A Differential Diagnosis Model For Diabetic Nephropathy And Non-Diabetic Renal Disease In Patients With Type 2 Diabetes Complicated With Chronic Kidney Disease

Assessment of the Added Value of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging in Identifying Non-Diabetic Renal Disease in Patients With Type 2 Diabetes Mellitus

BACKGROUND

Identification of non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus (T2DM) may help tailor treatment. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising tool to evaluate renal function but its potential role in the clinical differentiation between diabetic nephropathy (DN) and NDRD remains unclear.

PURPOSE

To investigate the added role of IVIM-DWI in the differential diagnosis between DN and NDRD in patients with T2DM.

STUDY TYPE

Prospective.

POPULATION

Sixty-three patients with T2DM (ages: 22-69 years, 17 females) confirmed by renal biopsy divided into two subgroups (28 DN and 35 NDRD).

FIELD STRENGTH/SEQUENCE

3 T/ T2 weighted imaging (T2WI), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

ASSESSMENT

The parameters derived from IVIM-DWI (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion fraction [f]) were calculated for the cortex and medulla, respectively. The clinical indexes related to renal function (eg cystatin C, etc.) and diabetes (eg diabetic retinopathy [DR], fasting blood glucose, etc.) were measured and calculated within 1 week before MRI scanning. The clinical model based on clinical indexes and the IVIM-based model based on IVIM parameters and clinical indexes were established and evaluated, respectively.

STATISTICAL TESTS

Student's t-test; Mann-Whitney U test; Fisher's exact test; Chi-squared test; Intraclass correlation coefficient; Receiver operating characteristic analysis; Hosmer-Lemeshow test; DeLong's test. P < 0.05 was considered statistically significant.

RESULTS

The cortex D*, DR, and cystatin C values were identified as independent predictors of NDRD in multivariable analysis. The IVIM-based model, comprising DR, cystatin C, and cortex D*, significantly outperformed the clinical model containing only DR, and cystatin C (AUC = 0.934, 0.845, respectively).

DATA CONCLUSION

The IVIM parameters, especially the renal cortex D* value, might serve as novel indicators in the differential diagnosis between DN and NDRD in patients with T2DM.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis: a retrospective case-control study

The purpose of this study was to describe and compare the clinical data, laboratory examination and imaging examination of tuberculous spondylitis (TS) and pyogenic spondylitis (PS), and to provide ideas for diagnosis and treatment intervention. The patients with TS or PS diagnosed by pathology who first occurred in our hospital from September 2018 to November 2021 were studied retrospectively. The clinical data, laboratory results and imaging findings of the two groups were analyzed and compared. The diagnostic model was constructed by binary logistic regression. In addition, an external validation group was used to verify the effectiveness of the diagnostic model. A total of 112 patients were included, including 65 cases of TS with an average age of 49 ± 15 years, 47 cases of PS with an average of 56 ± 10 years. The PS group had a significantly older age than the TS group (P = 0.005). In laboratory examination, there were significant differences in WBC, neutrophil (N), lymphocyte (L), ESR, CRP, fibrinogen (FIB), serum albumin (A) and sodium (Na). The difference was also statistically significant in the comparison of imaging examinations at epidural abscesses, paravertebral abscesses, spinal cord compression, involvement of cervical, lumbar and thoracic vertebrae. This study constructed a diagnostic model, which was Y (value of TS > 0.5, value of PS < 0.5) = 1.251 * X1 (thoracic vertebrae involved = 1, thoracic vertebrae uninvolved = 0) + 2.021 * X2 (paravertebral abscesses = 1, no paravertebral abscess = 0) + 2.432 * X3 (spinal cord compression = 1, no spinal cord compression = 0) + 0.18 * X4 (value of serum A)-4.209 * X5 (cervical vertebrae involved = 1, cervical vertebrae uninvolved = 0)-0.02 * X6 (value of ESR)-0.806 * X7 (value of FIB)-3.36. Furthermore, the diagnostic model was validated using an external validation group, indicating a certain value in diagnosing TS and PS. This study puts forward a diagnostic model for the diagnosis of TS and PS in spinal infection for the first time, which has potential guiding value in the diagnosis of them and provides a certain reference for clinical work.

Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning

BACKGROUND

The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately.

METHODS

Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients' electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated.

