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Methylenetetrahydrofolate Reductase

Methylenetetrahydrofolate Reductase (MTHFR) is an enzyme that plays a crucial role in folate metabolism.
It catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, the predominant circulating form of folate.
Variants in the MTHFR gene can lead to reduced enzyme activity, impacting folate-dependent processes such as DNA synthesis and repair, as well as homocysteine metabolism.
PubCompare.ai's AI-driven approach helps researchers optimize research protocols for MTHFR, locating the most reproducible and accuarate protocols from literature, preprints, and patents using an advanced comparison tool.
This can improve research outcomes and advance our understanding of this important enzyme.

Most cited protocols related to «Methylenetetrahydrofolate Reductase»

1
Determinants of Health in Central and Eastern Europe
Cited 41 times
To investigate the determinants of cardiovascular diseases and other chronic conditions in Central and Eastern Europe, we are conducting a prospective cohort study in Russia, Poland, the Czech Republic and Lithuania. The study will investigate the following specific hypotheses:
• Socioeconomic factors are key determinants of health in CEE/FSU; we will examine the pathways involved in their action, including factors hypothesised below.
• Psychosocial factors, both at individual and population level, are related to CVD and other non-communicable diseases.
• Low consumption of fresh fruits and vegetables and their nutrient biomarkers are associated with increased risk of CVD;
• Binge drinking and heavy alcohol consumption are related to all-cause mortality, CVD and injury;
• Elevated concentration of homocysteine and low levels of folate and related B vitamins are associated with increased risk of CVD;
• Interactions between different groups of risk factors, in particular between heavy drinking and folate deficiency, and between the MTHFR genotype and folate deficiency, are associated with CVD.
In addition to these specific hypotheses, the study will also investigate several more general questions:
• The role of childhood socioeconomic circumstances and biological markers of their effects, such as leg length and lung functions, in the risk of CVD and other conditions in adulthood;
• Biological, social, economic and psychosocial determinants of healthy ageing (cognitive function, physical functioning, and quality of life of elderly persons);
• Genetic predictors and non-conventional biomarkers of CVD and other chronic diseases.
Peasey A., Bobak M., Kubinova R., Malyutina S., Pajak A., Tamosiunas A., Pikhart H., Nicholson A, & Marmot M. (2006). Determinants of cardiovascular disease and other non-communicable diseases in Central and Eastern Europe: Rationale and design of the HAPIEE study. BMC Public Health, 6, 255.
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Publication 2006
Aged Biological Markers Biopharmaceuticals Birth Cardiovascular Diseases Chronic Condition Cognition Disease, Chronic Folate Folic Acid Deficiency Fruit Genotype Homocysteine Injuries Methylenetetrahydrofolate Reductase Noncommunicable Diseases Nutrients Physical Examination Respiratory Physiology Vegetables Vitamins
2
Evaluating Obesity-Associated Genetic Variants
Cited 16 times
We selected 74 candidate genes previously tested for association with obesity in humans [53 (link)]. For each gene, we first evaluated the ability of the Affymetrix SNPs to tag common SNPs (MAF > 0.05) within +/− 5 kb of the gene (r2 > 0.50 or r2 > 0.80) using the HapMap CEU database [54 (link)]. We then evaluated evidence for association using all Affymetrix SNPs within each gene as well as neighboring Affymetrix SNPs that could be used to improve coverage (r2 > 0.5). For each gene, we report coverage statistics as well as the SNP that showed strongest evidence for association.
We selected 74 genes that were previously targeted in associations studies aiming to identify genetic determinants of obesity in humans [53 (link)]: ACE, ACTN, ADIPOQ, ADIPOR1, ADIPOR2, ADRB1, ADRB2, AGER, AHSG, APOA2, APOA4, APOA5, AR, BDNF, CASQ1, COL1A1, COMT, CRP, CYP11B2, DIO1, ENPP1, ESR1, ESR2, FABP2, FOXC2, GAD2, GFPT1, GHRHR, GNAS, GNB3, GPR40, H6PD, HSD11B1, HTR2C, ICAM1, IGF1, IGF2, IL6, IL6R, KCNJ11, KL, LEP, LEPR, LIPC, LPL, LTA, MC4R, MCHR1, MKKS, MTHFR, MTTP, NMB, NOS3, NPY, NPY2R, NR0B2, NTRK2, PARD6A, PLIN, PPARG, PPARGC1A, PRDM2, PTPN1, PYY, RETN, SCD, SELE, SERPINE1, TAS2R38, TNF, UCP1, UCP2, UCP3, and VDR. We did not consider genes associated with drug-induced body weight gain or mitochondrial genes [53 (link)].
