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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.
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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.
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Biomarcadores , Automonitorización de la Glucosa Sanguínea , Glucemia , Valor Predictivo de las Pruebas , Índice de Severidad de la Enfermedad , Humanos , Estudios Transversales , Masculino , Persona de Mediana Edad , Femenino , Glucemia/metabolismo , China/epidemiología , Anciano , Factores de Tiempo , Automonitorización de la Glucosa Sanguínea/instrumentación , Biomarcadores/sangre , Factores de Riesgo , Enfermedad del Hígado Graso no Alcohólico/sangre , Enfermedad del Hígado Graso no Alcohólico/epidemiología , Enfermedad del Hígado Graso no Alcohólico/diagnóstico , Factores de Edad , Medición de Riesgo , Aprendizaje Automático , Hígado Graso/sangre , Hígado Graso/diagnóstico , Hígado Graso/epidemiología , Monitoreo Continuo de Glucosa , Pueblos del Este de AsiaRESUMEN
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%, host genetics 3.23%, and sociodemographic factors 5.95%. Specifically, GMM elucidated 5.5%-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 interval [CI]: 53%-95%) and 19% (95% CI: 11%-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 influence of GMM on SM profiles 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. FUNDING: This study was jointly supported by the National Natural Science Foundation of China, Key Research and Development Program of Guangzhou, 5010 Program for Clinical Research of Sun Yat-sen University, and the 'Pioneer' and 'Leading goose' R&D Program of Zhejiang.
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Microbioma Gastrointestinal , Síndrome Metabólico , Metaboloma , Metabolómica , Humanos , Masculino , Femenino , Anciano , Metabolómica/métodos , Síndrome Metabólico/sangre , Síndrome Metabólico/epidemiología , Proteómica/métodos , Metagenómica/métodos , Persona de Mediana Edad , Biomarcadores/sangre , Heces/microbiología , MultiómicaRESUMEN
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.
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Microbioma Gastrointestinal , Micobioma , Humanos , Femenino , Embarazo , Microbioma Gastrointestinal/fisiología , Adulto , Estudios Prospectivos , China , Metaboloma , Hongos/aislamiento & purificación , Recién NacidoRESUMEN
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.
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Diabetes Gestacional , Microbioma Gastrointestinal , Vitamina D , Humanos , Femenino , Diabetes Gestacional/microbiología , Diabetes Gestacional/sangre , Embarazo , Vitamina D/sangre , Vitamina D/análogos & derivados , Estudios Transversales , Microbioma Gastrointestinal/fisiología , Adulto , Estudios Prospectivos , Glucemia/metabolismoRESUMEN
Background: Evidence is insufficient to establish a longitudinal association between combined trajectories of body mass index (BMI) and waist circumference (WC) and dyslipidemia. Our study aimed to explore the association between multi-trajectories of BMI and WC and incident dyslipidemia and identify microbiota and metabolite signatures of these trajectories. Methods: Stratified by sex, we used a group-based trajectory modeling approach to identify distinct multi-trajectories of BMI and WC among 10,678 participants from the China Health and Nutrition Survey over a 24-year period. For each sex, we examined the associations between these multi-trajectories (1991-2015) and the onset dyslipidemia (2018) using multivariable logistic regression adjusting for sociodemographic and lifestyles factors. We characterized the gut microbial composition and performed LASSO and logistic regression to identify gut microbial signatures associated with these multi-trajectories in males and females, respectively. Results: We identified four multi-trajectories of BMI and WC among both males and females: Normal (Group 1), BMI&WC normal increasing (Group 2), BMI&WC overweight increasing (Group 3), and BMI&WC obesity increasing (Group 4). Among males, Group 2 (OR: 2.10, 95% CI: 1.28-3.46), Group 3 (OR: 2.69, 95% CI: 1.56-4.63) and Group 4 (OR: 3.56, 95% CI: 1.85-6.83) had higher odds of developing dyslipidemia. However, among females, only those in Group 2 (OR: 1.54, 95% CI: 1.03-2.30) were more likely to develop dyslipidemia. In males, compared with Group 1, we observed lower alpha-diversity within Groups 2,3, and 4, and significant beta-diversity differences within Groups 3 and 4 (p 0.001). We also identified 3, 8, and 4 characteristic bacterial genera in male Groups 2, 3 and 4, and 2 genera in female Group 2. A total of 23, 25 and 10 differential metabolites were significantly associated with the above genera, except for Group 2 in males. Conclusions: The ascending combined trajectories of BMI and WC are associated with a higher risk of dyslipidemia, even with normal baseline levels, especially in males. Shared and unique gut microbial and metabolic signatures among these high-risk trajectories could enhance our understanding of the mechanisms connecting obesity to dyslipidemia.
