BACKGROUND: Genes encoding enzymes involved in estrogen metabolism are held to be candidate genes for associations with breast disease. In these candidate genes, no critical combination of single‐nucleotide polymorphisms (SNPs) for assessing breast carcinoma risk has been reported to date.METHODS: In a large case–control study, the authors investigated 10 estrogen‐metabolizing SNPs in 396 patients with breast carcinoma, 154 patients with fibroadenoma, and 1936 healthy control patients without breast carcinoma in their personal history. The following 10 SNPs were analyzed using sequencing‐on‐chip technology via a solid‐phase polymerase chain reaction assay performed on oligonucleotide microarrays: catechol‐O‐methyltransferase Val158Met G→A, 17‐beta‐hydroxysteroid dehydrogenase type 1 vIV A→C, cytochrome P‐450 (CYP) family 17 A2 allele T→C, CYP1A1‐1 MspI restriction fragment length polymorphism (RFLP) T→C, CYP1A1‐2 Ile462Val A→G, CYP19‐1 Trp39Arg T→C, CYP19‐2 Arg264Cys C→T, CYP19‐3 Cys1558Thr C→T, steroid‐5‐alpha reductase type 2 Val89Leu G→C, and vitamin D receptor BsmI RFLP. A total of 21,350 genotypes were evaluated. Associations and two‐way interaction models were calculated using stepwise logistic regression.RESULTS: In a multiple model, CYP1A1‐1 (P = 0.004) and CYP1A1‐2 (P = 0.03) were found to be associated with significantly decreased and increased risks of breast carcinoma, respectively. When two‐way interactions involving investigated SNPs were ascertained, no significant interactions among polymorphisms were noted. Comparison of patients with fibroadenoma with control patients revealed significantly increased and decreased risks of fibroadenoma when the mutant alleles of CYP17 (P = 0.02) and CYP1A1‐1 (P = 0.04), respectively, were present.CONCLUSIONS: The authors obtained the first SNP data indicating that CYP17 and CYP1A1‐1 play a role in the pathogenesis of fibroadenoma. Although the authors were not able to develop interaction models involving SNPs, they did provide evidence that CYP1A1 is a low‐penetrance susceptibility gene with respect to breast carcinoma in a large series of Caucasian women. Cancer 2004. © 2004 American Cancer Society.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)–mass spectroscopy (MS) and flow injection analysis (FIA)–MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

Coronary artery disease (CAD) shares common risk factors with type 2 diabetes (T2DM). Variations in the transcription factor 7-like 2 (TCF7L2) gene, particularly rs7903146, increase T2DM risk. Potential links between genetic variants of the TCF7L2 locus and coronary atherosclerosis are uncertain. We therefore investigated the association between TCF7L2 polymorphisms and angiographically determined CAD in diabetic and non-diabetic patients. We genotyped TCF7L2 variants rs7903146, rs12255372, and rs11196205 in a cross-sectional study including 1,650 consecutive patients undergoing coronary angiography for the evaluation of established or suspected stable CAD. Significant CAD was diagnosed in the presence of coronary stenoses ≥50%. Variant rs7903146 in the total study cohort was significantly associated with significant CAD (adjusted additive OR = 1.29 [1.09–1.53]; p = 0.003). This association was strong and significant in T2DM patients (n = 393; OR = 1.91 [1.32–2.75]; p = 0.001) but not in non-diabetic subjects (OR = 1.09 [0.90–1.33]; p = 0.370). The interaction risk allele by T2DM was significant (p_interaction = 0.002), indicating a significantly stronger impact of the polymorphism on CAD in T2DM patients than in non-diabetic subjects. TCF7L2 polymorphisms rs12255372 and rs11196205 were also significantly associated with CAD in diabetic patients (adjusted additive OR = 1.90 [1.31–2.74]; p = 0.001 and OR = 1.75 [1.22–2.50]; p = 0.002, respectively). Further, haplotype analysis demonstrated that haplotypes including the rare alleles of all investigated variants were significantly associated with CAD in the whole cohort as well as in diabetic subjects (OR = 1.22 [1.04–1.43]; p = 0.013 and OR = 1.67 [1.19–2.22]; p = 0.003, respectively). These results suggest that TCF7L2 variants rs7903146 rs12255372, and rs11196205 are significantly associated with angiographically diagnosed CAD, specifically in patients with T2DM. TCF7L2 therefore appears as a genetic link between diabetes and atherosclerosis.