The following genes have previously been investigated for their role in obesity and related traits but are not well tagged by SNPs in the Affymetrix array: ADRB3, DRD4, INS, and APOE.
Scuteri A., Sanna S., Chen W.M., Uda M., Albai G., Strait J., Najjar S., Nagaraja R., Orrú M., Usala G., Dei M., Lai S., Maschio A., Busonero F., Mulas A., Ehret G.B., Fink A.A., Weder A.B., Cooper R.S., Galan P., Chakravarti A., Schlessinger D., Cao A., Lakatta E, & Abecasis G.R. (2007). Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits. PLoS Genetics, 3(7), e115.
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Publication 2007
ADRB1 protein, human ADRB2 protein, human APOA4 protein, human ApoE protein, human COMT protein, human DRD4 protein, human FABP2 protein, human Genes Genes, Mitochondrial GNB3 protein, human HapMap Homo sapiens IGF1 protein, human IL6R protein, human insulin-like growth factor 2, human Intercellular Adhesion Molecule-1 Kaufman-Mckusick Syndrome leptin receptor, human LIPC protein, human MC4R protein, human Methylenetetrahydrofolate Reductase NOS3 protein, human O(4)-methylthymidine triphosphate Obesity Pharmaceutical Preparations PPARGC1A protein, human PRDM2 protein, human PTPN1 protein, human RAGE receptor protein, human Reproduction SERPINE1 protein, human Single Nucleotide Polymorphism tropomyosin-related kinase-B, human UCP1 protein, human
3
Maternal Folate Supplements and Retinoblastoma
Cited 11 times
Between January 2000 and December 2009, 108 mothers of children with newly diagnosed unilateral retinoblastoma at two adjacent referral hospitals in Mexico City were invited to participate in this case-control study, which was approved by IRBs of all participating institutions. Mothers of children with a known family history of retinoblastoma were not eligible to participate in the study. The study was designed to recruit 100 cases and 100 controls. Two mothers declined to participate. Case mothers (N=106) were enrolled during their child’s initial visits to the treating hospital. Enrolled mothers were asked to refer a friend (not related by blood to the case mother) who had a child of the same age as their child (range ±1 year) to serve as a control mother. Eligibility criteria for control mothers also included not having a family member with retinoblastoma. Among the 97 control mothers, more than 81% were the first friend the case mother approached, while the remaining mothers were the second friend approached. Control mothers were enrolled during home visits in 18 states in central and southern Mexico. At the time of enrollment, all participating mothers gave signed consent. Blood samples were then obtained from all mothers and from most case and control children. Mothers were interviewed regarding supplement intake during the first trimester of pregnancy using a validated questionnaire.15 If women reported taking any supplements, they were asked about the supplement’s brand, dose and form of administration (tablet, powder etc.). Supplemental intake of folic acid was noted as present or absent after analyzing the folic acid content of the supplements reported using a nutrient content database for supplements available in Mexico.16 (link) Blood was drawn into Becton Dickinson CPT (Becton Dickinson, NJ) vials and kept at 4°C until centrifugation. Buffy coat were stored at −80°C until DNA extraction using standard non-organic methods (Qiagen, Valencia, CA). Genotyping for the MTHFR 677C>T and DHFR 19bpdel polymorphisms were performed by PCR amplification using RFLP and allelic specific methods, respectively.9 (link),10 (link),12 (link) Resulting PCR products were separated on 3% agarose gels and were visualized with ethidium bromide. About 20% of samples were randomly selected to be run in duplicate with 99% concordance. Laboratory personnel were blinded to sample origin. Batches contained equivalent proportions of case and control samples. Samples and questionnaires were linked through de-identified and bar-coded labels.
Orjuela M., Cabrera-Muñoz L., Paul L., Ramirez-Ortiz M., Liu X., Chen J., Mejia-Rodriguez F., Medina-Sanson A., Diaz-Carreño S., Suen I., Selhub J, & Ponce-Castañeda M. (2012). Risk of retinoblastoma is associated with a maternal polymorphism in dihydrofolatereductase (DHFR) and pre-natal folic acid intake: Folic acid, DHFR and retinoblastoma risk. Cancer, 118(23), 5912-5919.
