Dulaglutide ameliorates chronic stress-induced cognitive impairment via regulating gut microbiota and serum metabolites in mice

Dulaglutide ameliorates chronic stress-induced cognitive impairment via regulating gut microbiota and serum metabolites in mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dulaglutide ameliorates chronic stress-induced cognitive impairment via regulating gut microbiota and serum metabolites in mice Shipan Zhang, man Jin, Boya Huang, Zhaowei Hou, Di Fei, Xueru Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5279490/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chronic stress may lead to cognitive impairment. Prolonged use of dulaglutide could potentially alleviate cognitive impairment in individuals with type 2 diabetes, although its role in cognitive impairment induced by chronic stress remains elusive. This study aimed to explore the effect of dulaglutide on cognitive impairment caused by chronic stress and the underlying mechanisms. Forty-five mice were randomly divided into the following 3 groups (n = 15 per group): the CON group (the normal control group), the CMS-V group (mice treated with chronic mild stress and vehicle) and the CMS-D group (mice treated with chronic mild stress and 0.6 mg/kg dulaglutide). We found chronic mild stress resulted in cognitive impairment and anxiety-like behaviors in mice. Three weeks of dulaglutide treatment significantly alleviated cognitive impairment but had no effect on anxiety-like behaviors. Dulaglutide treatment induced alterations in gut microbiome homeostasis, particularly affecting the levels of f _ Bacteroidaceae , f _ Caulobacteraceae and f_ Helicobacteraceae . Meanwhile, dulaglutide had an effect on metabolic changes, especially in glycerophospholipids. Further analysis showed a correlation between gut microbiota and metabolite alterations following dulaglutide treatment. These results suggest that dulaglutide may potentially reverse cognitive impairment induced by chronic stress, possibly through its influence on the gut microbiota and metabolomic pathways. Health sciences/Neurology Health sciences/Medical research Health sciences/Medical research/Pre clinical studies chronic stress dulaglutide cognition anxiety-like behaviors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cognitive impairment is highly prevalent and is a primary or concomitant symptom of many disorders, such as Alzheimer’s disease, Parkinson's disease, frontotemporal dementia, depression, schizophrenia, infectious disease and so on 1 – 4 . Chronic stress is a significant hazard factor for cognitive impairment 5 . In humans, chronic stress occurring in early life and adulthood has an impact on different psychopathologies, such as mild cognitive impairment 6 . Chronic stress could induce alterations in the immune, endocrine, and nervous systems, which could lead to depression, cognitive impairment, and other behavioral manifestations 7 , 8 . Although some studies have found that antidepressants can improve stress-induced cognitive dysfunction, the side effects limit the use of these medications 9 , 10 . Therefore, there is an urgent need to investigate drugs that improve stress-induced cognitive dysfunction. Interactions between the gut and the brain have been shown to play a crucial role in chronic stress-induced cognitive impairment and depression. Studies have revealed that chronic mild stress (CMS) could change gut microbes (GMs), including p _ Muribaculaceae , p _ Lachnospiraceae , p _ Firmicutes , p _ Verrucomicrobia , p _ Actinobacteriota , p _ Desulfobacterota , and p _ Bacteroidetes 11 . Alterations in GMs can directly affect the excitability of peripheral neurons and the release metabolites into serum, influencing the blood‒brain barrier and playing a role in various physiological and pathophysiological processes, including depression and cognition 12 – 14 . This area has become a focal point for researching the mechanisms of depression, anxiety, cognitive impairment and the establishment of new treatment strategies. Numerous clinical studies have found that stress-related diseases such as depression are often accompanied by disorders of glycolipid metabolism 15 , 16 . Existing drugs that targeted glycolipid metabolism have been found to improve neurobehavioral abnormalities such as depression and anxiety. In Seo’s study, liraglutide exhibited antidepressant effects and improved cognitive function 17 . Current studies have demonstrated that the potential of metformin to ameliorate cognitive and behavioral alterations in conditions like Alzheimer’s disease and depression 18 . Dulaglutide (Trulicity TM) is a glucagon-like peptide-1 (GLP-1) receptor agonist. Long-term administration of dulaglutide has been shown to reduce cognitive impairments in patients with type 2 diabetes mellitus 19 . Darwish’s study revealed that dulaglutide reversed depression-like behaviors in the chronic social defeat stress model 20 . To date, there have been no studies elucidating the mechanisms by which dulaglutide ameliorates cognitive impairment. In this study, a mouse model of chronic mild stress was used and explored the effect of dulaglutide on chronic stress-induced cognitive impairment and anxiety through a series of behavioral tests. 16S rRNA sequencing and metabolomics analysis were further used to investigate the possible mechanisms by which dulaglutide improved abnormal behavioral phenotypes in mice, providing a basis for the establishment of new clinical treatment strategies. Materials and Methods Animals Forty-five male ICR mice (7 weeks old) were obtained from Beijing Weitonglihua Co., Ltd. The mice were adaptively fed for one week. All mice were kept at a constant temperature (22 ± 2°C) and humidity (50 ± 5%) for light / dark 12 h cycles and allowed free access to food and water. Mice were euthanized using isoflurane inhalation at day 63. All trials were approved by the local committee on Animal Care and Use and Protection of Hebei General Hospital (protocol code 202394) and conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals guidelines. CMS CMS mouse model were established as previously outlined 21 . Mice in the CMS-V group (exposed to chronic mild stress and saline) and CMS-D group (exposed to chronic mild stress and 0.6 mg/kg dulaglutide) were subjected to various stressors for 28 days continuously, experiencing 2 ~ 3 different stressors every day. These stressors included reversing the light/dark cycle for 24 hours, tilting cages 45 degrees for 24 hours, removing bedding for 24 hours, wet housing for 24 hours, overcrowding for 24 hours, water deprivation for 12 hours, food deprivation for 12 hours, restraint for 2 hours, forced cold swim for 5 minutes, and tail pinching for 2 minutes. The control group (CON) was maintained and treated daily without stress. Experimental design Following acclimatization, the mice were randomly divided into the following 3 groups (n = 15): the CON group, the CMS-V group, and the CMS-D group. The intestinal contents and serum of mice in each group were collected on day 63. They were placed in liquid nitrogen immediately after collection and maintained at − 80°C until they were subjected to further analysis. Behavioral tests All behavioral trials were conducted during a dark cycle. All experimenters were blinded to drug administration. Open field test (OFT) Mice were positioned in the center of an open field (40 × 40 × 35 cm 3 ) and allowed to freely explore for 5 minutes. Consistent with previous studies, Video tracking was used to track the total distance traveled and the time spent in center (20 × 20 cm 2 ) in mice 22 . Novel object recognition (NOR) The NOR test was carried out based on previous researches 23 , 24 . Before the beginning of the experiment, mice were positioned in an open field to acclimate. During this period, mice were free to explore for 5 minutes per day, one time a day for three consecutive days. The training period was carried out after 24 hours of acclimatization. In the training session, two identical objects (object A) were placed in the two central corners of the box, and the mice were allowed to explore for 5 minutes. The test session was carried out 24 hours after the training session. One of the objects was replaced with a new one (object B). Each mouse was allowed to explore for 5 minutes. Sniffing time was calculated as the time when the nose of the mouse was located within 2 centimeters of the object or in contact with it. Time spent climbing or supporting objects for the purpose of exploring the environment was not calculated. At the end of each exploratory experiment, the objects and the empty space were cleaned with 75% alcohol, and then the mice were returned to their cages. The sniffing time and recognition index (%) were calculated [e.g., object recognition index (%) = Tn ×100/ (Tn + Tf), Tn = time spent exploring the novel object, Tf = time spent exploring the familiar object]. Intestinal microbial diversity analysis Samples of cecal contents (n = 5) were frozen as soon as they were collected and maintained at -80°C. The DNeasy PowerSoil kit (Qiagen, Hilden, Germany) was used to isolate bacterial DNA from cecum contents in accordance with the manufacturer's instructions. DNA density and integrity were estimated by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. PCR amplification of the V3-V4 hypervariable regions of the bacterial 16S rRNA gene was performed using universal primer pairs (343F: 5′-TACGGRAGGCAGCAG-3′; 798R: 5′-AGGGTATCTAATCCT-3′). The reverse primer included a code for the amplicon, and each primer was attached to an Illumina sequencing adapter. The amplicon quality was checked by using gel electrophoresis, and PCR products were purified with Agencourt AMPure XP beads (Beckman Coulter Co., USA) and quantified using a Qubit dsDNA assay kit. The 16S rRNA gene amplicon sequencing and analysis were carried out by OE Biotech Co., Ltd. (Shanghai, China) Metabolomics profiling In our study, mouse serum (n = 5) was used for untargeted metabolomics analysis. Metabolites were obtained from mouse serum, and the metabolites were analyzed by UHPLC‒MS/MS. Raw LC‒MS data were subjected to processing by Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK), including baseline filtering, peak discrimination, integration, reservation time adjustment, peak registration, and generalization. Qualitative analyses were conducted using the Human Metabolome Database (HMDB), Lipidmaps (v2.3), Metlin, EMDB, PMDB, and self-constructed databases, and compounds were recognized based on exact mass-to-charge ratios (M/z), secondary fragments, and isotopic signatures. The matrices were introduced into R, and principal component analysis (PCA) was performed to determine the general distribution of the samples and the robustness of the complete analysis process. Orthogonal partial least squares discriminant analysis (OPLS-DA) and PLS-DA were employed to discern metabolites that varied across groups. To ensure that no overfitting occurred, 7-fold cross-validation and 200 response permutation tests (RPTs) were applied to assess the strength of the model. The overall contribution of each parameter to group differentiation was ranked according to the variable importance (VIP) values derived from the OPLS-DA model. A two-tailed Student's t test was utilized to check whether the variation in metabolites between the groups was significant. Differentially abundant metabolites with VIP values greater than 1.0 and p values less than 0.05 were screened. The untargeted metabolomics profiling was also provided by OE Biotech Co., Ltd. (Shanghai, China) Data analysis All data are presented as the mean ± standard error of the mean (SEM). Values that were more than two standard deviations from the mean were deleted. Statistical analyses were performed using SPSS version 26.0 software (IBM Corp., Armonk, NY). Comparisons between two groups were carried out using an independent-sample T test or Wilcoxon test (see the results for details). The data were analyzed by one-way ANOVA followed by Bonferroni’s test or the Kruskal-Wallis test when comparing the three groups. To determine the significance of correlations between continuous variables, correlation analyses were performed. GraphPad Prism software (8.0.2) was used to create graphics. A value of p < 0.05 was considered statistically significant. Results Dulaglutide had no effect on CMS-induced anxiety symptoms. The experimental protocol was shown in Fig. 1 A. After the mice underwent a 4-week CMS treatment, the OFT was used to assess the anxiety-like behavior of the mice. There was a significant difference in the time spent in center among the groups (F (2,37) = 9.255, p = 0.001, Fig. 1 B). Post hoc analysis revealed that compared with the CON group, the CMS-V group ( p = 0.001) and the CMS-D group ( p = 0.000) showed a significant decrease in the time spent in center. And there was no significant difference in the total distance traveled among the groups (F (2,37) = 0.052, p = 0.949; Fig. 1 C). After dulaglutide treatment, the time spent in center was also significantly different among groups (H = 2.68, p = 0.023; Fig. 1 D). Mice in the CMS-V group and the CMS-D group spent less time in the center compared to the CON group. And there was no significant difference between the CMS-V group and the CMS-D group. Additionally, there was no significant difference in the total distance traveled (F (2,36) = 0.0.006, p = 0.994, Fig. 1 E). These results suggested that a chronic stress model in mice has been successfully established and dulaglutide did not affect anxiety-like behaviors. Dulaglutide ameliorated cognitive impairment caused by CMS. The NOR test was used to evaluate the effects of dulaglutide on cognitive function in mice. As we expected, dulaglutide improved cognitive impairment induced by CMS. Throughout the training period, one-way ANOVA indicated that there were no significant differences in the recognition index among groups (F (2,35) = 2.521, p = 0.095; Fig. 1 F), as well as in the total sniffing time (F (2,35) = 3.093, p = 0.058; Fig. 1 G). However, in the test period, one-way ANOVA indicated that there was a significant difference in the recognition index among the groups (F (2,35) = 5.937, p = 0.006; Fig. 1 H). Post hoc analysis revealed that compared with the CON group, there was a significant decrease in the recognition index in the long-term memory test in the CMS-V group ( p = 0.004). And compared with the CMS-V group, the recognition index was higher in the CMS-D group ( p = 0.008). Additionally, there were no significant differences in the total sniffing time among groups (H = 4.208, p = 0.122; Fig. 1 I). Taken together, CMS induced cognitive deficits in mice, while dulaglutide could reverse this effect. Dulaglutide changed the GMs distribution in CMS-induced mice. 16S rRNA sequencing and Bray‒Curtis PCoA was used to identify variations in microbiome structure among the three groups (CON, CMS-V, and CMS-D). A total of 1,758 high-quality reads were detected across all 15 samples. The Venn diagram revealed that there were 92 equal OTUs in the 3 groups, while 530, 506 and 504 OTUs were unique in the CON, CMS-V and CMS-D mice, respectively (Fig. 2 A). There was no significant difference in bacterial richness and α-diversity among the three groups (CON, CMS-V, and CMS-D), as demonstrated by the Shannon index ( p = 0.733) (Fig. 2 B). The microbial composition among the three groups was significantly different showed by the PCoA plot of unweighted UniFrac distances in ( p = 0.007) (Fig. 2 C). The top 15 most abundant microbes at the family level were shown in Fig. 2 D. The two most dominant taxa at the family level were f_Lactobacillaceae and f_ Lachnospiraceae . CMS induced alterations in the microbiome, while dulaglutide partially reversed the changes. The proportion of f _ Bacteroidaceae in the CMS-V group was considerably lower than that in the CON group ( p = 0.047), and this difference was reversed by dulaglutide treatment ( p = 0.012, Fig. 2 E). The same trend was found with respect to f _ Caulobacteraceae (CMS-V vs. CON p = 0.016, CMS-D vs. CMS-V p = 0.001, Fig. 2 F). When compared with the CON group, the proportion f_Tannerellaceae was higher in the CMS-V group ( p = 0.032), while the difference between the CMS-V group and the CMS-D group was not significant ( p = 0.056, Fig. 2 G). There was no significant difference in the relative abundances of f_ Helicobacteraceae between the CMS-V group and the CON group ( p = 0.422, Fig. 2 H). While that was higher in the CMS-D group compared with the CMS-V group ( p = 0.006) (Fig. 2 H). f _ Caulobacteraceae (r = 0.616, p = 0.014, Fig. 2 I) was significantly positively correlated with the RI, while f_Tannerellaceae (r=-0.242, p = 0.385, Fig. 2 K), f _ Bacteroidaceae (r = 0.434, p = 0.106, Fig. 2 J) and f_ Helicobacteraceae (r = 0.240, p = 0.389, Fig. 2 L) had no correlation with the RI. The Linear discriminant analysis Effect Size (LEfSe) analysis was conducted to explore the differences in microbial communities among the three groups. In the CON group, Caulobacterales was the most abundant order; Gammaproteobacteria was the leading class; Bacteroidaceae, Caulobacteraceae and Enterococcaceae were the three most abundant families; Bacteroides and Enterococcus were predominant genus (Fig. 3 A). In the CMS-V group, Tannerellaceae was the most abundant family; Parabacteroides and Ruminococcus_torques_group were predominant genus (Fig. 3 A). In CMS-D group, Campylobacterales, Peptococcales and Caulobacterales were the leading orders; Campylobacteria was the predominant class; Bacteroidaceae, Peptococcaceae were and Caulobacteraceae were the three most abundant families; Helicobacter and Bacteroides were the richest genus (Fig. 3 B). Dulaglutide altered the serum metabolite levels in CMS mice. The serum was collected for non-targeted metabolomics analysis to investigate the metabolic changes in mice. To verify the reliability of the model, a permutation test was conducted (n = 200 times), and the results indicated that the model was robust (Fig. 4 A). The OPLS-DA model showed that the CMS-V group could be significantly separated from the CON group (Fig. 4 B). There were significant differences in the metabolomics between the CMS-V group and the CON group (Fig. 4 C). Compared to the CON group, the CMS-V group showed changes in the levels of 96 metabolites, with the levels of 40 metabolites decreased and 56 metabolites increased (Fig. 4 D). The top 50 differentially abundant metabolites are shown in Supplementary Table 1. 21 of them were glycerophospholipids, and most glycerophospholipids were elevated in the CMS-V group. Compared to the CMS-V group, 118 metabolites were significantly increased, and 35 metabolites were significantly decreased in in the CMS-D group (Fig. 4 F). The top 50 differentially abundant metabolites are shown in Supplementary Table 2. 44 of them were glycerophospholipids, and most glycerophospholipids were decreased in the CMS-D group. Compared to the CON group, the metabolic pathways that exhibited significant differences in the CMS-V group included glycerophospholipid metabolism, GABAergic synapse, choline metabolism in cancer, retrograde endocannabinoid signaling, the citrate cycle (TCA cycle), the glucagon signaling pathway, alanine, aspartate and glutamate metabolism, central carbon metabolism in cancer, autophagy-other, butanoate metabolism, lysine degradation, Kaposi sarcoma-associated herpesvirus infection, autophagy-animal, glyoxylate and dicarboxylate metabolism, D-amino acid metabolism, Fc gamma R-mediated phagocytosis, the apelin signaling pathway, the calcium signaling pathway and the phospholipase D signaling pathway(Fig. 4 E). Compared to the CMS-V group, the metabolic pathways that exhibited significant differences in the CMS-V group included purine metabolism, the cGMP-PKG signaling pathway, the cAMP signaling pathway, sulfur metabolism, autophagy-other, the neuroactive ligand‒receptor interaction, Kaposi sarcoma-associated herpesvirus infection, autophagy-animal, glycerophospholipid metabolism, olfactory transduction, phototransduction, the biosynthesis of unsaturated fatty acids, GABAergic synapse, morphine addiction and alcoholism (Fig. 4 G). Figure 4 E and Fig. 4 G showed the bubble chart of the KEGG pathway analysis results. Glycerophospholipid metabolism was identified in pathways associated with onset and treatment. The correlation analysis between microbiota and metabolites. The Spearman’s correlation analysis was conducted between the GMs at the family level and the top 20 differentially abundant metabolites between CMS-V vs CON and CMS-V vs CMS-D. When comparing the CMS-V group with the CON group, f_Caulobacteraceae , f _ Bacteroidaceae and f_Tannerellaceae were correlated with different metabolites. f _ Caulobacteraceae was negatively correlated with PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)), Dihydrocyclosporin, Sphingosine 