Rodrigo Perea

Characterizing the Role of Brain Derived Neurotrophic Factor Genetic Variation in Alzheimer’s Disease Neurodegeneration

There is accumulating evidence that neurotrophins, like brain-derived neurotrophic factor (BDNF), may impact aging and Alzheimer’s Disease. However, traditional genetic association studies have not found a clear relationship between BDNF and AD. Our goal was to test whether BDNF single nucleotide polymorphisms (SNPs) impact Alzheimer’s Disease-related brain imaging and cognitive markers of disease. We completed an imaging genetics study on 645 Alzheimer’s Disease Neuroimaging Initiative participants (ND=175, MCI=316, AD=154) who had cognitive, brain imaging, and genetics data at baseline and a subset of those with brain imaging data at two years. Samples were genotyped using the Illumina Human610-Quad BeadChip. 13 SNPs in BDNF were identified in the dataset following quality control measures (rs6265(Val66Met), rs12273363, rs11030094, rs925946, rs1050187, rs2203877, rs11030104, rs11030108, rs10835211, rs7934165, rs908867, rs1491850, rs1157459). We analyzed a subgroup of 8 SNPs that were in low linkage disequilibrium with each other. Automated brain morphometric measures were available through ADNI investigators, and we analyzed baseline cognitive scores, hippocampal and whole brain volumes, and rates of hippocampal and whole brain atrophy and rates of change in the ADAS-Cog over one and two years. Three out of eight BDNF SNPs analyzed were significantly associated with measures of cognitive decline (rs1157659, rs11030094, rs11030108). No SNPs were significantly associated with baseline brain volume measures, however six SNPs were significantly associated with hippocampal and/or whole brain atrophy over two years (rs908867, rs11030094, rs6265, rs10501087, rs1157659, rs1491850). We also found an interaction between the BDNF Val66Met SNP and age with whole brain volume. Our imaging-genetics analysis in a large dataset suggests that while BDNF genetic variation is not specifically associated with a diagnosis of AD, it appears to play a role in AD-related brain neurodegeneration.

The neuroanatomy of genetic subtype differences in Prader-Willi syndrome.

Despite behavioral differences between genetic subtypes of Prader–Willi syndrome (PWS), no studies have been published characterizing brain structure in these subgroups. Our goal was to examine differences in the brain structure phenotype of common subtypes of PWS [chromosome 15q deletions and maternal uniparental disomy 15 (UPD)]. Fifteen individuals with PWS due to a typical deletion [(DEL) type I; n = 5, type II; n = 10], eight with PWS due to UPD, and 25 age‐matched healthy‐weight individuals (HWC) participated in structural magnetic resonance imaging (MRI) scans. A custom voxel‐based morphometry processing stream was used to examine regional differences in gray and white matter volume (WMV) between groups, covarying for age, sex, and body mass index (BMI). Overall, compared to HWC, PWS individuals had lower gray matter volumes (GMV) that encompassed the prefrontal, orbitofrontal and temporal cortices, hippocampus and parahippocampal gyrus, and lower WMVs in the brain stem, cerebellum, medial temporal, and frontal cortex. Compared to UPD, the DEL subtypes had lower GMV primarily in the prefrontal and temporal cortices, and lower white matter in the parietal cortex. The UPD subtype had more extensive lower gray and WMVs in the orbitofrontal and limbic cortices compared to HWC. These preliminary findings are the first structural neuroimaging findings to support potentially separate neural mechanisms mediating the behavioral differences seen in these genetic subtypes.

Insulin resistance and gray matter volume in neurodegenerative disease.

The goal of this study was to compare insulin resistance in aging and aging-related neurodegenerative diseases, and to determine the relationship between insulin resistance and gray matter volume (GMV) in each cohort using an unbiased, voxel-based approach. Insulin resistance was estimated in apparently healthy elderly control (HC, n=21) and neurodegenerative disease (Alzheimers disease (AD), n=20; Parkinsons disease (PD), n=22) groups using Homeostasis Model Assessment of Insulin Resistance 2 (HOMA2) and intravenous glucose tolerance test (IVGTT). HOMA2 and GMV were assessed within groups through General Linear Model multiple regression. We found that HOMA2 was increased in both AD and PD compared to the HC group (HC vs. AD, p=0.002, HC vs. PD, p=0.003), although only AD subjects exhibited increased fasting glucose (p=0.005). Furthermore, our voxel-based morphometry analysis revealed that HOMA2 was related to GMV in all cohorts in a region-specific manner (p<0.001, uncorrected). Significant relationships were observed in the medial prefrontal cortex (HC), medial temporal regions (AD), and parietal regions (PD). Finally, the directionality of the relationship between HOMA2 and GMV was disease-specific. Both HC and AD subjects exhibited negative relationships between HOMA2 and brain volume (increased HOMA2 associated with decreased brain volume), while a positive relationship was observed in PD. This cross-sectional study suggests that insulin resistance is increased in neurodegenerative disease, and that individuals with AD appear to have more severe metabolic dysfunction than individuals with PD or PD dementia.