RESULTS

Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that DD-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD.

CONCLUSION

Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.

Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease

PURPOSE

Differentiating between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) in patients with Type 2 diabetes mellitus (T2DM) is important due to implications on treatment and prognosis. Clinical methods to accurately distinguish DKD from NDKD are lacking. We aimed to develop and validate a novel nomogram to predict DKD in patients with T2DM and proteinuric kidney disease to guide decision for kidney biopsy.

METHODS

A hundred and two patients with Type 2 Diabetes Mellitus (T2DM) who underwent kidney biopsy from 1st January 2007 to 31st December 2016 were analysed. Univariate and multivariate analyses were performed to identify predictive variables and construct a nomogram. The discriminative ability of the nomogram was assessed by calculating the area under the receiver operating characteristic curve (AUROC), while calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plot. Internal validation of the nomogram was assessed using bootstrap resampling.

RESULTS

Duration of T2DM, HbA1c, absence of hematuria, presence of diabetic retinopathy and absence of positive systemic biomarkers were found to be independent predictors of DKD in multivariate analysis and were represented as a nomogram. The nomogram showed excellent discrimination, with a bootstrap-corrected C statistic of 0.886 (95% CI 0.815-0.956). Both the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (p = 0.242) showed high degree of agreement between the prediction and actual outcome, with the bootstrap bias-corrected curve similarly indicating excellent calibration.

CONCLUSIONS

A novel nomogram incorporating 5 clinical parameters is useful in predicting DKD in type 2 diabetes mellitus patients with proteinuric kidney disease.

Prevalence of non-diabetic kidney disease and inability of clinical predictors to differentiate it from diabetic kidney disease: results from a prospectively performed renal biopsy study

INTRODUCTION

Renal involvement in type 2 diabetes mellitus (T2DM) may be due to diabetes (diabetic kidney disease (DKD)), causes other than diabetes (non-diabetic kidney disease (NDKD)) or overlap of DKD and NDKD (mixed kidney disease group). Prevalence of NDKD and predictive value of clinical or biochemical indicators have been explored in retrospective cohorts with preselection biases warranting the need for prospectively conducted unbiased renal biopsy study.

RESEARCH DESIGN AND METHODS

Consecutive subjects aged >18 years with T2DM and renal involvement with estimated glomerular filtration rate of 30-60 mL/min/m² and/or albumin:creatinine ratio of >300 mg/g were offered renal biopsy. Prevalence of DKD, NDKD and mixed kidney disease was documented. Clinical/laboratory parameters of subjects were recorded and compared between groups and were tested for ability to predict histopathological diagnosis.

RESULTS

We screened 6247 subjects with T2DM of which 869 fulfilled inclusion criteria for biopsy. Of the 869 subjects, biopsy was feasible in 818 subjects. Out of 818, we recruited first 110 subjects who agreed to undergo renal biopsy. Among those 110 subjects, 73 (66.4%) had DKD; 20 (18.2 %) had NDKD; and 17 (15.4 %) had mixed kidney disease. Subjects with NDKD as compared with DKD had shorter duration of diabetes (p<0.001), absence of retinopathy (p<0.001) and absence of neuropathy (p<0.001). Logistic regression revealed that only presence of retinopathy and duration of diabetes were statistically significant to predict histopathological diagnosis of DKD. 30% of DKD did not have retinopathy, thereby limiting the utility of the same as a discriminator. Use of traditional indicators of biopsy would have indicated a need for renal biopsy in 87.2% of subjects, though 64.5% of the subjects had DKD, who would not have benefitted from biopsy.

CONCLUSION

NDKD and mixed kidney disease in T2DM with renal involvement are very common and traditionally used parameters to select biopsies are of limited value in clinical decision making.

Clinical predictors for nondiabetic kidney diseases in patients with type 2 diabetes mellitus: a retrospective study from 2017 to 2021

BACKGROUND

Nondiabetic kidney disease (NDKD), which is prevalent among patients with diabetes mellitus (DM), is considerably different from diabetic kidney disease (DKD) in terms of the pathological features, treatment strategy and prognosis. Although renal biopsy is the current gold-standard diagnostic method, it cannot be routinely performed due to a range of risks. The aim of this study was to explore the predictors for differentiating NDKD from DKD to meet the urgent medical needs of patients who cannot afford kidney biopsy.