1-phosphate, 3,5-Dihydroxytetradecanoylcarnitine, PC(P-16:0/18:1(9Z)), PC(18:3(9Z,12Z,15Z)/18:1(11Z)), Uric acid, PE(P-18:0/19:0) and 3R,4S,5S,6S)-6-[4-Chloro-2-(furan-2-ylmethylamino)-5-sulfamoylbenzoyl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid. While f _ Caulobacteraceae was positively correlated with D-erythro-L-galacto-Nonulose. f_Bacteroidaceae was negatively correlated with 3,5-Dihydroxytetradecanoylcarnitine. f_Tannerellaceae was positively with 4-Chloro-L-phenylalanine, PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)), PE(18:0/20:1(11Z)), PE-NMe2(22:1(13Z)/14:1(9Z)), Thiabendazole, Mactraxanthin 3-linoleate 3'-dipalmitoleate, 1,2-Benzisothiazole, Dihydrocyclosporin, Sphingosine 1-phosphate, 3,5-Dihydroxytetradecanoylcarnitine, PC(P-16:0/18:1(9Z)), PC(18:3(9Z,12Z,15Z)/18:1(11Z)), PE(P-18:0/19:0), (3R,4S,5S,6S)-6-[4-Chloro-2-(furan-2-ylmethylamino)-5-sulfamoylbenzoyl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid and 17-phenyl trinor Prostaglandin D2 (Fig. 4 A). The metabolites belonged to Peptidomimetics, Sphingolipids, Fatty Acyls, Glycerophospholipids, Organoheterocyclic compounds, Organooxygen compounds, Organic oxygen compounds, Carboxylic acids and derivatives, Benzimidazoles, Prenol lipids, Peptidomimetics (Fig. 5 A). When compareing the CMS-D group with the CMS-V group, f _ Helicobacteraceae was negatively correlated with LysoPC(0:0/18:0), GM4(d18:1/16:0), GM4(d18:1/20:0), 2-Lysophosphatidylcholine, 1-(2-methoxy-octadecanyl)-sn-glycero-3-phosphoserine, Cyclopentanol, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0) and LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)), and positively correlated with PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)) and PE(18:0/20:1(11Z)). f _ Bacteroidaceae was negatively correlated with 2-Lysophosphatidylcholine, Docosahexaenoic acid, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0) and LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)). f _Caulobacteraceae was negatively correlated with GM4(d18:1/16:0), LysoPC(0:0/16:0), PC(P-16:0/22:6(5Z,7Z,10Z,13Z,16Z,19Z)-OH(4)), LysoPE(0:0/18:2(9Z,12Z)), LysoPE(20:4(5Z,8Z,11Z,14Z)/0:0), Cyclopentanol, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0), LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)) and LysoPE(18:1(9Z)/0:0), While positively correlated with PE(18:0/20:1(11Z)), L-Carnitine (Fig. 4 B). And the metabolites belonged to Glycerophospholipids, Acidic glycosphingolipids, Fatty Acyls, Organooxygen compounds, Sphingolipids, Organonitrogen compounds (Fig. 5 B). Discussion This was the first study to verify the effects of dulaglutide in an animal model of CMS-induced cognitive impairment. In our study, CMS led to cognitive impairment in mice, and chronic dulaglutide administration improved long-term memory formation without affecting anxiety-like behaviors. We further explored the possible mechanisms of this effect. These results provide insight into therapy for cognitive dysfunction. The synaptic plasticity of the hippocampus, specifically long-term potentiation (LTP), regulates the formation of learning and memory 25 . It has been shown that CMS significantly impaired memory formation by reducing LTP in the CA1 region of the dorsal hippocampus 26 . Consistent with the aforementioned research, we found that CMS significantly reduced the recognition index of mice in long-term memory test. The level of GLP-1 in serum was associated with cognitive function in diabetic patients 27 . Clinical follow-up found that dulaglutide, as a GLP-1 receptor agonist, could improve cognitive dysfunction in diabetic patients 28 . In guan’s study, dulaglutide effectively improved cognitive impairment in rats with vascular dementia without affecting anxiety-like behavior 29 . In conclusion, both clinical and animal studies have demonstrated the effectiveness of dulaglutide in treating cognitive impairment, and further exploration is needed to understand its potential mechanisms affecting cognitive function.The gut-brain axis, as a communication system between the gastrointestinal tract and the central nervous system, plays an important role in maintaining body homeostasis. 14 . The GMs is vital to human health, as it affects the blood‒brain barrier, the myelin sheath, neurogenesis, and other neuronal developmental processes 30 . Changes in the GMs may lead to neuroinflammation and psychiatric disorders, such as anxiety, depression and cognitive impairment 31 . The relationship between gut microbial α-diversity and cognitive function is controversial. Nicholas and colleagues found that gut microbial α-diversity in dementia patients was lower 32 , while some studies found no difference in α-diversity between people with dementia and healthy controls 33 , 34 . In this study, there was no significant difference in α-diversity among the three groups, which was consistent with previous researches. Therefore, we speculated that compared to individual microbial diversity, the stress-induced changes in microbial structure among individuals may have a greater impact on cognitive function. Animal studies have found that chronic stress led to a decrease in the relative abundance of g_Bacteroides in mice 35 . We found that the relative abundance of g_Bacteroidetes in the CMS-V group exhibited the same trend, while dulaglutide reversed this change. Katharina et al. found that dulaglutide increased the relative abundance of p _ Bacteroidetes in mice 36 . In Liang’s study, people with more g _ Bacteroides had better cognitive performance based on cognitive scores 34 . Caulobacteraceae is a family of bacteria in the proteobacteria phylum. We found that CMS significantly reduced the relative abundance of f_Caulobacteraceae , and this reduction was reversed after treatment with dulaglutide. Moreover, the relative abundance of f_Caulobacteraceae was positively correlated with the recognition index in long-term memory test. A study has reported that g_Caulobacteraceae may be related to immune gene expression, but its function in immunity is not clear 37 . Other studies indicated that f _ Caulobacteraceae were cholesterol-degrading bacteria 38 , and a high level of cholesterol is a risk factor for cognitive decline 39 . These researches supported our findings. Animals studies indicated that the increase of the relative abundance of f _ Helicobacteraceae might impair cognitive performance 40 .However, in our study, the relative abundance of f _ Helicobacteraceae was increased by dulaglutide, it had no correlation with the formation of long-term memory. At present, the effect and mechanism of Clostridium on cognitive function are still unclear, and there are few related studies, so further exploration is needed in the future. In previous study, sleep-deprived mice showed cognitive impairment and the increased relative abundance of f_Tannerellaceae , and f_Tannerellaceae was correlated with inflammatory response 41 . In our study, although CMS decreased the relative abundance of f_Tannerellaceae , dulaglutide could not reverse this phenomenon, and f_Tannerellaceae was not related to cognitive function. There are few studies on the relationship between Tannerellaceae and cognition, and further research is needed. Therefore, these results showed that dulaglutide regulated the relative abundance of f _ Caulobacteraceae and f _ Bacteroidetes to improve cognition. Metabolomics involves the comprehensive identification and quantification of metabolites, which are small molecules produced during metabolic processes, within a biological system 42 . In this study, our PLS-DA and OPLS-DA score plots displayed significant separation among the three groups. And CMS led to lipid metabolism disorders, especially with relation to glycerophospholipids. Lipids make up more than half of the brain’s dry weight, and lipid metabolism plays important roles in brain functions, including cognition 43 . Chronic stress led to metabolic disorders, including lipid disorders 44 . Many disorders that caused cognitive impairment were accompanied by a disturbance in lipid metabolism, such as major depressive disorder and Alzheimer's disease 45 , 46 . Our results were consistent with previous studies, suggesting that lipid metabolism disorders work in cognitive impairment induced by chronic stress. Glycerophospholipids can be divided into different subgroups, including phosphatidylethanolamine (PE) and phosphatidylcholine(PC) 47 . In our study, compared with the CON group, CMS led to a significant increase in the levels of most PC and PE, while dulaglutide partially reversed this effect. Consistent with our results, Tian et al. found that the differential lipid metabolites in the serum of mice subjected to the chronic social defeat stress were mainly glycerophospholipids 48 . In Chen’s study, the levels of PE and PC were elevated in diabetic rats with cognitive impairment 49 . Nho. et al. reported that the levels of PCs were increased in the serum of Alzheimer’s patients and were associated with cognition and amyloid-β deposition 50 . Du et al. found that dulaglutide could remodel glycerophospholipids in type 2 diabetes 51 . Considering that the different lipid metabolites in serum in this study were mainly glycerophospholipids, we concluded that dulaglutide alleviated cognitive impairment by modulating glycerophospholipids. We also explored the association between the differential microbiota and the differential metabolites. Some studies have reported that GMs influenced host energy metabolism and brain health through microbiota metabolites (e.g., short-chain fatty acids, amino acids and vitamins) 52 , 53 . GMs are key modifiers of lipid metabolism and can influence host lipid metabolism 54 . In this study, when comparing the CMS-V group and the CMS-D group, most of the differential metabolites that correlated to the differential microbiota belonged to Lipids and lipid-like molecules, including Glycerophospholipids, Sphingolipids, Fatty Acyls, Sphingolipids. Zhai et al found that Caulobacteraceae was correlated with serum lipid in mice 55 . Bacteroides was correlated with lipid-related metabolism, and increasing the abundance of Bacteroides contributed to hyperlipidemia alleviated 56 . In a word, we concluded that dulaglutide improved chronic stress induce cognitive function by affecting lipid metabolic changes caused by regulating the abundance of gut microbiota. The effect of dulaglutide on anxiety-like behaviors remains unclear. In existing studies, chronic application of GLP-1 receptor agonists has shown anxiolytic effects in obese or diabetic rodent models. 