Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: a retrospective study

Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in type 2 diabetes mellitus patients with chronic kidney disease

Chronic kidney disease (CKD) is often a common comorbidity in critically ill patients with type 2 diabetes mellitus (T2DM). This study explored the relationship between blood urea nitrogen to serum albumin ratio (BAR) and mortality in T2DM patients with CKD in intensive care unit (ICU). Patients were recruited from the Medical Information Mart database, retrospectively. The primary and secondary outcomes were 90-day mortality, the length of ICU stay, hospital mortality and 30-day mortality, respectively. Cox regression model and Kaplan-Meier survival curve were performed to explore the association between BAR and 90-day mortality. Subgroup analyses were performed to determine the consistency of this association. A total of 1920 patients were enrolled and divided into the three groups (BAR < 9.2, 9.2 ≤ BAR ≤ 21.3 and BAR > 21.3). The length of ICU stay, 30-day mortality, and 90-day mortality in the BAR > 21.3 group were significantly higher than other groups. In Cox regression analysis showed that high BAR level was significantly associated with increased greater risk of 90-day mortality. The adjusted HR (95%CIs) for the model 1, model 2, and model 3 were 1.768 (1.409-2.218), 1.934, (1.489-2.511), and 1.864, (1.399-2.487), respectively. Subgroup analysis also showed the consistency of results. The Kaplan-Meier survival curve analysis revealed similar results as well that BAR > 21.3 had lower 90-day survival rate. High BAR was significantly associated with increased risk of 90-day mortality. BAR could be a simple and useful prognostic tool in T2DM patients with CKD in ICU.

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.

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Purpose

To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

Patients and methods

A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA).

Results

T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively.

Conclusion

The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

BACKGROUND

Among older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.

AIM

To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.

METHODS

The clinical data of 210 patients diagnosed with T2DM and admitted to the First People's Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (n = 147) and a test set (n = 63) in a 7:3 ratio using a random function. The training set was used to construct the nomogram, decision tree, and random forest models, and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity, specificity, accuracy, recall, precision, and area under the receiver operating characteristic curve.

RESULTS

Among the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (P < 0.05), whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant (P > 0.05).

CONCLUSION

Among the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.

Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.

Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population.

METHODS

A cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models' discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model's effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software.

RESULTS

The area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%-80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count.

CONCLUSIONS

A dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.

Evaluation of Trace Elements Levels and Construction of Auxiliary Prediction Model in Patients with Diabetes Ketoacidosis in Type 1 Diabetes

Background

Trace elements play an important role in reflecting physical metabolic status, but have been rarely evaluated in diabetes ketoacidosis (DKA). Since clinical biochemical parameters are the first-line diagnostic data mastered by clinical doctors and DKA has a rapid progression, it is crucial to fully utilize clinical data and combine innovative parameters to assist in assessing disease progression. The aim of this study was to evaluate the levels of trace elements in DKA patients, followed by construction of predictive models combined with the laboratory parameters.

Methods

A total of 96 T1D individuals (48 DKA patients) were collected from the First Hospital of Jilin University. Serum calcium (Ca), magnesium (Mg), zinc (Zn), copper (Cu), iron (Fe) and selenium (Se) were measured by Inductively Coupled Plasma Mass Spectrometry, and the data of biochemical parameters were collected from the laboratory information system. Training and validation sets were used to construct the model and examine the efficiency of the model. The lambda-mu-sigma method was used to evaluate the changes in the model prediction efficiency as the severity of the patient's condition increases.

Results

Lower levels of serum Mg, Ca and Zn, but higher levels of serum Fe, Cu and Se were found in DKA patients. Low levels of total protein (TP), Zn and high levels of lipase would be an efficient combination for the prediction of DKA (Area under curves for training set and validation set were 0.867 and 0.961, respectively). The examination test confirmed the clinical applicability of the constructed models. The increasing predictive efficiency of the model was found with NACP.

Conclusion

More severe oxidative stress in DKA led to further imbalance of trace elements. The combination of TP, lipase and Zn could predict DKA efficiently, which would benefit the early identification and prevention of DKA to improve prognosis.