METHODS

This is a retrospective study conducted by reviewing the medical records of patients with type 2 DM who underwent percutaneous renal biopsy at the Affiliated Hospital of Guizhou Medical University between January 2017 and May 2021. The demographic data, clinical data, blood test results, and pathological examination results of the patients were obtained from their medical records. Multivariate regression analysis was performed to evaluate the predictive factors for NDKD.

RESULTS

A total of 244 patients were analyzed. The median age at biopsy was 55 (46, 62) years. Patients diagnosed with true DKD, those diagnosed with NDKD and those diagnosed with NDKD superimposed DKD represented 48.36% (118/244), 45.9% (112/244) and 5.74% (14/244), respectively, of the patient population. Immunoglobulin A nephropathy was the most common type of lesion in those with NDKD (59, 52.68%) and NDKD superimposed DKD (10, 71.43%). Independent predictive indicators for diagnosing NDKD included a DM duration of less than 5 years (odds ratio [OR] = 4.476; 95% confidence interval [CI]: 2.257-8.877; P < 0.001), an absence of diabetic retinopathy (OR = 4.174; 95% CI: 2.049-8.502; P < 0.001), a high RBC count (OR = 1.901; 95% CI: 1.251-2.889; P = 0.003), and a negative of urinary glucose excretion test result (OR = 2.985; 95% CI: 1.474-6.044; P = 0.002)..

CONCLUSIONS

A DM duration less than 5 years, an absence of retinopathy, a high RBC count and an absence of urinary glucose excretion were independent indicators for the diagnosis of NDKD, suggesting that patients with NDKD may require a different treatment regimen than those with DKD.

Determination of cumulative glucose levels HbA1C and some biochemical variables in the serum of Diabetic nephropathy patients

The study included measuring the concentrations of each of the cumulative sugar HbA1C Glycosylated hemoglobin, urea, creatinine, uric acid, glucose, glucose uric acid, albumin, total protein and calcium, in addition to identifying the concentrations of some electrolytes (sodium, potassium, chloride, calcium) in (35) blood samples from patients with diabetic nephropathy (14 males, 12 females) aged (16-62) years who came to some laboratories in the city of Mosul and compared them to control samples (26) samples. The results showed a significant increase in cumulative sugar, uric acid, glucose and potassium concentrations in the serum of patients of both sexes. An insignificant increase in urea and creatinine concentrations and a significant decrease in albumin, sodium and chloride concentrations, while calcium and total protein did not show a significant difference compared to control samples. When comparing cases of infection between males and females, the results showed a significant increase in the cumulative sugar level for males and an insignificant increase in glucose concentration. In contrast, creatinine, uric acid, total protein and albumin did not significantly differ for males compared to infected females. Keywords cumulative glucose, biochemical variables, Diabetic

New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

BACKGROUND

The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD.

METHODS

We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017-April 2019).

RESULTS

In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively.

CONCLUSION

We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov, NCT03865914.

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.

Manifestation of rs1888747 polymorphisms in the FRMD3 gene in diabetic kidney disease and diabetic retinopathy in type 2 diabetes patients

Background

FRMD3 polymorphisms has suggested that they could be an alternative test to differentiate diabetic nephropathy (DN) from nondiabetic renal disease (NDRD) in type 2 diabetes mellitus (DM) patients. This study was performed to investigate the relationship between the FRMD3 gene and clinical characteristics of DN.

Methods

Patients who already had renal pathologic results were tested for FRMD3 polymorphisms. The subjects were classified into three groups; DN with diabetic retinopathy (DR), DN without DR, and DM with NDRD. FRMD3 polymorphisms were analyzed in each group.

Results

The prevalence of GG, CG, and CC was 44.4%, 42.2%, and 13.3% respectively. There was no significant difference in clinical parameters, which consisted of disease duration, proteinuria, and complications in DN with or without DR and DM with NDRD. The G allele was mainly found in DN with DR patients (50.8%) whereas the C allele was found in DM with NDRD patients (43.5%) (p = 0.02). There was a significant association between the CC genotype in NDRD when compared to GG (p = 0.001). In addition, the C allele was 2.10-fold more often associated with NDRD than the G allele (p = 0.03). The CC genotype was correlated with risk for NDRD than the GG and GC genotypes, with odds ratios of 6.89 and 4.91, respectively (p = 0.02).

