Brain Imaging Phenotypes Research Articles

Spotlight on Hemorrhagic Destruction of the Brain, Subependymal Calcification, and Congenital Cataracts (HDBSCC).

Hemorrhagic Destruction of the Brain, Subependymal Calcification, and Congenital Cataracts (HDBSCC) is a rare syndrome caused by biallelic mutations in the JAM3 gene with significant intrafamilial variability in clinical presentation and brain imaging phenotypes. The clinical presentation of HDBSCC includes severe recurrent hemorrhages involving the brain parenchyma and the ventricles beginning in utero and continuing in infancy together with dense central cataracts present at birth. This comprehensive review documents reported cases on this unique condition and describes its genetic, neuroradiologic and ophthalmic features. It should be included in the differential diagnosis of children with congenital cataracts and neurodevelopmental abnormalities. Unique clinical, imaging findings and genetic testing can help the diagnosis.

TESC overexpression mitigates amyloid-β-induced hippocampal atrophy and memory decline

Background and objectivesGenome-wide association studies (GWASs) have identified numerous candidate genes for human brain-imaging phenotypes; however, the biological relevance of many of these genes remains unconfirmed. This study aimed to investigate the causal relationships among tescalcin (TESC) (a GWAS-indicated gene), hippocampal volume, Alzheimer’s disease (AD), and the underlying biological mechanisms. MethodsHuman transcriptional data were analyzed to confirm relative TESC expression in the hippocampus. In cell experiments, RNA-seq analysis was used to identify the potential biological pathways for TESC overexpression, and immunofluorescence imaging and cell viability assays were used to evaluate the effect of TESC overexpression on neuronal structure and survival. In animal experiments, the effects of TESC overexpression on hippocampal volume and cognitive function in normal mice and amyloid-β (Aβ)-induced AD mice were investigated by 9.4 T magnetic resonance imaging and behavioral tests. Underlying mechanisms were further assessed via western blotting and electrophysiological recordings. ResultsHuman transcriptional data demonstrated that TESC is primarily expressed in the hippocampus and neurons. TESC overexpression enhanced the viability of HT22 cells and reduced Aβ-induced cell death. In mouse models, Tesc-overexpressing mice revealed increased hippocampal volume, likely owing to enhanced cell viability and long-term potentiation (LTP), and reducing apoptotic- and oxidation-induced hippocampal damage. TESC overexpression could significantly mitigate Aβ-induced hippocampal atrophy and memory impairment, potentially by reducing Aβ-induced neuronal apoptosis and LTP weakening. ConclusionThis study exemplifies the translation of GWAS findings into actionable biological knowledge and suggests that upregulation of TESC may offer a promising therapeutic strategy for AD.

Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Exploring causal effects and potential mediating mechanisms of genetically linked environmental senses with intracerebral hemorrhage.

The occurrence mechanism of intracerebral hemorrhage remains unclear. Several recent studies have highlighted the close relationship between environmental senses and intracerebral hemorrhage, but the mechanisms of causal mediation are inconclusive. We aimed to investigate the causal relationships and potential mechanisms between environmental senses and intracerebral hemorrhage. Multiple Mendelian randomization methods were used to identify a causal relationship between environmental senses and intracerebral hemorrhage. Gut microbiota and brain imaging phenotypes were used to find possible mediators. Enrichment and molecular interaction analyses were used to identify potential mediators and molecular targets. No causal relationship between temperature and visual perception with intracerebral hemorrhage was found, whereas long-term noise was identified as a risk factor for intracerebral hemorrhage (OR 2.95, 95% CI: 1.25 to 6.93, PIVW = 0.01). The gut microbiota belonging to the class Negativicutes and the order Selenomonadales and the brain image-derived phenotypes ICA100 node 54, edge 803, edge 1149, and edge 1323 played mediating roles. "Regulation of signaling and function in synaptic organization" is the primary biological pathway of noise-induced intracerebral hemorrhage, and ARHGAP22 may be the critical gene. This study emphasized the importance of environmental noise in the prevention, disease management, and underlying biological mechanisms of intracerebral hemorrhage.

