Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study

Circ-0069561 as a novel diagnostic biomarker for progression of diabetic kidney disease

BACKGROUND

Circular RNAs (circRNAs) are non-coding RNAs that are key regulators of the initiation and progression of various human diseases. However, the role of circRNAs in diabetic kidney disease (DKD) remains unknown.

METHODS

Whole high-throughput RNA sequencing (RNA-seq) was performed on kidney tissues from clinical DKD patients and controls. Circ-0069561 with significantly up-regulated expression level was selected by real-time PCR (RT-PCR) analysis. RT-PCR and fluorescent in situ hybridization (FISH) further validated the expression and subcellular localization of circ-0069561 in type 2 diabetic mice and DKD patients. The clinical significance of circ-0069561 in DKD was evaluated. The circRNA-miRNA-ferroptosis associated mRNA network was constructed. The biological function of circ-0069561 in mouse podocyte clone 5 (MPC5) was analyzed.

RESULTS

The top 10 up-regulated circular RNAs were selected by RT-PCR validation, and the results demonstrated a significant elevation in the expression level of circ-0069561. The RT-PCR and FISH results demonstrated that the expression of circ-0069561 was elevated in renal tissues of type 2 diabetic mice and DKD patients, with a predominant localization in glomerulus. The ROC curves showed that circ-0069561 had a good diagnostic value in massive proteinuria (area under the curve = 0.889). Kaplan-Meier analysis showed that high expression of circ-0069561 was associated with an increased risk of primary endpoints. The circRNA-miRNA-mRNA network indicated that ferroptosis might be involved in the pathogenesis of DKD. In vitro experiments demonstrated that circ-0069561 aggravated glucose-induced podocyte damage and ferroptosis.

CONCLUSION

Circ-0069561 has the potential to be an ideal biomarker and therapeutic target for DKD progression.

Implementing novel complete blood count-derived inflammatory indices in the diabetic kidney diseases diagnostic models

Objectives

Hemogram inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red-cell distribution width (RDW), and mean platelet volume (MPV) have been associated with type 2 diabetes mellitus (T2DM) and its complications, namely diabetic kidney diseases (DKD). We aimed to develop and validate logistic regression (LR) and CatBoost diagnostic models and study the role of adding these markers to the models.

Methods

All individuals who were managed in our secondary care center from March 2020 to December 2023 were identified. After excluding the ineligible patients, train-test splitting, and data preprocessing, two baseline LR and CatBoost-based models were developed using demographic, clinical, and laboratory features. The AUC-ROC of the models with biomarkers (NLR, PLR, RDW, and MPV) was compared to the baseline models. We calculated net reclassification improvement (NRI) and integrated discrimination index (IDI).

Results

One thousand and eleven T2DM patients were eligible. The AUC-ROC of both LR (0.738) and CatBoost (0.715) models was comparable. Adding target inflammatory markers did not significantly change the AUC-ROC in both LR and CatBoost models. Adding RDW to the baseline LR model reclassified 41.7% of patients without DKD, in the cost of misclassification of 38.4% of DKD cases. This change was absent in CatBoost models, and other markers did not achieve improved NRI or IDI.

Conclusion

The basic models with demographical and clinical features had acceptable performance. Adding RDW to the basic LR model improved the reclassification of the non-DKD participants. However, adding other hematological indices did not significantly improve the LR and CatBoost models' performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01523-2.

A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital

BACKGROUND

Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM), with patients typically remaining asymptomatic until reaching an advanced stage. We aimed to develop and validate a predictive model for DKD in patients with an initial diagnosis of type 2 diabetes mellitus (T2DM) using real-world data.

METHODS

We retrospectively examined data from 3,291 patients (1740 men, 1551 women) newly diagnosed with T2DM at Ningbo Municipal Hospital of Traditional Chinese Medicine (2011-2023). The dataset was randomly divided into training and validation cohorts. Forty-six readily available medical characteristics at initial diagnosis of T2DM from the electronic medical records were used to develop prediction models based on linear, non-linear, and SuperLearner approaches. Model performance was evaluated using the area under the curve (AUC). SHapley Additive exPlanation (SHAP) was used to interpret the best-performing models.

RESULTS

Among 3291 participants, 563 (17.1%) were diagnosed with DKD during median follow-up of 2.53 years. The SuperLearner model exhibited the highest AUC (0.7138, 95% confidence interval: [0.673, 0.7546]) for the holdout internal validation set in predicting any DKD stage. Top-ranked features were WBC_Cnt*, Neut_Cnt, Hct, and Hb. High WBC_Cnt, low Neut_Cnt, high Hct, and low Hb levels were associated with an increased risk of DKD.

CONCLUSIONS

We developed and validated a DKD risk prediction model for patients with newly diagnosed T2DM. Using routinely available clinical measurements, the SuperLearner model could predict DKD during hospital visits. Prediction accuracy and SHAP-based model interpretability may help improve early detection, targeted interventions, and prognosis of patients with DM.

Targeting the NF-κB p65-MMP28 axis: Wogonoside as a novel therapeutic agent for attenuating podocyte injury in diabetic nephropathy

BACKGROUND

Although recent progress provides mechanistic insights into diabetic nephropathy (DN), effective treatments remain scarce. DN, characterized by proteinuria and a progressive decline in renal function, primarily arises from podocyte injury, which impairs the glomerular filtration barrier. Wogonoside, a bioactive compound from the traditional Chinese herb Scutellaria baicalensis, has not been explored for its role in DN.

