Brain Age
Introduction
Section titled “Introduction”Brain age refers to a concept that quantifies the structural and functional integrity of an individual’s brain relative to their chronological age. It is often estimated using neuroimaging techniques, such as magnetic resonance imaging (MRI), and comprehensive cognitive assessments. The difference between an individual’s predicted brain age and their chronological age, known as “brain age gap” (BAG), can serve as an indicator of accelerated or decelerated brain aging.
Biological Basis
Section titled “Biological Basis”The aging process affects the brain in various ways, leading to observable structural and functional changes. These changes can include alterations in total cerebral brain volume (TCBV), white matter hyperintensity volume (WMH), and volumes of specific brain regions like lobes and ventricles.[1]Cognitive functions such as verbal memory, visuospatial memory, attention, and executive function also typically decline with age.[1]Research indicates that these structural and functional brain aging phenotypes are heritable traits, meaning they are influenced by genetic factors.[1]Genome-wide association studies (GWAS) are employed to identify specific genes and single nucleotide polymorphisms (SNPs) that contribute to these traits, providing insights into the genetic underpinnings of brain aging.[1] For example, a SNP on the retinal cadherin gene CDH4, rs1970546 , has shown a strong association with TCBV. [1] Other genes like SORL1, associated with Alzheimer’s disease, have also been linked to cognitive performance.[1]
Clinical Relevance
Section titled “Clinical Relevance”Understanding brain age has significant clinical implications, particularly in identifying individuals at increased risk for neurodegenerative diseases and other brain-related conditions. The quantitative endophenotypes derived from brain MRI and cognitive tests are known to increase the risk of developing conditions such as stroke, dementia, and Alzheimer’s disease.[1]These indicators of accelerated brain aging often manifest years before the clinical diagnosis of such diseases, offering a potential window for early intervention and risk stratification.[1]Identifying genetic markers associated with brain aging can help predict individual susceptibility and inform personalized prevention strategies.
Social Importance
Section titled “Social Importance”The concept of brain age holds considerable social importance in an increasingly aging global population. By providing a metric for brain health, it contributes to public health initiatives aimed at promoting healthy aging and preventing age-related cognitive decline. Research into brain age can facilitate the development of targeted interventions, lifestyle recommendations, and therapeutic strategies to maintain cognitive function and independence for longer. This, in turn, can reduce the societal burden of neurodegenerative diseases and improve the quality of life for older adults.
Limitations
Section titled “Limitations”Constraints in Study Design and Statistical Inference
Section titled “Constraints in Study Design and Statistical Inference”The investigation into genetic correlates of brain aging was subject to several methodological and statistical limitations. The study utilized a relatively modest sample size of 705 related individuals, which was further reduced to 327 participants for analyses involving hippocampal volumes.[1]This limited sample size inherently constrains the statistical power, making it challenging to detect genetic associations with smaller effect sizes that could still hold biological significance for brain aging.[1] Consequently, the findings may primarily highlight genetic variants with more pronounced effects, potentially overlooking a broader spectrum of subtle genetic influences.
Furthermore, genome-wide association studies (GWAS) inherently involve a substantial number of statistical tests, which, despite the application of conservative correction methods, can increase the likelihood of false positive findings and potentially inflate the reported effect sizes of detected associations. [1] The cohort itself also presented a healthy survivor bias, as participants needed to survive beyond 1990 to provide DNA and meet specific health criteria to undergo MRI scans. [1] This selection process resulted in a healthier sample compared to the general Framingham population, limiting the representativeness of the findings and their direct generalizability to a broader, unselected population.
Genomic Coverage and Phenotypic Measurement Resolution
Section titled “Genomic Coverage and Phenotypic Measurement Resolution”The genetic data utilized in this study, derived from the 100K Affymetrix GeneChip, provided approximately 30% coverage of the human genome, with notable gaps in several gene-rich regions and for key candidate genes, such as APOE. [1]This incomplete genomic coverage means that a substantial portion of potential genetic variants influencing brain aging could not be assessed, thereby limiting the comprehensive identification of genetic correlates.[1]The resolution of the genotyping platform thus constrains the ability to fully elucidate the complex genetic architecture underlying brain aging phenotypes.