Risk factors and prognostic nomogram for patients with second primary cancers after lung cancer using classical statistics and machine learning

Previous studies have revealed an increased risk of secondary primary cancers (SPC) after lung cancer. The prognostic prediction models for SPC patients after lung cancer are particularly needed to guide screening. Therefore, we study retrospectively analyzed the Surveillance, Epidemiology, and End Results (SEER) database using classical statistics and machine learning to explore the risk factors and construct a novel overall survival (OS) prediction nomogram for patients with SPC after lung cancer. Data of patients with SPC after lung cancer, covering 2000 to 2016, were gathered from the SEER database. The incidence of SPC after lung cancer was calculated by Standardized incidence ratios (SIRs). Cox proportional hazards regression, machine learning (ML), Kaplan-Meier (KM) methods, and log-rank tests were conducted to identify the important prognostic factors for predicting OS. These significant prognostic factors were used for the development of an OS prediction nomogram. Totally, 10,487 SPC samples were randomly divided into training and validation cohorts (model construction and internal validation) from the SEER database. In the random forest (RF) and extreme gradient boosting (XGBoost) feature importance ranking models, age was the most important variable which was also reflected in the nomogram. And, the models that combined machine learning with cox proportional hazards had a better predictive performance than the model that only used cox proportional hazards (AUC = 0.762 in RF, AUC = 0.737 in XGBoost, AUC = 0.722 in COX). Calibration curves and decision curve analysis (DCA) curves also revealed that our nomogram has excellent clinical utility. The web-based dynamic nomogram calculator was accessible on https://httseer.shinyapps.io/DynNomapp/ . The prognosis characteristics of SPC following lung cancer were systematically reviewed. The dynamic nomogram we constructed can provide survival predictions to assist clinicians in making individualized decisions.

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Ferroptosis: new insight into the mechanisms of diabetic nephropathy and retinopathy

Diabetic nephropathy (DN) and diabetic retinopathy (DR) are the most serious and common diabetes-associated complications. DN and DR are all highly prevalent and dangerous global diseases, but the underlying mechanism remains to be elucidated. Ferroptosis, a relatively recently described type of cell death, has been confirmed to be involved in the occurrence and development of various diabetic complications. The disturbance of cellular iron metabolism directly triggers ferroptosis, and abnormal iron metabolism is closely related to diabetes. However, the molecular mechanism underlying the role of ferroptosis in DN and DR is still unclear, and needs further study. In this review article, we summarize and evaluate the mechanism of ferroptosis and its role and progress in DN and DR, it provides new ideas for the diagnosis and treatment of DN and DR.

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.

The effect of probiotics/synbiotics supplementation on renal and liver biomarkers in patients with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials

Despite the apparent beneficial effects of probiotics/synbiotics on glucose haemostasis, lipid profile and inflammatory responses, it is not clear whether these beneficial effects also impact renal and hepatic function in diabetes. Therefore, we sought to assess the effect of probiotics/synbiotics supplementation on renal and liver biomarkers in adults with type 2 diabetes mellitus (T2DM) using a systematic review and meta-analysis of randomised controlled trials (RCT). PubMed, Scopus, Web of Science and Cochrane Library were systematically searched, up to February 2021. The pooled weighted mean difference (WMD) was estimated using a random-effects model. The methodological quality of studies, as well as certainty of evidence, was assessed using standard scales. Fifteen related trials were identified. Meta-analysis of six trials, involving 426 participants, indicated that probiotics/synbiotics supplementation reduced serum levels of creatinine (WMD = -0·10 mg/dl, 95 % CI -0·20, -0·00; P = 0·01; I ² = 87·7 %; P-heterogeneity < 0·001), without any significant effect on blood urea nitrogen (BUN), glomerular filtration rate or microalbuminuria. No significant improvement was found on liver biomarkers following probiotics/synbiotics supplementation. The subgroup analysis showed a significant improvement in BUN when follow-up duration lasted for 12 weeks or more (WMD = -1·215 mg/dl, 95 % CI -1·933, -0·496; P = 0·001) and in creatinine levels in patients with renal dysfunction (WMD = -0·209 mg/dl, 95 % CI -0·322, -0·096; P < 0·001). Our results are insufficient to advocate the use of probiotics/synbiotics for improving renal or liver function in patients with T2DM. Indeed, due to the low certainty of evidence, these findings need to be affirmed in further high-quality RCT.

External validation of prognostic models for chronic kidney disease among type 2 diabetes

BACKGROUND

Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort.

METHODS

A nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting.

RESULTS

Six relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565-0.605) to 0.786 (0.765-0.806) for CKD and 0.657 (0.610-0.703) to 0.760 (0.705-0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975-1.024) to 1.009 (0.929-1.090). Hosmer-Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low's of 0.114 for CKD and Elley's of 0.025 for ESRD.

CONCLUSIONS

All models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low's (developed in Singapore) and Elley's model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.