Publication 2012
Alleles BLOOD Centrifugation Child Dietary Supplements Eligibility Determination Ethics Committees, Research Ethidium Bromide Familial Retinoblastoma Folic Acid Friend Gels Genetic Polymorphism Hospital Referral Laboratory Personnel Methylenetetrahydrofolate Reductase Mothers Nutrients Powder Restriction Fragment Length Polymorphism Retinoblastoma Sepharose Tablet Visit, Home Woman
4
MTHFR Polymorphism and B-Vitamin Levels
Cited 10 times
The association between the genotypes of the MTHFR polymorphism and allelic frequencies with the demographic variables was analyzed with Chi-square test. T-test was used to determine the association of homocysteine, vitamin B12 and folic acid levels between cases and controls irregardless of the genotypes. The association between the plasma homocysteine, vitamin B12 and folic acid levels and the MTHFR polymorphisms were analyzed using 3-way ANOVA. P values were considered statistically significant if they were less than 0.05. The sample size was determined using the power analysis, with the α error and β error set at 0.05 and 0.20 respectively. Approximately 40 subjects per group were sufficient for parametric test (Cohen, 1977) whilst for non-parametric test such as Chi-Square test, the expected frequency of a minimum 5 for each category was sufficient (Hays, 1994). The measure of skewness and kurtosis were used to assess the normality of the distribution of the parameters (homocysteine, vitamin B12 and folic acid levels).
Liew S.C., Das-Gupta E., Wong S.F., Lee N., Safdar N, & Jamil A. (2012). Association of Methylentetraydrofolate Reductase (MTHFR) 677 C > T gene polymorphism and homocysteine levels in psoriasis vulgaris patients from Malaysia: a case-control study. Nutrition Journal, 11, 1.
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Publication 2012
Cobalamins Folic Acid Genetic Polymorphism Genotype Homocysteine Methylenetetrahydrofolate Reductase neuro-oncological ventral antigen 2, human Plasma
5
Meta-Analysis of MTHFR Polymorphisms
Cited 8 times
All statistic tests performed in this study were two tailed and P<0.05 was taken as statistically significant, unless otherwise stated. Statistic analyses were performed using STATA package version 11.0 program (Stata corp, College Station, TX). Hardy-Weinberg equilibrium (HWE) in controls was calculated again in our meta-analysis. The chi-square goodness of fit was used to test deviation from HWE.
Crude ORs with corresponding 95% CIs were calculated to estimate the strength of the associations of the MTHFR C677T and A1298C polymorphisms with H and/or HIP. The significance of the pooled OR was determined by the Z test. Pooled frequency analysis was carried out using the method suggested by Thakkinstian [18] (link). The overall pooled ORs were calculated using allele contrast model, dominant model and recessive model. Moreover, comparisons of OR1 (AA vs. aa), OR2 (Aa vs. aa) and OR3 (AA vs. Aa) were explored with A as the risk allele. The above pairwise differences were used to determine the most appropriate genetic model. If OR1 = OR3 ≠ 1 and OR2 = 1, then a recessive model is selected. If OR1 = OR2 ≠ 1 and OR3 = 1, then a dominant model is selected. If OR2 = 1/OR3 ≠ 1 and OR1 = 1, then a complete overdominant model is selected. If OR1> OR2>1 and OR1> OR3>1 (or OR1< OR2<1 and OR1< OR3<1), then a codominant model is selected [19] (link). Additionally, if some genotypes were very rare or could not be identified in either case or control group in some studies, a recessive or dominant model is selected to combine rare homozygous and heterozygous [20] (link).
Between-study heterogeneity was calculated by Cochran’s Chi-square based Q-test [21] (link). Simultaneously, it was also detected using the I2 statistic (I2 = 0–25% represents no heterogeneity; I2 = 25–50% represents moderate heterogeneity; I2 = 50–75% represents large heterogeneity; I2 = 75–100% represents extreme heterogeneity) [22] (link). If the between-study heterogeneity was statistically significant (P<0.10 for Q-test or I2>50%), the Dersimonian and Laird random effects model was used; otherwise, the Mantel Haenszel method fixed effects model was applied [23] (link). Subgroup analysis based on ethnicity (East Asians, Caucasians, Latinos, Indians and Sri Lankans, Black Africans), source of controls (population based vs. hospital based), genotyping method (polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) vs. “others”), sample size (studies with ≥ median number of participants vs. studies with < median number) and study quality (high quality vs. low quality), respectively, were also performed under the most appropriate genetic model. Furthermore, meta-regression was employed to explore potential sources of heterogeneity including publication date, ethnicity, genotyping method, source of controls, study quality and sample size [24] (link). To explore the dynamic trends as studies accumulated over time, cumulative meta-analysis was performed by date of publication [25] (link). Sensitivity analysis was also conducted to examine the influence of excluding each study or some specific studies on the overall estimate [25] (link). Finally, potential publication bias was assessed using funnel plot and Egger’s regression test [26] (link).
