Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy.

OBJECTIVE: To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90). RESEARCH DESIGN AND METHODS: We retrospectively studied 985 CGM periods of 90 days with <10% missing data from 315 adults (86% of whom had type 1 diabetes) with paired HbA1c measurements. The impact of mean red blood cell age as a proxy for nonglycemic effects on HbA1c was estimated using published theoretical models and in comparison with empirical data. Given the lack of a gold standard measurement for AG90, we applied correction methods to generate a reference (eAG90) that we used to assess accuracy for HbA1c and CGM. RESULTS: Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error (95th percentile) of 14 (34) mg/dL when compared with eAG90. Nonglycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 (29) mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 (26) mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time. CONCLUSIONS: The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with an HbA1c-based estimate or extending the monitoring period beyond ∼26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable nonglycemic factors.

Primary care diabetes assessment when HbA1c and other measures of glycemia disagree.

AIMS: Although diabetes management decisions in primary care are typically based largely on HbA1c, mismatches between HbA1c and other measures of glycemia that are increasingly more available present challenges to optimal management. This study aimed to assess a systematic approach to identify the frequency of mismatches of potential clinical significance amongst various measures of glycemia in a primary care setting. METHODS: Following screening to exclude conditions known to affect HbA1c interpretation, HbA1c, and fructosamine were obtained and repeated after ∼90 days on 53 adults with prediabetes or type 2 diabetes. A subset of 13 participants with repeat labs wore continuous glucose monitoring (CGM) for 10 days. RESULTS: As expected, HbA1c and fructosamine only modestly correlated (initial R2 = 0.768/repeat R2 = 0.655). The HbA1c/fructosamine mismatch frequency of ± 0.5% (using the following regression HbA1c = 0.015 *fructosamine + 2.994 calculated from the initial sample) was 27.0%. Of the 13 participants with CGM data, HbA1c and CGM-based Glucose Management Indicator correlated at R2 = 0.786 with a mismatch frequency of ± 0.5% at 46.2% compared to a HbA1c/fructosamine mismatch frequency of ± 0.5% at 30.8%. CONCLUSIONS: HbA1c is frequently mismatched with fructosamine and CGM data. As each of the measures has strengths and weaknesses, the utilization of multiple different measures of glycemia may be informative for diabetes assessment in the clinical setting.

COVID-19 Precipitating Euglycaemic Diabetic Ketoacidosis with SGLT2 Inhibitor Use.

OBJECTIVE: To describe a patient who developed euglycaemic diabetic ketoacidosis (DKA) in the setting of SGLT2 inhibitor use precipitated by COVID-19. PATIENT AND METHODS: A 52-year-old male with type II diabetes on empagliflozin and no history of DKA presented with symptoms of COVID-19 as well as laboratory findings consistent with euglycaemic DKA. His hospital course was complicated by recurrent episodes of euglycaemic DKA as well as hyperglycaemic DKA. CONCLUSION: SGLT2 inhibitors should be held as early as possible in COVID-19 cases due to the risk of euglycaemic DKA. These patients should also have more intense glucose monitoring. LEARNING POINTS: COVID-19 can precipitate euglycaemic DKA in diabetic patients taking SGLT2 inhibitors.Clinicians should be cognizant that the effects of SGLT2 inhibitors can persist for more than 72 hours after the last dose.Diabetic patients with COVID-19 require closer strict glucose monitoring to reduce the risk of DKA.

Unwitting Accomplices: Endocrine Disruptors Confounding Clinical Care.

Burgeoning evidence over the last 25 years has identified myriad synthetic chemicals with the capacity to alter various aspects of hormone synthesis and action. These endocrine-disrupting chemicals (EDCs) have been linked to various diseases, including reproductive disorders, metabolic diseases, and developmental abnormalities, among others. Exposure to EDCs arises from industrial activity, use of personal and home care products, and consumption of contaminated food and water; however, the role of healthcare in exposing individuals to EDCs is grossly underappreciated. Indeed, through the use of medications as well as medical equipment and devices, healthcare providers are unknowing mediators of exposure to EDCs, chemicals that might not only promote disease but that may also antagonize the efficacy of treatments. The ethical implications of provider-dependent exposure are profound. A failure to disclose the endocrine-disrupting properties of medical interventions violates core principles of nonmaleficence, patient autonomy, and justice as well as the practice of informed consent. Furthermore, physicians' lack of knowledge regarding EDCs in medical practice artificially skews risk-benefit calculations that are fundamental to informed medical decision-making. To combat this underappreciated ethical challenge, urgent action is required. Healthcare providers must be educated about endocrine disruption. Known EDCs, defined by endocrinologists, should be clearly labeled on all medical products, and all medication components and devices should be screened for endocrine-disrupting properties. Finally, communication strategies must be devised to empower patients with knowledge about these risks. Providing ethically competent care requires an open acknowledgment of endocrine risks imposed by the medical community that have heretofore been ignored.