PURPOSE

This study aimed to investigate the therapeutic effects of wogonoside on podocyte injury in DN and its molecular mechanisms.

METHODS

The effects of wogonoside were examined using HFD/STZ-induced DN mouse models and high glucose (HG)-induced MPC-5 cells. Oxidative stress and inflammation markers were analyzed via Western blot and RT-qPCR. Wogonoside targets were identified through DARTS-MS and validated by SPR, molecular docking, alanine scanning, and CETSA. RNA-Seq analysis was employed to identify downstream targets, and the p65-MMP28 axis was explored through p65 knockdown and overexpression studies. The regulatory effect of p65 on Mmp28 was confirmed through dual-luciferase reporter assays and ChIP-qPCR.

RESULTS

Wogonoside treatment significantly reduced oxidative stress and inflammation in vivo and in vitro. Mechanistic studies identified p65 as a direct target of wogonoside, with SPR confirming a strong binding affinity (KD = 25.05 μM). Molecular docking and alanine scanning identified LYS221 as a critical binding site, which was further supported by CETSA using the p65 K221A mutant. RNA-Seq analysis revealed Mmp28 as a downstream effector of p65 involved in HG-induced podocyte injury. Functional studies demonstrated that wogonoside's protective effects on antioxidant and inflammatory pathways are mediated via the p65-MMP28 axis. Dual-luciferase reporter assays revealed that p65 regulates Mmp28 transcription, and ChIP-qPCR confirmed its direct promoter binding.

CONCLUSIONS

This study highlights wogonoside as a promising candidate for the treatment of podocyte injury in DN by targeting the NF-κB p65-MMP28 signaling axis. These findings provide novel insights into wogonoside's therapeutic potential and its molecular mechanisms, paving the way for its further development as a DN intervention.

The reporting quality and methodological quality of dynamic prediction models for cancer prognosis

BACKGROUND

To evaluate the reporting quality and methodological quality of dynamic prediction model (DPM) studies on cancer prognosis.

METHODS

Extensive search for DPM studies on cancer prognosis was conducted in MEDLINE, EMBASE, and the Cochrane Library databases. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and the Prediction model Risk of Bias Assessment Tool (PROBAST) were used to assess reporting quality and methodological quality, respectively.

RESULTS

A total of 34 DPM studies were identified since the first publication in 2005, the main modeling methods for DPMs included the landmark model and the joint model. Regarding the reporting quality, the median overall TRIPOD adherence score was 75%. The TRIPOD items were poorly reported, especially the title (23.53%), model specification, including presentation (55.88%) and interpretation (50%) of the DPM usage, and implications for clinical use and future research (29.41%). Concerning methodological quality, most studies were of low quality (n = 30) or unclear (n = 3), mainly due to statistical analysis issues.

CONCLUSIONS

The Landmark model and joint model show potential in DPM. The suboptimal reporting and methodological qualities of current DPM studies should be improved to facilitate clinical application.

Risk of onset of chronic kidney disease in type 2 diabetes mellitus (ROCK-DM): Development and validation of a 4-variable prediction model

AIMS

The aim of this study was to develop and validate a prediction model for incident chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM), defined as eGFR < 60 ml/min/1.73m2 and/or urine albumin:creatinine ratio (UACR) > 3 mg/mmol in ≥ 2 consecutive readings ≥ 3 months apart.

METHODS

Model derivation was performed in the SingHealth Diabetes Registry, including patients aged ≥ 21 years diagnosed with T2DM without pre-existing CKD. External validation was performed in a single-center prospective observational cohort. Cox Proportional Hazard model was created to evaluate predictors associated with time-to-onset of incident CKD. Increasingly parsimonious models were assessed for discrimination and calibration. Models underwent external validation, benchmarking against existing models, and decision curve analysis.

RESULTS

25,142 (59 %) of 42,552 patients in the derivation cohort developed CKD over a median 4.0 years (IQR 2.1-7.7) follow up. An 18-variable model, 12-variable model, and 4-variable model (including age, duration of T2DM, eGFR, and previous non-persistent albuminuria) was developed. The 4-variable model had a C-statistic of 0.78 and good calibration on plots of observed-versus-predicted risk. The 12-variable and 18-variable models performed similarly. In the external validation cohort of 2249 patients, of whom 1035 (46 %) developed incident CKD, the 4-variable model had a C-statistic of 0.87. All models had better discrimination than existing benchmarks. Decision curve analysis of the 4-variable model showed positive net benefit for any threshold probability above 16 % for 2-year and 28 % for 5-year CKD risk.

CONCLUSION

The 4-variable model for prediction of incident CKD in T2DM demonstrates good performance, predicts both eGFR and albuminuria endpoints, and is simple-to-use. This may guide personalized care, resource allocation and population health.

A cycle-based model to predict no usable blastocyst formation following cycles of in vitro fertilization in patients with normal ovarian reserve

OBJECTIVE

This study aimed to develop a predictive model for the risk of no usable blastocyst formation in patients with normal ovarian reserve undergoing IVF.

