Obesity associated with increased brain age from midlife

The mitochondrial quality control system: a new target for exercise therapeutic intervention in the treatment of brain insulin resistance-induced neurodegeneration in obesity

Obesity is a major global health concern because of its strong association with metabolic and neurodegenerative diseases such as diabetes, dementia, and Alzheimer's disease. Unfortunately, brain insulin resistance in obesity is likely to lead to neuroplasticity deficits. Since the evidence shows that insulin resistance in brain regions abundant in insulin receptors significantly alters mitochondrial efficiency and function, strategies targeting the mitochondrial quality control system may be of therapeutic and practical value in obesity-induced cognitive decline. Exercise is considered as a powerful stimulant of mitochondria that improves insulin sensitivity and enhances neuroplasticity. It has great potential as a non-pharmacological intervention against the onset and progression of obesity associated neurodegeneration. Here, we integrate the current knowledge of the mechanisms of neurodegenration in obesity and focus on brain insulin resistance to explain the relationship between the impairment of neuronal plasticity and mitochondrial dysfunction. This knowledge was synthesised to explore the exercise paradigm as a feasible intervention for obese neurodegenration in terms of improving brain insulin signals and regulating the mitochondrial quality control system.

Astrocytes at the intersection of ageing, obesity, and neurodegeneration

Once considered passive cells of the central nervous system (CNS), glia are now known to actively maintain the CNS parenchyma; in recent years, the evidence for glial functions in CNS physiology and pathophysiology has only grown. Astrocytes, a heterogeneous group of glial cells, play key roles in regulating the metabolic and inflammatory landscape of the CNS and have emerged as potential therapeutic targets for a variety of disorders. This review will outline astrocyte functions in the CNS in healthy ageing, obesity, and neurodegeneration, with a focus on the inflammatory responses and mitochondrial function, and will address therapeutic outlooks.

Childhood maltreatment influences adult brain structure through its effects on immune, metabolic, and psychosocial factors

Childhood maltreatment (CM) leads to a lifelong susceptibility to mental ill-health which might be reflected by its effects on adult brain structure, perhaps indirectly mediated by its effects on adult metabolic, immune, and psychosocial systems. Indexing these systemic factors via body mass index (BMI), C-reactive protein (CRP), and rates of adult trauma (AT), respectively, we tested three hypotheses: (H1) CM has direct or indirect effects on adult trauma, BMI, and CRP; (H2) adult trauma, BMI, and CRP are all independently related to adult brain structure; and (H3) childhood maltreatment has indirect effects on adult brain structure mediated in parallel by BMI, CRP, and AT. Using path analysis and data from N = 116,887 participants in UK Biobank, we find that CM is related to greater BMI and AT levels, and that these two variables mediate CM's effects on CRP [H1]. Regression analyses on the UKB MRI subsample (N = 21,738) revealed that greater CRP and BMI were both independently related to a spatially convergent pattern of cortical effects (Spearman's ρ = 0.87) characterized by fronto-occipital increases and temporo-parietal reductions in thickness. Subcortically, BMI was associated with greater volume, AT with lower volume and CPR with effects in both directions [H2]. Finally, path models indicated that CM has indirect effects in a subset of brain regions mediated through its direct effects on BMI and AT and indirect effects on CRP [H3]. Results provide evidence that childhood maltreatment can influence brain structure decades after exposure by increasing individual risk toward adult trauma, obesity, and inflammation.

Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Sarcopenic obesity is part of obesity paradox in dementia development: evidence from a population-based cohort study

BACKGROUND

Sarcopenic obesity, a clinical and functional condition characterized by the coexistence of obesity and sarcopenia, has not been investigated in relation to dementia risk and its onset.

METHODS

We included 208,867 participants from UK biobank, who aged 60 to 69 years at baseline. Dementia diagnoses were identified using hospital records and death register data. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models to evaluate the associations of obesity, sarcopenia, and sarcopenic obesity with dementia risk, stratified by sex. Stratified analyses were performed across dementia-related polygenic risk score (PRS). Restricted mean survival time models were established to estimate the difference and 95%CIs of dementia onset across different status. Additionally, linear regression models were employed to estimate associations of different status with brain imaging parameters. The mediation effects of chronic diseases were also examined.

RESULTS

Obese women with high PRS had a decreased risk (HR = 0.855 [0.761-0.961]), but obese men with low PRS had an increased risk (HR = 1.223 [1.045-1.431]). Additionally, sarcopenia was associated with elevated dementia risk (HRwomen = 1.323 [1.064-1.644]; HRmen = 2.144 [1.753-2.621]) in those with low PRS. Among those with high PRS, however, the association was only significant in early-life (HRwomen = 1.679 [1.355-2.081]; HRmen = 2.069 [1.656-2.585]). Of note, sarcopenic obesity was associated with higher dementia risk (HRwomen = 1.424 [1.227-1.653]; HRmen = 1.989 [1.702-2.323]), and results remained similar stratified by PRS. Considering dementia onset, obesity was associated with dementia by 1.114 years delayed in women, however, 0.170 years advanced in men. Sarcopenia (women: 0.080 years; men: 0.192 years) and sarcopenic obesity (women: 0.109 years; men: 0.511 years) respectively advanced dementia onset. Obesity, sarcopenia, and sarcopenic obesity were respectively related to alterations in different brain regions. Association between sarcopenic obesity and dementia was mediated by chronic diseases.

CONCLUSIONS

Sarcopenic obesity and sarcopenia were respectively associated with increased dementia risk and advanced dementia onset to vary degree. The role of obesity in dementia may differ by sex and genetic background.

Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk

In mice, periodic cycles of a fasting mimicking diet (FMD) protect normal cells while killing damaged cells including cancer and autoimmune cells, reduce inflammation, promote multi-system regeneration, and extend longevity. Here, we performed secondary and exploratory analysis of blood samples from a randomized clinical trial (NCT02158897) and show that 3 FMD cycles in adult study participants are associated with reduced insulin resistance and other pre-diabetes markers, lower hepatic fat (as determined by magnetic resonance imaging) and increased lymphoid to myeloid ratio: an indicator of immune system age. Based on a validated measure of biological age predictive of morbidity and mortality, 3 FMD cycles were associated with a decrease of 2.5 years in median biological age, independent of weight loss. Nearly identical findings resulted from  a second clinical study (NCT04150159). Together these results provide initial support for beneficial effects of the FMD on multiple cardiometabolic risk factors and biomarkers of biological age.

Novel insights into brain lipid metabolism in Alzheimer's disease: Oligodendrocytes and white matter abnormalities

Alzheimer's disease (AD) is the most common cause of dementia. A genome-wide association study has shown that several AD risk genes are involved in lipid metabolism. Additionally, epidemiological studies have indicated that the levels of several lipid species are altered in the AD brain. Therefore, lipid metabolism is likely changed in the AD brain, and these alterations might be associated with an exacerbation of AD pathology. Oligodendrocytes are glial cells that produce the myelin sheath, which is a lipid-rich insulator. Dysfunctions of the myelin sheath have been linked to white matter abnormalities observed in the AD brain. Here, we review the lipid composition and metabolism in the brain and myelin and the association between lipidic alterations and AD pathology. We also present the abnormalities in oligodendrocyte lineage cells and white matter observed in AD. Additionally, we discuss metabolic disorders, including obesity, as AD risk factors and the effects of obesity and dietary intake of lipids on the brain.

