Human 8-oxoguanine DNA N-glycosylase 1 (hOGG1) plays an important role in repairing oxidative DNA damage induced by tobacco carcinogens. In this case-control study, the authors examined the interactive effect of hOGG1 gene polymorphisms and cigarette smoking on the risk of lung cancer in Taiwan. A total of 1,096 cases and 1,007 controls were enrolled from 6 medical centers in Taiwan during 2002-2004. hOGG1 Ser326Cys genetic polymorphisms were determined using the MassARRAY system (SEQUENOM, Inc., San Diego, California). Tobacco smoking history was obtained through personal interview according to a structured questionnaire. Logistic regression analysis was used to estimate multivariate-adjusted odds ratios and 95% confidence intervals. The odds of developing lung cancer for persons with the Cys/Cys genotype versus the Ser/Ser genotype were 1.11 (95% confidence interval (CI): 0.74, 1.65) for never smokers, 1.45 (95% CI: 0.74, 2.83) for moderate smokers, and 3.52 (95% CI: 1.54, 8.06) for heavy smokers. The P value for interaction in the logistic model was 0.01. The increased risk associated with the Cys/Cys genotype among heavy smokers remained statistically significant for various histologic types of lung cancer, including adenocarcinoma, squamous cell carcinoma, and small cell carcinoma. The authors conclude that there was a noticeable modifying effect on the association between hOGG1 genotype and lung cancer risk by cigarette smoking status.

IHC4 and PAM50 assays have been shown to provide additional prognostic information for patients with early breast cancer. We evaluated whether incorporating TP53 mutation analysis can further enhance their prognostic accuracy. We examined TP53 mutation and the IHC4 score in tumors of 605 patients diagnosed with stage I–III breast cancer at National Taiwan University Hospital (the NTUH cohort). We obtained information regarding TP53 mutation and PAM50 subtypes in 699 tumors from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. We found that TP53 mutation was significantly associated with high-risk IHC4 group and with luminal B, HER2-enriched, and basal-like subtypes. Despite the strong associations, TP53 mutation independently predicted shorter relapse-free survival (hazard ratio [HR] = 1.63, P = 0.007) in the NTUH cohort and shorter breast cancer-specific survival (HR = 2.35, P = <0.001) in the METABRIC cohort. TP53 mutational analysis added significant prognostic information in addition to the IHC4 score (∆ LR-χ2 = 8.61, P = 0.002) in the NTUH cohort and the PAM50 subtypes (∆ LR-χ2 = 18.9, P = <0.001) in the METABRIC cohort. We conclude that incorporating TP53 mutation analysis can enhance the prognostic accuracy of the IHC4 and PAM50 assays.

For a genetic study in which there are concordant and discordant sibpairs for a complex disease trait and the measurements of other endophenotypes/intermediate phenotypes for each of the individuals are also available, we describe an allele-sharing based multipoint linkage test that utilizes nonparametrically the additional endophenotypes/intermediate phenotypes. The usefulness of this method is evaluated in simulation studies, which show that the gain in power is influenced by not only the endophenotypic value but also the correlation between the diagnosis-based phenotype and the endophenotype. In addition to reporting p values, our method also provides an index C(E), derived from the coefficients of the weight function associated with the endophenotype in the proposed statistic, to indicate the relevance of a specific endophenotype/intermediate phenotype in the genetic study. The simulation study indicates that a larger power, in general, corresponds to a larger value of the index C(E). The index C(E) is thus suggested as a quantity to be used in the choice of endophenotypes in linkage study. Data from the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe) are used to illustrate the method.

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Introduction A couple of studies investigated the energy balance in patients with obstructive sleep apnea (OSA) but the results were inconclusive. Moreover, OSA have been associated with visceral adiposity but the mechanism has not been fully elucidated. We hypothesized that OSA was associated with lower basal metabolic rate (BMR) and increased nutrition intake which further attributed to body adiposity. The aim of the present study is to determine the association between OSA, and BMR, nutrition intake, and body composition (BC). Methods Patients were recruited from referrals to sleep lab for suspect OSA. Measurement of the BMR with indirect calorimetry and BC with bioelectrical impedance analysis, and blood sampling were conducted in the morning next to the overnight polysomnography. Afterward, participants were evaluated with short -form IPAQ, 3-day intake dietary, 7-day sleep log, and wore Actiwatch for 7 days. The outcomes are resting energy expenditure (REE) and respiratory quotien (RQ), total fat mass (TFM), fat free mass (FFM), nutrition intake, daily total activity count, nightly sleep hour, and hormone. The association between OSA □apnea hypopnea index (AHI) &gt;=15/h□ and REE, RQ, TFM, and FFM was analyzed with multivariable linear regression. Results 85 patients were enrolled with median age 41.7 y/o, 78.6% male, body mass index (BMI) 25.4 kg/m2, and AHI 28.8/h. Compared to no OSA, patients with OSA had higher BMI, RQ, TFM, activity count, and similar age, gender, REE, FFM, nutrition intake, sleep hour, cortisol, leptin, and Ghrelin. OSA was independently associated with RQ (coefficient 0.031; 95% CI 0.004-0.057, p=0.022) with adjustment of age, gender, BMI, and activity count but not associated with REE, TFM, and FFM. Conclusion Though OSA may be associated with metabolic dysregulation, it was not associated with energy balance and BC. Further validation of the findings in a large scale and multi-ethnicity cohort to validate the findings of the present study is warranted. Support (if any) National Science and Technology Council, Taiwan (NST 111-2314-B-002-293; MOST 109-2314-B-002-252); Ministry of Education (NTU-107L900502, 108L900502, 109L900502)”, National Taiwan University Hospital (NTHU 108-S4331, 109-42, 111-S0298, 111-X0033); MediaTek Inc. (201802034 RIPD), and LARGAN Health AI-Tec CO., Ltd (202003021 RIPB)