metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Landscape of the gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy 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.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.

BACKGROUND: 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. METHODS: 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. RESULTS: 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% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. CONCLUSIONS: Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Erythrocyte Very Long-Chain Saturated Fatty Acids, Gut Microbiota-Bile Acid Axis, and Incident Coronary Artery Disease in Adults: A Prospective Cohort Study.

BACKGROUND: Prior research has highlighted inverse associations between concentrations of circulating very long-chain saturated fatty acids (VLCSFAs) and coronary artery disease (CAD). However, the intricate links involving VLCSFAs, gut microbiota, and bile acids remain underexplored. OBJECTIVES: This study examined the association of erythrocyte VLCSFAs with CHD incidence, focusing on the mediating role of gut microbiota and fecal bile acids. METHODS: This 10-y prospective study included 2383 participants without CHD at baseline. Erythrocyte VLCSFAs [arachidic acid (C20:0), behenic acid (C22:0), and lignoceric acid (C24:0)] were measured using gas chromatography at baseline, and 274 CHD incidents were documented in triennial follow-ups. Gut microbiota in 1744 participants and fecal bile acid metabolites in 945 participants were analyzed using 16S ribosomal ribonucleic acid sequencing and ultra-performance liquid chromatography-tandem mass spectrometry at middle-term. RESULTS: The multivariable-adjusted hazard ratios (95% confidence interval) for CHD incidence in highest compared with lowest quartiles were 0.87 (0.61, 1.25) for C20:0, 0.63 (0.42, 0.96) for C22:0, 0.59 (0.41, 0.85) for C24:0, and 0.57 (0.39, 0.83) for total VLCSFAs. Participants with higher total VLCSFA concentrations exhibited increased abundances of Holdemanella, Coriobacteriales Incertae Sedis spp., Ruminococcaceae UCG-005 and UCG-010, and Lachnospiraceae ND3007 group. These 5 genera generated overlapping differential microbial scores (ODMSs) that accounted for 11.52% of the total VLCSFAs-CHD association (Pmediation = 0.018). Bile acids tauro_α_ and tauro_ß_muricholic acid were inversely associated with ODMS and positively associated with incident CHD. Opposite associations were found for glycolithocholic acid and glycodeoxycholic acid. Mediation analyses indicated that glycolithocholic acid, glycodeoxycholic acid, and tauro_α_ and tauro_ß_muricholic acid explained 56.40%, 35.19%, and 26.17% of the ODMS-CHD association, respectively (Pmediation = 0.002, 0.008, and 0.020). CONCLUSIONS: Elevated erythrocyte VLCSFAs are inversely associated with CHD risk in the Chinese population, with gut microbiota and fecal bile acid profiles potentially mediating this association. The identified microbiota and bile acid metabolites may serve as potential intervention targets in future studies. This trial was registered at www. CLINICALTRIALS: gov as NCT03179657.

Early-life exposure to the Great Chinese Famine and gut microbiome disruption across adulthood for type 2 diabetes: three population-based cohort studies.

BACKGROUND: The early life stage is critical for the gut microbiota establishment and development. We aimed to investigate the lifelong impact of famine exposure during early life on the adult gut microbial ecosystem and examine the association of famine-induced disturbance in gut microbiota with type 2 diabetes. METHODS: We profiled the gut microbial composition among 11,513 adults (18-97 years) from three independent cohorts and examined the association of famine exposure during early life with alterations of adult gut microbial diversity and composition. We performed co-abundance network analyses to identify keystone taxa in the three cohorts and constructed an index with the shared keystone taxa across the three cohorts. Among each cohort, we used linear regression to examine the association of famine exposure during early life with the keystone taxa index and assessed the correlation between the keystone taxa index and type 2 diabetes using logistic regression adjusted for potential confounders. We combined the effect estimates from the three cohorts using random-effects meta-analysis. RESULTS: Compared with the no-exposed control group (born during 1962-1964), participants who were exposed to the famine during the first 1000 days of life (born in 1959) had consistently lower gut microbial alpha diversity and alterations in the gut microbial community during adulthood across the three cohorts. Compared with the no-exposed control group, participants who were exposed to famine during the first 1000 days of life were associated with consistently lower levels of keystone taxa index in the three cohorts (pooled beta - 0.29, 95% CI - 0.43, - 0.15). Per 1-standard deviation increment in the keystone taxa index was associated with a 13% lower risk of type 2 diabetes (pooled odds ratio 0.87, 95% CI 0.80, 0.93), with consistent results across three individual cohorts. CONCLUSIONS: These findings reveal a potential role of the gut microbiota in the developmental origins of health and disease (DOHaD) hypothesis, deepening our understanding about the etiology of type 2 diabetes.

Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health.

OBJECTIVE: The human gut fungal community, known as the mycobiome, plays a fundamental role in the gut ecosystem and health. Here we aimed to investigate the determinants and long-term stability of gut mycobiome among middle-aged and elderly adults. We further explored the interplay between gut fungi and bacteria on metabolic health. DESIGN: The present study included 1244 participants from the Guangzhou Nutrition and Health Study. We characterised the long-term stability and determinants of the human gut mycobiome, especially long-term habitual dietary consumption. The comprehensive multiomics analyses were performed to investigate the ecological links between gut bacteria, fungi and faecal metabolome. Finally, we examined whether the interaction between gut bacteria and fungi could modulate the metabolic risk. RESULTS: The gut fungal composition was temporally stable and mainly determined by age, long-term habitual diet and host physiological states. Specifically, compared with middle-aged individuals, Blastobotrys and Agaricomycetes spp were depleted, while Malassezia was enriched in the elderly. Dairy consumption was positively associated with Saccharomyces but inversely associated with Candida. Notably, Saccharomycetales spp interacted with gut bacterial diversity to influence insulin resistance. Bidirectional mediation analyses indicated that bacterial function or faecal histidine might causally mediate an impact of Pichia on blood cholesterol. CONCLUSION: We depict the sociodemographic and dietary determinants of human gut mycobiome in middle-aged and elderly individuals, and further reveal that the gut mycobiome may be closely associated with the host metabolic health through regulating gut bacterial functions and metabolites.

Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults.

AIMS/HYPOTHESIS: The gut microbiome is mainly shaped by diet, and varies across geographical regions. Little is known about the longitudinal association of gut microbiota with glycaemic control. We aimed to identify gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and examined the cross-sectional association of dietary or lifestyle factors with the identified gut microbiota. METHODS: The China Health and Nutrition Survey is a population-based longitudinal cohort covering 15 provinces/megacities across China. Of the participants in that study, 2772 diabetes-free participants with a gut microbiota profile based on 16S rRNA analysis were included in the present study (age 50.8 ± 12.7 years, mean ± SD). Using a multivariable-adjusted linear mixed-effects model, we examined the prospective association of gut microbiota with glycaemic traits (fasting glucose, fasting insulin, HbA1c and HOMA-IR). We constructed a healthy microbiome index (HMI), and used Poisson regression to examine the relationship between the HMI and incident type 2 diabetes. We evaluated the association of dietary or lifestyle factors with the glycaemic trait-related gut microbiota using a multivariable-adjusted linear regression model. RESULTS: After follow-up for 3 years, 123 incident type 2 diabetes cases were identified. We identified 25 gut microbial genera positively or inversely associated with glycaemic traits. The newly created HMI (per SD unit) was inversely associated with incident type 2 diabetes (risk ratio 0.69, 95% CI 0.58, 0.84). Furthermore, we found that several microbial genera that were favourable for the glycaemic trait were consistently associated with healthy dietary habits (higher consumption of vegetable, fruit, fish and nuts). CONCLUSIONS/INTERPRETATION: Our results revealed multiple gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and highlighted the potential of gut microbiota-based diagnosis or therapy for type 2 diabetes. DATA AVAILABILITY: The code for data analysis associated with the current study is available at https://github.com/wenutrition/Microbiota-T2D-CHNS.

Gut microbiota signatures of long-term and short-term plant-based dietary pattern and cardiometabolic health: a prospective cohort study.