Yang B., Fan S., Zhi X., Li Y., Liu Y., Wang D., He M., Hou Y., Zheng Q, & Sun G. (2014). Associations of MTHFR Gene Polymorphisms with Hypertension and Hypertension in Pregnancy: A Meta-Analysis from 114 Studies with 15411 Cases and 21970 Controls. PLoS ONE, 9(2), e87497.
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Publication 2014
African People Alleles Caucasoid Races East Asian People Ethnicity Genetic Heterogeneity Genetic Polymorphism Heterozygote Homozygote Hypersensitivity Latinos Methylenetetrahydrofolate Reductase queuine Restriction Fragment Length Polymorphism

Most recents protocols related to «Methylenetetrahydrofolate Reductase»

1
Inherited Thrombophilia and Adverse Pregnancy Outcomes
In this study, patients had been incorporated from a prospective cohort of 358 pregnant recruited between 2016 and 2018 at the Clinic for Obstetrics and Gynecology, University Clinical Centre of Serbia, Belgrade, Serbia12 (link)
. Briefly, the study endorsed all the referred women with inherited thrombophilia between 11 and 15 weeks of gestation and followed up to the delivery. The examined parameters were laboratory parameters and Doppler flows of the umbilical artery at 28th to 30th, 32nd to 34th, and 36th to 38th gestational weeks (gw), use of LMWH prophylaxis, and obstetric and perinatal outcomes. For this study, we incorporated the cases with the complete data on values of the RiAu between 36th and 38th weeks of gestation and values of D-dimer between 36th and 38th weeks of gestation.
The exclusion criteria were as follows:
The data for the study were drawn from the patient records in the hospital database, including age, comorbid conditions (pulmonary embolism, insulin resistance, thyroid dysfunction), adverse health outcomes in the family history (arterial hypertension, HA; deep venous thrombosis, DVT; myocardial infarction, MI; cerebrovascular insult, CVI; pulmonary embolism, PULME; thyroid dysfunction, THR) if thrombophilia was recognized prior to the current pregnancy, previous APO, type of mutation responsible for thrombophilia (plasminogen-activator inhibitor, PAI; factor V Leiden; MTHFR mutation; prothrombin G20210A; protein S deficiency; factors VII, IX, and XI; or anti-thrombin-related mutation), mode of delivery, APO in the current pregnancy (in our study, we recorded pregnancy losses in the third trimester—intrauterine fetal death—preterm birth, fetal growth restriction), values of resistance index of umbilical artery (RiAu) between 36th and 38th weeks of gestation, and values of D-dimer between 36th and 38th weeks of gestation, the LMWH therapy in the current pregnancy, gestational age at delivery, and fetal sex. The characteristics of the participants from the original cohort are presented elsewhere12 (link)
. From the cohort of 358 pregnant patients, 203 had complete data on values of the RiAu between 36th and 38th weeks of gestation and values of D-dimer between 36th and 38th weeks of gestation and were selected for the analysis. These cases were classified into two groups according to the presence of APO in the current pregnancy: group with APO (33 cases, 16.3%) and group without APO (170 cases, 83.7%).
The statistical analyses were conducted using the methods of descriptive and analytical statistics. To this end, the means, standard deviations, skewness, and kurtosis were calculated for numerical data, and categorical variables were presented by absolute numbers with percentages. The differences between the groups with APO and without APO were analyzed using the chi-square (χ2) test for categorical variables and the Student's t-test for numerical variables. The path analysis was conducted to examine the relationship between LMWH therapy, previous APO, gestational age at delivery, RiAu between 36th and 38th weeks of gestation, D-dimer value between 36th and 38th weeks of gestation, and APO. Multiple measures were used to assess the adequacy of model fit to the data: the chi-square test and the fit indices such as the comparative fit index (CFI), the normed fit index (NFI), the adjusted goodness-of-fit index (AGFI), and the root mean square error of approximation (RMSEA). The model consistency was evaluated by the chi-square test, which indicates, when nonsignificant, that the data are consistent. The acceptable model fitting values for fit indices were defined as follows: CFI ≥0.95, NFI≥0.95, AGFI ≥0.95, and RMSEA <0.05. In all the analyses, the significance level was set at 0.05, and the statistical analyses were performed using the Amos 21 (IBM SPSS Inc., Chicago, IL, USA) and IBM SPSS Statistics 25 software.