Sex differences in response to obesity and caloric restriction on cognition and hippocampal measures of autophagic-lysosomal transcripts and signaling pathways

BACKGROUND

Obesity rates in the U.S. continue to increase, with nearly 50% of the population being either obese or morbidly obese. Obesity, along with female sex, are leading risk factors for sporadic Alzheimer's Disease (AD) necessitating the need to better understand how these variables impact cellular function independent of age or genetic mutations. Animal and clinical studies both indicate that autophagy-lysosomal pathway (ALP) dysfunction is among the earliest known cellular systems to become perturbed in AD, preceding cognitive decline, yet little is known about how obesity and sex affects these cellular functions in the hippocampus, a brain region uniquely susceptible to the negative effects of obesity. We hypothesized that obesity would negatively affect key markers of ALP in the hippocampus, effects would vary based on sex, and that caloric restriction would counteract obesity effects.

METHODS

Female and male mice were placed on an obesogenic diet for 10 months, at which point half were switched to caloric restriction for three months, followed by cognitive testing in the Morris watermaze. Hippocampus was analyzed by western blot and qPCR.

RESULTS

Cognitive function in female mice responded differently to caloric restriction based on whether they were on a normal or obesogenic diet; male cognition was only mildly affected by caloric restriction and not obesity. Significant male-specific changes occurred in cellular markers of autophagy, including obesity increasing pAkt, Slc38a9, and Atg12, while caloric restriction reduced pRPS6 and increased Atg7. In contrast females experienced changes due to diet/caloric restriction predominately in lysosomal markers including increased TFE3, FLCN, FNIP2, and pAMPK.

CONCLUSIONS

Results support that hippocampal ALP is a target of obesity and that sex shapes molecular responses, while providing insight into how dietary manipulations affect learning and memory based on sex.

Brain Age Is Associated with Cognitive Outcomes of Cholinesterase Inhibitor Treatment in Patients with Mild Cognitive Impairment

Background

The effect of cholinesterase inhibitor (ChEI) on mild cognitive impairment (MCI) is controversial. Brain age has been shown to predict Alzheimer's disease conversion from MCI.

Objective

The study aimed to show that brain age is related to cognitive outcomes of ChEI treatment in MCI.

Methods

Brain MRI, the Clinical Dementia Rating (CDR) and Mini-Mental State Exam (MMSE) scores were retrospectively retrieved from a ChEI treatment database. Patients who presented baseline CDR of 0.5 and received ChEI treatment for at least 2 years were selected. Patients with stationary or improved cognition as verified by the CDR and MMSE were categorized to the ChEI-responsive group, and those with worsened cognition were assigned to the ChEI-unresponsive group. A gray matter brain age model was built with a machine learning algorithm by training T1-weighted MRI data of 362 healthy participants. The model was applied to each patient to compute predicted age difference (PAD), i.e. the difference between brain age and chronological age. The PADs were compared between the two groups.

Results

58 patients were found to fit the ChEI-responsive criteria in the patient data, and 58 matched patients that fit the ChEI-unresponsive criteria were compared. ChEI-unresponsive patients showed significantly larger PAD than ChEI-responsive patients (8.44±8.78 years versus 3.87±9.02 years, p = 0.0067).

Conclusions

Gray matter brain age is associated with cognitive outcomes after 2 years of ChEI treatment in patients with the CDR of 0.5. It might facilitate the clinical trials of novel therapeutics for MCI.

BASE: Brain Age Standardized Evaluation

Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2-3 year range, is achieved predominantly through deep neural networks. However, comparing study results is difficult due to differences in datasets, evaluation methodologies and metrics. Addressing this, we introduce Brain Age Standardized Evaluation (BASE), which includes (i) a standardized T1w MRI dataset including multi-site, new unseen site, test-retest and longitudinal data, and an associated (ii) evaluation protocol, including repeated model training and upon based comprehensive set of performance metrics measuring accuracy, robustness, reproducibility and consistency aspects of brain age predictions, and (iii) statistical evaluation framework based on linear mixed-effects models for rigorous performance assessment and cross-comparison. To showcase BASE, we comprehensively evaluate four deep learning based brain age models, appraising their performance in scenarios that utilize multi-site, test-retest, unseen site, and longitudinal T1w brain MRI datasets. Ensuring full reproducibility and application in future studies, we have made all associated data information and code publicly accessible at https://github.com/AralRalud/BASE.git.

Inflammatory Pathways in Overweight and Obese Persons as a Potential Mechanism for Cognitive Impairment and Earlier Onset Alzeihmer's Dementia in the General Population: A Narrative Review

The overweight status or obesity can be confirmed through classical methods such as the body mass index (BMI) and the waist-to-hip ratio (WHR). Apart from metabolic issues such as atherosclerosis, liver steatosis, or diabetes mellitus, long-term obesity or overweight status can pose a risk for cardiovascular and neurovascular complications. While some acute adverse events like coronary syndromes of strokes are well-documented to be linked to an increased body mass, there are also chronic processes that, due to their silent onset and evolution, are underdiagnosed and not as thoroughly studied. Through this review, we aimed to collect all relevant data with regard to the long-term impact of obesity on cognitive function in all ages and its correlation with an earlier onset of dementia such as Alzheimer's disease (AD). The exact mechanisms through which a decline in cognitive functions occurs in overweight or obese persons are still being discussed. A combination of factors has been acknowledged as potential triggers, such as a sedentary lifestyle and stress, as well as a genetic predisposition, for example, the apolipoprotein E (ApoE) alleles in AD. Most research highlights the impact of vascular dysfunction and systemic inflammation on the nervous system in patients with obesity and the subsequent neurological changes. Obesity during the early to mid-ages leads to an earlier onset of cognitive dysfunction in various forms. Also, lifestyle intervention can reverse cognitive dysfunction, especially dieting, to encourage weight loss.

Long-term lifestyle intervention is superior to transient modification for neuroprotection in D-galactose-induced aging rats

AIMS

To investigate whether transient dietary restriction or aerobic exercise in young adulthood exert long-lasting protection against brain aging later in life.

MAIN METHODS

Seven-week-old male Wistar rats were divided into 2 groups and given either normal saline as a vehicle (n = 8) or 150 mg/kg/day of D-galactose (n = 40) for 28 weeks, the D-galactose being used to induce aging. At week 13 of the experiment, D-galactose-treated rats were further divided into 5 groups, 1) no intervention, 2) transient dietary restriction for 6 weeks (week 13-18), 3) transient exercise for 6 weeks (week 13-18), 4) long-term dietary restriction for 16 weeks (week 13-28), and 5) long-term exercise for 16 weeks (week 13-28). At the end of week 28, cognitive function was examined, followed by molecular studies in the hippocampus.

KEY FINDINGS

Our results showed that either long-term dietary restriction or aerobic exercise effectively attenuated cognitive function in D-galactose-treated rats via the attenuation of oxidative stress, cellular senescence, Alzheimer's-like pathology, neuroinflammation, and improvements in mitochondria, brain metabolism, adult neurogenesis, and synaptic integrity. Although transient interventions provided benefits in some brain parameters in D-galactose-treated rats, an improvement in cognitive function was not observed.

SIGNIFICANCE

Our findings suggested that transient lifestyle interventions failed to exert a long-lasting protective effect against brain aging. Hence, novel drugs mimicking the neuroprotective effect of long-term dietary restriction or exercise and the combination of the two since young age appear to be more appropriate treatments for the elderly who are unable to engage in long-term dietary restriction or exercise.