IBS stress reactivity phenotype is associated with blood transcriptome profiles and microstructural and functional brain changes.

Clinical evidence suggests significant interindividual differences in stress reactivity (SR), but biological mechanisms and therapeutic implications of these differences are poorly understood. We aimed to identify the biological basis of increased SR by investigating associations between a psychometric-based phenotype with blood transcriptomics profiles of increased sympathetic nervous system (SNS) activation and brain imaging phenotypes in irritable bowel syndrome (IBS) participants and healthy controls (HCs). A cross-sectional observational study design, transcriptomics profiling, multimodal brain imaging, and psychosocial assessments were obtained in 291 female and male IBS participants and HCs. Prior to analyses, unsupervised clustering was applied to derive high and low SR subgroups across participants based on two measures of SR. General linear models tested for SR group differences in clinical and biological parameters. Exploratory analyses examined associations between SR group-specific brain alterations and gene expression. The high, compared to low SR group showed greater cyclic AMP response element-binding protein (CREB) gene expression consistent with tonic SNS activity and proinflammatory changes in whole blood. Brain imaging showed neuroplastic changes in the high SR group consistent with an upregulation of ascending arousal systems and sensory processing and integration regions, and functional connectivity changes in the central autonomic network. SR moderated the sex difference in extraintestinal symptoms. The findings support a model of tonically increased SNS activity as a plausible risk factor for increased autonomic reactivity to psychosocial stressors and low grade immune activation in both IBS and HCs, with a greater prevalence in IBS. These findings may have important implications for personalized treatment interventions in IBS.

Chronic Low-Grade Inflammation and Brain Structure in the Middle-Aged and Elderly Adults.

Low-grade inflammation (LGI) mainly acted as the mediator of the association of obesity and inflammatory diet with numerous chronic diseases, including neuropsychiatric diseases. However, the evidence about the effect of LGI on brain structure is limited but important, especially in the context of accelerating aging. This study was then designed to close the gap, and we leveraged a total of 37,699 participants from the UK Biobank and utilized inflammation score (INFLA-score) to measure LGI. We built the longitudinal relationships of INFLA-score with brain imaging phenotypes using multiple linear regression models. We further analyzed the interactive effects of specific covariates. The results showed high level inflammation reduced the volumes of the subcortex and cortex, especially the globus pallidus (β [95% confidence interval] = -0.062 [-0.083, -0.041]), thalamus (-0.053 [-0.073, -0.033]), insula (-0.052 [-0.072, -0.032]), superior temporal gyrus (-0.049 [-0.069, -0.028]), lateral orbitofrontal cortex (-0.047 [-0.068, -0.027]), and others. Most significant effects were observed among urban residents. Furthermore, males and individuals with physical frailty were susceptive to the associations. The study provided potential insights into pathological changes during disease progression and might aid in the development of preventive and control targets in an age-friendly city to promote great health and well-being for sustainable development goals.

Associations of long-term exposure to low-level PM2.5 and brain disorders in 260,922 middle-aged and older adults

Long-term exposure to high-level ambient PM2.5 was associated with increased risks of brain disorders, while the associations remain uncertain when the exposure is lower than current air quality standards in numerous countries. This study aimed to assess the effects of PM2.5 exposure on the brain system in the population with annual mean concentrations ≤15 μg/m3. We analyzed data from 260,922 participants without preexisting brain diseases at baseline in the UK Biobank. The geographical distribution of PM2.5 in 2010 was estimated by a land use regression model and linked with individual residential address. We investigated associations of ambient PM2.5 with incident neurological (dementia, Parkinson's diseases [PD], epilepsy, and migraine) and psychiatric (major depressive disorder [MDD] and anxiety disorder) diseases through Cox proportional hazard models. We further estimated the links with brain imaging phenotypes by neuroimaging analysis. Results showed that in the population with PM2.5 concentrations ≤15 μg/m3, each interquartile range (IQR, 1.28 μg/m3) increment in PM2.5 was related to incidence risks of dementia, epilepsy, migraine, MDD, and anxiety disorder with hazard ratios of 1.08 (95% confidence interval [CI]: 1.03, 1.13), 1.12 (1.05, 1.20), 1.07 (1.00, 1.13), 1.06 (1.03, 1.09), and 1.05 (1.02, 1.08), respectively. We did not observe a significant association with PD. The association with dementia was stronger among the population with poor cardiovascular health (measured by Life's Essential 8) than the counterpart (P for interaction = 0.037). Likewise, per IQR increase was associated with specific brain imaging phenotypes, including volumes of total brain (β = −0.036; 95% CI: −0.050, −0.022), white matter (−0.030; −0.046, −0.014), grey matter (−0.030; −0.042, −0.017), respectively. The findings suggest long-term exposure to ambient PM2.5 at low-level still has an adverse impact on the neuro-psychiatric systems. The brain-relevant epidemiological assessment suggests that each country should update the standard for ambient PM2.5 following the World Health Organization Air Quality Guidelines 2021.

