Serum Lipoprotein(a) and High-Density Lipoprotein Cholesterol Associate with Diabetic Nephropathy: Evidence from Machine Learning Perspectives

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis

Objective

This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.

Methods

We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.

Results

A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.

Conclusion

Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.

Registration

This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

The causal relationship between 5 serum lipid parameters and diabetic nephropathy: a Mendelian randomization study

Background

Serum lipids were found to be correlated with chronic kidney disease and cardiovascular disease. Here, we aimed to research the potential causal associations between five serum lipid parameters and the risk of diabetic nephropathy using several Mendelian Randomization methods.

Methods

Genetic data was obtained from the UK Biobank datasets. Causal effects were estimated using multiple MR methods. Heterogeneity and pleiotropy tests were performed.

Results

MR analysis revealed that HDL-C and TG exhibited causal associations with diabetic nephropathy (P<0.05). Similar trends were not observed for other lipid parameters.

Conclusions

Our research has suggested links between HDL-C, TG and diabetic nephropathy. The findings could contribute to further elucidation of the disease etiology.

Strengths and limitations of this study

This article only uses Mendel randomization method to analyze the relationship between blood lipids and diabetes nephropathy, which is more convincing when combined with population data.

Desmosterol-driven atypical macrophage polarization regulates podocyte dynamics in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) stands as a leading diabetes complication, with macrophages intricately involved in its evolution. While glucose metabolism's impact on macrophage activity is well-established, cholesterol metabolism's contributions remain less explored. Our study seeks to elucidate this association.

METHODS AND RESULTS

Methods and Results: Gene expression analysis of monocytes from the blood of both normal and diabetic patients was conducted using public databases, showing that cholesterol metabolism pathways, especially Bloch and Kandutsch-Russell, were more altered in diabetic monocytes/macrophages than glucose-responsive pathways. When bone marrow-derived macrophages (BMDMs) were subjected to desmosterol, they exhibited an unconventional polarization. These BMDMs displayed heightened levels of both M1-related pro-inflammatory cytokines and M2-linked anti-inflammatory factors. Further, in co-culture, desmosterol-conditioned BMDMs paralleled M2 macrophages in augmenting Ki-67 + podocyte populations while mimicking M1 macrophages in elevating TUNEL + apoptotic podocytes. Comparable outcomes on podocytes were obtained using conditioned media from the respective BMDMs.

CONCLUSIONS

Our data underscores the pivotal role of cholesterol metabolism, particularly via desmosterol, in steering macrophages toward an unconventional polarization marked by both inflammatory and regulatory traits. Such unique macrophage behavior concurrently impacts podocyte proliferation and apoptosis, shedding fresh light on DN pathogenesis and hinting at potential therapeutic interventions.