Global burden and influencing factors of chronic kidney disease due to type 2 diabetes in adults aged 20-59 years, 1990-2019

Beyond Blood Sugar: Low Awareness of Kidney Disease among Type 2 Diabetes Mellitus Patients in Dalmatia-Insights from the First Open Public Call

Background and Objectives: Kidney disease (KD) is a common complication of diabetes mellitus (DM) associated with adverse outcomes of renal failure, cardiovascular disease, and mortality. The aim of this study was to determine the prevalence and awareness of the KD among the DM type 2 (T2DM) patients. Materials and Methods: This cross-sectional study was conducted at the University Hospital of Split between November and December of 2023 during an open call for DM patients. For each participant, blood and urine samples, along with relevant medical information, were collected, and adherence to the Mediterranean diet (MeDi) was assessed using the Mediterranean Diet Service Score (MDSS). Furthermore, blood pressure was measured, along with body composition and anthropometric parameters. Results: Of 252 T2DM patients with a median age of 67 years (IQR: 60-73), 130 (51.6%) were women. The median duration of T2DM was 10 years (IQR: 6-20). Despite the fact that 80.95% of total participants reported receiving dietary guidelines from any source, only 53.2% reported adhering to the suggested instructions, while according to the MDSS, only 7.2% adhered to the MeDi. The median body mass index was 27.6 kg/m2 (24.2-31), with 70.1% of participants overweight or obese. Only 6% of participants believed they had KD, but after blood and urine sample analysis, 31% were found to have KD. Conclusions: This study highlights a significant gap in awareness of KD, low adherence to MeDi, and a high prevalence of obesity among T2DM patients. Due to the increasing number of T2DM patients, it is crucial to improve healthy lifestyle education and make modifications within this group, as well as perform regular screening for KD and medical check-ups.

Dialysis nurse demand in Europe: an estimated prediction based on modelling

Background

To estimate the projections of supply and demand for dialysis nurses (DNs) over 5 years in four European countries (France, Italy, Spain and the UK).

Methods

This study modelled the nursing labour workforce across each jurisdiction by estimating the current nursing labour force, number of nursing graduates and the attrition rate.

Results

France currently has the greatest demand for DNs (51 325 patients on dialysis), followed by Italy, the UK and Spain with 40 661, 30 301 and 28 007 patients on dialysis, respectively. The number of in-centre haemodialysis (HD) patients is expected to increase in the four countries, while the number of patients on home HD (HHD) or on peritoneal dialysis (PD) is expected to increase in the UK. Currently Italy has the greatest proportion of DNs (2.6%), followed by France (2.1%), Spain (1.7%) and the UK (1.5%). Estimation of the dialysis nursing staff growth rate over 5 years showed that the UK has the greatest estimated growth rate (6%), followed by Italy (2%), France (2%) and Spain (1%).

Conclusions

Dialysis demand will increase in the coming years, which may exacerbate the DN shortage. Additionally, competencies and training requirements of DNs should be precisely defined. Finally, implementing and facilitating PD and HHD strategies would be helpful for patients, healthcare professionals and healthcare systems and can even help ease the DN shortage.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Decomposing difference in the kidney cancer burden measures between 1990 and 2019 based on the global burden of disease study

The kidney cancer (KC) burden measures have changed dramatically in recent years due to changes in exposure to the determinants over time. We aimed to decompose the difference in the KC burden measures between 1990 and 2019. This ecological study included data on the KC burden measures as well as socio-demographic index (SDI), behavioral, dietary, and metabolic risk factors from the global burden of disease study. Non-linear multivariate decomposition analysis was applied to decompose the difference in the burden of KC. Globally, ASIR, ASMR, and ASDR of KC increased from 2.88 to 4.37, from 1.70 to 2.16, and from 46.13 to 54.96 per 100,000 people between 1990 and 2019, respectively. The global burden of KC was more concentrated in developed countries. From 1990 to 2019, the burden of KC has increased the most in Eastern European countries. More than 70% of the difference in the KC burden measures between 1990 and 2019 was due to changes in exposure to the risk factors over time. The SDI, high body mass index (BMI), and alcohol use had the greatest contribution to the difference in the KC burden measures. Changes in characteristics over time, including SDI, high BMI, and alcohol consumption, appear to be important in the evolving landscape of KC worldwide. This finding may help policymakers design policies and implement prevention programs to control and manage KC.

Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990-2019

Background

This study investigates the burden of chronic kidney disease attributed to type 2 diabetes (CKD-T2D) across different geographical locations and time periods from 1990 to 2019. A total of 204 countries and regions are included in the analysis, with consideration given to their socio-demographic indexes (SDI). The aim is to examine both spatial and temporal variations in CKD-T2D burden.

Methods

This research utilized data from the 2019 Global Burden of Diseases Study to evaluate the age-standardized incidence rates (ASIR), Disability-Adjusted Life Years (DALYs), and Estimated Annual Percentage Change (EAPC) associated with CKD-T2D.

Results

Since 1990, there has been a noticeable increase of CKD age-standardized rates due to T2D, with an EAPCs of 0.65 (95% confidence interval [CI]: 0.63 to 0.66) for ASIR and an EAPC of 0.92 (95% CI: 0.8 to 1.05) for age-standardized DALYs rate. Among these regions, Andean Latin America showed a significant increase in CKD-T2D incidence [EAPC: 2.23 (95% CI: 2.11 to 2.34) and North America showed a significant increase in CKD-T2D DALYs [EAPC: 2.73 (95% CI: 2.39 to 3.07)]. The burden was higher in male and increased across all age groups, peaking at 60-79 years. Furthermore, there was a clear correlation between SDI and age-standardized rates, with regions categorized as middle SDI and High SDI experiencing a significant rise in burden.

Conclusion

The global burden of CKD-T2D has significantly risen since 1990, especially among males aged 60-79 years and in regions with middle SDI. It is imperative to implement strategic interventions to effectively address this escalating health challenge.

Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort

BACKGROUND

Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition.

METHODS

The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform.

RESULTS

Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10- 45), innate immune system (q = 1.1 × 10- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls.

CONCLUSIONS

The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXT101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using five-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.

Screening immune-related blood biomarkers for DKD-related HCC using machine learning

Background

Diabetes mellitus is a significant health problem worldwide, often leading to diabetic kidney disease (DKD), which may also influence the occurrence of hepatocellular carcinoma (HCC). However, the relationship and diagnostic biomarkers between DKD and HCC are unclear.

Methods

Using public database data, we screened DKD secretory RNAs and HCC essential genes by limma and WGCNA. Potential mechanisms, drugs, and biomarkers for DKD-associated HCC were identified using PPI, functional enrichment, cMAP, and machine learning algorithms, and a diagnostic nomogram was constructed. Then, ROC, calibration, and decision curves were used to evaluate the diagnostic performance of the nomograms. In addition, immune cell infiltration in HCC was explored using CIBERSORT. Finally, the detectability of critical genes in blood was verified by qPCR.

Results

104 DEGs associated with HCC using WGCNA were identified. 101 DEGs from DKD were predicated on secreting into the bloodstream with Exorbase datasets. PPI analysis identified three critical modules considered causative genes for DKD-associated HCC, primarily involved in inflammation and immune regulation. Using lasso and RM, four hub genes associated with DKD-associated HCC were identified, and a diagnostic nomogram confirmed by DCA curves was established. The results of immune cell infiltration showed immune dysregulation in HCC, which was associated with the expression of four essential genes. PLVAP was validated by qPCR as a possible blood-based diagnostic marker for DKD-related HCC.

Conclusion

We revealed the inflammatory immune pathways of DKD-related HCC and developed a diagnostic nomogram for HCC based on PLVAP, C7, COL15A1, and MS4A6A. We confirmed with qPCR that PLVAP can be used as a blood marker to assess the risk of HCC in DKD patients.