Primary prevention from the epidemiology perspective: three examples from the practice

Background: Primary prevention programmes are of increasing importance to reduce the impact of chronic diseases on the individual, institutional and societal level. However, most initiatives that develop and implement primary prevention programmes are not evaluated with scientific rigor. On the basis of three different projects we discuss necessary steps on the road to evidence-based primary prevention. Discussion: We first discuss how to identify suitable target groups exploiting sophisticated statistical methods. This is illustrated using data from a health survey conducted in a federal state of Germany. A literature review is the more typical approach to identify target groups that is demonstrated using a European project on the prevention of childhood obesity. In the next step, modifiable risk factors and realistic targets of the intervention have to be specified. These determine the outcome measures that in turn are used for effect evaluation. Both, the target groups and the outcome measures, lay the ground for the study design and the definition of comparison groups as can be seen in our European project. This project also illustrates the development and implementation of a prevention programme. These may require active involvement of participants which can be achieved by participatory approaches taking into account the socio-cultural and living environment. Evaluation is of utmost importance for any intervention to assess structure, process and outcome according to rigid scientific criteria. Different approaches used for this are discussed and illustrated by a methodological project developed within a health promotion programme in a deprived area. Eventually the challenge of transferring an evidence-based intervention into practice and to achieve its sustainability is addressed. Summary: This article describes a general roadmap to primary prevention comprising (1) the identification of target groups and settings, (2) the identification of modifiable risk factors and endpoints, (3) the development and implementation of an intervention programme, (4) the evaluation of structure, process and outcome and (5) the transfer of an evidence-based intervention into practice

The role of neuromedin U in adiposity regulation. Haplotype analysis in European children from the IDEFICS Cohort

Background and aims: Neuromedin U (NMU) is a hypothalamic neuropeptide with important roles in several metabolic processes, recently suggested as potential therapeutic target for obesity. We analysed the associations between NMU gene variants and haplotypes and body mass index (BMI) in a large sample of European children. Methods and results: From a large European multi-center study on childhood obesity, 4,528 children (2.0–9.9 years, mean age 6.0±1.8 SD; boys 52.2%) were randomly selected, stratifying by age, sex and country, and genotyped for tag single nucleotide polymorphisms (SNPs; rs6827359, T:C; rs12500837, T:C; rs9999653,C:T) of NMU gene, then haplotypes were inferred. Regression models were applied to estimate the associations between SNPs or haplotypes and BMI as well as other anthropometric measures. BMI was associated with all NMU SNPs (p<0.05). Among five haplotypes inferred, the haplotype carrying the minor alleles (CCT, frequency = 22.3%) was the only associated with lower BMI values (beta = -0.16, 95%CI:-0.28,-0.04, p = 0.006; z-score, beta = -0.08, 95%CI:-0.14,-0.01, p = 0.019) and decreased risk of overweight/obesity (OR = 0.81, 95%CI:0.68,0.97, p = 0.020) when compared to the most prevalent haplotype (codominant model). Similar significant associations were also observed using the same variables collected after two years’ time (BMI, beta = -0.25, 95%CI:-0.41,-0.08, p = 0.004; z-score, beta = -0.10, 95%CI:-0.18,-0.03, p = 0.009; overweight/obesity OR = 0.81, 95%CI:0.66,0.99, p = 0.036). The association was age-dependent in girls (interaction between CCT haplotypes and age, p = 0.008), more evident between 7 and 9 years of age. The CCT haplotype was consistently associated with lower levels of fat mass, skinfold thickness, hip and arm circumferences both at T0 and at T1, after adjustment for multiple testing (FDR-adjusted p<0.05). Conclusions: This study shows an association between a NMU haplotype and anthropometric indices, mainly linked to fat mass, which appears to be age- and sex-specific in children. Genetic variations within or in linkage with this haplotype should be investigated to identify functional variants responsible for the observed phenotypic variation

Reference values for leptin and adiponectin in children below the age of 10 based on the IDEFICS cohort

OBJECTIVE: To establish age- and sex-specific reference values for serum leptin and adiponectin in normal-weight 3.0-8.9-year old European children. SUBJECTS AND METHODS: Blood samples for hormone analysis were taken from 1338 children of the IDEFICS (Identification and prevention of Dietary-and lifestyle-induced health Effects in Children and infantS) study cohort. Only normal-weight children aged 3.0-8.9 years were included (n = 539) in our analysis. Using the General Additive Model for Location Scale and Shape, age-and sex-specific percentiles were derived. The influence of under/overweight and obesity on the proposed reference curves based on normal-weight children was investigated in several sensitivity analyses using the sample without obese children (n = 1015) and the whole study sample (n = 1338). RESULTS: There was a negative age trend of adiponectin blood levels and a positive trend of leptin levels in boys and girls. Percentiles derived for girls were generally higher than those obtained for boys. The corresponding age-specific differences of the 97th percentile ranged from -2.2 to 4.6 mu g ml(-1) and from 2.2 to 4.8 ng ml(-1) for adiponectin and leptin, respectively. CONCLUSIONS: According to our knowledge, these are the first reference values of leptin and adiponectin in prepubertal, normal-weight children. The presented adiponectin and leptin reference curves may allow for a more differentiated interpretation of children's hormone levels in epidemiological and clinical studies

