High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome.

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications in health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve comprehensive understanding of complex microbial communities together with their hosts is therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive responses states among species in Prevotella and Roseburia genus and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated the smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-word situations and promises new perspectives in the understanding of human microbiomes.

Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states.

OBJECTIVES: This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission. METHODS: We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks. RESULTS: Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse. CONCLUSION: Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

Function analysis of transcription factor OSR1 regulating osmotic stress resistance in maize.

BACKGROUND: Maize is a major cereal crop world widely, however, the yield of maize is frequently limited by dehydration and even death of plants, which resulted from osmotic stress such as drought and salinity. Dissection of molecular mechanisms controlling stress tolerance will enable plant scientists and breeders to increase crops yield by manipulating key regulatory components. METHODS: The candidate OSR1 gene was identified by map-based cloning. The expression level of OSR1 was verified by qRT-PCR and digital PCR in WT and osr1 mutant. Electrophoretic mobility shift assay, transactivation activity assay, subcellular localization, transcriptome analysis and physiological characters measurements were conducted to analyze the function of OSR1 in osmotic stress resistance in maize. RESULTS: The osr1 mutant was significantly less sensitive to osmotic stress than the WT plants and displayed stronger water-holding capacity, and the OSR1 homologous mutant in Arabidopsis showed a phenotype similar with maize osr1 mutant. Differentially expressed genes (DEGs) were identified between WT and osr1 under osmotic stress by transcriptome analysis, the expression levels of many genes, such as LEA, auxin-related factors, PPR family members, and TPR family members, changed notably, which may primarily involve in osmotic stress or promote root development. CONCLUSIONS: OSR1 may serve as a negative regulatory factor in response to osmotic stress in maize. The present study sheds new light on the molecular mechanisms of osmotic stress in maize.

Memristor-based circuit design of episodic memory neural network and its application in hurricane category prediction.

Episodic memory, as a type of long-term memory (LTM), is used to learn and store the unique personal experience. Based on the episodic memory biological mechanism, this paper proposes a bionic episodic memory memristive neural network circuit. The proposed memristive neural network circuit includes a neocortical module, a parahippocampal module and a hippocampus module. The neocortical module with the two paths structure is used to receive the sensory signal, and is also used to separate and transmit the spatial information and the non-spatial information involved in the sensory signal. The parahippocampal module is composed of the parahippocampal cortex-MEA and the perirhinal cortex-LEA, which receives the two types of information from the neocortical module respectively. As the last module, the hippocampus module receives and integrates the output information of the parahippocampal module as well as generates the corresponding episodic memory. Meanwhile, the specific scenario information with the certain temporal signal from the generated episodic memory is also extracted by the hippocampus module. The simulation results in PSPICE show that the proposed memristive neural network circuit can generate the various episodic memories and extract the specific scenario information successfully. By configuring the memristor parameters, the proposed bionic episodic memory memristive neural network circuit can be applied to the hurricane category prediction, which verifies the feasibility of this work.

Miscarriage and stillbirth in relation to risk of cardiovascular diseases: A systematic review and meta-analysis.

INTRODUCTION: The relationship between pregnancy loss and the risk of cardiovascular diseases (CVDs) remains a matter of debate. Our intention in conducting this meta-analysis was to analyze the relationship between miscarriage and stillbirth and risk of CVDs. METHODS: PubMed, Embase, and Web of Science were systematically searched up to May 30, 2023 for all relevant studies. The random-effects model was applied to estimate the pooled relative risks (RRs) and 95% confidence intervals (95% CIs). We evaluated RR estimates for the risk of CVDs with each additional miscarriage and stillbirth through generalized least squares regression. RESULTS: Twenty-three articles were incorporated into the meta-analysis. For women with a history of miscarriage, the pooled RRs for the risk of total CVDs, coronary heart disease (CHD), stroke, and total CVD deaths were 1.16 (95 % CI 1.10-1.22), 1.26 (1.12-1.41), 1.13 (1.03-1.24), and 1.20 (1.01-1.42), respectively. For women with a history of stillbirth, the pooled RRs for the risk of total CVDs, CHD, stroke, and total CVD deaths were 1.60 (1.34-1.89), 1.30 (1.12-1.50), 1.37 (1.06-1.78), and 1.95 (1.05-3.63), respectively. With each additional miscarriage, the risk increased for total CVDs (1.08, 1.04-1.13), CHD (1.08, 1.04-1.13), and stroke (1.05, 1.00-1.10). With each additional stillbirth, the risk increased for total CVDs (1.11, 1.03-1.21) and CHD (1.13, 1.07-1.19). CONCLUSION: This meta-analysis indicates that both miscarriages and stillbirths are related to a higher risk of total CVDs, CHD, stroke, and total CVD deaths. The risk of total CVDs and CHD increased with the number of miscarriages or stillbirths.

