Mapping the gut microecological multi-omics signatures to serum metabolome and their impact on cardiometabolic health in elderly adults.

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.

Mitochondrial mechanisms in the pathogenesis of chronic inflammatory musculoskeletal disorders.

Chronic inflammatory musculoskeletal disorders characterized by prolonged muscle inflammation, resulting in enduring pain and diminished functionality, pose significant challenges for the patients. Emerging scientific evidence points to mitochondrial malfunction as a pivotal factor contributing to these ailments. Mitochondria play a critical role in powering skeletal muscle activity, but in the context of persistent inflammation, disruptions in their quantity, configuration, and performance have been well-documented. Various disturbances, encompassing alterations in mitochondrial dynamics (such as fission and fusion), calcium regulation, oxidative stress, biogenesis, and the process of mitophagy, are believed to play a central role in the progression of these disorders. Additionally, unfolded protein responses and the accumulation of fatty acids within muscle cells may adversely affect the internal milieu, impairing the equilibrium of mitochondrial functioning. The structural discrepancies between different mitochondrial subsets namely, intramyofibrillar and subsarcolemmal mitochondria likely impact their metabolic capabilities and susceptibility to inflammatory influences. The release of signals from damaged mitochondria is known to incite inflammatory responses. Intriguingly, migrasomes and extracellular vesicles serve as vehicles for intercellular transfer of mitochondria, aiding in the removal of impaired mitochondria and regulation of inflammation. Viral infections have been implicated in inducing stress on mitochondria. Prolonged dysfunction of these vital organelles sustains oxidative harm, metabolic irregularities, and heightened cytokine release, impeding the body's ability to repair tissues. This review provides a comprehensive analysis of advancements in understanding changes in the intracellular environment, mitochondrial architecture and distribution, biogenesis, dynamics, autophagy, oxidative stress, cytokines associated with mitochondria, vesicular structures, and associated membranes in the context of chronic inflammatory musculoskeletal disorders. Strategies targeting key elements regulating mitochondrial quality exhibit promise in the restoration of mitochondrial function, alleviation of inflammation, and enhancement of overall outcomes.

Gut microbiota-bile acid axis mediated the beneficial associations between dietary lignans and hyperuricemia: a prospective study.

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.

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.

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.

Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort.

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.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Multi-trajectories of body mass index, waist circumference, gut microbiota, and incident dyslipidemia: a 27-year prospective study.

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.

A metabolomics study on carcinogenesis of ground-glass nodules.

Objective: This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer. Material and Methods: Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure. Results: Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples. Conclusion: Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

Dietary intake of plant- and animal-derived protein and incident cardiovascular diseases: the pan-European EPIC-CVD case-cohort study.

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.

Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.

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.

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.

Influence of adjuvant chemotherapy on survival for patients with completely resected high-risk stage IB NSCLC.

BACKGROUND: The use of adjuvant chemotherapy (ACT) in completely resected stage IB NSCLC is still controversial. This study aims to investigate the efficacy of ACT in pathological stage IB non-small cell lung cancer (NSCLC) with high risk factors. METHODS: Patients with pT2aN0M0 stage IB NSCLC who underwent complete resection from 2013 to 2017 were retrospectively analyzed. Univariate and multivariable logistic regression analysis was used to assess potential independent risk factors associated with poor prognosis. To compare survival between patients who received ACT and those who did not. RESULTS: In univariate and multivariate analyses, adenocarcinomas with predominantly micropapillary (MIP) and solid patterns (SOL), poorly differentiated squamous cell carcinoma (SCC), number of lymph nodes dissected less than 16 and tumor size larger than 36 mm were identified as high-risk factors for recurrence. In patients with high risk factors for recurrence, ACT resulted in significantly longer DFS (HR, 0.4689, 95%CI, 1.193-3.818; p = 0.0108) and OS (HR, 0.4696, 95%CI, 0.6578-6.895; p = 0.2073), although OS failed to reach statistically significance. After propensity score matching (PSM), 67 pairs of patients were 1:1 matched in the two groups and all baseline characteristics were well balanced. The results also demonstrated that ACT was associated with improved DFS (HR, 0.4776, 95%CI, 0.9779-4.484; p = 0.0440) while OS was not significantly different (92.5% vs. 91.0%; HR, 0.6167, 95%CI, 0.1688-2.038; p = 0.7458). In patients with low-risk factors for recurrence, DFS (HR, 0.4831, 95%CI, 0.03025-7.715; p = 0.6068) and OS (HR, 0.969, 95%CI, 0.08364-11.21; p = 0.9794) was not significantly different between those who received ACT and those who did not. CONCLUSION: In patients with completely resected stage IB NSCLC, ACT can improve survival in patients with high risk for recurrence. Further large multicenter studies are needed to confirm these findings.

Model-based hypothesis tests for the causal mediation of semi-competing risks.

Analyzing the causal mediation of semi-competing risks has become important in medical research. Semi-competing risks refers to a scenario wherein an intermediate event may be censored by a primary event but not vice versa. Causal mediation analyses decompose the effect of an exposure on the primary outcome into an indirect (mediation) effect: an effect mediated through a mediator, and a direct effect: an effect not through the mediator. Here we proposed a model-based testing procedure to examine the indirect effect of the exposure on the primary event through the intermediate event. Under the counterfactual outcome framework, we defined a causal mediation effect using counting process. To assess statistical evidence for the mediation effect, we proposed two tests: an intersection-union test (IUT) and a weighted log-rank test (WLR). The test statistic was developed from a semi-parametric estimator of the mediation effect using a Cox proportional hazards model for the primary event and a series of logistic regression models for the intermediate event. We built a connection between the IUT and WLR. Asymptotic properties of the two tests were derived, and the IUT was determined to be a size [Formula: see text] test and statistically more powerful than the WLR. In numerical simulations, both the model-based IUT and WLR can properly adjust for confounding covariates, and the Type I error rates of the proposed methods are well protected, with the IUT being more powerful than the WLR. Our methods demonstrate the strongly significant effects of hepatitis B or C on the risk of liver cancer mediated through liver cirrhosis incidence in a prospective cohort study. The proposed method is also applicable to surrogate endpoint analyses in clinical trials.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments.

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.

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.