Conclusion

C allele presentation, especially homozygous CC, was associated with NDRD pathology in patients with overt proteinuria. Hence, kidney biopsy is suggested in those with the C allele or homozygous CC genotype, regardless of retinopathy manifestations.

Anemia in Diabetic Patients Reflects Severe Tubulointerstitial Injury and Aids in Clinically Predicting a Diagnosis of Diabetic Nephropathy

Objective A kidney biopsy is generally performed in diabetic patients to discriminate between diabetic nephropathy (DN) and non-diabetic kidney disease (NDKD) and to provide more specific treatments. This study investigated the impact of anemia on the renal pathology and the clinical course in patients who underwent a kidney biopsy. Methods We reviewed 81 patients with type 2 diabetes who underwent a percutaneous kidney biopsy. Patients were classified into two groups: isolated DN (DN group, n=30) and NDKD alone or concurrent DN (NDKD group, n=51) groups. The laboratory and pathological findings and clinical courses were investigated. Results In the NDKD group, membranous nephropathy was the most common finding (23.5%), followed by IgA nephropathy (17.6%) and crescentic glomerulonephritis (13.7%). In the logistic regression analysis, the absence of severe hematuria and presence of anemia were significantly associated with a diagnosis of DN. Akaike's information criterion (AIC) and net reclassification improvement (NRI) analyses revealed improved predictive performance by adding anemia to the conventional factors (AIC 100.152 to 91.844; NRI 27.0%). The tissues of patients in the DN group demonstrated more severe interstitial fibrosis and tubular atrophy (IF/TA) than those in the NDKD group (p<0.05) regardless of the rate of global glomerulosclerosis, and IF/TA was related to the prevalence of anemia (odds ratio: 7.31, 95% confidence interval: 2.33-23.00, p<0.01) according to a multivariable regression analysis. Furthermore, the isolated DN group demonstrated a poorer prognosis than the NDKD group. Conclusion DN is associated with anemia because of severe IF/TA regardless of the renal function, and anemia helps clinician discriminate clinically between isolated DN and NDKD.

A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis on computed tomography images

OBJECTIVES

To develop and evaluate a logistics regression diagnostic model based on computer tomography (CT) features to differentiate tuberculous spondylitis (TS) from pyogenic spondylitis (PS).

METHODS

Demographic and clinical features were collected from the Electronic Medical Record System. Data of bony changes seen on CT images were compared between the PS (n = 61) and TS (n = 51) groups using the chi-squared test or t test. Based on features that were identified to be significant, a diagnostic model was developed from a derivation set (two thirds) and evaluated in a validation set (one third). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

RESULTS

The width of bone formation around the vertebra and sequestrum was greater in the TS group. There were significant differences between the two groups in the horizontal and longitudinal location of erosion and the morphology of axial bone destruction and sagittal residual vertebra. Kyphotic deformity and overlapping vertebrae were more common in the TS group. A diagnostic model that included eight predictors was developed and simplified to include the following six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. The simplified model showed good sensitivity, specificity, and total accuracy (85.59%, 87.80%, and 86.50%, respectively); the AUC was 0.95, indicating good clinical predictive ability.

CONCLUSIONS

A diagnostic model based on bone destruction and formation seen on CT images can facilitate clinical differentiation of TS from PS.

KEY POINTS

• We have developed and validated a simple diagnostic model based on bone destruction and formation observed on CT images that can differentiate tuberculous spondylitis from pyogenic spondylitis. • The model includes six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. • The simplified model has good sensitivity, specificity, and total accuracy with a high AUC, indicating excellent predictive ability.

