysfunction-related diseases. The causal relationships were first assessed using two-sample and multivariable Mendelian randomization (MR). Then, sensitivity analyses were performed, followed by a reverse MR analysis to determine whether reverse causality is possible. Finally, we performed replication analyses and combined the findings from the above studies.

RESULTS

Our meta-analysis results showed NAFLD significantly increased the risk of anxiety disorders (OR = 1.016, 95% CI = 1.010-1.021, P value < 0.0001). In addition, major depressive disorder was the potential risk factor for NAFLD (OR = 1.233, 95% CI = 1.063-1.430, P value = 0.006). Multivariable MR analysis showed that the causal effect of major depressive disorder on NAFLD remained significant after considering body mass index, but the association disappeared after adjusting for the effect of waist circumference. Furthermore, other neuropsychiatric disorders and NAFLD were not found to be causally related.

CONCLUSIONS

These results implied causal relationships of NAFLD with anxiety disorders and Major Depressive Disorder. This study highlighted the need to recognize and understand the connection between neuropsychiatric disorders and NAFLD to prevent the development of related diseases.

Taming neuroinflammation in Alzheimer's disease: The protective role of phytochemicals through the gut-brain axis

Alzheimer's disease (AD) is a progressive degenerative neurological condition characterized by cognitive decline, primarily affecting memory and logical thinking, attributed to amyloid-β plaques and tau protein tangles in the brain, leading to neuronal loss and brain atrophy. Neuroinflammation, a hallmark of AD, involves the activation of microglia and astrocytes in response to pathological changes, potentially exacerbating neuronal damage. The gut-brain axis is a bidirectional communication pathway between the gastrointestinal and central nervous systems, crucial for maintaining brain health. Phytochemicals, natural compounds found in plants with antioxidant and anti-inflammatory properties, such as flavonoids, curcumin, resveratrol, and quercetin, have emerged as potential modulators of this axis, suggesting implications for AD prevention. Intake of phytochemicals influences the gut microbial composition and its metabolites, thereby impacting neuroinflammation and oxidative stress in the brain. Consumption of phytochemical-rich foods may promote a healthy gut microbiota, fostering the production of anti-inflammatory and neuroprotective substances. Early dietary incorporation of phytochemicals offers a non-invasive strategy for modulating the gut-brain axis and potentially reducing AD risk or delaying its onset. The exploration of interventions targeting the gut-brain axis through phytochemical intake represents a promising avenue for the development of preventive or therapeutic strategies against AD initiation and progression.

Sex as a Determinant of Age-Related Changes in the Brain

The notion of notable anatomical, biochemical, and behavioral distinctions within male and female brains has been a contentious topic of interest within the scientific community over several decades. Advancements in neuroimaging and molecular biological techniques have increasingly elucidated common mechanisms characterizing brain aging while also revealing disparities between sexes in these processes. Variations in cognitive functions; susceptibility to and progression of neurodegenerative conditions, notably Alzheimer's and Parkinson's diseases; and notable disparities in life expectancy between sexes, underscore the significance of evaluating aging within the framework of gender differences. This comprehensive review surveys contemporary literature on the restructuring of brain structures and fundamental processes unfolding in the aging brain at cellular and molecular levels, with a focus on gender distinctions. Additionally, the review delves into age-related cognitive alterations, exploring factors influencing the acceleration or deceleration of aging, with particular attention to estrogen's hormonal support of the central nervous system.

Non-ictal, interictal and ictal déjà vu: a systematic review and meta-analysis

Background

Déjà vu, French for "already seen," is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as "non-ictal déjà vu") and in epilepsy patients during seizures (as "ictal déjà vu") and between seizures (as "interictal déjà vu"). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients.

Methods

This study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence's of the phenomena. Study quality, heterogeneity, and publication bias were assessed.

Results

Database searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p < 0.001) of healthy individuals, whereas interictal déjà vu was experienced by 0.62 (95% CI [0.48, 0.75], p = 0.099) and ictal déjà vu by 0.22 (95% CI [0.15, 0.32], p = 0.001) of epilepsy patients. Examinations of phenomenological (sex, age, frequency, duration, emotional valence, and dissociative symptoms) and neuroscientific (brain structures and functions) data revealed significant variations between non-ictal, interictal and ictal déjà vu on several domains.

