A Validated Prediction Model for End-Stage Kidney Disease in Type 1 Diabetes

Diabetes-Related Macrovascular Complications Are Associated With an Increased Risk of Diabetic Microvascular Complications: A Prospective Study of 1518 Patients With Type 1 Diabetes and 20 802 Patients With Type 2 Diabetes in the UK Biobank

BACKGROUND

Diabetic vascular complications share common pathophysiological mechanisms, but the relationship between diabetes-related macrovascular complications (MacroVCs) and incident diabetic microvascular complications remains unclear. We aimed to investigate the impact of MacroVCs on the risk of microvascular complications.

METHODS AND RESULTS

There were 1518 participants with type 1 diabetes (T1D) and 20 802 participants with type 2 diabetes from the UK Biobank included in this longitudinal cohort study. MacroVCs were defined by the presence of macrovascular diseases diagnosed after diabetes at recruitment, including coronary heart disease, peripheral artery disease, stroke, and ≥2 MacroVCs. The primary outcome was incident microvascular complications, a composite of diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy. During a median (interquartile range) follow-up of 11.61 (5.84-13.12) years and 12.2 (9.50-13.18) years, 596 (39.3%) and 4113 (19.8%) participants developed a primary outcome in T1D and type 2 diabetes, respectively. After full adjustment for conventional risk factors, Cox regression models showed significant associations between individual as well as cumulative MacroVCs and the primary outcome, except for coronary heart disease in T1D (T1D: diabetes coronary heart disease: 1.25 [0.98-1.60]; diabetes peripheral artery disease: 3.00 [1.86-4.84]; diabetes stroke: 1.71 [1.08-2.72]; ≥2: 2.57 [1.66-3.99]; type 2 diabetes: diabetes coronary heart disease: 1.59 [1.38-1.82]; diabetes peripheral artery disease: 1.60 [1.01-2.54]; diabetes stroke: 1.50 [1.13-1.99]; ≥2: 2.66 [1.92-3.68]). Subgroup analysis showed that strict glycemic (glycated hemoglobin <6.5%) and blood pressure (<140/90 mm Hg) control attenuated the association.

CONCLUSIONS

Individual and cumulative MacroVCs confer significant risk of incident microvascular complications in patients with T1D and type 2 diabetes. Our results may facilitate cost-effective high-risk population identification and development of precise prevention strategies.

Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model

AIMS

To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes.

MATERIALS AND METHODS

A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank.

RESULTS

During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts.

CONCLUSIONS

The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation.

Proteinuria, measured or estimated albuminuria for risk prediction in patients with chronic kidney disease?

BACKGROUND

Although albuminuria is the gold standard for defining chronic kidney disease (CKD), total proteinuria has also been widely used in real-world clinical practice. Moreover, the superiority of the prognostic performance of albuminuria over proteinuria in patients with CKD remains inconclusive. Therefore, we aimed to compare the predictive performances of albuminuria and proteinuria in these patients.

METHODS

From the Korean Cohort Study for Outcome in Patients with CKD we included 2099 patients diagnosed with CKD grades 1-5 who did not require kidney replacement therapy. We measured the spot urine albumin:creatinine ratio (mACR) and protein:creatinine ratio (PCR) and estimated the ACR (eACR) using the PCR. Kidney failure risk equation (KFRE) scores were calculated using the mACR, PCR and eACR. The primary outcome was the 5-year risk of kidney failure with replacement therapy (KFRT).

RESULTS

The eACR significantly underestimated mACR in patients with low albuminuria levels. The time-dependent area under the receiver operating characteristics curve showed excellent predictive performance for all KFRE scores from the mACR, PCR and eACR. However, eACR was inferior to mACR based on the continuous net reclassification index (cNRI) and integrated discrimination improvement index (IDI) in all CKD cause groups, except for the group with an unclassified aetiology. Moreover, the cNRI and IDI statistics indicated that both eACR and PCR were inferior to mACR in patients with low albuminuria (<30 mg/g). Conversely, the predictive performance of PCR was superior in severe albuminuria and nephrotic-range proteinuria, in which the IDI and cNRI of the PCR were greater than those of the mACR.

CONCLUSIONS

The mACR, eACR and PCR showed excellent performance in predicting KFRT in patients with CKD. However, eACR was inferior to mACR in patients with low albuminuria, indicating that measuring rather than estimating albuminuria is preferred for these patients.

Potential Medicinal Value of Rhein for Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the primary cause of mortality among diabetic patients. With the increasing prevalence of diabetes, it has become a major concern around the world. The therapeutic effect of clinical use of drugs is far from expected, and therapy choices to slow the progression of DKD remain restricted. Therefore, research on new drugs and treatments for DKD has been a hot topic in the medical field. It has been found that rhein has the potential to target the pathogenesis of DKD and has a wide range of pharmacological effects on DKD, such as anti-nephritis, decreasing blood glucose, controlling blood lipids and renal protection. In recent years, the medical value of rhein in the treatment of diabetes, DKD and renal disease has gradually attracted worldwide attention, especially its potential in the treatment of DKD. Currently, DKD can only be treated with medications from a single symptom and are accompanied by adverse effects, while rhein improves DKD with a multi-pathway and multi-target approach. Therefore, this paper reviews the therapeutic effects of rhein on DKD, and proposes solutions to the limitations of rhein itself, in order to provide valuable references for the clinical application of rhein in DKD and the development of new drugs.