METHODS

The model was derived from 7,901 patients who underwent their first oocyte retrieval and subsequent blastocyst culture, of which 446 cases have no usable blastocysts formed. Univariate regression analyses, least absolute shrinkage and selection operator regression analysis were used to identify the association of patient and cycle characteristics with the presence of no available blastocyst and to create a nomogram. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve and calibration curve, the net benefit threshold of prediction was determined using decision curve analysis (DCA).

RESULTS

Multivariate analysis identified three independent predictors: the number of day 3 (D3) embryos, the number of high-quality D3 embryos, and the number of embryos used for blastocyst culture. A nomogram model was developed and internally validated using bootstrapping, demonstrating good discriminative ability with an area under the receiver operating characteristic curve (AUC) of 0.879(95%CI: 0.861-0.890).

CONCLUSIONS

The cycle-based nomogram can anticipate the probability of no available blastocyst formation in IVF/ICSI treatment. This can help doctors make appropriate clinical judgments and assist patients in managing their expectations effectively.

External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study

OBJECTIVE

Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment.

METHODS

A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model.

RESULTS

The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%.

CONCLUSION

Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions.

Drug Exposure As a Predictor in Diabetic Retinopathy Risk Prediction Models-A Systematic Review and Meta-Analysis

PURPOSE

To conduct a systematic review to assess drug exposure handling in diabetic retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs associated with DR and a meta-analysis to determine which drugs contributed to enhanced model performance.

DESIGN

Systematic review and meta-analysis.

METHODS

We included studies presenting DR models incorporating drug exposure as a predictor. We searched EMBASE, MEDLINE, and SCOPUS from inception to December 2023. We evaluated the quality of studies using the Prediction model Risk of Bias Assessment Tool and certainty using GRADE. We conducted network meta-analysis and meta-analysis to estimate the odds ratio (OR) and pooled C-statistic, respectively, and 95% confidence intervals (CI) (PROSPERO: CRD42022349764).

RESULTS

Of 5,653 records identified, we included 28 studies of 678,837 type 1 or 2 diabetes participants, of which 38,579 (5.7%) had DR. A total of 19, 3, and 7 studies were at high, unclear, and low risk of bias, respectively. Drugs included in models as predictors were: insulin (n = 24), antihypertensives (n = 5), oral antidiabetics (n = 12), lipid-lowering drugs (n = 7), antiplatelets (n = 2). Drug exposure was modelled primarily as a categorical variable (n = 23 studies). Two studies handled drug exposure as time-varying covariates, and one as a time-dependent covariate. Insulin was associated with an increased risk of DR (OR = 2.50; 95% CI: 1.61-3.86). Models that included insulin (n = 9) had a higher pooled C-statistic (C-statistic = 0.84, CI: 0.80-0.88), compared to models (n = 9) that incorporated a combination of drugs alongside insulin (C-statistic = 0.79, CI: 0.74-0.84), as well as models (n = 3) not including insulin (C-statistic = 0.70, CI: 0.64-0.75). Limitations include the high risk of bias and significant heterogeneity in reviewed studies.

CONCLUSION

This is the first review assessing drug exposure handling in DR prediction models. Drug exposure was primarily modelled as a categorical variable, with insulin associated with improved model performance. However, due to suboptimal drug handling, associations between other drugs and model performance may have been overlooked. This review proposes the following for future DR prediction models: (1) evaluation of drug exposure as a variable, (2) use of time-varying methodologies, and (3) consideration of drug regimen details. Improving drug exposure handling could potentially unveil novel variables capable of significantly enhancing the predictive capability of prediction models.

Clinical prediction models for patients undergoing total hip arthroplasty: an external validation based on a systematic review and the Dutch Arthroplasty Register

BACKGROUND AND PURPOSE

 External validation is a crucial step after prediction model development. Despite increasing interest in prediction models, external validation is frequently overlooked. We aimed to evaluate whether joint registries can be utilized for external validation of prediction models, and whether published prediction models are valid for the Dutch population with a total hip arthroplasty.

METHODS

 We identified prediction models developed in patients undergoing arthroplasty through a systematic literature search. Model variables were evaluated for availability in the Dutch Arthroplasty Registry (LROI). We assessed the model performance in terms of calibration and discrimination (area under the curve [AUC]). Furthermore, the models were updated and evaluated through intercept recalibration and logistic recalibration.

RESULTS

 After assessing 54 papers, 19 were excluded for not describing a prediction model (n = 16) or focusing on non-TJA populations (n = 3), leaving 35 papers describing 44 prediction models. 90% (40/44) of the prediction models used outcomes or predictors missing in the LROI, such as diabetes, opioid use, and depression. 4 models could be externally validated on LROI data. The models' discrimination ranged between poor and acceptable and was similar to that in the development cohort. The calibration of the models was insufficient. The model performance improved slightly after updating.

CONCLUSION

 External validation of the 4 models resulted in suboptimal predictive performance in the Dutch population, highlighting the importance of external validation studies.

External validation and calibration of risk equations for prediction of diabetic kidney diseases among patients with type 2 diabetes in Taiwan

BACKGROUND

Most existing risk equations for predicting/stratifying individual diabetic kidney disease (DKD) risks were developed using relatively dated data from selective and homogeneous trial populations comprising predominately Caucasian type 2 diabetes (T2D) patients. We seek to adapt risk equations for prediction of DKD progression (microalbuminuria, macroalbuminuria, and renal failure) using empiric data from a real-world population with T2D in Taiwan.