Additionally, the study relied on a single, cross-sectional measurement of brain MRI and cognitive tests for each participant. [1]This static snapshot approach prevents the investigation of dynamic changes in these brain aging phenotypes over time, which are crucial for understanding the progressive nature of both typical and pathological aging processes.[1]Genes associated with longitudinal changes in brain structure and function may offer more robust insights into the mechanisms of brain aging than those identified through cross-sectional analyses.
Generalizability Across Diverse Populations
Section titled “Generalizability Across Diverse Populations”A significant limitation concerning the applicability of the findings is the high homogeneity of ancestry within the study sample, which was predominantly European. [1] While this homogeneity helps mitigate issues of population stratification in genetic analyses, it simultaneously restricts the generalizability of the results to individuals of different racial or ethnic backgrounds. [1]Consequently, the study cannot identify or characterize genetic variations that might be specific to other populations, highlighting a critical gap in understanding the diverse genetic influences on brain aging across various ancestral groups.
Variants
Section titled “Variants”Variants in genes associated with neural development, transcriptional regulation, and cellular maintenance play crucial roles in determining individual differences in brain aging. TheSHH (Sonic Hedgehog) gene is fundamental for embryonic brain development, influencing patterning and cell differentiation. Variants within SHH, such as rs371967302 , could subtly alter these critical signaling pathways, potentially impacting brain structure and function throughout life and contributing to variations in brain aging trajectories.[2] Similarly, MAPT-AS1 (MAPT Antisense RNA 1) is a non-coding RNA that modulates the expression of the MAPT gene, which encodes the Tau protein, a key component of neuronal microtubules. Dysregulation of Tau is implicated in several neurodegenerative conditions, suggesting that variants like rs2106786 in MAPT-AS1may influence neuronal stability and overall brain health during aging.[1] Additionally, the regions encompassing DND1P1 (DND1 Pseudogene 1) and MAPK8IP1P2 (MAPK8 Interacting Protein 1 Pseudogene 2), with variants such as rs534115641 , might indirectly affect the regulation or function of their active gene counterparts, which are involved in processes like germline stem cell maintenance and neuronal survival and plasticity, thereby influencing the brain’s resilience over time. [3]
Further contributing to the genetic landscape of brain aging are genes involved in transcriptional control and general cellular processes.KLF3 (Kruppel-like Factor 3) is a transcription factor known for repressing gene expression across various cellular functions, including development and metabolism. Variants like rs13132853 in KLF3 or *rs337638 _ in its antisense regulator, KLF3-AS1(KLF3 Antisense RNA 1), could modify these regulatory roles, affecting the cellular mechanisms vital for preserving brain integrity and influencing the rate of brain aging.[1] The GMNC (Geminin Coiled-Coil Domain Containing) gene is important for DNA replication and cell cycle regulation, essential processes for neural progenitor cell division and repair within the brain. Alongside, OSTN (Osteocrin), a secreted protein found in the brain, may contribute to neural plasticity and repair mechanisms. A variant such as rs61067594 in this combined region could therefore impact fundamental cellular and developmental pathways, affecting the brain’s ability to maintain cognitive function with age.[3]
Ion channels, transporters, and kinase signaling pathways also hold significant relevance for brain aging.KCNK2(Potassium Two Pore Domain Channel Subfamily K Member 2), also known as TREK-1, encodes a potassium channel crucial for regulating neuronal excitability and providing neuroprotection. Variants likers1452628 near KCNK2 could influence neuronal communication and resilience to age-related stressors. [1] The SLC39A8 (Solute Carrier Family 39 Member 8) gene is responsible for encoding a zinc transporter, which is vital for maintaining zinc homeostasis, a process critical for synaptic transmission, neurogenesis, and protection against oxidative stress in the brain. A variant such as rs13135092 could disrupt this delicate balance, potentially impairing cognitive function and accelerating aspects of brain aging. Furthermore,NUAK1(NUAK Family Kinase 1), a serine/threonine kinase, plays a role in cellular stress responses, cell adhesion, and migration, all of which are essential for neuronal survival and proper brain function. Variants likers12146713 might alter kinase activity, influencing cellular resilience to age-related damage and thereby impacting brain aging trajectories.[3] The GAPDHP24(Glyceraldehyde-3-Phosphate Dehydrogenase Pseudogene 24) region, while a pseudogene, may contain regulatory elements that affect the expression of the functionalGAPDHgene, impacting metabolic health and neuronal processes relevant to brain aging.[1]