Sotagliflozin, a dual sodium-glucose co-transporter-1 and sodium-glucose co-transporter-2 inhibitor, reduces the risk of cardiovascular and kidney disease, as assessed by the Steno T1 Risk Engine in adults with type 1 diabetes

AIMS

Sotagliflozin (SOTA) as adjunct to insulin therapy improves glycemic control, reduces body weight and blood pressure, and increases time in range in adults with type 1 diabetes (T1D). SOTA demonstrated CV and kidney benefits in high-risk adults with type 2 diabetes. These potential benefits using SOTA for T1D may collectively outweigh the risk of diabetic ketoacidosis. The present analysis estimated the risk of CVD and kidney failure in adults with T1D treated with SOTA.

MATERIALS AND METHODS

Participant-level data were used from the inTandem trials evaluating 2980 adults with T1D randomized to once-daily placebo, SOTA 200 mg, or SOTA 400 mg for 24 weeks. For each participant, the cumulative risks of developing CVD and kidney failure were estimated using the Steno T1 Risk Engine. A subgroup analysis was performed in participants with BMI ≥ 27 kg/m² .

RESULTS

SOTA significantly reduced the predicted 5- and 10-year CVD risk in the SOTA 200 and 400 mg pooled group with a relative change in the SOTA group compared to the relative change in the placebo group of (mean [95%-confidence interval (CI)]) -6.6 (-7.9, -5.3) % and -6.4 (-7.6, -5.1) % (p < 0.0001 for both) respectively. For the 5-year ESKD risk there was a significant reduction with a relative change of -5.0 (-7.6, -2.3) % (p = 0.0003). Similar results were observed with the individual doses and in participants with BMI ≥ 27 kg/m² .

CONCLUSION

This analysis provides additional clinical results that may positively balance the benefit/risk assessment of SGLT inhibition use in T1D.

Childhood body mass index trajectories, adult-onset type 2 diabetes, and obesity-related cancers

BACKGROUND

Elevated childhood body mass index (BMI), commonly examined as a "once-only" value, increases the risk of cancer and type 2 diabetes (T2D) in adulthood. Continuous exposure to adiposity during childhood may further increase cancer risk. We examined whether longitudinal childhood BMI trajectories were associated with adult obesity-related cancer and the role of adult-onset T2D in these associations.

METHODS

Five sex-specific latent class BMI trajectories were generated for 301 927 children (149 325 girls) aged 6-15 years from the Copenhagen School Health Records Register. Information on obesity-related cancers and T2D was obtained from national health registers. Incidence rate ratios (IRR), cumulative incidences, and confidence intervals (CI) were estimated using Poisson regressions.

RESULTS

Compared with the average childhood BMI trajectory (containing approximately 40% of individuals), the rate of obesity-related cancer (excluding breast cancer) increased with higher childhood BMI trajectories among women. The highest rates occurred in the overweight (IRR = 1.27, 95% CI = 1.17 to 1.38) and obesity (IRR = 1.79, 95% CI = 1.53 to 2.08) BMI trajectories. Similar patterns were observed among men. In contrast, women with the obesity childhood BMI trajectory had the lowest rate of pre- and postmenopausal breast cancer (IRR = 0.59, 95% CI = 0.43 to 0.80, and IRR = 0.41, 95% CI = 0.30 to 0.57, respectively). For all trajectories, the cumulative risk of obesity-related cancer increased with adult-onset T2D.

CONCLUSION

Consistent childhood overweight or obesity may increase the rates of adult obesity-related cancer and decrease the rates of breast cancer. Adult-onset T2D conferred additional risk for obesity-related cancer, but the effect did not differ across childhood BMI trajectories.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.

Childhood body mass index trajectories and associations with adult-onset chronic kidney disease in Denmark: A population-based cohort study

BACKGROUND

Although excess adult adiposity is a strong risk factor for chronic kidney disease (CKD), evidence for associations with early life body size is limited. We investigated whether childhood body mass index (BMI) trajectories are associated with adult-onset CKD and end-stage kidney disease (ESKD) using a population-based cohort. Further, we examined the role of adult-onset type 2 diabetes (T2D) in these associations.