METHODS

Risk equations from three well-known simulation models: UKPDS-OM2, RECODe, and CHIME models, were adapted. Discrimination and calibration were determined using the area under the receiver operating characteristic curve (AUROC), a calibration plot (slope and intercept), and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was performed for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of risk equations to address risk variations among patients.

RESULTS

The RECODe equations for microalbuminuria and macroalbuminuria showed moderate discrimination (AUROC: 0.62 and 0.76) but underestimated the event risks (calibration slope > 1). The CHIME equation had the best discrimination for renal failure (AUROCs from CHIME, UKPDS-OM2 and RECODe: 0.77, 0.60 and 0.64, respectively). All three equations overestimated renal failure risk (calibration slope < 1). After rigorous updating, the calibration slope/intercept of the recalibrated RECODe for predicting microalbuminuria (0.87/0.0459) and macroalbuminuria (1.10/0.0004) risks as well as the recalibrated CHIME equation for predicting renal failure risk (0.95/-0.0014) were improved.

CONCLUSIONS

Risk equations for prediction of DKD progression in real-world Taiwanese T2D patients were established, which can be incorporated into a multi-state simulation model to project and differentiate individual DKD risks for supporting timely interventions and health economic research.

A novel predictive model for optimizing diabetes screening in older adults

INTRODUCTION

The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels.

MATERIALS AND METHODS

This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis.

RESULTS

Nine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility.

CONCLUSIONS

We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data

PURPOSE

To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP).

METHODS

This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated.

RESULTS

A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001).

CONCLUSION

The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.

Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms

Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings.

Metabolic Profiles of Type 2 Diabetes and Their Association With Renal Complications

CONTEXT

The components of metabolic syndrome (MetS) are interrelated and associated with renal complications in patients with type 2 diabetes (T2D).

OBJECTIVE

We aimed to reveal prevalent metabolic profiles in patients with T2D and identify which metabolic profiles were risk markers for renal progression.

METHODS

A total of 3556 participants with T2D from a hospital (derivation cohort) and 931 participants with T2D from a community survey (external validation cohort) were included. The primary outcome was the onset of diabetic kidney disease (DKD), and secondary outcomes included estimated glomerular filtration rate (eGFR) decline, macroalbuminuria, and end-stage renal disease (ESRD). In the derivation cohort, clusters were identified using the 5 components of MetS, and their relationships with the outcomes were assessed. To validate the findings, participants in the validation cohort were assigned to clusters. Multivariate odds ratios (ORs) of the primary outcome were evaluated in both cohorts, adjusted for multiple covariates at baseline.

RESULTS

In the derivation cohort, 6 clusters were identified as metabolic profiles. Compared with cluster 1, cluster 3 (severe hyperglycemia) had increased risks of DKD (hazard ratio [HR] [95% CI]: 1.72 [1.39-2.12]), macroalbuminuria (2.74 [1.84-4.08]), ESRD (4.31 [1.16-15.99]), and eGFR decline [P < .001]; cluster 4 (moderate dyslipidemia) had increased risks of DKD (1.97 [1.53-2.54]) and macroalbuminuria (2.62 [1.61-4.25]). In the validation cohort, clusters 3 and 4 were replicated to have significantly increased risks of DKD (adjusted ORs: 1.24 [1.07-1.44] and 1.39 [1.03-1.87]).

CONCLUSION

We identified 6 prevalent metabolic profiles in patients with T2D. Severe hyperglycemia and moderate dyslipidemia were validated as significant risk markers for DKD.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

IMPORTANCE AND AIMS

Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

METHODS

A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

RESULTS

Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

CONCLUSIONS AND RELEVANCE

There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Developing screening tools to estimate the risk of diabetic kidney disease in patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is an important microvascular complication of diabetes mellitus (DM).

OBJECTIVE

This study aimed to develop predictive nomograms to estimate the risk of DKD in patients with type 2 diabetes mellitus (T2DM).

METHODS

The medical records of patients with T2DM in our hospital from March 2022 to March 2023 were retrospectively reviewed. The enrolled patients were randomly selected for training and validation sets in a 7:3 ratio. The models for predicting risk of DKD were virtualized by the nomograms using logistic regression analysis.

RESULTS

Among the enrolled 597 patients, 418 were assigned to the training set, while 179 were assigned to the validation set. Using the predictors included glycated hemoglobin A1c (HbA1c), high density lipoprotein cholesterol (HDL-C), presence of diabetic retinopathy (DR) and duration of diabetes (DD), we constructed a full model (model 1) for predicting DKD. And using the laboratory indexes of HbA1c, HDL-C, and cystatin C (Cys-C), we developed a laboratory-based model (model 2). The C-indexes were 0.897 for model 1 and 0.867 for model 2, respectively. The calibration curves demonstrated a good agreement between prediction and observation in the two models. The decision curve analysis (DCA) curves showed that the two models achieved a net benefit across all threshold probabilities.

CONCLUSION

We successfully constructed two prediction models to evaluate the risk of DKD in patients with T2DM. The two models exhibited good predictive performance and could be recommended for DKD screening and early detection.

