Temporal relationship among adiposity, gut microbiota, and insulin resistance in a longitudinal human cohort

New insights into the interplay between autophagy, gut microbiota and insulin resistance in metabolic syndrome

Metabolic syndrome (MetS) is a widespread and multifactorial disorder, and the study of its pathogenesis and treatment remains challenging. Autophagy, an intracellular degradation system that maintains cellular renewal and homeostasis, is essential for maintaining antimicrobial defense, preserving epithelial barrier integrity, promoting mucosal immune response, maintaining intestinal homeostasis, and regulating gut microbiota and microbial metabolites. Dysfunctional autophagy is implicated in the pathological mechanisms of MetS, involving insulin resistance (IR), chronic inflammation, oxidative stress, and endoplasmic reticulum (ER) stress, with IR being a predominant feature. The study of autophagy represents a valuable field of research with significant clinical implications for identifying autophagy-related signals, pathways, mechanisms, and treatment options for MetS. Given the multifactorial etiology and various potential risk factors, it is imperative to explore the interplay between autophagy and gut microbiota in MetS more thoroughly. This will facilitate the elucidation of new mechanisms underlying the crosstalk among autophagy, gut microbiota, and MetS, thereby providing new insights into the diagnosis and treatment of MetS.

Temporal relationship between hepatic steatosis and blood pressure elevation and the mediation effect in the development of cardiovascular disease

The temporal relationship between non-alcoholic fatty liver disease (NAFLD) and hypertension remains highly controversial, with ongoing debates on whether NAFLD induces hypertension or vice versa. We employed cross-lagged panel models to investigate the temporal relationship between hepatic steatosis (assessed by Fatty Liver Index [FLI] in the main analysis, and by Proton Density Fat Fraction [PDFF] in the validation study) and blood pressure (systolic and diastolic blood pressure [SBP/ DBP]). Subsequently, we employed causal mediation models to explore the mediation effect in CVD development, including ischemic heart disease and stroke. The main analysis incorporated repeated measurement data of 5,047 participants from the China Multi-Ethnic Cohort (CMEC) and 5,685 participants from the UK Biobank (UKB). In both cohorts, the path coefficients from FLI to blood pressure were significant and greater than the path from blood pressure to FLI, with βFLI→SBP = 0.081, P < 0.001 versus βSBP→FLI = 0.020, P = 0.031; βFLI→DBP = 0.082, P < 0.001 versus βDBP→FLI = -0.006, P = 0.480 for CEMC, and βFLI→SBP = 0.057, P < 0.001 versus βSBP→FLI = -0.001, P = 0.727; βFLI→DBP = 0.061, P < 0.001, versus βDBP→FLI = -0.006, P = 0.263 for UKB. The validation study with 962 UKB participants using PDFF consistently supported these findings. In the mediation analyses encompassing 11,108 UKB participants, SBP and DBP mediated 12.2% and 5.2% of the hepatic steatosis-CVD association, respectively. The proportions were lower for ischemic heart disease (SBP: 6.1%, DBP: non-statistically significant -6.8%), and relatively stronger for stroke (SBP: 19.4%, DBP: 26.1%). In conclusion, hepatic steatosis more strongly contributes to elevated blood pressure than vice versa. Blood pressure elevation positively mediates the hepatic steatosis-CVD association, particularly in stroke compared to ischemic heart disease.

Total saponins from Panax japonicus regulated the intestinal microbiota to alleviate lipid metabolism disorders in aging mice

Total saponins from Panax japonicus (TSPJ) have many beneficial physiological activities, particularly in alleviating the damages of aging and abnormal lipid metabolism. This work used mice models to investigate if TSPJ reduced obesity and regulated metabolic functions via the intestinal microbiota, the disturbance of which has been shown to cause aging-related diseases. The results showed that TSPJ significantly reduced the weight and blood lipid level of aging mice. Further analyses showed that TSPJ significantly inhibited adipogenesis, changed the composition of the intestinal flora, and protected the integrity of the intestinal barrier. It was inferred from the accumulated experimental data that TSPJ helped to combat obesity in aging mice by regulating the intestinal microbiota and promoting microbial metabolism.

