CLN Article

The Sample

Prevalence of Residual C-peptide Levels Higher With Later Age of Type 1 Diabetes On-Set, Regardless of Duration of Disease

A study examining the prevalence of detectable C-peptide in type 1 diabetes found evidence of residual insulin secretion in nearly one-third of individuals 3 years or longer after their type 1 diabetes diagnosis (Diabetes Care 2014;doi:10.2337/dc14-1952). While this finding is consistent with other studies, the authors took pains to overcome limitations in these prior efforts, which looked at only selected or small cohorts, or individuals with limited duration of diabetes. Accordingly, the authors’ findings “[set] the stage for greater understanding of the heterogeneity of disease within these groupings.”

The authors tested the frequency of residual insulin secretion in 919 individuals with type 1 diabetes at 28 sites participating in the T1D Exchange Clinic Network, which includes a network of more than 70 clinics, a clinic registry with data from at least 26,000 people with type 1 diabetes, a biobank collection of biosamples, and Glu, a patient-caregiver online community. Study participants all had had type 1 diabetes for at least 3 years and had been diagnosed when they were between 6 months to 46 years old. The researchers stratified participants into subgroups based on their age at diagnosis and duration of disease. All participants with detectable C-peptide level at baseline were invited to undergo mixed meal tolerance testing (MMTT) to measure stimulated C-peptide. In addition, up to 10 participants in each subgroup with undetectable non-fasting C-peptide levels were asked to undergo MMTT as a control group. Samples with C-peptide ≥0.017 nmol/L were considered detectable.

The prevalence of patients with detectable nonfasting C-peptide declined with duration of type 1 diabetes, but was consistently higher when patients developed diabetes at age 18 or older. When the researchers included both diagnosis age and diabetes duration in their regression models, each factor was independently associated with detectable C-peptide. Overall, more than three-quarters of participants diagnosed when they were older than age 18 had residual C-peptide levels 3–5 years after diagnosis, in comparison to 46% of those diagnosed when they were younger than age 18.
The researchers concluded that while using a non-fasting random blood draw to measure C-peptide is “a reasonable but not exact measure,” they suggested there remains a need for MMTT in the context of clinical trial outcome evaluations. With the gradient of residual C-peptide levels between adult- and pediatric-onset disease, the authors also suggest that “important differences in the biological process of type 1 diabetes” might be at work in these two groups. The data also “reinforce the inadvisability of using C-peptide alone to differentiate between type 1 diabetes and other forms of diabetes.”

HDL Proteins SAA and SP-B Independently Associated with Cardiac Events and All-Cause Mortality in Patients with Diabetic Kidney Disease

In patients with diabetes on hemodialysis, the high-density lipoprotein cholesterol (HDL-C) proteins, serum amyloid A (SAA) and surfactant protein B (SP-B), are associated with cardiac events and all-cause mortality, independent of HDL-C (Clin J Am Soc Nephrol 2014; doi:10.2215/CJN.06560714). The findings suggest that remodeling of the HDL proteome contributes to the increased risk of cardiovascular events and mortality in patients with kidney disease, according to the authors.
The authors conducted the study because of emerging lines of investigation about HDL-C. First, research now shows that overall, the HDL-C level alone is not enough to estimate the cardioprotective function of HDL-C. In addition, emerging evidence suggests the renal function moderates the effect of HDL-C and that atheroprotective HDL particles “may be rendered dysfunctional” in the context of chronic kidney disease.

The investigators conducted a post hoc analysis of the 4D Study, a multicenter trial of more than 1,200 patients with type 2 diabetes. The researchers developed an enzyme-linked immuno assay and used it to measure SP-B and SAA in archived samples from baseline visits for the 4D Study. They evaluated 10 end-points in patients ranging from composite of cardiac death, nonfatal myocardial infarction or stroke, to non-cardiovascular disease mortality.

The authors found that high concentrations of SAA were significantly and positively associated with risk of cardiac events, and that high concentrations of SP-B were significantly associated with all-cause mortality. Adjustment for HDL-C did not affect these associations, according to the authors.

Delayed Extraction Influences DNA Integrity

Results from the first pan-European­ SPIDIA DNA external quality assessment (EQA) indicate that blood sample storage and DNA extraction procedures influence genomic DNA (gDNA) integrity and that PCR-based testing can yield different results if pre-analytical procedures are not standardized (Clin Chim Acta 2015; 440:205–10). SPIDIA was a 4-year project funded by the European Commission to develop quality guidelines and tools for molecular diagnostics and to standardize the related pre-analytical process. SPIDIA included implementation of an EQA to look at collection, transport and processing of blood samples for RNA- and DNA-based analyses.

In this report of the SPIDIA EQA, the SPIDIA laboratory in Florence, Italy looked specifically at the role of gDNA fragmentation in EQA samples on pre-analytical factors and on the results of a multiplex PCR test. Participating laboratories extracted DNA from a SPIDIA EQA-provided blood sample without restrictions on sample storage temperature or time. After DNA extraction, the labs sent back DNA samples to the SPIDIA laboratory. The great majority of participants stored the sample before extraction and returned it to SPIDIA at 4 degrees Centigrade.

In an evaluation of high molecular weight (HMW) DNA by pulsed field gel electrophoresis, the SPIDIA lab found that HMW DNA integrity showed high variability “probably reflecting the influence of some pre-analytical factors, such as DNA extraction procedures and/or time-interval from block collection to DNA isolation.” The SPIDIA lab also discovered a “relevant discrepancy” in values from samples extracted within 6 days, compared with those extracted between 6–10 days and after 10 days.

Additionally, the SPIDIA lab looked at the influence of gDNA integrity on a downstream multiplex PCR test. They found that short amplicons were not influenced by DNA integrity, resulting in the same number of successful PCRs independently of lab performance. However, with longer amplicons higher than 1500bp, high fragmented samples “tend to systematically have a lower number of successful PCRs compared to those classified as control (high gDNA integrity).”

Four MicroRNAs Associated With Heart Transplant Rejection

Four microRNAs discriminate “with a very high accuracy” between patients with heart transplant rejection and those without (Eur Heart J 2014; 35:3194–202). This differentiation occurs in both the tissue and serum, suggesting the microRNAs potentially could serve as non-invasive biomarkers of heart transplant rejection.
The investigators conducted the study because the gold standard for detecting acute heart transplant rejection is repeated endomyocardial biopsy, an invasive procedure with rare but potentially serious complications, discomfort for patients, and considerable costs. Emerging research suggests that microRNAs “may play a critical role” in regulating immune cell development and in modulating innate and adaptive immune responses.

The authors evaluated 113 heart transplant recipients, including 30 with biopsy-proven rejection matched to controls without rejection. They compared expression of 14 microRNAs in heart tissue and in serum. Of these, seven were differentially expressed in heart tissue, four of which also were differentially expressed in serum and correlated with tissue expression. These four microRNAs—miR-10a, miR-31, miR-92a, and miR-155—are associated with inflammatory burden in endothelial cells, inflammatory pathways, cardiomyocytes/interstitial cells, and endothelial cells, respectively.