Relationship of cardiorenal risk factors with albuminuria based on age, smoking, glycaemic status and BMI: a retrospective cohort study of the UK Biobank data

Introduction

Smoking is harmful, and its cessation is recommended to prevent chronic kidney disease, which often begins with abnormal leakage of albumin in the urine, called albuminuria. Smoking cessation's effect on albuminuria depends on the pack-years smoked, length of abstinence, body mass index (BMI) and glycosylated haemoglobin (HbA1c). Using the UK Biobank data, we examined the relationship between these cardiorenal variables and albuminuria.

Methods

For this study, we selected a UK Biobank cohort with urinary albumin concentration (UAC) in the first and second visits. Participants were divided into progressor and regressor groups, where progressors were defined as those with increased UAC value, and regressors were those with decreased UAC value. Three different logistic regression models were fitted. In model 1, with a cohort design, we explored the impact of a change in age, HbA1c and BMI between the first and second visits and the UAC. In model 2 and 3, in a cross-sectional design, we explored which cardiorenal risk factors were associated with a rise or fall of UAC at the time point of the second visit. Results are expressed in OR and 95% CI.

Results

The prevalence of albuminuria was highest in ex-smokers who started smoking between the ages of 13 and 18. With a mean duration of 51 months, there was no statistically significant relationship between smoking status and BMI with albuminuria. Each year of ageing and each unit of increase in HbA1c (mmol/mol) increased the odds of progression of albuminuria by 20% and 3%, respectively. In ex-smokers, at the time point of the second visit, each year of smoking increased, and each year of abstinence decreased the odds by 4% and 6%, respectively.

Conclusion

Smokers should be supported to stop smoking and remain abstinent despite short-term weight gain. Childhood smoking should be actively discouraged.

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets

BACKGROUND AND AIMS

It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets.

METHODS

Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892).

RESULTS

We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity.

CONCLUSIONS

Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention

Cardiovascular diseases (CVDs) are the leading causes of morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). Secular changes in CVD outcomes have occurred over the past few decades, mainly due to a decline in the incidence of ischaemic heart disease. The onset of T2DM at a young age (<40 years), leading to a greater number of life-years lost, has also become increasingly common. Researchers are now looking beyond established risk factors in patients with T2DM towards the role of ectopic fat and, potentially, haemodynamic abnormalities in mediating important outcomes (such as heart failure). T2DM confers a wide spectrum of risk and is not necessarily a CVD risk equivalent, indicating the importance of risk assessment strategies (such as global risk scoring, consideration of risk-enhancing factors and assessment of subclinical atherosclerosis) to inform treatment. Data from epidemiological studies and clinical trials demonstrate that successful control of multiple risk factors can reduce the risk of CVD events by ≥50%; however, only ≤20% of patients achieve targets for risk factor reduction (plasma lipid levels, blood pressure, glycaemic control, body weight and non-smoking status). Improvements in composite risk factor control with lifestyle management (including a greater emphasis on weight loss interventions) and evidence-based generic and novel pharmacological therapies are therefore needed when the risk of CVD is high.

Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study

PURPOSE

To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth.

MATERIAL AND METHODS

Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results.

RESULTS

296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion.

CONCLUSIONS

CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.

Optic Nerve Head Capillary Network Quantified by Optical Coherence Tomography Angiography and Decline of Renal Function in Type 2 Diabetes: A Three-Year Prospective Study

PURPOSE

To assess the association of optic capillary perfusion with decline in the estimated glomerular filtration rate (eGFR) and to clarify its added value.

DESIGN

Prospective, observational cohort study.

METHODS

Patients with type 2 diabetes mellitus without diabetic retinopathy (non-DR) underwent standardized examinations annually during a 3-year follow-up period. The superficial capillary plexus (SCP), deep capillary plexus (DCP), and radial peripapillary plexus (RPC) of optic nerve head (ONH) were visualized using optical coherence tomography angiography (OCTA), and the perfusion density (PD) and vascular density were quantified for the whole image and circumpapillary regions of the ONH.  The lowest tercile of annual eGFR slope was defined as the rapidly progressive group, and the highest tercile was considered the stable group.

RESULTS

A total of 906 patients were included for 3-mm × 3-mm OCTA analysis. After adjusting for other confounders, each 1% decrease in baseline whole en face PD in SCP and RPC was associated with accelerated rates of decline in eGFR by -0.53 mL/min/1.73/m2 per year (95% confidence interval [CI] -0.17 to -0.90; P = .004) and -0.60 mL/min/1.73/m2 per year (95% CI 0.28-0.91), respectively. Adding both whole-image PD in SCP and whole-image PD in RPC to the conventional model increased the area under the curve from 0.696 (95% CI 0.654-0.737) to 0.725 (95% CI 0.685-0.765; P = .031). Another cohort of 400 eligible patients with 6-mm × 6 mm OCTA imaging validated the significant associations between ONH perfusion and rate of eGFR decline (P < .05).

CONCLUSIONS

Reduced capillary perfusion of ONH in patients with type 2 diabetes mellitus is associated with a greater eGFR decline, and it has additional predictive value for detecting an early stage and progression.

