Pitfalls in Diagnosing Hypoglycemia Due to Exogenous Insulin: Validation and Utility of an Insulin Analog Assay

OBJECTIVE

To overcome the limitations of commercially available insulin immunoassays which have variable detection of analog insulin and can lead to clinically discordant results and misdiagnosis in the workup of factitious hypoglycemia.

PATIENTS AND METHODS

We performed analytical validation of a liquid chromatography high resolution accurate mass (LC-HRAM) immunoassay to detect insulin analogs. We completed clinical assessment using a large cohort of human serum samples from 78 unique individuals, and subsequently used the assay in the evaluation of eight individuals with high diagnostic suspicion for factitious hypoglycemia.

RESULTS

The performance characteristics show that the LC-HRAM immunoassay can be applied to detect five commonly used synthetic insulin analogs (lispro, glulisine, aspart, glargine metabolite, and detemir) in human serum. Our clinical cases show that this assay could be used in the diagnosis of factitious hypoglycemia by identifying the analog insulin(s) in question.

CONCLUSION

The LC-HRAM immunoassay reported here overcomes a gap in our diagnostic pathway for hypoglycemia. The results obtained from our studies suggest that this method is appropriate for use in clinical laboratories when factitious hypoglycemia is considered as a differential diagnosis.

Abstract WP237: Uric Acid Expressed Within Carotid Atherosclerotic Plaque And Serum Uric Acid Are Associated With Cerebrovascular Events

Introduction: Uric acid (UA) level within carotid plaque specimens and its association with cerebrovascular events has not been detected or quantified. Systemically, serum uric acid (SUA) is a marker of inflammation and risk factor for atherosclerosis. However, its association with carotid plaque instability and subsequent cerebrovascular events remains unclear.

Hypothesis: We hypothesized that UA may play a role in carotid artery atherosclerosis and stroke pathogenesis. In patients undergoing carotid endarterectomy (CEA), we aimed to determine whether UA is present differentially in carotid plaques of symptomatic versus asymptomatic patients and whether SUA is associated with cerebrovascular symptoms before surgery and major adverse cardiovascular events (MACE) in the follow-up to CEA.

Methods: We prospectively collected 32 carotid plaques during CEA. We qualitatively assessed the presence of UA by using Gomori methenamine silver (GMS) staining and immunohistochemistry with anti-UA antibodies. We extracted and measured the quantity of UA in carotid plaques by using an enzymatic colorimetric assay. A retrospective analysis of a clinical database of 534 consecutive patients who underwent CEA was performed. We defined as symptomatic those patients with history of stroke, TIA and amaurosis fugax.

Results: UA positive staining was detected in higher number of carotid plaques from symptomatic compared to asymptomatic patients on both staining, GMS [20 (86.9%) vs 2 (22.2%); p=0.001] and immunohistochemistry anti-UA [16 (69.5%) vs 1 (11.1%); p=0.004]. Moreover, we found a significantly higher concentration of UA in carotid plaque from symptomatic compared to asymptomatic patients [25.1 (9.5) μg/g vs 17.9 (3.8) μg/g; p=0.021]. SUA level in patients prior to CEA was significantly higher in the symptomatic compared to the asymptomatic group [6.0 (4.8-7.1) mg/dL vs 5.2 (4.4-6.3) mg/dL; p<0.001] and it was significantly higher in patients who developed MACE after CEA versus those that did not [6.0 (4.8-7.1) mg/dL vs 5.4 (4.5-6.6) mg/dL; p=0.021].

Conclusions: UA may provide a mechanistic explanation as systemic biomarker and potential tissue contributor for carotid plaque instability and subsequent cerebrovascular and cardiovascular symptoms.

Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol

The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (&gt;0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.

Impact of Lipoprotein(a)-Cholesterol on Accurate Classification of Familial Hypercholesterolemia

Lipoprotein(a) [Lp(a)] is a pro-atherogenic and pro-thrombotic LDL-like particle recognized as an independent risk factor for cardiovascular disease (CVD) that is resistant to typical lipid-lowering treatments. The cholesterol within Lp(a) (Lp(a)-C) contributes to the reported LDL-cholesterol (LDL-C) concentration by nearly all available methods including beta-quantification, direct homogenous assays, and all estimating equations. Accurate LDL-C measurements are critical for identification of genetic hyperlipidemia conditions such as familial hypercholesterolemia (FH). FH risk estimators such as the Dutch Lipid Clinic Network (DLCN) criteria utilize LDL-C concentration cut-offs and other clinical inputs to assess the likelihood of FH. Therefore, failure to adjust for Lp(a)-C can impact accurate FH classification, appropriate follow-up testing and treatments, and interpretation of cholesterol-lowering treatment efficacy. Lp(a)-C can be estimated from Lp(a) mass as measured by immunoassay using an average cholesterol content per particle. However, Lp(a)-C size and composition varies significantly within individuals resulting in inaccurate Lp(a)-C estimates. In this study, we use direct Lp(a)-C measurements to assess the potential misclassification of FH risk due to the contribution of Lp(a)-C to LDL-C in patient samples submitted for advanced lipoprotein profiling. A total of 28,200 samples submitted for lipoprotein profiling were included. The profiling included lipid testing in a CDC-certified laboratory on Roche cobas 501 (cholesterol and triglycerides by enzymatic method, high-density lipoprotein cholesterol by MgCl2/dextran sulfate precipitation). LDL-C was measured by beta-quantification, and Lp(a)-C by quantitative lipoprotein electrophoresis (SPIFE Vis Cholesterol, Helena Laboratories). The DLCN LDL-C cut-offs (155, 190, 250, and 330mg/dL) were applied to LDL-C results before and after accounting for Lp(a)-C contribution. Lp(a)-C was detected in 3,728 (13.2%) samples. The median (range) concentrations of Lp(a)-C and LDL-C were 11mg/dL (5-108mg/dL) and 121mg/dL (27-678mg/dL), respectively. Overall, subtracting Lp(a)-C would reclassify 6.5% of all samples into a lower LDL-C category within the DLCN algorithm. Within the LDL-C scoring categories, 7.0% (n=222) of subjects with LDL-C 155-189mg/dL, 5.6% (n=66) of subjects with LDL-C 190-249mg/dL, 5.2% (n=10) of subjects with LDL-C 250-329mg/dL, and 3.4% (n=4) of subjects with LDL-C &gt;330mg/dL would be down-classified after adjusting for Lp(a)-C. Limiting to subjects with measurable Lp(a)-C, reclassification to a lower diagnostic threshold occurred in 47.4% of subjects with LDL-C 155-189mg/dL, 37.5% with LDL-C 190-249mg/dL, 41.6% with LDL-C 250-329mg/dL, and 33.3% with LDL-C &gt;330mg/dL after adjustment. Current guidelines recommend screening for elevated Lp(a) in patients with family history of CVD. Our data show that a high percentage of samples evaluated for advanced lipid testing contain measurable Lp(a)-C that could cause mis-classification in FH prediction algorithms. If labeled high probability of FH, these mis-classifications could trigger inappropriate work-up for suspected FH. As clinical follow-up and therapeutic strategies differ between FH and elevated Lp(a), proper distinction between LDL-C and Lp(a)-C is needed to guide appropriate patient management.