57 . However, in Rozita’s study, chronic central administration of a GLP-1 analog did not influence anxiety-like behaviors 58 . Moreover, the effect of dulaglutide on anxiety-like behaviors was not related to the behavioral test method used. In this study, chronic administration of dulaglutide did not affect CMS-induced anxiety-like behaviors. Therefore, further investigation is needed to explore the effects and mechanisms of dulaglutide on anxiety-like behaviors in the future. However, there were still some limitations in our study. First, we only used serum non-targeted metabolomics analysis to examine the differential metabolites and their functions among the three groups. This approach was relatively singular, and some metabolites are rare in serum, making it difficult to clarify the specific effects of dulaglutide on a particular metabolite. Second, we should further explore whether there are interactions between these differential metabolites and differential microbiota, as well as their specific roles in the process of CMS-induced cognitive impairment. Conclusions In conclusion, modulating the GMs and serum metabolites is an effective method to improve CMS-induced cognitive impairment. We first verified the effect of dulaglutide on cognition and anxiety-like behaviors induced by CMS. In addition to discovering this new beneficial effect of dulaglutide, the results of this study also suggest that dulaglutide may exert this effect by regulating the GMs and serum metabolites. Further exploration of the mechanisms underlying the neuroprotective effects of dulaglutide will be the focus of our future research. Declarations Competing interests The authors declare no competing interests. Funding This research was funded by Hebei Natural Science Foundation[H2022307075], Hebei Provincial Government subsidizes the Clinical Outstanding Talent Project[ZF2025017] and Hebei Provincial Medical Science Research Project [20240535]. Author Contribution Conceptualization, L.P.Y.,S.H.S. and W.T.J.; methodology, H.B.Y.,Z.S.P.; software, Z.S.P.; validation, S.H.S., L,P,Y. and W.T.J.; formal analysis, Z.S.P., J.M. and H.B.Y.; investigation, Z.S.P., J.M. and H.B.Y.; resources, H.Z.W.,F.D.,Z.X.R.,G.C.Y.; data curation, Z.S.P., J.M. and H.B.Y.; writing-original draft preparation, Z.S.P.; writing-review and editing, S.H.S., L,P,Y. and W.T.J.; visualization, Z.S.P.; supervision, S.H.S.; project administration, T.W.J.; funding acquisition, L.P.Y. All authors have read and agreed to the published version of the manuscript . Acknowledgments This work was supported by Hebei Natural Science Foundation [H2022307075], Hebei Provincial Medical Science Research Project [20240535] and Hebei Provincial Government subsidizes the Clinical Outstanding Talent Project[ZF2025017]. Data Availability The original sequence data have been uploaded to the Sequence Read Archive (SRA) (NCBI, USA) with the Accession Number: PRJNA1180755. References McCollum, L. & Karlawish, J. Cognitive Impairment Evaluation and Management. Med. Clin. North. Am. 104 , 807–825. 10.1016/j.mcna.2020.06.007 (2020). McCutcheon, R. A., Keefe, R. S. E. & McGuire, P. K. Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment. Mol. Psychiatry . 28 , 1902–1918. 10.1038/s41380-023-01949-9 (2023). Townsend, A. K., Sewall, K. B., Leonard, A. S. & Hawley, D. M. Infectious disease and cognition in wild populations. Trends Ecol. Evol. 37 , 899–910. 10.1016/j.tree.2022.06.005 (2022). Price, R. B. & Duman, R. 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(B). Time spent in center in OFT after CMS. (C) Total distance in OFT after CMS. (D) Time spent in center in OFT after the treatment. (E) Total distance in OFT after the treatment. (F) Object recognition index in the training period. (G) Total sniffing time in the training period. (H) Object recognition index in the long-term memory test. (I) Total sniffing time in the long-term memory test. The data are expressed as the mean±SEM. n = 15 per group. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 versus the control group. # #\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 versus the CMS-V group.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/4cef5b495adcaac75adf9a20.jpg"},{"id":69571847,"identity":"97d0f9ef-c3df-4a7f-9928-5d2087393919","added_by":"auto","created_at":"2024-11-21 19:19:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of Dulaglutide on gut microbiome distribution.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram showing OTU and the overlap of CON \u003csup\u003e45\u003c/sup\u003e, CMS-V (blue) and CMS-D (yellow) groups. (B) Shannon index (C) Principal Coordinate Analysis (PCoA) analysis of mice gut microbiota. (D) Barplot of gut microbiota abundance of different groups in family level. (E) (F) (G) (H) The abundance of Bacteroidaceae, Caulobacteraceae, Enterobacteriaceae and Helicobacteraceae in different groups at family. (I) (J) (K) (L) The correlation between Caulobacteraceae, Bacteroidaceae, Enterobacteriaceae and Helicobacteraceae with recognition index. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 versus the control group. # #\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 versus the CMS-V group.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/ce73b5ab9045044a12087b81.jpg"},{"id":69571842,"identity":"6e692ed9-5e6c-4188-b386-efb18168d981","added_by":"auto","created_at":"2024-11-21 19:19:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLEfSe analysis of gut microbiota in different groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLDA value histogram was performed to different bacteria in different groups. A is for CON vs CMS-V, B is for CMS-V vs CMS-D.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/4ab0b6266d9ffcc1c6ddc396.jpg"},{"id":69572750,"identity":"89530ffa-c34c-4ae7-97b9-efd219ebf641","added_by":"auto","created_at":"2024-11-21 19:35:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDulaglutide adjusted the metabolite in CMS mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) OPLS-DA model validation diagram of the CON and CMS-V. (B) (C) OPLS-DA score plots from CON, CMS-V and CMS-D. (D)Volcano plot of CON and CMS-V. (E) Analysis of the top 20 metabolic pathways in comparison combinations according to the impact factors (bubble plot) of the CON and CMS-V. (F)Volcano plot of CMS-V and CMS-D. (G) Analysis of the top 20 metabolic pathways in comparison combinations according to the impact factors (bubble plot) of the CMS-V and CMS-D. The results of the metabolic pathway analysis were presented as bubble plots. The bubble color represents the \u003cem\u003ep\u003c/em\u003e value of the enrichment analysis, while the size of the point represents the number of different metabolites enriched in the pathway (n =5).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/2400e11851ce37509caba53c.jpg"},{"id":69572520,"identity":"de4f07cb-53ac-4081-8f01-4173fd801b11","added_by":"auto","created_at":"2024-11-21 19:27:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221703,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation heatmap between differential family-level bacteria and the top 20 differential metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis between gut microbiota and the top 20 differentially abundant metabolites at family level. A is for CON vs CMS-V, B is for CMS-V vs CMS-D. All different bacteria and metabolites were filtrated according to the following conditions: VIP \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003cem\u003e *p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/f32f0ec81c0ccd5ad5367c1d.jpg"},{"id":78906655,"identity":"677b889b-b68a-43d9-9418-db121faeacb1","added_by":"auto","created_at":"2025-03-20 15:17:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2004455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/97bb59c1-468e-4d67-9c6d-d24a71b68e6d.pdf"},{"id":69571848,"identity":"3b683b51-8950-4e6f-86d2-a0f1b6e88ec2","added_by":"auto","created_at":"2024-11-21 19:19:20","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":109056,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-5279490/v1/1ae84d3fd39bc343358066b7.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dulaglutide ameliorates chronic stress-induced cognitive impairment via regulating gut microbiota and serum metabolites in mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCognitive impairment is highly prevalent and is a primary or concomitant symptom of many disorders, such as Alzheimer\u0026rsquo;s disease, Parkinson's disease, frontotemporal dementia, depression, schizophrenia, infectious disease and so on \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Chronic stress is a significant hazard factor for cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In humans, chronic stress occurring in early life and adulthood has an impact on different psychopathologies, such as mild cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Chronic stress could induce alterations in the immune, endocrine, and nervous systems, which could lead to depression, cognitive impairment, and other behavioral manifestations \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although some studies have found that antidepressants can improve stress-induced cognitive dysfunction, the side effects limit the use of these medications \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, there is an urgent need to investigate drugs that improve stress-induced cognitive dysfunction.\u003c/p\u003e \u003cp\u003eInteractions between the gut and the brain have been shown to play a crucial role in chronic stress-induced cognitive impairment and depression. Studies have revealed that chronic mild stress (CMS) could change gut microbes (GMs), including \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eMuribaculaceae\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eVerrucomicrobia\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eDesulfobacterota\u003c/em\u003e, and \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eBacteroidetes\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Alterations in GMs can directly affect the excitability of peripheral neurons and the release metabolites into serum, influencing the blood‒brain barrier and playing a role in various physiological and pathophysiological processes, including depression and cognition \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This area has become a focal point for researching the mechanisms of depression, anxiety, cognitive impairment and the establishment of new treatment strategies.