Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population and Setting
2.2. Demographic Data
2.3. Genoty** and Matabolites Data
2.4. Data Analysis
3. Results
3.1. Characteristics of Study Participants
3.2. Most Influential Factors—The Ensemble Method
3.3. Predictive Modeling for Healthy Eating—Generalized Regression Analysis
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Factors | Control (Groups 1, 2) | Cancer (Groups 3, 4) | |||
---|---|---|---|---|---|
n (%) or M ± SD (Ranges) | 1-Healthy | 2-Chronic Diseases | 3-Cancer | 4-Advanced | p |
(n = 4) | (n = 11) | (n = 5) | (n = 10) | ||
Gender | |||||
Male | 0 (0%) | 4 (36.4%) | 5 (100%) | 2 (20%) | 0.008 |
Female | 4 (100%) | 11 (63.6%) | 0 (0%) | 8 (80%) | |
Age (Years) | 34 ± 14 | 43 ± 12 | 50 ± 11 | 60 ± 9 | 0.006 |
(19–51) | (21–58) | (38–62) | (44–72) | ||
Posthoc | <4 (p = 0.013) | <4 (p = 0.048) | |||
BMI | 24 ± 3.2 | 28 ± 8.5 | 24 ± 2.2 | 31 + 8.6 | 0.24 |
(17–28) | (21–49) | (19–29) | (19–51) | ||
Weight (Kg) | 63 ± 6.8 | 77 ± 26 | 72 ± 11 | 79 + 26 | 0.59 |
(57–71) | (52–141) | (59–88) | (45–138) | ||
Vegetable intake | 2.3 ± 0.0 | 2 ± 0.8 | 2.6 ± 0.6 | 1.6 ± 0.7 | 0.087 |
Cup Servings | (2–3) | (1–3) | (2–3) | (1–3) | |
Posthoc | <3 (p = 0.027) | ||||
Fruit | 1.3 ± 1.0 | 1.5 ± 0.7 | 1.8 ± 0.5 | 0.9 ± 0.7 | 0.073 |
Cup Servings | (0–2) | (0–2) | (1–2) | (0–2) | |
Posthoc | <3 (p = 0.015) | ||||
Whole grain cups | 1.5 ± 0.6 | 1.7 ± 0.7 | 1.8 ± 0.8 | 1.8 ± 0.8 | 0.92 |
(1–2) | (1–3) | (1–2) | (0–2) | ||
Liquid cups | 5.8 ± 1.5 | 5.5 ± 1.6 | 6.2 ± 1.6 | 5.3 ± 1.5 | 0.56 |
(5–8) | (4–8) | (5–8) | (4–8) | ||
Race | |||||
White (10) | 1 (25%) | 3 (27.3%) | 2 (20%) | 4 (40%) | 0.68 |
Asian (9) | 2 (50%) | 3 (27.3%) | 3 (30%) | 1 (10%) | |
Hispanic (9) | 1 (25%) | 4 (36.4%) | 0 (0%) | 4 (40%) | |
African (2) | 0 (0%) | 1 (9.1%) | 0 (0%) | 1 (10%) |
Genotype | Control (Groups 1, 2) | Cancer (Groups 3, 4) | p | ||
---|---|---|---|---|---|
Enzyme Deficiency | 1-Healthy | 2-Chronic Disease | 3-Cancer | 4-Advanced | |
(n = 4) | (n = 11) | (n = 5) | (n = 10) | ||
MTHFR 677 | |||||
0 (CC) | 2 (50%) | 5 (45.4%) | 2 (40%) | 2 (20%) | 0.70 |
1 (CT) | 1 (25%) | 5 (45.4%) | 2 (40%) | 7 (70%) | |
2 (TT) | 1 (25%) | 1 (9.1%) | 1 (20%) | 1 (10%) | |
MTHFR 1298 | |||||
0 (AA) | 2 (50%) | 7 (63.6%) | 4 (80%) | 7 (70%) | 0.82 |
1 (AC) | 2 (50%) | 4 (36.4%) | 1 (20%) | 2 (20%) | |
2 (CC) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (10%) | |
MTR 2756 | |||||
