Gut microbiome causal impacts on the prognosis of breast cancer: a Mendelian randomization study

The association between serum albumin levels and metabolic syndrome based on the NHANES and two sample Mendelian randomization study

Previous studies have shown that serum albumin levels are associated with a greater risk of metabolic syndrome (MetS). However, it is unclear whether this association is causal or only influenced by confounding factors, so further investigation is needed to determine the causal relationships. Researchers selected participants with serum albumin, metabolic syndrome, and related covariates from the National Health and Nutrition Examination Survey (NHANES) database for a total of 14,036 individuals, including 5483 individuals with MetS and 8553 individuals without MetS. The association of serum albumin levels with metabolic syndrome and its components was estimated using weighted multivariable logistic regression, with its nonlinearity being examined by restricted cubic spline (RCS) regression. Bidirectional two-sample Mendelian randomization (MR) analysis was performed using Genome-Wide Association Study (GWAS) data on serum albumin and MetS to assess the causal relationship between serum albumin levels and MetS and its components. The primary MR analyses were performed via an inverse variance weighting (IVW) approach. In addition, several sensitivity analyses were performed to assess the robustness of the results. The STROBE-MR checklist for the reporting of MR studies was used in this study. After confounder adjustment, when the serum albumin levels were analyzed as a continuous variable, the multivariable logistic analysis revealed a significant association between it and metabolic syndrome (OR: 1.032, 95% CI: 1.012-1.052). When the serum albumin levels were used as categorical variables, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for metabolic syndrome across higher serum albumin levels quartiles were 0.981 (0.842-1.143), 1.290 (1.115-1.492), and 1.244 (1.064-1.454) compared to the lowest quartile, respectively. In the forward MR study, the IVW method revealed that genetic predicted increased levels of serum albumin were positively correlated with metabolic syndrome (OR: 1.149, 95% CI: 1.016-1.299) and its components, including hypertension (OR: 1.130, 95% CI: 1.013-1.260) and triglycerides (OR: 1.343, 95% CI: 1.209-1.492). In the reverse MR study, the IVW method showed no significant causal relationship between MetS, hypertension, fasting blood glucose and HDL-C with serum albumin levels. The results from the NHANES and MR analysis have revealed a causal relationship between serum albumin levels and both metabolic syndrome and hypertension, indicating that elevated levels of serum albumin are a risk factor for these conditions. Our results provide new biomarkers for preventive and therapeutic strategies for metabolic syndrome.

Role of a Whole Plant Foods Diet in Breast Cancer Prevention and Survival

Breast cancer (BC) is one of the leading causes of death and morbidity among women worldwide. Epidemiologic evidence shows that the risk of BC and other chronic diseases decreases as the proportion of whole plant foods increases, while the proportion of animal foods (fish, meat, poultry, eggs, seafood, and dairy products) and non-whole plant foods (e.g., refined grains, added sugars, French fries) in the diet decreases. Whole plant foods include fruits, vegetables, roots, tubers, whole grains, legumes, nuts, and seeds from which no edible part has been removed and to which no non-whole food been added. A whole plant foods diet lowers insulin resistance, inflammation, excess body fat, cholesterol, and insulin-like growth factor 1 and sex hormone bioavailability; it also increases estrogen excretion, induces favorable changes in the gut microbiota, and may also favorably affect mammary microbiota composition and decrease the risk of early menarche, all contributing to reduced BC incidence, recurrence, and mortality. This review explores the connection between a whole plant foods diet and BC risk and mortality as well as the potential mechanisms involved. Additionally, this diet is compared with other dietary approaches recommended for BC. A whole plant foods diet seems the optimal dietary pattern for BC and overall disease prevention as it exclusively consists of whole plant foods which, based on existing evidence, lead to the best health outcomes.

Exploring the relationship between gut microbiota and breast diseases using Mendelian randomization analysis

Background

Growing evidence suggests a relationship between gut microbiota composition and breast diseases, although the precise nature of this association remains uncertain. To investigate the causal relationship between gut microbiota and breast diseases, we utilized two-way Mendelian randomization (MR) analysis.