Conclusion

This systematic review and meta-analysis do not support the notion that non-ictal, interictal and ictal déjà vu are homogenous experiences. Instead, it provides insight into ictal déjà vu as a symptom of epilepsy that should be considered included in future revisions of the ICD-11.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=394239, CRD42023394239.

Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study

OBJECTIVES

This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions.

METHODS

The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject.

RESULTS

MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers.

CONCLUSIONS

Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.

Investigating nutrient biomarkers of healthy brain aging: a multimodal brain imaging study

The emerging field of Nutritional Cognitive Neuroscience aims to uncover specific foods and nutrients that promote healthy brain aging. Central to this effort is the discovery of nutrient profiles that can be targeted in nutritional interventions designed to promote brain health with respect to multimodal neuroimaging measures of brain structure, function, and metabolism. The present study therefore conducted one of the largest and most comprehensive nutrient biomarker studies examining multimodal neuroimaging measures of brain health within a sample of 100 older adults. To assess brain health, a comprehensive battery of well-established cognitive and brain imaging measures was administered, along with 13 blood-based biomarkers of diet and nutrition. The findings of this study revealed distinct patterns of aging, categorized into two phenotypes of brain health based on hierarchical clustering. One phenotype demonstrated an accelerated rate of aging, while the other exhibited slower-than-expected aging. A t-test analysis of dietary biomarkers that distinguished these phenotypes revealed a nutrient profile with higher concentrations of specific fatty acids, antioxidants, and vitamins. Study participants with this nutrient profile demonstrated better cognitive scores and delayed brain aging, as determined by a t-test of the means. Notably, participant characteristics such as demographics, fitness levels, and anthropometrics did not account for the observed differences in brain aging. Therefore, the nutrient pattern identified by the present study motivates the design of neuroscience-guided dietary interventions to promote healthy brain aging.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

Causal influences of neuropsychiatric disorders on Alzheimer's disease

Previous studies have observed a significant comorbidity between Alzheimer's disease (AD) and some other neuropsychiatric disorders. However, the mechanistic connections between neuropsychiatric disorders and AD are not well understood. We conducted a Mendelian randomization analysis to appraise the potential influences of 18 neurodegenerative and neuropsychiatric disorders on AD. We found that four disorders are causally associated with increased risk for AD, including bipolar disorder (BD) (OR: 1.09), migraine (OR: 1.09), schizophrenia (OR: 1.05), and Parkinson's disease (PD) (OR: 1.07), while attention-deficit/hyperactivity disorder (ADHD) was associated with a decreased risk for AD (OR: 0.80). In case of amyotrophic lateral sclerosis (OR: 1.04) and Tourette's syndrome (OR: 1.05), there was suggestive evidence of their causal effects of on AD. Our study shows that genetic components predisposing to BD, migraine, schizophrenia, and PD may promote the development of AD, while ADHD may be associated with a reduced risk of AD. The treatments aimed at alleviating neuropsychiatric diseases with earlier onset may also influence the risk of AD-related cognitive decline, which is typically observed later in life.

Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research

The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.

Insights into the Role of Histone Methylation in Brain Aging and Potential Therapeutic Interventions

Epigenetic mechanisms play a primary role in the cellular damage associated with brain aging. Histone posttranslational modifications represent intrinsic molecular alterations essential for proper physiological functioning, while divergent expression and activity have been detected in several aspects of brain aging. Aberrant histone methylation has been involved in neural stem cell (NSC) quiescence, microglial deficits, inflammatory processes, memory impairment, cognitive decline, neurodegenerative diseases, and schizophrenia. Herein, we provide an overview of recent studies on epigenetic regulation of brain tissue aging, mainly focusing on the role of histone methylation in different cellular and functional aspects of the aging process. Emerging targeting strategies of histone methylation are further explored, including neuroprotective drugs, natural compounds, and lifestyle modifications with therapeutic potential towards the aging process of the brain.

Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.

