Biomarkers of diabetic kidney disease

Subclinical inflammation and depressive symptoms in patients with type 1 and type 2 diabetes

Depression is a frequent comorbidity of type 1 diabetes (T1D) and type 2 diabetes (T2D). Depression and diabetes are linked by a bidirectional relationship, but the underlying mechanisms are still incompletely understood. Experimental, observational and intervention studies showed that inflammatory processes contribute to the development of depression in animal models and humans. Given the high risk of morbidity and mortality in patients with the double burden of diabetes and depression, this review provides an overview of epidemiological studies that addressed the relationship between biomarkers of inflammation and depressive symptoms or depression in diabetes patients. In patients with T1D, there is some evidence that higher levels of high-sensitivity C-reactive protein (hsCRP), IL-6, IL-1 receptor antagonist (IL-1RA) and sICAM-1 may be related to depressive symptoms or (for hsCRP) lower treatment response. For T2D, hsCRP, IL-1RA, CCL2 and adiponectin or its isoforms were associated with depressive symptoms in at least two studies, whereas positive associations of IL-1β, IL-6 and IL-18 with depressive symptoms or depression were reported from single cohorts. However, the number of studies is too low for any meaningful meta-analysis. Prospective life course studies including both patients with T1D and T2D, a comprehensive assessment of systemic inflammation and repeated assessment of depressive symptoms should represent a future research priority to clarify to what extent subclinical inflammation affects the risk of depression in patients with diabetes. A better understanding of the role of inflammatory processes may help to identify subtypes of depression with partly different pathogenesis, which could have consequences with respect to therapeutic options including immunomodulation.

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

AIMS/HYPOTHESIS

As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts.

METHODS

We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min^-1 [1.73 m]^-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function.

RESULTS

Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β₂-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets.

CONCLUSIONS/INTERPRETATION

Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30-75 ml min^-1 [1.73 m]^-2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

The incessant search for renal biomarkers: is it really justified?

PURPOSE OF REVIEW

This review summarizes the most recent and relevant findings in the search for novel biomarkers for the most common renal diseases.

RECENT FINDINGS

Unprecedented, fast-paced technical advances in biomedical research have offered an opportunity to identify novel and more specific renal biomarkers in several clinical settings. However, despite the huge efforts made, the molecules identified so far have generally failed to provide relevant information beyond what has already been generated by established biomarkers, such as serum creatinine and proteinuria, whereas the complexity and costs of these technology platforms hamper their widespread implementation.

SUMMARY

No novel renal biomarkers have added clear-cut additional value in clinical decision-making. The only exception is anti-phospholipase A2 receptor antibodies, which have been implemented successfully as a diagnostic and prognostic biomarker of membranous nephropathy. This achievement, along with the large number of ongoing collaborative projects worldwide, should lead the renal community to be quite confident regarding the successful qualification of novel and effective diagnostic, prognostic and therapeutic response biomarkers for kidney diseases, hopefully in the next few years.