\u003c/p\u003e \u003cp\u003eNumerous clinical studies have found that stress-related diseases such as depression are often accompanied by disorders of glycolipid metabolism \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Existing drugs that targeted glycolipid metabolism have been found to improve neurobehavioral abnormalities such as depression and anxiety. In Seo\u0026rsquo;s study, liraglutide exhibited antidepressant effects and improved cognitive function \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Current studies have demonstrated that the potential of metformin to ameliorate cognitive and behavioral alterations in conditions like Alzheimer\u0026rsquo;s disease and depression \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Dulaglutide (Trulicity TM) is a glucagon-like peptide-1 (GLP-1) receptor agonist. Long-term administration of dulaglutide has been shown to reduce cognitive impairments in patients with type 2 diabetes mellitus \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Darwish\u0026rsquo;s study revealed that dulaglutide reversed depression-like behaviors in the chronic social defeat stress model \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To date, there have been no studies elucidating the mechanisms by which dulaglutide ameliorates cognitive impairment.\u003c/p\u003e \u003cp\u003eIn this study, a mouse model of chronic mild stress was used and explored the effect of dulaglutide on chronic stress-induced cognitive impairment and anxiety through a series of behavioral tests. 16S rRNA sequencing and metabolomics analysis were further used to investigate the possible mechanisms by which dulaglutide improved abnormal behavioral phenotypes in mice, providing a basis for the establishment of new clinical treatment strategies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eForty-five male ICR mice (7 weeks old) were obtained from Beijing Weitonglihua Co., Ltd. The mice were adaptively fed for one week. All mice were kept at a constant temperature (22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C) and humidity (50\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) for light / dark 12 h cycles and allowed free access to food and water. Mice were euthanized using isoflurane inhalation at day 63. All trials were approved by the local committee on Animal Care and Use and Protection of Hebei General Hospital (protocol code 202394) and conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals guidelines.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCMS\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCMS mouse model were established as previously outlined \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Mice in the CMS-V group (exposed to chronic mild stress and saline) and CMS-D group (exposed to chronic mild stress and 0.6 mg/kg dulaglutide) were subjected to various stressors for 28 days continuously, experiencing 2\u0026thinsp;~\u0026thinsp;3 different stressors every day. These stressors included reversing the light/dark cycle for 24 hours, tilting cages 45 degrees for 24 hours, removing bedding for 24 hours, wet housing for 24 hours, overcrowding for 24 hours, water deprivation for 12 hours, food deprivation for 12 hours, restraint for 2 hours, forced cold swim for 5 minutes, and tail pinching for 2 minutes. The control group (CON) was maintained and treated daily without stress.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFollowing acclimatization, the mice were randomly divided into the following 3 groups (n\u0026thinsp;=\u0026thinsp;15): the CON group, the CMS-V group, and the CMS-D group. The intestinal contents and serum of mice in each group were collected on day 63. They were placed in liquid nitrogen immediately after collection and maintained at \u0026minus;\u0026thinsp;80\u0026deg;C until they were subjected to further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBehavioral tests\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll behavioral trials were conducted during a dark cycle. All experimenters were blinded to drug administration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eOpen field test (OFT)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMice were positioned in the center of an open field (40 \u0026times; 40 \u0026times; 35 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) and allowed to freely explore for 5 minutes. Consistent with previous studies, Video tracking was used to track the total distance traveled and the time spent in center (20 \u0026times; 20 cm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) in mice \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNovel object recognition (NOR)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe NOR test was carried out based on previous researches \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Before the beginning of the experiment, mice were positioned in an open field to acclimate. During this period, mice were free to explore for 5 minutes per day, one time a day for three consecutive days. The training period was carried out after 24 hours of acclimatization. In the training session, two identical objects (object A) were placed in the two central corners of the box, and the mice were allowed to explore for 5 minutes. The test session was carried out 24 hours after the training session. One of the objects was replaced with a new one (object B). Each mouse was allowed to explore for 5 minutes. Sniffing time was calculated as the time when the nose of the mouse was located within 2 centimeters of the object or in contact with it. Time spent climbing or supporting objects for the purpose of exploring the environment was not calculated. At the end of each exploratory experiment, the objects and the empty space were cleaned with 75% alcohol, and then the mice were returned to their cages. The sniffing time and recognition index (%) were calculated [e.g., object recognition index (%)\u0026thinsp;=\u0026thinsp;Tn \u0026times;100/ (Tn\u0026thinsp;+\u0026thinsp;Tf), Tn\u0026thinsp;=\u0026thinsp;time spent exploring the novel object, Tf\u0026thinsp;=\u0026thinsp;time spent exploring the familiar object].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntestinal microbial diversity analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSamples of cecal contents (n\u0026thinsp;=\u0026thinsp;5) were frozen as soon as they were collected and maintained at -80\u0026deg;C. The DNeasy PowerSoil kit (Qiagen, Hilden, Germany) was used to isolate bacterial DNA from cecum contents in accordance with the manufacturer's instructions. DNA density and integrity were estimated by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. PCR amplification of the V3-V4 hypervariable regions of the bacterial 16S rRNA gene was performed using universal primer pairs (343F: 5\u0026prime;-TACGGRAGGCAGCAG-3\u0026prime;; 798R: 5\u0026prime;-AGGGTATCTAATCCT-3\u0026prime;). The reverse primer included a code for the amplicon, and each primer was attached to an Illumina sequencing adapter. The amplicon quality was checked by using gel electrophoresis, and PCR products were purified with Agencourt AMPure XP beads (Beckman Coulter Co., USA) and quantified using a Qubit dsDNA assay kit. The 16S rRNA gene amplicon sequencing and analysis were carried out by OE Biotech Co., Ltd. (Shanghai, China)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMetabolomics profiling\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn our study, mouse serum (n\u0026thinsp;=\u0026thinsp;5) was used for untargeted metabolomics analysis. Metabolites were obtained from mouse serum, and the metabolites were analyzed by UHPLC‒MS/MS. Raw LC‒MS data were subjected to processing by Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK), including baseline filtering, peak discrimination, integration, reservation time adjustment, peak registration, and generalization. Qualitative analyses were conducted using the Human Metabolome Database (HMDB), Lipidmaps (v2.3), Metlin, EMDB, PMDB, and self-constructed databases, and compounds were recognized based on exact mass-to-charge ratios (M/z), secondary fragments, and isotopic signatures. The matrices were introduced into R, and principal component analysis (PCA) was performed to determine the general distribution of the samples and the robustness of the complete analysis process. Orthogonal partial least squares discriminant analysis (OPLS-DA) and PLS-DA were employed to discern metabolites that varied across groups. To ensure that no overfitting occurred, 7-fold cross-validation and 200 response permutation tests (RPTs) were applied to assess the strength of the model. The overall contribution of each parameter to group differentiation was ranked according to the variable importance (VIP) values derived from the OPLS-DA model. A two-tailed Student's t test was utilized to check whether the variation in metabolites between the groups was significant. Differentially abundant metabolites with VIP values greater than 1.0 and p values less than 0.05 were screened. The untargeted metabolomics profiling was also provided by OE Biotech Co., Ltd. (Shanghai, China)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Values that were more than two standard deviations from the mean were deleted. Statistical analyses were performed using SPSS version 26.0 software (IBM Corp., Armonk, NY). Comparisons between two groups were carried out using an independent-sample T test or Wilcoxon test (see the results for details). The data were analyzed by one-way ANOVA followed by Bonferroni\u0026rsquo;s test or the Kruskal-Wallis test when comparing the three groups. To determine the significance of correlations between continuous variables, correlation analyses were performed. GraphPad Prism software (8.0.2) was used to create graphics. A value of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDulaglutide had no effect on CMS-induced anxiety symptoms.