Triangulating causality between childhood obesity and neurobehavior: Behavioral genetic and longitudinal evidence

Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. We filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9-10. First, correlations between the age- and sex-specific 95th BMI percentile (%BMIp95) and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Behavioral genetic Direction of Causation modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. %BMIp95 correlated with impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Greater %BMIp95 was also associated with reduced cortical thickness in frontal and temporal brain areas but with increased thickness in parietal and occipital areas. Similar although weaker patterns emerged for cortical surface area and volume. Behavioral genetic modeling suggested causal effects of %BMIp95 on eating behavior (β = 0.26), cognition (β = 0.05), cortical thickness (β = 0.15), and cortical surface area (β = 0.07). Personality/psychopathology (β = 0.09) and eating behavior (β = 0.16) appeared to influence %BMIp95. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. Results supported causal effects of obesity on brain functioning and morphology. The present study highlights the importance of physical health for brain development and may inform interventions aimed at preventing or reducing pediatric obesity. RESEARCH HIGHLIGHTS: A continuous measure related to obesity, %BMIp95, has correlations with various measures of brain functioning and structure Behavioral genetic and longitudinal modeling suggest causal links from personality, psychopathology, and eating behavior to %BMIp95 Results also indicate directional links from %BMIp95 to eating behavior, cognition, cortical thickness, and cortical surface area Obesity may play a role for healthy brain development during childhood.

Explainable Brain Age Prediction using coVariance Neural Networks

In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.

Machine-learning-derived heart and brain age are independently associated with cognition

BACKGROUND AND PURPOSE

A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated.

METHODS

Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis.

RESULTS

Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3-36) of the HDA effect on the tapping test score was mediated through BDA.

DISCUSSION

Heart delta age, representing the cumulative effects of life-long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA.

Brain Specific Estrogen Ameliorates Cognitive Effects of Surgical Menopause in Mice

Menopause is a major endocrinological shift that leads to an increased vulnerability to the risk factors for cognitive impairment and dementia. This is thought to be due to the loss of circulating estrogens, which exert many potent neuroprotective effects in the brain. Systemic replacement of estrogen post-menopause has many limitations, including increased risk for estrogen-sensitive cancers. A more promising therapeutic approach therefore might be to deliver estrogen only to the brain thus limiting adverse peripheral side effects. We examined whether we could enhance cognitive performance by delivering estrogen exclusively to the brain in post-menopausal mice. We modeled surgical menopause via bilateral ovariectomy (OVX). We treated mice with the pro-drug 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED), which can be administered systemically but is converted to 17β-estradiol only in the brain. Young (2.5-month) and middle-aged (11-month-old) female C57BL/6J mice received ovariectomy and a subcutaneous implant containing vehicle (cholesterol) or DHED. At 3.5 months old (young group) and 14.5 months old (middle-aged group), mice underwent behavior testing to assess memory. DHED did not significantly alter metabolic status in middle-aged, post-menopausal mice. In both young and middle-aged mice, the brain-specific estrogen DHED improved spatial memory. Additional testing in middle-aged mice also showed that DHED improved working and recognition memory. These promising results lay the foundation for future studies aimed at determining if this intervention is as efficacious in models of dementia that have comorbid risk factors.

Brain age and cognitive functioning in first-episode bipolar disorder

BACKGROUND

There is significant heterogeneity in cognitive function in patients with bipolar I disorder (BDI); however, there is a dearth of research into biological mechanisms that might underlie cognitive heterogeneity, especially at disease onset. To this end, this study investigated the association between accelerated or delayed age-related brain structural changes and cognition in early-stage BDI.

METHODS

First episode patients with BDI (n = 80) underwent cognitive assessment to yield demographically normed composite global and domain-specific scores in verbal memory, non-verbal memory, working memory, processing speed, attention, and executive functioning. Structural magnetic resonance imaging data were also collected from all participants and subjected to machine learning to compute the brain-predicted age difference (brainPAD), calculated by subtracting chronological age from age predicted by neuroimaging data (positive brainPAD values indicating age-related acceleration in brain structural changes and negative values indicating delay). Patients were divided into tertiles based on brainPAD values, and cognitive performance compared amongst tertiles with ANCOVA.

RESULTS

Patients in the lowest (delayed) tertile of brainPAD values (brainPAD range -17.9 to -6.5 years) had significantly lower global cognitive scores (p = 0.025) compared to patients in the age-congruent tertile (brainPAD range -5.3 to 2.4 yrs), and significantly lower verbal memory scores (p = 0.001) compared to the age-congruent and accelerated (brainPAD range 2.8 to 16.1 yrs) tertiles.

CONCLUSION

These results provide evidence linking cognitive dysfunction in the early stage of BDI to apparent delay in typical age-related brain changes. Further studies are required to assess how age-related brain changes and cognitive functioning evolve over time.

Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review

This comprehensive narrative review explores the concept of neuro-vulnerability in energy metabolism regulation and its implications for metabolic disorders. The review highlights the complex interactions among the neural, hormonal, and metabolic pathways involved in the regulation of energy metabolism. The key topics discussed include the role of organs, hormones, and neural circuits in maintaining metabolic balance. The review investigates the association between neuro-vulnerability and metabolic disorders, such as obesity, insulin resistance, and eating disorders, considering genetic, epigenetic, and environmental factors that influence neuro-vulnerability and subsequent metabolic dysregulation. Neuroendocrine interactions and the neural regulation of food intake and energy expenditure are examined, with a focus on the impact of neuro-vulnerability on appetite dysregulation and altered energy expenditure. The role of neuroinflammation in metabolic health and neuro-vulnerability is discussed, emphasizing the bidirectional relationship between metabolic dysregulation and neuroinflammatory processes. This review also evaluates the use of neuroimaging techniques in studying neuro-vulnerability and their potential applications in clinical settings. Furthermore, the association between neuro-vulnerability and eating disorders, as well as its contribution to obesity, is examined. Potential therapeutic interventions targeting neuro-vulnerability, including pharmacological treatments and lifestyle modifications, are reviewed. In conclusion, understanding the concept of neuro-vulnerability in energy metabolism regulation is crucial for addressing metabolic disorders. This review provides valuable insights into the underlying neurobiological mechanisms and their implications for metabolic health. Targeting neuro-vulnerability holds promise for developing innovative strategies in the prevention and treatment of metabolic disorders, ultimately improving metabolic health outcomes.

Associations Between Socioeconomic Status, Obesity, Cognition, and White Matter Microstructure in Children

Importance

Lower neighborhood and household socioeconomic status (SES) are associated with negative health outcomes and altered brain structure in children. It is unclear whether such findings extend to white matter and via what mechanisms.

Objective

To assess whether and how neighborhood and household SES are independently associated with children's white matter microstructure and examine whether obesity and cognitive performance (reflecting environmental cognitive and sensory stimulation) are plausible mediators.

Design, Setting, and Participants

This cross-sectional study used baseline data from participants in the Adolescent Brain Cognitive Development (ABCD) study. Data were collected at 21 US sites, and school-based recruitment was used to represent the US population. Children aged 9 to 11 years and their parents or caregivers completed assessments between October 1, 2016, and October 31, 2018. After exclusions, 8842 of 11 875 children in the ABCD study were included in the analyses. Data analysis was conducted from July 11 to December 19, 2022.

Exposures

Neighborhood disadvantage was derived from area deprivation indices at participants' primary residence. Household SES factors were total income and highest parental educational attainment.

