Risk of type 1 diabetes at 3 years is high for initially multiple and single Ab+ IT and multiple Ab+ NT. Genetic predisposition, age, and male sex are significant risk factors for development of Ab+ in twins.
Objective: To determine whether the use of an inhaled insulin would improve HbA1c. Methods: This study was performed in 20 type 2 diabetes mellitus (T2DM) participants with HbA1c values !7.5 (58) to 11.5% (102 mmol/mol) on a variety of glucose-lowering regimens. Prandial Technosphere insulin (TI) was rapidly titrated based on a treatment algorithm using postprandial blood glucose to calculate premeal doses. A 2-week baseline period was followed by 12 weeks of active treatment with TI. The primary outcome was change in HbA1c. Secondary outcomes included glucose time in range (time in range: 70-180 mg/dL) obtained by a blinded continuous glucose monitoring during the baseline period and at the end of 12 weeks. Goals were to assess how to rapidly and safely initiate TI intensification, determine dosing requirements, and establish an effective dose range in uncontrolled T2DM. Results: Mean HbA1c decreased by À1.6% (À17 mmol/mol) from 9.0% (75 mmol/mol) at baseline to 7.4% (57 mmol/mol) at 12 weeks (P < .0001). Mean time in range increased from 42.2% to 65.7% (P < .0002). Mean prandial doses of TI were 18 or 19 units for all meals. Time below range was 1.1% baseline and 2.6% post treatment (P ¼ .01). Conclusion: Treatment with inhaled TI dosed using a simple algorithm improved glycemic control measured by both HbA1c and time in range, with low rates of hypoglycemia. These data add significantly to understanding TI in the management of T2DM patients for whom prandial insulin is a consideration.
OBJECTIVE
To implement, disseminate, and evaluate a sustainable method for identifying, diagnosing, and promoting individualized therapy for monogenic diabetes.
RESEARCH DESIGN AND METHODS
Patients were recruited into the implementation study through a screening questionnaire completed in the waiting room or through the patient portal, physician recognition, or self-referral. Patients suspected of having monogenic diabetes based on the processing of their questionnaire and other data through an algorithm underwent next-generation sequencing for 40 genes implicated in monogenic diabetes and related conditions.
RESULTS
Three hundred thirteen probands with suspected monogenic diabetes (but most diagnosed with type 2 diabetes) were enrolled from October 2014 to January 2019. Sequencing identified 38 individuals with monogenic diabetes, with most variants found in GCK or HNF1A. Positivity rates for ascertainment methods were 3.1% for clinic screening, 5.3% for electronic health record portal screening, 16.5% for physician recognition, and 32.4% for self-referral. The algorithmic criterion of non–type 1 diabetes before age 30 years had an overall positivity rate of 15.0%.
CONCLUSIONS
We successfully modeled the efficient incorporation of monogenic diabetes diagnosis into the diabetes care setting, using multiple strategies to screen and identify a subpopulation with a 12.1% prevalence of monogenic diabetes by molecular testing. Self-referral was particularly efficient (32% prevalence), suggesting that educating the lay public in addition to clinicians may be the most effective way to increase the diagnosis rate in monogenic diabetes. Scaling up this model will assure access to diagnosis and customized treatment among those with monogenic diabetes and, more broadly, access to personalized medicine across disease areas.
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