Validation of a risk prediction model for early chronic kidney disease in patients with type 2 diabetes: Data from the German/Austrian Diabetes Prospective Follow-up registry

AIM

To validate a recently proposed risk prediction model for chronic kidney disease (CKD) in type 2 diabetes (T2D).

MATERIALS AND METHODS

Subjects from the German/Austrian Diabetes Prospective Follow-up (DPV) registry with T2D, normoalbuminuria, an estimated glomerular filtration rate of 60 ml/min/1.73m² or higher and aged 39-75 years were included. Prognostic factors included age, body mass index (BMI), smoking status and HbA1c. Subjects were categorized into low, moderate, high and very high-risk groups. Outcome was CKD occurrence.

RESULTS

Subjects (n = 10 922) had a mean age of 61 years, diabetes duration of 6 years, BMI of 31.7 kg/m² , HbA1c of 6.9% (52 mmol/mol); 9.1% had diabetic retinopathy and 16.3% were smokers. After the follow-up (~59 months), 37.4% subjects developed CKD. The area under the curve (AUC; unadjusted base model) was 0.58 (95% CI 0.57-0.59). After adjustment for diabetes and follow-up duration, the AUC was 0.69 (95% CI 0.68-0.70), indicating improved discrimination. After follow-up, 15.0%, 20.1%, 27.7% and 40.2% patients in the low, moderate, high and very high-risk groups, respectively, had developed CKD. Increasing risk score correlated with increasing cumulative risk of incident CKD over a median of 4.5 years of follow-up (P < .0001).

CONCLUSIONS

The predictive model achieved moderate discrimination but good calibration in a German/Austrian T2D population, suggesting that the model may be relevant for determining CKD risk.

Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach

PURPOSE

This study aims to develop and validate a risk score to predict the occurrence of DKD in individuals with type 2 diabetes using a machine learning (ML) approach.

METHODS

By implementing Recursive Feature Elimination with Cross-Validation (RFECV) and RFE on the Diabetes Clinic of Imam Khomeini Hospital Complex (IKHC) dataset, the most critical features were identified. These features were used in the multivariate logistic regression (LR) analysis, and the discrimination and calibration of the model were evaluated. Finally, external validation of the model was assessed.

RESULTS

The development dataset included 1907 type 2 diabetic patients, 763 of whom developed DKD over 5 years. The predictive model performed well in the development dataset by implementing RFECV with the RF algorithm and considering six features (AUC: 79%). Using these features, the LR-based risk score indicated appropriate discrimination (AUC: 75.5%, 95% CI 73-78%) and acceptable calibration ([Formula: see text]= 7.44; p value = 0.49). This risk score was then used for 1543 diabetic patients in the validation dataset, including 633 patients with DKD over 5 years. The results showed sufficient discrimination (AUC: 75.8%, 95% CI 73-78%) of the risk score in the validation dataset.

CONCLUSIONS

We developed and validated a new risk score for predicting DKD via ML approach, which used common features in the periodic screening of type 2 diabetic patients that are readily available. In addition, a web-based online tool that is readily available to the public was developed to calculate the DKD risk score.

A Health Survey-Based Prediction Equation for Incident CKD

BACKGROUND

Prediction tools that incorporate self-reported health information could increase CKD awareness, identify modifiable lifestyle risk factors, and prevent disease. We developed and validated a survey-based prediction equation to identify individuals at risk for incident CKD (eGFR <60 ml/min per 1.73 m2), with and without a baseline eGFR.

METHODS

A cohort of adults with an eGFR ≥70 ml/min per 1.73 m2 from Ontario, Canada, who completed a comprehensive general population health survey between 2000 and 2015 were included (n=22,200). Prediction equations included demographics (age, sex), comorbidities, lifestyle factors, diet, and mood. Models with and without baseline eGFR were derived and externally validated in the UK Biobank (n=15,522). New-onset CKD (eGFR <60 ml/min per 1.73 m2) with ≤8 years of follow-up was the primary outcome.

RESULTS

Among Ontario individuals (mean age, 55 years; 58% women; baseline eGFR, 95 (SD 15) ml/min per 1.73 m2), new-onset CKD occurred in 1981 (9%) during a median follow-up time of 4.2 years. The final models included lifestyle factors (smoking, alcohol, physical activity) and comorbid illnesses (diabetes, hypertension, cancer). The model was discriminating in individuals with and without a baseline eGFR measure (5-year c-statistic with baseline eGFR: 83.5, 95% confidence interval [CI], 82.2 to 84.9; without: 81.0, 95% CI, 79.8 to 82.4) and well calibrated. In external validation, the 5-year c-statistic was 78.1 (95% CI, 74.2 to 82.0) and 66.0 (95% CI, 61.6 to 70.4), with and without baseline eGFR, respectively, and maintained calibration.

CONCLUSIONS

Self-reported lifestyle and health behavior information from health surveys may aid in predicting incident CKD.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast.aspx?p=CJASN&e=2023_01_10_CJN05650522.mp3.