Relevance of the Pyroptosis-Related Inflammasome Pathway in the Pathogenesis of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a major cause of chronic kidney disease (CKD) in many developed and developing countries. Pyroptosis is a recently discovered form of programmed cell death (PCD). With progress in research on DKD, researchers have become increasingly interested in elucidating the role of pyroptosis in DKD pathogenesis. This review focuses on the three pathways of pyroptosis generation: the canonical inflammasome, non-canonical inflammasome, and caspase-3-mediated inflammasome pathways. The molecular and pathophysiological mechanisms of the pyroptosis-related inflammasome pathway in the development of DKD are summarized. Activation of the diabetes-mediated pyroptosis-related inflammasomes, such as nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), Toll-like receptor 4 (TLR4), caspase-1, interleukin (IL)-1β, and the IL-18 axis, plays an essential role in DKD lesions. By inhibiting activation of the TLR4 and NLRP3 inflammasomes, the production of caspase-1, IL-1β, and IL-18 is inhibited, thereby improving the pathological changes associated with DKD. Studies using high-glucose-induced cell models, high-fat diet/streptozotocin-induced DKD animal models, and human biopsies will help determine the spatial and temporal expression of DKD inflammatory components. Recent studies have confirmed the relationship between the pyroptosis-related inflammasome pathway and kidney disease. However, these studies are relatively superficial at present, and the mechanism needs further elucidation. Linking these findings with disease activity and prognosis would provide new ideas for DKD research.

Aptamer-functionalised magnetic particles for highly selective detection of urinary albumin in clinical samples of diabetic nephropathy and other kidney tract disease

We report a new highly selective detection platform for human albumin (HA) in urine based on aptamer-functionalised magnetic particles. Magnetic separation and re-dispersion was utilised to expose the HA-bound particles to a methylene blue solution. A second magnetic collection step was then used to allow the methylene blue supernatant to be reduced at an unmodified screen-printed electrode. Since methylene blue adsorbs to HA, the reduction current fell in proportion to HA concentration. There was no interference from compounds such as dopamine, epinephrine, vanillylmandelic acid, normetanephrine, metanephrine and creatinine in artificial urine at the concentrations at which they would be expected to appear. A calibration equation was derived to allow for the effect of pH on the response. This enabled measurement to be made directly in clinical urine samples of varying pH. After optimisation of experimental parameters, the total assay time was 40 min and the limit of detection was between 0.93 and 1.16 μg mL^-1, depending on the pH used. HA could be detected up to 400 μg mL^-1, covering the range from normoalbuminuria to macroalbuminuria. Analysis of urine samples of patients, with diabatic nephropathy, type I & II diabetes mellitus and chronic kidney disease, from a local hospital showed good agreement with the standard urinary human albumin detection method.

A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes

Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer-Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670-0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes.

Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility.

RESULT

A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis.

CONCLUSION

Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Discrimination of Chronic Kidney Disease and Diabetic Nephropathy and Analysis of Their Related Influencing Factors

PURPOSE

Clinically there are not many clinical indicators to differentiate diabetic kidney disease (DKD) and chronic kidney disease (CKD). Data from laboratory inspections on admission of clinical patients were used to complete the relationship and discrimination analysis of the two diseases.

PATIENTS AND METHODS

All subjects were taken from the Department of Nephrology of the Second Hospital of Jilin University from January 2019 to September 2020 with clinical diagnosis of CKD or diabetic nephropathy and no other diseases. After querying the hospital's medical record system, the basic demographic information was obtained, and data on cardiovascular, metabolism, renal function, blood function, and other relevant indicators were extracted as well. IBM SPSS 24.0 software was used for data collation and analysis.

RESULTS

A total of 1726 inpatients (986 males and 740 females) over 18 years old were included, 1407 were CKD patients, 319 were DKD patients. Female accounted for 55.4% in CKD patients, 64.6% in DKD patients. Compared to men, women may be more susceptible to DKD (OR=2.234). DKD patients were more likely to be have higher DP, GLU, eGFR, TCHO, and abnormal TVU (OR=1.746, 3.404, 1.107, 3.004, 14.03) while VB₁₂ was the relative risk factor for CKD; thus, low VB₁₂ level is more likely to happen in CKD patients (OR=0.054, OR95%CI: 0.005-0.552, P=0.014) compared with DKD patients. The stepwise discriminant analysis was completed, only 11 of the 34 variables had discriminative significance. The discriminant score (DS) was set to explore its test efficiency of DKD prediction by drawing ROC curve. Discriminant formula used for CKD and DN identification was given in the study.

CONCLUSION

Female, higher DP, fasting blood GLU and TCHO level seemed to be more indicative for DKD, while lower eGFR level and VB₁₂ deficiency were more likely to point to CKD. Doctors can refer to the discriminant formula to assist in the differential diagnosis of the two diseases after completing the detection of DP, fasting blood GLU, Cys-C, eGFR, TVU, TCHO, FA, VB12, CK, and CK-Mb.