Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

The causal relationship between bacterial pneumonia and diabetes: a two-sample mendelian randomization study

BACKGROUND

Previous observational studies have established the high prevalence of bacterial pneumonia in diabetic patients, which in turn leads to increased mortality. However, the presence of a causal connection between bacterial pneumonia and diabetes remains unobserved.

METHODS

We chose genome-wide significant (Ρ < 1 × 10-5 and Ρ < 1 × 10-6) and independent (r2 < 0.001) single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to proceed a bidirectional two-sample MR study. The extracted SNPs explored the relationship between bacterial pneumonia and diabetes by Inverse variance weighted (IVW), MR-Egger, and weighted median methods. In addition, we conducted the Heterogeneity test, the Pleiotropy test, MR-presso and the Leave-one-out (LOO) sensitivity test to validate the reliability of results.

RESULTS

In an MR study with bacterial pneumonia as an exposure factor, four different types of diabetes as outcome. It was observed that bacterial pneumonia increases the incidence of GDM (OR = 1.150 (1.027-1.274, P = 0.011) and T1DM (OR = 1.277 (1.024-1.531), P = 0.016). In the reverse MR analysis, it was observed that GDM (OR = 1.112 (1.023-1.201, P = 0.009) is associated with an elevated risk of bacterial pneumonia. However, no significant association was observed bacterial pneumonia with T1DM and other types of diabetes (P > 0.05).

CONCLUSION

This study utilizing MR methodology yields robust evidence supporting a bidirectional causal association between bacterial pneumonia and GDM. Furthermore, our findings suggest a plausible causal link between bacterial pneumonia and T1DM.

Relative poverty is associated with increased risk of diabetic ketoacidosis at onset of type 1 diabetes in children. A Swedish national population-based study in 2014-2019

AIMS

The aim of the study was to estimate the effect of household relative poverty on the risk of diabetic ketoacidosis at diagnosis of children with type 1 diabetes using an international standard measurement of relative poverty.

METHODS

A national population-based retrospective study was conducted. The Swedish National Diabetes Register (NDR) was linked with data from Sweden's public statistical agency (Statistics Sweden). Children who were diagnosed with new-onset type 1 diabetes in the period of 2014-2019 were common identifiers. The definition of diabetic ketoacidosis was venous pH <7.30 or a serum bicarbonate level <18 mmol/L. The exposure variable was defined according to the standard definition of the persistent at-risk-of-poverty rate used by the statistical office of the European Union (Eurostat) and several other European public statistical agencies. Univariate and multi-variable analyses were used to calculate the effect of relative poverty on the risk of diabetic ketoacidosis.

RESULTS

Children from households with relative poverty had a 41% higher risk of diabetic ketoacidosis (1.41, CI 1.12-1.77, p = 0.004) and more than double the risk of severe diabetic ketoacidosis (pH <7.10) (RR 2.10, CI 1.35-3.25, p = 0.001), as compared to children from households without relative poverty.

CONCLUSIONS

Relative poverty significantly increases the risk of diabetic ketoacidosis at onset of type 1 diabetes in children, even in a high-income country with publicly reimbursed health care.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Multiple reports on the causal relationship between various chronic pain and gut microbiota: a two-sample Mendelian randomization study

Background

Previous evidence suggests a link between gut microbiota and chronic pain, but the causal relationship is not yet fully understood.

Methods

We categorized gut microbiota based on phylum, class, order, family, and genus levels and gathered pain-related information from the UKB and FinnGen GWAS project. Then, we conducted MR analysis to explore the potential causal relationship between gut microbiota and chronic pain at 12 specific locations.

Results

We have discovered a direct connection between genetic susceptibility in the gut microbiota (gut metabolites) and pain experienced at 12 specific locations. Notably, Serotonin (5-HT) and Glycine were found to be associated with a higher risk of pain in the extremities. On the other hand, certain microbial families and orders were found to have a protective effect against migraines. Specifically, the family Bifidobacteriaceae (IVW, FDR p = 0.013) was associated with a lower risk of migraines. Furthermore, the genus Oxalobacter (IVW, FDR p = 0.044) was found to be linked to an increased risk of low back pain. Importantly, these associations remained significant even after applying the Benjamini-Hochberg correction test. Our analysis did not find any heterogeneity in the data (p > 0.05), as confirmed by the Cochrane's Q-test. Additionally, both the MR-Egger and MR-PRESSO tests indicated no significant evidence of horizontal pleiotropy (p > 0.05).

Conclusion

Our MR analysis demonstrated a causal relationship between the gut microbiota and pain, highlighting its potential significance in advancing our understanding of the underlying mechanisms and clinical implications of microbiota-mediated pain.