Prediction model for the risk of ESKD in patients with primary FSGS

The purpose of this study is to build a prediction model for accurate assessment of the risk of end-stage kidney disease (ESKD) in individuals with primary focal segmental glomerulosclerosis (FSGS) by integrating clinical and pathological features at biopsy. The prediction model was created based on a retrospective study of 99 patients with biopsy-proven primary FSGS diagnosed at our hospital between December 2012 and December 2019. We assessed discriminative ability and predictive accuracy of the model by C-index and calibration plot. Internal validation of the prediction model was performed with 1000-bootstrap procedure. Eight patients (8.1%) progressed to ESKD before 31 March 2021. Univariate analysis revealed that disease duration before biopsy, hematuria, hemoglobin, eGFR, and percentages of sclerosis and global sclerosis were associated with renal outcome. In multivariate analysis, three predictors were included in final prediction model: eGFR, hematuria, and percentage of sclerosis. The C-index of the model was 0.811 and 5-year calibration plot showed good agreement between predicted renal survival probability and actual observation. A nomogram and an online risk calculator were built on the basis of the prediction model. In conclusion, we constructed and internally validated the first prediction model for risk of ESKD in primary FSGS, which showed good discriminative ability and calibration performance. The prediction model provides an accurate and simple strategy to predict renal prognosis which may help to identify patients at high risk of ESKD and guide the management for patients with primary FSGS in clinical practice.

Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study

Purpose

To develop a nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in type 2 diabetes mellitus (T2DM) patients.

Methods

We collect information from electronic medical record systems. The data were split into a training set (n=521) containing 73.8% of patients and a validation set (n=185) holding the remaining 26.2% of patients based on the date of data collection. Stepwise and multivariable logistic regression analyses were used to screen out DN risk factors. A predictive model including selected risk factors was developed by logistic regression analysis. The results of binary logistic regression are presented through forest plots and nomogram. Lastly, the c-index, calibration plots, and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram in internal and external validation. The clinical benefit of the model was evaluated by decision curve analysis.

Results

Predictors included serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and Diabetic peripheral neuropathy (DPN). Harrell’s C-indexes were 0.773 (95% CI:0.726–0.821) and 0.758 (95% CI:0.679–0.837) in the training and validation sets, respectively. Decision curve analysis (DCA) demonstrated that the novel nomogram was clinically valuable.

Conclusion

Our simple nomogram with seven factors may help clinicians predict the risk of DN incidence in patients with T2DM.

Association Between the Risk of Non-Alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes and Chronic Kidney Disease

OBJECTIVE

To explore the relationship between non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM).

METHODS

A total of 1168 patients with T2DM were divided into the non-CKD and CKD groups, and the difference in the prevalence of NAFLD was compared. The differences in serum creatinine (SCr) and urine albumin-to-creatinine ratio (UACR) levels were compared between the non-NAFLD and NAFLD groups. Patients with T2DM were divided into three groups according to their UACR levels (UACR < 30 mg/g [U1 group]; UACR ≤ 30 mg/g to < 300 mg/g [U2 group]; and UACR ≥ 300 mg/g [U3 group]) or estimated glomerular filtration rate (eGFR) levels (≥ 90 mL/min [G1 group]; eGFR ≤ 60 mL/min to < 90 mL/min [G2 group]; and eGFR < 60 mL/min (G3 group]). The difference in the prevalence and risks of NAFLD in the different UACR or eGFR level groups was analyzed.

RESULTS

The prevalence of NAFLD in the CKD group was higher than that in the non-CKD group (63.5% vs 50.5%, p < 0.001). The SCr and UACR levels in the NAFLD group were higher than those in the non-NAFLD group (both p<0.05). The prevalence of NAFLD in the U3 group (75.6%) was higher than that in the U1 (50.5%, p < 0.05) and U2 (60.1%, p < 0.05) groups, and the prevalence of NAFLD in the U2 group (60.1%) was higher than that in the U1 group (50.5%, p < 0.05). The risk of NAFLD in the U3 group was higher than that in the U2 group (odds ratio [OR] = 3.032 and 1.473). Despite adjusting the parameters further, the NAFLD risk in the U3 group remained higher than that in the U2 group (OR = 1.660 and 2.342).

CONCLUSION

The risk of NAFLD in patients with T2DM is closely related to CKD.

Establishment and Validation of a Nomogram Model for Prediction of Diabetic Nephropathy in Type 2 Diabetic Patients with Proteinuria

PURPOSE

To establish and validate the nomogram model for predicting diabetic nephropathy (DN) in type 2 diabetes mellitus (T2DM) patients with proteinuria.

METHODS

A total of 102 patients with T2DM and proteinuria who underwent renal biopsy were included in this study. According to pathological classification of the kidney, the patients were divided into two groups, namely, a DN group (52 cases) and a non-diabetic renal disease (NDRD) group (50 cases). The clinical data were collected, and the factors associated with diabetic nephropathy (DN) were analyzed with multivariate logistic regression. A nomogram model for predicting DN risk was constructed by using R4.1 software. Receiver operator characteristic (ROC) curves were generated, and the K-fold cross-validation method was used for validation. A consistency test was performed by generating the correction curve.

RESULTS

Systolic blood pressure (SBP), diabetic retinopathy (DR), hemoglobin (Hb), fasting plasma glucose (FPG) and triglyceride/cystatin C (TG/Cys-C) ratio were independent factors for DN in T2DM patients with proteinuria (P<0.05). The nomogram model had good prediction efficiency. If the total score of the nomogram exceeds 200, the probability of DN is as high as 95%. The area under the ROC curve was 0.9412 (95% confidence interval (CI) = 0.8981-0.9842). The 10-fold cross-validation showed that the prediction accuracy of the model was 0.8427. The Hosmer-Lemeshow (H-L) test showed that there was no significant difference between the predicted value and the actual observed value (X ² = 6.725, P = 0.567). The calibration curve showed that the fitting degree of the DN nomogram prediction model was good.

CONCLUSION

The nomogram model constructed in the present study improves the diagnostic efficiency of DN in T2DM patients with proteinuria, and it has a high clinical value.

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

BACKGROUND

Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models.

METHODS

Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D).

RESULTS

In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively.

CONCLUSIONS

Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018105287.

Predicting the Failure Risk of Internal Fixation Devices in Chinese Patients Undergoing Spinal Internal Fixation Surgery: Development and Assessment of a New Predictive Nomogram

The current study is aimed at developing and validating a nomogram of the risk of failure of internal fixation devices in Chinese patients undergoing spinal internal fixation. We collected data from a total of 1139 patients admitted for spinal internal fixation surgery at the First Affiliated Hospital of Guangxi Medical University from May 2012 to February 2019. Of these, 1050 patients were included in the spinal internal fixation group and 89 patients in the spinal internal fixation device failure group. Patients were divided into training and validation tests. The risk assessment of the failure of the spinal internal fixation device used 14 characteristics. In the training test, the feature selection of the failure model of the spinal internal fixation device was optimized using the least absolute shrinkage and selection operator (LASSO) regression model. Based on the characteristics selected in the LASSO regression model, multivariate logistic regression analysis was used for constructing the model. Identification, calibration, and clinical usefulness of predictive models were assessed using C-index, calibration curve, and decision curve analysis. A validation test was used to validate the constructed model. In the training test, the risk prediction nomogram included gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. The model demonstrated moderate predictive power with a C-index of 0.722 (95% confidence interval: 0.644-0.800) and the area under the curve (AUC) of 0.722. Decision curve analysis depicted that the failure risk nomogram was clinically useful when the probability threshold for internal fixation device failure was 3%. The C-index of the validation test was 0.761. This novel nomogram of failure risk for spinal instrumentation includes gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. It can be used for evaluating the risk of instrumentation failure in patients undergoing spinal instrumentation surgery.

A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study

OBJECTIVE

To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM).

METHODS

Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation.

RESULTS

Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783-0.853) and an AUC of 0.813 (95%CI: 0.778-0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763-0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%.

CONCLUSION

Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.

Effect of probiotics on oxidative stress and inflammatory status in diabetic nephropathy: A systematic review and meta-analysis of clinical trials

This systematic review and meta-analysis was performed to evaluate the effect of probiotics on serum high sensitivity-C reactive protein (hs-CRP) and oxidative stress biomarkers among patients with Diabetic Nephropathy (DN). Electronic databases were searched through May 10, 2020. Seven trials that included 340 patients were identified for analysis. Meta-analysis indicated that probiotics significantly reduced hs-CRP (WMD = -1.53 mg/L; 95% CI = -2.38, -0.69; P < 0.001) and Malondialdehyde (MDA) (WMD = -0.62 ɥmol/L; 95% CI = -1.18, -0.06; P = 0.030) levels in DN patients, whereas they increased Glutathione (GSH) (WMD = 73.84 ɥmol/L; 95% CI = 24.3, 123.29; P = 0.003) and Total Antioxidant Capacity (TAC) (WMD = 26.54 mmol/L; 95% CI = 6.23, 46.85; P = 0.010). Therefore, probiotics may improve hs-CRP and oxidative stress biomarkers in DN population.