Methods

Four common diseases were included as outcomes: breast cancer, breast cysts, inflammatory disorders of the breast, and infections of the breast associated with childbirth, along with their subtypes. Genetic data on gut microbiota were extracted from genome-wide association studies (GWAS). The primary approach used to investigate the association between these genetic factors and gut microbiota was the inverse-variance-weighted (IVW) method with random-effects types. Sensitivity analyses, such as Cochran's Q test, the MR-Egger intercept test, and leave-one-out analysis, were conducted to ensure the stability and reliability of the MR findings.

Results

We discovered plausible causal links between 20 microbial categories and the breast diseases, with a significance level of p < 0.05. Notably, Family.Rikenellaceae (p: 0.0013) maintained a significant inverse relationship with overall breast cancer (BC), after the Bonferroni correction. In the reverse MR analysis, interactions were observed between Genus.Adlercreutzia and estrogen receptor-positive cancer. In addition, Genus.Sellimonas, Family.Rikenellaceae, and Genus.Paraprevotella were associated with ER+ and overall breast cancer, whereas Genus.Dorea was linked to both estrogen receptor-negative and overall breast cancer. Family.Prevotellaceae was the only category correlated with inflammatory breast disorders. Moreover, Genus Eubacteriumruminantiumgroup, Genus.Lactococcus, and Family.Alcaligenaceae were associated with breast cysts, while Genus.Anaerofilum, Genus.Butyricimonas, Order.Coriobacteriales, Order.Pasteurellales, and Order.Verrucomicrobiales showed significant associations with infections of the breast associated with childbirth. No evidence of heterogeneity or horizontal pleiotropy was found.

Conclusion

Our Mendelian randomization analysis confirmed a causal relationship between gut microbiota and breast diseases. Early stool tests may be a viable method for screening diseases to identify people at higher risk of breast diseases.

Exploring the genetic associations and causal relationships between antibody responses, immune cells, and various types of breast cancer

BACKGROUND

There may be potential associations between various pathogens, antibody immune responses, and breast cancer (BC), but the specific mechanisms and causal relationships remain unclear.

METHODS

First, multiple Mendelian randomization (MR) methods were used for univariable MR analysis to explore potential causal relationships between 34 antibody immune responses (related to 12 pathogens), 46 antibody immune responses (related to 13 pathogens), antibody responses post-COVID-19 vaccination, 731 immune cell types, and various BC subtypes (including overall BC, ER-positive, ER-negative, Luminal A, Luminal B, Luminal B HER2-negative, HER2-positive, and triple-negative BC). The primary results were then subjected to reverse MR analysis, heterogeneity testing using Cochran's Q, and horizontal pleiotropy testing. Robust findings were further used to design mediation pathways involving antibody immune responses, immune cells, and BC. After adjusting the effect estimates using multivariable MR (MVMR), a two-step mediation analysis was conducted to explore mediation pathways and mediation proportions. Finally, linkage disequilibrium score regression (LDSC) was applied to analyze the genetic correlation between phenotypes along mediation pathways, and cross-phenotype association analysis (CPASSOC) was performed to identify pleiotropic SNPs among three phenotypes along these pathways. Bayesian colocalization tests were conducted on pleiotropic SNPs using the multiple-trait-coloc (moloc).

RESULTS

We identified potential causal relationships between 15 antibody immune responses to 8 pathogens (Hepatitis B virus, Herpes Simplex Virus 2, Human Herpesvirus 6, Polyomavirus 2, BK polyomavirus, Cytomegalovirus, Helicobacter pylori, Chlamydia trachomatis), 250 immune cell phenotypes, and various BC subtypes. MVMR-adjusted mediation analysis revealed four potential mediation pathways. LDSC results showed no significant genetic correlation between phenotypes pairwise. CPASSOC analysis identified two potential mediation pathways with common pleiotropic SNPs (rs12121677, rs281378, rs2894250). However, none of these SNPs passed the Bayesian colocalization test by moloc. These results excluded horizontal pleiotropy, stabilizing MR analysis results.