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe experimental protocol was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. After the mice underwent a 4-week CMS treatment, the OFT was used to assess the anxiety-like behavior of the mice. There was a significant difference in the time spent in center among the groups (F (2,37)\u0026thinsp;=\u0026thinsp;9.255, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Post hoc analysis revealed that compared with the CON group, the CMS-V group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and the CMS-D group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) showed a significant decrease in the time spent in center. And there was no significant difference in the total distance traveled among the groups (F (2,37)\u0026thinsp;=\u0026thinsp;0.052, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.949; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). After dulaglutide treatment, the time spent in center was also significantly different among groups (H\u0026thinsp;=\u0026thinsp;2.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Mice in the CMS-V group and the CMS-D group spent less time in the center compared to the CON group. And there was no significant difference between the CMS-V group and the CMS-D group. Additionally, there was no significant difference in the total distance traveled (F (2,36)\u0026thinsp;=\u0026thinsp;0.0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.994, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These results suggested that a chronic stress model in mice has been successfully established and dulaglutide did not affect anxiety-like behaviors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDulaglutide ameliorated cognitive impairment caused by CMS.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe NOR test was used to evaluate the effects of dulaglutide on cognitive function in mice. As we expected, dulaglutide improved cognitive impairment induced by CMS. Throughout the training period, one-way ANOVA indicated that there were no significant differences in the recognition index among groups (F (2,35)\u0026thinsp;=\u0026thinsp;2.521, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.095; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), as well as in the total sniffing time (F (2,35)\u0026thinsp;=\u0026thinsp;3.093, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). However, in the test period, one-way ANOVA indicated that there was a significant difference in the recognition index among the groups (F (2,35)\u0026thinsp;=\u0026thinsp;5.937, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Post hoc analysis revealed that compared with the CON group, there was a significant decrease in the recognition index in the long-term memory test in the CMS-V group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). And compared with the CMS-V group, the recognition index was higher in the CMS-D group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Additionally, there were no significant differences in the total sniffing time among groups (H\u0026thinsp;=\u0026thinsp;4.208, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.122; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). Taken together, CMS induced cognitive deficits in mice, while dulaglutide could reverse this effect.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDulaglutide changed the GMs distribution in CMS-induced mice.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e16S rRNA sequencing and Bray‒Curtis PCoA was used to identify variations in microbiome structure among the three groups (CON, CMS-V, and CMS-D). A total of 1,758 high-quality reads were detected across all 15 samples. The Venn diagram revealed that there were 92 equal OTUs in the 3 groups, while 530, 506 and 504 OTUs were unique in the CON, CMS-V and CMS-D mice, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). There was no significant difference in bacterial richness and α-diversity among the three groups (CON, CMS-V, and CMS-D), as demonstrated by the Shannon index (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.733) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The microbial composition among the three groups was significantly different showed by the PCoA plot of unweighted UniFrac distances in (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThe top 15 most abundant microbes at the family level were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. The two most dominant taxa at the family level were \u003cem\u003ef_Lactobacillaceae\u003c/em\u003e and \u003cem\u003ef_ Lachnospiraceae\u003c/em\u003e. CMS induced alterations in the microbiome, while dulaglutide partially reversed the changes. The proportion of \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidaceae\u003c/em\u003e in the CMS-V group was considerably lower than that in the CON group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), and this difference was reversed by dulaglutide treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The same trend was found with respect to \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e (CMS-V vs. CON \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, CMS-D vs. CMS-V \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). When compared with the CON group, the proportion \u003cem\u003ef_Tannerellaceae\u003c/em\u003e was higher in the CMS-V group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), while the difference between the CMS-V group and the CMS-D group was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). There was no significant difference in the relative abundances of \u003cem\u003ef_ Helicobacteraceae\u003c/em\u003e between the CMS-V group and the CON group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.422, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). While that was higher in the CMS-D group compared with the CMS-V group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.616, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI) was significantly positively correlated with the RI, while \u003cem\u003ef_Tannerellaceae\u003c/em\u003e (r=-0.242, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.385, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK), \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidaceae\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.434, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.106, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ) and \u003cem\u003ef_ Helicobacteraceae\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.240, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.389, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL) had no correlation with the RI.\u003c/p\u003e\u003cp\u003eThe Linear discriminant analysis Effect Size (LEfSe) analysis was conducted to explore the differences in microbial communities among the three groups. In the CON group, \u003cem\u003eCaulobacterales\u003c/em\u003e was the most abundant order; \u003cem\u003eGammaproteobacteria\u003c/em\u003e was the leading class; \u003cem\u003eBacteroidaceae, Caulobacteraceae\u003c/em\u003e and \u003cem\u003eEnterococcaceae\u003c/em\u003e were the three most abundant families; \u003cem\u003eBacteroides and Enterococcus\u003c/em\u003e were predominant genus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the CMS-V group, Tannerellaceae was the most abundant family; Parabacteroides and Ruminococcus_torques_group were predominant genus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In CMS-D group, \u003cem\u003eCampylobacterales, Peptococcales\u003c/em\u003e and \u003cem\u003eCaulobacterales\u003c/em\u003e were the leading orders; \u003cem\u003eCampylobacteria\u003c/em\u003e was the predominant class; \u003cem\u003eBacteroidaceae, Peptococcaceae\u003c/em\u003ewere and Caulobacteraceae were the three most abundant families; \u003cem\u003eHelicobacter\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e were the richest genus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDulaglutide altered the serum metabolite levels in CMS mice.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe serum was collected for non-targeted metabolomics analysis to investigate the metabolic changes in mice. To verify the reliability of the model, a permutation test was conducted (n\u0026thinsp;=\u0026thinsp;200 times), and the results indicated that the model was robust (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The OPLS-DA model showed that the CMS-V group could be significantly separated from the CON group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). There were significant differences in the metabolomics between the CMS-V group and the CON group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eCompared to the CON group, the CMS-V group showed changes in the levels of 96 metabolites, with the levels of 40 metabolites decreased and 56 metabolites increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The top 50 differentially abundant metabolites are shown in Supplementary Table\u0026nbsp;1. 21 of them were glycerophospholipids, and most glycerophospholipids were elevated in the CMS-V group. Compared to the CMS-V group, 118 metabolites were significantly increased, and 35 metabolites were significantly decreased in in the CMS-D group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The top 50 differentially abundant metabolites are shown in Supplementary Table\u0026nbsp;2. 44 of them were glycerophospholipids, and most glycerophospholipids were decreased in the CMS-D group. Compared to the CON group, the metabolic pathways that exhibited significant differences in the CMS-V group included glycerophospholipid metabolism, GABAergic synapse, choline metabolism in cancer, retrograde endocannabinoid signaling, the citrate cycle (TCA cycle), the glucagon signaling pathway, alanine, aspartate and glutamate metabolism, central carbon metabolism in cancer, autophagy-other, butanoate metabolism, lysine degradation, Kaposi sarcoma-associated herpesvirus infection, autophagy-animal, glyoxylate and dicarboxylate metabolism, D-amino acid metabolism, Fc gamma R-mediated phagocytosis, the apelin signaling pathway, the calcium signaling pathway and the phospholipase D signaling pathway(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Compared to the CMS-V group, the metabolic pathways that exhibited significant differences in the CMS-V group included purine metabolism, the cGMP-PKG signaling pathway, the cAMP signaling pathway, sulfur metabolism, autophagy-other, the neuroactive ligand‒receptor interaction, Kaposi sarcoma-associated herpesvirus infection, autophagy-animal, glycerophospholipid metabolism, olfactory transduction, phototransduction, the biosynthesis of unsaturated fatty acids, GABAergic synapse, morphine addiction and alcoholism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG showed the bubble chart of the KEGG pathway analysis results. Glycerophospholipid metabolism was identified in pathways associated with onset and treatment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe correlation analysis between microbiota and metabolites.