Investigating genetic links between blood metabolites and preeclampsia

BACKGROUND

Observational studies have revealed that metabolic disorders are closely related to the development of preeclampsia (PE). However, there is still a research gap on the causal role of metabolites in promoting or preventing PE. We aimed to systematically explore the causal association between circulating metabolites and PE.

METHODS

Single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) of 486 blood metabolites (7,824 participants) were extracted as instrumental variables (P < 1 × 10- 5), GWAS summary statistics for PE were obtained from FinnGen consortium (7,212 cases and 194,266 controls) as outcome, and a two-sample Mendelian randomization (MR) analysis was conducted. Inverse variance weighted (IVW) was set as the primary method, with MR-Egger and weighted median as auxiliary methods; the instrumental variable strength and confounding factors were also assessed. Sensitivity analyses including MR-Egger, Cochran's Q test, MR-PRESSO and leave-one-out analysis were performed to test the robustness of the MR results. For significant associations, repeated MR and meta-analysis were performed by another metabolite GWAS (8,299 participants). Furthermore, significantly associated metabolites were subjected to a metabolic pathway analysis.

RESULTS

The instrumental variables for the metabolites ranged from 3 to 493. Primary analysis revealed a total of 12 known (e.g., phenol sulfate, citrulline, lactate and gamma-glutamylglutamine) and 11 unknown metabolites were associated with PE. Heterogeneity and pleiotropy tests verified the robustness of the MR results. Validation with another metabolite GWAS dataset revealed consistency trends in 6 of the known metabolites with preliminary analysis, particularly the finding that genetic susceptibility to low levels of arachidonate (20:4n6) and citrulline were risk factors for PE. The pathway analysis revealed glycolysis/gluconeogenesis and arginine biosynthesis involved in the pathogenesis of PE.

CONCLUSIONS

This study identifies a causal relationship between some circulating metabolites and PE. Our study presented new perspectives on the pathogenesis of PE by integrating metabolomics with genomics, which opens up avenues for more accurate understanding and management of the disease, providing new potential candidate metabolic molecular markers for the prevention, diagnosis and treatment of PE. Considering the limitations of MR studies, further research is needed to confirm the causality and underlying mechanisms of these findings.

Association between gut microbiota and onset of type 2 diabetes mellitus: a two-sample Mendelian randomization study

Aim

Mendelian randomization (MR) analysis has been used in the exploration of the role of gut microbiota (GM) in type 2 diabetes mellitus (T2DM); however, it was limited to the genus level. This study herein aims to investigate the relationship of GM, especially at the species level, with T2DM in order to provide some evidence for further exploration of more specific GM taxa and pathway abundance in T2DM.

Methods

This two-sample MR study was based on the summary statistics of GM from the available genome-wide association study (GWAS) meta-analysis conducted by the MiBioGen consortium as well as the Dutch Microbiome Project (DMP), whereas the summary statistics of T2DM were obtained from the FinnGen consortium released data. Inverse variance weighted (IVW), MR-Egger, strength test (F), and weighted median methods were used to examine the causal association between GM and the onset of T2DM. Cochran's Q statistics was employed to quantify the heterogeneity of instrumental variables. Bonferroni's correction was conducted to correct the bias of multiple testing. We also performed reverse causality analysis.

Results

The corrected IVW estimates suggested the increased relative abundance of family Oxalobacteraceae (OR = 1.0704) and genus Oxalobacter (OR = 1.0874), respectively, were associated with higher odds of T2DM, while that of species faecis (OR = 0.9460) had a negative relationship with T2DM. The relationships of class Betaproteobacteria, family Lactobacillaceae, species finegoldii, and species longum with T2DM were also significant according to the IVW results (all P < 0.05).

Conclusions

GM had a potential causal association with T2DM, especially species faecis, finegoldii, and longum. Further studies are still needed to clarify certain results that are contradictory with previous findings.

Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system

The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

The Causal Effect of Obesity on the Risk of 15 Autoimmune Diseases: A Mendelian Randomization Study

INTRODUCTION

Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases.

METHODS

MR analysis employed instrumental variables, specifically single-nucleotide polymorphisms associated with obesity measures such as body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio. The study utilized UK Biobank and FinnGen data to estimate the causal relationship between obesity and autoimmune diseases.

RESULTS

Genetically predicted BMI was associated with risk for five autoimmune diseases. The odds ratio per 1-SD increase in genetically predicted BMI, the OR was 1.28 (95% CI, 1.18-1.09; p < 0.001) for asthma, 1.37 (95% CI, 1.24-1.51; p < 0.001) for hypothyroidism, 1.52 (95% CI, 1.27-1.83; p < 0.001) for psoriasis, 1.22 (95% CI, 1.06-1.40; p = 0.005) for rheumatoid arthritis, and 1.55 (95% CI, 1.32-1.83; p < 0.001) for type 1 diabetes. However, after adjusting for genetic susceptibility to drinking and smoking, the correlation between BMI and rheumatoid arthritis was not statistically significant. Genetically predicted waist circumference, hip circumference, and waist and hip circumference were associated with 6, 6, and 1 autoimmune disease, respectively.

