Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine
Abstract
:1. Introduction
2. Clinically Tested Genetic Variants within Genes Involved in Phase I/Phase II Detox Reactions
2.1. Phase I Detox Enzymes
2.1.1. Cytochrome P-450 1A2
2.1.2. Cytochrome P450 1B1
2.2. Phase II Detox Enzymes
2.2.1. Glutathione S-Transferases Mu 1 (GSTM1) and Theta 1 (GSTT1)
2.2.2. Catechol-O-Methyltransferase (COMT)
2.2.3. Bilirubin Uridine Diphosphate Glucuronosyl Transferase
3. Validation of Genetic Variants of Detox Metabolism in Real-World Clinical Settings
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase I Detox Enzymes | ||
---|---|---|
CYP1A2 | ||
Effect allele | Allele frequency | Effects on enzymatic function |
rs762551-C Strength of evidence: Convincing (A). | 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. | |
CYP1B1 | ||
Effect allele | Allele frequency | Effects on enzymatic function |
rs1056836-C Strength of evidence: Possible (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. | |
Phase II detox enzymes | ||
GSTM1 | ||
Effect allele | Allele frequency | Effects on enzymatic function |
GSTM1 deletion GSTT1 deletion Strength of evidence: Probable (B). | * -: deletion; +: present | Individuals carrying GSTM1 or GSTT1 double deletions (-/- genotype) may have a decreased ability to detoxify environmental toxicants, carcinogens, and products of oxidative stress. Gene deletions are more frequent among Caucasian and Asian populations and less frequent in African populations. Different segmental deletions have different frequencies in the population and between different ethnicities. |
COMT | ||
Effect allele | Allele frequency | Effects on enzymatic function |
rs4680-A Strength of evidence: Probable (B). | 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. | |
UGT1A1 | ||
Effect allele | Allele frequency | Effects on enzymatic function |
rs3064744-TA Strength of evidence: Possible (C). | 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. |
Food/Nutrient | Gene | Effects on Enzymatic Function |
---|---|---|
Caffeine | CYP1A2 | Caffeine is an inducer and substrate of CYP1A2. rs762551-C allele carriers are “slow” caffeine metabolizers, and they should limit coffee consumption to <1 cup/day or caffeine from other drinks to <100 mg/day to avoid being at higher risks of hypertension and myocardial infarction. In contrast, those with the AA genotype are “rapid” caffeine metabolizers and may benefit from consuming 1–4 cups of coffee/day due to increased consumption of phytonutrients presumed to be protective against heart disease. |
Cruciferous vegetables (broccoli, Brussels sprouts, cauliflower, watercress, and cabbage) | CYP1A2 | May increase CYP1A2 activity, but it is unclear whether the magnitude of this effect may depend on CYP1A genotype. |
GSTM1, GSTT1 | Individuals carrying gene deletions in GSTM1 or GSTT1, especially those carrying deletions in both genes, may have a more rapid excretion of bioactive nutrients found in cruciferous vegetables such as isothiocyanates and sulforaphane. Consequently, they may need to consume greater amounts of cruciferous vegetables than those who carry at least one copy of either GSTM1 or GSTT1. On the other side, double-deletion carriers tend to experience a greater increase in GST activity and GST-mediated detoxification upon consumption of cruciferous vegetables or cruciferous-based supplements such as 2-phenethyl isothiocyanate (PEITC). The GST-inducing effects of cruciferous vegetables are more pronounced in females than in males. | |
UGT1A1 | 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.) | CYP1A2 | May decrease CYP1A2 activity, but it is unclear whether the magnitude of this effect may depend on CYP1A genotype. May exert inhibitory effects on GSTM-1 in men, not women, who carry at least one copy of the GSTM1 gene. |
GSTM1, GSTT1 | May exert inhibitory effects on GSTM1 in men, not women, who carry at least one copy of the GSTM1 gene. | |
UGTA1 | 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.) | CYP1B1 | 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). |
GSTM1, GSTT1 | 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 | COMT | Individuals with the rs4680 AA genotype, who have slow COMT activity, may be slow catechin metabolizers and retain more catechins in the blood than those with the GG genotype. As a result, they may benefit from a lower intake of tea catechins. In contrast, those with the GG genotype, who have higher COMT activity, may be more sensitive to the short-term effects of tea catechins, such as an increase in insulin secretion and blood pressure (BP). |
Olive oil, red wine | COMT | Individuals with the rs4680 GG genotype, who have higher COMT activity, may experience the health benefits of olive oil and red wine at lower intakes than those with the AA genotype. This is due to a greater ability to convert hydroxytyrosol, a phenolic compound in virgin olive oil and red wine, into its cardioprotective metabolite homovanillyl alcohol (HVAL). |
Citrus fruit | UGT1A1 | May help lower serum bilirubin in women with the rs3064744 TA/TA genotype. These effects may be noticeable in all TA allele carriers. |
Mean (SD) | |
---|---|
n = 157 | |
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%) |
CYP1A2|rs762551-C | CYP1B1|rs1056836-C | |||||||
---|---|---|---|---|---|---|---|---|
Genotype | Subjects (%) | Mean (SD) | p-Value | Genotype | Subjects (%) | Mean (SD) | p-Value | |
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) | |||
COMT|rs4680-A | Polygenic Risk Score | |||||||
Genotype | Subjects (%) | Mean (SD) | p-Value | Genetic Risk | Subjects (%) | Mean (SD) | p-Value | |
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) |
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Aronica, L.; Ordovas, J.M.; Volkov, A.; Lamb, J.J.; Stone, P.M.; Minich, D.; Leary, M.; Class, M.; Metti, D.; Larson, I.A.; et al. Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine. Nutrients 2022, 14, 768. https://doi.org/10.3390/nu14040768
Aronica L, Ordovas JM, Volkov A, Lamb JJ, Stone PM, Minich D, Leary M, Class M, Metti D, Larson IA, et al. Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine. Nutrients. 2022; 14(4):768. https://doi.org/10.3390/nu14040768
Chicago/Turabian StyleAronica, Lucia, Jose M. Ordovas, Andrey Volkov, Joseph J. Lamb, Peter Michael Stone, Deanna Minich, Michelle Leary, Monique Class, Dina Metti, Ilona A. Larson, and et al. 2022. "Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine" Nutrients 14, no. 4: 768. https://doi.org/10.3390/nu14040768
APA StyleAronica, L., Ordovas, J. M., Volkov, A., Lamb, J. J., Stone, P. M., Minich, D., Leary, M., Class, M., Metti, D., Larson, I. A., Contractor, N., Eck, B., & Bland, J. S. (2022). Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine. Nutrients, 14(4), 768. https://doi.org/10.3390/nu14040768