Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.

Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles in scientific, engineering, and medical journals in English up to August 31st, 2023. Out of a total of 22,693 articles under review, 612 articles are included in the final analysis. The majority of articles are proof-of-concepts studies, and only 5.2% are studies with real-life application of FL. Radiology and internal medicine are the most common specialties involved in FL. FL is robust to a variety of machine learning models and data types, with neural networks and medical imaging being the most common, respectively. We highlight the need to address the barriers to clinical translation and to assess its real-world impact in this new digital data-driven healthcare scene.

Privacy-preserving continual learning methods for medical image classification: a comparative analysis.

Background: The implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution. Methods: We evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal (47.21%), drusen (7.96%), choroidal neovascularization (CNV) (34.35%), and diabetic macular edema (DME) (10.48%). Each class consisted of 250 testing images. For continuous training, the first task involved CNV and normal classes, the second task focused on DME class, and the third task included drusen class. All selected algorithms were further experimented with different training sequence combinations. The final model's average class accuracy was measured. The performance of the joint model obtained through retraining and the original finetune model without continual learning algorithms were compared. Additionally, a publicly available medical dataset for colon cancer detection based on histology slides was selected as a proof of concept, while the CIFAR10 dataset was included as the continual learning benchmark. Results: Among the continual learning algorithms, Brain-inspired-replay (BIR) outperformed the others in the continual learning-based classification of retinal diseases from OCT images, achieving an accuracy of 62.00% (95% confidence interval: 59.36-64.64%), with consistent top performance observed in different training sequences. For colon cancer histology classification, Efficient Feature Transformations (EFT) attained the highest accuracy of 66.82% (95% confidence interval: 64.23-69.42%). In comparison, the joint model achieved accuracies of 90.76% and 89.28%, respectively. The finetune model demonstrated catastrophic forgetting in both datasets. Conclusion: Although the joint retraining model exhibited superior performance, continual learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models. Thus, it presents a highly promising solution for the long-term clinical deployment of such models.

Explainable artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. RECENT FINDINGS: Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. SUMMARY: We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

Artificial intelligence and digital health in global eye health: opportunities and challenges.

Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress. The accelerated development of digital health and artificial intelligence (AI) applications provides an opportunity to transform eye health, from facilitating and increasing access to eye care to supporting clinical decision making with an objective, data-driven approach. Here, we explore the opportunities and challenges presented by digital health and AI in global eye health and describe how these technologies could be leveraged to improve global eye health. AI, telehealth, and emerging technologies have great potential, but require specific work to overcome barriers to implementation. We suggest that a global digital eye health task force could facilitate coordination of funding, infrastructural development, and democratisation of AI and digital health to drive progress forwards in this domain.

Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer.

Objective: To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance. Materials and methods: We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set. B-Ultrasound videos (BUS), mpMRI T2 sequence (T2), and ADC sequence (ADC) were obtained from all patients. We extracted 851 features of each patient in the BUS, T2, and ADC groups and used a t-test, the Mann-Whitney U test, and LASSO regression to screen the features. Support vector machine (SVM), random forest (RF), adaptive boosting (ADB), and gradient boosting machine (GBM) models were used to establish radiomics models. In addition, we fused the features screened via LASSO regression from three groups as new features and rebuilt ML models. The performance of the ML models in diagnosing PCa in the BUS, T2, ADC, and fusion groups was compared using the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. Results: In the test cohort, the AUC of each model in the ADC group was higher than that of in the.BUS and T2 groups. Among the models, the RF model had the best diagnostic performance, with an AUC of 0.85, sensitivity of 0.78 (0.61-0.89), specificity of 0.84 (0.69-0.94), and accuracy of 0.83 (0.66-0.93). The SVM model in both the BUS and T2 groups performed best. Based on the features screened in the BUS, T2, and ADC groups fused to construct the models, the SVM model was found to perform best, with an AUC of 0.87, sensitivity of 0.73 (0.56-0.86), specificity of 0.79 (0.63-0.90), and accuracy of 0.77 (0.59-0.89). The difference in the results was statistically significant (p<0.05). Conclusion: The ML prediction models had a good diagnostic ability for PCa. Among them, the SVM model in the fusion group showed the best performance in diagnosing PCa. These prediction models can help radiologists make better diagnoses.