BACKGROUND: The interplay among the plant-based dietary pattern, gut microbiota, and cardiometabolic health is still unclear, and evidence from large prospective cohorts is rare. We aimed to examine the association of long-term and short-term plant-based dietary patterns with gut microbiota and to assess the prospective association of the identified microbial features with cardiometabolic biomarkers. METHODS: Using a population-based prospective cohort study: the China Health and Nutrition Survey, we included 3096 participants from 15 provinces/megacities across China. We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The average PDIs were calculated using repeat food frequency questionnaires collected in 2011 and 2015 to represent a long-term dietary pattern. Short-term dietary pattern was estimated using 3-day 24-h dietary recalls collected in 2015. Fecal samples were collected in 2015 and measured using 16S rRNA sequencing. We investigated the association of long-term and short-term plant-based dietary patterns with gut microbial diversity, taxonomies, and functional pathways using linear mixed models. Furthermore, we assessed the prospective associations between the identified gut microbiome signatures and cardiometabolic biomarkers (measured in 2018) using linear regression. RESULTS: We found a significant association of short-term hPDI with microbial alpha-diversity. Both long-term and short-term plant-based diet indices were correlated with microbial overall structure, whereas long-term estimates explained more variance. Long-term and short-term PDIs were differently associated with microbial taxonomic composition, yet only microbes related to long-term estimates showed association with future cardiometabolic biomarkers. Higher long-term PDI was associated with the lower relative abundance of Peptostreptococcus, while this microbe was positively correlated with the high-sensitivity C-reactive protein and inversely associated with high-density lipoprotein cholesterol. CONCLUSIONS: We found shared and distinct gut microbial signatures of long-term and short-term plant-based dietary patterns. The identified microbial genera may provide insights into the protective role of long-term plant-based dietary pattern for cardiometabolic health, and replication in large independent cohorts is needed.

Circulating vitamin C concentration and risk of cancers: a Mendelian randomization study.

BACKGROUND: Circulating vitamin C concentrations have been associated with several cancers in observational studies, but little is known about the causal direction of the associations. This study aims to explore the potential causal relationship between circulating vitamin C and risk of five most common cancers in Europe. METHODS: We used summary-level data for genetic variants associated with plasma vitamin C in a large vitamin C genome-wide association study (GWAS) meta-analysis on 52,018 Europeans, and the corresponding associations with lung, breast, prostate, colon, and rectal cancer from GWAS consortia including up to 870,984 participants of European ancestry. We performed two-sample, bi-directional Mendelian randomization (MR) analyses using inverse-variance-weighted method as the primary approach, while using 6 additional methods (e.g., MR-Egger, weighted median-based, and mode-based methods) as sensitivity analysis to detect and adjust for pleiotropy. We also conducted a meta-analysis of prospective cohort studies and randomized controlled trials to examine the association of vitamin C intakes with cancer outcomes. RESULTS: The MR analysis showed no evidence of a causal association of circulating vitamin C concentration with any examined cancer. Although the odds ratio (OR) per one standard deviation increase in genetically predicted circulating vitamin C concentration was 1.34 (95% confidence interval 1.14 to 1.57) for breast cancer in the UK Biobank, this association could not be replicated in the Breast Cancer Association Consortium with an OR of 1.05 (0.94 to 1.17). Smoking initiation, as a positive control for our reverse MR analysis, showed a negative association with circulating vitamin C concentration. However, there was no strong evidence of a causal association of any examined cancer with circulating vitamin C. Sensitivity analysis using 6 different analytical approaches yielded similar results. Moreover, our MR results were consistent with the null findings from the meta-analysis exploring prospective associations of dietary or supplemental vitamin C intakes with cancer risk, except that higher dietary vitamin C intake, but not vitamin C supplement, was associated with a lower risk of lung cancer (risk ratio: 0.84, 95% confidence interval 0.71 to 0.99). CONCLUSIONS: These findings provide no evidence to support that physiological-level circulating vitamin C has a large effect on risk of the five most common cancers in European populations, but we cannot rule out very small effect sizes.

Individual Postprandial Glycemic Responses to Diet in n-of-1 Trials: Westlake N-of-1 Trials for Macronutrient Intake (WE-MACNUTR).