Increased brain age and relationships with blood-based biomarkers following concussion in younger populations

OBJECTIVE

Brain age is increasingly being applied to the spectrum of brain injury to define neuropathological changes in conjunction with blood-based biomarkers. However, data from the acute/sub-acute stages of concussion are lacking, especially among younger cohorts.

METHODS

Predicted brain age differences were independently calculated in large, prospectively recruited cohorts of pediatric concussion and matched healthy controls (total N = 446), as well as collegiate athletes with sport-related concussion and matched non-contact sport controls (total N = 184). Effects of repetitive head injury (i.e., exposure) were examined in a separate cohort of contact sport athletes (N = 82), as well as by quantifying concussion history through semi-structured interviews and years of contact sport participation.

RESULTS

Findings of increased brain age during acute and sub-acute concussion were independently replicated across both cohorts, with stronger evidence of recovery for pediatric (4 months) relative to concussed athletes (6 months). Mixed evidence existed for effects of repetitive head injury, as brain age was increased in contact sport athletes, but was not associated with concussion history or years of contact sport exposure. There was no difference in brain age between concussed and contact sport athletes. Total tau decreased immediately (~ 1.5 days) post-concussion relative to the non-contact group, whereas pro-inflammatory markers were increased in both concussed and contact sport athletes. Anti-inflammatory markers were inversely related to brain age, whereas markers of axonal injury (neurofilament light) exhibited a trend positive association.

CONCLUSION

Current and previous findings collectively suggest that the chronicity of brain age differences may be mediated by age at injury (adults > children), with preliminary findings suggesting that exposure to contact sports may also increase brain age.

Different Aspects of Aging in Migraine

Migraine is a common neurological disease displaying an unusual dependence on age. For most patients, the peak intensity of migraine headaches occurs in 20s and lasts until 40s, but then headache attacks become less intense, occur less frequently and the disease is more responsive to therapy. This relationship is valid in both females and males, although the prevalence of migraine in the former is 2-4 times greater than the latter. Recent concepts present migraine not only as a pathological event, but rather as a part of evolutionary adaptive response to protect organism against consequences of stress-induced brain energy deficit. However, these concepts do not fully explain that unusual dependence of migraine prevalence on age. Many aspects of aging, both molecular/cellular and social/cognitive, are interwound in migraine pathogenesis, but they neither explain why only some persons are affected by migraine, nor suggest any causal relationship. In this narrative/hypothesis review we present information on associations of migraine with chronological aging, brain aging, cellular senescence, stem cell exhaustion as well as social, cognitive, epigenetic, and metabolic aging. We also underline the role of oxidative stress in these associations. We hypothesize that migraine affects only individuals who have inborn, genetic/epigenetic, or acquired (traumas, shocks or complexes) migraine predispositions. These predispositions weakly depend on age and affected individuals are more prone to migraine triggers than others. Although the triggers can be related to many aspects of aging, social aging may play a particularly important role as the prevalence of its associated stress has a similar age-dependence as the prevalence of migraine. Moreover, social aging was shown to be associated with oxidative stress, important in many aspects of aging. In perspective, molecular mechanisms underlying social aging should be further explored and related to migraine with a closer association with migraine predisposition and difference in prevalence by sex.

Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank

BACKGROUND

Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear.

METHODS

In this study, we focused on N  = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets.

RESULTS

The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P  < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P  < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P  = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69.

CONCLUSION

High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Diagnostic accuracy of brain age prediction in a memory clinic population and comparison with clinically available volumetric measures

The aim of this study was to assess the diagnostic validity of a deep learning-based method estimating brain age based on magnetic resonance imaging (MRI) and to compare it with volumetrics obtained using NeuroQuant (NQ) in a clinical cohort. Brain age prediction was performed on minimally processed MRI data using deep convolutional neural networks and an independent training set. The brain age gap (difference between chronological and biological age) was calculated, and volumetrics were performed in 110 patients with dementia (Alzheimer's disease, frontotemporal dementia (FTD), and dementia with Lewy bodies), and 122 with non-dementia (subjective and mild cognitive impairment). Area-under-the-curve (AUC) based on receiver operating characteristics and logistic regression analyses were performed. The mean age was 67.1 (9.5) years and 48.7% (113) were females. The dementia versus non-dementia sensitivity and specificity of the volumetric measures exceeded 80% and yielded higher AUCs compared to BAG. The explained variance of the prediction of diagnostic stage increased when BAG was added to the volumetrics. Further, BAG separated patients with FTD from other dementia etiologies with > 80% sensitivity and specificity. NQ volumetrics outperformed BAG in terms of diagnostic discriminatory power but the two methods provided complementary information, and BAG discriminated FTD from other dementia etiologies.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Genetic architecture of brain age and its causal relations with brain and mental disorders