Cross-ancestry genome-wide association studies of brain imaging phenotypes.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes.

Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ l2,1 -norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.

Gut microbiome‐related metabolites in plasma are associated with general cognition

AbstractBackgroundIncreasing evidence suggests a role of metabolism in neurological disorders including Alzheimer’s disease (AD). The human metabolome is determined by genes and modifiable factors such as gut microbiota, diet, medication, and other lifestyle choices. We hypothesized that metabolites related to endophenotypes of brain health maybe derived or influenced by these modifiable factors. Identifying the determinants of metabolites associated with AD‐related endophenotypes may help to discover new targets for drugs and prevention.MethodWe profiled plasma samples of 1082 participants of the Rotterdam study for 1387 metabolites using a non‐targeted metabolomics platform. After preprocessing and quality control, plasma levels of 991 metabolites were available for analysis of 1068 cognitively normal participants. We assessed the role of metabolites in general cognition (G‐factor) and magnetic resonance imaging (MRI) markers using linear regression. Replication analysis was performed for general cognition in an independent dataset. To identify the source of brain health‐associated metabolites, we estimated the explained variance (EV) of metabolites by genes, gut‐microbiome, medication use, and clinical features using gradient boosting decision tree (GBDT). Multiple testing correction was performed based on the false discovery rate (FDR) by Benjamini Hochberg (FDR&lt;0.05).ResultAmong the 991 analyzed metabolites, 14 showed significant associations with general cognition, 21 metabolites with total brain volume, and one with total white matter lesions. In the replication analysis of metabolites associated with cognition, 9 metabolites showed consistent association with G‐factor. While genetics explained more than 5% of the variance of all associated metabolites, for three cognition‐associated metabolites (ergothioneine, 3‐hydroxy‐2‐methylpyridine sulfate, 4‐vinylguaiacol sulfate), modifiable lifestyle factors, medication and microbiota additionally explained up to 15% of their variance, with lifestyle factors showing the largest influence. Among the metabolites associated with brain imaging phenotypes, clinical factors and medication explained more than 5% of the variance of four metabolites (3‐hydroxysebacate, 3‐hydroxybutyrylcarnitine, sphingomyelin (d18:2/24:2), unnamed metabolite).ConclusionOur study provides evidence for the association between circulating metabolites and cognition or brain imaging phenotypes in participants without cognitive impairment. The levels of these metabolites are influenced by genetics and in part by modifiable factors. Further, studies are warranted to elucidate the complex relationship between gut microbiota, diet, genome, metabolome, and cognition.

Brain imaging endophenotypes by unsupervised deep learning

AbstractBackgroundUnderstanding the genetic architecture of brain structure and Alzheimer’s disease is challenging, partly due to difficulties in designing robust, non‐biased descriptors of the brain.MethodWe present approaches to derive robust brain imaging phenotypes using unsupervised deep representation learning. Training a 3‐D convolutional autoencoder model with reconstruction loss, we derived a vector representation (termed endophenotype) that captures rich morphological information of the brain. Further, we developed a perturbation‐based decoder interpretation approach that can highlight brain regions that are most relevant to individual endophenotypes.ResultUsing UK Biobank data, we trained the model using over 6,000 UK Biobank (UKBB) participants’ T1 or T2‐FLAIR (T2) brain MRIs. The model is used to derive 128 endophenotypes. The endophenotypes have a mean heritability of 0.3 and the GWAS of which have identified 43 independent loci in the held‐out UKBiobank dataset (discovery n = 22,962/replication n = 12,848), among which 13 loci have not been previously reported by the UK Biobank Big40 study.ConclusionUsing UK Biobank data, we show that, compared to traditional brain image‐derived phenotypes (IDPs), our endophenotypes are more heritable and have higher power for genetic discovery.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Neuroimaging Genetics and Network Analysis in Alzheimer's Disease.