0 (AA) | 2 (50%) | 7 (63.6%) | 4 (80%) | 3 (30%) | 0.40 |
1 (AG) | 2 (50%) | 2 (18.2%) | 1 (20%) | 6 (60%) | |
2 (GG) | 0 (0%) | 2 (18.2%) | 0 (0%) | 1 (10%) | |
MTRR 66 | |||||
0 (AA) | 2 (66.7%) | 6 (54.5%) | 4 (40%) | 0.93 | |
1 (AG) | 0 (0%) | 3 (27.3%) | 1 (20%) | 4 (40%) | |
2 (GG) | 1 (33.3%) | 2 (18.2%) | 1 (20%) | 2 (20%) | |
DHFR 19 | |||||
00 (++) | 1 (25%) | 5 (45.4%) | 0 (0%) | 3 (30%) | 0.69 |
01 (+−) | 2 (50%) | 4 (36.4%) | 2 (40%) | 4 (40%) | |
11 (−−) | 1 (25%) | 2 (18.2%) | 3 (60%) | 3 (30%) | |
Total Mutation | |||||
≥4 | 1 (25%) | 4 (36.4%) | 1 (20%) | 8 (80%) | 0.077 |
3.25 ± 0.50 | 3.36 ± 1.57 | 2.20 ± 1.30 | 3.90 ± 1.45 | 0.16 | |
(3–4) | (1–6) | (1–4) | (1–6) | ||
Posthoc | <4 (p = 0.049) |
Metabolites | Control (Groups 1, 2) | Cancer (Groups 3, 4) | p | ||
---|---|---|---|---|---|
M + SD (ranges) | 1-Healthy n = 4 | 2-Chronic Disease n = 11 | 3-Cancer n = 5 | 4-Advanced n = 10 | |
Homocysteine (µmol/L) | 4.5 ± 1.8 (3.1–7) | 5.1 ± 1.0 (4.2–7.2) | 8.6 ± 3.8 (5.8–14) | 9.1 ± 4.2 (4–17) | 0.014 |
Posthoc | <4 (p = 0.023) | <3 (p = 0.028)<4 (p = 0.019) | |||
SAM (nmol/L) | 85 ± 24 (70–122) | 89 ± 17 (63–120) | 129 ± 61 (77–233) | 102± 21 (67–134) | 0.12 |
SAH (nmol/L) | 25 ± 14 (11–43) | 23 ± 7.2 (12–38) | 52 ± 51 (23–142) | 29 + 13 (16–56) | 0.25 |
Posthoc | <3 (p = 0.041) | ||||
SAM/SAH Ratio | 4.1 ± 1.9 (1.7–6.3) | 4.2 ± 1.1 (2.8–6.3) | 3.2 ± 1.1 (1.6–4.6) | 3.9 ± 1.1 (0–5.2) | 0.56 |
ADMA (nmol/L) | 573 ± 198 (393–849) | 519 ± 110 (278–720) | 666 ± 223 (472–917) | 557 ± 110 (406–754) | 0.77 |
SDMA (nmol/L) | 488 ± 130 (324–642) | 466 ± 78 (340–589) | 885 ± 671 (401–2050) | 516 ± 109 (425–778) | 0.44 |
Methionine (nmol/L) | 37 ± 10 (27–51) | 30 ± 7.3 (20–46) | 32 ± 4.8 (26–39) | 26 ± 6.2( 18–38) | 0.14 |
Posthoc | <3 (p = 0.041) | ||||
MMA (nmol/L) | 249 ± 48 (185–301) | 285 ± 229 (178–972) | 359 ± 72 (304–480) | 274 ± 97 (186–521) | 0.025 |
Posthoc | <3 (p = 0.02) | <3 (p = 0.013) | |||
Cystathionine (nmol/L) | 423 ± 267 (227–796) | 243 ± 147 (107–600) | 470 ± 221 (193–692) | 244 ± 102 (149–502) | 0.043 |
Posthoc | <3 (p = 0.041) | <3 (p = 0.043) | |||
Betaine (nmol/L) | 71 ± 18 (48–89) | 63 ± 20 (38–111) | 61 ± 24 (37–96) | 53 ± 11 (36–67) | 0.45 |
Vitamin B-6 (nmol/L) | 50 ± 16 (29–67) | 60 ± 42 (14–155) | 64 ± 52 (5.3–128) | 46 ± 24 (20–88) | 0.95 |
5-MTHF (nmol/L) | 30 ± 10 (18–43) | 48 ± 19 (30–97) | 36 ± 5.3 (32–45) | 36 ± 16 (18–78) | 0.063 |
Posthoc | <2 (p = 0.045) | ||||