CONCLUSION

This study utilized MR methods to analyze potential causal relationships between various antibody immune responses, immune cell types, and BC subtypes, identifying four potential regulatory mediation pathways. The findings of this study offer potential targets and research directions for virus-related and immunotherapy-related studies, providing a certain level of theoretical support. However, limitations such as GWAS sample size constraints and unclear specific pathophysiological mechanisms need further improvement and validation in future studies.

Gut microbiota causally impacts adrenal function: a two-sample mendelian randomization study

Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002-0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004-0.013, P = 2 × 10- 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

Guideline for designing microbiome studies in neoplastic diseases

Oncobiosis has emerged as a key contributor to the development, and modulator of the treatment efficacy of cancer. Hereby, we review the modalities through which the oncobiome can support the progression of tumors, and the emerging therapeutic opportunities they present. The review highlights the inherent challenges and limitations faced in sampling and accurately characterizing oncobiome. Additionally, the review underscores the critical need for the standardization of microbial analysis techniques and the consistent reporting of microbiome data. We provide a suggested metadata set that should accompany microbiome datasets from oncological settings so that studies remain comparable and decipherable.

High-fat diet during early life reshapes the gut microbiome and is associated with the disrupted mammary microenvironment in later life in mice

The influence of gut microbiota on gut health is well-documented, but it remains obscure for extraintestinal diseases such as breast cancer. Moreover, it is entirely unknown how gut dysbiosis during early life contributes to breast tumorigenesis later in life. In this study, we hypothesized that a high-fat diet during early life leads to alterations in the gut microbiome and is associated with disruptions in the mammary microenvironment. Female C57BL/6 mice were fed a low-fat diet (10% kcal fat) or a high-fat diet (HF, 60% kcal fat) for 8 weeks from the age of 4 to 12 weeks, which is equivalent to human childhood and adolescence. Twelve mice were sacrificed immediately after the 8-week feeding, the remainder were euthanized after switching to a normal lifecycle-supporting diet for an additional 12 weeks; the gut microbiome was then sequenced. The 8-week HF diet feeding altered the beta-diversity (Bray & Jaccard P < .01), and the difference remained significant after switching the diet (Bray & Jaccard P < .05). Immediately after HF feeding, a greater number of microbial taxa (>50) were altered, and about half of the taxa (25) remained significantly changed after switching the diet. The abundance of Alistipes, Bilophila, and Rikenellaceae stood out as significantly associated with multiple metabolic and inflammatory biomarkers in mammary tissue, including aromatase, Ccl2, and Cox2. In conclusion, an 8-week early-life HF feeding reshaped the gut microbiome, which connected with disrupted mammary microenvironments.

Mendelian randomization reveals the causal association between gout and hearing impairment in older adults

With the global aging trend escalating, the holistic well-being of the elderly has become a paramount concern within public health. Traditional observational studies often struggle with confounding factors and establishing causality, leaving the relationship between age-related hearing loss (ARHL) and gout largely unexplored. Employing bidirectional two-sample Mendelian randomization (MR) analysis, this investigation elucidated the genetic underpinnings associated with age-related hearing impairment, gout, and urate levels within the IEU Open-GWAS database, thereby uncovering potential causal connections that underlie the intricate interplay between gout, serum urate concentrations, and auditory decline in the geriatric demographic. In the forward MR phase, a cohort of 30 single nucleotide polymorphisms was leveraged to dissect the causal dynamics between ARHL and both gout and urate concentrations. Conversely, in the reverse MR phase, gout and urate levels were posited as the exposome to delineate their impact on hearing acuity, employing an array of models for rigorous validation and sensitivity scrutiny. In the forward MR analysis, a statistically significant correlation was discerned between ARHL and gout (P = .003, odds ratio = 1.01, 95% confidence interval: 1.00-1.02), alongside a notable association with serum urate levels (P = .031, odds ratio = 1.39, 95% confidence interval: 1.03-1.88), intimating that ARHL could potentially influence the incidence of gout and urate concentrations. Conversely, the reverse MR investigation revealed that neither gout nor serum urate levels exerted significant impact on auditory degradation (P > .05), insinuating that these factors might not predominantly contribute to hearing loss. Sensitivity analyses concurred with this inference. This study enriches the comprehension of geriatric health intricacies and unveils that ARHL potentially influences gout and serum urate concentrations. This suggests that monitoring ARHL may play a crucial role in the early identification and management of gout and hyperuricemia, potentially contributing to a comprehensive approach to improving geriatric health outcomes.

