Determinants of life satisfaction in older adults with diabetes in China: a national cross-sectional study

BackgroundThe life satisfaction (LS) of individuals among older adults with diabetes should not be neglected. However, current research provides limited insight into the LS of older adults with diabetes in China. Therefore, the primary objective of this study is to assess the current life satisfaction status of older adults with diabetes in China, to delve into the factors influencing it, and to identify the key factors.MethodsThis study selected 1,304 patients with diabetes from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database for analysis. A multivariate logistic regression model was used to analyze the factors influencing life satisfaction among diabetic patients, and a random forest model was further utilized to rank the importance of significant influencing factors.Results30.14% of older adults with diabetes were dissatisfied with their lives. Multivariate Logistic regression analysis shows that self-assessed health status, self-assessed economic status, depressive symptoms, exercise, living arrangements, hearing impairment, and cognitive impairment all significantly affect the life satisfaction of older adults with diabetics. The OR values for self-assessed health and self-assessed economic status are relatively high, patients with fair and poor self-assessed health was 5.03 times and 9.72 times higher risk of life dissatisfaction compared to those with good self-assessed health (fair: OR = 5.03, 95% CI: 3.46–7.31; poor: OR = 9.72, 95% CI: 6.20–15.26). The risk of feeling dissatisfied with life was 7.69 times higher in patients with poor self-assessed economic status than in those with good self-assessed economic status (OR = 7.69, 95%CI: 4.25–13.89). The random forest results showed that the order of importance from highest to lowest was self-assessed health status, self-assessed economic status, depressive symptoms, exercise, living arrangements, hearing impairment, and cognitive impairment.ConclusionOur study reveals that the current rate of life satisfaction among older adults with diabetes is significantly high. Therefore, it is essential to implement measures from multiple perspectives for effective prevention and intervention. Among these factors, priority should be given to interventions focusing on economic support and health management, as these measures may serve as crucial protective factors in enhancing the well-being of older adults with diabetes

Orchestrating inflammation: non-coding RNAs as master regulators of macrophage function in chronic obstructive pulmonary disease-an update

Chronic obstructive pulmonary disease (COPD) represents a major global health burden, characterized by dysregulated macrophage function and persistent inflammation. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs, have emerged as critical orchestrators of macrophage polarization and inflammatory responses in COPD pathogenesis. This comprehensive review synthesizes current evidence demonstrating how ncRNA-macrophage regulatory axes drive disease progression. Pro-inflammatory miRNAs promote pathological M1 polarization through NF-κB and STAT3 pathways, while protective miRNAs facilitate inflammation resolution. LncRNAs exhibit sophisticated regulatory mechanisms through transcriptional scaffolding and competitive endogenous RNA networks. Clinical studies have successfully translated these mechanistic insights, establishing diagnostic biomarkers and therapeutic targets in human COPD patients. Despite significant progress, challenges remain including methodological heterogeneity, limited understanding of integrated regulatory networks, and clinical translation barriers. Future directions emphasize precision medicine approaches through ncRNA-based diagnostics and combination therapeutics. The evidence strongly supports the therapeutic potential of targeting ncRNA-macrophage regulatory axes, offering transformative opportunities for personalized COPD management and improved patient outcomes

Depressive symptoms and its influencing factors of older people with cataracts in China: a national cross-sectional survey

BackgroundDepressive symptoms are a common complication in patients with cataracts and may exacerbate cataract symptoms. Therefore, it is important to focus on depressive symptoms and their influencing factors in older people with cataracts. The purpose of this study was to investigate the prevalence rate of depressive symptoms and influencing factors in Chinese older people with cataracts.MethodsDescriptive analyses were used to report the sociodemographic characteristics, lifestyle, health status, and depressive symptoms of old people with cataracts in China. The chi-square test was used to compare differences between subjects with different demographic characteristics. Binary logistic regression was used to analyze the factors that influenced the depressive symptoms of cataract patients. Meanwhile, a random forest model was developed in this study to rank the importance of the influencing factors.ResultsThree hundred and six (25.27%) of 1,211 cataract patients included in this study suffered from depressive symptoms. Logistic regression analysis suggested that poor economic situation (AOR = 3.162, 95%CI: 1.719–5.817), social participation (AOR = 1.530, 95%CI: 1.053–2.222), having hearing disorder (AOR = 1.445, 95%CI: 1.040–2.008), poor self-reported health status (AOR = 2.646, 95%CI: 1.705–4.106), poor life satisfaction (AOR = 3.586, 95%CI: 1.652–7.784) were risk factors for depressive symptoms in cataract patients and consumption of fresh fruits (AOR = 0.587, 95%CI: 0.369–0.933) was a protective factor for depressive symptoms in cataract patients. The results of the random forest showed that self-reported health status was the most important factor influencing depressive symptoms in cataract patients. The other factors, in order of importance, were life satisfaction, economic situation, fruits, hearing disorder, and social participation.ConclusionThe results suggested that the development of depressive symptoms in cataract patients was influenced by various factors. Medical staff should monitor these influencing factors more closely when treating and caring for patients with cataracts

Neuroprotective effects of bavachalcone in a mouse model of Parkinson’s disease: linking the gut-brain axis and systemic metabolism