Empagliflozin ameliorates diabetic cardiomyopathy via regulated branched-chain amino acid metabolism and mTOR/p-ULK1 signaling pathway-mediated autophagy.

BACKGROUND: Empagliflozin, a sodium-glucose co-transporter 2 inhibitor (SGLT2i), has been reported to significantly reduce the risk of heart failure in multiple clinical studies. However, the underlying mechanisms remain elusive. This study aimed to investigate the effect of empagliflozin on branched-chain amino acid (BCAA) metabolism in diabetic cardiomyopathy. METHODS: Thirty male 8-week KK Cg-Ay/J mice were used to study diabetic cardiomyopathy; here, 15 were used as the model group, and the remaining 15 were administered empagliflozin (3.75 mg/kg/day) by gavage daily for 16 weeks. The control group consisted of fifteen male 8-week C57BL/6J mice, whose blood glucose and body weight were measured simultaneously with the diabetic mice until 16 weeks without additional intervention. Echocardiography and histopathology were performed to evaluate cardiac structure and function. Proteomic sequencing and biogenic analysis were performed on mouse hearts. Parallel Reaction Monitoring and western blotting were performed to validate the expression levels of differentially expressed proteins. RESULTS: The results showed that empagliflozin improved ventricular dilatation and ejection fraction reduction in diabetic hearts, as well as the elevation of myocardial injury biomarkers hs-cTnT and NT-proBNP. At the same time, empagliflozin alleviates myocardial inflammatory infiltration, calcification foci deposition, and fibrosis caused by diabetes. The results of the proteomics assay showed that empagliflozin could improve the metabolism of various substances, especially promoting the BCAA metabolism of diabetic hearts by up-regulating PP2Cm. Furthermore, empagliflozin could affect the mTOR/p-ULK1 signaling pathway by reducing the concentration of BCAA in diabetic hearts. When mTOR/p-ULK1 protein was inhibited, ULK1, the autophagy initiation molecule, increased. Moreover, autophagy substrate p62 and autophagy marker LC3B were significantly reduced, indicating that the autophagy activity of diabetes inhibition was reactivated. CONCLUSIONS: Empagliflozin may attenuate diabetic cardiomyopathy-related myocardial injury by promoting the catabolism of BCAA and inhibiting mTOR/p-ULK1 to enhance autophagy. These findings suggest that empagliflozin could be a potential candidate drug against BCAA increase and could be used for other cardiovascular diseases with a metabolic disorder of BCAA.

Haze caused by NOx oxidation under restricted residential and industrial activities in a mega city in the south of North China Plain.

The formation of secondary aerosol species, including nitrate and sulfate, induces severe haze in the North China Plain. However, despite substantial reductions in anthropogenic pollutants due to severe restriction of residential and industrial activities in 2020 to stop the spread of COVID-19, haze still formed in Zhengzhou. We compared ionic compositions of PM2.5 during the period of the restriction with that immediately before the restriction and in the comparison period in 2019 to investigate the processes that caused the haze. The average concentration of PM2.5 was 83.9 µg m-3 in the restriction period, 241.8 µg m-3 before the restriction, and 94.0 µg m-3 in 2019. Nitrate was the largest contributor to the PM2.5 in all periods, with an average mass fraction of 24%-30%. The average molar concentration of total nitrogen compounds (NOx + nitrate) was 0.89 µmol m-3 in the restriction period, which was much lower than that in the non-restriction periods (1.85-2.74 µmol m-3). In contrast, the concentration of sulfur compounds (SO2 + sulfate) was 0.34-0.39 µmol m-3 in all periods. The conversion rate of NOx to nitrate (NOR) was 0.35 in the restriction period, significantly higher than that before the restriction (0.26) and in 2019 (0.25). NOR was higher with relative humidity in 40-80% in the restriction period than in the other two periods, whereas the conversion rate of SO2 to sulfate did not, indicating nitrate formation was more efficient during the restriction. When O3 occupied more than half of the oxidants (Ox = O3 + NO2), NOR increased rapidly with the ratio of O3 to Ox and was much higher in the daytime than nighttime. Therefore, haze in the restriction period was caused by increased NOx-to-nitrate conversion driven by photochemical reactions.