Dugalic S., Petronijevic M., Sengul D., Detanac D.A., Sengul I., Veiga E.C., Stanisavljevic T., Macura M., Todorovic J, & Gojnic M. (2023). Hereditary thrombophilia and low -molecular -weight heparin in women: useful determinants, including thyroid dysfunction, incorporating the management of treatment and outcomes of the entity. Revista da Associação Médica Brasileira, 69(2), 335-340.
Publication 2023
Factor VII factor V Leiden Fetal Death Fetal Growth Retardation Fetus fibrin fragment D Gestational Age Heparin, Low-Molecular-Weight High Blood Pressures Insulin Resistance Methylenetetrahydrofolate Reductase Mutation Myocardial Infarction Obstetric Delivery Patients Plant Roots Plasminogen Inactivators Pregnancy Premature Birth Protein S Deficiency Prothrombin Pulmonary Embolism Therapeutics Thrombin Thrombophilia Thrombophilia, hereditary Thyroid Gland Umbilical Arteries Woman
2
MTHFR Gene Genotyping from Blood DNA
Genomic DNA was extracted from peripheral blood samples using Nucleon BACC1 (GE Healthcare, Chalfont Saint Giles, UK) or QIAamp DNA Blood Mini (Qiagen, Hilden, Germany) kits, according to the manufacturer’s instructions. The DNA was titrated by a Qubit 2.0 fluorometer (Invitrogen, Singapore) using a Qubit dsDNA BR assay kit (Life Technologies, Carlsbad, CA, USA). The genotyping of the two SNPs in the MTHFR gene (including c.665C>T (rs1801133) and c.1298A>C (rs1801131)) was performed using pre-designed TaqMan 5′ exonuclease assays (Applied Biosystems, Foster City, CA, USA); subsequently, the fluorescence signals of the probes were detected by an ABI 7300 Real-Time PCR System (Applied Biosystems), according to the supplier’s protocol.
Ravaei A., Pulsatelli L., Assirelli E., Ciaffi J., Meliconi R., Salvarani C., Govoni M, & Rubini M. (2023). MTHFR c.665C>T and c.1298A>C Polymorphisms in Tailoring Personalized Anti-TNF-α Therapy for Rheumatoid Arthritis. International Journal of Molecular Sciences, 24(4), 4110.
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Publication 2023
Biological Assay BLOOD DNA, Double-Stranded Exonuclease Fluorescence Genes Genome Methylenetetrahydrofolate Reductase Single Nucleotide Polymorphism
3
MTHFR C677T Genotype Analysis
The data were represented as means ± SD and statistical analysis was performed using GraphPad Prism 5 (version 5.01). Statistically significant differences among the study groups were set at p < 0.05 and analyzed by using one way ANOVA and Tukey’s Multiple Comparison test. The significant difference in genotype frequency of MTHFR C677T (rs1801133) among the study groups was calculated by using Fisher’s exact test.
Mallhi T.H., Shahid M., Rehman K., Khan Y.H., Alanazi A.S., Alotaibi N.H., Akash M.S, & Butt M.H. (2023). Biochemical Association of MTHFR C677T Polymorphism with Myocardial Infarction in the Presence of Diabetes Mellitus as a Risk Factor. Metabolites, 13(2), 251.
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Publication 2023
Genotype Methylenetetrahydrofolate Reductase neuro-oncological ventral antigen 2, human prisma
4
Genotyping of MTHFR C677T Polymorphism
To genotype the SNP of the MTHFR C677T (rs1801133) gene, Tetra-ARMS PCR was used. Three types of genotypes (C/C, C/T, and T/T) were found for the MTHFR C677T (rs1801133) gene. The base pair size used for Tetra ARMS PCR was 86 bp and 146 bp. For the amplification of the MTHFR C677T (rs1801133) gene, two forward primers and two reverse primers were used (Table 1). At the start of the reaction, two non-allele specific primers were amplified by the outer primers which produced outer fragments that served as a template for the attachment and elongation of inner primers.