Non-traumatic osteonecrosis of the femoral head induced by steroid and alcohol exposure is associated with intestinal flora alterations and metabolomic profiles

OBJECTIVE

Osteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives.

METHODS

Utilizing fecal samples from 68 NONFH patients (32 steroid-induced, 36 alcohol-induced), high-throughput 16 S rDNA sequencing and liquid chromatography with tandem mass spectrometry (LC-MS/MS) metabolomics analyses were conducted. Univariate and multivariate analyses were applied to the omics data, employing linear discriminant analysis effect size to identify potential biomarkers. Additionally, functional annotation of differential metabolites and associated pathways was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Subsequently, Spearman correlation analysis was employed to assess the potential correlations between differential gut microbiota and metabolites.

RESULTS

High-throughput 16 S rDNA sequencing revealed significant gut microbial differences. At the genus level, the alcohol group had higher Lactobacillus and Roseburia, while the steroid group had more Megasphaera and Akkermansia. LC-MS/MS metabolomic analysis indicates significant differences in fecal metabolites between steroid- and alcohol-induced ONFH patients. Alcohol-induced ONFH (AONFH) showed elevated levels of L-Lysine and Oxoglutaric acid, while steroid-induced ONFH(SONFH) had increased Gluconic acid and Phosphoric acid. KEGG annotation revealed 10 pathways with metabolite differences between AONFH and SONFH patients. Correlation analysis revealed the association between differential gut flora and differential metabolites.

CONCLUSIONS

Our results suggest that hormones and alcohol can induce changes in the gut microbiota, leading to alterations in fecal metabolites. These changes, driven by different pathways, contribute to the progression of the disease. The study opens new research directions for understanding the pathogenic mechanisms of hormone- or alcohol-induced NONFH, suggesting that differentiated preventive and therapeutic approaches may be needed for NONFH caused by different triggers.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Effect of the gut microbiome, plasma metabolome, peripheral cells, and inflammatory cytokines on obesity: a bidirectional two-sample Mendelian randomization study and mediation analysis

Background

Obesity is a metabolic and chronic inflammatory disease involving genetic and environmental factors. This study aimed to investigate the causal relationship among gut microbiota abundance, plasma metabolomics, peripheral cell (blood and immune cell) counts, inflammatory cytokines, and obesity.

Methods

Summary statistics of 191 gut microbiota traits (N = 18,340), 1,400 plasma metabolite traits (N = 8,299), 128 peripheral cell counts (blood cells, N = 408,112; immune cells, N = 3,757), 41 inflammatory cytokine traits (N = 8,293), and 6 obesity traits were obtained from publicly available genome-wide association studies. Two-sample Mendelian randomization (MR) analysis was applied to infer the causal links using inverse variance-weighted, maximum likelihood, MR-Egger, weighted median, weighted mode, and Wald ratio methods. Several sensitivity analyses were also utilized to ensure reliable MR results. Finally, we used mediation analysis to identify the pathway from gut microbiota to obesity mediated by plasma metabolites, peripheral cells, and inflammatory cytokines.

Results

MR revealed a causal effect of 44 gut microbiota taxa, 281 plasma metabolites, 27 peripheral cells, and 8 inflammatory cytokines on obesity. Among them, five shared causal gut microbiota taxa belonged to the phylum Actinobacteria, order Bifidobacteriales, family Bifidobacteriaceae, genus Lachnospiraceae UCG008, and species Eubacterium nodatum group. Furthermore, we screened 42 shared causal metabolites, 7 shared causal peripheral cells, and 1 shared causal inflammatory cytokine. Based on known causal metabolites, we observed that the metabolic pathways of D-arginine, D-ornithine, linoleic acid, and glycerophospholipid metabolism were closely related to obesity. Finally, mediation analysis revealed 20 mediation relationships, including the causal pathway from gut microbiota to obesity, mediated by 17 metabolites, 2 peripheral cells, and 1 inflammatory cytokine. Sensitivity analysis represented no heterogeneity or pleiotropy in this study.