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging

BACKGROUND

Ischemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging-derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles.

OBJECTIVES

The authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function.

METHODS

Brain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237).

RESULTS

Prevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia.

CONCLUSIONS

Prevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

A Role for Advanced Glycation End Products in Molecular Ageing

Ageing is a composite process that involves numerous changes at the cellular, tissue, organ and whole-body levels. These changes result in decreased functioning of the organism and the development of certain conditions, which ultimately lead to an increased risk of death. Advanced glycation end products (AGEs) are a family of compounds with a diverse chemical nature. They are the products of non-enzymatic reactions between reducing sugars and proteins, lipids or nucleic acids and are synthesised in high amounts in both physiological and pathological conditions. Accumulation of these molecules increases the level of damage to tissue/organs structures (immune elements, connective tissue, brain, pancreatic beta cells, nephrons, and muscles), which consequently triggers the development of age-related diseases, such as diabetes mellitus, neurodegeneration, and cardiovascular and kidney disorders. Irrespective of the role of AGEs in the initiation or progression of chronic disorders, a reduction in their levels would certainly provide health benefits. In this review, we provide an overview of the role of AGEs in these areas. Moreover, we provide examples of lifestyle interventions, such as caloric restriction or physical activities, that may modulate AGE formation and accumulation and help to promote healthy ageing.

BrainAge of patients with severe late-life depression referred for electroconvulsive therapy

BACKGROUND

Severe depression is associated with accelerated brain aging. BrainAge gap, the difference between predicted and observed BrainAge, was investigated in patients with late-life depression (LLD). We aimed to examine BrainAge gap in LLD and its associations with clinical characteristics indexing LLD chronicity, current severity, prior to electroconvulsive therapy (ECT) and ECT outcome.

METHODS

Data was analyzed from the Mood Disorders in Elderly treated with Electroconvulsive Therapy (MODECT) study. A previously established BrainAge algorithm (BrainAge R by James Cole, (https://github.com/james-cole/brainageR)) was applied to pre-ECT T1-weighted structural MRI-scans of 42 patients who underwent ECT.

RESULTS

A BrainAge gap of 1.8 years (SD = 5.5) was observed, Cohen's d = 0.3. No significant associations between BrainAge gap, number of previous episodes, current episode duration, age of onset, depression severity, psychotic symptoms or ECT outcome were observed.

LIMITATIONS

Limited sample size.

CONCLUSIONS

Our initial findings suggest an older BrainAge than chronological age in patients with severe LLD referred for ECT, however with high degree of variability and direction of the gap. No associations were found with clinical measures. Larger samples are needed to better understand brain aging and to evaluate the usability of BrainAge gap as potential biomarker of prognosis an treatment-response in LLD.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02667353.

Longitudinal brain age prediction and cognitive function after stroke

Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD.

Brain Macro-Structural Alterations in Aging Rats: A Longitudinal Lifetime Approach

Aging is accompanied by macro-structural alterations in the brain that may relate to age-associated cognitive decline. Animal studies could allow us to study this relationship, but so far it remains unclear whether their structural aging patterns correspond to those in humans. Therefore, by applying magnetic resonance imaging (MRI) and deformation-based morphometry (DBM), we longitudinally screened the brains of male RccHan:WIST rats for structural changes across their average lifespan. By combining dedicated region of interest (ROI) and voxel-wise approaches, we observed an increase in their global brain volume that was superimposed by divergent local morphologic alterations, with the largest aging effects in early and middle life. We detected a modality-dependent vulnerability to shrinkage across the visual, auditory, and somato-sensory cortical areas, whereas the piriform cortex showed partial resistance. Furthermore, shrinkage emerged in the amygdala, subiculum, and flocculus as well as in frontal, parietal, and motor cortical areas. Strikingly, we noticed the preservation of ectorhinal, entorhinal, retrosplenial, and cingulate cortical regions, which all represent higher-order brain areas and extraordinarily grew with increasing age. We think that the findings of this study will further advance aging research and may contribute to the establishment of interventional approaches to preserve cognitive health in advanced age.

