Genetic effect of MTHFR C677T polymorphism on the structural covariance network and white‐matter integrity in Alzheimer's disease

Chang, Yu Tzu; Hsu, Shih‐Wei; Tsai, Shih‐Jen; Chang, Ya‐Ting; Huang, Chi‐Wei; Liu, Mu-En; Chen, Nai-Ching; Chang, Wen-Neng; Hsu, Jen‐Hwa; Lee, Chen-Chang; Chang, Chiung-Chih

doi:10.1002/hbm.23572

Cited by 12 publications

(10 citation statements)

References 62 publications

(64 reference statements)

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“…To achieve SCN t-maps, we performed the general linear analysis on modulated GM images. Specifically, we extracted the GM volume from a 4-mm radius sphere (Montembeault et al, 2016; Chang et al, 2018; Chang et al, 2017) around those coordinates on modulated images. Then, we performed four separate correlation analysis by entering the GM volumes of each ROI as a regressor and the total intracranial volume, gender, age, and education as covariates.…”

Section: Methodsmentioning

confidence: 99%

“…We set the threshold at P ≤ 0.01 corrected for false discovery rate (FDR). Considering the physiological meanings of the SCN clusters, we reported clusters showing cluster size >100 voxels (Chang et al, 2018; Chang et al, 2017). To achieve qualitative group-wise comparisons, we displayed the results on a standard brain template (ICBM152).…”

Section: Methodsmentioning

confidence: 99%

“…To explore the clinical significance of the identified peak voxel volume, we performed the correlation analysis (Chang et al, 2018; Chang et al, 2017). To be specific, for the peak clusters showing significant between-group differences, we extracted the GM volume using a 4-mm radius sphere placing on the peak voxel.…”

Section: Methodsmentioning

confidence: 99%

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Gray matter structural covariance networks changes along the Alzheimer's disease continuum

Luo

Zeng

et al. 2019

NeuroImage: Clinical

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Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta 1–42 (A) and phosphorylated tau protein 181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T−), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Gray matter structural covariance networks changes along the Alzheimer's disease continuum

Luo

Zeng

et al. 2019

NeuroImage: Clinical

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“…A significant polymorphism at the level of the MTHFR gene is C677T, which induces increased homocysteine concentrations and DNA hypomethylation. Furthermore, it has been shown to be associated with neural tube defects, white matter integrity in Alzheimer patients, venous thrombosis, colorectal cancer survival, breast cancer and leukemia [143,144,145,146,147,148,149]. Continuing on, the links between MTHFR polymorphisms and lung cancer have also been extensively studied.…”

Section: Vitaminsmentioning

confidence: 99%

Role of Key Micronutrients from Nutrigenetic and Nutrigenomic Perspectives in Cancer Prevention

et al. 2019

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Regarding cancer as a genetic multi-factorial disease, a number of aspects need to be investigated and analyzed in terms of cancer’s predisposition, development and prognosis. One of these multi-dimensional factors, which has gained increased attention in the oncological field due to its unelucidated role in risk assessment for cancer, is diet. Moreover, as studies advance, a clearer connection between diet and the molecular alteration of patients is becoming identifiable and quantifiable, thereby replacing the old general view associating specific phenotypical changes with the differential intake of nutrients. Respectively, there are two major fields concentrated on the interrelation between genome and diet: nutrigenetics and nutrigenomics. Nutrigenetics studies the effects of nutrition at the gene level, whereas nutrigenomics studies the effect of nutrients on genome and transcriptome patterns. By precisely evaluating the interaction between the genomic profile of patients and their nutrient intake, it is possible to envision a concept of personalized medicine encompassing nutrition and health care. The list of nutrients that could have an inhibitory effect on cancer development is quite extensive, with evidence in the scientific literature. The administration of these nutrients showed significant results in vitro and in vivo regarding cancer inhibition, although more studies regarding administration in effective doses in actual patients need to be done.

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“…N-5, N-10-methylenetetrahydrofolate reductase (MTHFR) is the rate limiting enzyme in the metabolism of Hcy. Studies have found that MTHFR has a certain correlation with cerebrovascular disease [1,2] and has a predictive value in ischemic stroke [25]. However, the relationship between MTHFR gene C677T polymorphism and WML is still unclear.…”

Section: Introductionmentioning

confidence: 99%

Correlations between plasma homocysteine and MTHFR gene polymorphism and white matter lesions

Li¹,

Fu²,

Dong³

2018

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This study aims to investigate the correlation between the plasma homocysteine (Hcy) level and methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism, and white matter lesions (WML). The plasma Hcy level and MTHFR C677T gene polymorphism in 104 patients with white matter lesions and 74 controls were measured. The severity of cerebral white matter was scored on the magnetic resonance imaging (MRI) images by the modified Scheltens scale (score of 0-30). The plasma Hcy level in the WML group was remarkably higher than that in the control group (p < 0.05), and the proportion of the patients with a high Hcy level in the WML group was notably higher than that in the control group (p < 0.05). Moreover, the patients with TT genotype in MTHFR gene had a significantly higher plasma Hcy level than those with CT or CC genotype. In addition, the plasma Hcy level in the patients with CT genotype was also significantly different from that in the patients with CC genotype (p < 0.05). The severity of cerebral white matter lesions among the patients with different genotypes in MTHFR gene was not significantly different (p > 0.05). The plasma Hcy level is positively correlated with WML and significantly correlated with C677T gene polymorphism in MTHFR gene. The severity of white matter lesions is not correlated with MTHFR C677T gene polymorphism.

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Genetic effect of MTHFR C677T polymorphism on the structural covariance network and white‐matter integrity in Alzheimer's disease

Cited by 12 publications

References 62 publications

Gray matter structural covariance networks changes along the Alzheimer's disease continuum

Gray matter structural covariance networks changes along the Alzheimer's disease continuum

Role of Key Micronutrients from Nutrigenetic and Nutrigenomic Perspectives in Cancer Prevention

Correlations between plasma homocysteine and MTHFR gene polymorphism and white matter lesions

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