Main Outcomes and Measures

A restriction spectrum imaging (RSI) model was used to quantify restricted normalized directional (RND; reflecting oriented myelin organization) and restricted normalized isotropic (RNI; reflecting glial and neuronal cell bodies) diffusion in 31 major white matter tracts. The RSI measurements were scanner harmonized. Obesity was assessed through body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), age- and sex-adjusted BMI z scores, and waist circumference, and cognition was assessed through the National Institutes of Health Toolbox Cognition Battery. Analyses were adjusted for age, sex, pubertal development stage, intracranial volume, mean head motion, and twin or siblingship.

Results

Among 8842 children, 4543 (51.4%) were boys, and the mean (SD) age was 9.9 (0.7) years. Linear mixed-effects models revealed that greater neighborhood disadvantage was associated with lower RSI-RND in the left superior longitudinal fasciculus (β = -0.055; 95% CI, -0.081 to -0.028) and forceps major (β = -0.040; 95% CI, -0.067 to -0.013). Lower parental educational attainment was associated with lower RSI-RND in the bilateral superior longitudinal fasciculus (eg, right hemisphere: β = 0.053; 95% CI, 0.025-0.080) and bilateral corticospinal or pyramidal tract (eg, right hemisphere: β = 0.042; 95% CI, 0.015-0.069). Structural equation models revealed that lower cognitive performance (eg, lower total cognition score and higher neighborhood disadvantage: β = -0.012; 95% CI, -0.016 to -0.009) and greater obesity (eg, higher BMI and higher neighborhood disadvantage: β = -0.004; 95% CI, -0.006 to -0.001) partially accounted for the associations between SES and RSI-RND. Lower household income was associated with higher RSI-RNI in most tracts (eg, right inferior longitudinal fasciculus: β = -0.042 [95% CI, -0.073 to -0.012]; right anterior thalamic radiations: β = -0.045 [95% CI, -0.075 to -0.014]), and greater neighborhood disadvantage had similar associations in primarily frontolimbic tracts (eg, right fornix: β = 0.046 [95% CI, 0.019-0.074]; right anterior thalamic radiations: β = 0.045 [95% CI, 0.018-0.072]). Lower parental educational attainment was associated with higher RSI-RNI in the forceps major (β = -0.048; 95% CI, -0.077 to -0.020). Greater obesity partially accounted for these SES associations with RSI-RNI (eg, higher BMI and higher neighborhood disadvantage: β = 0.015; 95% CI, 0.011-0.020). Findings were robust in sensitivity analyses and were corroborated using diffusion tensor imaging.

Conclusions and Relevance

In this cross-sectional study, both neighborhood and household contexts were associated with white matter development in children, and findings suggested that obesity and cognitive performance were possible mediators in these associations. Future research on children's brain health may benefit from considering these factors from multiple socioeconomic perspectives.

Sex differences in the effects of high fat diet on underlying neuropathology in a mouse model of VCID

BACKGROUND

Damage to the cerebral vasculature can lead to vascular contributions to cognitive impairment and dementia (VCID). A reduction in blood flow to the brain leads to neuropathology, including neuroinflammation and white matter lesions that are a hallmark of VCID. Mid-life metabolic disease (obesity, prediabetes, or diabetes) is a risk factor for VCID which may be sex-dependent (female bias).

METHODS

We compared the effects of mid-life metabolic disease between males and females in a chronic cerebral hypoperfusion mouse model of VCID. C57BL/6J mice were fed a control or high fat (HF) diet starting at ~ 8.5 months of age. Three months after diet initiation, sham or unilateral carotid artery occlusion surgery (VCID model) was performed. Three months later, mice underwent behavior testing and brains were collected to assess pathology.

RESULTS

We have previously shown that in this VCID model, HF diet causes greater metabolic impairment and a wider array of cognitive deficits in females compared to males. Here, we report on sex differences in the underlying neuropathology, specifically white matter changes and neuroinflammation in several areas of the brain. White matter was negatively impacted by VCID in males and HF diet in females, with greater metabolic impairment correlating with less myelin markers in females only. High fat diet led to an increase in microglia activation in males but not in females. Further, HF diet led to a decrease in proinflammatory cytokines and pro-resolving mediator mRNA expression in females but not males.

CONCLUSIONS

The current study adds to our understanding of sex differences in underlying neuropathology of VCID in the presence of a common risk factor (obesity/prediabetes). This information is crucial for the development of effective, sex-specific therapeutic interventions for VCID.

Brain structural and functional alterations in individuals with combined overweight/obesity and mood disorders: A systematic review of neuroimaging studies

Growing evidence suggests there is a bidirectional relationship between depression and obesity, which are associated with structural and functional brain abnormalities. However, the underlying neurobiological mechanisms subserving the foregoing associations have yet to be characterized. It is necessary to summarize the neuroplastic brain changes in relation to depression and obesity. We systematically searched articles from 1990 to November 2022 on databases including MEDLINE/PubMed, Web of Science, PsycINFO. Only neuroimaging studies within the scope of potential differences in brain function and structure in individuals with depression and obesity/ BMI changes were included. Twenty-four eligible studies were included in the review herein, consisting of 17 studies reporting changes in brain structure, 4 studies reporting abnormal brain function, and 3 studies reporting both changes in brain structure and function. Results indicated an interaction between depression and obesity on brain functions, and their influence on brain structure is both extensive and specific. Overall, reduced whole brain, intracranial, and gray matter volume (e.g. frontal, temporal gyri, thalamic, and hippocampal) and impaired white matter integrity was observed in persons with depression and obesity comorbidity. Additional evidence on resting state fMRI reveals select brain regions associated with cognitive control, emotion regulation, and reward functions. Due to the diversity of tasks in task fMRI, the distinct neural activation patterns are revealed separately. The bidirectional relationship between depression and obesity reflects different characteristics in brain structure and function. Longitudinal designs should be reinforced in follow-up studies.

The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity

Background

Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by weight loss following a lifestyle intervention.

Methods

In a sub-study of 102 participants of the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age based on magnetic resonance imaging (MRI)-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age.

Results

To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months' attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18 months of intervention. Finally, we showed that lower consumption of processed food, sweets and beverages were associated with attenuated brain age.

Conclusions

Successful weight loss following lifestyle intervention might have a beneficial effect on the trajectory of brain aging.

Funding

The German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11, Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission 09933838 SFB 105 (to I Shai).

The Preventive Effect of Exercise and Oral Branched-Chain Amino Acid Supplementation on Obesity-Induced Brain Changes in Ldlr−/−.Leiden Mice

Exercise and dietary interventions are promising approaches to tackle obesity and its obesogenic effects on the brain. We investigated the impact of exercise and possible synergistic effects of exercise and branched-chain amino acids (BCAA) supplementation on the brain and behavior in high-fat-diet (HFD)-induced obese Ldlr−/−.Leiden mice. Baseline measurements were performed in chow-fed Ldlr−/−.Leiden mice to assess metabolic risk factors, cognition, and brain structure using magnetic resonance imaging. Thereafter, a subgroup was sacrificed, serving as a healthy reference. The remaining mice were fed an HFD and divided into three groups: (i) no exercise, (ii) exercise, or (iii) exercise and dietary BCAA. Mice were followed for 6 months and aforementioned tests were repeated. We found that exercise alone changed cerebral blood flow, attenuated white matter loss, and reduced neuroinflammation compared to non-exercising HFD-fed mice. Contrarily, no favorable effects of exercise on the brain were found in combination with BCAA, and neuroinflammation was increased. However, cognition was slightly improved in exercising mice on BCAA. Moreover, BCAA and exercise increased the percentage of epididymal white adipose tissue and muscle weight, decreased body weight and fasting insulin levels, improved the circadian rhythm, and transiently improved grip strength. In conclusion, BCAA should be supplemented with caution, although beneficial effects on metabolism, behavior, and cognition were observed.

