Cohort profile: the Food Chain Plus (FoCus) cohort

Prediabetes and type 2 diabetes but not obesity are associated with alterations in bile acid related gut microbe-microbe and gut microbe-host community metabolism

The interplay between bile acids (BAs) and metabolic diseases has gained importance in recent years, with a variety of studies investigating their relationship with diverging results. Therefore, in the present study we performed a detailed analysis of BA metabolism in 492 subjects with different metabolic phenotypes. Besides microbiomics and metabolomics this investigation included in silico analysis of community metabolism to examine metabolic interchange between different microbes as well as microbes and the human host. Our findings revealed distinct changes in the BA profiles of patients with diabetes and prediabetes, whereas obesity alone had no influence on circulating BAs. Impaired glycemic control led to increased circulating BAs, a shift toward more secondary BAs, and an increase in the ratio of glycine to taurine-conjugated BAs. Additional analyses revealed that the ratio of glycine to taurine conjugation demonstrated variations between the single BAs, cholic acid (CA), chenodeoxycholic acid (CDCA) and deoxycholic acid (DCA), regardless of the metabolic status, with CA having a higher fraction of taurine conjugation. Furthermore, we found that microbiome alterations are associated with BAs, independent of diabetes or obesity. Analysis of microbial community metabolism revealed differential relative pathway abundance in relation to diabetes, particularly those related to membrane and polyamine synthesis. Increased bacterial cross-feeding of polyamines, galactose, and D-arabinose also coincided with an increase in BA. Notably, our serum metabolome analysis mirrored several of the previously in silico predicted exchanged metabolites, especially amino acid metabolism. Therefore, targeting BA metabolism may be a future approach for the treatment of metabolic diseases, especially prediabetes and type 2 diabetes.

Comprehensive evaluation of diabetes subtypes in a European cohort reveals stronger differences of lifestyle, education and psychosocial parameters compared to metabolic or inflammatory factors

BACKGROUND

The traditional binary classification of diabetes into Type 1 and Type 2 fails to capture the heterogeneity among diabetes patients. This study aims to identify and characterize diabetes subtypes within the German FoCus cohort, using the ANDIS cohort's classification framework, and to explore subtype-specific variations in metabolic markers, gut microbiota, lifestyle, social factors, and comorbidities.

METHODS

We utilized data from 416 participants (208 with diabetes and 208 matched metabolically healthy controls) from the German FoCus cohort. Participants were classified into five subtypes: severe autoimmune diabetes (SAID)-like, severe insulin-deficient diabetes (SIDD)-like, severe insulin-resistant diabetes (SIRD)-like, mild obesity-related diabetes (MOD)-like, and mild age-related diabetes (MARD)-like. Comprehensive characterization included anthropometric measurements, dietary and physical activity questionnaires, blood biomarker analysis, and gut microbiota profiling.

RESULTS

The subtype distribution in the FoCus cohort accounted to SAID-like: 2.84%, SIDD-like: 30.81%, SIRD-like: 32.23%, MOD-like: 17.54%, MARD-like: 16.59%. Of interest, inflammatory markers (C-reactive protein (CRP) and Interleukin-6 (IL-6)) and glucagon-like peptide-1 (GLP-1) levels were similarly elevated across all subtypes compared to controls, indicating common aspects in Type 2 diabetes molecular pathology despite different clinical phenotypes. While the gut microbiota and dietary patterns only showed minor differences, smoking status, sleep duration, physical activity and psychological aspects varied significantly between the subtypes. In addition, we observed a lower educational status especially for SIDD-like and SIRD-like groups, which should be considered in establishing future diabetes-related patient education programs. In respect to the development of cardio-metabolic comorbidities, we observe not only significant differences in the presence of the diseases but also for their age-of onset, highlighting the need for early preventive intervention strategies.

CONCLUSIONS

The study validates the ANDIS classification framework's applicability not only at the time point of manifestation but also in cohorts with pre-existing diabetes. While we did not find major differences regarding the classical metabolic, microbial and nutritional parameters, we identified several significant associations with lifestyle factors. Our findings underscore the importance of personalized, subtype-specific therapies not solely focusing on anthropometric and laboratory markers but comprehensively addressing the patient's own personality and situation of life.

Causal relationships between gut microbiome and aplastic anemia: a Mendelian randomization analysis

BACKGROUND

Previous observational studies have hinted at a potential correlation between aplastic anemia (AA) and the gut microbiome. However, the precise nature of this bidirectional causal relationship remains uncertain.

METHODS

We conducted a bidirectional two-sample Mendelian randomization (MR) study to investigate the potential causal link between the gut microbiome and AA. Statistical analysis of the gut microbiome was based on data from an extensive meta-analysis (genome-wide association study) conducted by the MiBioGen Alliance, involving 18,340 samples. Summary statistical data for AA were obtained from the Integrative Epidemiology Unit database. Single -nucleotide polymorphisms (SNPs) were estimated and summarized using inverse variance weighted (IVW), MR Egger, and weighted median methods in the bidirectional MR analysis. Cochran's Q test, MR Egger intercept test, and sensitivity analysis were employed to assess SNP heterogeneity, horizontal pleiotropy, and stability.