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Spearman\u0026rsquo;s correlation analysis was conducted between the GMs at the family level and the top 20 differentially abundant metabolites between CMS-V vs CON and CMS-V vs CMS-D.\u003c/p\u003e \u003cp\u003eWhen comparing the CMS-V group with the CON group, \u003cem\u003ef_Caulobacteraceae\u003c/em\u003e, \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidaceae\u003c/em\u003e and \u003cem\u003ef_Tannerellaceae\u003c/em\u003e were correlated with different metabolites. \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e was negatively correlated with PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)), Dihydrocyclosporin, Sphingosine 1-phosphate, 3,5-Dihydroxytetradecanoylcarnitine, PC(P-16:0/18:1(9Z)), PC(18:3(9Z,12Z,15Z)/18:1(11Z)), Uric acid, PE(P-18:0/19:0) and 3R,4S,5S,6S)-6-[4-Chloro-2-(furan-2-ylmethylamino)-5-sulfamoylbenzoyl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid. While \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e was positively correlated with D-erythro-L-galacto-Nonulose. \u003cem\u003ef_Bacteroidaceae\u003c/em\u003e was negatively correlated with 3,5-Dihydroxytetradecanoylcarnitine. \u003cem\u003ef_Tannerellaceae\u003c/em\u003e was positively with 4-Chloro-L-phenylalanine, PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)), PE(18:0/20:1(11Z)), PE-NMe2(22:1(13Z)/14:1(9Z)), Thiabendazole, Mactraxanthin 3-linoleate 3'-dipalmitoleate, 1,2-Benzisothiazole, Dihydrocyclosporin, Sphingosine 1-phosphate, 3,5-Dihydroxytetradecanoylcarnitine, PC(P-16:0/18:1(9Z)), PC(18:3(9Z,12Z,15Z)/18:1(11Z)), PE(P-18:0/19:0), (3R,4S,5S,6S)-6-[4-Chloro-2-(furan-2-ylmethylamino)-5-sulfamoylbenzoyl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid and 17-phenyl trinor Prostaglandin D2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The metabolites belonged to Peptidomimetics, Sphingolipids, Fatty Acyls, Glycerophospholipids, Organoheterocyclic compounds, Organooxygen compounds, Organic oxygen compounds, Carboxylic acids and derivatives, Benzimidazoles, Prenol lipids, Peptidomimetics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWhen compareing the CMS-D group with the CMS-V group, \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eHelicobacteraceae\u003c/em\u003e was negatively correlated with LysoPC(0:0/18:0), GM4(d18:1/16:0), GM4(d18:1/20:0), 2-Lysophosphatidylcholine, 1-(2-methoxy-octadecanyl)-sn-glycero-3-phosphoserine, Cyclopentanol, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0) and LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)), and positively correlated with PE(22:1(13Z)/20:4(5Z,8Z,11Z,14Z)-OH(20)) and PE(18:0/20:1(11Z)). \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidaceae\u003c/em\u003e was negatively correlated with 2-Lysophosphatidylcholine, Docosahexaenoic acid, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0) and LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)). \u003cem\u003ef\u003c/em\u003e_Caulobacteraceae was negatively correlated with GM4(d18:1/16:0), LysoPC(0:0/16:0), PC(P-16:0/22:6(5Z,7Z,10Z,13Z,16Z,19Z)-OH(4)), LysoPE(0:0/18:2(9Z,12Z)), LysoPE(20:4(5Z,8Z,11Z,14Z)/0:0), Cyclopentanol, GlcNAcbeta1-4Manbeta1-4Glcbeta-Cer(d18:1/18:0), LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)) and LysoPE(18:1(9Z)/0:0), While positively correlated with PE(18:0/20:1(11Z)), L-Carnitine (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). And the metabolites belonged to Glycerophospholipids, Acidic glycosphingolipids, Fatty Acyls, Organooxygen compounds, Sphingolipids, Organonitrogen compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis was the first study to verify the effects of dulaglutide in an animal model of CMS-induced cognitive impairment. In our study, CMS led to cognitive impairment in mice, and chronic dulaglutide administration improved long-term memory formation without affecting anxiety-like behaviors. We further explored the possible mechanisms of this effect. These results provide insight into therapy for cognitive dysfunction.\u003c/p\u003e \u003cp\u003eThe synaptic plasticity of the hippocampus, specifically long-term potentiation (LTP), regulates the formation of learning and memory \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It has been shown that CMS significantly impaired memory formation by reducing LTP in the CA1 region of the dorsal hippocampus \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Consistent with the aforementioned research, we found that CMS significantly reduced the recognition index of mice in long-term memory test. The level of GLP-1 in serum was associated with cognitive function in diabetic patients \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Clinical follow-up found that dulaglutide, as a GLP-1 receptor agonist, could improve cognitive dysfunction in diabetic patients \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In guan\u0026rsquo;s study, dulaglutide effectively improved cognitive impairment in rats with vascular dementia without affecting anxiety-like behavior \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In conclusion, both clinical and animal studies have demonstrated the effectiveness of dulaglutide in treating cognitive impairment, and further exploration is needed to understand its potential mechanisms affecting cognitive function.The gut-brain axis, as a communication system between the gastrointestinal tract and the central nervous system, plays an important role in maintaining body homeostasis.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The GMs is vital to human health, as it affects the blood‒brain barrier, the myelin sheath, neurogenesis, and other neuronal developmental processes \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Changes in the GMs may lead to neuroinflammation and psychiatric disorders, such as anxiety, depression and cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The relationship between gut microbial α-diversity and cognitive function is controversial. Nicholas and colleagues found that gut microbial α-diversity in dementia patients was lower \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, while some studies found no difference in α-diversity between people with dementia and healthy controls \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this study, there was no significant difference in α-diversity among the three groups, which was consistent with previous researches. Therefore, we speculated that compared to individual microbial diversity, the stress-induced changes in microbial structure among individuals may have a greater impact on cognitive function.\u003c/p\u003e \u003cp\u003eAnimal studies have found that chronic stress led to a decrease in the relative abundance of \u003cem\u003eg_Bacteroides\u003c/em\u003e in mice \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. We found that the relative abundance of \u003cem\u003eg_Bacteroidetes\u003c/em\u003e in the CMS-V group exhibited the same trend, while dulaglutide reversed this change. Katharina et al. found that dulaglutide increased the relative abundance of \u003cem\u003ep\u003c/em\u003e_\u003cem\u003eBacteroidetes\u003c/em\u003e in mice \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In Liang\u0026rsquo;s study, people with more \u003cem\u003eg\u003c/em\u003e_\u003cem\u003eBacteroides\u003c/em\u003e had better cognitive performance based on cognitive scores \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCaulobacteraceae\u003c/em\u003e is a family of bacteria in the \u003cem\u003eproteobacteria\u003c/em\u003e phylum. We found that CMS significantly reduced the relative abundance of \u003cem\u003ef_Caulobacteraceae\u003c/em\u003e, and this reduction was reversed after treatment with dulaglutide. Moreover, the relative abundance of \u003cem\u003ef_Caulobacteraceae\u003c/em\u003e was positively correlated with the recognition index in long-term memory test. A study has reported that \u003cem\u003eg_Caulobacteraceae\u003c/em\u003e may be related to immune gene expression, but its function in immunity is not clear \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Other studies indicated that \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e were cholesterol-degrading bacteria \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and a high level of cholesterol is a risk factor for cognitive decline \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These researches supported our findings. Animals studies indicated that the increase of the relative abundance of \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eHelicobacteraceae\u003c/em\u003e might impair cognitive performance \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.However, in our study, the relative abundance of \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eHelicobacteraceae\u003c/em\u003e was increased by dulaglutide, it had no correlation with the formation of long-term memory. At present, the effect and mechanism of Clostridium on cognitive function are still unclear, and there are few related studies, so further exploration is needed in the future. In previous study, sleep-deprived mice showed cognitive impairment and the increased relative abundance of \u003cem\u003ef_Tannerellaceae\u003c/em\u003e, and \u003cem\u003ef_Tannerellaceae\u003c/em\u003e was correlated with inflammatory response \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In our study, although CMS decreased the relative abundance of \u003cem\u003ef_Tannerellaceae\u003c/em\u003e, dulaglutide could not reverse this phenomenon, and \u003cem\u003ef_Tannerellaceae\u003c/em\u003e was not related to cognitive function. There are few studies on the relationship between \u003cem\u003eTannerellaceae\u003c/em\u003e and cognition, and further research is needed. Therefore, these results showed that dulaglutide regulated the relative abundance of \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae\u003c/em\u003e and \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidetes\u003c/em\u003e to improve cognition.