| Literature DB >> 35215417 |
Lucia Aronica1,2, Jose M Ordovas3,4,5, Andrey Volkov6, Joseph J Lamb7, Peter Michael Stone7,8,9,10, Deanna Minich8,11, Michelle Leary12, Monique Class8,13, Dina Metti7, Ilona A Larson1, Nikhat Contractor14, Brent Eck1, Jeffrey S Bland15.
Abstract
Metabolic detoxification (detox)-or biotransformation-is a physiological function that removes toxic substances from our body. Genetic variability and dietary factors may affect the function of detox enzymes, thus impacting the body's sensitivity to toxic substances of endogenous and exogenous origin. From a genetic perspective, most of the current knowledge relies on observational studies in humans or experimental models in vivo and in vitro, with very limited proof of causality and clinical value. This review provides health practitioners with a list of single nucleotide polymorphisms (SNPs) located within genes involved in Phase I and Phase II detoxification reactions, for which evidence of clinical utility does exist. We have selected these SNPs based on their association with interindividual variability of detox metabolism in response to certain nutrients in the context of human clinical trials. In order to facilitate clinical interpretation and usage of these SNPs, we provide, for each of them, a strength of evidence score based on recent guidelines for genotype-based dietary advice. We also present the association of these SNPs with functional biomarkers of detox metabolism in a pragmatic clinical trial, the LIFEHOUSE study.Entities:
Keywords: LIFEHOUSE study; biomarkers; biotransformation; detoxification; environmental health; functional medicine; nutrigenomics; personalized lifestyle medicine; pragmatic clinical trials; single nucleotide polymorphisms
Mesh:
Substances:
Year: 2022 PMID: 35215417 PMCID: PMC8876337 DOI: 10.3390/nu14040768
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Common genetic variants within genes involved in Phase I/Phase II detox reactions associated with variability of response to foods or nutrients that modulate detox metabolism.
| Phase I Detox Enzymes | ||
|---|---|---|
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| Effect allele | Allele frequency | Effects on enzymatic function |
| rs762551-C |
| C-allele carriers produce an enzyme variant with 62–70% lower activity and are less inducible by xenobiotics. Low CYP1A activity can result in decreased clearance of toxins, a lower 2/16-alpha hydroxyestrone ratio, and a higher risk of certain cancers. Consequently, lower production of reactive detoxification intermediates may reduce oxidative stress. |
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| Effect allele | Allele frequency | Effects on enzymatic function |
| rs1056836-C |
| Individuals with the CC genotype tend to have higher enzymatic activity than G-allele carriers, which may result in greater activation of toxicants, greater production of 4-hydroxy estrogens, and greater oxidative damage. The effects of this SNP are affected by age, ethnicity, and menopausal status. |
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| Effect allele | Allele frequency | Effects on enzymatic function |
| Individuals carrying | ||
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| Effect allele | Allele frequency | Effects on enzymatic function |
| rs4680-A |
| The A allele (Met) produces an enzyme with 40 % lower activity than that encoded by the G allele (Val). A-allele carriers may have a decreased ability to degrade neurotransmitters, estrogen, and various xenobiotics. This may result in increased sensitivity to environmental toxicants, a higher risk of developing neuropsychiatric disorders, and impaired estrogen metabolism. |
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| Effect allele | Allele frequency | Effects on enzymatic function |
| rs3064744-TA |
| Individuals carrying two insertion alleles (TA/TA genotype) may have a lower enzymatic activity than those carrying at most one copy of the deletion allele (-). This may result in increased toxicity in response to certain drugs (acetaminophen) and to a benign cardio-protective condition known as Gilbert syndrome, characterized by increased serum levels of total and unconjugated bilirubin. |
Abbreviations: SNP identification numbers (noted as “rs...”) are the unique SNP identifiers from the NCBI dbSNP database. CYP1A2: Cytochrome P-450 1A2; CYP1B1: Cytochrome P-450 1B1; GSTM1: Glutathione S-transferase mu 1; GSTT1: Glutathione S-transferase theta 1; COMT: Catechol-O-methyltransferase; UGT1A1: UDP-glucuronosyltransferase A-1.
Foods and nutrients that modulate the activity of Phase I/Phase II enzymes and their interaction with genotype.
| Food/Nutrient | Gene | Effects on Enzymatic Function |
|---|---|---|
| Caffeine |
| Caffeine is an inducer and substrate of |
| Cruciferous vegetables (broccoli, Brussels sprouts, cauliflower, watercress, and cabbage) |
| May increase |
| Individuals carrying gene deletions in | ||
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| May decrease serum bilirubin levels in rs3064744-TA allele carriers with greater effects observed for TA/TA homozygous. | |
| Apiaceous vegetables (carrots, celery, dill, parsley, parsnips, etc.) |
| May decrease |
| May exert inhibitory effects on GSTM1 in men, not women, who carry at least one copy of the | ||
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| May decrease serum bilirubin levels in rs3064744-TA allele carriers with greater effects observed for TA/TA homozygous. | |
| Quercetin and antioxidant rich foods (citrus fruits, apples, onions, red wine, olive oil, dark berries, etc.) |
| Quercetin may reduce oxidative stress to a greater extent in rs1056836-G allele carriers than in those with the CC genotype. These findings were made with quercetin from fruit juices at doses significantly lower (~100 mg) than those typically used for supplementation (500–1000 mg). |
| Quercetin and other antioxidants from blueberry, apples, and purple grapes may reduce oxidative stress to a greater extent in GST double deletion carriers than GST-positive individuals. Smokers who carry GST deletions may especially benefit from supplementation with antioxidants because carcinogens in cigarette smoke can overload their detox capacity and induce a higher production of ROS byproducts. However, quercetin and other antioxidants seem to improve certain oxidative stress markers such as glutathione levels and vitamin C to a greater extent in those with at least one copy of GSTM-1 or GSTT-1. | ||
| Tea catechins |
| Individuals with the rs4680 AA genotype, who have slow |
| Olive oil, red wine |
| Individuals with the rs4680 GG genotype, who have higher |
| Citrus fruit |
| May help lower serum bilirubin in women with the rs3064744 TA/TA genotype. These effects may be noticeable in all TA allele carriers. |
Abbreviations: SNP identification numbers (noted as “rs...”) are the unique SNP identifiers from the NCBI dbSNP database. CYP1A2: Cytochrome P-450 1A2; CYP1B1: Cytochrome P-450 1B1; GST: Glutathione S-transferase; GSTM1: Glutathione S-transferase mu 1; GSTT1: Glutathione S-transferase theta 1; COMT: Catechol-O-methyltransferase; UGT1A1: UDP-glucuronosyltransferase A-1; ROS: Radical Oxygen Species.