Interconnected MXene/Graphene Network Constructed by Soft Template for Multi-Performance Improvement of Polymer Composites.

The multi-functionalization of polymer composites refers to the ability to connect multiple properties through simple structural design and simultaneously achieve multi-performance optimization. The large-scale design and mass production to realize the reasonable structure design of multifunctional polymer composites are urgently remaining challenges. Herein, the multifunctional MXene/graphene/polymer composites with three-dimensional thermally and electrically conductive network structures are fabricated via the utilization of the microstructure of the soft template, and a facile dispersion dip-coating approach. As a result, the polymer composites have a multi-performance improvement. At the MXene and graphene content of 18.7 wt%, the superior through-plane thermal conductivity of polymer composite is 2.44 W m-1 K-1, which is 1118% higher than that of the polymer matrix. The electromagnetic interference (EMI) shielding effectiveness of the sample reaches 43.3 dB in the range of X-band. And the mechanical property of the sample has advanced 4 times compared with the polymer matrix. The excellent EMI shielding and thermal management performance, along with the effortless and easy-to-scalable producing techniques, imply promising perspectives of the polymer composites in the next-generation smart electronic devices.

Isolated Solid Wall-Assisted Thermal Conductive Performance of Three-Dimensional Anisotropic MXene/Graphene Polymeric Composites.

The introduction of three-dimensional (3D) continuous conformations in polymer materials is a convincing proposal for acquiring the desirable multifunction to fulfill the urgent demands of highly integrated electronic devices. However, the limited functional design of the filled aligned network remains challenging. Herein, directional self-assembly 3D MXene/graphene aerogels are fabricated as conductive networks for polyethylene glycol (PEG) matrix. Based on the uniaxial and biaxial ice template method, the temperature gradient affects the aligned arrangement of the 3D microstructure. The biaxial PEG/MXene/GR composites exhibit an enhanced through-plane thermal conductivity of 1.64 W m-1 K-1 at 10.6 vol % content, which is 522% higher than that of pure PEG. The influence of the biaxial self-assembly strategy compared with that of the uniaxial one on the thermal conductivity reaches the highest 333% when the weight ratio equals 1:1. Meanwhile, the same difference also occurs in the electromagnetic shielding interference (EMI) property. The advanced EMI-shielding effectiveness of the biaxial PM1G1 composites reaches ∼36 dB at the 2.5 mm thickness. This research provides valuable guidance for designing high-performance applications of anisotropic thermal management and EMI shielding in 5G telecommunications and mobile electronic devices.

Functions of Monocytes and Macrophages and the Associated Effective Molecules and Mechanisms at the Early Stage of Atherosclerosis.

OBJECTIVE: This study aimed to explore the functions and possible underlying regulatory molecules and mechanisms of monocytes and macrophages under early atherosclerotic conditions. METHODS: THP-1-derived monocytes or macrophages were induced by 50 µg/ml oxidized low density lipoprotein (ox-LDL) for 24 hours, and the degree of lipid metabolism and inflammation were determined. In addition, we identified differentially expressed genes, noncoding ribonucleic acids (RNAs), pathways and mechanisms by RNA sequencing, and performed further correlation analysis and molecular expression verification. RESULTS: Monocytes could not form foam cells with oil red O staining directly and had low levels of lipids as determined by total cholesterol and triglycerides assays, cholesterol uptake molecules CD36, the class A macrophage scavenger receptor and lectin-like oxidized low-density lipoprotein receptor-1 and cholesterol efflux molecules ATP binding cassette transporter A1, ATP binding cassette transporter G1 and liver X receptor α, and inflammatory factors, which were markedly different from those in macrophages. Additionally, sequencing data showed obviously differentially expressed genes, microRNAs and long noncoding RNAs in the atherosclerotic group. We identified 15 upregulated and downregulated genes, and 10 biological processes and pathways involved in atherosclerosis. Specifically, fatty acid desaturase 2 and apolipoprotein A1 in the peroxisome proliferator-activated receptor signaling pathway were differentially expressed in stimulated macrophages, whereas no changes were observed in the monocyte groups. Furthermore, correlation analysis showed differential expressed lncRNAs targeting miRNAs and mRNAs, and 24 competing endogenous RNA (ceRNA) networks of long noncoding RNA-microRNA-messenger RNA in early oxidative macrophages. CONCLUSIONS: Monocytes did not directly participate in lipid metabolism before differentiation into macrophages at the early stage in vitro. Furthermore, noncoding RNAs and ceRNA networks might play important roles in regulating the lipid metabolism of macrophages at the early stage of atherosclerosis.