Dietary Flavonoids and Adult Neurogenesis: Potential Implications for Brain Aging

Adult neurogenesis deficiency has been proposed to be a common hallmark in different age-related neurodegenerative diseases. The administration of flavonoids is currently reported as a potentially beneficial strategy for preventing brain aging alterations, including adult neurogenesis decline. Flavonoids are a class of plant-derived dietary polyphenols that have drawn attention for their neuroprotective and pro-cognitive effects. Although they undergo extensive metabolism and localize in the brain at low concentrations, flavonoids are now believed to improve cerebral vasculature and interact with signal transduction cascades involved in the regulation of adult neurogenesis. Furthermore, many dietary flavonoids have been shown to reduce oxidative stress and neuroinflammation, improving the neuronal microenvironment where adult neurogenesis occurs. The overall goal of this review is to summarize the evidence supporting the role of flavonoids in modulating adult neurogenesis as well as to highlight how these dietary agents may be promising candidates in restoring healthy brain function during physiological and pathological aging.

Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction

Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength (R ² = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R ² = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.

Role of 3D Printing in the Development of Biodegradable Implants for Central Nervous System Drug Delivery

Increased life expectancy has led to a rise in age-related disorders including neurological diseases such as Alzheimer's disease and Parkinson's disease. Limited progress has been made in the development of clinically translatable therapies for these central nervous system (CNS) diseases. Challenges including the blood-brain barrier, brain complexity, and comorbidities in the elderly population are some of the contributing factors toward lower success rates. Various invasive and noninvasive ways are being employed to deliver small and large molecules across the brain. Biodegradable, implantable drug-delivery systems have gained lot of interest due to advantages such as sustained and targeted delivery, lower side effects, and higher patient compliance. 3D printing is a novel additive manufacturing technique where various materials and printing techniques can be used to fabricate implants with the desired complexity in terms of mechanical properties, shapes, or release profiles. This review discusses an overview of various types of 3D-printing techniques and illustrative examples of the existing literature on 3D-printed systems for CNS drug delivery. Currently, there are various technical and regulatory impediments that need to be addressed for successful translation from the bench to the clinical stage. Overall, 3D printing is a transformative technology with great potential in advancing customizable drug treatment in a high-throughput manner.

Interplay between aging and other factors of the pathogenesis of age-related macular degeneration

Age-related macular degeneration (AMD) is a complex eye disease with the retina as the target tissue and aging as per definition the most serious risk factor. However, the retina contains over 60 kinds of cells that form different structures, including the neuroretina and retinal pigment epithelium (RPE) which can age at different rates. Other established or putative AMD risk factors can differentially affect the neuroretina and RPE and can differently interplay with aging of these structures. The occurrence of β-amyloid plaques and increased levels of cholesterol in AMD retinas suggest that AMD may be a syndrome of accelerated brain aging. Therefore, the question about the real meaning of age in AMD is justified. In this review we present and update information on how aging may interplay with some aspects of AMD pathogenesis, such as oxidative stress, amyloid beta formation, circadian rhythm, metabolic aging and cellular senescence. Also, we show how this interplay can be specific for photoreceptors, microglia cells and RPE cells as well as in Bruch's membrane and the choroid. Therefore, the process of aging may differentially affect different retinal structures. As an accurate quantification of biological aging is important for risk stratification and early intervention for age-related diseases, the determination how photoreceptors, microglial and RPE cells age in AMD may be helpful for a precise diagnosis and treatment of this largely untreatable disease.

