There is increasing evidence showing that the dynamic changes in the gut microbiota can alter brain physiology and behavior. Cognition was originally thought to be regulated only by the central nervous system. However, it is now becoming clear that many non-nervous system factors, including the gut-resident bacteria of the gastrointestinal tract, regulate and influence cognitive dysfunction as well as the process of neurodegeneration and cerebrovascular diseases. Extrinsic and intrinsic factors including dietary habits can regulate the composition of the microbiota. Microbes release metabolites and microbiota-derived molecules to further trigger host-derived cytokines and inflammation in the central nervous system, which contribute greatly to the pathogenesis of host brain disorders such as pain, depression, anxiety, autism, Alzheimer’s diseases, Parkinson’s disease, and stroke. Change of blood–brain barrier permeability, brain vascular physiology, and brain structure are among the most critical causes of the development of downstream neurological dysfunction. In this review, we will discuss the following parts: Overview of technical approaches used in gut microbiome studies Microbiota and immunity Gut microbiota and metabolites Microbiota-induced blood–brain barrier dysfunction Neuropsychiatric diseases ■ Stress and depression ■ Pain and migraine ■ Autism spectrum disorders Neurodegenerative diseases ■ Parkinson’s disease ■ Alzheimer’s disease ■ Amyotrophic lateral sclerosis ■ Multiple sclerosis Cerebrovascular disease ■ Atherosclerosis ■ Stroke ■ Arteriovenous malformation Conclusions and perspectives

Background: The association between tobacco use and hypertension risk has been extensively researched but remains controversial, and few existing studies have considered the role of tobacco type and dosage response in this association. In this context, this study aims to provide epidemiological evidence for the possible relationship between tobacco smoking and future hypertension risk, with the tobacco type and consumption dose into consideration.Methods: This study was based on 10-year follow-up data from the Guizhou Population Health Cohort conducted in southwest China. Multivariate Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals [95% confidence intervals (CIs)], and restricted cubic spline analyses were performed to visualize the dose-response association.Results: A total of 5,625 participants (2,563 males and 3,062 females) were included in the final analysis. Heavy smokers of machine-rolled cigarettes had an elevated hypertension risk compared with non-smokers (HR: 1.50, 95% CI: 1.05–2.16). The interaction effects of heavy smoking-heavy drinking patterns increased the future hypertension risk, with an adjusted HR of 2.58 (95% CI: 1.06–6.33).Conclusion: This study did not find a significant association between overall tobacco use status and the risk of hypertension. However, heavy machine-rolled cigarette smokers had a statistically significant increased risk of hypertension compared with non-smokers, and a J-shape association has been found between the average daily consumption of machine-rolled cigarettes and the risk of hypertension. Besides, tobacco and alcohol consumption jointly increased the long-term hypertension risk.

Exploration of plasma proteins associated with osteoporosis can offer insights into its pathological development, identify novel biomarkers for screening high-risk populations, and facilitate the discovery of effective therapeutic targets. The present study aimed to identify potential proteins associated with osteoporosis and to explore the underlying mechanisms from a proteomic perspective. The study included 42,325 participants without osteoporosis in the UK Biobank (UKB), of whom 1,477 developed osteoporosis during the follow-up. We used Cox regression and Mendelian randomization analysis to examine the association between plasma proteins and osteoporosis. Machine learning was utilized to explore proteins with strong predictive power for osteoporosis risk. Of 2,919 plasma proteins, we identified 134 significantly associated with osteoporosis, with sclerostin (SOST), adiponectin (ADIPOQ), and creatine kinase B-type (CKB) exhibiting strong associations. Twelve of these proteins showed significant associations with bone mineral density (BMD) T-score at the femoral neck, lumbar spine, and total body. Mendelian randomization further supported causal relationships between 17 plasma proteins and osteoporosis. Moreover, follitropin subunit beta (FSHB), SOST, and ADIPOQ demonstrated high importance in predictive modeling. Utilizing a predictive model built with 10 proteins, we achieved relatively accurate prediction of osteoporosis onset up to 5 years in advance (AUC = 0.803). Finally, we identified three osteoporosis-related protein modules associated with immunity, lipid metabolism, and follicle-stimulating hormone (FSH) regulation from a network perspective, elucidating their mediating roles between various risk factors (smoking, sleep, physical activity, polygenic risk score (PRS), and menopause) and osteoporosis. We identified several proteins associated with osteoporosis and highlighted the role of plasma proteins in influencing its progression through three primary pathways: immunity, lipid metabolism, and FSH regulation. This provides further insights into the distinct molecular patterns and pathogenesis of bone loss and may contribute to strengthening early diagnosis and long-term monitoring of the condition.