Mechanistic Investigation of Palladium-Catalyzed meta-C-H Bond Activation of Arenes with a Carboxyl Directing Group.

The mechanism of Pd(II)-catalyzed meta-C-H bond olefination of arenes with a carboxyl directing group (DG)-containing template has been investigated with density functional theory. The reaction includes three major steps: C-H bond activation, alkene insertion, and ß-hydride elimination. The C-H activation step, which proceeds via a concerted metalation-deprotonation pathway, is found to be the rate- and regioselectivity-determining step. We proposed a mono-N-protected amino acid (MPAA)/DG-assisted C-H activation model, in which the carboxyl DG coordinates with the Pd center and delivers it to the meta-position of arene, and the bidentate dianionic MPAA acts as a base for deprotonation. There is a hydrogen bonding interaction between the carboxyl DG and the carboxylate group of MPAA. An alternative Pd(OAc)2-catalyzed mechanism without involvement of MPAA is also operative. The template is conformationally flexible, and multiple low-energy transition-state conformations contribute to the regioselectivity.

Enhance anti-lung tumor efficacy of chimeric antigen receptor-T cells by ectopic expression of C-C motif chemokine receptor 6.

Chimeric antigen receptor-T (CAR-T) cells have limited therapeutic efficacy against solid tumors, partially due to their limited ability to reach and invade into the neoplastic foci. By gene expression profiling interactive analysis, we identified that the C-C motif chemokine ligand (CCL) 20 is highly expressed in lung and other most incidence and/or mortality cancers such as colon, rectum, stomach, and liver cancers. Forced expression of C-C motif chemokine receptor 6 (CCR6), the biunique receptor of CCL20, results in robust trafficking of CAR-T cells toward CCL20-secreting tumor cells. In a lung cancer xenograft mouse model, CCR6-expressing CAR-T cells efficiently migrate to and infiltrate into solid tumors upon infusion, leading to effective tumor clearance and significantly prolonged survival of tumor-bearing mice. In addition, culturing CCR6-CAR-T cells with interleukin (IL)-7 and IL-15 further improved their anti-lung cancer activity. Our findings provide supporting evidence for the clinical development of chemokine receptor-engineered CAR-T cells for solid tumor immunotherapy.

Computational Studies on the Mechanism and Origin of the Different Regioselectivities of Manganese Porphyrin-Catalyzed C-H Bond Hydroxylation and Amidation of Equilenin Acetate.