Investigating the causal relationship of gut microbiota with GERD and BE: a bidirectional mendelian randomization

BACKGROUND

Gut microbiota(GM) have been proven associated with lots of gastrointestinal diseases, but its causal relationship with Gastroesophageal reflux disease(GERD) and Barrett's esophagus(BE) hasn't been explored. We aimed to uncover the causal relation between GM and GERD/BE and potential mediators by utilizing Mendelian Randomization(MR) analysis.

METHODS

Summary statistics of GM(comprising 301 bacteria taxa and 205 metabolism pathways) were extracted from MiBioGen Consortium(N = 18,340) and Dutch Microbiome Project(N = 7,738), GERD and BE from a multitrait meta-analysis(NGERD=602,604, NBE=56,429). Bidirectional two-sample MR analysis and linkage disequilibrium score regression(LDSC) were used to explore the genetic correlation between GM and GERD/BE. Mediation MR analysis was performed for the risk factors of GERD/BE, including Body mass index(BMI), weight, type 2 diabetes, major depressive disorder(MDD), smoking initiation, alcohol consumption, and dietary intake(including carbohydrate, sugar, fat, protein intake), to detect the potential mediators between GM and GERD/BE.

RESULTS

11 bacterial taxa and 13 metabolism pathways were found associated with GERD, and 18 taxa and 5 pathways exhibited causal relationship with BE. Mediation MR analysis suggested weight and BMI played a crucial role in these relationships. LDSC identified 1 taxon and 4 metabolism pathways related to GERD, and 1 taxon related to BE. Specie Faecalibacterium prausnitzii had a suggestive impact on both GERD(OR = 1.087, 95%CI = 1.01-1.17) and BE(OR = 1.388, 95%CI = 1.03-1.86) and LDSC had determined their correlation. Reverse MR indicated that BE impacted 10 taxa and 4 pathways.

CONCLUSIONS

This study established a causal link between gut microbiota and GERD/BE, and identified the probable mediators. It offers new insights into the role of gut microbiota in the development and progression of GERD and BE in the host.

Causal relationships between gut microbrome and digestive system diseases: A two-sample Mendelian randomization study

Growing evidences of recent studies have shown that gut microbrome are causally related to digestive system diseases (DSDs). However, causal relationships between the gut microbiota and the risk of DSDs still remain unclear. We utilized identified gut microbiota based on class, family, genus, order and phylum information and digestive system diseases genome-wide association study (GWAS) dataset for two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method was used to evaluate causal relationships between gut microbiota and 7 DSDs, including chronic gastritis, colorectal cancer, Crohn's disease, gastric cancer, gastric ulcer, irritable bowel syndrome and esophageal cancer. Finally, we verified the robustness of MR results based on heterogeneity and pleiotropy analysis. We discovered 15 causal associations with genetic liabilities in the gut microbiota and DSDs, such as genus Victivallis, genus RuminococcaceaeUCG005, genus Ruminococcusgauvreauiigroup, genus Oxalobacter and so on. Our MR analysis revealed that the gut microbiota is causally associated with DSDs. Further researches of the gut microbiota and the pathogenesis of DSDs are still significant and provide new methods for the prevention and treatment of DSDs.

