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Abstract Background Numerous reports indicate that both obesity and type 2 diabetes mellitus (T2DM) are factors associated with cognitive impairment (CI). The objective was to assess the relationship between abdominal obesity as measured by waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and CI in middle-aged and elderly patients with T2DM. Methods A cross-sectional study was conducted, in which a total of 1154 patients with T2DM aged ≥ 40 years were included. WHRadjBMI was calculated based on anthropometric measurements and CI was assessed utilizing the Montreal Cognitive Assessment (MoCA). Participants were divided into CI group (n = 509) and normal cognition group (n = 645). Correlation analysis and binary logistic regression were used to explore the relationship between obesity-related indicators including WHRadjBMI, BMI as well as waist circumference (WC) and CI. Meanwhile, the predictive power of these indicators for CI was estimated by receiver operating characteristic (ROC) curves. Results WHRadjBMI was positively correlated with MoCA scores, independent of sex. The Area Under the Curve (AUC) for WHRadjBMI, BMI and WC were 0.639, 0.521 and 0.533 respectively, and WHRadjBMI had the highest predictive power for CI. Whether or not covariates were adjusted, one-SD increase in WHRadjBMI was significantly related to an increased risk of CI with an adjusted OR of 1.451 (95% CI: 1.261–1.671). After multivariate adjustment, the risk of CI increased with rising WHRadjBMI quartiles (Q4 vs. Q1 OR: 2.980, 95%CI: 2.032–4.371, P for trend

BackgroundThe results of current studies on metabolic-dysfunction associated steatotic liver disease (MASLD)-related diseases, cognition and dementia are inconsistent. This study aimed to elucidate the effects of MASLD-related diseases on cognition and dementia.MethodsBy using single-nucleotide polymorphisms (SNPs) associated with different traits of NAFLD (chronically elevated serum alanine aminotransferase levels [cALT], imaging-accessed and biopsy-proven NAFLD), metabolic dysfunction-associated steatohepatitis, and liver fibrosis and cirrhosis, we employed three methods of mendelian randomization (MR) analysis (inverse-variance weighted [IVW], weighted median, and MR-Egger) to determine the causal relationships between MASLD-related diseases and cognition and dementia. We used Cochran's Q test to examine the heterogeneity, and MR-PRESSO was used to identify outliers (NbDistribution = 10000). The horizontal pleiotropy was evaluated using the MR-Egger intercept test. A leave-one-out analysis was used to assess the impact of individual SNP on the overall MR results. We also repeated the MR analysis after excluding SNPs associated with confounding factors.ResultsThe results of MR analysis suggested positive causal associations between MASLD confirmed by liver biopsy (p of IVW = 0.020, OR = 1.660, 95%CI = 1.082-2.546) and liver fibrosis and cirrhosis (p of IVW = 0.009, OR = 1.849, 95%CI = 1.169-2.922) with vascular dementia (VD). However, there was no evidence of a causal link between MASLD-related diseases and cognitive performance and other types of dementia (any dementia, Alzheimer's disease, dementia with lewy bodies, and frontotemporal dementia). Sensitivity tests supported the robustness of the results.ConclusionsThis two-sample MR analysis suggests that genetically predicted MASLD and liver fibrosis and cirrhosis may increase the VD risk. Nonetheless, the causal effects of NAFLD-related diseases on VD need more in-depth research.

Objective The aim of this study was to investigate risk factors for cognitive impairment (CI) and explore the relationship between obesity and cognition in hospitalised middle-aged patients with type 2 diabetes (T2DM).Methods Subjects were divided into normal cognitive function (NCF) (n=320) and CI (n=204) groups based on the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The risk factors for CI were determined by logistic regression analysis and generalised linear modelling. The associations between obesity parameters (body mass index (BMI) and waist circumference (WC)) and cognitive ability were studied with the use of linear regression analysis, piecewise regression modelling and interaction analysis. The receiver operating characteristic curve analysis was used to examine the diagnostic value of influencing factors for ccResults The prevalence of CI was 38.9% in hospitalised middle-aged T2DM patients (median age, 58 years). Age, WC, hypoglycaemic episode within past 3 months and cerebrovascular disease (CVD) were identified as independent risk factors for CI, while the independent protective factors were education, diabetic dietary pattern, overweight and obesity. BMI was a protective factor for the MoCA score within a certain range, whereas WC was a risk factor for the MMSE and MoCA scores. The area under the curve for the combination of BMI and WC was 0.754 (p

Abstract Background The purpose of the study was to compare the efficacy of two novel obesity indices, lipid accumulation product (LAP) and visceral adiposity index (VAI), with traditional obesity indices in predicting early-onset type 2 diabetes (T2DM). Methods In this cross-sectional study, a total of 744 participants, including 605 patients newly diagnosed with T2DM and 139 non-diabetic control subjects, were enrolled from a tertiary care hospital in Tianjin, China. Participants with T2DM were divided into two groups based on their age at diagnosis, namely early-onset T2DM (age less than 40 years, n = 154) and late-onset T2DM (age 40 years or older, n = 451). The predictive power of each obesity index was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, binary logistic regression analysis was conducted to examine the independent relationship between LAP and VAI with early-onset T2DM risk. The relationship between novel obesity indices and the age of T2DM onset was also evaluated through correlation and multiple linear regression analysis. Results In males, LAP had the highest predictive power for early-onset T2DM with an area under the ROC curve (AUC) of 0.742 (95% CI 0.684–0.799, P

TiO2 with the merits of non‐toxicity, high stability, strong redox capability, and low cost, has garnered considerable attention in the fields of renewable energy. However, the practical application is limited by the rapid recombination of photogenerated electron–hole pairs, posing a challenge to enhance electron utilization without compromising catalytic activity. Herein, S‐scheme TiO2@Co(OH)F‐Pt heterojunction through a simple hydrothermal and photo‐deposition method is constructed. The experimental tests and theoretical computation indicate that Co(OH)F possesses a smaller work function and a more negative conduction band (CB) position, significantly accelerating the separation of photogenerated charge carriers. Furthermore, the built‐in electric field, band bending between TiO2 and Co(OH)F, and the electron sink of Pt nanoparticles, facilitate the reduction of protons to hydrogen. The as‐prepared TiO2@Co(OH)F‐Pt exhibits high‐performance solar hydrogen evolution with an evolution rate of 1401 µmol h−1. The apparent quantum yield (AQY) is determined to be 22.8% at a single wavelength of 365 nm. After reacting 12 h for three cycles, no noticeable performance degradation occurs, showing good stability of the catalyst. This work provides a rational strategy for the design of heterojunction photocatalysts for driving the production of new energy and useful chemicals. [ABSTRACT FROM AUTHOR]

Single-index model is one of the most popular semiparametric models in applied econometrics. Estimation of the derivative function is often of crucial importance, as studying "marginal effects" serves as a cornerstone of microeconomics. A prerequisite for the successful application of nonparametric/semiparametric kernel estimation methods is to select smoothing parameters properly to balance the estimation squared bias and variance. Henderson et al. (2015) propose a novel method for selecting the smoothing parameters optimally for derivative function estimation. However, their method suffers from the "curse of diemnsionality" problem in a multivariate nonparametric regression model. In this article, we extend the work of Henderson et al. (2015) to estimation of the derivative function of a single-index model. Specifically, we propose a data-driven method to select smoothing parameters optimally for single-index model derivative function estimation. We also derive the asymptotic distribution of the resulting local linear estimator of the derivative function. Both simulations and empirical applications show that the proposed method works well in practice. [ABSTRACT FROM AUTHOR]