The manganese porphyrin-catalyzed C-H bond hydroxylation and amidation of equilenin acetate developed by Breslow and his co-worker have been investigated with density functional theory (DFT) calculations. The hydroxylation of C(sp2)-H bond of equilenin acetate leading to the 6-hydroxylated product is more favorable than the hydroxylation of C(sp3)-H bond of equilenin acetate, leading to the 11ß-hydroxylation product. The computational results suggest that the C(sp2)-H bond hydroxylation of equilenin acetate undergoes an oxygen-atom-transfer mechanism, which is more favorable than the C(sp3)-H bond hydroxylation undergoing the hydrogen-atom-abstraction/oxygen-rebound (HAA/OR) mechanism by 1.6 kcal/mol. That is why, the 6-hydroxylated product is the major product and the 11ß-hydroxylated product is the minor product. In contrast, the 11ß-amidated product is the only observed product in manganese porphyrin-catalyzed amidation reaction. The benzylic amidation undergoes a hydrogen-atom-abstraction/nitrogen-rebound (HAA/NR) mechanism, in which hydrogen atom abstraction is followed by nitrogen rebound, leading to the 11ß-amidated product. The benzylic C(sp3)-H bond amidation at the C-11 position is more favorable than aromatic amidation at the C-6 position by 4.9 kcal/mol. Therefore, the DFT computational results are consistent with the experiments that manganese porphyrin-catalyzed C-H bond hydroxylation and amidation of equilenin acetate have different regioselectivities.

Investigation of Indigoidine Synthetase Reveals a Conserved Active-Site Base Residue of Nonribosomal Peptide Synthetase Oxidases.

Nonribosomal peptide synthetase (NRPS) oxidase (Ox) domains oxidize protein-bound intermediates to install crucial structural motifs in bioactive natural products. The mechanism of this domain remains elusive. Here, by studying indigoidine synthetase, a single-module NRPS involved in the biosynthesis of indigoidine and several other bacterial secondary metabolites, we demonstrate that its Ox domain utilizes an active-site base residue, tyrosine 665, to deprotonate a protein-bound l-glutaminyl residue. We further validate the generality of this active-site residue among NRPS Ox domains. These findings not only resolve the biosynthetic pathway mediated by indigoidine synthetase but enable mechanistic insight into NRPS Ox domains.

Mesenchymal stem cells ameliorate myocardial fibrosis in diabetic cardiomyopathy via the secretion of prostaglandin E2.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a cardiac complication of long-term uncontrolled diabetes and is characterized by myocardial fibrosis and abnormal cardiac function. Mesenchymal stem cells (MSCs) are multipotent cells with immunoregulatory and secretory functions in diabetes and heart diseases. However, very few studies have focused on the effect and the underlying mechanism of MSCs on myocardial fibrosis in DCM. Therefore, we aimed to explore the therapeutic potential of MSCs in myocardial fibrosis and its underlying mechanism in vivo and in vitro. METHODS: A DCM rat model was induced using a high-fat diet (HFD) combined with a low-dose streptozotocin (STZ) injection. After four infusions of MSCs, rat serum and heart tissues were collected, and the levels of blood glucose and lipid, cardiac structure, and function, and the degree of myocardial fibrosis including the expression levels of pro-fibrotic factor and collagen were analyzed using biochemical methods, echocardiography, histopathology, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA). We infused prostaglandin E2 (PGE2)-deficient MSCs to DCM rats in vivo and established a system mimicking diabetic myocardial fibrosis in vitro by inducing cardiac fibroblasts with high glucose (HG) and coculturing them with MSCs or PGE2-deficient MSCs to further explore the underlying mechanism of amelioration of myocardial fibrosis by MSCs. RESULTS: Metabolic abnormalities, myocardial fibrosis, and cardiac dysfunction in DCM rats were significantly ameliorated after treatment with MSCs. Moreover, the levels of TGF-ß, collagen I, collagen III, and collagen accumulation were markedly decreased after MSC infusion compared to those in DCM hearts. However, PGE2-deficient MSCs had decreased ability to alleviate cardiac fibrosis and dysfunction. In addition, in vitro study revealed that the concentration of PGE2 in the MSC group was enhanced, while the proliferation and collagen secretion of cardiac fibroblasts were reduced after MSC treatment. However, MSCs had little effect on alleviating fibrosis when the fibroblasts were pretreated with cyclooxygenase-2 (COX-2) inhibitors, which also inhibited PGE2 secretion. This phenomenon could be reversed by adding PGE2. CONCLUSIONS: Our results indicated that MSC infusion could ameliorate cardiac fibrosis and dysfunction in DCM rats. The underlying mechanisms might involve the function of PGE2 secreted by MSCs.

