Genetic Variation and Neuroimaging Measures in Alzheimer Disease

Biffi, Alessandro; Anderson, Chris; Desikan, Rahul S.; Sabuncu, Mert R.; Cortellini, Lynelle; Schmansky, Nick; Salat, David H.; Rosand, Jonathan

doi:10.1001/archneurol.2010.108

Cited by 209 publications

(220 citation statements)

References 33 publications

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“…124 More specifically, the inheritance of APOE ε4 and a maternal history of the disease were linked to hippocampal atrophy. 125 With regard to comorbid conditions, obesity is linked to lower brain volumes in AD and in mild cognitive impairment (MCI), a condition likely to be prodromal AD.…”

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confidence: 99%

Neuroinflammatory Cytokines—The Common Thread in Alzheimer Pathogenesis

Griffin¹,

Barger²

2010

US Neurology

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This article discusses the potential role of the cytokine cycle and its corollary as drivers of the relentless progression of Alzheimer's neuropathologies, whether they are the result of gene mutations, gene polymorphisms, and/or environmental and comorbid conditions. Based on the discovery of cytokine overexpression as an accompaniment to the dementia-related glial activation, the cytokine hypothesis wasproposed. This states that in response to the negative impact on neurons of known and unknown risk factors-which include genetic inheritance, comorbid and environmental factors-microglia and astrocytes become activated and produce excess amounts of the immune-modulating cytokine interleukin-1 (IL-1) and the neuritogenic cytokine S100B, respectively. Finding that these glial events occur in fetuses and neonates with Down syndrome provided the first evidence that productive immune responses by activated glia precede rather than follow overt AD-related pathology. This finding can be added to the demonstration of IL-1 induction of amyloid β (Aβ) precursor protein and astrocyte activation with excess production of neuritogenic factor S100B. This combination suggests that IL-1 and S100B overexpression would favor the Aβ production and dystrophic neurite growth necessary for laying down neuritic Aβ plaques. This, together with demonstration of IL-1 induction of excessive production of the precursors of other features common in AD prompted a corollary to the cytokine hypothesis. The corollary states that regardless of the primary cause of the neuronal insult, the result will be chronic glial activation, which in turn will result in further neuronal injury, still more glial activation with excess cytokine expression and so on. This article discusses known causes, genetic and environmental risk factors, and comorbid conditions, and the potential contribution of glial activation with excessive cytokine expression to each. KeywordsAlzheimer's disease (AD), amyloid β (Aβ), apolipoprotein E (ApoE), β−amyloid precursor protein (βAPP), cytokines, Down syndrome (DS), excitotoxicity, glutamate, interleukin-1 (IL-1), S100B, tumor necrosis factor alpha (TNFα) Disclosure: The authors have no conflicts of interest to declare. Acknowledgments:Much of the work cited in this article was supported by National Institutes of Health (NIH) grants P01AG12411 (to WSTG) and R01AG17498 (to SWB). The authors thank Orwa Aboud, MD, for helpful comments and editing. A progression of these neuropathological events seems apparent. For example, the plethora of Aβ plaques at end stages-from several to many-and an inclusion of neurofibrillary tangles-in a few to numerous neurons-strongly suggests that these neuropathological Received

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confidence: 99%

Neuroinflammatory Cytokines—The Common Thread in Alzheimer Pathogenesis

Griffin¹,

Barger²

2010

US Neurology

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“…It is also comparable to previous findings in the literature for multilocus models of a brain-imaging phenotype. Biffi et al (2010) found that 3% of the variance in MRI-derived volumes of several brain regions could be explained from a number of candidate genes for Alzheimer's disease. Nikolova et al (2011) showed 11% of the variance in ventral striatal reactivity could be explained from their panel of five polymorphisms.…”

Section: Discussionmentioning

confidence: 99%

“…Carayol et al (2010), for instance, reported on the cumulative effect of four candidate SNPs on the risk for autism, using a casecontrol approach. These models are beginning to be applied to brain imaging in the context of neuropsychiatric disorders (Biffi et al, 2010;Hibar et al, 2011a;Nikolova et al, 2011), and may provide more biologically meaningful predictions with implications for personalized diagnosis and therapy.…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, individual effects of single genetic variants on white matter structure have been explored, but a multilocus approach has not yet been taken. The utility of a multilocus candidate gene model in predicting an imagingderived outcome was recently explored in the context of structural MRI (Biffi et al, 2010) and functional MRI (Nikolova et al, 2011), but its applications in DTI and detailed three-dimensional maps of brain structure appear novel. In this paper, we incorporate a subject's genetic signature, at key loci, into a multilocus model.…”

Section: Introductionmentioning

confidence: 99%

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Predicting White Matter Integrity from Multiple Common Genetic Variants

Kohannim

Jahanshad

Braskie

et al. 2012

Neuropsychopharmacol

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Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate singlenucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained B6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.

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“…The study of Biffi et al 2010 Another study utilizing the available genotyping and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative tried to assess the association of GWAS-validated and GWAS-promising novel AD loci on hippocampal volume, amygdale volume, white matter lesion volume (WML), entorhinal cortex thickness (ECT), parahippocampal gyrus thickness and temporal pole cortex thickness (TPT) (Biffi et al, 2010) …”

Section: Quantitative-trait Association Studies In Admentioning

confidence: 99%