Conclusion

Our findings support a causal relationship among gut microbiota, plasma metabolites, peripheral cells, inflammatory cytokines, and obesity. These biomarkers provide new insights into the mechanisms underlying obesity and contribute to its prevention, diagnosis, and treatment.

Univariable and multivariable Mendelian randomization study identified the key role of gut microbiota in immunotherapeutic toxicity

BACKGROUND

In cancer patients receiving immune checkpoint inhibitors (ICIs), there is emerging evidence suggesting a correlation between gut microbiota and immune-related adverse events (irAEs). However, the exact roles of gut microbiota and the causal associations are yet to be clarified.

METHODS

To investigate this, we first conducted a univariable bi-directional two-sample Mendelian randomization (MR) analysis. Instrumental variables (IVs) for gut microbiota were retrieved from the MiBioGen consortium (18,340 participants). GWAS summary data for irAEs were gathered from an ICIs-treated cohort with 1,751 cancer patients. Various MR analysis methods, including inverse variance weighted (IVW), MR PRESSO, maximum likelihood (ML), weighted median, weighted mode, and cML-MA-BIC, were used. Furthermore, multivariable MR (MVMR) analysis was performed to account for possible influencing instrumental variables.

RESULTS

Our analysis identified fourteen gut bacterial taxa that were causally associated with irAEs. Notably, Lachnospiraceae was strongly associated with an increased risk of both high-grade and all-grade irAEs, even after accounting for the effect of BMI in the MVMR analysis. Akkermansia, Verrucomicrobiaceae, and Anaerostipes were found to exert protective roles in high-grade irAEs. However, Ruminiclostridium6, Coprococcus3, Collinsella, and Eubacterium (fissicatena group) were associated with a higher risk of developing high-grade irAEs. RuminococcaceaeUCG004, and DefluviitaleaceaeUCG011 were protective against all-grade irAEs, whereas Porphyromonadaceae, Roseburia, Eubacterium (brachy group), and Peptococcus were associated with an increased risk of all-grade irAEs.

CONCLUSIONS

Our analysis highlights a strong causal association between Lachnospiraceae and irAEs, along with some other gut microbial taxa. These findings provide potential modifiable targets for managing irAEs and warrant further investigation.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Analysis of influencing factors and interaction of body weight and disease outcome in patients with prediabetes

BACKGROUND

The trend of prediabetes progressing to type 2 diabetes mellitus (T2DM) is prominent, and effective intervention can lead to a return to prediabetes. Exploring the factors influencing the outcome of prediabetes is helpful to guide clinical intervention. The weight change in patients with prediabetes has not attracted much attention.

AIM

To explore the interaction between body weight and the factors affecting the progression of prediabetes to T2DM.

METHODS

We performed a retrospective analysis of 236 patients with prediabetes and 50 with normal glucose tolerance (NGT), and collected clinical data and follow-up results of all patients. Based on natural blood glucose outcomes, we classified 66 patients with progression to T2DM into the disease progression (DP) group, and 170 patients without progression to T2DM into the disease outcome (DO) group. We analyzed the factors that influenced prediabetes outcome and the influence of body weight on prediabetes blood glucose outcome by unconditional logistic regression. A general linear model (univariate) was used to analyze the inter-action between body weight and independent influencing factors.

