Background The interplay between diet and gut microbiome substantially influences host metabolism, but uncertainties remain regarding their relationships tailored for each subject given the huge inter-individual variability. Here we aim to investigate diet-gut microbiome interaction at single-subject resolution and explore its effects on blood glucose homeostasis. Methods We conducted a series of nutritional n-of-1 trials (NCT04125602), in which 30 participants were assigned high-carbohydrate (HC) and low-carbohydrate (LC) diets in a randomized sequence across 3 pair of cross-over periods lasting 72 days. We used shotgun metagenomic sequencing and continuous glucose monitoring systems to profile the gut microbiome and blood glucose, respectively. An independent cohort of 1219 participants with available metagenomics data are included as a validation cohort. Findings We demonstrated that the gut microbiome exhibited both intra-individually dynamic and inter-individually personalized signatures during the interventions. At the single-subject resolution, we observed person-specific response patterns of gut microbiota to interventional diets. Furthermore, we discovered a personal gut microbial signature represented by a carb-sensitivity score, which was closely correlated with glycemic phenotypes during the HC intervention, but not LC intervention. We validate the role of this score in the validation cohort and fi nd that it reflects host glycemic sensitivity to the personal gut microbiota profile when sensing the dietary carbohydrate inputs. Interpretation Our fi nding suggests that the HC diet modulates gut microbiota in a person-specific manner and facilitates the connection between gut microbiota and glycemic sensitivity. This study represents a new paradigm for investigating the diet-microbiome interaction in the context of precision nutrition. Copyright (c) 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Importance:Understanding the interplay between trajectories of sleep duration, sleep onset timing, and glycemic dynamics is crucial for improving preventive strategies against diabetes and related metabolic diseases. Objective:To examine the associations of sleep duration and onset timing trajectories with continuous glucose monitoring (CGM)-derived glycemic metrics in adults. Design, Setting, and Participants:This cohort study analyzed data collected from January 2014 to December 2023 in the Guangzhou Nutrition and Health Study, a prospective cohort in Guangdong province, China, among participants aged 46 to 83. Participants who had repeated sleep assessments at several study visits and were equipped with CGM devices at the last visit were included. Data analyses were conducted between January and June 2024. Exposures:The trajectories of sleep duration and onset timing were constructed using self-report sleep duration and sleep onset timing, recorded at multiple study visit points. Main Outcomes and Measures:Measurements of glycemic variability and glycemic control were collected using a masked CGM device worn by patients for 14 consecutive days. Huber robust regression models were used to assess the associations between sleep trajectories and CGM-derived metrics. Results:In this study of 1156 participants (mean [SD] age, 63.0 [5.1] years, 816 [70.6%] women), we identified 4 distinct sleep duration trajectory groups: severe inadequate, moderate inadequate, mild inadequate, and adequate. Severe sleep inadequacy was associated with an increment of glycemic variability indicators: 2.87% (95% CI, 1.23%-4.50%) for coefficient of variation and 0.06 (95% CI, 0.02-0.09) mmol/L for mean of daily differences. We found 2 trajectories of sleep onset timing: persistent early and persistent late groups. Late sleep onset was associated with larger coefficient of variation (β = 1.18%; 95% CI, 0.36%-2.01%) and mean of daily differences (β = 0.02 mmol/L; 95% CI, 0.01-0.04 mmol/L). Inappropriate sleep duration and timing trajectories in combination were associated with greater glycemic variability. Conclusions and Relevance:In this cohort study of middle-aged and older participants, persistent inadequate sleep duration and late sleep onset, whether alone or in combination, were associated with greater glycemic variability. These findings emphasize the importance of considering both sleep duration and timing for optimizing glycemic control in the general population.

The blood proteome contains biomarkers of ageing and age-associated diseases, but such markers are rarely validated longitudinally. Here we map the longitudinal proteome in 7,565 serum samples from a cohort of 3,796 middle-aged and elderly adults across three time points over a 9-year follow-up period. We pinpoint 86 ageing-related proteins that exhibit signatures associated with 32 clinical traits and the incidence of 14 major ageing-related chronic diseases. Leveraging a machine-learning model, we pick 22 of these proteins to generate a proteomic healthy ageing score (PHAS), capable of predicting the incidence of cardiometabolic diseases. We further identify the gut microbiota as a modifiable factor influencing the PHAS. Our data constitute a valuable resource and offer useful insights into the roles of serum proteins in ageing and age-associated cardiometabolic diseases, providing potential targets for intervention with therapeutics to promote healthy ageing. Tang, Yue, Xu and colleagues map the proteome of several thousand individuals over a 9-year period to identify potential biomarkers of ageing and of age-associated diseases.

