Genotype data

Microsatellite genotype of 869 Myriophyllum spicatum inviduals from 58 populations

Data_Sheet_4_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Data_Sheet_1_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Data_Sheet_3_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Mechanism and Kinetic Model for Autocatalysis in Liquid–Liquid System: Oxidation of Dibutyl Sulfide with Aqueous Hydrogen Peroxide

The oxidation of dibutyl sulfide with aqueous hydrogen peroxide as a liquid–liquid reaction was investigated. The autocatalysis, solubility of H₂O₂ in organic phase, and effects of temperature, stirring speed, initial organic DBSO concentration, and initial aqueous H₂O₂ concentration were studied. Solvent effect of dibutyl sulfoxide was proposed for liquid–liquid autocatalysis. The intrinsic reaction was considered as the determining step, and all the other steps were considered as equilibrium processes. Considering interfacial reaction and dynamic equilibrium of hydrogen peroxide between the two phases, the reaction was divided into exterior and interior stages. Exterior and interior mechanisms were proposed for the corresponding stages, and kinetic models were established. The parameters of kinetic model were estimated with the experimental data, and the activation energies of exterior and interior reaction were 30.62 and 73.50 kJ/mol. The validity of the kinetic models with estimated parameters was studied, and good agreements were observed between the experimental results and the model results

Elimination of interference from environmental policies.

Elimination of interference from environmental policies.</p

The impact of power market reform on technological progress.

The impact of power market reform on technological progress.</p

Replacement dependent variables.

The market-oriented reform of China’s power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006–2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform.</div

S1 Data -

The market-oriented reform of China’s power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006–2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform.</div