Finally, non-coding RNAs and complex genetic loci contribute to the intricate regulation of brain aging. TheLINC02210-CRHR1 locus includes LINC02210 (Long Intergenic Non-Coding RNA 02210), which can regulate gene expression, and CRHR1(Corticotropin Releasing Hormone Receptor 1), a key component of the body’s stress response system. Variants likers593720 in this region could modulate both gene regulation and stress pathway signaling, potentially affecting how the brain responds to chronic stress and its overall aging trajectory.[3] Y_RNA (Y RNA) are small non-coding RNAs involved in RNA processing, DNA replication, and the formation of stress granules. Although rs371967302 is associated with the SHH - Y_RNA region, variants in or near Y_RNAgenes could influence cellular responses to stress and genomic stability, factors intricately linked to healthy brain aging.[1]These diverse genetic variants highlight the multifaceted nature of brain aging, encompassing developmental, cellular, metabolic, and stress-response pathways.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs2106786 | MAPT-AS1 | erythrocyte count brain volume brain age measurement |
| rs534115641 | DND1P1 - MAPK8IP1P2 | brain age measurement |
| rs61067594 | GMNC - OSTN | brain age measurement brain connectivity attribute brain attribute amygdala volume |
| rs13132853 | KLF3 | brain connectivity attribute neuroimaging measurement body fat percentage serum alanine aminotransferase amount triglyceride measurement |
| rs337638 | KLF3-AS1 | acute myeloid leukemia brain age measurement |
| rs371967302 | SHH - Y_RNA | brain age measurement |
| rs593720 | LINC02210-CRHR1, LINC02210, LINC02210 | cerebral cortex area attribute brain age measurement |
| rs1452628 | GAPDHP24 - KCNK2 | cortical thickness cerebral cortex area attribute brain connectivity attribute brain attribute brain age measurement |
| rs13135092 | SLC39A8 | high density lipoprotein cholesterol measurement alcohol consumption quality, high density lipoprotein cholesterol measurement alcohol drinking, high density lipoprotein cholesterol measurement risk-taking behaviour cerebral cortex area attribute |
| rs12146713 | NUAK1 | cerebral cortex area attribute cortical thickness brain connectivity attribute thalamus volume white matter microstructure measurement |
Classification, Definition, and Terminology of Brain Aging
Section titled “Classification, Definition, and Terminology of Brain Aging”Defining Brain Aging as an Endophenotype
Section titled “Defining Brain Aging as an Endophenotype”Brain aging is conceptualized as a collection of structural and functional phenotypes that reflect the cumulative cellular and vascular changes occurring in the brain over time.[1]These phenotypes are considered heritable, reproducible, and quantitative endophenotypes, which are measurable traits that indicate an individual’s genetic predisposition to develop diseases like dementia or stroke.[1]Such endophenotypes often manifest years before the clinical and pathological diagnostic criteria for these conditions are fully met, positioning brain aging as a critical intermediate biological marker for studying the progression and genetic underpinnings of age-related neurological decline.
Measurement and Operationalization of Brain Aging
Section titled “Measurement and Operationalization of Brain Aging”The operational definition of brain aging is primarily based on quantifiable measures derived from magnetic resonance imaging (MRI) and comprehensive neuropsychological testing. Structural indicators obtained from MRI include Total Cerebral Brain Volume (ATCBV), Frontal Brain Volume (AFBV), Parietal Brain Volume (APBV), Occipital Brain Volume (AOBV), Temporal Brain Volume (ATBV), Hippocampal Volume (AHPV), Lateral Ventricular Volume (ALVV), Temporal Horn Volume (LTHBV), and White Matter Hyperintensity Volume (BMRIZLWMHVMV).[1]These volumetric measures are typically adjusted for differences in head size, often expressed as a ratio to total intracranial volume (TCV), and can be derived through semi-automated pixel distribution analyses or software likeSIENAX for normalized brain parenchymal volume. [1]Cognitive function, a key component of brain aging assessment, is evaluated through standardized neuropsychological test batteries. These tests yield scores that are frequently grouped into factors representing specific cognitive domains, such as Verbal Memory (F1), Visuospatial Memory and Organization (F2), and Attention and Executive Function (F3).[1] Individual tests, including the Boston Naming Test (BNT), Similarities (Sim), and Wide Range Achievement Tests (WRAT), further contribute to a detailed cognitive profile. [1] To ensure consistency and comparability across tests and individuals, raw cognitive scores undergo transformations, including natural logarithmic normalization for skewed distributions, followed by regression on age and standardization into z-scores. [1]Both MRI and cognitive measures are routinely adjusted for relevant covariates, such as age, sex, smoking status, blood pressure, diabetes, education level, and apolipoprotein E genotype, to refine the assessment of brain aging effects.[1]
Terminology and Associated Concepts
Section titled “Terminology and Associated Concepts”The core terminology in this field is “brain aging,” which encapsulates both the structural and functional alterations in the brain over time. A critical related term is “endophenotypes,” defined as heritable traits that precede and increase the risk of clinical disease, serving as measurable biological markers of disease susceptibility.[1]Specific MRI measures, such as Total Cerebral Brain Volume (TCBV) and White Matter Hyperintensity Volume (WMH), are considered primary structural indicators reflecting “cellular and vascular brain damage.”[1]Conversely, cognitive factors, particularly Verbal Memory (F1) and Attention and Executive Function (F3), are identified as primary indicators associated with “amnestic, Alzheimer-type and vascular” cognitive impairments.[1]This nomenclature underscores the complex, multi-faceted nature of brain aging, integrating macroscopic structural changes with their corresponding cognitive manifestations.