Causal associations between gut microbiota and cutaneous melanoma: a Mendelian randomization study

Background

Cutaneous melanoma (CM) of the skin stands as the leading cause of mortality among skin cancer-related deaths. Despite the successes achieved with novel therapies such as immunotherapy and targeted therapy, their efficacy remains limited, necessitating further exploration of new treatment modalities. The gut microbiota and CM may be linked, as indicated by a growing body of preclinical and observational research. Nevertheless, the exact correlation between the intestinal microbiota and CM remains to be determined. Therefore, this study aims to assess the potential causal relationship between the gut microbiota and CM.

Methods

The study utilized exposure data obtained from the MiBioGen consortium's microbiome GWAS, which included a total of 18,340 samples gathered from 24 population-based cohorts. Data at the summary level for CM were acquired from the UK Biobank investigation. The main analytical strategy utilized in this research was the inverse variance weighted (IVW) technique, supported by quality assurance measures like the weighted median model, MR-Egger, simple model, and weighted model approaches. The Cochran's Q test was used to evaluate heterogeneity. To ascertain potential pleiotropy, we employed both the MR-Egger regression and the MR-PRESSO test. Sensitivity analysis was conducted using the leave-one-out method.

Results

The study found that the class Bacteroidia (OR = 0.997, 95% CI: 0.995-0.999, p = 0.027), genus Parabacteroides (OR = 0.997, 95% CI: 0.994-0.999, p = 0.037), order Bacteroidales (OR = 0.997, 95% CI: 0.995-0.999, p = 0.027), and genus Veillonella (OR = 0.998, 95% CI: 0.996-0.999, p = 0.046) have protective effects on CM. On the order hand, the genus Blautia (OR = 1.003, 95% CI: 1-1.006, p = 0.001) and phylum Cyanobacteria (OR = 1.002, 95% CI: 1-1.004, p = 0.04) are identified as risk factors for CM.

Conclusion

We comprehensively assessed the potential causal relationship between the gut microbiota and CM and identified associations between six gut microbiota and CM. Among these, four gut microbiota were identified as protective factors for CM, while two gut microbiota were identified as risk factors for CM. This study effectively established a causal relationship between the gut microbiota and CM, thereby providing valuable insights into the mechanistic pathways through which the microbiota impacts the progression of CM.

The association between lipid biomarkers and osteoarthritis based on the National Health and Nutrition Examination Survey and Mendelian randomization study

To explore the association between lipid markers and osteoarthritis (OA). First, the National Health and Nutrition Examination Survey (NHANES) database was used to screen participants with lipid markers, OA and relevant covariates, and logistic regression was used to analyze the association between lipid markers and OA; Then, under the theoretical framework of Mendelian randomization (MR), two-sample MR was performed using GWAS data of lipid markers and OA to explore the causal association between the two, which was analyzed by inverse variance weighting (IVW) method. Heterogeneity test, sensitivity analysis and pleiotropy analysis were also performed. The NHANES database screened a total of 3706 participants, of whom 836 had OA and 2870 did not have OA. When lipid markers were used as continuous variables, multivariate logistic results showed an association between HDL, LDL and OA (HDL, OR (95%):1.01 (1.00, 1.01); LDL, OR (95%):1.00 (0.99, 1.00)). When lipid markers were used as categorical variables, multivariate logistic results showed the fourth quartile result of 0.713 (0.513, 0.992) for LDL relative to the first quartile. In MR study, the results of the IVW method for TG, TL, HDL and LDL showed OR (95% CI) of 1.06 (0.97-1.16), 0.95 (0.85-1.06), 0.94 (0.86-1.02) and 0.89 (0.80-0.998) with P-values of 0.21, 0.37. 013, 0.046. The heterogeneity tests and multiplicity analyses showed P-values greater than 0.05, and sensitivity analyses showed no abnormal single nucleotide polymorphisms. Through NHANES database and MR analyses, LDL was found to be a protective factor for OA, while HDL still needs further study. Our results provide new biomarkers for preventive and therapeutic strategies for OA.