The value of an MRI-based radiomics model in predicting the survival and prognosis of patients with extrahepatic cholangiocarcinoma.

OBJECTIVES: The study aimed to establish radiomics models based on magnetic resonance imaging (MRI) multiparameter images to predict the survival and prognosis of patients with extrahepatic cholangiocarcinoma (ECC). METHODS: Seventy-eight patients with ECC confirmed by pathology were collected retrospectively. The radiomics model_a/b/c were constructed based on the 1/2/3-year survival of patients with ECC. The best texture features were selected according to postoperative survival time and ECC patient status to calculate the radiomics score (Rad-score). A cutoff value was selected, and patients were divided into high-risk and low-risk groups. RESULTS: Model_a, model_b, and model_c were used to predict 1-, 2-, and 3-year postoperative survival rates, respectively. The area under the curve values in the training and test groups were 1.000 and 0.933 for model_a, 0.909 and 0.907 for model_b, 1.000 and 0.975 for model_c, respectively. The survival prediction model based on the Rad-score showed that the postoperative mortality risk differed significantly between risk groups (p < 0.0001). CONCLUSIONS: The MRI radiomics model could be used to predict the survival and prognosis of patients with ECC.

Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging.

Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.

Association of methylation risk score with incident type 2 diabetes mellitus: A nested case-control study.

AIMS: To investigate the association of methylation risk score (MRS) and its interactions with environmental factors with type 2 diabetes mellitus (T2DM) risk. METHODS: We conducted a nested case-control study with 241 onset cases and 241 matched controls. Conditional logistic regression models were employed to identify risk CpG sites. Simple and weighted MRSs were constructed based on the methylation levels of ATP-binding cassette G1 gene, fat mass and obesity associated gene, potassium voltage-gated channel member 1 gene, and thioredoxin-interacting protein gene previously associated with T2DM to estimate the association of MRS with T2DM risk. Stratified analyses were used to investigate interactions between MRS and environmental factors. RESULTS: A total of 10 CpG loci were identified from the aforementioned genes to calculate MRS. After controlling for potential confounding factors, taking tertile 1 as reference, the odds ratios (ORs) and 95% confidence intervals (CIs) for T2DM of tertile 3 was 2.39 (1.36-4.20) for simple MRS and 2.59 (1.45-4.63) for weighted MRS. With per SD score increment in MRS, the OR (95% CI) was 1.66 (1.29-2.14) and 1.60 (1.24-2.08) for simple and weighted MRSs, respectively. J-curved associations were observed between both simple and weighted MRSs and T2DM risks. Additionally, multiplication interactions for smoking and hypertension with simple MRS on the risk of T2DM were found, similarly for smoking and obesity with weighted MRS on the risk of T2DM (all Pinteraction < .05). CONCLUSION: Elevated simple and weighted MRSs were associated with increased risk of T2DM. Environmental risk factors may influence the association between MRS and T2DM.

Trade-offs between the accumulation of cuticular wax and jasmonic acid-mediated herbivory resistance in maize.

Plants have evolved complex physical and chemical defense systems that allow them to withstand herbivory infestation. Composed of a complex mixture of very-long-chain fatty acids (VLCFAs) and their derivatives, cuticular wax constitutes the first physical line of defense against herbivores. Here, we report the function of Glossy 8 (ZmGL8), which encodes a 3-ketoacyl reductase belonging to the fatty acid elongase complex, in orchestrating wax production and jasmonic acid (JA)-mediated defenses against herbivores in maize (Zea mays). The mutation of GL8 enhanced chemical defenses by activating the JA-dependent pathway. We observed a trade-off between wax accumulation and JA levels across maize glossy mutants and 24 globally collected maize inbred lines. In addition, we demonstrated that mutants defective in cuticular wax biosynthesis in Arabidopsis thaliana and maize exhibit enhanced chemical defenses. Comprehensive transcriptomic and lipidomic analyses indicated that the gl8 mutant confers chemical resistance to herbivores by remodeling VLCFA-related lipid metabolism and subsequent JA biosynthesis and signaling. These results suggest that VLCFA-related lipid metabolism has a critical role in regulating the trade-offs between cuticular wax and JA-mediated chemical defenses.