Causes of Brain Age
Section titled “Causes of Brain Age”Genetic Foundations and Heritable Influences
Section titled “Genetic Foundations and Heritable Influences”Brain aging, as characterized by structural brain MRI measures and cognitive test performance, demonstrates a substantial genetic basis and is considered a highly heritable trait.[1]Twin studies have confirmed the significant heritability of these endophenotypes, which are quantitative traits reflecting genetic predispositions to conditions like dementia or stroke, often manifesting years before clinical disease onset.[1] Genome-wide association studies (GWAS) employ an unbiased approach to identify genes, including those with modest phenotypic effects, that contribute to these age-related changes in brain structure and function. [1]For instance, white matter hyperintensity (WMH) volume, a marker of brain aging, has shown high heritability, indicating a strong genetic influence on its development.[1]
The genetic architecture of brain aging involves a complex interplay of numerous inherited variants, contributing to a polygenic risk profile. These genetic factors collectively influence various aspects of brain health, from total cerebral brain volume (TCBV) to specific cognitive domains like abstract reasoning.[1]While individual single nucleotide polymorphisms (SNPs) may exert small effects, their cumulative impact can significantly determine an individual’s trajectory of brain aging. The identification of specific genetic loci through GWAS helps to uncover the underlying biological pathways and mechanisms that contribute to the observed variability in brain aging phenotypes across populations.[1]
Specific Genetic Associations and Molecular Pathways
Section titled “Specific Genetic Associations and Molecular Pathways”Research has identified several candidate genes and specific genetic variants associated with brain aging phenotypes, shedding light on the molecular mechanisms involved. For example, a strong association has been found between a SNP on theSORL1 gene, rs1131497 , and performance in abstract reasoning, a key cognitive function.[1] SORL1is biologically significant due to its known role in amyloid precursor protein processing, a pathway central to Alzheimer’s disease pathology.[1] Similarly, the CDH4 gene, specifically rs1970546 , has shown a strong association with Total Cerebral Brain Volume (TCBV), suggesting its involvement in maintaining overall brain structure. [1]
Further investigations have implicated other genes with potential roles in various aspects of brain aging and related neurological conditions. Genes likePDE3A have been linked to thrombosis, while SCN8A is associated with cerebellar ataxia and mental retardation, and PDE4Bwith schizophrenia.[1]Additionally, a wide array of candidate genes previously associated with stroke, Alzheimer’s disease, and memory impairment in other studies, such asPDE4D, LTA4H, NFBG, NTRK2, NTRK3, BACE1, PRNP, A2M, VLDLR, and LRRK2, have been found to correlate with brain MRI and cognitive endophenotypes. [1]These findings suggest pleiotropic effects, where individual genetic variants or genes may influence multiple, seemingly distinct, brain-related phenotypes, contributing to the complex etiology of brain aging.