Cognitive Function and White Matter Changes Associated with Renal Transplantation.

Background: End-stage renal disease (ESRD) is a disease with an aging population and a high prevalence of cognitive impairment affecting quality of life, health care costs and mortality. Structural changes in the brain with decreased white matter integrity have been observed in ESRD. Understanding the changes in cognition and associated changes in brain structure after renal transplantation can help define the mechanisms underlying cognitive impairment in ESRD. Methods: We conducted a prospective, observational cohort study in ESRD patients listed for renal transplantation and followed them post-transplantation. We assessed their cognitive function with a battery of neuropsychological tests and brain white matter integrity with diffusion tensor imaging (DTI) both before transplant and 3 months after transplant. Results: Eleven patients, aged 56.5 ± 10.7 years, completed the study. Cognitive measures of memory and executive function improved after the transplant, specifically on tests of logical memory I (p = 0.004), logical memory II (p = 0.003) and digit symbol (p < 0.0001). DTI metrics also improved post the transplant with an increase in fractional anisotropy (p = 0.01) and decrease in mean diffusivity (p = 0.004). These changes were more prominent in tracts associated with memory and executive function. Conclusions: Cognitive function, particularly memory and executive function, improve post the transplant with concurrent improvements in white matter integrity in tracts associated with memory and executive function. These data suggest that abnormalities in cognition and brain structure seen in the ESRD population are at least partially reversible.

Voxel-based morphometry reveals brain gray matter volume changes in successful dieters.

To compare regional brain volume predictors of percent weight loss (WL) in dieters with obesity (DwO) and in the same participants categorized as “successful” (≥7% WL) or “unsuccessful” dieters (<7% WL).

Cardiorespiratory fitness and white matter integrity in Alzheimer’s disease

The objective of this study was to investigate the relationship between cardiorespiratory (CR) fitness and the brain’s white matter tract integrity using diffusion tensor imaging (DTI) in the Alzheimer’s disease (AD) population. We recruited older adults in the early stages of AD (n = 37; CDR = 0.5 and 1) and collected cross-sectional fitness and diffusion imaging data. We examined the association between CR fitness (peak oxygen consumption [VO2peak]) and fractional anisotropy (FA) in AD-related white matter tracts using two processing methodologies: a tract-of-interest approach and tract-based spatial statistic (TBSS). Subsequent diffusivity metrics (radial diffusivity [RD], mean diffusivity [MD], and axial diffusivity [A × D]) were also correlated with VO2peak. The tract-of-interest approach showed that higher VO2peak was associated with preserved white matter integrity as measured by increased FA in the right inferior fronto-occipital fasciculus (p = 0.035, r = 0.36). We did not find a significant correlation using TBSS, though there was a trend for a positive association between white matter integrity and higher VO2peak measures (p < 0.01 uncorrected). Our findings indicate that higher CR fitness levels in early AD participants may be related to preserved white matter integrity. However to draw stronger conclusions, further study on the relationship between fitness and white matter deterioration in AD is necessary.

AEROBIC EXERCISE REDUCES HIPPOCAMPAL ATROPHY IN INDIVIDUALS WITH EARLY ALZHEIMER'S DISEASE

Owen Thomas Carmichael, Tom Meade, Dementia Research Centre, London, England, United Kingdom; London School of Hygiene and Tropical Medicine, London, United Kingdom; UCL, London, United Kingdom; London School of Hygiene and Tropical Medicine, London; University College London, London, United Kingdom; UCL Institute of Neurology, London, United Kingdom; University of California, Davis, Davis, California, United States. Contact e-mail: jennifer.nicholas@lshtm. ac.uk

A diffusion-tensor imaging study of white matter microstructure in neurodegenerative disease

Age Mean (SD) 63.4 (10.90) 61.8 (10.10) 0.5730 45-54 8 (21.6%) 6 (26.1%) 0.7476 55-64 14 (37.8%) 10 (43.5%) 65-74 8 (21.6%) 5 (21.7%) 75+ 7 (18.9%) 2 (8.7%) Race Non-White 9 (24.3%) 5 (21.7%) 0.8179 White 28 (75.7%) 18 (78.3%) Gender Male 26 (70.3%) 15 (65.2%) 0.6825 Female 11 (29.7%) 8 (34.8%) Education, yrs Mean (SD) 14.2 (2.99) 15.7 (2.56) 0.0421 <12 yrs 4 (11.1%) 1 (4.3%) 0.3630 eGFR Mean (SD) 26.0 (9.45) –* <15 7 (18.9%) 0 <.0001 15-29 15 (40.5%) 0 30-44 15 (40.5%) 0 45-59 0 10 (43.5%) 60+ 0 13 (56.5%) Diabetes 20 (55.6%) 5 (21.7%) 0.0104 Hypertension 32 (88.9%) 13 (56.5%) 0.0044 Stroke/TIA history 5 (13.5%) 1 (4.3%) 0.2499 Depression history 14 (38.9%) 9 (39.1%) 0.7210