While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

Context Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking. Objective To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor. Design Multicenter, prospective study. Setting Twelve centers within the T1D Exchange Clinic Network. Patients or Participants Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index. Intervention Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days. Main Outcome Measures CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. Results A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d). Conclusion By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.

ImportanceSARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.ObjectiveTo develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.Design, Setting, and ParticipantsProspective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.ExposureSARS-CoV-2 infection.Main Outcomes and MeasuresPASC and 44 participant-reported symptoms (with severity thresholds).ResultsA total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.Conclusions and RelevanceA definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

Intestinal microbial dysbiosis contributes to the dysmetabolism of luminal factors, including steroid hormones (sterones) that affect the development of chronic gastrointestinal inflammation and the incidence of sterone-responsive cancers of the breast, prostate, and colon. Little is known, however, about the role of specific host sterone nucleoreceptors, including estrogen receptor ␤ (ER␤), in microbiota maintenance. Herein, we test the hypothesis that ER␤ status affects microbiota composition and determine if such compositionally distinct microbiota respond differently to changes in diet complexity that favor Proteobacteria enrichment. To this end, conventionally raised female ER␤ ؉/؉ and ER␤ ؊/؊ C57BL/6J mice (mean age of 27 weeks) were initially reared on 8604, a complex diet containing estrogenic isoflavones, and then fed AIN-76, an isoflavone-free semisynthetic diet, for 2 weeks. 16S rRNA gene surveys revealed that the fecal microbiota of 8604-fed mice and AIN-76-fed mice differed, as expected. The relative diversity of Proteobacteria, especially the Alphaproteobacteria and Gammaproteobacteria, increased significantly following the transition to AIN-76. Distinct patterns for beneficial Lactobacillales were exclusive to and highly abundant among 8604-fed mice, whereas several Proteobacteria were exclusive to AIN-76-fed mice. Interestingly, representative orders of the phyla Proteobacteria, Bacteroidetes, and Firmicutes, including the Lactobacillales, also differed as a function of murine ER␤ status. Overall, these interactions suggest that sterone nucleoreceptor status and diet complexity may play important roles in microbiota maintenance. Furthermore, we envision that this model for gastrointestinal dysbiosis may be used to identify novel probiotics, prebiotics, nutritional strategies, and pharmaceuticals for the prevention and resolution of Proteobacteria-rich dysbiosis.

Background Pediatric studies examining the association between obstructive sleep apnea (OSA) and insulin sensitivity/cardiometabolic risk are limited and conflicting. Objective To determine if cardiometabolic risk markers are increased among obese youth with obstructive sleep apnea as compared with their equally obese peers without OSA. Methods We performed a retrospective analysis of 96 patients (age 14.2 ± 1.4 years) who underwent polysomnography for suspected OSA. Fasting lipids, glucose, insulin, and hemoglobin A1c (HbA1c) were performed as part of routine clinical evaluation. Patients were categorized into two groups by degree of OSA as measured by the apnea hypopnea index (AHI): none or mild OSA (AHI < 5) and moderate or severe OSA (AHI ≥ 5). Results Despite similar degrees of obesity, patients with moderate or severe OSA had higher fasting insulin (p = 0.037) and homeostasis model assessment-insulin resistance [HOMA-IR (p = 0.0497)], as compared with those with mild or no OSA. After controlling for body mass index, there was a positive association between the AHI and log HOMA-IR (p = 0.005). There was a positive relationship between arousals plus awakenings during the polysomnography and fasting triglycerides. Conclusions OSA is linked with greater cardiometabolic risk markers in obese youth.

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To evaluate the contemporary prevalence of diabetic peripheral neuropathy (DPN) in participants with type 1 diabetes in the T1D Exchange Clinic Registry throughout the U.S. RESEARCH DESIGN AND METHODSDPN was assessed with the Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ) in adults with ‡5 years of type 1 diabetes duration. A score of ‡4 defined DPN. Associations of demographic, clinical, and laboratory factors with DPN were assessed. RESULTSAmong 5,936 T1D Exchange participants (mean 6 SD age 39 6 18 years, median type 1 diabetes duration 18 years [interquartile range 11, 31], 55% female, 88% non-Hispanic white, mean glycated hemoglobin [HbA 1c ] 8.1 6 1.6% [65.3 6 17.5 mmol/mol]), DPN prevalence was 11%. Compared with those without DPN, DPN participants were older, had higher HbA 1c , had longer duration of diabetes, were more likely to be female, and were less likely to have a college education and private insurance (all P < 0.001). DPN participants also were more likely to have cardiovascular disease (CVD) (P < 0.001), worse CVD risk factors of smoking (P 5 0.008), hypertriglyceridemia (P 5 0.002), higher BMI (P 5 0.009), retinopathy (P 5 0.004), reduced estimated glomerular filtration rate (P 5 0.02), and Charcot neuroarthropathy (P 5 0.002). There were no differences in insulin pump or continuous glucose monitor use, although DPN participants were more likely to have had severe hypoglycemia (P 5 0.04) and/or diabetic ketoacidosis (P < 0.001) in the past 3 months. CONCLUSIONSThe prevalence of DPN in this national cohort with type 1 diabetes is lower than in prior published reports but is reflective of current clinical care practices. These data also highlight that nonglycemic risk factors, such as CVD risk factors, severe hypoglycemia, diabetic ketoacidosis, and lower socioeconomic status, may also play a role in DPN development.Diabetic neuropathy is a prevalent complication in patients with diabetes and a major cause of morbidity and mortality (1). Among the various forms of diabetic neuropathy, distal symmetric polyneuropathy (DPN) and diabetic autonomic neuropathies are by far the most studied (1).

Objective To examine relationships between blood pressure (BP), adiposity, and sleep quality using overnight polysomnography (PSG) in obese adolescents. Study design Overnight PSG and morning BP measurements were performed in obese (BMI >97th %ile) non-diabetic adolescents (eligible age range 12-18 years, n=49). Subjects were stratified into two groups, one with normal BP, and one with elevated BP, and demographic and clinical characteristics compared between the groups. Multiple linear regression analysis was used to assess the BP effects of sleep quality measures. Results Participants (n=27) had normal morning BP, and 22 (44.9%) had elevated morning BP. There were no differences in age (p=0.53), sex (p=0.44), race (p=0.58) or BMI (p=0.56) between the two BP groups. The group with elevated BP spent shorter percentages of time in rapid eye movement (REM; p=0.006) and slow-wave sleep (SWS; p=0.024). Multiple linear regression analysis showed a lower percent of both REM and SWS were associated with increased morning BP, after adjusting for pubertal stage, sex, race, and BMI. Conclusion Lack of deeper stages of sleep, REM sleep and SWS, is associated with higher morning BP in obese adolescents, independent of BMI. Poor sleep quality should be considered in the work-up of obese youth with hypertension. Intervention studies are needed to evaluate whether improving the quality of sleep will reduce blood pressure elevation.