Associations between socioeconomic status and white matter microstructure in children: indirect effects via obesity and cognition

Importance

Both neighborhood and household socioeconomic disadvantage relate to negative health outcomes and altered brain structure in children. It is unclear whether such findings extend to white matter development, and via what mechanisms socioeconomic status (SES) influences the brain.

Objective

To test independent associations between neighborhood and household SES indicators and white matter microstructure in children, and examine whether body mass index and cognitive function (a proxy of environmental cognitive/sensory stimulation) may plausibly mediate these associations.

Design

This cross-sectional study used baseline data from the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing 10-year cohort study tracking child health.

Setting

School-based recruitment at 21 U.S. sites.

Participants

Children aged 9 to 11 years and their parents/caregivers completed baseline assessments between October 1 ^st , 2016 and October 31 ^st , 2018. Data analysis was conducted from July to December 2022.

Exposures

Neighborhood disadvantage was derived from area deprivation indices at primary residence. Household SES indicators were total income and the highest parental education attainment.

Main Outcomes and Measures

Thirty-one major white matter tracts were segmented from diffusion-weighted images. The Restriction Spectrum Imaging (RSI) model was implemented to measure restricted normalized directional (RND; reflecting oriented myelin organization) and isotropic (RNI; reflecting glial/neuronal cell bodies) diffusion in each tract. Obesity-related measures were body mass index (BMI), BMI z -scores, and waist circumference, and cognitive performance was assessed using the NIH Toolbox Cognition Battery. Linear mixed-effects models tested the associations between SES indicators and scanner-harmonized RSI metrics. Structural equation models examined indirect effects of obesity and cognitive performance in the significant associations between SES and white mater microstructure summary principal components. Analyses were adjusted for age, sex, pubertal development stage, intracranial volume, and head motion.

Results

The analytical sample included 8842 children (4299 [48.6%] girls; mean age [SD], 9.9 [0.7] years). Greater neighborhood disadvantage and lower parental education were independently associated with lower RSI-RND in forceps major and corticospinal/pyramidal tracts, and had overlapping associations in the superior longitudinal fasciculus. Lower cognition scores and greater obesity-related measures partially accounted for these SES associations with RSI-RND. Lower household income was related to higher RSI-RNI in almost every tract, and greater neighborhood disadvantage had similar effects in primarily frontolimbic tracts. Lower parental education was uniquely linked to higher RSI-RNI in forceps major. Greater obesity-related measures partially accounted for these SES associations with RSI-RNI. Findings were robust in sensitivity analyses and mostly corroborated using traditional diffusion tensor imaging (DTI).

Conclusions and Relevance

These cross-sectional results demonstrate that both neighborhood and household contexts are relevant to white matter development in children, and suggest cognitive performance and obesity as possible pathways of influence. Interventions targeting obesity reduction and improving cognition from multiple socioeconomic angles may ameliorate brain health in low-SES children.

Key Points

Question: Are neighborhood and household socioeconomic levels associated with children’s brain white matter microstructure, and if so, do obesity and cognitive performance (reflecting environmental stimulation) mediate the associations?Findings: In a cohort of 8842 children, higher neighborhood disadvantage, lower household income, and lower parental education had independent and overlapping associations with lower restricted directional diffusion and greater restricted isotropic diffusion in white matter. Greater body mass index and poorer cognitive performance partially mediated these associations.Meaning: Both neighborhood and household poverty may contribute to altered white matter development in children. These effects may be partially explained by obesity incidence and poorer cognitive performance.

Naringin Alleviates Glucose-Induced Aging by Reducing Fat Accumulation and Promoting Autophagy in Caenorhabditis elegans

Naringin (Nar) is a dihydroflavonoid compound, widely found in citrus fruit and used in Chinese herbal medicine. As a phytochemical, it acts as a dietary supplement that can delay aging and prevent aging-related disease, such as obesity and diabetes. However, its exact mechanism remains unclear. In this study, the high-glucose-induced (HGI) Caenorhabditis elegans model was used to evaluate the anti-aging and anti-obesity effects of Nar. The mean lifespan and fast movement span of HGI worms were extended roughly 24% and 11%, respectively, by Nar treatment. Oil red O staining revealed a significant reduction in fat accumulation and dFP::LGG-labeled worms showed the promotion of autophagy. Additionally, whole transcriptome sequencing and gene set variation analysis suggested that Nar upregulated the lipid biosynthesis and metabolism pathways, as well as the TGF-β, Wnt and longevity signaling pathways. Protein-protein interaction (PPI) network analysis identified hub genes in these pathways for further analysis. Mutant worms and RNA interference were used to study mechanisms; the suppression of hlh-30, lgg-1, unc-51, pha-4, skn-1 and yap-1 disabled the fat-lowering, lifespan-prolonging, and health-promoting properties of Nar. Collectively, our findings indicate that Nar plays an important role in alleviating HGI-aging and anti-obesity effects by reducing fat accumulation and promoting autophagy.

Relationship Between Weight-Change Patterns and Cognitive Function: A Retrospective Study

BACKGROUND

Obesity has been linked to cognitive impairment. However, how changes in body mass index (BMI) over the life course influence cognitive function remains unclear.

OBJECTIVE

The influence of distinct weight-change patterns from young adulthood to midlife and late adulthood on cognitive function in older adults was explored.

METHODS

A total of 5,809 individuals aged≥60 years were included and categorized into four groups on the basis of BMI change patterns. Cognitive function was assessed using four cognition tests in the baseline survey. The relationship between the weight-change patterns and cognition was evaluated using regression models.

RESULTS

In comparison with participants who remained at non-obese, those moving from the non-obese to obese weight-change pattern from young (25 years of age) to middle adulthood showed lower Digit Symbol Substitution Test (DSST) scores (β= -1.28; 95% confidence interval [CI]: -2.24 to -0.32). A non-obese to obese change pattern from age 25 years of age to 10 years before baseline was associated with a higher risk of DSST impairment (odds ratio = 1.40; 95% CI: 1.09 to 1.79). In comparison with participants whose heaviest weight was recorded after 60 years of age, those with the heaviest weight between 18 and 40 years of age had lower DSST scores (β= -1.46; 95% CI: -2.77 to -1.52).

CONCLUSION

Our results suggest that the transition from the non-obese to obese category in early adulthood and appearance of the heaviest weight between 18 and 40 years of age are associated with lower cognitive function in later life.

Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities

Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18-88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.

Modifiable risk factors of dementia linked to excitation-inhibition imbalance

Recent evidence identifies 12 potentially modifiable risk factors for dementia to which 40% of dementia cases are attributed. While the recognition of these risk factors has paved the way for the development of new prevention measures, the link between these risk factors and the underlying pathophysiology of dementia is yet not well understood. A growing number of recent clinical and preclinical studies support a role of Excitation-Inhibition (E-I) imbalance in the pathophysiology of dementia. In this review, we aim to propose a conceptual model on the links between the modifiable risk factors and the E-I imbalance in dementia. This model, which aims to address the current gap in the literature, is based on 12 mediating common mechanisms: the hypothalamic-pituitary-adrenal (HPA) axis dysfunction, neuroinflammation, oxidative stress, mitochondrial dysfunction, cerebral hypo-perfusion, blood-brain barrier (BBB) dysfunction, beta-amyloid deposition, elevated homocysteine level, impaired neurogenesis, tau tangles, GABAergic dysfunction, and glutamatergic dysfunction. We believe this model serves as a framework for future studies in this field and facilitates future research on dementia prevention, discovery of new biomarkers, and developing new interventions.