Assisted reproductive technology induces different secondary sex ratio: parental and embryonic impacts.

BACKGROUND: Assisted reproduction technology (ART) has advanced significantly, raising concerns regarding its impact on the secondary sex ratio (SSR), which is the sex ratio at birth in offspring. This study aimed to explore factors affecting SSR in singletons, singletons from twin gestation, and twins from twin gestation within the context of ART. METHODS: A retrospective analysis was conducted on data from 8335 births involving 6,223 couples undergoing ART. Binary logistic regression assessed relationships between parental and embryonic factors and SSR in singletons and singletons from twin gestation. Multinomial logistic regression models were utilized to identify factors influencing SSR in twins from twin gestation. RESULTS: Secondary infertility (OR = 1.164, 95% CI: 1.009-1.342), advanced paternal age (OR = 1.261, 95% CI: 1.038-1.534), and blastocyst embryo transfer (OR = 1.339, 95% CI: 1.030-1.742) were associated with an increased SSR, while frozen embryo transfer (FET) showed a negative association with SSR (OR = 0.738, 95% CI: 0.597-0.912) in singletons. A longer duration of gonadotropin (Gn) usage reduced SSR in singletons (OR = 0.961, 95% CI: 0.932-0.990) and singletons from twin gestation (OR = 0.906, 95% CI: 0.838-0.980). In singletons from twin gestation, male-induced infertility (OR = 2.208, 95% CI: 1.120-4.348) and higher Gn dosage (OR = 1.250, 95% CI: 1.010-1.548) were significantly associated with an increased SSR. Women aged > 35 years and intracytoplasmic sperm injection (ICSI) were associated with lower SSR (OR = 0.539, 95% CI: 0.293-0.990 and OR = 0.331, 95% CI: 0.158-0.690, respectively). In twins from twin gestation, paternal age exceeded maternal age (OR = 0.682, 95% CI: 0.492-0.945) and higher Gn dosage (OR = 0.837, 95% CI: 0.715-0.980) were associated with a higher proportion of male twins. Cleavage stage transfer (OR = 1.754, 95% CI: 1.133-2.716) resulted in a higher percentage of boy-girl twins compared to blastocyst transfer. CONCLUSION: This study demonstrates the complex interplay of various factors in determining the SSR in ART, highlighting the importance of considering infertility type, paternal age, fertilization method, embryo transfer stage, and Gn use duration when assessing SSR. Nevertheless, further research with a large sample size is necessary to confirm and expand upon the findings of this study.

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS: We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS: Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION: The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Coupled Effect of Low Temperature and Electrolyte Immersion on the Tensile Properties of Separators in Lithium-Ion Batteries.

The performance degradation at low temperatures and frequent safety accidents have aggravated security risks and inhibited the long-term service of lithium-ion batteries (LIBs). As a key component of LIBs, the separator has a great impact on the performance and safety of the battery. In this study, tensile tests of two commercial polyolefin separators (Celgard 2325 and Celgard PE) are performed under low-temperature and immersion conditions. Four representative temperature points and dimethyl carbonate [(DMC), the common solvent in electrolytes] are selected to investigate the coupling effect on the mechanical properties of the separators. The results show that both the separators have anisotropy, but the performance of Celgard 2325 varies more significantly than that of Celgard PE along different directions. Additionally, it is found that with a decrease in the temperature, the tensile strength of the two separators increases, while the elongation decreases. Electrolyte immersion induces a softening tendency in Celgard 2325. Due to the special effect of the residual electrolyte on polyethylene fibers, Celgard PE shows the opposite result. Furthermore, the effect of low temperature is revealed by the analysis of the crystallinity and molecular structure, which can be obtained by X-ray diffraction and Raman spectroscopy, respectively. In addition, the contact angle is measured to describe the wettability variation related to low temperature. The present work provides a theoretical basis and experimental data for the application and development of separators.

Long-Term Exposure to Traffic Noise and Risk of Incident Cardiovascular Diseases: a Systematic Review and Dose-Response Meta-Analysis.