Context-dependency of synthetic minimal promoters in driving gene expression: a case study.

Synthetic promoters are considered ideal candidates in driving robust gene expression. Most of the available synthetic promoters are minimal promoters, for which the upstream sequence of the 5' end of the core region is usually excluded. Although the upstream sequence has been shown to mediate transcription of natural promoters, its impact on synthetic promoters has not been widely studied. Here, a library of chromosomal DNA fragments is randomly fused with the 5' end of the J23119 synthetic promoter, and the transcriptional performance of the promoter is evaluated through ß-galactosidase assay, fluorescence intensity and chemical biosynthesis. Results show that changes in the upstream sequence can induce significant variation in the promoter strength of up to 5.8-fold. The effect is independent of the length of the insertions and the number of potential transcription factor binding sites. Several DNA fragments that are able to enhance the transcription of both the natural and the synthetic promoters are identified. This study indicates that the synthetic minimal promoters are susceptible to the surrounding sequence context. Therefore, the upstream sequence should be treated as an indispensable component in the design and application of synthetic promoters, or as an independent genetic part for the fine-tuning of gene expression.

Mesenchymal stem cells promote type 2 macrophage polarization to ameliorate the myocardial injury caused by diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a common complication of diabetes and is characterized by chronic myocardial inflammation. Mesenchymal stem cell (MSC) infusions have recently been suggested to alleviate myocardial injury and ameliorate cardiac function. However, few studies have focused on the effects of MSCs in DCM. Therefore, we explored the effects of MSC-regulated macrophage polarization on myocardial repair in DCM. METHODS: A DCM rat model was induced by a high-fat diet and streptozotocin (STZ) administration and infused 4 times with MSCs. Rat blood and heart tissue were analyzed for blood glucose levels, lipid levels, echocardiography, histopathology, macrophage phenotype ratios and inflammatory cytokines, respectively. We mimicked chronic inflammation in vitro by inducing peritoneal macrophages with high glucose and LPS, then cocultured these macrophages with MSCs to explore the specific mechanism of MSCs on macrophage polarization. RESULTS: DCM rats exhibited abnormal blood glucose levels and lipid metabolism, cardiac inflammation and dysfunction. MSC infusion ameliorated metabolic abnormalities and preserved cardiac structure and function in DCM rats. Moreover, MSC infusion significantly increased the M2 phenotype macrophages and alleviated cardiac inflammation. Interestingly, this in vitro study revealed that the MSCs pretreated with a COX-2 inhibitor had little effect on M2 macrophage polarization, but this phenomenon could be reversed by adding prostaglandin E2 (PGE2). CONCLUSIONS: Our results suggested that MSC infusions can protect against cardiac injury in DCM rats. The underlying mechanisms may include MSC-enhanced M2 macrophage polarization via the COX-2-PGE2 pathway.

PCSK9 positively correlates with plasma sdLDL in community-dwelling population but not in diabetic participants after confounder adjustment.

This study aimed to investigate the relationship between plasma proprotein convertase subtilisin kexin 9 (PCSK9) and small dense low-density lipoprptein (sdLDL) in diabetic and non-diabetic participants in a community-dwelling cohort.The plasma levels of PCSK9 and sdLDL were detected in 1766 participants (median age: 61.40 years; 733 males vs 1033 females; 383 diabetic vs 1383 non-diabetic patients) from the Pingguoyuan community of Beijing, China.Results showed that Pearson correlation analysis revealed a positive correlation between PCSK9 and sdLDL (r = 0.263, P < .001). Multiple linear regression analysis showed a significant positive correlation between plasma PCSK9 and sdLDL in the whole population study. sdLDL was used as the dependent variable, and the potential cofounders were adjusted. However, any independent relationship was not observed between circulating PCSK9 and sdLDL in the diabetic subpopulation (r = 0.269, P < .05, ß = 9.591, P > .05).Thus, there is a positive correlation between plasma PCSK9 and sdLDL in a community-dwelling cohort, but not in type 2 diabetic subpopulation, after confounder adjustment.