Examples of sex/gender sensitivity in epidemiological research: results of an evaluation of original articles published in JECH 2006-2014.

BACKGROUND: During the last decades, sex and gender biases have been identified in various areas of biomedical and public health research, leading to compromised validity of research findings. As a response, methodological requirements were developed but these are rarely translated into research practice. The aim of this study is to provide good practice examples of sex/gender sensitive health research. METHODS: We conducted a systematic search of research articles published in JECH between 2006 and 2014. An instrument was constructed to evaluate sex/gender sensitivity in four stages of the research process (background, study design, statistical analysis, discussion). RESULTS: In total, 37 articles covering diverse topics were included. Thereof, 22 were evaluated as good practice example in at least one stage; two articles achieved highest ratings across all stages. Good examples of the background referred to available knowledge on sex/gender differences and sex/gender informed theoretical frameworks. Related to the study design, good examples calculated sample sizes to be able to detect sex/gender differences, selected sex/gender sensitive outcome/exposure indicators, or chose different cut-off values for male and female participants. Good examples of statistical analyses used interaction terms with sex/gender or different shapes of the estimated relationship for men and women. Examples of good discussions interpreted their findings related to social and biological explanatory models or questioned the statistical methods used to detect sex/gender differences. CONCLUSIONS: The identified good practice examples may inspire researchers to critically reflect on the relevance of sex/gender issues of their studies and help them to translate methodological recommendations of sex/gender sensitivity into research practice.

The Stem Cell Factor HMGA2 Is Expressed in Non-HPV-Associated Head and Neck Squamous Cell Carcinoma and Predicts Patient Survival of Distinct Subsites.

BACKGROUND: The transcription factor high-mobility AT-hook 2 (HMGA2) is involved in stem cell renewal and is expressed in many tumor tissues. Head and neck squamous cell carcinomas (HNSCC) comprise tumors of the upper aerodigestive tract and are characterized by high recurrence rates that represent a challenge to patient management. The study addresses the potential of HMGA2 as a molecular biomarker for HNSCC patient survival. METHODS: Patients with HNSCC of the larynx, pharynx, tonsils, or oral cavity were recruited in a hospital-based case-control study (n = 202). Quantitative expression of HMGA2 in tumor tissues was measured by RT-PCR. In a 6- to 10-year follow-up, secondary cancers, vital status, and cause of death were ascertained. The HR and 95% confidence intervals (CI) for overall, tumor-specific, and progression-free survival were estimated by Cox proportional hazards with HMGA2 expression level as the independent variable. RESULTS: High HMGA2 expression in tumor tissues of HNSCC patients was significantly correlated with negative HPV status (P = 0.01), and associated with shorter overall survival time. In Cox regression modeling, HMGA2 expression yielded a risk increase for overall and tumor-specific death in subsets of HNSCC patients, that is, laryngeal cancer patients (overall survival: HR = 4.00; 95% CI, 1.18-13.62) and in oral cancer patients (tumor-specific survival: HR = 2.88; 95% CI, 1.06-7.84), but not in patients with pharyngeal and tonsillar HNSCC. CONCLUSIONS: HMGA2 expression is associated with a risk increase for adverse outcomes in patients with HNSCC of the larynx and oral cavity. IMPACT: The understanding of stem cell signaling in HNSCC may offer new strategies for cancer treatment. Cancer Epidemiol Biomarkers Prev; 26(2); 197-205. ©2016 AACR.

Pretreatment oral hygiene habits and survival of head and neck squamous cell carcinoma (HNSCC) patients.

BACKGROUND: The survival time of patients with head and neck squamous cell carcinoma (HNSCC) is related to health behavior, such as tobacco smoking and alcohol consumption. Poor oral health (OH), dental care (DC) and the frequent use of mouthwash have been shown to represent independent risk factors for head and neck cancerogenesis, but their impact on the survival of HNSCC patients has not been systematically investigated. METHODS: Two hundred seventy-six incident HNSCC cases recruited for the ARCAGE study were followed through a period of 6-10 years. Interview-based information on wearing of dentures, gum bleeding, teeth brushing, use of floss and dentist visits were grouped into weighted composite scores, i.e. oral health (OH) and dental care (DH). Use of mouthwash was assessed as frequency per day. Also obtained were other types of health behavior, such as smoking, alcohol drinking and diet, appreciated as both confounding and study variables. Endpoints were progression-free survival, overall survival and tumor-specific survival. Prognostic values were estimated using Kaplan-Meier analysis and Cox proportional hazards regression models. RESULTS: A good dental care score, summarizing annual dental visits, daily teeth cleaning and use of floss was associated with longer overall survival time (p = .001). The results of the Cox regression models similarly suggested a higher risk of tumor progression and shortened overall survival in patients with poor dental care, but the results lost their statistical significance after other types of health behavior had been controlled for. Frequent use of mouthwash (≥ 2 times/day) significantly increased the risk of tumor-specific death (HR = 2.26; CI = 1.19-4.32). Alcohol consumption and tobacco smoking were dose-dependently associated with tumor progression and shorter overall survival. CONCLUSION: Frequent mouthwash use of ≥ 2 times/day seems to elevate the risk of tumor-specific death in HNSCC patients. Good dental care scores are associated with longer overall survival.

The influence of aerobic fitness on obesity and its parent-offspring correlations in a cross-sectional study among German families.

BACKGROUND: Overweight/obesity is an important public health burden worldwide, increasing the risk for the development of cardiovascular diseases or the metabolic syndrome. This risk may be reduced by a good aerobic fitness (AF) that can be improved by physical activity but is also influenced by genetic factors. The aim of this study was to test for familial aggregation of AF measured by maximal oxygen uptake (VO2max) and to estimate its heritability. Furthermore, an exploratory analysis of the association between overweight/obesity and AF was performed. In contrast to previous studies, all analyses were adjusted for additional environmental and behavioral factors, in particular for objectively measured physical activity (PA) in addition to body mass index (BMI). METHODS: 79 families (157 parents, 132 children) performed a maximum exercise test (spiroergometry) to assess maximum oxygen uptake. PA was measured by accelerometry. Familial aggregation of AF was determined using a two-step design: AF was adjusted for age, sex and age*sex using linear regression. Afterwards, the residuals were used to determine the intraclass correlation coefficient (ICC) by ANOVA. Heritability and associations were estimated by generalized linear mixed models. RESULTS: Familial aggregation of AF (ICC = 0.22, p < 0.001) was significant but decreased when adjusted for PA or BMI. Its heritability was estimated as 40% (adjusted for PA) using the mid-parent-offspring design. Relative to the middle quintile of AF residuals, the odds of being overweight/obese were three- to tenfold reduced in the upper quintile (adjusted for age, sex, age*sex, PA). CONCLUSIONS: AF clustered in families after controlling for PA, BMI and parental smoking. Heritability was stronger for mother-child pairs as compared to father-child pairs after controlling for PA and BMI. Above average AF was negatively associated with overweight/obesity.

Artificial neural networks modeling gene-environment interaction.

BACKGROUND: Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. RESULTS: In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. CONCLUSION: Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

Neural networks for modeling gene-gene interactions in association studies.

BACKGROUND: Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied. RESULTS: The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction. CONCLUSIONS: Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.