The metabolic signature of blood lipids: a causal inference study using twins.

Dyslipidemia is one of the cardiometabolic risk factors that influences mortality globally. Unraveling the causality between blood lipids and metabolites and the complex networks connecting lipids, metabolites, and other cardiometabolic traits can help to more accurately reflect the body's metabolic disorders and even cardiometabolic diseases. We conducted targeted metabolomics of 248 metabolites in 437 twins from the Chinese National Twin Registry. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) analysis was used for causal inference between metabolites and lipid parameters. Bidirectional mediation analysis was performed to explore the linkages between blood lipids, metabolites, and other seven cardiometabolic traits. We identified 44, 1, and 31 metabolites associated with triglyceride (TG), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), most of which were gut microbiota-derived metabolites. There were 9, 1, and 14 metabolites that showed novel associations with TG, TC, and HDL-C, respectively. ICE FALCON analysis found that TG and HDL-C may have a predicted causal effect on 23 and six metabolites, respectively, and one metabolite may have a predicted causal effect on TG. Mediation analysis discovered 14 linkages connecting blood lipids, metabolites, and other cardiometabolic traits. Our study highlights the significance of gut microbiota-derived metabolites in lipid metabolism. Most of the identified cross-sectional associations may be due to the lipids having a predicted causal effect on metabolites, but not vice versa, nor are they due to family confounding. These findings shed new light on lipid metabolism and personalized management of cardiometabolic diseases.

Five years of change in adult twins: longitudinal changes of genetic and environmental influence on epigenetic clocks.

BACKGROUND: Epigenetic clocks were known as promising biomarkers of aging, including original clocks trained by individual CpG sites and principal component (PC) clocks trained by PCs of CpG sites. The effects of genetic and environmental factors on epigenetic clocks are still unclear, especially for PC clocks. METHODS: We constructed univariate twin models in 477 same-sex twin pairs from the Chinese National Twin Registry (CNTR) to estimate the heritability of five epigenetic clocks (GrimAge, PhenoAge, DunedinPACE, PCGrimAge, and PCPhenoAge). Besides, we investigated the longitudinal changes of genetic and environmental influences on epigenetic clocks across 5 years in 134 same-sex twin pairs. RESULTS: Heritability of epigenetic clocks ranged from 0.45 to 0.70, and those for PC clocks were higher than those for original clocks. For five epigenetic clocks, the longitudinal stability was moderate to high and was largely due to genetic effects. The genetic correlations between baseline and follow-up epigenetic clocks were moderate to high. Special unique environmental factors emerged both at baseline and at follow-up. PC clocks showed higher longitudinal stability and unique environmental correlations than original clocks. CONCLUSIONS: For five epigenetic clocks, they have the potential to identify aging interventions. High longitudinal stability is mainly due to genetic factors, and changes of epigenetic clocks over time are primarily due to changes in unique environmental factors. Given the disparities in genetic and environmental factors as well as longitudinal stability between PC and original clocks, the results of studies with original clocks need to be further verified with PC clocks.

A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision.

Granular materials are widespread in nature and human production, and their macro-mechanical behavior is significantly affected by granule movement. The development of computer vision has brought some new ideas for measuring the numerical information (including the amount of translation, the rotation angle, velocity, acceleration, etc.) of dynamic granular materials. In this paper, we propose a numerical measurement method for dynamic granular materials based on computer vision. Firstly, an improved video instance segmentation (VIS) network is introduced to perform end-to-end multi-task learning, and its temporal feature fusion module and tracking head with long-sequence external memory can improve the problems of poor video data quality and high similarity in appearance of granular materials, respectively. Secondly, the numerical information can be extracted through a series of post-processing steps. Finally, the effectiveness of the measurement method is verified by comparing the numerical measurement results with the real values. The experimental results indicate that our improved VIS obtains an average precision (AP) of 76.6, the relative errors and standard deviations are maintained at a low level, and this method can effectively be used to measure the numerical information of dynamic granular materials. This study provides an intelligent proposal for the task of measuring numerical information of dynamic granular materials, which is of great significance for studying the spatial distribution, motion mode and macro-mechanical behavior of granular materials.