Brain Aging as a Precursor to Neurological Disease
Section titled “Brain Aging as a Precursor to Neurological Disease”Brain aging is intrinsically linked to the risk of developing age-related neurological diseases, serving as a subclinical phenotype that often precedes the onset of conditions like stroke and dementia.[1]The endophenotypes measured by brain MRI and cognitive tests are not merely indicators of normal aging but are also associated with an increased risk for these debilitating diseases.[1]This connection highlights that the genetic factors influencing brain aging also contribute significantly to the susceptibility to neurological disorders, particularly in middle-aged to elderly populations.[1]
The genetic underpinnings of brain aging overlap considerably with those of major neurological diseases, meaning that genes previously associated with clinical disease can have detectable effects on these subclinical brain aging phenotypes.[1]For instance, genetic variants that increase the risk of Alzheimer’s disease, stroke, or vascular dementia can also manifest as accelerated brain aging, characterized by changes in brain volume or cognitive function.[1]This perspective suggests that brain aging itself can be viewed as a continuum, where genetic predispositions and age-related changes progressively increase an individual’s vulnerability to developing more severe neurological conditions later in life.
Biological Background
Section titled “Biological Background”Genetic Foundations of Brain Aging
Section titled “Genetic Foundations of Brain Aging”The intricate process of brain aging has a significant genetic component, with studies revealing substantial heritability for both structural brain characteristics and cognitive performance.[1]This genetic predisposition plays a crucial role not only in healthy cognitive aging but also in determining an individual’s susceptibility to age-related neurological diseases such as stroke and dementia.[1]For instance, a considerable portion of the variability in the age of onset for Alzheimer’s disease has been attributed to additive genetic effects.[1]
Genome-wide association studies (GWAS) are powerful tools used to identify novel genetic loci and regulatory elements that influence brain aging phenotypes.[1] By comprehensively scanning the human genome, these studies can detect genes that exert even subtle effects on brain structure and function. [1]The identification of such genes provides critical insights into the underlying biological pathways and genetic architecture that contribute to the diverse trajectories of brain aging observed across individuals.[1]
Structural and Functional Markers of Brain Age
Section titled “Structural and Functional Markers of Brain Age”Brain aging is characterized by quantifiable changes in both its physical structure and its functional capabilities, which are considered heritable endophenotypes.[1] Structural alterations are commonly assessed through volumetric brain MRI, yielding measures such as Total Cerebral Brain Volume (TCBV), along with specific regional volumes like those of the frontal, parietal, occipital, temporal, and hippocampal regions. [1]Increases in lateral ventricular and temporal horn volumes also serve as indicators of tissue and organ-level biological changes associated with advancing brain age.[1]
Beyond overall brain size and specific region volumes, White Matter Hyperintensity (WMH) volume is a significant structural marker reflecting the integrity of the brain’s white matter, and it is closely linked to vascular brain aging.[1]Functionally, brain aging is evaluated through comprehensive cognitive testing that measures domains such as verbal memory, visuospatial memory and organization, and attention and executive function.[1]These structural and functional phenotypes are interconnected, collectively illustrating the complex systemic consequences of aging on brain health and cognitive performance.[1]
Molecular and Cellular Mechanisms
Section titled “Molecular and Cellular Mechanisms”The complex process of brain aging is driven by an intricate network of molecular and cellular pathways, involving critical proteins, enzymes, and receptors essential for maintaining neuronal health. Genes like_CDH4_, a retinal cadherin gene, have been associated with Total Cerebral Brain Volume, suggesting its role in cell adhesion and the overall structural integrity of the brain. [1] Similarly, _CLDN10_, or claudin 10, a protein vital for tight junction formation, is a biologically plausible candidate linked to white matter hyperintensity, emphasizing the importance of vascular barrier function in brain health.[1]
Key signaling pathways are fundamental to neuronal communication and plasticity, and their dysregulation can contribute significantly to brain aging. These include the glutamate signaling pathway, involving genes such as_GRIN2A_ and _HOMER2_; calcium-mediated signaling, influenced by _EGFR_, _PIP5K3_, and _MCTP2_; and G-protein signaling, involving _DGKG_, _EDNRB_, and _EGFR_. [2]Metabolic processes, particularly amino acid metabolism, are also crucial for cellular energy and neurotransmitter synthesis, with genes like_EGFR_, _MSRA_, _SLC6A6_, _UBE1DC1_, and _SLC7A5_ playing roles in these pathways, highlighting their impact on brain function. [2] Furthermore, processes such as CNS development and axon guidance, involving genes like _CNTN6_, _GRIK1_, _PBX1_, _PCP4_, _SLIT2_, and _NRXN1_, are vital for establishing and maintaining the brain’s complex architecture and connectivity throughout life. [2]