RESULTS

The IVW analysis revealed a significant correlation between AA and 10 bacterial taxa. However, there is currently insufficient evidence to support a causal relationship between AA and the composition of gut microbiome.

CONCLUSION

This study suggests a causal connection between the prevalence of specific gut microbiome and AA. Further investigation into the interaction between particular bacterial communities and AA could enhance efforts in prevention, monitoring, and treatment of the condition.

Metabolic and lifestyle factors accelerate disease onset and alter gut microbiome in inflammatory non-communicable diseases

BACKGROUND

Biomedical and lifestyle factors in Western populations have significantly shifted in recent decades, influencing public health and contributing to the increasing prevalence of non-communicable diseases (NCDs) that share inflammation as common pathology.

METHODS

We investigated the relationship between these factors and 11 NCDs in the cross-sectional FoCus cohort (n = 1220), using logistic regression models. Associations with age-at-disease-onset were specifically analyzed for type 2 diabetes (T2D, low-grade chronic inflammation) and inflammatory bowel disease (IBD, high-grade chronic inflammation) in disease-specific cohorts (FoCus-T2D, n = 514; IBD-KC, n = 1110). Important factors for disease risk were identified using Cox-PH-regression models and time-to-event analysis. We further explored the interaction between identified risk factors and gut microbiome composition using linear models.

RESULTS

Lifestyle factors were clearly linked to disease phenotypes, particularly in T2D and IBD. Still, some factors affected only the age-at-onset, but not disease prevalence. High-quality nutrition significantly delayed onset for both IBD and T2D (IBD: HR = 0.81 [0.66; 0.98]; T2D: HR = 0.45 [0.28; 0.72]). Smoking accelerated T2D onset (HR = 1.82 [1.25; 2.65]) but delayed onset in ulcerative colitis (UC: HR = 0.47 [0.28; 0.79]). Higher microbiota diversity delayed IBD onset (Shannon: HR = 0.58 [0.49; 0.71]) but had no effect on T2D. The abundance of specific microbial genera was strongly associated with various biomedical and lifestyle factors in T2D and IBD. In unaffected controls, these effects were smaller or reversed, potentially indicating a greater susceptibility of the gut microbiome to negative influences in T2D and IBD.

CONCLUSIONS

The dual insights into age-at-disease-onset and gut microbiota composition in disease emphasize the role of certain biomedical and lifestyle factors, e.g., nutrition quality, in disease prevention and management. Understanding these relationships provides a foundation for developing targeted strategies to mitigate the impact of metabolic and inflammatory diseases through lifestyle modifications and gut health management.

Effects of lifestyle and associated diseases on serum CC16 suggest complex interactions among metabolism, heart and lungs

INTRODUCTION

Clara cell 16-kDa protein (CC16) is an anti-inflammatory, immunomodulatory secreted pulmonary protein with reduced serum concentrations in obesity according to recent data.

OBJECTIVE

Studies focused solely on bodyweight, which does not properly reflect obesity-associated implications of the metabolic and reno-cardio-vascular system. The purpose of this study was therefore to examine CC16 in a broad physiological context considering cardio-metabolic comorbidities of primary pulmonary diseases.

METHODS

CC16 was quantified in serum samples in a subset of the FoCus (N = 497) and two weight loss intervention cohorts (N = 99) using ELISA. Correlation and general linear regression analyses were applied to assess CC16 effects of lifestyle, gut microbiota, disease occurrence and treatment strategies. Importance and intercorrelation of determinants were validated using random forest algorithms.

RESULTS

CC16 A38G gene mutation, smoking and low microbial diversity significantly decreased CC16. Pre-menopausal female displayed lower CC16 compared to post-menopausal female and male participants. Biological age and uricosuric medications increased CC16 (all p < 0.01). Adjusted linear regression revealed CC16 lowering effects of high waist-to-hip ratio (est. -11.19 [-19.4; -2.97], p = 7.99 × 10-3), severe obesity (est. -2.58 [-4.33; -0.82], p = 4.14 × 10-3) and hypertension (est. -4.31 [-7.5; -1.12], p = 8.48 × 10-3). ACEi/ARB medication (p = 2.5 × 10-2) and chronic heart failure (est. 4.69 [1.37; 8.02], p = 5.91 × 10-3) presented increasing effects on CC16. Mild associations of CC16 were observed with blood pressure, HOMA-IR and NT-proBNP, but not manifest hyperlipidemia, type 2 diabetes, diet quality and dietary weight loss intervention.

CONCLUSION

A role of metabolic and cardiovascular abnormalities in the regulation of CC16 and its modifiability by behavioral and pharmacological interventions is indicated. Alterations by ACEi/ARB and uricosurics could point towards regulatory axes comprising the renin-angiotensin-aldosterone system and purine metabolism. Findings altogether strengthen the importance of interactions among metabolism, heart and lungs.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.