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Abstract Background Ammonia-oxidizing archaea (AOA) of the phylum Thaumarchaeota mediate the rate-limiting step of nitrification and remove the ammonia that inhibits the aerobic metabolism of methanotrophs. However, the AOA that inhabit deep-sea methane-seep surface sediments (DMS) are rarely studied. Here, we used global DMS metagenomics and metagenome-assembled genomes (MAGs) to investigate the metabolic activity, evolutionary history, and ecological contributions of AOA. Expression of AOA-specific ammonia-oxidizing gene (amoA) was examined in the sediments collected from the South China Sea (SCS) to identify their active ammonia metabolism in the DMS. Results Our analysis indicated that AOA contribute > 75% to the composition of ammonia-utilization genes within the surface layers (above 30 cm) of global DMS. The AOA-specific ammonia-oxidizing gene was actively expressed in the DMS collected from the SCS. Phylogenomic analysis of medium-/high-quality MAGs from 18 DMS-AOA indicated that they evolved from ancestors in the barren deep-sea sediment and then expanded from the DMS to shallow water forming an amoA-NP-gamma clade-affiliated lineage. Molecular dating suggests that the DMS-AOA origination coincided with the Neoproterozoic oxidation event (NOE), which occurred ~ 800 million years ago (mya), and their expansion to shallow water coincided with the Sturtian glaciation (~ 713 mya). Comparative genomic analysis suggests that DMS-AOA exhibit higher requirement of carbon source for protein synthesis with enhanced genomic capability for osmotic regulation, motility, chemotaxis, and utilization of exogenous organic compounds, suggesting it could be more heterotrophic compared with other lineages. Conclusion Our findings provide new insights into the evolutionary history of AOA within the Thaumarchaeota, highlighting their critical roles in nitrogen cycling in the global DMS ecosystems. Video Abstract

Abstract Background Available data about the effects of circulating polyunsaturated fatty acids (PUFAs) on ischemic stroke (IS) and its main risk factors remains limited and conflicting. Therefore, we conducted Mendelian randomization (MR) to assess whether genetically predicted PUFA affected IS, lipids and blood pressure (BP). Methods Genetic instruments associated with IS were derived from ISGC Consortium (n = 29,633), with lipids were derived from GLGC(n = 188,577), with BP were derived from Neale Lab(n = 337,000). The inverse-variance weighted method was the main analysis to estimate the effect of exposure on outcome. Sensitivity analyses included principal components analysis, MR-Egger, weighted median, and weighted mode. Results Per SD increases in serum α-linolenic acid (ALA) were associated with lower IS risk, with odd ratio (OR) of 0.867(0.782,0.961), arachidonic acid (AA) were associated with higher IS risk (OR: 1.053(1.014,1.094)). Likewise, Per SD increases in ALA were associated with the lower-level low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC) (β:-0.122(− 0.144, − 0.101), − 0.159(− 0.182, − 0.135), − 0.148(− 0.171, − 0.126), respectively), AA were associated with the higher-level of LDL-C, HDL-C and TC (β:0.045(0.034,0.056), 0.059(0.050,0.067), 0.055(0.046,0.063), respectively). Linoleic acid (LA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA) had little or no association with IS, lipids or BP at Bonferroni-corrected significance. Different analytic methods supported these findings. The intercept test of MR-Egger implied no pleiotropy. Conclusions High-level plasma ALA was protective for IS but AA was the opposite. LA, EPA, DHA, and DPA had no effects on IS.

Abstract Background Prevention and control of cardiometabolic conditions and cardiovascular disease (CVD) in China may contribute to sustainable CVD reduction globally, given the fact that one-fifth of the worldwide population is in China. Knowing the distribution of behavioral risk factors (e.g., smoking and physical inactivity), especially at a national level in China, would be extremely relevant to the field of public health and CVD prevention. The objectives of this study were to investigate the nationwide prevalence of obesity, smoking, heavy drinking, and physical inactivity in Chinese adults, and further explore whether cardiometabolic conditions would modify the distribution of behavioral risk factors. Methods This population-based study is based on the China Health and Retirement Longitudinal Study (2011–2012), including 17,302 adults (≥45 years, mean age 59.67 years, female 51.66%) from 25 provinces in China. Data on demographics, lifestyle factors, health status and history of diseases were collected via structured interviews and laboratory tests. Smoking, heavy drinking, obesity, and physical inactivity were defined following standard guidelines. We performed descriptive analysis and logistic regressions in this study. Results The overall prevalence of heavy drinking, obesity, current smoking, and physical inactivity among middle-aged and older adults was 7.23% (95% confidence interval 6.53—7.29%), 11.53% (10.43—12.62%), 27.46% (26.30—28.62%), and 44.06% (41.19—46.92%), respectively. The prevalence varied between rural and urban areas as well as among geographic areas, with higher prevalence in the Northern and Northeastern regions. Heavy drinking and obesity were significantly associated with incident hypertension, diabetes, and high cholesterol; while current smoking was significantly associated with incident hypertension. Compared with healthy individuals, participants who self-reported a diagnosis of hypertension, high cholesterol, or diabetes were less likely to smoke currently and drink alcohol heavily, but more likely to be physically inactive and obese. Conclusions Among Chinese middle-aged and older adults, the prevalence of behavioral risk factors varies by geographic region. Further effort is required to improve physical activity and fitness for Chinese adults, especially those with cardiometabolic conditions.

Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. Conclusions All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.