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.

Testing for associations between HbA1c levels, polygenic risk and brain health in UK Biobank (N = 39 283).

To investigate whether continuous HbA1c levels and HbA1c-polygenic risk scores (HbA1c-PRS) are significantly associated with worse brain health independent of type 2 diabetes (T2D) diagnosis (vs. not), by examining brain structure and cognitive test score phenotypes. Using UK Biobank data (n = 39 283), we tested whether HbA1c levels and/or HbA1c-PRS were associated with cognitive test scores and brain imaging phenotypes. We adjusted for confounders of age, sex, Townsend deprivation score, level of education, genotyping chip, eight genetic principal components, smoking, alcohol intake frequency, cholesterol medication, body mass index, T2D and apolipoprotein (APOE) e4 dosage. We found an association between higher HbA1c levels and poorer performance on symbol digit substitution scores (standardized beta [β] = -0.022, P = .001) in the fully adjusted model. We also found an association between higher HbA1c levels and worse brain MRI phenotypes of grey matter (GM; fully-adjusted β = -0.026, P < .001), whole brain volume (β = -0.072, P = .0113) and a general factor of frontal lobe GM (β = -0.022, P < .001) in partially and fully adjusted models. HbA1c-PRS were significantly associated with GM volume in the fully adjusted model (β = -0.010, P = .0113); however, when adjusted for HbA1c levels, the association was not significant. Our findings suggest that measured HbA1c is associated with poorer cognitive health, and that HbA1c-PRS do not add significant information to this.

Dissecting the causal association between social or physical inactivity and depression: a bidirectional two-sample Mendelian Randomization study

A growing body of research suggests that social or physical activity can affect the risk of Major depressive disorder (MDD). However, the bidirectional relationship between them remains to be clarified further, especially between inactivity and MDD. Here, we performed a two-sample Mendelian Randomization analysis using genetic variants associated with social/physical activities and MDD, and assessed the mediating effect of obesity-related measures and brain imaging phenotypes. The dataset on MDD, social activities, and physical activities included 500,199; 461,369; 460,376 individuals, respectively. Information regarding body mass index (BMI), body fat percentage (BFP), IDPs for 454,633; 461,460; 8,428 participants, respectively. We identified bidirectional causal relationships between sport clubs or gyms, strenuous sports, heavy do-it-youself, other exercises and MDD. We also observed that leisure/social inactivity (odds ratio [OR] = 1.64; P = 5.14 × 10−5) or physical inactivity (OR = 3.67; P = 1.99 × 10−5) caused an increased risk of MDD, which were partially mediated by BMI or BFP and masked by the weighted-mean orientation dispersion index of left acoustic radiation or volume of right caudate. Furthermore, we discovered that MDD increased the risk of leisure/social inactivity (OR = 1.03; P = 9.89 × 10−4) or physical inactivity (OR = 1.01; P = 7.96 × 10−4). In conclusions, we found that social/physical activities reduced the risk of MDD, while MDD in turn hindered social/physical activities. Inactivity may increase the risk of MDD, which was mediated or masked by brain imaging phenotypes. These results help to understand the manifestations of MDD and provide evidence and direction for the advancement of intervention and prevention.

Association of smoking with brain gray and white matter volume: a Mendelian randomization study.

Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association. Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect. Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, -0.100; 95% CI, -0.156 to -0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division. This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.

Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality.

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.

Telomere length and brain imaging phenotypes in UK Biobank.

Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.

Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method.

Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance. To this end, this paper proposed a hypergraph-based netNMF (HG-netNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles. Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses. Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively.