Advanced Brain Aging Research Articles

Associations between sleep apnea and advanced brain aging in a large-scale population study

Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer's dementia, and it was suggested that sleep disorders such as obstructive sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect [95% CI]: 0.07 [0.03; 0.12], p-value: 0.002; ODI std. effect [95% CI]: 0.09 [0.04; 0.13], p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Measurements of Brain Age May Help Target Therapy

Measurements of Brain Age May Help Target Therapy

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing.

Alzheimer's disease (AD) and sleep‐disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self‐reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self‐reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray‐matter volume was measured using voxel‐wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age‐prediction model fitted on gray‐matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB‐by‐cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by‐cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic‐dependent association of SDB with either gray‐matter volumetric or brain aging.

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity.MethodsThe developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6–25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.ResultsMachine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity.LimitationsThis work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable.ConclusionsFindings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

88. Advanced Brain Age and its Clinical Correlates in Bipolar Disorder: A Global, Multi-Site Analysis of Data From the ENIGMA Bipolar Disorders Working Group

Subcortical volumes and cortical thickness are reduced in bipolar disorder (BD), including in regions that show age-related decline. We studied whether brain age was advanced in 23 samples of BD participants compared to non-mentally-ill comparison (NC) participants from the ENIGMA Bipolar Disorder Working Group and examined relationships with clinical features.

Inflammatory markers and imaging patterns of advanced brain aging in the general population.

Inflammaging describes the complexity between low-grade chronic inflammation with the pathogenesis of brain aging and Alzheimer´s disease (AD). We aimed to find associations of inflammatory markers: i) white blood cell count (WBC), ii) high-sensitivity C-reactive protein (hs-CRP), and iii) fibrinogen with brain structures, sensitive neuroimaging markers of advanced brain aging and AD-like atrophy, and cognitive aging scores. We analyzed magnetic resonance imaging (MRI) scans of 2204 participants from the Study of Health in Pomerania-2 (SHIP-2) and SHIP-Trend (55.6% women, mean age 52.4±13.7 years). Associations of the inflammatory markers with specific brain signatures of brain aging (SPARE-BA), AD-like brain atrophy (SPARE-AD) and white matter disease (white matter hyperintensities volume (WMHV)) were investigated. Furthermore we explored their association with general brain structures including total brain volume (TBV), gray matter volume (GMV), and white matter volume (WMV), as well as cognitive scores (Nurnberger Age Inventory (NAI); Verbal Learning and Memory Test (VLMT). We adjusted for multiple vascular risk factors (VRF; e.g. smoking and blood pressure) and corresponding medication use to take their brain aging effects into account and corrected for false-discovery rate (FDR). Results:WBC was inversely associated with SPARE-BA (FDR-adjusted p=0.003), TBV (FDR-adjusted p=0.019) and GMV (FDR-adjusted p= 0.017). GMV was also inversely associated with hs-CRP (FDR-adjusted p=0.039) and fibrinogen (FDR-adjusted p=0.039). None of the inflammatory markers was associated with WMHV. Regression analysis also revealed a trend-level interaction between intake of antiinflammatory medication and hs-CRP with brain aging (SPARE-BA; FDR-adjusted p=0.062). Inflammatatory markers are associated with neuroimaging markers, with elevated WBC leading to significant acceleration in brain aging patterns but not with AD-like imaging structural changes. Given the overlap between accelerated brain aging and AD-like atrophy, increased WBC might be associated with global dementia symptoms due to this overlap in atrophy patterns. Elevated WBC may be not causal to preclinical AD dementia, but an accessory symptom of inflammaging. At population level, our results support the relevant roles of inflammatory markers on brain aging related atrophy.

NEW RESEARCH ON OLDER-AGE BIPOLAR DISORDER (OABD) AND BIPOLAR DISORDER ACROSS THE LIFE-SPAN: AN UPDATE FROM THE INTERNATIONAL SOCIETY OF BIPOLAR DISORDERS OABD TASKFORCE: Session 416

Bipolar disorder (BD) is a prevalent mental disorder that causes disability throughout the life-span. Because of demographic trends, there is a growing population of individuals with older-age bipolar disorder (OABD). This symposium, presented by members of the International Society of Bipolar Disorders OABD taskforce, will highlight new data relevant to OABD and to aging with BD. The session chair, Martha Sajatovic, will first speak about the development of the Aging and Geriatric Experiments in BD (AGE-BD) integrated dataset, and present initial results regarding the interplay of clinical, demographic, and medical variables across adulthood. Second, Lisa Eyler will discuss the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) BD Working Group, a large, ecologically-valid sample of 3020 participants from 13 sites worldwide and present her analysis of ENIGMA data that showed advanced “brain age” among those with BD compared to those without and examined clinical correlates of advanced brain age. The third speaker, David Harper, will present recently analyzed resting state fMRI data in older bipolar depressed individuals vs healthy controls. The fourth speaker, Ariel Gildengers, will present on the relationship between bipolar disorder and medical burden on cognition and brain health in older adults.

Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.

Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.

Negative fateful life events in midlife and advanced predicted brain aging.

