Variable selection in linear regression models: Choosing the best subset is not always the best choice.

We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often considered the "gold standard," with its use being restricted only by its NP-hard nature. Alternatives such as the least absolute shrinkage and selection operator (Lasso) or the Elastic net (Enet) have become methods of choice in high-dimensional settings. A recent proposal represents BSS as a mixed-integer optimization problem so that large problems have become computationally feasible. We present an extensive neutral comparison assessing the ability to select the correct direct predictors of BSS compared to forward stepwise selection (FSS), Lasso, and Enet. The simulation considers a range of settings that are challenging regarding dimensionality (number of observations and variables), signal-to-noise ratios, and correlations between predictors. As fair measure of performance, we primarily used the best possible F1-score for each method, and results were confirmed by alternative performance measures and practical criteria for choosing the tuning parameters and subset sizes. Surprisingly, it was only in settings where the signal-to-noise ratio was high and the variables were uncorrelated that BSS reliably outperformed the other methods, even in low-dimensional settings. Furthermore, FSS performed almost identically to BSS. Our results shed new light on the usual presumption of BSS being, in principle, the best choice for selecting the correct direct predictors. Especially for correlated variables, alternatives like Enet are faster and appear to perform better in practical settings.

Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort.

AIMS/HYPOTHESIS: There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution. METHODS: Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin. RESULTS: A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin. CONCLUSIONS/INTERPRETATION: The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.

Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation.

BACKGROUND: Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation. METHODS: Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures. RESULTS: We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data. CONCLUSION: N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.

In atrial fibrillation epilepsy risk differs between oral anticoagulants: active comparator, nested case-control study.

AIMS: Atrial fibrillation (AF) is a risk factor for brain infarction, which can lead to epilepsy. We aimed to investigate whether treatment of AF with direct oral anticoagulants (DOACs) affects the risk of epilepsy in comparison to treatment with the vitamin K antagonist phenprocoumon (PPC). METHODS AND RESULTS: We performed an active comparator, nested case-control study based on the German Pharmacoepidemiological Research Database that includes claims data from statutory health insurance providers of about 25 million persons since 2004. In 2011-17, 227 707 AF patients initiated treatment with a DOAC or PPC, of which 1828 cases developed epilepsy on current treatment with an oral anticoagulant. They were matched to 19 084 controls without epilepsy. Patients with DOAC treatment for AF had an overall higher risk of epilepsy with an odds ratio of 1.39, 95% CI (1.24; 1.55) compared to current PPC treatment. Cases had higher baseline CHA2DS2-VASc scores and more frequently a history of stroke than controls. After excluding patients with ischaemic stroke prior to the diagnosis of epilepsy, the risk of epilepsy was still higher on DOACs than on PPC. In contrast, within a cohort of patients with venous thromboembolism, the risk of epilepsy on treatment with DOACs was less elevated [adjusted odds ratio 1.15, 95% CI (0.98; 1.34)]. CONCLUSION: In patients with AF initiating oral anticoagulation, treatment with a DOAC was associated with an increased risk of epilepsy compared to the vitamin K antagonist PPC. Covert brain infarction may explain the observed elevated risk of epilepsy.

Longitudinal association of inflammatory markers with markers of glycaemia and insulin resistance in European children.

PURPOSE: Subclinical systemic inflammation may lead to development of type 2 diabetes, but there has been no investigation into its relationship with early progression of glycaemic deterioration and insulin resistance, especially in younger population. In this study we assessed longitudinal associations of pro- and anti-inflammatory markers with markers that evaluate glycaemia and insulin resistance. METHODS: This study includes 6537 initially nondiabetic children (mean age at baseline = 6.2 years) with repeated measurements from the IDEFICS/I.Family cohort study (mean follow-up = 5.3 years) from eight European countries. Markers of inflammation were used as independent variables and markers of glycaemia/insulin resistance as dependent variables. Associations were examined using two-level growth model. Models were adjusted for sex, age, major lifestyle, metabolic risk factors, early life markers, and other inflammatory markers in final model. RESULTS: Children with 6 years of follow-up showed that a one-unit increase in z-score of leptin level was associated with 0.38 (95% CI = 0.32 to 0.44) unit increase in HOMA-IR z-scores. Leptin continued to be associated with HOMA-IR even when analysis was limited to children with no overall obesity, no abdominal obesity, and low to normal triglyceride levels. An inverse association was observed between IL-15 and HOMA-IR (ß = -0.11, 95% CI = -0.15 to -0.07). CONCLUSIONS: IL-15 should be evaluated further in the prevention or treatment of prediabetes whereas leptin may prove to be useful in early detection of prediabetes via their association with markers of insulin resistance in European children.

Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data.

Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focusing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when building the graph. Until recently, these algorithms have been unable to handle missing values. In this article, we investigate two alternative solutions: test-wise deletion and multiple imputation. We establish necessary and sufficient conditions for the recoverability of causal structures under test-wise deletion, and argue that multiple imputation is more challenging in the context of causal discovery than for estimation. We conduct an extensive comparison by simulating from benchmark causal graphs: as one might expect, we find that test-wise deletion and multiple imputation both clearly outperform list-wise deletion and single imputation. Crucially, our results further suggest that multiple imputation is especially useful in settings with a small number of either Gaussian or discrete variables, but when the dataset contains a mix of both neither method is uniformly best. The methods we compare include random forest imputation and a hybrid procedure combining test-wise deletion and multiple imputation. An application to data from the IDEFICS cohort study on diet- and lifestyle-related diseases in European children serves as an illustrating example.

Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents.

BACKGROUND: Childhood obesity is a complex multifaceted condition, which is influenced by genetics, environmental factors, and their interaction. However, these interactions have mainly been studied in twin studies and evidence from population-based cohorts is limited. Here, we analyze the interaction of an obesity-related genome-wide polygenic risk score (PRS) with sociodemographic and lifestyle factors for BMI and waist circumference (WC) in European children and adolescents. METHODS: The analyses are based on 8609 repeated observations from 3098 participants aged 2-16 years from the IDEFICS/I.Family cohort. A genome-wide polygenic risk score (PRS) was calculated using summary statistics from independent genome-wide association studies of BMI. Associations were estimated using generalized linear mixed models adjusted for sex, age, region of residence, parental education, dietary intake, relatedness, and population stratification. RESULTS: The PRS was associated with BMI (beta estimate [95% confidence interval (95%-CI)] = 0.33 [0.30, 0.37], r2 = 0.11, p value = 7.9 × 10-81) and WC (beta [95%-CI] = 0.36 [0.32, 0.40], r2 = 0.09, p value = 1.8 × 10-71). We observed significant interactions with demographic and lifestyle factors for BMI as well as WC. Children from Southern Europe showed increased genetic liability to obesity (BMI: beta [95%-CI] = 0.40 [0.34, 0.45]) in comparison to children from central Europe (beta [95%-CI] = 0.29 [0.23, 0.34]), p-interaction = 0.0066). Children of parents with a low level of education showed an increased genetic liability to obesity (BMI: beta [95%-CI] = 0.48 [0.38, 0.59]) in comparison to children of parents with a high level of education (beta [95%-CI] = 0.30 [0.26, 0.34]), p-interaction = 0.0012). Furthermore, the genetic liability to obesity was attenuated by a higher intake of fiber (BMI: beta [95%-CI] interaction = -0.02 [-0.04,-0.01]) and shorter screen times (beta [95%-CI] interaction = 0.02 [0.00, 0.03]). CONCLUSIONS: Our results highlight that a healthy childhood environment might partly offset a genetic predisposition to obesity during childhood and adolescence.

The temporal relationship between parental concern of overeating and childhood obesity considering genetic susceptibility: longitudinal results from the IDEFICS/I.Family study.

BACKGROUND: Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood. METHODS: The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean ± SD follow-up 5.8 ± 0.4). Children examined in 2007/2008 (wave 1) (mean ± SD age: 4.4 ± 1.1, range: 2-9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire. RESULTS: In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI (ß = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (ß = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 (ß = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (ß = 0.09, 95% CI: 0.07, 0.12). CONCLUSIONS: The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain. TRIAL REGISTRATION: Registry number: ISRCTN62310987 Retrospectively registered 17 September 2018.

Circulating miRNAs are associated with sleep duration in children/adolescents: Results of the I.Family Study.

