Change in albuminuria as a surrogate endpoint for cardiovascular and renal outcomes in patients with diabetes

Early reduction in albuminuria is associated with a steeper 'dip' in initial estimated glomerular filtration rate but favourable long-term kidney outcomes in people with diabetes receiving sodium-glucose cotransporter-2 inhibitors

AIM

To assess if early change in albuminuria was linked to an initial change in estimated glomerular filtration rate (eGFR) and long-term kidney outcomes in people with type 2 diabetes (T2D) receiving sodium-glucose cotransporter-2 (SGLT2) inhibitors.

METHODS

Using a medical database from a multicentre healthcare institute in Taiwan, we retrospectively enrolled 8310 people receiving SGLT2 inhibitors from 1 June 2016 to 31 December 2021. We compared the risks of initial eGFR decline, major adverse renal events (MARE; >50% eGFR reduction or development of end-stage kidney disease), major adverse cardiovascular events (MACE), or hospitalization for heart failure (HHF) using a Cox proportional hazards model.

RESULTS

In all, 36.8% (n = 3062) experienced a >30% decrease, 21.0% (n = 1743) experienced a 0%-30% decrease, 14.4% (n = 1199) experienced a 0%-30% increase, and 27.7% (n = 2306) experienced a >30% increase in urine albumin-to-creatine ratio (UACR) after 3 months of SGLT2 inhibitor treatment. Greater acute eGFR decline at 3 months correlated with greater UACR reduction: -3.6 ± 10.9, -2.0 ± 9.5, -1.1 ± 8.6, and -0.3 ± 9.7 mL/min/1.73 m2 for the respective UACR change groups (p < 0.001). Over a median of 29.0 months, >30% UACR decline was associated with a higher risk of >30% initial eGFR decline (hazard ratio [HR] 2.68, 95% confidence interval [CI] 1.61-4.47]), a lower risk of MARE (HR 0.66, 95% CI 0.48-0.89), and a comparable risk of MACE or HHF after multivariate adjustment (p < 0.05). The nonlinear analysis showed early UACR decline was linked to a lower risk of MARE but a higher risk of initial steep eGFR decline of >30%.

CONCLUSION

Physicians should be vigilant for the potential adverse effects of abrupt eGFR dipping associated with a profound reduction in UACR, despite the favourable long-term kidney outcomes in the population with T2D receiving SGLT2 inhibitor treatment.

The triglyceride-glucose index is superior to homeostasis model assessment of insulin resistance in predicting metabolic syndrome in an adult population in the United States

BACKGROUND

Metabolic Syndrome (MetS) is a cluster of cardio-metabolic features portending an increased risk for both type 2 diabetes mellitus (T2DM) and premature atherosclerotic cardiovascular disease (ASCVD). Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is a widely used surrogate measure of insulin resistance. The triglyceride-glucose (TyG) index is another validated measure of insulin resistance that predicts both diabetes and cardiovascular disease in low and medium-income countries, but only diabetes in high income countries.

OBJECTIVE

Due to the paucity of data on the TyG index in the US population, we compared the validity of the TyG index and HOMA-IR in predicting MetS.

METHODS

Data from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 on Non-Hispanic White(NHW), Hispanic American(HA), and African American(AA) individuals(n = 5380) aged 20-80 years were used for analysis. Individuals were classified as having MetS based on three or more of its components. HOMA-IR and the TyG index were determined from fasting samples.

RESULTS

Both the TyG index and HOMA-IR were significantly increased in MetS and increased significantly with increasing severity of the syndrome. Also both indices correlated significantly with all 5 features of MetS, hsCRP and non-HDL-C. ROC-AUC analysis for TyG index was significantly greater than that of HOMA-IR in predicting MetS: 0.87(95 % CI 0.85-0.88) versus 0.82(95 % CI 0.81-0.83) respectively, p < 0.0001. This was not evident for the small AA subgroup.

CONCLUSION

The TyG index outperformed HOMA-IR in predicting MetS, a proxy for both T2DM and ASCVD, in a general US population and is a valuable biomarker.

Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability

OBJECTIVE

To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally.

METHODS

Firstly, the data was cleaned and enhanced, and was divided into training and test sets according to the 7:3 ratio. Then, the metrics related to DN were filtered by difference analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and Max-relevance and Min-redundancy (MRMR) algorithms. Ten machine learning models were constructed based on the key variables. The best model was filtered by Receiver Operating Characteristic (ROC), Precision-Recall (PR), Accuracy, Matthews Correlation Coefficient (MCC), and Kappa, and was internally and externally validated. Based on the best model, an online platform had been constructed.

RESULTS

15 key variables were selected, and among the 10 machine learning models, the Random Forest model achieved the best predictive performance. In the test set, the area under the ROC curve was 0.912, and in two external validation cohorts, the area under the ROC curve was 0.828 and 0.863, indicating excellent predictive and generalization abilities.

CONCLUSION

The model has a good predictive value and is expected to help in the early diagnosis and screening of clinical DN.

