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.

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.

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.

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.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

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.

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.

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.

Gut microbiome, cognitive function and brain structure: a multi-omics integration analysis.

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 (ß, 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.

Associations of dietary diversity with the gut microbiome, fecal metabolites, and host metabolism: results from 2 prospective Chinese cohorts.

BACKGROUND: Dietary diversity is essential for human health. The gut ecosystem provides a potential link between dietary diversity, host metabolism, and health, yet this mechanism is poorly understood. OBJECTIVES: Here, we aimed to investigate the relation between dietary diversity and the gut environment as well as host metabolism from a multiomics perspective. METHODS: Two independent longitudinal Chinese cohorts (a discovery and a validation cohort) were included in the present study. Dietary diversity was evaluated with FFQs. In the discovery cohort (n = 1916), we performed shotgun metagenomic and 16S ribosomal ribonucleic acid (rRNA) sequencing to profile the gut microbiome. We used targeted metabolomics to quantify fecal and serum metabolites. The associations between dietary diversity and the microbial composition were replicated in the validation cohort (n = 1320). RESULTS: Dietary diversity was positively associated with α diversity of the gut microbiota. We identified dietary diversity-related gut environment features, including the microbial structure (ß diversity), 68 microbial genera, 18 microbial species, 8 functional pathways, and 13 fecal metabolites. We further found 332 associations of dietary diversity and related gut environment features with circulating metabolites. Both the dietary diversity and diversity-related features were inversely correlated with 4 circulating secondary bile acids. Moreover, 16 mediation associations were observed among dietary diversity, diversity-related features, and the 4 secondary bile acids. CONCLUSIONS: These results suggest that high dietary diversity is associated with the gut microbial environment. The identified key microbes and metabolites may serve as hypotheses to test for preventing metabolic diseases.

Interaction of n-3 polyunsaturated fatty acids with host CD36 genetic variant for gut microbiome and blood lipids in human cohorts.

BACKGROUND & AIMS: Previous studies suggest an interaction of CD36 genetic variant rs1527483 with n-3 polyunsaturated fatty acids (PUFAs) to modulate blood lipids. However, successful replication is lacking and the role of gut microbiome remains unclear. Here, we aimed to replicate these gene-diet interactions on blood lipids and investigate their possible associations with gut microbiome. METHODS: We evaluated the n-3 PUFA-rs1527483 interaction on blood lipids in two population-based cohorts (n = 4,786). We profiled fecal microbiome and short-chain fatty acids among 1,368 participants. The associations between n-3 PUFAs and bacterial alpha-diversity, taxonomies and short-chain fatty acids by rs1527483 genotypes were analyzed using regression models. RESULTS: CD36 rs1527483-GG carriers responded better to high n-3 PUFA exposure; higher blood HDL-C (beta (95% CI): 0.05 (0.01, 0.08) mmol/L) and lower TG (log-transformed, beta (95% CI): -0.08 (-0.14, -0.02)) were observed among participants whose n-3 PUFA exposure ranked in the top quartile comparing with those in the bottom quartile. We identified docosahexaenoic acid (DHA) as the driven individual n-3 PUFA biomarker, which showed interaction with rs1527483. Among the rs1527483-GG carriers, but not other genotype groups, DHA exposure was positively associated with bacterial Faith's phylogenetic diversity, Observed OTUs, Shannon's diversity index, Dorea, Coriobacteriales Incertae Sedis spp, and fecal propionic acid levels. Another independent longitudinal cohort validated the DHA-rs1527483 interaction on gut microbiome. The identified microbial features were correlated with blood lipids, and the host biosynthesis and metabolism pathways of bile acids and aromatic amino acids. CONCLUSIONS: The present study found that higher n-3 PUFAs were associated with improved blood lipids and gut microbial features only among rs1527483-GG carriers. These findings highlight a potential role of gut microbiome to link the CD36 genetic variant, n-3 PUFAs and blood lipids, revealing a new research direction to interpret the gene-diet interaction for cardiometabolic health.

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.

The gut microbiota-bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases.

Evidence from human cohorts indicates that chronic insomnia is associated with higher risk of cardiometabolic diseases (CMD), yet whether gut microbiota plays a role is unclear. Here, in a longitudinal cohort (n = 1809), we find that the gut microbiota-bile acid axis may link the positive association between chronic insomnia and CMD. Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 are the main genera mediating the positive association between chronic insomnia and CMD. These results are also observed in an independent cross-sectional cohort (n = 6122). The inverse associations between those gut microbial biomarkers and CMD are mediated by certain bile acids (isolithocholic acid, muro cholic acid and nor cholic acid). Habitual tea consumption is prospectively associated with the identified gut microbiota and bile acids in an opposite direction compared with chronic insomnia. Our work suggests that microbiota-bile acid axis may be a potential intervention target for reducing the impact of chronic insomnia on cardiometabolic health.