Pathophysiological Processes and Neurodegeneration
Section titled “Pathophysiological Processes and Neurodegeneration”The biological underpinnings of brain aging are closely linked to the pathophysiological processes that lead to age-related neurological disorders, including Alzheimer’s disease (AD), stroke, and vascular dementia.[1]Genes previously identified in connection with clinical neurological diseases, such as those associated with AD and schizophrenia, have demonstrable effects on subclinical phenotypes of brain aging, suggesting common or overlapping disease mechanisms.[1] For example, _SORL1_, a gene implicated in AD, has been associated with performance in abstract reasoning tasks, establishing a link between specific genetic variations and aspects of cognitive decline.[1]
Homeostatic disruptions and specific disease mechanisms contribute to the progressive changes observed in brain structure and cognitive function, often manifesting years before the fulfillment of clinical diagnostic criteria for full-blown disease.[1] Genes like _PDE4D_ and _ALOX5AP_, known to influence stroke risk, underscore the critical role of vascular health in the broader context of brain aging.[1] Insights into these pathophysiological connections, including processes like hemopoiesis involving _JAG1_, _LRMP_, and _BCL11A_, and the regulation of cell migration involving _JAG1_ and _EGFR_, are essential for identifying potential targets for interventions aimed at promoting healthy brain aging and mitigating the risk of neurodegenerative diseases.[2]
Clinical Relevance of Brain Age
Section titled “Clinical Relevance of Brain Age”Early Risk Identification and Prognosis
Section titled “Early Risk Identification and Prognosis”Brain aging phenotypes, as assessed through volumetric brain MRI and comprehensive cognitive testing, represent heritable and quantitative endophenotypes that are closely linked to the risk of developing conditions such as dementia and stroke.[1] These measures are particularly valuable because they can manifest years before an individual meets the clinical or pathological diagnostic criteria for these diseases. This early manifestation provides significant prognostic value, enabling the identification of individuals at high risk for future neurodegenerative and cerebrovascular conditions well in advance of overt symptoms. [1]Such proactive risk stratification allows for the implementation of personalized medicine approaches, where tailored preventative strategies and interventions could potentially delay disease onset or mitigate its severity.
Diagnostic Utility and Monitoring Treatment Response
Section titled “Diagnostic Utility and Monitoring Treatment Response”The assessment of brain age holds promise for enhancing diagnostic utility by offering objective markers of neurological health that extend beyond chronological age. While initial genome-wide association studies identifying genetic correlates for these endophenotypes are largely hypothesis-generating and require further replication, they contribute to a deeper understanding of underlying disease mechanisms.[1]Clinically, these measures can refine risk assessment by providing a more nuanced insight into an individual’s brain health trajectory. Furthermore, tracking changes in brain age over time could serve as a valuable tool for monitoring the effectiveness of various treatments or lifestyle interventions, thereby informing treatment selection and optimizing patient care strategies, even as more longitudinal data are collected to solidify these applications.[1]
Associations with Neurodegenerative and Cerebrovascular Conditions
Section titled “Associations with Neurodegenerative and Cerebrovascular Conditions”Brain aging endophenotypes are significantly associated with a spectrum of neurodegenerative and cerebrovascular conditions, highlighting their role in understanding disease comorbidities and overlapping phenotypes. Genetic analyses have revealed links between specific brain structural and functional traits and genes previously implicated in severe clinical diseases. For instance, genes likeBACE1, PRNP, and A2Mhave been associated with Alzheimer’s disease, whilePDE4D and LTA4Hare related to stroke, andLRRK2to Parkinson’s disease.[1]These findings suggest that accelerated brain aging, as captured by these endophenotypes, may reflect a common predisposition or pathway towards developing multiple age-related neurological disorders. The complex interplay of genetic factors with covariates such as smoking status, diabetes, blood pressure, andAPOEgenotype further underscores the multifactorial nature of brain aging and its profound impact on related clinical complications.[1]
References
Section titled “References”[1] Seshadri, S. et al. “Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S15.
[2] Baranzini, S. E. et al. “Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis.”Human Molecular Genetics, 2008.
[3] Lunetta, Kathryn L., et al. “Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S13.