Role of metabolism in neurodegenerative disorders

Procaccini, Claudio; Santopaolo, Marianna; Faicchia, Deriggio; Colamatteo, Alessandra; Formisano, Luigi; Candia, Paola de; Galgani, Mario; Rosa, Veronica De; Matarese, Giuseppe

doi:10.1016/j.metabol.2016.05.018

Cited by 168 publications

(119 citation statements)

References 210 publications

(198 reference statements)

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“…Patients in advanced states of the disease are known to exhibit poor glycemic control and progressive weight loss (Aziz, Swaab, Pijl, & Roos, 2007). Furthermore, mouse models have also indicated that mutations in the huntingtin gene expressed in the hypothalamus may be the causal factor for metabolic abnormalities seen in the disease, such as: impaired glucose metabolism, insulin resistance, and leptin resistance (Hult et al, 2011;Procaccini et al, 2016). For instance, it has been demonstrated that insulin resistance and decreased circulating insulinlike growth factor 1 (IGF-1) can accelerate HD onset in individuals with the genetic predisposition (Lalic et al, 2008).…”

Section: The Appetitive Networkmentioning

confidence: 99%

A neural signature of metabolic syndrome

Kotkowski

Price

Franklin

et al. 2019

Human Brain Mapping

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That metabolic syndrome (MetS) is associated with age‐related cognitive decline is well established. The neurobiological changes underlying these cognitive deficits, however, are not well understood. The goal of this study was to determine whether MetS is associated with regional differences in gray‐matter volume (GMV) using a cross‐sectional, between‐group contrast design in a large, ethnically homogenous sample. T1‐weighted MRIs were sampled from the genetics of brain structure (GOBS) data archive for 208 Mexican‐American participants: 104 participants met or exceeded standard criteria for MetS and 104 participants were age‐ and sex‐matched metabolically healthy controls. Participants ranged in age from 18 to 74 years (37.3 ± 13.2 years, 56.7% female). Images were analyzed in a whole‐brain, voxel‐wise manner using voxel‐based morphometry (VBM). Three contrast analyses were performed, a whole sample analysis of all 208 participants, and two post hoc half‐sample analyses split by age along the median (35.5 years). Significant associations between MetS and decreased GMV were observed in multiple, spatially discrete brain regions including the posterior cerebellum, brainstem, orbitofrontal cortex, bilateral caudate nuclei, right parahippocampus, right amygdala, right insula, lingual gyrus, and right superior temporal gyrus. Age, as shown in the post hoc analyses, was demonstrated to be a significant covariate. A further functional interpretation of the structures exhibiting lower GMV in MetS reflected a significant involvement in reward perception, emotional valence, and reasoning. Additional studies are needed to characterize the influence of MetS's individual clinical components on brain structure and to explore the bidirectional association between GMV and MetS.

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Section: The Appetitive Networkmentioning

confidence: 99%

A neural signature of metabolic syndrome

Kotkowski

Price

Franklin

et al. 2019

Human Brain Mapping

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“…Fluctuations in carbohydrate metabolism may also lead to an imbalance of glucose tolerance and insulin resistance. Indeed, the metabolic homeostasis of leptin, ghrelin, insulin, and IGF-1 provides a neuroprotective and neurotrophic effect in AD and PD (Procaccini et al, 2016).…”

Section: Figmentioning

confidence: 99%

Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Kori

Aydın

Unal

et al. 2016

OMICS: A Journal of Integrative Biology

118

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Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) lack robust diagnostics and prognostic biomarkers. Metabolomics is a postgenomics field that offers fresh insights for biomarkers of common complex as well as rare diseases. Using data on metabolite-disease associations published in the previous decade (2006-2016) in PubMed, ScienceDirect, Scopus, and Web of Science, we identified 101 metabolites as putative biomarkers for these three neurodegenerative diseases. Notably, uric acid, choline, creatine, L-glutamine, alanine, creatinine, and N-acetyl-L-aspartate were the shared metabolite signatures among the three diseases. The disease-metabolite-pathway associations pointed out the importance of membrane transport (through ATP binding cassette transporters), particularly of arginine and proline amino acids in all three neurodegenerative diseases. When disease-specific and common metabolic pathways were queried by using the pathway enrichment analyses, we found that alanine, aspartate, glutamate, and purine metabolism might act as alternative pathways to overcome inadequate glucose supply and energy crisis in neurodegeneration. These observations underscore the importance of metabolite-based biomarker research in deciphering the elusive pathophysiology of neurodegenerative diseases. Future research investments in metabolomics of complex diseases might provide new insights on AD, PD, and ALS that continue to place a significant burden on global health.

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“…Many biochemical processes are affected in AD, including amyloid precursor protein metabolism, phosphorylation of tau protein, oxidative stress, impaired energetics, mitochondrial dysfunction, inflammation, membrane lipid dysregulation, and neurotransmitter pathway disruption [3,4]. Impaired cerebral glucose uptake occurs decades before the onset of cognitive dysfunction in AD [5], and neurotoxicity associated with Aβ is thought to participate in impaired neuronal energetics including mitochondrial dysfunction and release of reactive oxygen species.…”

Section: Introductionmentioning

confidence: 99%

Metabolic network failures in Alzheimer's disease: A biochemical road map

Toledo

Arnold

Kastenmüller

et al. 2017

Alzheimer's & Dementia

373

378

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Introduction The Alzheimer’s Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer’s disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer’s Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

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Role of metabolism in neurodegenerative disorders

Cited by 168 publications

References 210 publications

A neural signature of metabolic syndrome

A neural signature of metabolic syndrome

Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Metabolic network failures in Alzheimer's disease: A biochemical road map

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