Improving performance robustness of subject-based brain segmentation software

Purpose

Artificial intelligence (AI)-based image analysis tools to quantify the brain have become commercialized. However, insufficient data for learning and scanner specificity is a limitation for achieving high quality. In the present study, the performance of personalized brain segmentation software when applied to multicenter data using an AI model trained on data from a single institution was improved.

Methods

Preindicators of brain white matter (WM) information from the training dataset were utilized for preprocessing. During learning, data of cognitively normal (CN) individuals from a single center were utilized, and data of CN individuals and Alzheimer disease (AD) patients enrolled in multiple centers were considered the test set.

Results

The preprocessing based on the preindicator (dice similarity coefficient [DSC], 0.8567) resulted in a better performance than without (DSC, 0.7921). The standard deviation (SD) of the WM region intensity (DSC, 0.8303) had a more substantial influence on the performance than the average intensity (DSC, 0.6591). When the SD of the test data WM intensity was smaller than the learning data, the performance improved (0.03 increase in lower SD, 0.05 decrease in higher SD). Furthermore, preindicator-based pretreatment increased the correlation of mean cortical thickness of the entire gray matter between Atroscan and FreeSurfer, and data augmentation without preprocessing did not.Both preindicator processing and data augmentation improved the correlation coefficient from 0.7584 to 0.8165.

Conclusion

Data augmentation and preindicator-based preprocessing of training data can improve the performance of AI-based brain segmentation software, both increasing the generalizability and stability of brain segmentation software.

Correlation between Body Mass Index and Cognitive Function in Patients with Atrial Fibrillation

Background

Evidence regarding the relationship between body mass index (BMI) and cognitive function was limited. Therefore, the objective of this research is to investigate whether BMI is independently related to cognitive function in Chinese patients with atrial fibrillation after adjusting for other covariates.

Methods

The present study is a cross-sectional study. A total of 281 patients with atrial fibrillation who were hospitalized at the Affiliated Hospital of Jining Medical University in Shandong Province from January 2021 to November 2021 were included in the study. The target independent variable and the dependent variable were BMI and cognitive function in patients with atrial fibrillation, respectively. The patients' general information, BMI, past history, medication history, and other disease-related data were collected. The Montreal cognitive assessment scale (MoCA) was used to evaluate cognitive function.

Results

A total of 244 patients with atrial fibrillation were collected in this study, with an average age of (67.28 ± 10.33) years, of whom 55.3% were male. The average BMI was (25.33 ± 4.27) kg/m², and the average cognitive function score was (19.25 ± 6.88) points. The results of the smooth curve fitting and threshold effect tests showed that there was a curve correlation between BMI and cognitive function score, and its inflection point was 24.56 kg/m². To the left of the inflection point, the relationship was significant; the effect size and the confidence interval were 0.43 and 0.01-0.85, respectively. To the right of the inflection point, there was no significant correlation between BMI and cognitive function (P=0.152).

Conclusion

When BMI is lower than 24.56 kg/m², the cognitive function score increases by 0.43 points for each unit increase in BMI in patients with atrial fibrillation. An increase in BMI at this time is a protective factor for cognitive function. Within the normal range of BMI, the higher the BMI in atrial fibrillation patients, the higher the cognitive function score. We encourage atrial fibrillation patients with normal BMI to maintain their current weight.

Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging

Objective

Brain tissue changes dynamically during aging. The purpose of this study was to use synthetic magnetic resonance imaging (syMRI) to evaluate the changes in relaxation values in different brain regions during brain aging and to construct a brain age prediction model.

Materials and methods

Quantitative MRI was performed on 1,000 healthy people (≥ 18 years old) from September 2020 to October 2021. T1, T2 and proton density (PD) values were simultaneously measured in 17 regions of interest (the cerebellar hemispheric cortex, pons, amygdala, hippocampal head, hippocampal tail, temporal lobe, occipital lobe, frontal lobe, caudate nucleus, lentiform nucleus, dorsal thalamus, centrum semiovale, parietal lobe, precentral gyrus, postcentral gyrus, substantia nigra, and red nucleus). The relationship between the relaxation values and age was investigated. In addition, we analyzed the relationship between brain tissue values and sex. Finally, the participants were divided into two age groups: < 60 years old and ≥ 60 years old. Logistic regression analysis was carried out on the two groups of data. According to the weight of related factors, a brain age prediction model was established and verified.

Results

We obtained the specific reference value range of different brain regions of individuals in different age groups and found that there were differences in relaxation values in brain tissue between different sexes in the same age group. Moreover, the relaxation values of most brain regions in males were slightly higher than those in females. In the study of age and brain relaxation, it was found that brain relaxation values were correlated with age. The T1 values of the centrum semiovale increased with age, the PD values of the centrum semiovale increased with age, while the T2 values of the caudate nucleus and lentiform nucleus decreased with age. Seven brain age prediction models were constructed with high sensitivity and specificity, among which the combined T1, T2 and PD values showed the best prediction efficiency. In the training set, the area under the curve (AUC), specificity and sensitivity were 0.959 [95% confidence interval (CI): 0.945–0.974], 91.51% and 89.36%, respectively. In the test cohort, the above indicators were 0.916 (95% CI: 0.882–0.951), 89.24% and 80.33%, respectively.

Conclusion

Our study provides specific reference ranges of T1, T2, and PD values in different brain regions from healthy adults of different ages. In addition, there are differences in brain relaxation values in some brain regions between different sexes, which help to provide new ideas for brain diseases that differ according to sex. The brain age model based on synthetic MRI is helpful to determine brain age.

Cerebral Blood Flow Alterations and Obesity: A Systematic Review and Meta-Analysis

Background: Reduction in cerebral blood flow (CBF) plays an essential role in the cognitive impairment and dementia in obesity. However, current conclusions regarding CBF changes in patients with obesity are inconsistent. Objective: A systematic review and meta-analysis was performed to evaluate the relationship between obesity and CBF alterations. Methods: We systematically screened published cross-sectional and longitudinal studies focusing on the differences in CBF between obese and normal-weight individuals. Eighteen studies including 24,866 participants, of which seven articles reported longitudinal results, were evaluated in the present study. Results: The results of the meta-analysis showed that in cross-sectional studies, body mass index (BMI) was negatively associated with CBF (β= –0.31, 95% confidence interval [CI]: –0.44, –0.19). Moreover, this systematic review demonstrated that obese individuals showed global and regional reductions in the CBF and increased CBF in diverse functional areas of the frontal lobe, including the prefrontal cortex, left frontal superior orbital, right frontal mid-orbital cortex, and left premotor superior frontal gyrus. Conclusion: Our findings suggest that BMI, rather than waist circumference and waist-to-hip ratio, is inversely associated with CBF in cross-sectional studies. The CBF of obese individuals showed global and regional reductions, including the frontal lobe, temporal and parietal lobes, cerebellum, hippocampus, and thalamus.

Mechanisms linking obesity and its metabolic comorbidities with cerebral grey and white matter changes

Obesity is a preventable risk factor for cerebrovascular disorders and it is associated with cerebral grey and white matter changes. Specifically, individuals with obesity show diminished grey matter volume and thickness, which seems to be more prominent among fronto-temporal regions in the brain. At the same time, obesity is associated with lower microstructural white matter integrity, and it has been found to precede increases in white matter hyperintensity load. To date, however, it is unclear whether these findings can be attributed solely to obesity or whether they are a consequence of cardiometabolic complications that often co-exist with obesity, such as low-grade systemic inflammation, hypertension, insulin resistance, or dyslipidemia. In this narrative review we aim to provide a comprehensive overview of the potential impact of obesity and a number of its cardiometabolic consequences on brain integrity, both separately and in synergy with each other. We also identify current gaps in knowledge and outline recommendations for future research.