Importance Physical activity, as a modifiable factor, emerges as a primary intervention strategy for the prevention and management of gestational diabetes (GD). Among women with GD, the association of physical activity during pregnancy with preterm birth remains unclear. Objective To examine the association of accelerometer-derived physical activity metrics and patterns with preterm birth among women with GD. Design, Setting, and Participants This prospective cohort study recruited pregnant women with GD in Hangzhou, China, from August 2019 to August 2023 as part of the Westlake Precision Birth Cohort study. Statistical analysis was performed between August and November 2023. Exposures Wearable accelerometer-derived physical activity metrics and patterns. Measurements of physical activity via wearable accelerometer were performed at a median (IQR) of 25.4 (24.6-26.6) weeks' gestation. Main Outcomes and Measures Preterm birth was determined through the examination of delivery records. Incident preterm birth was defined as the delivery of infants before completing 37 weeks of gestation. Results Among the 1427 women meeting the inclusion criteria, the mean (SD) age was 31.3 (3.8) years, and there were 80 cases of preterm birth. An increase in moderate-to-vigorous intensity physical activity (MVPA) and the fraction of physical activity energy expenditure derived from MVPA exhibited an inverse association with preterm birth, with an odds ratio per 30 minutes of 0.64 (95% CI, 0.42-0.98) and an odds ratio per SD of 0.69 (95% CI, 0.55-0.88). In the dose-response analysis, there was a progressive decrease in the odds of preterm birth with increasing duration of MVPA per day, reaching a plateau at approximately 74 minutes per day. Furthermore, the findings indicated that active MVPA (MVPA >= 30 minutes per day), whether it was concentrated into a few days or followed a more regular pattern, had similar beneficial association with preterm birth. Conclusions and Relevance In this prospective cohort study, MVPA during pregnancy exhibited an inverse association with preterm birth among women with GD. Concentrated physical activity was associated with similar benefits in reducing preterm birth risk as regular physical activity.

Background Mapping gut microecological features to serum metabolites (SMs) will help identify functional links between gut microbiome and cardiometabolic health. Methods This study encompassed 836-1021 adults over 9.7 year in a cohort, assessing metabolic syndrome (MS), carotid atherosclerotic plaque (CAP), and other metadata triennially. We analyzed mid-term microbial metagenomics, targeted fecal and serum metabolomics, host genetics, and serum proteomics. Findings Gut microbiota and metabolites (GMM) accounted for 15.1% overall variance in 168 SMs, with individual GMM factors explaining 5.65%-10.1%, - 10.1%, host genetics 3.23%, and sociodemographic factors 5.95%. Specifically, fi cally, GMM elucidated 5.5%-49.6% - 49.6% variance in the top 32 GMM-explained SMs. Each 20% increase in the 32 metabolite score (derived from the 32 SMs) correlated with 73% (95% confidence fi dence interval [CI]: 53%-95%) - 95%) and 19% (95% CI: 11%-27%) - 27%) increases in MS and CAP incidences, respectively. Among the 32 GMM-explained SMs, sebacic acid, indoleacetic acid, and eicosapentaenoic acid were linked to MS or CAP incidence. Serum proteomics revealed certain proteins, particularly the apolipoprotein family, mediated the relationship between GMM-SMs and cardiometabolic risks. Interpretation This study reveals the significant fi cant influence fl uence of GMM on SM profiles fi les and illustrates the intricate connections between GMM-explained SMs, serum proteins, and the incidence of MS and CAP, providing insights into the roles of gut dysbiosis in cardiometabolic health via regulating blood metabolites.

SCOPE:Little is known about the effect of blood vitamin D status on the gut mycobiota (i.e., fungi), a crucial component of the gut microbial ecosystem. The study aims to explore the association between 25-hydroxyvitamin D [25(OH)D] and gut mycobiota and to investigate the link between the identified mycobial features and blood glycemic traits. METHODS AND RESULTS:The study examines the association between serum 25(OH)D levels and the gut mycobiota in the Westlake Precision Birth Cohort, which includes pregnant women with gestational diabetes mellitus (GDM). The study develops a genetic risk score (GRS) for 25(OH)D to validate the observational results. In both the prospective and cross-sectional analyses, the vitamin D is associated with gut mycobiota diversity. Specifically, the abundance of Saccharomyces is significantly lower in the vitamin D-sufficient group than in the vitamin D-deficient group. The GRS of 25(OH)D is inversely associated with the abundance of Saccharomyces. Moreover, the Saccharomyces is positively associated with blood glucose levels. CONCLUSION:Blood vitamin D status is associated with the diversity and composition of gut mycobiota in women with GDM, which may provide new insights into the mechanistic understanding of the relationship between vitamin D levels and metabolic health.

