Metabolic profiling distinguishes three subtypes of Alzheimer's disease

Bredesen, Dale E.

doi:10.18632/aging.100801

Cited by 47 publications

(53 citation statements)

References 19 publications

(22 reference statements)

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“…Our findings correlate with other similar studies (21). Level of CRP in our study did not correlate significantly with global cognitive function unlike previous findings (22)(23)(24)(25). Our study showed that elevated PTH levels and severity of secondary hyperparathyroidism statistically significantly correlates with space and time orientation disorders.…”

Section: Discussionsupporting

confidence: 79%

Analysis of Risk Factors for Development of Cognitive Disorders in Maintenance Hemodialysis Patients – Pilot Study

Jovanović¹,

Todorovic

Milovanovic

et al. 2017

Serbian Journal of Experimental and Clinical Research

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Section: Discussionsupporting

confidence: 79%

Analysis of Risk Factors for Development of Cognitive Disorders in Maintenance Hemodialysis Patients – Pilot Study

Jovanović¹,

Todorovic

Milovanovic

et al. 2017

Serbian Journal of Experimental and Clinical Research

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“…There is a growing consensus that the most effective treatments will intervene early in disease progression and halt disease pathophysiological processes prior to conversion to LOAD 2 . In addition, there is increasing recognition that LOAD may in fact be a spectrum of related diseases that have similar clinical and neuropathological manifestations 3,4 . Devising successful therapeutic strategies will likely require targeting potentially diverse early-stage disease processes that occur prior to a high burden of neuropathology or cognitive impairment.…”

Section: Introductionmentioning

confidence: 99%

Molecular estimation of neurodegeneration pseudotime in older brains

Mukherjee

Preuss

Jayadev

et al. 2019

Preprint

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26Therapeutic treatments for late-onset Alzheimer's disease (LOAD) are hindered by an incomplete 27 understanding of the temporal molecular changes that lead to disease onset and progression. Here, we 28 evaluate the ability of manifold learning to develop a molecular model for the unobserved temporal 29 disease progression from RNA-Seq data collected from human postmortem brain samples collected 30 within the ROS/MAP and Mayo Clinic RNA-Seq studies of the AMP-AD consortium. This approach 31 defines a cross-sectional ordering across samples based on their relative similarity in RNA-Seq profiles 32 and uses this information to define an estimate of molecular disease stage -or disease progression 33 pseudotime -for each sample. This transcriptional estimate of disease progression is strongly concordant 34 with burden of tau pathology (Braak score, P = 1.0x10 -5 ), amyloid pathology (CERAD score, P = 1.8x10 -35 5 ), and cognitive diagnosis (P = 3.5x10 -7 ) of LOAD. Further, the disease progression estimate 36 recapitulates known changes in cell type abundance and impact of genes that harbor known AD risk loci. 37Samples estimated to reside early in disease progression were enriched for control and early stage AD 38 cases, and demonstrated changes in basic cellular functions. Samples estimated to reside late in disease 39 progression were enriched for late-stage AD cases, and demonstrated changes in known disease processes 40 including neuroinflammation and amyloid pathology. We also identified a set of control samples with 41 late-stage estimated disease progression who also showed compensatory changes in genes involved in 42 affected pathways are protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid 43 cleavage. In summary, we present a disease specific method for ordering patients based on their LOAD 44 disease progression from CNS transcriptomic data. 45

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“…inflammatory, noninflammatory, and atypical), and that the type of each patient can be determined by profiling a set of biomarkers dominant in each type (e.g. APOE type, homocysteine, hs-CRP, IL-6, TNF-α, HbA1C, insulin, progesterone, estradiol, cortisol, vitamin D, zinc, heavy metal, mycotoxin) [92]. Factors with prominent effects in the Sgo1 −/+ model are being investigated, and will be determined with further time-course study focusing on these biomarkers and proposed facilitators.…”

Section: Late-onset Accumulation Of Amyloid-beta In Sgo1 Brainmentioning

confidence: 99%

Critical role of mitosis in spontaneous late-onset Alzheimer’s disease; from a Shugoshin 1 cohesinopathy mouse model

et al. 2018

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From early-onset Alzheimer's disease (EOAD) studies, the amyloid-beta hypothesis emerged as the foremost theory of the pathological causes of AD. However, how amyloid-beta accumulation is triggered and progresses toward senile plaques in spontaneous late-onset Alzheimer's disease (LOAD) in humans remains unanswered. Various LOAD facilitators have been proposed, and LOAD is currently considered a complex disease with multiple causes. Mice do not normally develop LOAD. Possibly due to the multiple causes, proposed LOAD facilitators have not been able to replicate spontaneous LOAD in mice, representing a disease modeling issue. Recently, we reported spontaneous late-onset development of amyloid-beta accumulation in brains of Shugoshin 1 (Sgo1) haploinsufficient mice, a cohesinopathy-mediated chromosome instability model. The result for the first time expands disease relevance of mitosis studies to a major disease other than cancers. Reverse-engineering of the model would shed light on the process of late-onset amyloid-beta accumulation in the brain and spontaneous LOAD development, and contribute to development of interventions for LOAD. This review will discuss the Sgo1 model, our current "three-hit hypothesis" regarding LOAD development with an emphasis on critical role of prolonged mitosis in amyloid-beta accumulation, and implications for human LOAD intervention and treatment. Abbreviations: Alzheimer's disease (AD); Late-onset Alzheimer's disease (LOAD); Early-onset Alzheimer's disease (EOAD); Shugoshin-1 (Sgo1); Chromosome Instability (CIN); apolipoprotein (Apoe); Central nervous system (CNS); Amyloid precursor protein (APP); N-methyl-d-aspartate (NMDA); Hazard ratio (HR); Cyclin-dependent kinase (CDK); Chronic Atrial Intestinal Dysrhythmia (CAID); beta-secretase 1 (BACE); phosphor-Histone H3 (p-H3); Research and development (R&D); Non-steroidal anti-inflammatory drugs (NSAIDs); Brain blood barrier (BBB).

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Metabolic profiling distinguishes three subtypes of Alzheimer's disease

Cited by 47 publications

References 19 publications

Analysis of Risk Factors for Development of Cognitive Disorders in Maintenance Hemodialysis Patients – Pilot Study

Analysis of Risk Factors for Development of Cognitive Disorders in Maintenance Hemodialysis Patients – Pilot Study

Molecular estimation of neurodegeneration pseudotime in older brains

Critical role of mitosis in spontaneous late-onset Alzheimer’s disease; from a Shugoshin 1 cohesinopathy mouse model

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