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The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.
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Background: The escalating prevalence of hyperuricemia is emerging as a significant public health concern. The association between dietary lignans and hyperuricemia is yet to be fully elucidated. Our study aims to evaluate the relationships between dietary lignan intake and hyperuricemia among middle-aged and elderly Chinese individuals, with an additional focus on investigating the underlying mechanisms. Methods: Dietary lignan intake was measured using a validated Food Frequency Questionnaire in 3801 participants at the baseline. Among them, 2552 participants were included in the longitudinal study with a median follow-up of 10.5 years. The gut microbiota was analyzed by shotgun metagenome sequencing in 1789 participants, and the targeted fecal metabolome was determined in 987 participants using UPLC-MS/MS at the midpoint of follow-up. Results: The multivariable-adjusted HRs (95% CIs) for hyperuricemia incidence in the highest quartile (vs. the lowest quartile) of dietary intake of total lignans, matairesinol, pinoresinol, and secoisolariciresinol were 0.93 (0.78-1.10), 0.77 (0.66-0.90), 0.83 (0.70-0.97), and 0.85 (0.73-1.00), respectively. The gut microbial and fecal metabolic compositions were significantly different across the dietary lignan groups and the hyperuricemia groups. The beneficial associations between dietary lignans and hyperuricemia might be mediated by several gut microbes (e.g., Fusobacterium mortiferum and Blautia sp. CAG-257) and the downstream bile acid products (e.g., NorCA, glycochenodeoxycholic acid, and glycoursodeoxycholic acid). Conclusion: We found that dietary lignans were inversely associated with hyperuricemia incidence, and the gut microbiota-bile acid axis might mediate this association. Our findings provide new perspectives on precise therapeutic targets and underlying mechanisms for conditions associated with elevated uric acid.
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Ácidos y Sales Biliares , Microbioma Gastrointestinal , Hiperuricemia , Lignanos , Microbioma Gastrointestinal/efectos de los fármacos , Humanos , Lignanos/administración & dosificación , Persona de Mediana Edad , Masculino , Femenino , Estudios Prospectivos , Anciano , Ácidos y Sales Biliares/metabolismo , Estudios Longitudinales , Heces/microbiología , Dieta , Bacterias/clasificación , Bacterias/genética , Bacterias/aislamiento & purificación , Bacterias/metabolismo , China , AdultoAsunto(s)
Encéfalo , Proteoma , Proteoma/genética , Encéfalo/metabolismo , Animales , Mapeo Cromosómico , Humanos , RatonesRESUMEN
BACKGROUND: Epidemiological evidence suggests that a potential association between dietary protein intake and cardiovascular disease (CVD) may depend on the protein source, that is, plant- or animal-derived, but past research was limited and inconclusive. OBJECTIVES: To evaluate the association of dietary plant- or animal-derived protein consumption with risk of CVD, and its components ischemic heart disease (IHD) and stroke. METHODS: This analysis in the European Prospective Investigation into Cancer and Nutrition (EPIC)-CVD case-cohort study included 16,244 incident CVD cases (10,784 IHD and 6423 stroke cases) and 15,141 subcohort members from 7 European countries. We investigated the association of estimated dietary protein intake with CVD, IHD, and stroke (total, fatal, and nonfatal) using multivariable-adjusted Prentice-weighted Cox regression. We estimated isocaloric substitutions of replacing fats and carbohydrates with plant- or animal-derived protein and replacing food-specific animal protein with plant protein. Multiplicative interactions between dietary protein and prespecified variables were tested. RESULTS: Neither plant- nor animal-derived protein intake was associated with incident CVD, IHD, or stroke in adjusted analyses without or with macronutrient-specified substitution analyses. Higher plant-derived protein intake was associated with 22% lower total stroke incidence among never smokers [HR 0.78, 95% confidence intervals (CI): 0.62, 0.99], but not among current smokers (HR 1.08, 95% CI: 0.83, 1.40, P-interaction = 0.004). Moreover, higher plant-derived protein (per 3% total energy) when replacing red meat protein (HR 0.52, 95% CI: 0.31, 0.88), processed meat protein (HR 0.39, 95% CI: 0.17, 0.90), and dairy protein (HR 0.54, 95% CI: 0.30, 0.98) was associated with lower incidence of fatal stroke. CONCLUSION: Plant- or animal-derived protein intake was not associated with overall CVD. However, the association of plant-derived protein consumption with lower total stroke incidence among nonsmokers, and with lower incidence of fatal stroke highlights the importance of investigating CVD subtypes and potential interactions. These observations warrant further investigation in diverse populations with varying macronutrient intakes and dietary patterns.