Objective The remodelling of gut mycobiome (ie, fungi) during pregnancy and its potential influence on host metabolism and pregnancy health remains largely unexplored. Here, we aim to examine the characteristics of gut fungi in pregnant women, and reveal the associations between gut mycobiome, host metabolome and pregnancy health. Design Based on a prospective birth cohort in central China (2017 to 2020): Tongji-Huaxi-Shuangliu Birth Cohort, we included 4800 participants who had available ITS2 sequencing data, dietary information and clinical records during their pregnancy. Additionally, we established a subcohort of 1059 participants, which included 514 women who gave birth to preterm, low birthweight or macrosomia infants, as well as 545 randomly selected controls. In this subcohort, a total of 750, 748 and 709 participants had ITS2 sequencing data, 16S sequencing data and serum metabolome data available, respectively, across all trimesters. Results The composition of gut fungi changes dramatically from early to late pregnancy, exhibiting a greater degree of variability and individuality compared with changes observed in gut bacteria. The multiomics data provide a landscape of the networks among gut mycobiome, biological functionality, serum metabolites and pregnancy health, pinpointing the link between Mucor and adverse pregnancy outcomes. The prepregnancy overweight status is a key factor influencing both gut mycobiome compositional alteration and the pattern of metabolic remodelling during pregnancy. Conclusion This study provides a landscape of gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health, which lays the foundation of the future gut mycobiome investigation for healthy pregnancy.

BACKGROUND:The EAT-Lancet diet was reported to be mutually beneficial for the human cardiometabolic system and planetary health. However, mechanistic evidence linking the EAT-Lancet diet and human cardiometabolic health is lacking. OBJECTIVES:We aimed to investigate the role of blood proteins in the association between the EAT-Lancet diet and cardiometabolic health and explore the underlying gut microbiota-blood protein interplay. METHODS:Our study was based on a prospective cohort including 3742 Chinese participants enrolled from 2008-2013 with serum proteome data repeatedly measured ≤3 times (Nproteome = 7514) and 1195 with gut metagenomic data measured ≤2 times over 9 y (Nmicrobiota = 1695). Least absolute shrinkage and selection operator and multivariable linear regression were used to explore the associations of the EAT-Lancet diet (assessed by semi-quantitative food frequency questionnaire) with serum proteins and gut microbes. Linear mixed-effect model and logistic regression were used to examine the associations of selected proteins with 11 cardiometabolic risk factors and 4 cardiometabolic diseases, respectively. Mediation analysis was used to identify potential mediation effects. Multiple comparisons were adjusted using the Benjamini-Hochberg method. RESULTS:The mean (standard deviation) age of enrolled participants was 58.4 (6.1) y (31.6% men). The EAT-Lancet diet was prospectively associated with 4 core proteins, including α-2-macroglobulin (A2M) (pooled β: 0.12; 95% confidence interval [CI]: 0.05, 0.2), retinol-binding protein 4 (pooled β: -0.14; 95% CI: -0.24, -0.04), TBC1 domain family member 31 (pooled β: -0.11; 95% CI: -0.22, 0), and adenylate kinase 4 (pooled β: -0.19; 95% CI: -0.3, -0.08). The identified proteins were prospectively associated with cardiometabolic diseases (pooled odds ratio ranged from 0.8-1.18) and risk factors (pooled β ranged from -0.1 to 0.12), mediating the association between the EAT-Lancet diet and blood triglycerides. We then identified 5 gut microbial biomarkers of the EAT-Lancet diet, and discovered a potential gut microbiota-blood protein interplay (EAT-Lancet diet→Rothia mucilaginosa→A2M) underlying the EAT-Lancet diet-cardiometabolic health association. CONCLUSIONS:Our study presents key molecular evidence to support the role of EAT-Lancet diet adherence in promoting cardiometabolic health.

Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95

Background Pregnant women with gestational diabetes mellitus(GDM)are often comorbid with hypertensive disorders of pregnancy(HDP),which can severely impact pregnancy health and delivery outcomes.The relationship between gut microbiota and pregnancy health has received increasing attention,but its association with concurrent HDP in GDM remains to be investigated.Objective To explore the association between gut microbiota characteristics and concurrent HDP in GDM patients.Methods The 204 patients with GDM who underwent prenatal examination at the Hangzhou Women's Hospital from August 2019 to January 2020 were selected as the study subjects.Pregnant women diagnosed with GDM only were categorized as the GDM group(n=181),while those concurrently diagnosed with GDM and HDP were categorized as the GDM with concurrent HDP group(n=23).Clinical data and inflammation detection markers of the enrolled pregnant women were collected,and fecal samples of the same period were retained for gut microbiota 16S rDNA amplicon sequencing analysis.LEfSe analysis was used to compare microbiota composition between the two groups at the phylum,family and genus levels,and to identify distinct bacterial enrichments.Logistic regression analysis was performed to identify gut microbiota characteristics associated with concurrent HDP in GDM.Spearman's rank correlation analysis was performed to explore the association between gut microbiota and inflammatory markers.Results No significant differences were found in overall gut microbiota composition and relative abundances of major phyla between the two groups(P>0.05).LEfSe analysis on family level showed that the Veillonellaceae family was enriched in the GDM with concurrent HDP group(P<0.05),while Mollicutes RF39 unclassified family and Lachnospiraceae were depleted(P<0.05).At the genus level,Dialister,Intestinibacter,Eubacterium and Parasutterella were enriched in the GDM with concurrent HDP group(P<0.05),whereas[Eubacterium]xylanophilum group,Ruminiclostridium 6,Mollicutes RF39 unclassified genus and Lachnospiraceae unclassified genus were enriched in the GDM group(P<0.05).Logistic regression analysis results showed increased abundances of Veillonellaceae(OR=1.06,95%CI=1.01-1.11),Dialister(OR=1.26,95%CI=1.10-1.45)and Intestinibacter(OR=2.07,95%CI=1.12-3.84)were independent risk factors for concurrent HDP in GDM(P<0.05),while increased Lachnospiraceae was a protective factor(P<0.05).Spearman's rank correlation analysis results showed Veillonellaceae was positively correlated with the proportion of monocytes(rs=0.149,P<0.05);Dialister was positively correlated with leukocyte count,eosinophil leukocyte,and eosinophil count(rs=0.151,0.163,0.171,P<0.05).Conclusion Increased abundances of Veillonellaceae,Dialister and Intestinibacter are independent risk factors for concurrent HDP in GDM pregnant women,while increased abundance of Lachnospiraceae unclassified genus is a protective factor.Veillonellaceae and Dialister are positively correlated with multiple inflammatory markers.Gut microbiota may be an important risk factor for concurrent HDP in GDM.

Background The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD. Methods The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort ( n = 377), and the prospective validation cohort ( n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites. Results Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota ( f__veillonellaceae ) was associated with increased NAFLD risk, whereas f__rikenellaceae , f__barnesiellaceae , and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD. Conclusions We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.

We established a prospective birth cohort, the Westlake Precision Birth Cohort (WeBirth), based on 2000 pregnant women with gestational diabetes mellitus (GDM) in the second trimester and their offspring. The WeBirth provides a new framework for prospective birth cohort study with sophisticated integration of precision nutrition, wearable devices, and multiomics data collection among patients with GDM.image

While the human gut is home to a complex and diverse community of microbes, including bacteria and fungi, research on the gut microbiome has largely focused on bacteria, with relatively little attention given to the gut mycobiome. This study aims to investigate how diets with different dietary macronutrient distributions impact the gut mycobiome. We investigated gut mycobiome response to high-carbohydrate, low-fat (HC) and low-carbohydrate high-fat (LC) diet interventions based on a series of 72-day feeding-based n-of-1 clinical trials. A total of 30 participants were enrolled and underwent three sets of HC and LC dietary interventions in a randomized sequence. Each set lasted for 24 days with a 6-day washout period between dietary interventions. We collected and analyzed the fungal composition of 317 stool samples before and after each intervention period. To account for intra-individual variation across the three sets, we averaged the mycobiome data from the repeated sets for analysis. Of the 30 participants, 28 (aged 22–34 years) completed the entire intervention. Our results revealed a significant increase in gut fungal alpha diversity (p < 0.05) and significant changes in fungal composition (beta diversity, p < 0.05) after the HC dietary intervention. Specifically, we observed the enrichment of five fungal genera (Pleurotus, Kazachstania, Auricularia, Paraphaeosphaeria, Ustilaginaceae sp.; FDR < 0.052) and depletion of one fungal genus (Blumeria; FDR = 0.03) after the HC intervention. After the LC dietary intervention, one fungal genus was enriched (Ustilaginaceae sp.; FDR = 0.003), and five fungal genera were depleted (Blumeria, Agaricomycetes spp., Malassezia, Rhizopus, and Penicillium; FDR < 0.1). This study provides novel evidence on how the gut mycobiome structure and composition change in response to the HC and LC dietary interventions and reveals diet-specific changes in the fungal genera.

