Continuous Glucose Monitoring in Dogs and Cats: Application of New Technology to an Old Problem

Accuracy of the Freestyle Libre 3 Continuous Glucose Monitoring System in Hypo- and Euglycemic Cats

BACKGROUND

The FreeStyle Libre 3 (FSL3) has several improvements compared to previous FreeStyle Libre systems, but its accuracy has not yet been determined in cats. In diabetic people, FSL3 offers increased accuracy, and its smaller size could be advantageous for use in veterinary patients.

OBJECTIVES

Assess the accuracy of FSL3 in cats with experimentally induced hypoglycemia.

ANIMALS

Seven healthy, purpose-bred cats.

METHODS

Hyperinsulinemic-hypoglycemic clamps were performed. Interstitial glucose concentration (IG), measured by FSL3, was compared to blood glucose concentration (BG) measured by AlphaTrak2. Data were analyzed for all paired measurements (n = 474) and during stable BG (≤ 1 mg/dL/min change over 10 min). Pearson's r test, Bland-Altman test, and Parkes Error Grid analysis (EGA) respectively used to determine correlation, bias, and clinical accuracy.

RESULTS

Blood glucose concentration and IG correlated strongly (r = 0.86, p < 0.0001) in stable glycemia and moderately at all rates of change (r = 0.73, p < 0.0001). Analytical accuracy was not achieved, whereas clinical accuracy was demonstrated with 99%-100% of the results in zones A + B of the Parkes EGA. Interstitial glucose concentration underestimated BG in euglycemia and mild hypoglycemia (mean -11.7 ± 11.2, -5.5 ± 9.1, -1.5 ± 6.0 mg/dL in the ranges 91-120, 66-90, and 56-65 mg/dL, respectively), but overestimated BG in marked hypoglycemia (mean 6.3 ± 5.7, 15.7 ± 5.6 mg/dL in the ranges 46-55 and < 45 mg/dL, respectively).

CONCLUSIONS

The FSL3 underestimates BG across most of the hypo-euglycemic range but overestimates BG in marked hypoglycemia (< 55 mg/dL). Recognizing the proportional, glycemic-dependent bias of FSL3 improves the safety of its clinical application in cats.

Recent Advances in Biosensor Technologies for Meat Production Chain

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity and specificity for the rapid and accurate point-of-care (POC) quantitative detection of selected biomolecules. In the meat production chain, their application has gained attention due to the increasing demand for enhanced food safety, quality assurance, food fraud detection, and regulatory compliance. Biosensors can detect foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria and indicators, contaminants (pesticides, dioxins, and mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters and stress hormones), and metabolites (acute-phase proteins as inflammation markers) at different modules along the meat chain, from livestock farming to packaging in the farm-to-fork (F2F) continuum. By providing real-time data from the meat chain, biosensors enable early interventions, reducing the health risks (foodborne outbreaks) associated with contaminated meat/meat products or sub-standard meat products. Recent advancements in micro- and nanotechnology, microfluidics, and wireless communication have further enhanced the sensitivity, specificity, portability, and automation of biosensors, making them suitable for on-site field applications. The integration of biosensors with blockchain and Internet of Things (IoT) systems allows for acquired data integration and management, while their integration with artificial intelligence (AI) and machine learning (ML) enables rapid data processing, analytics, and input for risk assessment by competent authorities. This promotes transparency and traceability within the meat chain, fostering consumer trust and industry accountability. Despite biosensors' promising potential, challenges such as scalability, reliability associated with the complexity of meat matrices, and regulatory approval are still the main challenges. This review provides a broad overview of the most relevant aspects of current state-of-the-art biosensors' development, challenges, and opportunities for prospective applications and their regular use in meat safety and quality monitoring, clarifying further perspectives.

Comparison between a Flash Glucose Monitoring System and a Portable Blood Glucose Meter for Monitoring of Cats with Diabetic Ketosis or Ketoacidosis

Cats with diabetic ketosis or ketoacidosis DK(A) require intensive glucose monitoring. The aim of this study was to assess the agreement between a portable blood glucose meter (PBGM) and a flash glucose monitoring system (FGMS; FreeStyle Libre 2.0 Abbott®) measuring interstitial glucose in cats with DK(A). Ten client-owned cats with naturally occurring DK(A) were prospectively enrolled. Glucose concentrations were assessed with both methods every 1-3 h until resolution of DK(A), and every 4-8 h thereafter. While the median FGMS measured glucose concentration (14.3 mmol/L) was significantly lower than the median PBGM measured glucose concentration (19 mmol/L) (p < 0.001), the overall correlation between the FGMS and PBGM was high (r = 0.88; p < 0.001). In the Parkes error grid analysis, 98.3% of measurements fell in zones A and B. Bland-Altman plot analysis demonstrated that in the low glycaemic range (BG < 5.5 mmol/L), 50% of FGMS measurements deviated more than ±0.83 mmol/L, and in the high glycaemic range (BG > 5.5 mmol/L), 81% of results deviated >15% from the PBGM measurements. There was significant inter-individual variation in the difference between glucose concentrations measured by the FGMS and PBGM (p < 0.001). In spite of being more easily tolerated and easier to use, currently this method cannot be recommended for routine monitoring of cats with DK(A).

A dose titration protocol for once-daily insulin glargine 300 U/mL for the treatment of diabetes mellitus in dogs

BACKGROUND

In purpose-bred dogs, insulin glargine 300 U/mL (IGla300) has long duration of action, peakless time-action profile, and low potency, making it suitable for use as a basal insulin.

HYPOTHESIS

To evaluate IGla300 in client-owned diabetic dogs monitored using a flash glucose monitoring system (FGMS).

ANIMALS

Ninety-five client-owned diabetic dogs, newly diagnosed or previously treated with other insulin formulations, with or without concurrent diseases.

METHODS

Prospective multi-institutional study. Clinical signs and standardized assessment of FGMS data, using treatment and monitoring guidelines established a priori, guided dose adjustments and categorization into levels of glycemic control.

RESULTS

The initial IGla300 dose was 0.5 U/Kg q24h for newly diagnosed dogs and (median dose [range]) 0.8 U/Kg (0.2-2.5) q24h for all dogs. Glycemic control was classified as good or excellent in 87/95 (92%) dogs. The IGla300 was administered q24h (1.9 U/kg [0.2-5.2]) and q12h (1.9 U/kg/day [0.6-5.0]) in 56/95 (59%) and 39/95 (41%) dogs, respectively. Meal-time bolus injections were added in 5 dogs (0.5 U/kg/injection [0.3-1.0]). Clinical hypoglycemia occurred in 6/95 (6%) dogs. Dogs without concurrent diseases were more likely to receive IGla300 q24h than dogs with concurrent diseases (72% vs 50%, respectively; P = .04).

CONCLUSIONS AND CLINICAL IMPORTANCE

Insulin glargine 300 U/mL can be considered a suitable therapeutic option for once-daily administration in diabetic dogs. Clinicians should be aware of the low potency and wide dose range of IGla300. In some dogs, twice-daily administration with or without meal-time bolus injections may be necessary to achieve glycemic control. Monitoring with FGMS is essential for dose titration of IGla300.