RESULTS

There were 98 cases of impaired fasting glucose (IFG), 90 cases of impaired glucose tolerance (IGT), and 48 cases of coexistent IFG and IGT. The body weight, waist circumference, body mass index, fasting blood glucose, and 2 h plasma glucose of patients with IFG, IGT, and coexistent IFG and IGT were higher than those in patients with NGT (P < 0.05). Logistic regression analysis showed that body weight, glycosylated hemoglobin, uric acid, fasting insulin, and homeostatic model assessment for insulin resistance were independent factors affecting progression of prediabetes to T2DM (P < 0.05). Receiver operating characteristic curve analysis showed that the area under the curve predicted by the above indicators combined was 0.905 [95% confidence interval (CI): 0.863-0.948], which was greater than that predicted by each indicator alone. Logistic regression analysis with baseline body weight as an independent variable showed that compared with body weight 1, the odds ratio (95%CI) of body weight 3 was 1.399 (1.142-2.126) (P = 0.033). There was a multiplicative interaction between body weight and uric acid (β = 1.953, P = 0.005).

CONCLUSION

High body weight in patients with prediabetes is an independent risk factor for progression to T2DM, and the risk of progression is increased when coexisting with high uric acid level.

Effects of vitamin D supplementation on glucose and lipid metabolism in patients with type 2 diabetes mellitus and risk factors for insulin resistance

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease featured by insulin resistance (IR) and decreased insulin secretion. Currently, vitamin D deficiency is found in most patients with T2DM, but the relationship between vitamin D and IR in T2DM patients requires further investigation.

AIM

To explore the risk factors of IR and the effects of vitamin D supplementation on glucose and lipid metabolism in patients with T2DM.

METHODS

Clinical data of 162 T2DM patients treated in First Affiliated Hospital of Harbin Medical University between January 2019 and February 2022 were retrospectively analyzed. Based on the diagnostic criteria of IR, the patients were divided into a resistance group (n = 100) and a non-resistance group (n = 62). Subsequently, patients in the resistance group were subdivided to a conventional group (n = 44) or a joint group (n = 56) according to the treatment regimens. Logistic regression was carried out to analyze the risk factors of IR in T2DM patients. The changes in glucose and lipid metabolism indexes in T2DM patients with vitamin D deficiency were evaluated after the treatment.

RESULTS

Notable differences were observed in age and body mass index (BMI) between the resistance group and the non-resistance group (both P < 0.05). The resistance group exhibited a lower 25-hydroxyvitamin D3 (25(OH)D3) level, as well as notably higher levels of 2-h postprandial blood glucose (2hPG), fasting blood glucose (FBG), and glycosylated hemoglobin (HbA1c) than the non-resistance group (all P < 0.0001). Additionally, the resistance group demonstrated a higher triglyceride (TG) level but a lower high-density lipoprotein-cholesterol (HDL-C) level than the non-resistance group (all P < 0.0001). The BMI, TG, HDL-C, 25(OH)D3, 2hPG, and HbA1c were found to be risk factors of IR. Moreover, the post-treatment changes in levels of 25(OH)D3, 2hPG, FBG and HbA1c, as well as TG, total cholesterol, and HDL-C in the joint group were more significant than those in the conventional group (all P < 0.05).

CONCLUSION

Patients with IR exhibit significant abnormalities in glucose and lipid metabolism parameters compared to the non-insulin resistant group. Logistic regression analysis revealed that 25(OH)D3 is an independent risk factor influencing IR. Supplementation of vitamin D has been shown to improve glucose and lipid metabolism in patients with IR and T2DM.

The association between BMI and serum uric acid is partially mediated by gut microbiota

Obesity is a risk factor for the development of hyperuricemia, both of which were related to gut microbiota. However, whether alterations in the gut microbiota lie in the pathways mediating obesity's effects on hyperuricemia is less clear. Body mass index (BMI) and serum uric acid (SUA) were separately important indicators of obesity and hyperuricemia. Our study aims to investigate whether BMI-related gut microbiota characteristics would mediate the association between BMI and SUA levels. A total of 6,280 participants from Guangdong Gut Microbiome Project were included in this study. Stool samples were collected for 16S rRNA gene sequencing. The results revealed that BMI was significantly and positively associated with SUA. Meanwhile, BMI was significantly associated with the abundance of 102 gut microbial genera, 16 of which were also significantly associated with SUA. The mediation analysis revealed that the association between BMI and SUA was partially mediated by the abundance of Proteobacteria (proportion mediated: 0.94%, P < 0.05). At the genus level, 25 bacterial genera, including Ralstonia, Oscillospira, Faecalibacterium, etc., could also partially mediate the association of BMI with SUA (the highest proportion is mediated by Ralstonia, proportion mediated: 2.76%, P < 0.05). This study provided evidence for the associations among BMI, gut microbiota, and SUA, and the mediation analysis suggested that the association of BMI with SUA was partially mediated by the gut microbiota. IMPORTANCE Using 16S rRNA sequencing analysis, local interpretable machine learning technique analysis and mediation analysis were used to explore the association between BMI with SUA, and the mediating effects of gut microbial dysbiosis in the association were investigated.