NEW FINDINGS: What is the central question of this study? Are differential patterns of circulating miRNAs associated with sleep duration in normal-weight European children and adolescents? What is the main finding and its importance? Differences in the expression level of circulating miR-26b-3p and miR-485-5p are positively associated with total sleep duration in healthy normal-weight children and adolescents. ABSTRACT: It is commonly recognized that sleep is essential for children's health, and that insufficient sleep duration is associated with negative health outcomes. In humans, sleep duration and quality are influenced by genetic, environmental and social factors. Epigenetic mechanisms, likewise, regulate circadian rhythms and sleep patterns. In the present study, we aimed to identify circulating microRNAs associated with sleep duration in a subsample of normal-weight European children/adolescents (n = 111) participating in the I.Family Study. Subjects were divided into two groups based upon self-reported sleep duration, according to the recommended amount of sleep for paediatric populations. Sleep needs for children <13 years were at least 9 h per day, and for children >13 were at least 8 h per day. There were group differences (short sleepers versus normal sleepers) in circulating levels of miR-26b-3p (mean (95% CI) = 2.0 (1.3-2.7) versus 2.3 (1.9-2.7), P = 0.05) and miR-485-5p (mean (95% CI) = 0.6 (0.3-0.9) versus 0.9 (0.7 - 1.0), P < 0.001), adjusting for country of origin, age, sex, pubertal status, screen time and highest educational level of parents. Our findings show for the first time that sleep duration reflects the profile of specific circulating microRNAs in school-aged children and adolescents. It is conceivable that epigenetic modifications, mainly related to circadian rhythm control, may be modulated or interfere with sleep duration.

Adverse drug reaction or innocent bystander? A systematic comparison of statistical discovery methods for spontaneous reporting systems.

PURPOSE: Spontaneous reporting systems (SRSs) are used to discover previously unknown relationships between drugs and adverse drug reactions (ADRs). A plethora of statistical methods have been proposed over the years to identify these drug-ADR pairs. The objective of this study is to compare a wide variety of methods in their ability to detect these signals, especially when their detection is complicated by the presence of innocent bystanders (drugs that are mistaken to be associated with the ADR, since they are prescribed together with the drug that is the ADR's actual cause). METHODS: Twelve methods, 24 measures in total, ranging from simple disproportionality measures (eg, the reporting odds ratio), hypothesis tests (eg, test of the Poisson mean), Bayesian shrinkage estimates (eg, the Bayesian confidence propagation neural network, BCPNN) to sparse regression (LASSO), are compared in their ability to detect drug-ADR pairs in a large number of simulated SRSs with varying numbers of innocent bystanders and effect sizes. The area under the precision-recall curve is used to assess the measures' performance. RESULTS: Hypothesis tests (especially the test of the Poisson mean) perform best when the associations are weak and there is little to no confounding by other drugs. When the level of confounding increases and/or the effect sizes become larger, Bayesian shrinkage methods should be preferred. The LASSO proves to be the most robust against the innocent bystander effect. CONCLUSIONS: There is no absolute "winner". Which method to use for a particular SRS depends on the effect sizes and the level of confounding present in the data.

Association between variants of neuromedin U gene and taste thresholds and food preferences in European children: Results from the IDEFICS study.

AIM: The neuropeptide neuromedin U (NMU) known for its role in appetite, feeding and energy balance could be involved in the control of food choice and taste sensitivity. We examined the association between NMU polymorphisms/haplotypes and taste thresholds and food preferences in a population of European children. METHODS: A total of 578 subjects from the IDEFICS study (mean age 7.5 ±â€¯0.8 SD, boys 53.6%) with NMU genotype data and food preference (salty, fatty, sweet, flavour and umami food) and taste threshold (salt, fat, sweet, umami) tests available were analysed. Three single nucleotide polymorphisms (SNPs; rs6827359, T:C; rs12500837, T:C; rs9999653, C:T) of NMU gene were analyzed and five major haplotypes were inferred. The associations between genotypes and food preferences or taste thresholds were investigated (odds ratios -OR, adjusted for age, sex and country). A p < 0.05 after false discovery rate adjustment (pFDR) was considered statistically significant. RESULTS: The association between NMU genotypes and food preference showed two NMU SNPs associated with preference for food containing sodium glutamate (umami taste; rs6827359C, OR = 1.61, 95% confidence interval (CI):1.20-2.17; rs9999653T, OR = 1.59, 95%CI:1.18-2.13). In the haplotype analysis, the CTT haplotype showed an OR of 1.70 (95%CI:1.16-2.5) for the umami food preference, while CCT haplotype showed an OR of 1.63 (95%CI:1.11-2.40), compared to the most frequent haplotype (TTC). Carriers of CCT/CCT vs subjects with no CCT haplotype showed an OR of 4.78 (95%CI:1.86-12.30). Umami food preference was associated with low values of BMI z-score, arm circumferences, skinfolds and fat mass (pFDR<0.05). No association between NMU genetic variants and taste thresholds was found. CONCLUSIONS: This study shows for the first time in children an association between preference for umami food and a NMU haplotype, previously found associated with low BMI values.