A Thermocycler Master Cycler Gradient was used to perform PCR. PCR was carried out in a 20 µL container with 1 µL of DNA sample, 10 µL Master Mix, 0.1–0.5 mM of each primer, and 8 µL of DD-H2O. The initial denaturation temperature was 95 °C for 5 min, further followed by 40 cycles of denaturation at 94 °C for 30 s. Annealing was performed at 52 °C for 75 s, followed by extension at 72 °C for 40 s. The final extension temperature was again 72 °C for about 7 min. After the completion of the PCR reaction, 20 μL of PCR resultant product was placed in the well with 2% agarose gel, dyed with ethidium bromide, soaked in TAE buffer, and let to run in an electric field. The gel was then examined under UV light using a gel documentation system (InGenius3, Syngene, UK).
Mallhi T.H., Shahid M., Rehman K., Khan Y.H., Alanazi A.S., Alotaibi N.H., Akash M.S, & Butt M.H. (2023). Biochemical Association of MTHFR C677T Polymorphism with Myocardial Infarction in the Presence of Diabetes Mellitus as a Risk Factor. Metabolites, 13(2), 251.
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Publication 2023
Alleles Arm, Upper Base Pairing Electricity Ethidium Bromide Genes Genotype Methylenetetrahydrofolate Reductase Oligonucleotide Primers Sepharose Tetragonopterus tris-acetate-EDTA buffer Ultraviolet Rays
5
MTHFR C677T Polymorphism in MI and DM
The current study was a case–control study that was conducted to determine the relationship between the frequencies of MTHFR C677T (rs1801133) gene mutation in MI patients in the presence of DM as a risk factor by analyzing the polymorphism using Tetra-ARMS PCR. The sampling was performed in the Allied Hospital, Faisalabad and the Faisalabad institute of Cardiology, Faisalabad, Pakistan, according to the guidelines approved by the ethical committee of Government College University, Faisalabad, Pakistan. We recruited a total of 300 study participants who were divided into three groups: Group I included the control patients (n = 100), group II included MI patients (n = 100), and group III (n = 100) included MI with diabetic patients (MI-DM). The study participants were briefly informed of the purpose of the research and written consent was obtained from all the participants before collecting blood. The blood specimen was collected from the target population and underwent various biochemical analyses and Tetra-ARMS PCR for genotyping to observe the genetic polymorphism.
Mallhi T.H., Shahid M., Rehman K., Khan Y.H., Alanazi A.S., Alotaibi N.H., Akash M.S, & Butt M.H. (2023). Biochemical Association of MTHFR C677T Polymorphism with Myocardial Infarction in the Presence of Diabetes Mellitus as a Risk Factor. Metabolites, 13(2), 251.
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Publication 2023
A 300 Arm, Upper BLOOD Cardiovascular System factor A Genetic Polymorphism Methylenetetrahydrofolate Reductase Mutation Patients Target Population Tetragonopterus

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More about "Methylenetetrahydrofolate Reductase"

Methylenetetrahydrofolate reductase (MTHFR) is a critical enzyme involved in folate metabolism, catalyzing the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate.
This latter form is the predominant circulating folate species.
Variants or mutations in the MTHFR gene can lead to reduced enzyme activity, impacting important folate-dependent processes such as DNA synthesis, DNA repair, and homocysteine metabolism.
Understanding MTHFR and its genetic variants is crucial for researchers studying folate-related disorders, including neural tube defects, cardiovascular disease, and cancer.
Techniques like TaqMan SNP Genotyping Assays, QIAamp DNA extraction kits, and MassARRAY systems can be used to accurately genotype MTHFR polymorphisms.
Quantitative PCR methods on instruments like the ABI Prism 7900HT or LightCycler 480 can also be employed to measure MTHFR expression and activity.
PubCompare.ai's AI-powered approach helps researchers optimize their MTHFR-related research protocols by identifying the most reproducible and accurate methods from the scientific literature, preprints, and patents.
This can improve research outcomes and advance our understanding of this important enzyme and its role in folate metabolism and associated diseases.
By leveraging the latest technologies and analytical tools, researchers can gain deeper insights into the complexities of MTHFR and its impact on human health and disease.