Negative fateful life events (FLEs) such as interpersonal conflict, death in the family, financial hardship, and serious medical emergencies can act as allostatic stressors that accelerate biological aging. However, the relationship between FLEs and neuroanatomical aging is not well understood. We examined 359 men (mean age 62years) participating in the Vietnam Era twin study of aging (VETSA) to determine whether negative midlife FLEs are associated with advanced brain aging after controlling for physical, psychological, and lifestyle factors. At two different time points, participants were assessed for negative FLEs, health and well-being, general cognitive ability, socioeconomic status, depression, and ethnicity. Participants underwent a magnetic resonance imaging examination, and T1-weighted images were processed with FreeSurfer. Subsequent neuroanatomical measurements were entered into the Brain-Age Regression Analysis and Computation Utility software (BARACUS) to predict brain age. Having more midlife FLEs, particularly relating to interpersonal relationships, was associated with advanced predicted brain aging (i.e., higher predicted brain age relative to chronological age). This association remained after controlling for the significant covariates of alcohol consumption, cardiovascular risk, adult socioeconomic status, and ethnicity.

Obesity, dyslipidemia and brain age in first-episode psychosis

IntroductionObesity and dyslipidemia may negatively affect brain health and are frequent medical comorbidities of schizophrenia and related disorders. Despite the high burden of metabolic disorders, little is known about their effects on brain structure in psychosis. We investigated, whether obesity or dyslipidemia contributed to brain alterations in first-episode psychosis (FEP). Methods120 participants with FEP, who were undergoing their first psychiatric hospitalization, had <24 months of untreated psychosis and were 18–35 years old and 114 controls within the same age range participated in the study. We acquired 3T brain structural MRI, fasting lipids and body mass index. We used machine learning trained on an independent sample of 504 controls to estimate the individual brain age of study participants and calculated the BrainAGE score by subtracting the chronological from the estimated brain age. ResultsIn a multiple regression model, the diagnosis of FEP (B = 1.15, SE B = 0.31, p < 0.001) and obesity/overweight (B = 0.92, SE B = 0.35, p = 0.008) were each additively associated with BrainAGE scores (R2 = 0.22, F(3, 230) = 21.92, p < 0.001). BrainAGE scores were highest in participants with FEP and obesity/overweight (3.83 years, 95%CI = 2.35-5.31) and lowest in normal weight controls (−0.27 years, 95%CI = −1.22-0.69). LDL-cholesterol, HDL-cholesterol or triglycerides were not associated with BrainAGE scores. ConclusionsOverweight/obesity may be an independent risk factor for diffuse brain alterations manifesting as advanced brain age already early in the course of psychosis. These findings raise the possibility that targeting metabolic health and intervening already at the level of overweight/obesity could slow brain ageing in FEP.

Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns.

We systematically compared structural imaging patterns of advanced brain aging (ABA) in the general-population, herein defined as significant deviation from typical BA to those found in Alzheimer disease (AD). The hypothesis that ABA would show different patterns of structural change compared with those found in AD was tested via advanced pattern analysis methods. In particular, magnetic resonance images of 2705 participants from the Study of Health in Pomerania (aged 20–90 years) were analyzed using an index that captures aging atrophy patterns (Spatial Pattern of Atrophy for Recognition of BA (SPARE-BA)), and an index previously shown to capture atrophy patterns found in clinical AD (Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease (SPARE-AD)). We studied the association between these indices and risk factors, including an AD polygenic risk score. Finally, we compared the ABA-associated atrophy with typical AD-like patterns. We observed that SPARE-BA had significant association with: smoking (P<0.05), anti-hypertensive (P<0.05), anti-diabetic drug use (men P<0.05, women P=0.06) and waist circumference for the male cohort (P<0.05), after adjusting for age. Subjects with ABA had spatially extensive gray matter loss in the frontal, parietal and temporal lobes (false-discovery-rate-corrected q<0.001). ABA patterns of atrophy were partially overlapping with, but notably deviating from those typically found in AD. Subjects with ABA had higher SPARE-AD values; largely due to the partial spatial overlap of associated patterns in temporal regions. The AD polygenic risk score was significantly associated with SPARE-AD but not with SPARE-BA. Our findings suggest that ABA is likely characterized by pathophysiologic mechanisms that are distinct from, or only partially overlapping with those of AD.

Targeting AGEs Signaling Ameliorates Central Nervous System Diabetic Complications in Rats.

Diabetes is a chronic endocrine disorder associated with several complications as hypertension, advanced brain aging, and cognitive decline. Accumulation of advanced glycation end products (AGEs) is an important mechanism that mediates diabetic complications. Upon binding to their receptor (RAGE), AGEs mediate oxidative stress and/or cause cross-linking with proteins in blood vessels and brain tissues. The current investigation was designed to investigate the effect of agents that decrease AGEs signaling, perindopril which increases soluble RAGE (sRAGE) and alagebrium which cleaves AGEs cross-links, compared to the standard antidiabetic drug, gliclazide, on the vascular and central nervous system (CNS) complications in STZ-induced (50 mg/kg, IP) diabetes in rats. Perindopril ameliorated the elevation in blood pressure seen in diabetic animals. In addition, both perindopril and alagebrium significantly inhibited memory decline (performance in the Y-maze), neuronal degeneration (Fluoro-Jade staining), AGEs accumulation in serum and brain, and brain oxidative stress (level of reduced glutathione and activities of catalase and malondialdehyde). These results suggest that blockade of AGEs signaling after diabetes induction in rats is effective in reducing diabetic CNS complications.