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.

Circulating Proteome and Progression of Type 2 Diabetes.

CONTEXT: Circulating proteomes may provide intervention targets for type 2 diabetes (T2D). OBJECTIVE: We aimed to identify proteomic biomarkers associated with incident T2D and assess its joint effect with dietary or lifestyle factors on the T2D risk. METHODS: We established 2 nested case-control studies for incident T2D: discovery cohort (median 6.5 years of follow-up, 285 case-control pairs) and validation cohort (median 2.8 years of follow-up, 38 case-control pairs). We integrated untargeted mass spectrometry-based proteomics and interpretable machine learning to identify T2D-related proteomic biomarkers. We constructed a protein risk score (PRS) with the identified proteomic biomarkers and used a generalized estimating equation to evaluate PRS-T2D relationship with repeated profiled proteome. We evaluated association of PRS with trajectory of glycemic traits in another non-T2D cohort (n = 376). Multiplicative interactions of dietary or lifestyle factors with PRS were evaluated using logistic regression. RESULTS: Seven proteins (SHBG, CAND1, APOF, SELL, MIA3, CFH, IGHV1-2) were retained as the proteomic biomarkers for incident T2D. PRS (per SD change) was positively associated with incident T2D across 2 cohorts, with an odds ratio 1.29 (95% CI, 1.08-1.54) and 1.84 (1.19-2.84), respectively. Participants with a higher PRS had a higher probability showing unfavored glycemic trait trajectory in the non-T2D cohort. Red meat intake and PRS showed a multiplicative interaction on T2D risk in the discovery (P = 0.003) and validation cohort (P = 0.017). CONCLUSION: This study identified proteomic biomarkers for incident T2D among the Chinese populations. The higher intake of red meat may synergistically interact with the proteomic biomarkers to exaggerate the T2D risk.

Human Gut Antibiotic Resistome and Progression of Diabetes.

The antibiotic resistance crisis underlies globally increasing failures in treating deadly bacterial infections, largely due to the selection of antibiotic resistance genes (ARG) collection, known as the resistome, in human gut microbiota. So far, little is known about the relationship between gut antibiotic resistome and host metabolic disorders such as type 2 diabetes (T2D). Here, metagenomic landscape of gut antibiotic resistome is profiled in a large multiomics human cohort (n = 1210). There is a significant overall shift in gut antibiotic resistome structure among healthy, prediabetes, and T2D groups. It is found that larger ARG diversity is associated with a higher risk of T2D. The novel diabetes ARG score is positively associated with glycemic traits. Longitudinal validation analysis confirms that the ARG score is associated with T2D progression, characterized by the change of insulin resistance. Collectively, the data describe the profiles of gut antibiotic resistome and support its close relationship with T2D progression.

Dynamics of the Gut Bacteria and Fungi Accompanying Low-Carbohydrate Diet-Induced Weight Loss in Overweight and Obese Adults.

BACKGROUND: Low-carbohydrate (e.g., Atkins) dietary pattern is one of the most effective diets for weight loss, but little is known about the characteristics of the gut microbiota accompanying low-carbohydrate diets-induced weight loss. This study aims to profile dynamics of gut bacteria and fungi accompanying modified Atkins diets-induced weight loss among overweight and obese adults. METHODS: Overweight and obese adults were screened to follow a modified Atkins diet plan (30% of energy from protein, 40% from carbohydrate and 30% from fat). We longitudinally profiled dynamics of gut bacteria and fungi based on 16S rRNA and ITS rRNA gene sequencing data, respectively. RESULTS: A total of 65 participants followed the modified Atkins diets for 20-231 days, with 61 and 27 participants achieving a weight loss of at least 5 and 10%, respectively. Most of the participants who achieved 10% weight loss also experienced improvements on metabolic health. The diversity of gut bacteria and fungi increased after a weight loss of 5% and kept stable thereafter. Bacteria genera including Lachnoclostridium and Ruminococcus 2 from Firmicutes phylum were depleted, while Parabacteroides and Bacteroides from Bacteroidetes phylum were enriched after weight loss. The inter-kingdom analysis found an intensive covariation between gut fungi and bacteria, involving more than half of the weight loss-associated bacteria. CONCLUSIONS: This study confirmed the modulation of bacterial and fungal composition during weight loss with the low-carbohydrate diets and showed previously unknown links between intestinal bacteria and fungi accompanying the weight loss.

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.

Gut microbiota, inflammation, and molecular signatures of host response to infection.

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.