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction and dopaminergic neuronal loss. Emerging evidence suggests that gut microbiota dysbiosis and systemic metabolic disturbances contribute to the pathogenesis of PD. This study aimed to investigate the neuroprotective effects of bavachalcone, a prenylated chalcone isolated from Psoralea corylifolia, in an MPTP-induced mouse model of PD, with a particular focus on its effects on motor function, inflammation, gut microbiota, and serum metabolism.MethodsMale C57BL/6 mice were divided into Control, MPTP, Bavac-L (low-dose bavachalcone), and Bavac-H (high-dose bavachalcone) groups. Bavachalcone was administered by gavage, followed by MPTP injection to induce PD. Behavioral assessments (open field test, pole test, and rotarod test), western blotting, immunohistochemistry, immunofluorescence, 16S rDNA sequencing of fecal microbiota, and untargeted metabolomics of serum were performed to evaluate the effects of bavachalcone.ResultsBavachalcone significantly alleviated MPTP-induced motor impairment, preserved dopaminergic neurons in the substantia nigra and striatum, and reduced systemic inflammation and glial activation. Gut microbiota analysis showed that bavachalcone improved microbial richness and diversity, enriched beneficial genera, such as Allobaculum, and suppressed harmful taxa, such as Ligilactobacillus and Helicobacter. Metabolomic profiling revealed that bavachalcone modulated pathways, including pyruvate metabolism, folate biosynthesis, and phenylalanine metabolism.ConclusionBavachalcone exerts neuroprotective effects in mice with PD by improving motor function, preserving dopaminergic neurons, reducing inflammation, modulating gut microbiota composition, and remodeling systemic metabolism. These findings highlight bavachalcone as a promising therapeutic candidate for PD

Histone H3 proline 16 hydroxylation regulates mammalian gene expression

Histone post-translational modifications (PTMs) are important forregulating various DNA-templated processes. Here, we report theexistence of a histone PTM in mammalian cells, namely histone H3 withhydroxylation of proline at residue 16 (H3P16oh), which is catalyzed by theproline hydroxylase EGLN2. We show that H3P16oh enhances direct bindingof KDM5A to its substrate, histone H3 with trimethylation at the fourthlysine residue (H3K4me3), resulting in enhanced chromatin recruitmentof KDM5A and a corresponding decrease of H3K4me3 at target genes.Genome- and transcriptome-wide analyses show that the EGLN2–KDM5Aaxis regulates target gene expression in mammalian cells. Specifically, ourdata demonstrate repression of the WNT pathway negative regulator DKK1through the EGLN2-H3P16oh-KDM5A pathway to promote WNT/β-cateninsignaling in triple-negative breast cancer (TNBC). This study characterizesa regulatory mark in the histone code and reveals a role for H3P16oh inregulating mammalian gene expressio

Discrete-time survival models with long-term survivors

Discrete-time survival data typically possess three features: discreteness, ties, and concomitant information, which require appropriate discrete-time models to analyze. In this paper, we first review some existing discrete-time survival models and then extend them to discrete-time cure survival models, which account for the presence of long-term survivors (cured individuals). The maximum likelihood estimation as well as approximate partial likelihood approaches are used to estimate the model parameters. Simulation results are shown to support the suitability of such models for discrete-time survival data with long-term survivors. An example of applications on a set of bladder tumor recurrence data is also presented.21 page(s

Semiparametric estimation in transformation models with cure fraction

Semiparametric transformation model has been extensively investigated in the literature. The model, however, has little dealt with survival data with cure fraction. In this article, we consider a class of semi-parametric transformation models, where an unknown transformation of the survival times with cure fraction is assumed to be linearly related to the covariates and the error distributions are parametrically specified by an extreme value distribution with unknown parameters. Estimators for the coefficients of covariates are obtained from pseudo Z-estimator procedures allowing censored observations. We show that the estimators are consistent and asymptotically normal. The bootstrap estimation of the variances of the estimators is also investigated.18 page(s

Sufficient dimension reduction on marginal regression for gaps of recurrent events

A semiparametric linear transformation of gap time is proposed to model recurrent event data with high-dimensional covariates and informative censoring. It is derived from a proportional hazards model for the conditional intensity function of a renewal process. To overcome the difficulty arising from high-dimensional covariates, we develop a modified sliced regression for censored data and use a sufficient dimension reduction procedure to transform them to a lower dimensional space. Simulation studies are performed to confirm and evaluate the theoretical findings, and to compare the proposed method with existing methods in the literature. An example of application on a set of medical data is demonstrated as well. The proposed model together with the dimension reduction method offers an effective alternative for the analysis of recurrent event with high-dimensional covariates and informative censoring.16 page(s

Sufficient dimension reduction on the mean and rate functions of recurrent events

The counting process with a Cox-type intensity function has been extensively applied to analyze recurrent event data, which assume that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative or additive effects on the mean and rate functions of the counting process. The existing statistical inference, however, often encounters difficulties due to high-dimensional covariates, such as in gene expression and single nucleotide polymorphism data that have revolutionized our understanding of cancer recurrence and other diseases. In this paper, a technique of sufficient dimension reduction is applied to the mean and rate function for the number of occurrences of events over time. A two-step procedure is proposed to estimate the model components: first, a nonparametric estimator is proposed for the baseline, and then the basis of the central subspace and its dimension are estimated through a modified slicing inverse regression. On the basis of the estimated structural dimension and on the basis of the central subspace, we can estimate the regression function by using the local linear regression. A simulation is performed to confirm and assess the theoretical findings, and an application is demonstrated on a set of chronic granulomatous disease data.17 page(s

Applying copula models to individual claim loss reserving methods

The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Recently, individual claim loss models have attracted a great deal of interest in the actuarial literature, which overcome some shortcomings of aggregated claim loss models. The dependence of the event times with the delays is a crucial issue for estimating the claim loss reserving. In this article, we propose to use semi-competing risks copula and semi-survival copula models to fit the dependence structure of the event times with delays in the individual claim loss model. A nonstandard two-step procedure is applied to our setting in which the associate parameter and one margin are estimated based on an ad hoc estimator of the other margin. The asymptotic properties of the estimators are established as well. A simulation study is carried out to evaluate the performance of the proposed methods.10 page(s