Choline (nmol/L) | 12 ± 5.7 (7.9–21) | 9.7 ± 2.8 (5.7–16) | 14 ± 7.5 (8–27) | 10 ± 3.1 (6.9–18) | 0.50 |
Logistic Regression Original Model | Generalized Regression Elastic Net Model | |||||
---|---|---|---|---|---|---|
AICc Validation | Leave-One-Out Validation | |||||
Parameters | Estimate | p (X2) | Estimate | p (X2) | Estimate | p (X2) |
(Intercept) | −5.6 | 0.93 | 0.4 | 0.78 | 1.1 | 0.45 |
MMA * Gene mutations | −42 | 0.68 | −30 | <0.0001 | −11 | <0.0001 |
Homocysteine | −15 | 0.77 | −12 | <0.0001 | −5.7 | <0.0001 |
Methyl-folate | 14 | 0.69 | 9.1 | <0.0001 | 3.4 | 0.0019 |
Gene mutations | 14 | 0.86 | 11 | <0.0001 | 4.0 | 0.0188 |
Vegetable intake | 28 | 0.62 | 17 | <0.0001 | 5.6 | 0.0005 |
Age | −14 | 0.63 | −8.7 | <0.0001 | −2.9 | 0.0024 |
MMA | −0.4 | 0.996 | −1.7 | 0.28 | 0 | 1.0 |
Misclassification Rate | 0.2 | – | 0.03 | – | 0.04 | – |
AICc | 27 | – | 26 | – | – | – |
Area under the curve | 1.0 | – | 0.998 | – | 0.997 | – |
Logistic Regression Original Model | Generalized Regression Elastic Net Model | |||||
---|---|---|---|---|---|---|
AICc Validation | Leave-One-Out Validation | |||||
Parameters | Estimate | p (X2) | Estimate | p (X2) | Estimate | p (X2) |
(Intercept) | −0.4 | 0.997 | −0.36 | 0.79 | 1.2 | 0.38 |
MMA * Gene mutations | −35 | 0.77 | −29 | <0.0001 | −9.2 | <0.0001 |
Homocysteine | −13 | 0.63 | −12 | <0.0001 | −4.9 | <0.0001 |
Methyl-folate (MTHF) | 10 | 0.48 | 8.7 | <0.0001 | 2.8 | 0.0093 |
Gene mutations ≥4 | 17 | 0.92 | 12 | 0.0007 | 3.2 | 0.0496 |
Vegetable intake | 20 | 0.35 | 16 | <0.0001 | 4.4 | 0.0033 |
Age | −10 | 0.45 | −8.1 | <0.0001 | −2.5 | 0.0096 |
MMA | −1.9 | 0.99 | −0.7 | 0.64 | 0 | 1.0 |
MTHF * Gene mutations | −4.0 | 0.98 | −0.2 | 0.92 | 0 | 1.0 |
Misclassification Rate | 0.03 | – | 0.03 | – | 0.04 | – |
AICc | 30 | – | 30 | – | – | – |
Area under the curve | 0.998 | – | 0.998 | – | 0.997 | – |
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Shiao, S.P.K.; Grayson, J.; Yu, C.H. Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families. J. Pers. Med. 2018, 8, 26. https://doi.org/10.3390/jpm8030026
Shiao SPK, Grayson J, Yu CH. Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families. Journal of Personalized Medicine. 2018; 8(3):26. https://doi.org/10.3390/jpm8030026
Chicago/Turabian StyleShiao, S. Pamela K., James Grayson, and Chong Ho Yu. 2018. "Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families" Journal of Personalized Medicine 8, no. 3: 26. https://doi.org/10.3390/jpm8030026