Helicobacter pylori Seropositivity: Prevalence, Associations, and the Impact on Incident Metabolic Diseases/Risk Factors in the Population-Based KORA Study

Introduction:Helicobacter pylori (H. pylori) is a common infection and known risk factor for gastric cancer. We assessed cross-sectional and longitudinal associations to study the impact of H. pylori seropositivity on metabolic diseases.Methods:Helicobacter pylori seropositivity in serum samples of the KORA study was analyzed by multiplex serology. We calculated sex-specific prevalence of H. pylori seropositivity for the year 2007 based on the first follow-up survey (termed F4) of the KORA study S4. We identified factors associated with H. pylori seropositivity in the F4 survey. Further, we assessed relative risks of incident metabolic diseases/risk factors at the time of the second follow-up survey of S4 (termed FF4) and H. pylori seropositivity at the F4 survey as a determinant. Models were adjusted for age, sex, overweight status, physical activity, smoking status, education level, alcohol intake, and other metabolic diseases.Results: Based on 3,037 persons aged 32 to 82 years, the H. pylori prevalence for 2007 was 30.2% in men (n = 1,465) and 28.1% in women (n = 1,572). Increasing age, current smoking, low education and no alcohol intake were significantly associated with H. pylori seropositivity in the F4 survey. However, no association between H. pylori seropositivity and BMI, metabolic diseases (type 2 diabetes, hypertension and dyslipidemia, gout or increased uric acid) and gastrointestinal diseases (gastritis, inflammatory bowel disease, and gastric or duodenal ulcer) was observed. No significant associations between H. pylori seropositivity and one of the five investigated incident metabolic diseases/risk factors were detected in the longitudinal analysis.Conclusion: We identified associations between age, smoking, education and alcohol intake and H. pylori seropositivity but no impact of H. pylori seropositivity on incident metabolic diseases/risk factors

Optimized metabotype definition based on a limited number of standard clinical parameters in the population-based KORA study

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions

Fecal bile acids and neutral sterols are associated with latent microbial subgroups in the human gut

Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with “healthy” factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with “unhealthy” factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health

Association of habitual dietary intake with liver iron: a population-based imaging study

Iron-related disorders of the liver can result in serious health conditions, such as liver cirrhosis. Evidence on the role of modifiable lifestyle factors like nutrition in liver iron storage is lacking. Thus, we aimed to assess the association of habitual diet with liver iron content (LIC). We investigated 303 participants from the population-based KORA-MRI study who underwent whole-body magnetic resonance imaging (MRI). Dietary habits were evaluated using repeated 24 h food lists and a food frequency questionnaire. Sex-stratified multiple linear regression models were applied to quantify the association between nutrition variables of interest and LIC, adjusting for liver fat content (LFC), energy intake, and age. Mean age of participants was 56.4 ± 9.0 years and 44.2% were female. Mean LIC was 1.23 ± 0.12 mg/g dry weight, with higher values in men than in women (1.26 ± 0.13 and 1.20 ± 0.10 mg/g, p < 0.001). Alcohol intake was positively associated with LIC (men: β = 1.94; women: β = 4.98, p-values < 0.03). Significant negative associations with LIC were found for fiber (β = −5.61, p < 0.001) and potassium (β = −0.058, p = 0.034) for female participants only. Furthermore, LIC was highly correlated with liver fat content in both sexes. Our findings suggests that there are sex-specific associations of habitual dietary intake and LIC. Alcohol, fiber, and potassium may play a considerable role in liver iron metabolism

Association between usual dietary intake of food groups and DNA methylation and effect modification by metabotype in the KORA FF4 cohort