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Enfermedades Cardiovasculares , Humanos , Masculino , Femenino , Persona de Mediana Edad , Enfermedades Cardiovasculares/epidemiología , Europa (Continente)/epidemiología , Estudios Prospectivos , Anciano , Proteínas de Vegetales Comestibles/administración & dosificación , Proteínas Dietéticas Animales/administración & dosificación , Incidencia , Accidente Cerebrovascular/epidemiología , Estudios de Cohortes , Adulto , Factores de Riesgo , Proteínas en la Dieta/administración & dosificación , Dieta , Estudios de Casos y ControlesRESUMEN
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.
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Microbioma Gastrointestinal , Microbiota , Enfermedad del Hígado Graso no Alcohólico , Persona de Mediana Edad , Humanos , Animales , Ratones , Anciano , Enfermedad del Hígado Graso no Alcohólico/epidemiología , Enfermedad del Hígado Graso no Alcohólico/metabolismo , Hígado/metabolismo , Vida IndependienteRESUMEN
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.
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Diabetes Mellitus Tipo 2 , Adulto , Persona de Mediana Edad , Anciano , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/metabolismo , Multiómica , China/epidemiología , Biomarcadores , Material ParticuladoRESUMEN
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.
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Osteoporosis , Proteoma , Humanos , Estudios Prospectivos , Proteómica , Cromatografía Liquida , Espectrometría de Masas en Tándem , Osteoporosis/genética , EnvejecimientoRESUMEN
Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.
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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.
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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.
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Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Efectos Tardíos de la Exposición Prenatal , Inanición , Adulto , Humanos , Persona de Mediana Edad , China , Estudios de Cohortes , Diabetes Mellitus Tipo 2/complicaciones , Pueblos del Este de Asia , Hambruna , Microbiota , Inanición/complicaciones , Adolescente , Adulto Joven , Anciano , Anciano de 80 o más AñosRESUMEN
BACKGROUND: The Guangzhou Nutrition and Health Study (GNHS) aims to assess the determinants of metabolic disease in nutritional aspects, as well as other environmental and genetic factors, and explore possible biomarkers and mechanisms with multi-omics integration. METHODS: The population-based sample of adults in Guangzhou, China (baseline: 40-83 years old; n = 5118) was followed up about every 3 years. All will be tracked via on-site follow-up and health information systems. We assessed detailed information on lifestyle factors, physical activities, dietary assessments, psychological health, cognitive function, body measurements, and muscle function. Instrument tests included dual-energy X-ray absorptiometry scanning, carotid artery and liver ultrasonography evaluations, vascular endothelial function evaluation, upper-abdomen and brain magnetic resonance imaging, and 14-d real-time continuous glucose monitoring tests. We also measured multi-omics, including host genome-wide genotyping, serum metabolome and proteome, gut microbiome (16S rRNA sequencing, metagenome, and internal transcribed spacer 2 sequencing), and fecal metabolome and proteome. RESULTS: The baseline surveys were conducted from 2008 to 2015. Now, we have completed 3 waves. The 3rd and 4th follow-ups have started but have yet to end. A total of 5118 participants aged 40-83 took part in the study. The median age at baseline was approximately 59.0 years and the proportion of female participants was about 69.4%. Among all the participants, 3628 (71%) completed at least one on-site follow-up with a median duration of 9.48 years. CONCLUSION: The cohort will provide data that have been influential in establishing the role of nutrition in metabolic diseases with multi-omics.
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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 â¼20,000 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.
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Síndrome Metabólico , Humanos , Síndrome Metabólico/diagnóstico , Síndrome Metabólico/epidemiología , Pronóstico , Proteómica , Proteoma , Aprendizaje AutomáticoRESUMEN
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.
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Diabetes Mellitus Tipo 2 , Ácidos Grasos Omega-3 , Enfermedades Renales , Animales , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/etiología , Ácidos Grasos , Estudios de Cohortes , Riesgo , Estudios Prospectivos , Ácidos Grasos Insaturados , Biomarcadores , FuranosRESUMEN
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.