Inside the Diabetic Brain

Abstract CNS complications resulting from diabetes mellitus (DM) are a problem gaining more acceptance and attention in the recent years. Both types 1 and 2 DM represent an significant risk factor for decreased cognitive functions, memory and learning deficits as well as development of Alzheimer’s disease. Chronic hyperglycemia through protein glycation and increased oxidative stress contributes to brain dysfunction, however increasing evidences suggest that the pathology of DM in the brain involves a progressive and coordinated disruption of insulin signaling, with profound consequences for brain function and plasticity. Since many of the CNS changes observed in diabetic patients and animal models of DM are reminiscent of the changes seen in aging, the theory of advanced brain aging in DM has been proposed. This review summarizes the findings of the literature regarding the effects of DM on the brain in the terms of diabetes-related metabolic derangements and intracellular signaling.

The Impact of HAART on Advanced Brain Aging: Implications for Mitochondrial Dysfunction and APP Processing

Highly Active Antiretroviral Therapy (HAART) has significantly reduced AIDS-related morbidity and mortality. However the prevalence of HIV-1-Associated Neurocognitive Disorders (HAND) has been on the rise in the post- HAART era. A majority of the side effects of HAART can in part at least be attributed directly, or indirectly, to mitochondrial dysfunction. Indeed the rapid early clinical phase-in of HAART required dose de-escalations secondary to toxicities suggested to be related to drug side effects affecting mitochondria. Central to central nervous system (CNS) function is the amyloid precursor protein (APP), the parent protein from which amyloid-beta (Aβ) peptide is generated. Aβ generation and aggregation as plaques are well known in the age related dementia, Alzheimer’s disease (AD). It has been demonstrated that Aβ is common feature of the HIV infected brain as well. Further, reactive oxygen species (ROS) production is upregulated by HAART. Importantly, ROS promote β-secretase expression; a mechanism by which HAART may promote cognitive dysfunction, even in immune-competent HIV infected individuals.

Advanced BrainAGE in older adults with type 2 diabetes mellitus

Aging alters brain structure and function and diabetes mellitus (DM) may accelerate this process. This study investigated the effects of type 2 DM on individual brain aging as well as the relationships between individual brain aging, risk factors, and functional measures. To differentiate a pattern of brain atrophy that deviates from normal brain aging, we used the novel BrainAGE approach, which determines the complex multidimensional aging pattern within the whole brain by applying established kernel regression methods to anatomical brain magnetic resonance images (MRI). The “Brain Age Gap Estimation” (BrainAGE) score was then calculated as the difference between chronological age and estimated brain age. 185 subjects (98 with type 2 DM) completed an MRI at 3Tesla, laboratory and clinical assessments. Twenty-five subjects (12 with type 2 DM) also completed a follow-up visit after 3.8 ± 1.5 years. The estimated brain age of DM subjects was 4.6 ± 7.2 years greater than their chronological age (p = 0.0001), whereas within the control group, estimated brain age was similar to chronological age. As compared to baseline, the average BrainAGE scores of DM subjects increased by 0.2 years per follow-up year (p = 0.034), whereas the BrainAGE scores of controls did not change between baseline and follow-up. At baseline, across all subjects, higher BrainAGE scores were associated with greater smoking and alcohol consumption, higher tumor necrosis factor alpha (TNFα) levels, lower verbal fluency scores and more severe deprepession. Within the DM group, higher BrainAGE scores were associated with longer diabetes duration (r = 0.31, p = 0.019) and increased fasting blood glucose levels (r = 0.34, p = 0.025). In conclusion, type 2 DM is independently associated with structural changes in the brain that reflect advanced aging. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of abnormal patterns of brain aging associated with type 2 DM.

Regional network of magnetic resonance imaging gray matter volume in healthy aging

Healthy aging has been associated with brain volume reductions preferentially affecting the frontal cortex, but also involving other regions. We used a network model of regional covariance, the Scaled Subprofile Model, with magnetic resonance imaging voxel-based morphometry to identify the regional distribution of gray matter associated with aging in 26 healthy adults, 22-77 years old. Scaled Subprofile Model analysis identified a pattern that was highly correlated with age (R2=0.66, P<or=0.0001). Older age was associated with less gray matter in the bilateral frontal, temporal,thalamic, and right cerebellar regions. Gender differences suggested more advanced brain aging in the men. In this healthy adult sample, aging was associated with a regional pattern of gray matter atrophy most prominently involving the frontal and temporal cortices. Scaled Subprofile Model network analysis may aid in the detection and tracking of brain aging and in the evaluation of putative antiaging therapies.