Inferring High-Dimensional Dynamic Networks Changing with Multiple Covariates

High-dimensional networks play a key role in understanding complex relationships. These relationships are often dynamic in nature and can change with multiple external factors (e.g., time and groups). Methods for estimating graphical models are often restricted to static graphs or graphs that can change with a single covariate (e.g., time). We propose a novel class of graphical models, the covariate-varying network (CVN), that can change with multiple external covariates. In order to introduce sparsity, we apply a $L_1$-penalty to the precision matrices of $m \geq 2$ graphs we want to estimate. These graphs often show a level of similarity. In order to model this 'smoothness', we introduce the concept of a 'meta-graph' where each node in the meta-graph corresponds to an individual graph in the CVN. The (weighted) adjacency matrix of the meta-graph represents the strength with which similarity is enforced between the $m$ graphs. The resulting optimization problem is solved by employing an alternating direction method of multipliers. We test our method using a simulation study and we show its applicability by applying it to a real-world data set, the gene expression networks from the study 'German Cancer in childhood and molecular-epidemiology' (KiKme). An implementation of the algorithm in R is publicly available under https://github.com/bips-hb/cv

An Exposure Model Framework for Signal Detection based on Electronic Healthcare Data

Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In addition to traditional spontaneous reporting systems (SRSs), electronic health (EHC) data is being used for signal detection as well. Unlike SRS, EHC data is longitudinal and thus requires assumptions about the patient's drug exposure history and its impact on ADR occurrences over time, which many current methods do implicitly. We propose an exposure model framework that explicitly models the longitudinal relationship between the drug and the ADR. By considering multiple such models simultaneously, we can detect signals that might be missed by other approaches. The parameters of these models are estimated using maximum likelihood, and the Bayesian Information Criterion (BIC) is employed to select the most suitable model. Since BIC is connected to the posterior distribution, it servers the dual purpose of identifying the best-fitting model and determining the presence of a signal by evaluating the posterior probability of the null model. We evaluate the effectiveness of this framework through a simulation study, for which we develop an EHC data simulator. Additionally, we conduct a case study applying our approach to four drug-ADR pairs using an EHC dataset comprising over 1.2 million insured individuals. Both the method and the EHC data simulator code are publicly accessible as part of the R package https://github.com/bips-hb/expard.Comment: 40 pages, 7 figure

An Alternating Direction Method of Multipliers Algorithm for the Weighted Fused LASSO Signal Approximator

We present an Alternating Direction Method of Multipliers (ADMM) algorithm designed to solve the Weighted Generalized Fused LASSO Signal Approximator (wFLSA). First, we show that wFLSAs can always be reformulated as a Generalized LASSO problem. With the availability of algorithms tailored to the Generalized LASSO, the issue appears to be, in principle, resolved. However, the computational complexity of these algorithms is high, with a time complexity of $O(p^4)$ for a single iteration, where $p$ represents the number of coefficients. To overcome this limitation, we propose an ADMM algorithm specifically tailored for wFLSA-equivalent problems, significantly reducing the complexity to $O(p^2)$. Our algorithm is publicly accessible through the R package wflsa

Socioeconomic inequalities in cancer incidence and mortality - a spatial analysis in Bremen, Germany

Aim: Several international studies have already investigated the influence of socioeconomic factors on the risk of cancer. For Germany, however, the data are still insufficient. We examined the effects of social differences on cancer incidence and mortality on the population of Bremen, a town in northwest Germany. Subjects and methods: Data were obtained from the Bremen Cancer Registry, a population-based registry. The database comprised 27,430 incident cases, newly diagnosed between 2000 and 2006. The allocation of social class for each patient was based on the home address at the time of diagnosis, which led to the corresponding town district, which again could be linked to the “Bremen discrimination index.” Based on this index, cases were allocated to five categories, for which we compared standardized incidence ratios (SIR) and mortality ratios (SMR) for different cancers: prostate, breast, lung, colorectal, bladder, uterine, ovarian, cervical, malignant melanoma of the skin, non-melanoma skin cancer and all cancer sites summarized. Results: The influence of social status was observed for different cancer sites. An inverse association was ascertained for all cancer sites (only men) and for tumors of the oral cavity and pharynx, and for lung, cervical and bladder cancers. A positive correlation was observed for female breast cancer, malignant melanoma, non-melanoma skin tumors and prostate cancer. Conclusions: In spite of the methodical restrictions, our analyses suggest an association between social factors and cancer incidence and mortality. The results are in agreement with international studies. Many of the observed social class differences could probably be explained by known risk factors, such as smoking, alcohol consumption, diet and physical activity

Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure

Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We present an algorithm that efficiently exploits temporal structure, so-called tiered background knowledge, for estimating causal structures. Tiered background knowledge is readily available from, e.g., cohort or registry data. When used efficiently it renders the algorithm more robust to statistical errors and ultimately increases accuracy in finite samples. We describe the algorithm and illustrate how it proceeds. Moreover, we offer formal proofs as well as examples of desirable properties of the algorithm, which we demonstrate empirically in an extensive simulation study. To illustrate its usefulness in practice, we apply the algorithm to data from a children's cohort study investigating the interplay of diet, physical activity and other lifestyle factors for health outcomes

Behavioural Susceptibility Theory: Professor Jane Wardle and the Role of Appetite in Genetic Risk of Obesity

Purpose of Review: There is considerable variability in human body weight, despite the ubiquity of the 'obesogenic' environment. Human body weight has a strong genetic basis and it has been hypothesised that genetic susceptibility to the environment explains variation in human body weight, with differences in appetite being implicated as the mediating mechanism; so-called 'behavioural susceptibility theory' (BST), first described by Professor Jane Wardle. This review summarises the evidence for the role of appetite as a mediator of genetic risk of obesity. Recent Findings: Variation in appetitive traits is observable from infancy, drives early weight gain and is highly heritable in infancy and childhood. Obesity-related common genetic variants identified through genome-wide association studies show associations with appetitive traits, and appetite mediates part of the observed association between genetic risk and adiposity. Summary: Obesity results from an interaction between genetic susceptibility to overeating and exposure to an 'obesogenic' food environment

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 (beta = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (beta = 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 (beta = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (beta = 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.Peer reviewe