BACKGROUND: The role of different types and quantities of macronutrients on human health has been controversial, and the individual response to dietary macronutrient intake needs more investigation. OBJECTIVES: We aimed to use an 'n-of-1' study design to investigate the individual variability in postprandial glycemic response when eating diets with different macronutrient distributions among apparently healthy adults. METHODS: Thirty apparently healthy young Chinese adults (women, 68%) aged between 22 and 34 y, with BMI between 17.2 and 31.9 kg/m2, were provided with high-fat, low-carbohydrate (HF-LC, 60-70% fat, 15-25% carbohydrate, 15% protein, of total energy) and low-fat, high-carbohydrate (LF-HC, 10-20% fat, 65-75% carbohydrate, 15% protein) diets, for 6 d wearing continuous glucose monitoring systems, respectively, in a randomized sequence, interspersed by a 6-d wash-out period. Three cycles were conducted. The primary outcomes were the differences of maximum postprandial glucose (MPG), mean amplitude of glycemic excursions (MAGE), and AUC24 between intervention periods of LF-HC and HF-LC diets. A Bayesian model was used to predict responders with the posterior probability of any 1 of the 3 outcomes reaching a clinically meaningful difference. RESULTS: Twenty-eight participants were included in the analysis. Posterior probability of reaching a clinically meaningful difference of MPG (0.167 mmol/L), MAGE (0.072 mmol/L), and AUC24 (13.889 mmol/L·h) between LF-HC and HF-LC diets varied among participants, and those with posterior probability >80% were identified as high-carbohydrate responders (n = 9) or high-fat responders (n = 6). Analyses of the Bayesian-aggregated n-of-1 trials among all participants showed a relatively low posterior probability of reaching a clinically meaningful difference of the 3 outcomes between LF-HC and HF-LC diets. CONCLUSIONS: N-of-1 trials are feasible to characterize personal response to dietary intervention in young Chinese adults.

Dietary fruit and vegetable intake, gut microbiota, and type 2 diabetes: results from two large human cohort studies.

BACKGROUND: Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk. METHODS: This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008-2013). Fecal samples were collected at follow-up (2015-2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants. RESULTS: In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p < 0.001) and inversely associated with T2D risk (odds ratio (OR) 0.83, 95%CI 0.71-0.97). The FMI-fruit association (p = 0.003) and the FMI-T2D association (OR 0.90, 95%CI 0.84-0.97) were both successfully replicated in the independent cohort. The FMI-positive associated metabolite sebacic acid was inversely associated with T2D risk (OR 0.67, 95%CI 0.51-0.86). The FMI-negative associated metabolites cholic acid (OR 1.35, 95%CI 1.13-1.62), 3-dehydrocholic acid (OR 1.30, 95%CI 1.09-1.54), oleylcarnitine (OR 1.77, 95%CI 1.45-2.20), linoleylcarnitine (OR 1.66, 95%CI 1.37-2.05), palmitoylcarnitine (OR 1.62, 95%CI 1.33-2.02), and 2-hydroglutaric acid (OR 1.47, 95%CI 1.25-1.72) were positively associated with T2D risk. CONCLUSIONS: Higher fruit intake-associated gut microbiota and metabolic alteration were associated with a lower risk of T2D, supporting the public dietary recommendation of adopting high fruit intake for the T2D prevention.

Proteome-wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8-year prospective study.

The role of circulatory proteomics in osteoporosis is unclear. Proteome-wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography-tandem mass spectrometry (LC-MS/MS) at baseline, and the 2nd, and 3rd follow-ups (7704 person-tests) in the prospective Chinese cohorts with 9.8 follow-up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA) at follow-ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)-related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed-effects model (LMM). Meta-analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta-analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD-year increase in KDM-Proage was associated with higher risk of LS-OP (hazard ratio [HR], 1.25; 95% CI, 1.14-1.36, p = 4.96 × 10-06 ), and FN-OP (HR, 1.13; 95% CI, 1.02-1.23, p = 9.71 × 10-03 ). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.

The temporal dynamics of the gut mycobiome and its association with cardiometabolic health in a nationwide cohort of 12,641 Chinese adults.

The dynamics of the gut mycobiome and its association with cardiometabolic health remain largely unexplored. Here, we employ internal transcribed spacer (ITS) sequencing to capture the gut mycobiome composition and dynamics within a nationwide human cohort of 12,641 Chinese participants, including 1,946 participants with repeated measurements across three years. We find that the gut mycobiome is associated with cardiometabolic diseases and related biomarkers in both cross-sectional and dynamic analyses. Fungal alpha diversity indices and 19 mycobiome genera are the major contributors to the mycobiome-cardiometabolic disease link. Particularly, Saccharomyces emerges as an effect modifier of traditional risk factors in promoting type 2 diabetes risk. Further integration of multi-omics data reveals key metabolites such as γ-linolenic acid and L-valine linking the gut mycobiome to type 2 diabetes. This study advances our understanding of the potential roles of the gut mycobiome in cardiometabolic health.

Cross-Sectional and Prospective Association of Serum 25-Hydroxyvitamin D with Gut Mycobiota during Pregnancy among Women with Gestational Diabetes.

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.

Mapping multi-omics characteristics related to short-term PM2.5 trajectory and their impact on type 2 diabetes in middle-aged and elderly adults in Southern China.