Poly(l-lactic acid) Scaffold Releasing an α4β1 Integrin Agonist Promotes Nonfibrotic Skin Wound Healing in Diabetic Mice

Skin wound healing is a highly complex process that continues to represent a major medical problem, due to chronic nonhealing wounds in several classes of patients and to possible fibrotic complications, which compromise the function of the dermis. Integrins are transmembrane receptors that play key roles in this process and that offer a recognized druggable target. Our group recently synthesized GM18, a specific agonist for α4β1, an integrin that plays a role in skin immunity and in the migration of neutrophils, also regulating the differentiated state of fibroblasts. GM18 can be combined with poly(l-lactic acid) (PLLA) nanofibers to provide a controlled release of this agonist, resulting in a medication particularly suitable for skin wounds. In this study, we first optimized a GM18-PLLA nanofiber combination with a 7-day sustained release for use as skin wound medication. When tested in an experimental pressure ulcer in diabetic mice, a model for chronic nonhealing wounds, both soluble and GM18-PLLA formulations accelerated wound healing, as well as regulated extracellular matrix synthesis toward a nonfibrotic molecular signature. In vitro experiments using the adhesion test showed fibroblasts to be a principal GM18 cellular target, which we then used as an in vitro model to explore possible mechanisms of GM18 action. Our results suggest that the observed antifibrotic behavior of GM18 may exert a dual action on fibroblasts at the α4β1 binding site and that GM18 may prevent profibrotic EDA-fibronectin-α4β1 binding and activate outside-in signaling of the ERK1/2 pathways, a critical component of the wound healing process.

Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study

In view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) models were applied to evaluate the relationship between serum Mb and DKD. Serum Mb-mediated renal function impairment induced by MetS components was verified by causal mediation effect analysis. The area under the receiver operating characteristic curve of the DKD machine learning models incorporating serum Mb and MetS components reached 0.85. Feature importance analysis and SHAP showed that serum Mb and MetS components were important features. Further RCS models of DKD showed that the odds ratio was greater than 1 when serum Mb was > 80. Serum Mb showed a significant indirect effect in renal function impairment when using MetS components such as HOMA-IR, HGI and HDL-C/TC as a reason. Moderately elevated serum Mb is associated with the risk of DKD. Serum Mb may mediate MetS component-caused renal function impairment.

Comparison of a chronic kidney disease predictive model for type 2 diabetes mellitus in Malaysia using Cox regression versus machine learning approach

Background

Diabetes is one of the leading causes of chronic kidney disease (CKD) and end-stage renal disease. This study aims to develop and validate different risk predictive models for incident CKD and CKD progression in people with type 2 diabetes (T2D).

Methods

We reviewed a cohort of people with T2D seeking care from two tertiary hospitals in the metropolitan cities of the state of Selangor and Negeri Sembilan from January 2012 to May 2021. To identify the 3-year predictor of developing CKD (primary outcome) and CKD progression (secondary outcome), the dataset was randomly split into a training and test set. A Cox proportional hazards (CoxPH) model was developed to identify predictors of developing CKD. The resultant CoxPH model was compared with other machine learning models on their performance using C-statistic.

Results

The cohorts included 1992 participants, of which 295 had developed CKD and 442 reported worsening of kidney function. Equation for the 3-year risk of developing CKD included gender, haemoglobin A1c, triglyceride and serum creatinine levels, estimated glomerular filtration rate, history of cardiovascular disease and diabetes duration. For risk of CKD progression, the model included systolic blood pressure, retinopathy and proteinuria. The CoxPH model was better at prediction compared with other machine learning models examined for incident CKD (C-statistic: training 0.826; test 0.874) and CKD progression (C-statistic: training 0.611; test 0.655). The risk calculator can be found at https://rs59.shinyapps.io/071221/.

Conclusions

The Cox regression model was the best performing model to predict people with T2D who will develop a 3-year risk of incident CKD and CKD progression in a Malaysian cohort.

Development and Validation of a Lifetime Risk Model for Kidney Failure and Treatment Benefit in Type 2 Diabetes: 10-Year and Lifetime Risk Prediction Models

BACKGROUND AND OBJECTIVES

Individuals with type 2 diabetes are at a higher risk of developing kidney failure. The objective of this study was to develop and validate a decision support tool for estimating 10-year and lifetime risks of kidney failure in individuals with type 2 diabetes as well as estimating individual treatment effects of preventive medication.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

The prediction algorithm was developed in 707,077 individuals with prevalent and incident type 2 diabetes from the Swedish National Diabetes Register for 2002-2019. Two Cox proportional regression functions for kidney failure (first occurrence of kidney transplantation, long-term dialysis, or persistent eGFR <15 ml/min per 1.73 m2) and all-cause mortality as respective end points were developed using routinely available predictors. These functions were combined into life tables to calculate the predicted survival without kidney failure while using all-cause mortality as the competing outcome. The model was externally validated in 256,265 individuals with incident type 2 diabetes from the Scottish Care Information Diabetes database between 2004 and 2019.

RESULTS

During a median follow-up of 6.8 years (interquartile range, 3.2-10.6), 8004 (1%) individuals with type 2 diabetes in the Swedish National Diabetes Register cohort developed kidney failure, and 202,078 (29%) died. The model performed well, with c statistics for kidney failure of 0.89 (95% confidence interval, 0.88 to 0.90) for internal validation and 0.74 (95% confidence interval, 0.73 to 0.76) for external validation. Calibration plots showed good agreement in observed versus predicted 10-year risk of kidney failure for both internal and external validation.