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.

Alteration in peritoneal cells with the chemokine CX3CL1 reverses age-associated impairment of recognition memory

Cognitive function progressively declines with advancing age. The aging process can be promoted by obesity and attenuated by exercise. Both conditions affect levels of the chemokine CX3CL1 in peripheral tissues; however, its role in cognitive aging is unknown. In the current study, we administered CX3CL1 into the peritoneal cavity of aged mice to investigate its impact on the aging process. In the peritoneal cavity, CX3CL1 not only reversed the age-associated accumulation of cells expressing the senescence marker p16^INK4a but also increased peritoneal phagocytic activity, indicating that CX3CL1 affected the phenotypes of peritoneal cells. In the hippocampus of aged mice, intraperitoneal administration of CX3CL1 increased the number of Type-2 neural stem cells and promoted brain-derived neurotrophic factor (BDNF) expression. This treatment, furthermore, improved novel object recognition memory impaired with advancing age. Intraperitoneal transplantation of peritoneal cells from CX3CL1-treated aged mice improved novel object recognition memory in recipient aged mice. It indicates that peritoneal cells have a critical role in the CX3CL1-induced improvement of recognition memory in aged mice. Vagotomy inhibited the CX3CL1-induced increase in BDNF expression, demonstrating that the vagus nerve is involved in the hippocampal BDNF expression induced by intraperitoneal administration of CX3CL1. Thus, our results demonstrate that a novel connection among the peritoneal cells, the vagus nerve, and the hippocampus can reverse the age-associated decline in recognition memory.

Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years

BACKGROUND

The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk.

OBJECTIVE

Predicted age difference (PAD) was proposed to predict CDR change.

METHODS

All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction.

RESULTS

PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up.

CONCLUSION

Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.

Does obesity put your brain at risk?

BACKGROUND AND AIMS

The negative impact of obesity on the brain is an issue of increasing clinical interest. Hence, this review summarized evidence linking obesity with brain morphology (gray and white matter volume), brain function (functional activation and connectivity), and cognitive function.

METHODS

A criticals review of the relevant published English articles between 2008 and 2022, using PubMed, Google Scholar and Science Direct. Studies were included if (1) an experimental/intervention study was conducted (2) the experiment/intervention included both high fat diet or body weight, whether it could counteract the negative effect brain morphological or functional change. Critical analysis for a supporting study was also carried out.

RESULTS

Brain dysfunction can be recognized as result from neuroinflammation, oxidative stress, change in gut-brain hormonal functionality decrease regional blood flow or diminished hippocampal size and change in gut-brain hormonal functionality; which collectively translate into a cycle of deranged metabolic control and cognitive deficits, often obesity referred as changes in brain biochemistry and brain function. Recently, a few changes in brain structure and functions could be traced back even to obese children or adult. Evidence here suggested that obesity elicits early neuroinflammation effects, which likely disrupt the normal metabolism in hypothalamus, and hippocampus result from brain insulin resistance. The mechanisms of these robust effects are discussed herein.

CONCLUSION

Brain disease is inseparable from obesity itself and requires a better recognition to allow future therapeutic targeting for treatment of obesity. Additional research is needed to identify the best treatment targets and to identify if these changes reversible.

Obesity Increases Gene Expression of Markers Associated With Immunosenescence in Obese Middle-Aged Individuals

Recently, it has been argued that obesity leads to a chronic pro-inflammatory state that can accelerate immunosenescence, predisposing to the early acquisition of an immune risk profile and health problems related to immunity in adulthood. In this sense, the present study aimed to verify, in circulating leukocytes, the gene expression of markers related to early immunosenescence associated with obesity and its possible relationships with the physical fitness in obese adults with type 2 diabetes or without associated comorbidities. The sample consisted of middle-aged obese individuals (body mass index (BMI) between 30-35 kg/m²) with type 2 diabetes mellitus (OBD; n = 17) or without associated comorbidity (OB; n = 18), and a control group of eutrophic healthy individuals (BMI: 20 - 25 kg/m²) of same ages (E; n = 18). All groups (OBD, OB and E) performed the functional analyses [muscle strength (1RM) and cardiorespiratory fitness (VO_2max)], anthropometry, body composition (Air Displacement Plethysmograph), blood collections for biochemical (anti-CMV) and molecular (gene expression of leptin, IL-2, IL-4, IL-6, IL-10, TNF-α, PD-1, P16^ink4a, CCR7, CD28 and CD27) analyses of markers related to immunosenescence. Increased gene expression of leptin, IL-2, IL-4, IL-10, TNF-α, PD-1, P16^ink4a, CCR7 and CD27 was found for the OBD and OB groups compared to the E group. Moreover, VO_2max for the OBD and OB groups was significantly lower compared to E. In conclusion, obesity, regardless of associated disease, induces increased gene expression of markers associated with inflammation and immunosenescence in circulating leukocytes in obese middle-aged individuals compared to a eutrophic group of the same age. Additionally, increased adipose tissue and markers of chronic inflammation and immunosenescence were associated to impairments in the cardiorespiratory capacity of obese middle-aged individuals.

Effect of MIND diet intervention on cognitive performance and brain structure in healthy obese women: a randomized controlled trial

Previous studies suggested adherence to recently developed Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) associated with cognitive performance. This study aimed to examine the effect of MIND dietary pattern on cognitive performance features and changes in brain structure in healthy obese women. As a total of 50 obese women were assessed for eligibility, we randomly allocated 40 participants with mean BMI 32 ± 4.31 kg/m2 and mean age 48 ± 5.38 years to either calorie-restricted modified MIND diet or a calorie-restricted standard control diet. Change in cognitive performance was the primary outcome measured with a comprehensive neuropsychological test battery. We also performed voxel-based morphometry as a secondary outcome to quantify the differences in brain structure. All of the measurements were administered at baseline and 3 months follow-up. Thirty-seven participants (MIND group = 22 and control group = 15) completed the study. The results found in the MIND diet group working memory + 1.37 (95% CI 0.79, 1.95), verbal recognition memory + 4.85 (95% CI 3.30, 6.40), and attention + 3.75 (95% CI 2.43, 5.07) improved more compared with the control group (ps < 0.05). Results of brain MRI consist of an increase in surface area of the inferior frontal gyrus in the MIND diet group. Furthermore, the results showed a decrease in the cerebellum-white matter and cerebellum-cortex in two groups of study. Still, the effect in the MIND group was greater than the control group. The study findings declare for the first time that the MIND diet intervention can reverse the destructive effects of obesity on cognition and brain structure, which could be strengthened by a modest calorie restriction.

Clinical trial registration ClinicalTrials.gov ID: NCT04383704 (First registration date: 05/05/2020).

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

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There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions.

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We investigated adipose tissue distribution from body magnetic resonance imaging (MRI) in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI).

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The results indicated older-appearing brains in people with higher measures of adipose tissue, and accelerated ageing over the course of the study period in people with higher measures of adipose tissue.