METHODS AND FINDINGS

We included 151,506 boys and 148,590 girls from the Copenhagen School Health Records Register, born 1930 to 1987 with information on measured weights and heights at ages 6 to 15 years. Five sex-specific childhood BMI trajectories were analyzed. Information on the main outcomes CKD and ESKD, as well as T2D, came from national health registers. Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated using Poisson regression adjusted for year of birth. During a median of 30.8 person-years of follow-up, 5,968 men and 3,903 women developed CKD and 977 men and 543 women developed ESKD. For both sexes, the rates of CKD and ESKD increased significantly with higher child BMI trajectories in comparison with the average BMI trajectory (40% to 43% of individuals) and the below-average BMI trajectory (21% to 23% of individuals) had the lowest rates. When including T2D, most associations were significant and men (IRR = 1.39, 95% CI: 1.13 to 1.72) and women (IRR = 1.54, 95% CI: 1.28 to 1.86) with the obese childhood BMI trajectory (2% of individuals) had significantly higher CKD rates than the average BMI trajectory, whereas for ESKD, the associations were positive, but nonsignificant, for men (IRR = 1.38, 95% CI: 0.83 to 2.31) but significant for women (IRR = 1.97, 95% CI: 1.25 to 3.11) with the obese BMI trajectory. A main study limitation is the use of only hospital-based CKD diagnoses.

CONCLUSIONS

Individuals with childhood BMI trajectories above average had higher rates of CKD and ESKD than those with an average childhood BMI trajectory. When including T2D, most associations were significant, particularly with CKD, emphasizing the potential information that the early appearance of above-average BMI growth patterns provide in relation to adult-onset CKD beyond the information provided by T2D development.

Sodium-glucose cotransporter 2 inhibitors as adjunct therapy for type 1 diabetes and the benefit on cardiovascular and renal disease evaluated by Steno risk engines

AIMS

Sodium-glucose cotransporter inhibitors (SGLTi) have beneficial cardiovascular and renal effects in persons with type 2 diabetes. No studies have shown whether this can be demonstrated in type 1 diabetes (T1D). We aimed to estimate the risk of cardiovascular disease (CVD) and end-stage kidney disease (ESKD) in persons with T1D with and without treatment with SGLTi.

METHODS

The study is based on 3660 adults with T1D. The Steno Type 1 Risk Engines were used to calculate 5-year risks of ESKD and 5- and 10-year risk of CVD. The effect of SGLTi was simulated by changing the HbA_1c and systolic blood pressure values in accordance with results from the DEPICT studies with mean (standard deviation (SD)) of -3.6 (0.9) mmol/mol (-2.5 % (2.2)) and -1.12 (2.8) mmHg. eGFR and albuminuria were changed in accordance with results from the Tandem studies; no change in eGFR and mean (SD) %-change in albuminuria of -23.7 (12.9).

RESULTS

We found a 5-year CVD relative risk reduction of 6.1 % (95%CI 5.9,6.3) and 11.1 % (10.0,12.2) in the subgroup with albuminuria with similar results for the 10-year CVD risk. For the estimated 5-year risk of ESKD, we found a relative risk reduction of 5.3 % (5.1,5.4) with 7.6 % (6.9,8.4) in the subgroup with albuminuria.

CONCLUSION

We found a significant CVD and ESKD risk reduction, especially in the subgroup with albuminuria.

Development and implementation of patient-level prediction models of end-stage renal disease for type 2 diabetes patients using fast healthcare interoperability resources

This study aimed to develop a model to predict the 5-year risk of developing end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning (ML). It also aimed to implement the developed algorithms into electronic medical records (EMR) system using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). The final dataset used for modeling included 19,159 patients. The medical data were engineered to generate various types of features that were input into the various ML classifiers. The classifier with the best performance was XGBoost, with an area under the receiver operator characteristics curve (AUROC) of 0.95 and area under the precision recall curve (AUPRC) of 0.79 using three-fold cross-validation, compared to other models such as logistic regression, random forest, and support vector machine (AUROC range, 0.929–0.943; AUPRC 0.765–0.792). Serum creatinine, serum albumin, the urine albumin-to-creatinine ratio, Charlson comorbidity index, estimated GFR, and medication days of insulin were features that were ranked high for the ESRD risk prediction. The algorithm was implemented in the EMR system using HL7 FHIR through an ML-dedicated server that preprocessed unstructured data and trained updated data.

Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes

Background

Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.

Methods

Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.

Findings

The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.

Interpretation

With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.

Funding

This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

Trajectories of kidney function in diabetes: a clinicopathological update

Diabetic nephropathy has been traditionally diagnosed based on persistently high albuminuria and a subsequent decline in glomerular filtration rate (GFR), which is widely recognized as the classical phenotype of diabetic kidney disease (DKD). Several studies have emphasized that trajectories of kidney function in patients with diabetes (specifically, changes in GFR and albuminuria over time) can differ from this classical DKD phenotype. Three alternative DKD phenotypes have been reported to date and are characterized by albuminuria regression, a rapid decline in GFR, or non-proteinuric or non-albuminuric DKD. Although kidney biopsies are not typically required for the diagnosis of DKD, a few studies of biopsy samples from patients with DKD have demonstrated that changes in kidney function associate with specific histopathological findings in diabetes. In addition, various clinical and biochemical parameters are related to trajectories of GFR and albuminuria. Collectively, pathological and clinical characteristics can be used to predict trajectories of GFR and albuminuria in diabetes. Furthermore, cohort studies have suggested that the risks of kidney and cardiovascular outcomes might vary among different phenotypes of DKD. A broader understanding of the clinical course of DKD is therefore crucial to improve risk stratification and enable early interventions that prevent adverse outcomes.