[Ethical consideration of studies involving human subjects outside the regulatory framework: not mandatory, but of high relevance]. / Ethische Bewertung von Studien am Menschen außerhalb des regulatorischen Rahmens: nicht bindend, aber von großer Wichtigkeit.

According to the Declaration of Helsinki, ethics committees are obliged to evaluate any type of medical research involving human subjects in order to ensure an objective view on ethical considerations. This does not only mean considering whether the risks to study participants are ethically justifiable or not, but also checking whether the scientific quality of a study is sufficient. However, the role of ethics committees differs depending on whether the study to be considered is, for example, an approval study according to the German Medicines Act (AMG) or whether the study is outside the regulatory framework. For these so-called unregulated studies it is not always mandatory to obtain approval from an ethics committee or an institutional review board.In this paper, we first explain the term "unregulated studies" in detail and elaborate for which types of unregulated studies an application for ethical approval is required before we deal with the application for ethical approval as such and in particular with the study protocol as one of its major components. Registry studies, postmarketing surveillance studies, analyses of secondary data, surveys, intervention, and prognostic studies serve as examples to illustrate the broad range of unregulated studies.Finally, we discuss crucial aspects of the role of ethics committees with respect to the consideration of unregulated studies. In our conclusion, we point out the necessity of having ethics committees at each university in Germany that are also responsible for unregulated studies. In addition, the German legislature should define a stricter regulation such that unregulated studies also have to adhere to the vote of the ethics committee.

[Detection of drug risks after approval : Methods development for the use of routine statutory health insurance data]. / Aufdeckung von Arzneimittelrisiken nach der Zulassung : Methodenentwicklung zur Nutzung von Routinedaten der gesetzlichen Krankenversicherungen.

Adverse drug reactions are among the leading causes of death. Pharmacovigilance aims to monitor drugs after they have been released to the market in order to detect potential risks. Data sources commonly used to this end are spontaneous reports sent in by doctors or pharmaceutical companies. Reports alone are rather limited when it comes to detecting potential health risks. Routine statutory health insurance data, however, are a richer source since they not only provide a detailed picture of the patients' wellbeing over time, but also contain information on concomitant medication and comorbidities.To take advantage of their potential and to increase drug safety, we will further develop statistical methods that have shown their merit in other fields as a source of inspiration. A plethora of methods have been proposed over the years for spontaneous reporting data: a comprehensive comparison of these methods and their potential use for longitudinal data should be explored. In addition, we show how methods from machine learning could aid in identifying rare risks. We discuss these so-called enrichment analyses and how utilizing pharmaceutical similarities between drugs and similarities between comorbidities could help to construct risk profiles of the patients prone to experience an adverse drug event.Summarizing these methods will further push drug safety research based on healthcare claim data from German health insurances which form, due to their size, longitudinal coverage, and timeliness, an excellent basis for investigating adverse effects of drugs.

Clone temporal centrality measures for incomplete sequences of graph snapshots.