Associations between diet and DNA methylation may vary among subjects with different metabolic states, which can be captured by clustering populations in metabolically homogenous subgroups, called metabotypes. Our aim was to examine the relationship between habitual consumption of various food groups and DNA methylation as well as to test for effect modification by metabotype. A cross-sectional analysis of participants (median age 58 years) of the population-based prospective KORA FF4 study, habitual dietary intake was modeled based on repeated 24-h diet recalls and a food frequency questionnaire. DNA methylation was measured using the Infinium MethylationEPIC BeadChip providing data on >850,000 sites in this epigenome-wide association study (EWAS). Three metabotype clusters were identified using four standard clinical parameters and BMI. Regression models were used to associate diet and DNA methylation, and to test for effect modification. Few significant signals were identified in the basic analysis while many significant signals were observed in models including food group-metabotype interaction terms. Most findings refer to interactions of food intake with metabotype 3, which is the metabotype with the most unfavorable metabolic profile. This research highlights the importance of the metabolic characteristics of subjects when identifying associations between diet and white blood cell DNA methylation in EWAS

Projected health and economic impacts of sugar-sweetened beverage taxation in Germany: A cross-validation modelling study.

BackgroundTaxes on sugar-sweetened beverages (SSBs) have been implemented globally to reduce the burden of cardiometabolic diseases by disincentivizing consumption through increased prices (e.g., 1 peso/litre tax in Mexico) or incentivizing industry reformulation to reduce SSB sugar content (e.g., tiered structure of the United Kingdom [UK] Soft Drinks Industry Levy [SDIL]). In Germany, where no tax on SSBs is enacted, the health and economic impact of SSB taxation using the experience from internationally implemented tax designs has not been evaluated. The objective of this study was to estimate the health and economic impact of national SSBs taxation scenarios in Germany.Methods and findingsIn this modelling study, we evaluated a 20% ad valorem SSB tax with/without taxation of fruit juice (based on implemented SSB taxes and recommendations) and a tiered tax (based on the UK SDIL) in the German adult population aged 30 to 90 years from 2023 to 2043. We developed a microsimulation model (IMPACTNCD Germany) that captures the demographics, risk factor profile and epidemiology of type 2 diabetes, coronary heart disease (CHD) and stroke in the German population using the best available evidence and national data. For each scenario, we estimated changes in sugar consumption and associated weight change. Resulting cases of cardiometabolic disease prevented/postponed and related quality-adjusted life years (QALYs) and economic impacts from healthcare (medical costs) and societal (medical, patient time, and productivity costs) perspectives were estimated using national cost and health utility data. Additionally, we assessed structural uncertainty regarding direct, body mass index (BMI)-independent cardiometabolic effects of SSBs and cross-validated results with an independently developed cohort model (PRIMEtime). We found that SSB taxation could reduce sugar intake in the German adult population by 1 g/day (95%-uncertainty interval [0.05, 1.65]) for a 20% ad valorem tax on SSBs leading to reduced consumption through increased prices (pass-through of 82%) and 2.34 g/day (95%-UI [2.32, 2.36]) for a tiered tax on SSBs leading to 30% reduction in SSB sugar content via reformulation. Through reductions in obesity, type 2 diabetes, and cardiovascular disease (CVD), 106,000 (95%-UI [57,200, 153,200]) QALYs could be gained with a 20% ad valorem tax and 192,300 (95%-UI [130,100, 254,200]) QALYs with a tiered tax. Respectively, €9.6 billion (95%-UI [4.7, 15.3]) and €16.0 billion (95%-UI [8.1, 25.5]) costs could be saved from a societal perspective over 20 years. Impacts of the 20% ad valorem tax were larger when additionally taxing fruit juice (252,400 QALYs gained, 95%-UI [176,700, 325,800]; €11.8 billion costs saved, 95%-UI [€6.7, €17.9]), but impacts of all scenarios were reduced when excluding direct health effects of SSBs. Cross-validation with PRIMEtime showed similar results. Limitations include remaining uncertainties in the economic and epidemiological evidence and a lack of product-level data.ConclusionsIn this study, we found that SSB taxation in Germany could help to reduce the national burden of noncommunicable diseases and save a substantial amount of societal costs. A tiered tax designed to incentivize reformulation of SSBs towards less sugar might have a larger population-level health and economic impact than an ad valorem tax that incentivizes consumer behaviour change only through increased prices

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p