Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires.

Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from 'reduced mastication' to 'weight gain' was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.

Prediction and causal inference of hyperuricemia using gut microbiota.

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40 years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.

Impact of gut microbiome on serum IgG4 levels in the general population: Shika-machi super preventive health examination results.

Introduction: Immunoglobulin G4 (IgG4) is a member of the human immunoglobulin G (IgG) subclass, a protein involved in immunity to pathogens and the body's resistance system. IgG4-related diseases (IgG4-RD) are intractable diseases in which IgG4 levels in the blood are elevated, causing inflammation in organs such as the liver, pancreas, and salivary glands. IgG4-RD are known to be more prevalent in males than in females, but the etiology remains to be elucidated. This study was conducted to investigate the relationship between gut microbiota (GM) and serum IgG4 levels in the general population. Methods: In this study, the relationship between IgG4 levels and GM evaluated in male and female groups of the general population using causal inference. The study included 191 men and 207 women aged 40 years or older from Shika-machi, Ishikawa. GM DNA was analyzed for the 16S rRNA gene sequence using next-generation sequencing. Participants were bifurcated into high and low IgG4 groups, depending on median serum IgG4 levels. Results: ANCOVA, Tukey's HSD, linear discriminant analysis effect size, least absolute shrinkage and selection operator logistic regression model, and correlation analysis revealed that Anaerostipes, Lachnospiraceae, Megasphaera, and [Eubacterium] hallii group were associated with IgG4 levels in women, while Megasphaera, [Eubacterium] hallii group, Faecalibacterium, Ruminococcus.1, and Romboutsia were associated with IgG4 levels in men. Linear non-Gaussian acyclic model indicated three genera, Megasphaera, [Eubacterium] hallii group, and Anaerostipes, and showed a presumed causal association with IgG4 levels in women. Discussion: This differential impact of the GM on IgG4 levels based on sex is a novel and intriguing finding.

Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results.

The renin-angiotensin-aldosterone system (RAAS) is a regulatory mechanism of the endocrine system and is associated with various diseases, including hypertension and renal and cardiovascular diseases. The gut microbiota (GM) have been associated with various diseases, mainly in animal models. However, to our knowledge, no studies have examined the relationship between the RAAS and GM in humans. The present study aimed to assess the association between the systemic RAAS and GM genera and their causal relationships. The study participants were 377 members of the general population aged 40 years or older in Shika-machi, Japan. Plasma renin activity (PRA), plasma aldosterone concentration (PAC), aldosterone-renin ratio (ARR), and GM composition were analyzed using the 16S rRNA method. The participants were divided into high and low groups according to the PRA, PAC, and ARR values. U-tests, one-way analysis of covariance, and linear discriminant analysis of effect size were used to identify the important bacterial genera between the two groups, and binary classification modeling using Random Forest was used to calculate the importance of the features. The results showed that Blautia, Bacteroides, Akkermansia, and Bifidobacterium were associated with the RAAS parameters. Causal inference analysis using the linear non-Gaussian acyclic model revealed a causal effect of Blautia on PAC via SBP. These results strengthen the association between the systemic RAAS and GM in humans, and interventions targeting the GM may provide new preventive measures and treatments for hypertension and renal disease.