Background Continuous glucose monitoring (CGM) has shown potential in improving maternal and neonatal out-comes in individuals with type 1/2 diabetes, but data in gestational diabetes mellitus (GDM) is limited. We aimed to explore the relationship between CGM-derived metrics during pregnancy and pregnancy outcomes among women with GDM.Methods We recruited 1302 pregnant women with GDM at a mean gestational age of 26.0 weeks and followed them until delivery. Participants underwent a 14-day CGM measurement upon recruitment. The primary outcome was any adverse pregnancy outcome, defined as having at least one of the outcomes: preterm birth, large-for-gestational-age (LGA) birth, fetal distress, premature rupture of membranes, and neonatal intensive care unit (NICU) admission. The individual outcomes included in the primary outcome were considered as secondary outcomes. We conducted multivariable logistic regression to evaluate the association of CGM-derived metrics with these outcomes.Findings Per 1-SD difference in time above range (TAR), glucose area under the curve (AUC), nighttime mean blood glucose (MBG), daytime MBG, and daily MBG was associated with higher risk of any adverse pregnancy outcome, with odds ratio: 1.22 (95% CI 1.08-1.36), 1.22 (95% CI 1.09-1.37), 1.18 (95% CI 1.05-1.32), 1.21 (95% CI 1.07-1.35), and 1.22 (95% CI 1.09-1.37), respectively. Time in range, TAR, AUC, nighttime MBG, daytime MBG, daily MBG, and mean amplitude of glucose excursions were positively associated, while time blow range was inversely associated with the risk of LGA. Additionally, higher value for TAR was associated with higher risk of NICU admission. We further summarized the potential thresholds of TAR (2.5%) and daily MBG (4.8 mmol/L) to distinguish individuals with and without any adverse pregnancy outcome.Interpretation The CGM-derived metrics may help identify individuals at higher risk of adverse pregnancy outcomes. These CGM biomarkers could serve as potential new intervention targets to maintain a healthy pregnancy status among women with GDM.

Identification of protein quantitative trait loci (pQTL) helps understand the underlying mechanisms of diseases and discover promising targets for pharmacological intervention. For most important class of drug targets, genetic evidence needs to be generalizable to diverse populations. Given that the majority of the previous studies were conducted in European ancestry populations, little is known about the protein-associated genetic variants in East Asians. Based on data-independent acquisition mass spectrometry technique, we conduct genome-wide association analyses for 304 unique proteins in 2,958 Han Chinese participants. We identify 195 genetic variant-protein associations. Colocalization and Mendelian randomization analyses highlight 60 gene-protein-phenotype associations, 45 of which (75%) have not been prioritized in Europeans previously. Further cross-ancestry analyses uncover key proteins that contributed to the differences in the obesity-induced diabetes and coronary artery disease susceptibility. These findings provide novel druggable proteins as well as a unique resource for the trans-ancestry evaluation of protein-targeted drug discovery.

Background Microbiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes, but evidence from large human cohorts is lacking. This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations. Methods We included 1430 participants from the Guangzhou Nutrition and Health Study (GNHS) with both gut microbiome and cognitive assessment data available as a discovery cohort, of whom 272 individuals provided fecal samples twice before cognitive assessment. We selected 208 individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit. Fecal 16S rRNA and shotgun metagenomic sequencing, targeted serum metabolomics, and cytokine measurements were performed in the GNHS. The validation analyses were conducted in an Alzheimer’s disease case–control study (replication study 1, n = 90) and another community-based cohort (replication study 2, n = 1300) with cross-sectional dataset. Results We found protective associations of specific gut microbial genera ( Odoribacter , Butyricimonas , and Bacteroides ) with cognitive impairment in both the discovery cohort and the replication study 1. Result of Bacteroides was further validated in the replication study 2. Odoribacter was positively associated with hippocampal volume (β, 0.16; 95% CI 0.06–0.26, P = 0.002), which might be mediated by acetic acids. Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment. We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition. Conclusions Our findings reveal that specific gut microbial features are closely associated with cognitive impairment and decreased hippocampal volume, which may play an important role in dementia development.