While noise pollution from transportation has become an important public health problem, the relationships between different sources of traffic noise and cardiovascular diseases (CVDs) remain inconclusive. A comprehensive meta-analysis was therefore conducted to quantitatively assess the effects of long-term exposure to road traffic, railway, and aircraft noise on CVDs and relevant subtypes. We systematically retrieved PubMed, Embase, and Web of Science for articles published before April 4, 2022. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated by the fixed- or random-effects models. In total, 23 articles were included in our meta-analysis. The risk of CVDs increased by 2% (RR 1.020, 95% CI 1.006-1.035) and 1.6% (RR 1.016, 95% CI 1.000-1.032) for every 10 dB increment of road traffic and aircraft noise. For CVD subtypes, the risk increased by 3.4% (1.034, 1.026-1.043) for stroke and 5% (1.050, 1.006-1.096) for heart failure with each 10 dB increment of road traffic noise; the risk of atrial fibrillation increased by 1.1% (1.011, 1.002-1.021) with each 10 dB increment of railway noise; and the risk increased by 1% (1.010, 1.003-1.017) for myocardial infarction, 2.7% (1.027, 1.004-1.050) for atrial fibrillation, and 2.3% (1.023, 1.016-1.030) for heart failure with each 10 dB increment in aircraft noise. Further, effects from road traffic, railway, and aircraft noise all followed positive linear trends with CVDs. Long-term exposure to traffic noise is positively related to the incidence risk of cardiovascular events, especially road traffic noise which significantly increases the risk of CVDs, stroke, and heart failure.

Prediction of angiogenesis in extrahepatic cholangiocarcinoma using MRI-based machine learning.

Purpose: Reliable noninvasive method to preoperative prediction of extrahepatic cholangiocarcinoma (eCCA) angiogenesis are needed. This study aims to develop and validate machine learning models based on magnetic resonance imaging (MRI) for predicting vascular endothelial growth factor (VEGF) expression and the microvessel density (MVD) of eCCA. Materials and methods: In this retrospective study from August 2011 to May 2020, eCCA patients with pathological confirmation were selected. Features were extracted from T1-weighted, T2-weighted, and diffusion-weighted images using the MaZda software. After reliability testing and feature screening, retained features were used to establish classification models for predicting VEGF expression and regression models for predicting MVD. The performance of both models was evaluated respectively using area under the curve (AUC) and Adjusted R-Squared (Adjusted R2). Results: The machine learning models were developed in 100 patients. A total of 900 features were extracted and 77 features with intraclass correlation coefficient (ICC) < 0.75 were eliminated. Among all the combinations of data preprocessing methods and classification algorithms, Z-score standardization + logistic regression exhibited excellent ability both in the training cohort (average AUC = 0.912) and the testing cohort (average AUC = 0.884). For regression model, Z-score standardization + stochastic gradient descent-based linear regression performed well in the training cohort (average Adjusted R2 = 0.975), and was also better than the mean model in the test cohort (average Adjusted R2 = 0.781). Conclusion: Two machine learning models based on MRI can accurately predict VEGF expression and the MVD of eCCA respectively.

High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq.

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.

Comparison of the clinical features of HIV-positive and HIV-negative hosts infected with Talaromyces marneffei: A multicenter, retrospective study.

OBJECTIVES: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is rapidly increasing. Nevertheless, there is no sufficient comprehensive report on this issue, and awareness needs to be raised among clinicians. METHODS: We analyzed the differences in the clinical data of patients who are HIV-negative and HIV-positive with Talaromyces marneffei infection (TMI) from 2018 to 2022. RESULTS: A total of 848 patients were included, among whom 104 were HIV-negative. The obvious differences between the HIV-positive and HIV-negative groups were as follows: (i) the patients who are HIV-negative were older and more likely to exhibit cough and rash, (ii) the time in days from symptom onset to diagnosis among patients who are HIV-negative was longer, (iii) the laboratory findings and radiological presentations seemed more severe in patients who are HIV-negative, (iv) differences were observed regarding the underlying conditions and co-infection pathogens, and correlation analysis showed that correlations existed for many indicators, (v) and persistent infection was more likely to occur in patients who are HIV-negative. CONCLUSION: TMI in patients who are HIV-negative differs from that in patients who are HIV-positive in many aspects, and more investigations are needed. Clinicians should be more aware of TMI in patients who are HIV-negative.

Interaction of methionine sulfoxide reductase B5 with SlMYC2 stimulates the transcription of MeJA-mediated autophagy-related genes in tomato fruit.