BACKGROUND: Different phenomena like the spread of a disease, social interactions or the biological relation between genes can be thought of as dynamic networks. These can be represented as a sequence of static graphs (so called graph snapshots). Based on this graph sequences, classical vertex centrality measures like closeness and betweenness centrality have been extended to quantify the importance of single vertices within a dynamic network. An implicit assumption for the calculation of temporal centrality measures is that the graph sequence contains all information about the network dynamics over time. This assumption is unlikely to be justified in many real world applications due to limited access to fully observed network data. Incompletely observed graph sequences lack important information about duration or existence of edges and may result in biased temporal centrality values. RESULTS: To account for this incompleteness, we introduce the idea of extending original temporal centrality metrics by cloning graphs of an incomplete graph sequence. Focusing on temporal betweenness centrality as an example, we show for different simulated scenarios of incomplete graph sequences that our approach improves the accuracy of detecting important vertices in dynamic networks compared to the original methods. An age-related gene expression data set from the human brain illustrates the new measures. Additional results for the temporal closeness centrality based on cloned snapshots support our findings. We further introduce a new algorithm called REN to calculate temporal centrality measures. Its computational effort is linear in the number of snapshots and benefits from sparse or very dense dynamic networks. CONCLUSIONS: We suggest to use clone temporal centrality measures in incomplete graph sequences settings. Compared to approaches that do not compensate for incompleteness our approach will improve the detection rate of important vertices. The proposed REN algorithm allows to calculate (clone) temporal centrality measures even for long snapshot sequences.

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.

[Methodological challenges for genome-based prediction of diseases]. / Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen.

The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.

A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents.

Childhood obesity is a complex disorder that appears to be influenced by an interacting system of many factors. Taking this complexity into account, we aim to investigate the causal structure underlying childhood obesity. Our focus is on identifying potential early, direct or indirect, causes of obesity which may be promising targets for prevention strategies. Using a causal discovery algorithm, we estimate a cohort causal graph (CCG) over the life course from childhood to adolescence. We adapt a popular method, the so-called PC-algorithm, to deal with missing values by multiple imputation, with mixed discrete and continuous variables, and that takes background knowledge such as the time-structure of cohort data into account. The algorithm is then applied to learn the causal structure among 51 variables including obesity, early life factors, diet, lifestyle, insulin resistance, puberty stage and cultural background of 5112 children from the European IDEFICS/I.Family cohort across three waves (2007-2014). The robustness of the learned causal structure is addressed in a series of alternative and sensitivity analyses; in particular, we use bootstrap resamples to assess the stability of aspects of the learned CCG. Our results suggest some but only indirect possible causal paths from early modifiable risk factors, such as audio-visual media consumption and physical activity, to obesity (measured by age- and sex-adjusted BMI z-scores) 6 years later.

Self-administered questionnaire assessing childhood cancer treatments and associated risks for adverse health outcomes - The KiKme study.

Background: Childhood cancer survivors (CCS) are at particularly high risk for therapy-related late sequelae, with secondary primary neoplasms (SPN) being the most detrimental. Since there is no standardized questionnaire for retrospective assessment of associations between prior cancer treatments and late health effects, we developed a self-administered questionnaire and validated it in a cohort of CCS. Methods: CCS of a first primary neoplasm (FPN, N=340) only or with a subsequent SPN (N=101) were asked whether they had received cancer therapies. Self-reports were compared to participants' medical records on cancer therapies from hospitals and clinical studies (N=242). Cohen's Kappa (κ) was used to measure their agreement and logistic regression was used to identify factors influencing the concordance. Associations between exposure to cancer therapies and late health effects (overweight/obesity, diseases of the lipid metabolism and the thyroid gland, cardiovascular diseases, occurrence of SPN) were analyzed in all participants by applying generalized linear mixed models to calculate odds ratios (OR) and 95% confidence intervals (95%CI). Results: For CCS of SPN, a perfect agreement was found between self-reports and medical records for chemotherapy (CT, κ=1.0) while the accordance for radiotherapy (RT) was lower but still substantial (κ=0.8). For the CCS of FPN the accordance was less precise (CT: κ=0.7, RT: κ=0.3). Cancer status, tumors of the central nervous system, sex, age at recruitment, vocational training, follow-up time, and comorbidities had no impact on agreement. CCS with exposure to CT were found to be less often overweight or obese compared to those without CT (OR=0.6 (95%CI 0.39; 0.91)). However, they were found to suffer more likely from thyroid diseases excluding thyroid cancers (OR=9.91 (95%CI 4.0; 24.57)) and hypercholesterolemia (OR=4.45 (95%CI 1.5; 13.23)). All other analyses did not show an association. Conclusion: Our new questionnaire proved reliable for retrospective assessment of exposure to CT and RT in CCS of SPN. For the CCS of FPN, self-reported RT was very imprecise and should not be used for further analyses. We revealed an association between late health outcomes occurring as hypercholesterolemia and thyroid diseases, excluding thyroid cancer, and the use of CT for the treatment of childhood cancer.