Albuminuria, Forgotten No More: Underlining the Emerging Role in CardioRenal Crosstalk

Kidneys have an amazing ability to adapt to adverse situations, both acute and chronic. In the presence of injury, the kidney is able to activate mechanisms such as autoregulation or glomerular hyperfiltration to maintain the glomerular filtration rate (GFR). While these adaptive mechanisms can occur in physiological situations such as pregnancy or high protein intake, they can also occur as an early manifestation of diseases such as diabetes mellitus or as an adaptive response to nephron loss. Although over-activation of these mechanisms can lead to intraglomerular hypertension and albuminuria, other associated mechanisms related to the activation of inflammasome pathways, including endothelial and tubular damage, and the hemodynamic effects of increased activity of the renin-angiotensin-aldosterone system, among others, are recognized pathways for the development of albuminuria. While the role of albuminuria in the progression of chronic kidney disease (CKD) is well known, there is increasing evidence of its negative association with cardiovascular events. For example, the presence of albuminuria is associated with an increased likelihood of developing heart failure (HF), even in patients with normal GFR, and the role of albuminuria in atherosclerosis has recently been described. Albuminuria is associated with adverse outcomes such as mortality and HF hospitalization. On the other hand, it is increasingly known that the systemic effects of congestion are mainly preceded by increased central venous pressure and transmitted retrogradely to organs such as the liver or kidney. With regard to the latter, a new entity called congestive nephropathy is emerging, in which increased renal venous pressure can lead to albuminuria. Fortunately, the presence of albuminuria is modifiable and new treatments are now available to reverse this common risk factor in the cardiorenal interaction.

Potential application of Klotho as a prognostic biomarker for patients with diabetic kidney disease: a meta-analysis of clinical studies

Background

Diabetic kidney disease (DKD) is a serious diabetic complication and the performance of serum Klotho in DKD's prognostic evaluation is controversial.

Objective

To assess the association of serum Klotho with adverse kidney and non-kidney clinical outcomes in patients with DKD.

Design

Clinical studies regarding the relationship of serum Klotho with DKD were included. Study quality was assessed using the Newcastle-Ottawa scale. Subgroup and sensitive analyses were performed to search for the source of heterogeneity.

Data sources and methods

We comprehensively searched PubMed, Embase, Web of Science, and Cochrane library databases up to 27 September 2022. The associations of Klotho with albuminuria, such as the urinary albumin creatinine ratio (UACR), kidney outcomes such as persistent albuminuria, estimated glomerular filtration rate decline, and non-kidney outcomes such as diabetic retinopathy, cardiovascular morbidity, and mortality, were evaluated. The indicators, such as the correlation coefficient (r), odds ratio (OR), relative risk, and hazard ratio, were retrieved or calculated from the eligible studies.

Results

In all, 17 studies involving 5682 participants fulfilled the inclusion criteria and were included in this meta-analysis. There was no significant association of serum Klotho with UACR in DKD patients [summary r, -0.28 (-0.55, 0.04)] with high heterogeneity. By contrast, a strong association was observed regarding serum Klotho with kidney outcomes [pooled OR, 1.60 (1.15, 2.23)], non-kidney outcomes [pooled OR, 2.78 (2.11, 3.66)], or combined kidney and non-kidney outcomes [pooled OR, 1.96 (1.45, 2.65)] with moderate heterogeneity. Subgroup analysis indicated that age, study design, and the estimated glomerular filtration rate may be the sources of heterogeneity.

Conclusion

A decreased serum Klotho level is possibly associated with an increased risk of developing kidney and non-kidney clinical outcomes in DKD patients; thus, Klotho may be a possible biomarker to predict DKD clinical outcomes. Additional studies are needed to clarify and validate Klotho's prognostic value.

Early diagnosis of chronic kidney disease in patients with diabetes in France: multidisciplinary expert opinion, prevention value and practical recommendations

Diabetes is the leading cause of end-stage kidney disease (ESKD), accounting for approximately 50% of patients starting dialysis. However, the management of these patients at the stage of chronic kidney disease (CKD) remains poor, with fragmented care pathways among healthcare professionals (HCPs). Diagnosis of CKD and most of its complications is based on laboratory evidence. This article provides an overview of critical laboratory evidence of CKD and their limitations, such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), Kidney Failure Risk Equation (KFRE), and serum potassium. eGFR is estimated using the CKD-EPI 2009 formula, more relevant in Europe, from the calibrated dosage of plasma creatinine. The estimation formula and the diagnostic thresholds have been the subject of recent controversies. Recent guidelines emphasized the combined equation using both creatinine and cystatin for improved estimation of GFR. UACR on a spot urine sample is a simple method that replaces the collection of 24-hour urine. Albuminuria is the preferred test because of increased sensitivity but proteinuria may be appropriate in some settings as an alternative or in addition to albuminuria testing. KFRE is a new tool to estimate the risk of progression to ESKD. This score is now well validated and may improve the nephrology referral strategy. Plasma or serum potassium is an important parameter to monitor in patients with CKD, especially those on renin-angiotensin-aldosterone system (RAAS) inhibitors or diuretics. Pre-analytical conditions are essential to exclude factitious hyperkalemia. The current concept is to correct hyperkalemia using pharmacological approaches, resins or diuretics to be able to maintain RAAS blockers at the recommended dose and discontinue them at last resort. This paper also suggests expert recommendations to optimize the healthcare pathway and the roles and interactions of the HCPs involved in managing CKD in patients with diabetes.

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease

Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.