Srikanth Palanisamy

Importance: Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control. Objective: To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control. Design, Setting, and Participants: In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023. Interventions: Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care. Main Outcomes and Measures: Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis. Results: The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03). Conclusions and Relevance: In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication titration. Trial Registration: ClinicalTrials.gov Identifier: NCT05081011.

We report persistent severe acute respiratory syndrome coronavirus 2 infection in a patient with HIV/AIDS; the virus developed spike N terminal domain and receptor binding domain neutralization resistance mutations. Our findings suggest that immunocompromised patients can harbor emerging variants of severe acute respiratory syndrome coronavirus 2.

Overactivation of the renin–angiotensin–aldosterone system (RAAS) has been shown to be pathologic in heart failure and albuminuric chronic kidney disease (CKD), triggering pro-inflammatory and pro-fibrotic cellular pathways. The standard of care in these disease states includes treatment with angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers. Mineralocorticoid receptor antagonists (MRAs) are also a mainstay in the treatment of heart failure with reduced ejection fraction; however, therapy is often limited by treatment-related hyperkalemia. In albuminuric CKD, the risk of hyperkalemia, acute kidney injury (AKI), and hypotension also remains significant. Finerenone is a novel non-steroidal MRA that may obviate some of these concerns and have therapeutic potential in additional patient populations. Finerenone was developed using the chemical structure of a dihydropyridine channel blocker but optimized to create a bulky MRA without any activity at the L-type calcium channel. It has several novel cellular mechanisms that may account for its ability to reduce cardiac hypertrophy and proteinuria more efficiently than an equinatriuretic dose of a steroidal MRA, while retaining anti-inflammatory and anti-fibrotic properties. Finerenone also has a lower rate of treatment-related hyperkalemia and AKI than steroidal MRAs with a smaller effect on systolic blood pressure, greatly expanding its therapeutic utility. The recently published FIGARO-DKD and FIDELIO-DKD trials demonstrate that treatment with finerenone in patients with type II diabetes and albuminuric CKD results in improved cardiovascular outcomes and a lower risk of CKD progression. Patients enrolled in these studies were already on maximally tolerated ACE inhibitor or angiotensin receptor blocker therapy. Trials investigating finerenone’s therapeutic effect in patients with heart failure with preserved ejection fraction (HFpEF) and non-diabetic CKD, as well sodium–glucose cotransporter 2 (SGLT2) and finerenone combination therapy in patients with diabetic nephropathy, are ongoing.