Assessing brain and biological aging trajectories associated with Alzheimer's disease

The development of effective treatments to prevent and slow Alzheimer's disease (AD) pathogenesis is needed in order to tackle the steady increase in the global prevalence of AD. This challenge is complicated by the need to identify key health shifts that precede the onset of AD and cognitive decline as these represent windows of opportunity for intervening and preventing disease. Such shifts may be captured through the measurement of biomarkers that reflect the health of the individual, in particular those that reflect brain age and biological age. Brain age biomarkers provide a composite view of the health of the brain based on neuroanatomical analyses, while biological age biomarkers, which encompass the epigenetic clock, provide a measurement of the overall health state of an individual based on DNA methylation analysis. Acceleration of brain and biological ages is associated with changes in cognitive function, as well as neuropathological markers of AD. In this mini-review, we discuss brain age and biological age research in the context of cognitive decline and AD. While more research is needed, studies show that brain and biological aging trajectories are variable across individuals and that such trajectories are non-linear at older ages. Longitudinal monitoring of these biomarkers may be valuable for enabling earlier identification of divergent pathological trajectories toward AD and providing insight into points for intervention.

Brain age predicts long-term recovery in post-stroke aphasia

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.

Cognitive and behavioural but not motor impairment increases brain age in amyotrophic lateral sclerosis

Age is the most important single risk factor of sporadic amyotrophic lateral sclerosis. Neuroimaging together with machine-learning algorithms allows estimating individuals' brain age. Deviations from normal brain-ageing trajectories (so called predicted brain age difference) were reported for a number of neuropsychiatric disorders. While all of them showed increased predicted brain-age difference, there is surprisingly few data yet on it in motor neurodegenerative diseases. In this observational study, we made use of previously trained algorithms of 3377 healthy individuals and derived predicted brain age differences from volumetric MRI scans of 112 amyotrophic lateral sclerosis patients and 70 healthy controls. We correlated predicted brain age difference scores with voxel-based morphometry data and multiple different motoric disease characteristics as well as cognitive/behavioural changes categorized according to Strong and Rascovsky. Against our primary hypothesis, there was no higher predicted brain-age difference in the amyotrophic lateral sclerosis patients as a group. None of the motoric phenotypes/characteristics influenced predicted brain-age difference. However, cognitive/behavioural impairment led to significantly increased predicted brain-age difference, while slowly progressive as well as cognitive/behavioural normal amyotrophic lateral sclerosis patients had even younger brain ages than healthy controls. Of note, the cognitive/behavioural normal amyotrophic lateral sclerosis patients were identified to have increased cerebellar brain volume as potential resilience factor. Younger brain age was associated with longer survival. Our results raise the question whether younger brain age in amyotrophic lateral sclerosis with only motor impairment provides a cerebral reserve against cognitive and/or behavioural impairment and faster disease progression. This new conclusion needs to be tested in subsequent samples. In addition, it will be interesting to test whether a potential effect of cerebral reserve is specific for amyotrophic lateral sclerosis or can also be found in other neurodegenerative diseases with primary motor impairment.

Neurodegenerative diseases and blood pressure variability: A comprehensive review from HOPE Asia

Asia has an enormous number of older people and is the primary contributor to the rise in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. The therapy of many neurodegenerative diseases has not yet progressed to the point where it is possible to alter the course of the disease. Mid-life hypertension is an important predictor of later-life cognitive impairment and brain neurodegenerative conditions. These findings highlight the pivotal role of preventing and managing hypertension as a risk factor for neurodegenerative disease. Autonomic dysfunction, neuropsychiatric and sleep disturbances can arise in neurodegenerative diseases, resulting in blood pressure variability (BPV). The BPV itself can worsen the progression of the disease. In older people with neurodegenerative disease and hypertension, it is critical to consider 24-h blood pressure monitoring and personalized blood pressure therapy.

