LncRNA LINK-A Remodels Tissue Inflammatory Microenvironments to Promote Obesity

High-fat diet (HFD)-induced obesity is a crucial risk factor for metabolic syndrome, mainly due to adipose tissue dysfunctions associated with it. However, the underlying mechanism remains unclear. This study has used genetic screening to identify an obesity-associated human lncRNA LINK-A as a critical molecule bridging the metabolic microenvironment and energy expenditure in vivo by establishing the HFD-induced obesity knock-in (KI) mouse model. Mechanistically, HFD LINK-A KI mice induce the infiltration of inflammatory factors, including IL-1β and CXCL16, through the LINK-A/HB-EGF/HIF1α feedback loop axis in a self-amplified manner, thereby promoting the adipose tissue microenvironment remodeling and adaptive thermogenesis disorder, ultimately leading to obesity and insulin resistance. Notably, LINK-A expression is positively correlated with inflammatory factor expression in individuals who are overweight. Of note, targeting LINK-A via nucleic acid drug antisense oligonucleotides (ASO) attenuate HFD-induced obesity and metabolic syndrome, pointing out LINK-A as a valuable and effective therapeutic target for treating HFD-induced obesity. Briefly, the results reveale the roles of lncRNAs (such as LINK-A) in remodeling tissue inflammatory microenvironments to promote HFD-induced obesity.

Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data

A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.

The Association Between Statin Use and Risk of Chronic Kidney Disease in Community-Dwelling Older People in Shanghai, China

Purpose

The effects of statins on renal outcomes have already been studied in patients with chronic kidney disease (CKD); however, data on the general population are limited. We evaluated the association between statin use and risk of CKD in community-dwelling older people in Shanghai, China.

Patients and Methods

This registry-based cohort study was conducted in four communities in four districts in Shanghai. Participants with an estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m² in 2016 were eligible for the study, and new-onset CKD in 2017, 2018, and 2019 was recorded. Poisson generalized linear models were conducted to examine the relationships among statin therapy, dyslipidemia, and CKD; linear mixed-effects models were conducted to examine the relationships between statin therapy and changes in eGFR. All analyses were performed with both conventional adjustment and propensity score-matching methods.

Results

Of the study cohort of 2455 participants (41.1% men; average age, 68.06 years), 624 (25.4%) were treated with stains. Two propensity score-matched cohorts of 604 participants each were analyzed (statin users and nonusers). Statin use was significantly associated with a decreased risk of new-onset CKD with hazard ratios (HRs) and 95% confidence intervals (CIs) of 0.73 (0.59 to 0.91) (p<0.01) in the unmatched cohort and 0.75 (0.59 to 0.97) (p=0.02) in the matched cohort. There were significant differences in the eGFR decline between statin users and nonusers from baseline to 3 years in the unmatched and matched cohorts (both p<0.05). In addition, both statin users and nonusers with dyslipidemia experienced more new-onset CKD (both p<0.05).

Conclusion

Statin use was significantly associated with a decreased risk of new-onset CKD and a slower decline in eGFR in community-dwelling older people. Meanwhile, dyslipidemia was a risk factor for CKD progression among both statin users and nonusers.