Baseline demographics and functional biomarkers.
| Mean (SD) | |
|---|---|
| Age, years | 43 (11) |
| Sex | |
| Female | 106 (68%) |
| Male | 51 (32%) |
| Ethnicity | |
| Caucasian | 77 (49%) |
| Asian | 20 (13%) |
| African American | 4 (2%) |
| Mediterranean | 5 (3%) |
| Northern European | 14 (9%) |
| Native American | 6 (4%) |
| Other | 19 (12%) |
| Homocysteine (µmol/L) | 9.1 (3) |
| Missing | 3 (2%) |
| oxLDL (U/L) | 44 (13.8) |
| Missing | 26 (16%) |
| GGT (U/L) | 22.5 (16.7) |
| Missing | 3 (2%) |
Association between genetic variants in Phase I/Phase II detox enzymes and levels of homocysteine, oxLDL, and GGT. For each biomarker (rows), we report the mean levels and standard deviation in groups of subjects carrying the same genotype of the selected genetic variants.
| CYP1A2|rs762551-C | CYP1B1|rs1056836-C | |||||||
|---|---|---|---|---|---|---|---|---|
| Genotype | Subjects (%) | Mean (SD) | Genotype | Subjects (%) | Mean (SD) | |||
| Hcy | AA | 85 (55.2) | 9.21 (2.89) | 0.796 | GG | 58 (37.7) | 8.87 (2.34) | 0.232 |
| AC | 57 (37.0) | 8.91 (2.90) | CG | 66 (42.9) | 8.77 (2.46) | |||
| CC | 12 (7.8) | 8.71 (1.85) | CC | 30 (19.5) | 10.05 (4.02) | |||
| oxLDL | AA | 66 (50.4) | 46.21 (13.80) | 0.018 | GG | 45 (34.4) | 44.82 (11.52) | 0.459 |
| AC | 55 (42.0) | 43.23 (13.75) | CG | 58 (44.3) | 43.01 (16.12) | |||
| CC | 10 (7.6) | 34.00 (10.08) | CC | 28 (21.4) | 44.85 (12.29) | |||
| GGT | AA | 85 (55.2) | 22.58 (17.08) | 0.862 | GG | 58 (37.7) | 22.67 (19.29) | 0.555 |
| AC | 57 (37.0) | 21.86 (15.43) | CG | 66 (42.9) | 23.77 (16.82) | |||
| CC | 12 (7.8) | 24.33 (21.39) | CC | 30 (19.5) | 19.1 (9.95) | |||
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| Hcy | GG | 57 (37.0) | 9.02 (2.49) | 0.853 | Low | 88 (57.14) | 8.79 (2.15) | 0.215 |
| AG | 68 (44.2) | 8.88 (2.46) | Medium | 45 (29.22) | 8.96 (2.84) | |||
| AA | 29 (18.8) | 9.54 (4.02) | High | 21 (13.64) | 10.40 (4.53) | |||
| oxLDL | GG | 48 (31.2) | 43.08 (12.81) | 0.728 | Low | 71 (54.20) | 45.92 (13.32) | 0.103 |
| AG | 59 (38.3) | 43.85 (14.59) | Medium | 40 (30.53) | 40.45 (13.72) | |||
| AA | 24 (15.6) | 46.38 (14.16) | High | 20 (15.27) | 44.50 (15.07) | |||
| GGT | GG | 57 (37.0) | 22.93 (19.36) | 0.23 | Low | 88 (57.14) | 24.00 (19.25) | 0.890 |
| AG | 68 (44.2) | 24.44 (17.04) | Medium | 45 (29.22) | 20.16 (12.15) | |||
| AA | 29 (18.8) | 16.83 (6.82) | High | 21 (13.64) | 20.86 (13.57) | |||
Abbreviations: Hcy: homocysteine; oxLDL: oxidized low-density lipoprotein; GGT: gamma-glutamyltransferase; PRS: Polygenic Risk Score.
Figure 1Distribution of homocysteine, oxLDL, and GGT values by genotype. The violin plots show the density plot and statistical summary of homocysteine, oxLDL, and GGT values for each genotype of the CYP1A1, CYP1B2, and COMT genes. The white dot of the box depicts the median, the thick gray bar in the center represents the interquartile range (IQR), which indicates the spread of the middle half of the distribution, and the thin line extending from the gray bar represents the rest of the distribution lying between the ±1.5× IQR range. The points represent the actual distribution of individual data. On each side of the gray line is a smoothed estimation of probabilities for new points, calculated using the Kernel Density Estimation, showing the distribution shape of the data. Wider sections of the violin plot represent a higher probability that members of the population will have the given value; the skinnier sections represent a lower probability.