CONCLUSION

This study suggests that obesity may be associated with an increased risk of several autoimmune diseases, such as asthma, hypothyroidism, psoriasis, rheumatoid arthritis, and type 1 diabetes.

Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses

We conduct proteome-wide Mendelian randomization and colocalization analyses to decipher the associations of blood proteins with the risk of type 2 diabetes and diabetic complications. Genetic data on plasma proteome are obtained from 54,306 UK Biobank participants and 35,559 Icelanders. Summary-level data on type 2 diabetes are obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis consortium) consortium (74,124 cases) and FinnGen study (33,043 cases). Data on 10 diabetic complications are obtained from FinnGen and corresponding studies. Among 1,886 proteins, genetically predicted levels of 47 plasma proteins are associated with type 2 diabetes. Eleven of these proteins have strong support of colocalization. Seventeen proteins are associated with at least one diabetic complication, although a few have colocalization support. HLA-DRA, AGER, HSPA1A, and HSPA1B are associated with most microvascular complications. This study reveals causal proteins for the onset of type 2 diabetes and diabetic complications, which enhances the understanding of molecular etiology and development of therapeutics.

The association between gut microbiome and PCOS: evidence from meta-analysis and two-sample mendelian randomization

BACKGROUND

Increasing evidence from observational studies and clinical experimentation has indicated a link between the gut microbiotas (GMs) and polycystic ovary syndrome (PCOS), however, the causality and direction of causality between gut microbiome and PCOS remains to be established.

METHODS

We conducted a comprehensive search of four databases-PubMed, Cochrane Library, Web of Science, and Embase up until June 1, 2023, and subjected the results to a meta-analysis. In this study, a bidirectional two-sample Mendelian randomization (MR) analysis was employed to investigate the impact of gut microbiota on polycystic ovary syndrome (PCOS). The genome-wide association study (GWAS) data for PCOS comprised 113,238 samples, while the GWAS data for gut microbiota were derived from the MiBioGen consortium, encompassing a total sample size of 18,340 individuals. As the largest dataset of its kind, this study represents the most comprehensive genome-wide meta-analysis concerning gut microbiota composition to date. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables at various taxonomic levels, including Phylum, Class, Order, Family, and Genus. The causal associations between exposures and outcomes were assessed using four established MR methods. To correct for multiple testing, the false discovery rate (FDR) method was applied. The reliability and potential biases of the results were evaluated through sensitivity analysis and F-statistics.

RESULTS

The meta-analysis incorporated a total of 20 studies that met the criteria, revealing a close association between PCOS and specific gut microbiota species. As per the results from our MR analysis, we identified six causal associations between the gut microbiome and polycystic ovary syndrome (PCOS). At the genus level, Actinomyces (ORIVW = 1.369, FDR = 0.040), Streptococcus (ORIVW = 1.548, FDR = 0.027), and Ruminococcaceae UCG-005 (ORIVW = 1.488, FDR = 0.028) were identified as risk factors for PCOS. Conversely, Candidatus Soleaferrea (ORIVW = 0.723, FDR = 0.040), Dorea (ORIVW = 0.580, FDR = 0.032), and Ruminococcaceae UCG-011 (ORIVW = 0.732, FDR = 0.030) were found to be protective factors against PCOS. Furthermore, the MR-PRESSO global test and MR-Egger regression indicated that our study results were not affected by horizontal pleiotropy (p > 0.05). Finally, the leave-one-out analysis corroborated the robustness of the MR findings.

CONCLUSION

Both our meta-analysis and MR study indicates that there is a causal relationship between the gut microbiome and PCOS, which may contribute to providing novel insights for the development of new preventive and therapeutic strategies for PCOS.

Physiopathological mechanisms related to inflammation in obesity and type 2 diabetes mellitus

Overweight, obesity, and type 2 diabetes mellitus pose global health problems that are ever-increasing. A chronic low-grade inflammatory status and the presence of various pro-inflammatory markers either in circulation or within dysfunctional metabolic tissues are well established. The presence of these factors can, to some extent, predict disease development and progression. A central role is played by the presence of dysfunctional adipose tissue, liver dysfunction, and skeletal muscle dysfunction, which collectively contribute to the increased circulatory levels of proinflammatory factors. Weight loss and classical metabolic interventions achieve a decrease in many of these factors’ circulating levels, implying that a better understanding of the processes or even the modulation of inflammation may alleviate these diseases. This review suggests that inflammation plays a significant role in the development and progression of these conditions and that measuring inflammatory markers may be useful for assessing disease risk and development of future treatment methods.