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

Correlation between Body Mass Index (BMI) and Performance on the Montreal Cognitive Assessment (MoCA) in a Cohort of Adult Women in South Africa

Objective. Recent evidence suggests that obesity is increasing worldwide and may negatively impact neurocognition. Local studies on the association of weight status with neurocognitive function are sparse. This study is aimed at examining the association between body mass index (BMI) and neurocognitive functioning scores in a cohort of adult women. Methods. A convenience sample of 175 women aged 18 to 59 years ( $28.03\pm 8.87$ ) recruited in a community-based quantitative study completed the Montreal Cognitive Assessment (MoCA). The BMI metric was used to measure body fat based on weight and height and was stratified as high BMI (overweight or obese) or low BMI (normal weight). The Beck Depression Inventory (BDI) was used to assess depression. Pearson’s correlation analysis and the student’s $t$ -test analysis were performed. Results. We observed a significant inverse association between BMI and performance on MoCA ( $r\left(173\right)=-0.32$ , $p<0.001$ ). Performance on subtest of attention, memory, constructive abstraction, and executive functions significantly and inversely correlated with BMI. Significantly lower scores on the MoCA were found in women with a high BMI compared to women with a low BMI ( $23\pm 4$ vs. $26\pm 3$ ), $t\left(173\right)=4.12$ , $p<0.0001$ ). Conclusions. BMI and MoCA were inversely associated on both global and domain-specific neurocognitive test of attention, memory, and executive function; key neurocognitive control; and regulatory functions underlying behavior and decision-making. The findings provide a rationale for further research into the long-term effects of BMI on neurocognition.

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

Brain-Predicted Age Difference Moderates the Association Between Muscle Strength and Mobility

Background

Approximately 35% of individuals over age 70 report difficulty with mobility. Muscle weakness has been demonstrated to be one contributor to mobility limitations in older adults. The purpose of this study was to examine the moderating effect of brain-predicted age difference (an index of biological brain age/health derived from structural neuroimaging) on the relationship between leg strength and mobility.

Methods

In community dwelling older adults (N = 57, 74.7 ± 6.93 years; 68% women), we assessed the relationship between isokinetic leg extensor strength and a composite measure of mobility [mobility battery assessment (MBA)] using partial Pearson correlations and multifactorial regression modeling. Brain predicted age (BPA) was calculated from T1 MR-images using a validated machine learning Gaussian Process regression model to explore the moderating effect of BPA difference (BPAD; BPA minus chronological age).

Results

Leg strength was significantly correlated with BPAD (r = −0.317, p < 0.05) and MBA score (r = 0.541, p < 0.001). Chronological age, sex, leg strength, and BPAD explained 63% of the variance in MBA performance (p < 0.001). BPAD was a significant moderator of the relationship between strength and MBA, accounting for 7.0% of MBA score variance [△R² = 0.044, F(1,51) = 6.83, p = 0.01]. Conditional moderation effects of BPAD indicate strength was a stronger predictor of mobility in those with a great BPAD.

Conclusion

The relationship between strength and mobility appears to be influenced by brain aging, with strength serving as a possible compensation for decline in neural integrity.

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Gut Microbiome, Inflammation, and Cerebrovascular Function: Link Between Obesity and Cognition

Obesity affects 13% of the adult population worldwide and this number is only expected to increase. Obesity is known to have a negative impact on cardiovascular and metabolic health, but it also impacts brain structure and function; it is associated with both gray and white matter integrity loss, as well as decreased cognitive function, including the domains of executive function, memory, inhibition, and language. Especially midlife obesity is associated with both cognitive impairment and an increased risk of developing dementia at later age. However, underlying mechanisms are not yet fully revealed. Here, we review recent literature (published between 2010 and March 2021) and discuss the effects of obesity on brain structure and cognition, with a main focus on the contributions of the gut microbiome, white adipose tissue (WAT), inflammation, and cerebrovascular function. Obesity-associated changes in gut microbiota composition may cause increased gut permeability and inflammation, therewith affecting cognitive function. Moreover, excess of WAT in obesity produces pro-inflammatory adipokines, leading to a low grade systemic peripheral inflammation, which is associated with decreased cognition. The blood-brain barrier also shows increased permeability, allowing among others, peripheral pro-inflammatory markers to access the brain, leading to neuroinflammation, especially in the hypothalamus, hippocampus and amygdala. Altogether, the interaction between the gut microbiota, WAT inflammation, and cerebrovascular integrity plays a significant role in the link between obesity and cognition. Future research should focus more on the interplay between gut microbiota, WAT, inflammation and cerebrovascular function to obtain a better understanding about the complex link between obesity and cognitive function in order to develop preventatives and personalized treatments.

High Caloric Diet Induces Memory Impairment and Disrupts Synaptic Plasticity in Aged Rats

The increasing consumption of sugar and fat seen over the last decades and the consequent overweight and obesity, were recently linked with a deleterious effect on cognition and synaptic function. A major question, which remains to be clarified, is whether obesity in the elderly is an additional risk factor for cognitive impairment. We aimed at unravelling the impact of a chronic high caloric diet (HCD) on memory performance and synaptic plasticity in aged rats. Male rats were kept on an HCD or a standard diet (control) from 1 to 24 months of age. The results showed that under an HCD, aged rats were obese and displayed significant long-term recognition memory impairment when compared to age-matched controls. Ex vivo synaptic plasticity recorded from hippocampal slices from HCD-fed aged rats revealed a reduction in the magnitude of long-term potentiation, accompanied by a decrease in the levels of the brain-derived neurotrophic factor receptors TrkB full-length (TrkB-FL). No alterations in neurogenesis were observed, as quantified by the density of immature doublecortin-positive neurons in the hippocampal dentate gyrus. This study highlights that obesity induced by a chronic HCD exacerbates age-associated cognitive decline, likely due to impaired synaptic plasticity, which might be associated with deficits in TrkB-FL signaling.

Sex Differences in the Association between Different Obesity Parameters and Cognitive Function in Older Adults: A Cross-Sectional Study in Rural China

BACKGROUND

Early identification of risk factors for cognition decline may contribute to the interventions for Alzheimer's disease. Obesity is a common modifiable risk factor for chronic diseases. The association between obesity and cognition in older adults is limited, and sex differences in this area have not been well recognized.

OBJECTIVE

The aim of the study was to observe the sex differences in the relationship between obesity and cognition in a rural community-dwelling older population of Guizhou, China.

METHODS

Data were gathered from the baseline survey of a cohort study of older people in rural areas of Guizhou, China. Demographic and behavioral data (sex, age, education, household income, smoking history, drinking history, history of head injury, diet, and level of physical exercise time) were collected. The Mini-Mental State Examination (MMSE) was used to assess cognitive function. Body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) were used as different measures of obesity. Comparisons between the groups were made by the Wilcoxon rank-sum test or Kruskal-Wallis H test. Restricted cubic spline regression was used to examine a dose-response relationship between obesity indicators and cognitive function. Linear relationships were performed by the multivariable linear regression model.

RESULTS

A total of 1,654 participants including 964 women and 690 men were enrolled in this study. After adjustment, BMI showed a nonlinear relationship with MMSE scores in women. There was a significant trend toward increasing MMSE scores at the low end of BMI (13.52-20.10 kg/m2, p = 0.014). The multivariable linear regression model showed that MMSE increased by 0.631 (p < 0.001) for every one standard deviation increase in HC in women. No association was found between obesity parameters and cognitive function in men.

CONCLUSION

Our results suggest that there are significant sex differences in some obesity parameters and cognition in an older Chinese population. BMI and HC are positively associated with cognitive function in women. No association was found between obesity measures and cognitive function in men.

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.

Machine learning for brain age prediction: Introduction to methods and clinical applications

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.