\u003c/p\u003e \u003cp\u003eMetabolomics involves the comprehensive identification and quantification of metabolites, which are small molecules produced during metabolic processes, within a biological system \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In this study, our PLS-DA and OPLS-DA score plots displayed significant separation among the three groups. And CMS led to lipid metabolism disorders, especially with relation to glycerophospholipids. Lipids make up more than half of the brain\u0026rsquo;s dry weight, and lipid metabolism plays important roles in brain functions, including cognition \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Chronic stress led to metabolic disorders, including lipid disorders \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Many disorders that caused cognitive impairment were accompanied by a disturbance in lipid metabolism, such as major depressive disorder and Alzheimer's disease \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our results were consistent with previous studies, suggesting that lipid metabolism disorders work in cognitive impairment induced by chronic stress.\u003c/p\u003e \u003cp\u003eGlycerophospholipids can be divided into different subgroups, including phosphatidylethanolamine (PE) and phosphatidylcholine(PC) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In our study, compared with the CON group, CMS led to a significant increase in the levels of most PC and PE, while dulaglutide partially reversed this effect. Consistent with our results, Tian et al. found that the differential lipid metabolites in the serum of mice subjected to the chronic social defeat stress were mainly glycerophospholipids \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In Chen\u0026rsquo;s study, the levels of PE and PC were elevated in diabetic rats with cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Nho. et al. reported that the levels of PCs were increased in the serum of Alzheimer\u0026rsquo;s patients and were associated with cognition and amyloid-β deposition \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Du et al. found that dulaglutide could remodel glycerophospholipids in type 2 diabetes \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Considering that the different lipid metabolites in serum in this study were mainly glycerophospholipids, we concluded that dulaglutide alleviated cognitive impairment by modulating glycerophospholipids.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also explored the association between the differential microbiota and the differential metabolites. Some studies have reported that GMs influenced host energy metabolism and brain health through microbiota metabolites (e.g., short-chain fatty acids, amino acids and vitamins) \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. GMs are key modifiers of lipid metabolism and can influence host lipid metabolism \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In this study, when comparing the CMS-V group and the CMS-D group, most of the differential metabolites that correlated to the differential microbiota belonged to Lipids and lipid-like molecules, including Glycerophospholipids, Sphingolipids, Fatty Acyls, Sphingolipids. Zhai et al found that Caulobacteraceae was correlated with serum lipid in mice\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBacteroides\u003c/em\u003e was correlated with lipid-related metabolism, and increasing the abundance of \u003cem\u003eBacteroides\u003c/em\u003e contributed to hyperlipidemia alleviated\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In a word, we concluded that dulaglutide improved chronic stress induce cognitive function by affecting lipid metabolic changes caused by regulating the abundance of gut microbiota.\u003c/p\u003e \u003cp\u003eThe effect of dulaglutide on anxiety-like behaviors remains unclear. In existing studies, chronic application of GLP-1 receptor agonists has shown anxiolytic effects in obese or diabetic rodent models. \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. However, in Rozita\u0026rsquo;s study, chronic central administration of a GLP-1 analog did not influence anxiety-like behaviors \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Moreover, the effect of dulaglutide on anxiety-like behaviors was not related to the behavioral test method used. In this study, chronic administration of dulaglutide did not affect CMS-induced anxiety-like behaviors. Therefore, further investigation is needed to explore the effects and mechanisms of dulaglutide on anxiety-like behaviors in the future.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHowever, there were still some limitations in our study. First, we only used serum non-targeted metabolomics analysis to examine the differential metabolites and their functions among the three groups. This approach was relatively singular, and some metabolites are rare in serum, making it difficult to clarify the specific effects of dulaglutide on a particular metabolite. Second, we should further explore whether there are interactions between these differential metabolites and differential microbiota, as well as their specific roles in the process of CMS-induced cognitive impairment.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, modulating the GMs and serum metabolites is an effective method to improve CMS-induced cognitive impairment. We first verified the effect of dulaglutide on cognition and anxiety-like behaviors induced by CMS. In addition to discovering this new beneficial effect of dulaglutide, the results of this study also suggest that dulaglutide may exert this effect by regulating the GMs and serum metabolites. Further exploration of the mechanisms underlying the neuroprotective effects of dulaglutide will be the focus of our future research.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Hebei Natural Science Foundation[H2022307075], Hebei Provincial Government subsidizes the Clinical Outstanding Talent Project[ZF2025017] and Hebei Provincial Medical Science Research Project [20240535].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, L.P.Y.,S.H.S. and W.T.J.; methodology, H.B.Y.,Z.S.P.; software, Z.S.P.; validation, S.H.S., L,P,Y. and W.T.J.; formal analysis, Z.S.P., J.M. and H.B.Y.; investigation, Z.S.P., J.M. and H.B.Y.; resources, H.Z.W.,F.D.,Z.X.R.,G.C.Y.; data curation, Z.S.P., J.M. and H.B.Y.; writing-original draft preparation, Z.S.P.; writing-review and editing, S.H.S., L,P,Y. and W.T.J.; visualization, Z.S.P.; supervision, S.H.S.; project administration, T.W.J.; funding acquisition, L.P.Y. All authors have read and agreed to the published version of the manuscript .\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by Hebei Natural Science Foundation [H2022307075], Hebei Provincial Medical Science Research Project [20240535] and Hebei Provincial Government subsidizes the Clinical Outstanding Talent Project[ZF2025017].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original sequence data have been uploaded to the Sequence Read Archive (SRA) (NCBI, USA) with the Accession Number: PRJNA1180755.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCollum, L. \u0026amp; Karlawish, J. Cognitive Impairment Evaluation and Management. \u003cem\u003eMed. Clin. North. 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GLP-1 is both anxiogenic and antidepressant; divergent effects of acute and chronic GLP-1 on emotionality. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e. \u003cb\u003e65\u003c/b\u003e, 54\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psyneuen.2015.11.021\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2015.11.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic stress, dulaglutide, cognition, anxiety-like behaviors","lastPublishedDoi":"10.21203/rs.3.rs-5279490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5279490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic stress may lead to cognitive impairment. Prolonged use of dulaglutide could potentially alleviate cognitive impairment in individuals with type 2 diabetes, although its role in cognitive impairment induced by chronic stress remains elusive. This study aimed to explore the effect of dulaglutide on cognitive impairment caused by chronic stress and the underlying mechanisms. Forty-five mice were randomly divided into the following 3 groups (n\u0026thinsp;=\u0026thinsp;15 per group): the CON group (the normal control group), the CMS-V group (mice treated with chronic mild stress and vehicle) and the CMS-D group (mice treated with chronic mild stress and 0.6 mg/kg dulaglutide). We found chronic mild stress resulted in cognitive impairment and anxiety-like behaviors in mice. Three weeks of dulaglutide treatment significantly alleviated cognitive impairment but had no effect on anxiety-like behaviors. Dulaglutide treatment induced alterations in gut microbiome homeostasis, particularly affecting the levels of \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003ef\u003c/em\u003e_\u003cem\u003eCaulobacteraceae and f_ Helicobacteraceae\u003c/em\u003e. Meanwhile, dulaglutide had an effect on metabolic changes, especially in glycerophospholipids. Further analysis showed a correlation between gut microbiota and metabolite alterations following dulaglutide treatment. These results suggest that dulaglutide may potentially reverse cognitive impairment induced by chronic stress, possibly through its influence on the gut microbiota and metabolomic pathways.\u003c/p\u003e","manuscriptTitle":"Dulaglutide ameliorates chronic stress-induced cognitive impairment via regulating gut microbiota and serum metabolites in mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-21 19:19:15","doi":"10.21203/rs.3.rs-5279490/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b5e4ad5-7770-487e-a695-05b5581fe1bd","owner":[],"postedDate":"November 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40003504,"name":"Health sciences/Neurology"},{"id":40003505,"name":"Health sciences/Medical research"},{"id":40003506,"name":"Health sciences/Medical research/Pre clinical studies"}],"tags":[],"updatedAt":"2025-03-20T15:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-21 19:19:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5279490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5279490","identity":"rs-5279490","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}