CONCLUSIONS

This study derived and externally validated a prediction tool for estimating 10-year and lifetime risks of kidney failure as well as life years free of kidney failure gained with preventive treatment in individuals with type 2 diabetes using easily available clinical predictors.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2022_11_04_CJN05020422.mp3.

Development of minimal resource pre-screening tools for chronic kidney disease in people with type 2 diabetes

Regular chronic kidney disease (CKD) screening can facilitate earlier diagnosis of CKD and preventative action to reduce the risk of CKD progression. People with type 2 diabetes are at a higher risk of developing CKD; hence, it is recommended that they undergo annual screening. However, resources may be limited, particularly in lower-to-middle income countries, and those at the highest risk of having an abnormal CKD screening result should be prioritised for screening. We have developed models to determine which patients are at a high risk of renal impairment. We have shown that, for people with type 2 diabetes and no previous diagnosis of CKD stage 3-5, it is possible to use age, gender, body mass index, duration of type 2 diabetes and blood pressure information to detect those at a higher risk of a reduced glomerular filtration rate. When blood measurements are available, triglyceride and cholesterol measurements can be used to improve the estimate of the risk. Even though risk factors were associated with an increased urine albumin:creatinine ratio, we found no clinical benefit of using the model over a screen-all approach.

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy

Diabetic nephropathy (DN), a frequent microvascular complication of diabetes, has been recognized as a primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies found that autophagy of renal tubular epithelial cells plays an important role in DN pathogenesis. Our research aimed to investigate the differentially expressed autophagy-related genes (DEARGs) between DN and healthy renal tubule samples and identify a novel autophagy-related biomarker associated with tubulointerstitial injury in DN. In this study, gene expression profiles of renal tubules from 10 DN patients and 24 healthy controls in the GSE30122 dataset were analyzed, and 43 DEARGs were identified by bioinformatics analysis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and correlation analysis were performed on DEARGs, and the hub gene prolyl 4-hydroxylase subunit beta (P4HB) was screened by protein–protein interaction and verified by utilizing other datasets and stimulating HK-2 cells under high glucose concentration. We found that the expression of P4HB in renal tubules was correlated with renal function. In summary, our research provided novel insights for comprehension of DN molecular mechanisms and identified P4HB as a novel autophagy-related biomarker of DN.

A novel kidney disease index reflecting both the albumin-to-creatinine ratio and estimated glomerular filtration rate, predicted cardiovascular and kidney outcomes in type 2 diabetes

Background

The estimated glomerular filtration rate (eGFR) and the albumin-to-creatinine ratio (ACR) are risk factors for diabetes-related outcomes. A composite that captures information from both may provide a simpler way of assessing risk.

Methods

9115 of 9901 Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) participants with both an ACR and eGFR at baseline were included in this post hoc epidemiologic analysis. The hazard of higher baseline levels of 1/eGFR and natural log transformed ACR (calculated as ln [ACR × 100] to eliminate negative values) and their interaction for incident major adverse cardiovascular events (MACE), kidney outcomes, and deaths was estimated. The hazard of the geometric mean of these two baseline measures (the kidney disease index or KDI) was also assessed.

Results

A non-linear relationship was observed between 1/eGFR and all three outcomes, and between ln [ACR × 100] and the kidney outcome. There was also a negative interaction between these two risk factors with respect to MACE and death. Conversely, a linear relationship was noted between the KDI and all three outcomes. People in the highest KDI fifth experienced the highest incidence of MACE, death, and the kidney outcome (4.43, 4.56, and 5.55/100 person-years respectively). C statistics for the KDI were similar to those for eGFR and albuminuria.

Conclusions

The KDI combines the baseline eGFR and ACR into a novel composite risk factor that has a simple linear relationship with incident serious outcomes in people with diabetes and additional CV risk factors.

Trial Registration clinicaltrials.gov NCT01394952.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01594-6.

Bibliometric Study of Trends in the Diabetic Nephropathy Research Space from 2016 to 2020

Background

Diabetic nephropathy (DN) is one of the most common microvascular complications of diabetes mellitus (DM), but no bibliometric studies pertaining to DN have been published within the last 5 years.

Objectives

Most prior studies have focused on specific problems in the DN field. This study attempts to sort out and visualize the knowledge framework in this research space from a holistic and highly generalized perspective. Readers can quickly understand and master the knowledge regarding DN research conducted from 2016 to 2020, in addition to predicting future research hotspots and possible directions for development in this field in a comprehensive and scientifically valid manner.

Methods

Literature information, discourse matrices, and co-occurrence matrices were generated using BICOMB. gCLUTO was used for biclustering analyses and visualization. Strategic diagrams were generated using GraphPad Prism 5. The social network analysis (SNA) was analyzed and plotted using Ucinet 6.0 and Netdraw.

Results

In total, 55 high-frequency MeSH terms/MeSH subheadings were selected and grouped into 5 clusters in a biclustering analysis. These analyses revealed that extensive studies of the etiology, diagnosis, and treatment of DN have been conducted over the last 5 years, while further research regarding DN-related single nucleotide polymorphisms, miRNAs, and signal transduction are warranted as these research areas remain relatively immature.

Conclusion

Together, these results outline a robust knowledge structure pertaining to the field of DN-related research over the last 5 years, providing a valuable resource for readers by enabling the easy comprehension of relevant information. In addition, this analysis highlights predicted DN-related research directions and hotspots.