The relationship between PM2.5 and metabolic diseases, including type 2 diabetes (T2D), has become increasingly prominent, but the molecular mechanism needs to be further clarified. To help understand the mechanistic association between PM2.5 exposure and human health, we investigated short-term PM2.5 exposure trajectory-related multi-omics characteristics from stool metagenome and metabolome and serum proteome and metabolome in a cohort of 3267 participants (age: 64.4 ± 5.8 years) living in Southern China. And then integrate these features to examine their relationship with T2D. We observed significant differences in overall structure in each omics and 193 individual biomarkers between the high- and low-PM2.5 groups. PM2.5-related features included the disturbance of microbes (carbohydrate metabolism-associated Bacteroides thetaiotaomicron), gut metabolites of amino acids and carbohydrates, serum biomarkers related to lipid metabolism and reducing n-3 fatty acids. The patterns of overall network relationships among the biomarkers differed between T2D and normal participants. The subnetwork membership centered on the hub nodes (fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182) related to high-PM2.5, which well predicted higher T2D prevalence and incidence and a higher level of fasting blood glucose, HbA1C, insulin, and HOMA-IR. Our findings underline crucial PM2.5-related multi-omics biomarkers linking PM2.5 exposure and T2D in humans.

Nutri-microbiome epidemiology, an emerging field to disentangle the interplay between nutrition and microbiome for human health.

Diet and nutrition have a substantial impact on the human microbiome, and interact with the microbiome, especially gut microbiome, to modulate various diseases and health status. Microbiome research has also guided the nutrition field to a more integrative direction, becoming an essential component of the rising area of precision nutrition. In this review, we provide a broad insight into the interplay among diet, nutrition, microbiome, and microbial metabolites for their roles in the human health. Among the microbiome epidemiological studies regarding the associations of diet and nutrition with microbiome and its derived metabolites, we summarize those most reliable findings and highlight evidence for the relationships between diet and disease-associated microbiome and its functional readout. Then, the latest advances of the microbiome-based precision nutrition research and multidisciplinary integration are described. Finally, we discuss several outstanding challenges and opportunities in the field of nutri-microbiome epidemiology.

Furan fatty acid metabolite CMPF is associated with lower risk of type 2 diabetes, but not chronic kidney disease: a longitudinal population-based cohort study.

BACKGROUND: Furan fatty acid metabolite 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) is a strong biomarker of fish and n-3 polyunsaturated fatty acid (PUFA) intake. The relationship of CMPF with human health has been controversial, especially for type 2 diabetes and chronic kidney disease. OBJECTIVE: We performed a prospective cohort study to examine the association of serum CMPF with incident type 2 diabetes and chronic kidney disease. METHODS: In the Guangzhou Nutrition and Health Study, during a median follow-up of 8.8 y, we used a multivariable-adjusted Poisson regression model to investigate the association of baseline serum CMPF with the incidence of type 2 diabetes (1470 participants and 170 incident cases) and chronic kidney disease (1436 participants and 112 incident cases). We also examined the association of serial measures of serum CMPF with glycemic and renal function biomarkers. Mediation analysis was also performed to examine the contribution of CMPF in the association between marine n-3 PUFAs and risk of type 2 diabetes or chronic kidney disease. RESULTS: Each standard deviation increase in baseline serum CMPF was associated with an 18% lower risk of type 2 diabetes (relative risk: 0.82, 95% confidence interval [CI]: 0.68, 0.99) but was not associated with chronic kidney disease (relative risk: 0.95; 95% CI: 0.77-1.16). Correlation analyses of CMPF with glycemic and renal function biomarkers showed similar results. Mediation analysis suggested that serum CMPF contributed to the inverse association between erythrocyte marine n-3 PUFAs and incident type 2 diabetes (proportion mediated 37%, P-mediation = 0.022). CONCLUSIONS: Our findings suggest that serum CMPF was associated with a lower risk of type 2 diabetes but not chronic kidney disease. This study also suggests that CMPF may be a functional metabolite underlying the protective relationship between marine n-3 PUFA intake and type 2 diabetes.

Continuous glucose monitoring-derived glycemic metrics and adverse pregnancy outcomes among women with gestational diabetes: a prospective cohort study.

Background: Continuous glucose monitoring (CGM) has shown potential in improving maternal and neonatal outcomes 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. Funding: National Key R&D Program of China, National Natural Science Foundation of China, and Westlake Laboratory of Life Sciences and Biomedicine.

Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility.

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.