Deep neural networks learn general and clinically relevant representations of the ageing brain

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

GLP-1 Receptor Agonists in Neurodegeneration: Neurovascular Unit in the Spotlight

Defects in brain energy metabolism and proteopathic stress are implicated in age-related degenerative neuronopathies, exemplified by Alzheimer’s disease (AD) and Parkinson’s disease (PD). As the currently available drug regimens largely aim to mitigate cognitive decline and/or motor symptoms, there is a dire need for mechanism-based therapies that can be used to improve neuronal function and potentially slow down the underlying disease processes. In this context, a new class of pharmacological agents that achieve improved glycaemic control via the glucagon-like peptide 1 (GLP-1) receptor has attracted significant attention as putative neuroprotective agents. The experimental evidence supporting their potential therapeutic value, mainly derived from cellular and animal models of AD and PD, has been discussed in several research reports and review opinions recently. In this review article, we discuss the pathological relevance of derangements in the neurovascular unit and the significance of neuron–glia metabolic coupling in AD and PD. With this context, we also discuss some unresolved questions with regard to the potential benefits of GLP-1 agonists on the neurovascular unit (NVU), and provide examples of novel experimental paradigms that could be useful in improving our understanding regarding the neuroprotective mode of action associated with these agents.

Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery

Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87, age: 44.0±9.2 years, BMI: 43.9±4.2 kg/m2) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7.08, p-value < 0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0.0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-value_BMI=4.29, T-value_SBP=4.67, T-value_DBP=4.12, T-value_HOMA-IR=3.16, all p-values < 0.05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.

Alzheimer's disease and depression in the elderly: A trajectory linking gut microbiota and serotonin signaling

The occurrence of neuropsychiatric symptoms in the elderly is viewed as an early sign of subsequent cognitive deterioration and conversion from mild cognitive impairment to Alzheimer's disease. The prognosis in terms of both the severity and progression of clinical dementia is generally aggravated by the comorbidity of neuropsychiatric symptoms and decline in cognitive function. Undeniably, aging and in particular unhealthy aging, is a silent "engine of neuropathology" over which multiple changes take place, including drastic alterations of the gut microbial ecosystem. This narrative review evaluates the role of gut microbiota changes as a possible unifying concept through which the comorbidity of neuropsychiatric symptoms and Alzheimer's disease can be considered. However, since the heterogeneity of neuropsychiatric symptoms, it is improbable to describe the same type of alterations in the bacteria population observed in patients with Alzheimer's disease, as well as it is improbable that the variety of drugs used to treat neuropsychiatric symptoms might produce changes in gut bacterial diversity similar to that observed in the pathophysiology of Alzheimer's disease. Depression seems to be another very intriguing exception, as it is one of the most frequent neuropsychiatric symptoms in dementia and a mood disorder frequently associated with brain aging. Antidepressants (i.e., serotonin reuptake inhibitors) or tryptophan dietary supplementation have been shown to reduce Amyloid β-loading, reinstate microbial diversity and reduce the abundance of bacterial taxa dominant in depression and Alzheimer's disease. This review briefly examines this trajectory by discussing the dysfunction of gut microbiota composition, selected bacterial taxa, and alteration of tryptophan and serotonin metabolism/neurotransmission as overlapping in-common mechanisms involved with depression, Alzheimer's disease, and unhealthy aging.

Brain-predicted age difference is associated with cognitive processing in later-life

Brain age is a neuroimaging-based biomarker of aging. This study examined whether the difference between brain age and chronological age (brain-PAD) is associated with cognitive function at baseline and longitudinally. Participants were relatively healthy, predominantly white community-dwelling older adults (n = 531, aged ≥70 years), with high educational attainment (61% ≥12 years) and socioeconomic status (59% ≥75th percentile). Brain age was estimated from T1-weighted magnetic resonance images using an algorithm by Cole et al., 2018. After controlling for age, gender, education, depression and body mass index, brain-PAD was negatively associated with psychomotor speed (Symbol Digit Modalities Test) at baseline (Bonferroni p < 0.006), but was not associated with baseline verbal fluency (Controlled Oral Word Association Test), delayed recall (Hopkins Learning Test Revised), or general cognitive status (Mini-Mental State Examination). Baseline brain-PAD was not associated with 3-year change in cognition (Bonferroni p > 0.006). These findings indicate that even in relatively healthy older people, accelerated brain aging is associated with worse psychomotor speed, but future longitudinal research into changes in brain-PAD is needed.