The Effect of Short-Term Consumption of Lactic Acid Bacteria on the Gut Microbiota in Obese People

Obesity is a problem of modern health care that causes the occurrence of many concomitant diseases: arterial hypertension, diabetes mellitus, non-alcoholic fatty liver disease, and cardiovascular diseases. New strategies for the treatment and prevention of obesity are being developed that are based on using probiotics for modulation of the gut microbiota. Our study aimed to evaluate the bacterial composition of the gut of obese patients before and after two weeks of lactic acid bacteria (Lactobacillus acidophilus, Lactiplantibacillus plantarum, Limosilactobacillus fermentum, and Lactobacillus delbrueckii) intake. The results obtained showed an increase in the number of members of the phylum Actinobacteriota in the group taking nutritional supplements, while the number of phylum Bacteroidota decreased in comparison with the control group. There has also been an increase in potentially beneficial groups: Bifidobacterium, Blautia, Eubacterium, Anaerostipes, Lactococcus, Lachnospiraceae ND3007, Streptococcus, Escherichia-Shigella, and Lachnoclostridium. Along with this, a decrease in the genera was demonstrated: Faecalibacterium, Pseudobutyrivibrio, Subdoligranulum, Faecalibacterium, Clostridium sensu stricto 1 and 2, Catenibacterium, Megasphaera, Phascolarctobacterium, and the Oscillospiraceae NK4A214 group, which contribute to the development of various metabolic disorders. Modulation of the gut microbiota by lactic acid bacteria may be one of the ways to treat obesity.

Gut Microbiota Characteristics of People with Obesity by Meta-Analysis of Existing Datasets

Obesity is a complex chronic, relapsing, progressive disease. Association studies have linked microbiome alterations with obesity and overweight. However, the results are not always consistent. An integrated analysis of 4282 fecal samples (2236 control (normal weight) group, 1152 overweight, and 894 simple obesity) was performed to identify obesity-associated microbial markers. Based on a random effects model and a fixed effects model, we calculated the odds ratios of the metrics, including bacterial alpha-diversity, beta-diversity, Bacteroidetes/Firmicutes ratio, common genera, and common pathways, between the simple obesity and control groups as well as the overweight and control groups. The random forest model was trained based on a single dataset at the genus level. Feature selection based on feature importance ranked by mean decrease accuracy and leave-one-out cross-validation was conducted to improve the predictive performance of the models. Chao1 and evenness possessed significant ORs higher than 1.0 between the obesity and control groups. Significant bacterial community differences were observed between the simple obesity and the control. The ratio of Bacteroidetes/Firmicutes was significantly higher in simple obesity patients. The relative abundance of Lachnoclostridium and Faecalitalea were higher in people with simple obesity, while 23 genera, including Christensenellaceae_R-7_group, Akkermansia, Alistipes, and Butyricimonas, etc., were significantly lower. The random forest model achieved a high accuracy (AUC = 0.83). The adenine and adenosine salvage pathway (PWY-6609) and the L-histidine degradation I pathway (HISDEG-PWY) were clustered in obese patients, while amino acid biosynthesis and degradation pathways (HISDEG-PWY, DAPLYSINESYN-PWY) were decreased. This study identified obesity microbial biomarkers, providing fertile targets for the management of obesity.