Methyl jasmonate (MeJA) has been shown to induce autophagy in various plant stress responses and metabolic pathways. MYC2 is involved in MeJA-mediated postharvest fruit biological metabolism, but it is unclear how it affects MeJA-induced fruit autophagy. In this study, we noticed that silencing SlMYC2 significantly reduced the increase in autophagy-related genes (SlATGs) expression induced by MeJA. SlMYC2 could also bind to the promoters of several SlATGs, including SlATG13a, SlATG13b, SlATG18a, and SlATG18h, and activate their transcript levels. Moreover, SlMsrB5, a methionine sulfoxide reductase, could interact with SlMYC2. Methionine oxidation in SlMYC2 and mimicking sulfoxidation in SlMYC2 by mutation of methionine-542 to glutamine reduced the DNA-binding ability and transcriptional activity of SlMYC2, respectively. SlMsrB5 partially repaired oxidized SlMYC2 and restored its DNA-binding ability. On the other hand, silencing SlMsrB5 inhibited the transcript levels of SlMYC2-targeted genes (SlATG13a, SlATG13b, SlATG18a, and SlATG18h). Similarly, dual-luciferase reporter (DLR) analysis revealed that SlMsrB5-SlMYC2 interaction significantly increased the ability of SlMYC2-mediated transcriptional activation of SlATG13a, SlATG13b, SlATG18a, and SlATG18h. These findings demonstrate that SlMsrB5-mediated cyclic oxidation/reduction of methionine in SlMYC2 influences SlATGs expression. Collectively, these findings reveal the mechanism of SlMYC2 in SlATGs transcriptional regulation, providing insight into the mechanism of MeJA-mediated postharvest fruit quality regulation.

All-cause mortality risk with different metabolic abdominal obesity phenotypes: the Rural Chinese Cohort Study.

We aimed to investigate the association of metabolic obesity phenotypes with all-cause mortality risk in a rural Chinese population. This prospective cohort study enrolled 15 704 Chinese adults (38·86 % men) with a median age of 51·00 (interquartile range: 41·00-60·00) at baseline (2007-2008) and followed up during 2013-2014. Obesity was defined by waist circumference (WC: ≥ 90 cm for men and ≥ 80 cm for women) or waist-to-height ratio (WHtR: ≥ 0·5). The hazard ratio (HR) and 95 % CI for the risk of all-cause mortality related to metabolic obesity phenotypes were calculated using the Cox hazards regression model. During a median follow-up of 6·01 years, 864 deaths were identified. When obesity was defined by WC, the prevalence of participants with metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO) at baseline was 12·12 %, 2·80 %, 41·93 % and 43·15 %, respectively. After adjusting for age, sex, alcohol drinking, smoking, physical activity and education, the risk of all-cause mortality was higher with both MUNO (HR = 1·20, 95 % CI 1·14, 1·26) and MUO (HR = 1·20, 95 % CI 1·13, 1·27) v. MHNO, but the risk was not statistically significant with MHO (HR = 0·99, 95 % CI 0·89, 1·10). This result remained consistent when stratified by sex. Defining obesity by WHtR gave similar results. MHO does not suggest a greater risk of all-cause mortality compared to MHNO, but participants with metabolic abnormality, with or without obesity, have a higher risk of all-cause mortality. These results should be cautiously interpreted as the representation of MHO is small.

Elevated remnant cholesterol increase 6-year type 2 diabetes mellitus onset risk.

AIM: To investigate the association of remnant cholesterol (RC) with future type 2 diabetes mellitus (T2DM) risk, and to assess the underlying impact of some recognized risk factors on it. METHODS: A total of 11468 nondiabetic adults in rural China were recruited in 2007-2008 and followed up in 2013-2014. Logistic regression was used to assess the risk of incident T2DM by quartiles of baseline RC, estimating odds ratios (ORs) and 95% confidence intervals (CIs). Association of the combinations of RC and low-density lipoprotein cholesterol (LDL-C) with T2DM risk were further evaluated. RESULTS: Multivariable adjusted OR (95% CI) for incident T2DM associated with quartile 4 versus quartile 1 of RC was 2.72 (2.05-3.62). Per 1-standard deviation (SD) increases in RC levels was associated with a 34% higher T2DM risk. However, gender modified the specific association (Pinteraction < 0.05), with the association stronger among females. When combining the low LDL-C and low RC groups as reference, individuals with RC levels ≥ 0.56 mmol/L had more than a 2-fold T2DM risk, regardless of LDL-C level. CONCLUSIONS: Elevated RC levels increase T2DM risk in rural Chinese populations. In those who cannot control their risk by lowering LDL-C levels, the goal of lipid-lowering therapy can be shifted to RC.