Evaluating the interactive effects of dietary habits and human gut microbiome on the risks of depression and anxiety

Microbial and metabolomic profiles of type 1 diabetes with depression: A case-control study

BACKGROUND

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

METHODS

A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model.

RESULTS

In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone.

CONCLUSIONS

T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Gut microbiota and child behavior in early puberty: does child sex play a role?

A growing number of studies have indicated relations between the gut microbiota and mental health. However, to date, there is a scarcity of microbiota studies in community samples in early puberty. The current preregistered study (https://osf.io/wu2vt) investigated gut microbiota composition in relation to sex in low-risk children and explored behavioral associations with gut microbiota composition and metabolites in the same samples, together with the potential role of sex. Fecal microbiota composition was analyzed in 12-year-old children (N = 137) by 16S rRNA gene sequencing and quantitative PCR. Modest sex differences were observed in beta diversity. Generalized linear models showed consistent behavioral relations to both relative and absolute abundances of individual taxa, including positive associations between Parasutterella and mother-reported internalizing behavior, and negative associations between Odoribacter and mother-reported externalizing behavior. Additionally, Prevotella 9 was positively related to mother-reported externalizing behavior, confirming earlier findings on the same cohort at 5 years of age. Sex-related differences were found in behavioral relations to Ruminiclostridium 5, Alistipes, Streptococcus, Ruminiclostridium 9, Ruminococcaceae UCG-5, and Dialister, for relative abundances, as well as to Family XIII AD3011 group and an unidentified bacterium within the Tenericutes, for absolute abundances. Limited behavioral relations were observed regarding alpha diversity and fecal metabolites. Our findings describe links between the gut microbiota and child behavior, together with differences between child sexes in these relations, in low-risk early pubertal children. Importantly, this study confirmed earlier findings in this cohort of positive relations between Prevotella 9 and externalizing behavior at age 10 years. Results also show the merit of including absolute abundances in microbiota studies.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Genetic correlations between Alzheimer's disease and gut microbiome genera

A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a replication sample (GenADA case/control: 799/778) and then performed a meta-analysis with the PRS results from both samples. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes. In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Of these 20, three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were independently significant in the replication sample. Meta-analysis with discovery and replication samples confirmed that ten genera had a significant correlation with AD, four of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. Overall, the host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.

Administration Time and Dietary Patterns Modified the Effect of Inulin on CUMS-Induced Anxiety and Depression

SCOPE

Prebiotics exert anxiolytic and antidepressant effects through the microbiota-gut-brain axis in animal models. However, the influence of prebiotic administration time and dietary pattern on stress-induced anxiety and depression is unclear. In this study, whether administration time can modify the effect of inulin on mental disorders within normal and high-fat diets are investigated.

METHODS AND RESULTS

Mice subjected to chronic unpredicted mild stress (CUMS) are administered with inulin in the morning (7:30-8:00 am) or evening (7:30-8:00 pm) for 12 weeks. Behavior, intestinal microbiome, cecal short-chain fatty acids, neuroinflammatory responses, and neurotransmitters are measured. A high-fat diet aggravated neuroinflammation and is more likely to induce anxiety and depression-like behavior (p < 0.05). Morning inulin treatment improves the exploratory behavior and sucrose preference better (p < 0.05). Both inulin treatments decrease the neuroinflammatory response (p < 0.05), with a more evident trend for the evening administration. Furthermore, morning administration tends to affect the brain-derived neurotrophic factor and neurotransmitters.

CONCLUSION

Administration time and dietary patterns seem to modify the effect of inulin on anxiety and depression. These results provide a basis for assessing the interaction of administration time and dietary patterns, providing guidance for the precise regulation of dietary prebiotics in neuropsychiatric disorders.

A genetic association study reveals the relationship between the oral microbiome and anxiety and depression symptoms

BACKGROUND

Growing evidence supports that alterations in the gut microbiota play an essential role in the etiology of anxiety, depression, and other psychiatric disorders. However, the potential effect of oral microbiota on mental health has received little attention.

METHODS

Using the latest genome-wide association study (GWAS) summary data of the oral microbiome, polygenic risk scores (PRSs) of 285 salivary microbiomes and 309 tongue dorsum microbiomes were conducted. Logistic and linear regression models were applied to evaluate the relationship between salivary-tongue dorsum microbiome interactions with anxiety and depression. Two-sample Mendelian randomization (MR) was utilized to compute the causal effects between the oral microbiome, anxiety, and depression.

RESULTS

We observed significant salivary-tongue dorsum microbiome interactions related to anxiety and depression traits. Significantly, one common interaction was observed to be associated with both anxiety score and depression score, Centipeda periodontii SGB 224 × Granulicatella uSGB 3289 (P _{depressionscore} = 1.41 × 10^-8, P _anxietyscore = 5.10 × 10^-8). Furthermore, we detected causal effects between the oral microbiome and anxiety and depression. Importantly, we identified one salivary microbiome associated with both anxiety and depression in both the UKB database and the Finngen public database, Eggerthia (P _{IVW - majordepression - UKB} = 2.99 × 10^-6, P _{IVW - Self - reportedanxiety/panicattacks - UKB} = 3.06 × 10^-59, P _{IVW - depression - Finngen} = 3.16 × 10 , - 16 P _{IVW - anxiety - Finngen} = 1.14 × 10^-115).

CONCLUSION

This study systematically explored the relationship between the oral microbiome and anxiety and depression, which could help improve our understanding of disease pathogenesis and propose new diagnostic targets and early intervention strategies.