High-fiber diet interventions have been proven to be beneficial for gut microbiota and glycemic control in diabetes patients. However, the effect of a low level of fiber in habitual diets remains unclear. This study aims to examine the associations of habitual dietary fiber intake with gut microbiome profiles among Chinese diabetes patients and identify differential taxa that mediated associations of dietary fiber with HbA1c level. Two cross-sectional studies and one longitudinal study were designed based on two follow-up surveys in a randomized trial conducted during 2015–2017. The study included 356 and 310 participants in the first and second follow-ups, respectively, with 293 participants in common in both surveys. Dietary fiber intake was calculated based on a 3-day 24-h diet recall at each survey and was classified into a lower or a higher group according to the levels taken based on the two surveys using 7.2 g/day as a cut-off value. HbA1c was assayed to assess glycemic status using a cut-off point of 7.0% and 8.0%. Microbiome was profiled by 16S rRNA sequencing. A high habitual dietary fiber intake was associated with a decrease in α-diversity in both the cross-sectional and longitudinal analyses. At the first follow–up, phylum Firmicutes and Fusobacteria were negatively associated with a higher dietary fiber intake (p < 0.05, Q < 0.15); at the second follow-up, genus Adlercreutzia, Prevotella, Ruminococcus, and Desulfovibrio were less abundant in patients taking higher dietary fiber (p < 0.05, Q < 0.15); genus Desulfovibrio and Ruminococcaceae (Unknown), two identified differential taxa by HbA1c level, were negatively associated with dietary fiber intake in both the cross-sectional and longitudinal analyses, and mediated the dietary fiber-HbA1c associations among patients taking dietary fiber ≥ 7.2 g/day (mediation effect β [95%CI]: −0.019 [−0.043, −0.003], p = 0.018 and −0.019 [−0.046, −0.003], p = 0.016). Our results suggest that habitual dietary fiber intake has a beneficial effect on gut microbiota in Chinese diabetes patients. Further studies are needed to confirm our results.

The plant-based dietary pattern has been recommended for its potential health and environmental benefits, but its association with bone loss needs to be further explored. This study aimed to investigate the association between three plant-based diet indexes and bone loss in 16,085 adults, using data from the National Health and Nutrition Examination Survey. Three plant-based diet indexes (PDI, hPDI, and uPDI) were calculated from two NHANES 24-h dietary recall interviews, to characterize a plant-based diet. A multinomial logistic regression model was used to estimate the odds ratios (OR) and 95% confidence intervals (95% CI). Higher hPDI and PDI were associated with increased risk of bone loss (OR_{Q5 vs. Q1} = 1.50; 95% CI: 1.24–1.81 for hPDI; OR_{Q5 vs. Q1} = 1.22; 95% CI: 1.03–1.45 for PDI), while higher uPDI was associated with increased risk of osteoporosis (OR_{Q5 vs. Q1} = 1.48; 95% CI: 1.04–2.11). A harmful association between plant-based diet indexes (hPDI and PDI) and osteopenia was observed at the lumbar spine rather than the femoral neck. We conducted several sensitivity analyses to ensure the robustness of results, including subgroup analysis, exclusion of people taking anti-osteoporotic and estrogenic drugs, further adjustment for menopausal status, corticosteroid usage, and dietary supplements, and calculation of E-value. Our study demonstrates the deleterious effects of a plant-based diet on bone health and emphasizes the importance of a balanced diet.