Safety and prescribing recommendations for verapamil in newly diagnosed pediatric type 1 diabetes (T1D): The CLVer experience.

Objectives: To report the safety and side effects associated with taking verapamil for beta-cell preservation in children with newly-diagnosed T1D. Research Design and Methods: Eighty-eight participants aged 8.5 to 17.9 years weighing ≥ 30 kg were randomly assigned to verapamil (N = 47) or placebo (N = 41) within 31 days of T1D diagnosis and followed for 12 months from diagnosis, main CLVer study. Drug dosing was weight-based with incremental increases to full dosage. Side effect monitoring included serial measurements of pulse, blood pressure, liver enzymes, and electrocardiograms (ECGs). At study end, participants were enrolled in an observational extension study (CLVerEx), which is ongoing. No study drug is provided during the extension, but participants may use verapamil if prescribed by their diabetes care team. Results: Overall rates of adverse events were low and comparable between verapamil and placebo groups. There was no difference in the frequency of liver function abnormalities. Three CLVer participants reduced or discontinued medication due to asymptomatic ECG changes. One CLVerEx participant (18 years old), treated with placebo during CLVer, who had not had a monitoring ECG, experienced complete AV block with a severe hypotensive episode 6 weeks after reaching his maximum verapamil dose following an inadvertent double dose on the day of the event. Conclusions: The use of verapamil in youth newly-diagnosed with T1D appears generally safe and well tolerated with appropriate monitoring. We strongly recommend monitoring for potential side effects including an ECG at screening and an additional ECG once full dosage is reached.ClinicalTrials.gov number: NCT04233034.

Plastid phylogenomics reveals evolutionary relationships in the mycoheterotrophic orchid genus Dipodium and provides insights into plastid gene degeneration.

The orchid genus Dipodium R.Br. (Epidendroideae) comprises leafy autotrophic and leafless mycoheterotrophic species, with the latter confined to sect. Dipodium. This study examined plastome degeneration in Dipodium in a phylogenomic and temporal context. Whole plastomes were reconstructed and annotated for 24 Dipodium samples representing 14 species and two putatively new species, encompassing over 80% of species diversity in sect. Dipodium. Phylogenomic analysis based on 68 plastid loci including a broad outgroup sampling across Orchidaceae found that sect. Leopardanthus is the sister lineage to sect. Dipodium. Dipodium ensifolium, the only leafy autotrophic species in sect. Dipodium, was found to be a sister to all leafless, mycoheterotrophic species, supporting a single evolutionary origin of mycoheterotrophy in the genus. Divergence-time estimations found that Dipodium arose ca. 33.3 Ma near the lower boundary of the Oligocene and that crown diversification commenced in the late Miocene, ca. 11.3 Ma. Mycoheterotrophy in the genus was estimated to have evolved in the late Miocene, ca. 7.3 Ma, in sect. Dipodium. The comparative assessment of plastome structure and gene degradation in Dipodium revealed that plastid ndh genes were pseudogenised or physically lost in all Dipodium species, including in leafy autotrophic species of both Dipodium sections. Levels of plastid ndh gene degradation were found to vary among species as well as within species, providing evidence of relaxed selection for retention of the NADH dehydrogenase complex within the genus. Dipodium exhibits an early stage of plastid genome degradation, as all species were found to have retained a full set of functional photosynthesis-related genes and housekeeping genes. This study provides important insights into plastid genome degradation along the transition from autotrophy to mycoheterotrophy in a phylogenomic and temporal context.

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study.

Background: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D. Objective: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study. Methods: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available). Results: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min). Conclusions: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

Multicenter Evaluation of Ultra-Rapid Lispro Insulin with Control-IQ Technology in Adults, Adolescents, and Children with Type 1 Diabetes.

Objective: To evaluate the safety and explore the efficacy of use of ultra-rapid lispro (URLi, Lyumjev) insulin in the Tandem t:slim X2 insulin pump with Control-IQ 1.5 technology in children, teenagers, and adults living with type 1 diabetes (T1D). Methods: At 14 U.S. diabetes centers, youth and adults with T1D completed a 16-day lead-in period using lispro in a t:slim X2 insulin pump with Control-IQ 1.5 technology, followed by a 13-week period in which URLi insulin was used in the pump. Results: The trial included 179 individuals with T1D (age 6-75 years). With URLi, 1.7% (3 participants) had a severe hypoglycemia event over 13 weeks attributed to override boluses or a missed meal. No diabetic ketoacidosis events occurred. Two participants stopped URLi use because of infusion-site discomfort, and one stopped after developing a rash. Mean time 70-180 mg/dL increased from 65% ± 15% with lispro to 67% ± 13% with URLi (P = 0.004). Mean insulin treatment satisfaction questionnaire score improved from 75 ± 13 at screening to 80 ± 11 after 13 weeks of URLi use (mean difference = 6; 95% confidence interval 4-8; P < 0.001), with the greatest improvement reported for confidence avoiding symptoms of high blood sugar. Mean treatment-related impact measure-diabetes score improved from 74 ± 12 to 80 ± 12 (P < 0.001), and mean TRIM-Diabetes Device (score improved from 82 ± 11 to 86 ± 12 (P < 0.001). Conclusions: URLi use in the Tandem t:slim X2 insulin pump with Control-IQ 1.5 technology was safe for adult and pediatric participants with T1D, with quality-of-life benefits of URLi use perceived by the study participants. Clinicaltrials.gov registration: NCT05403502.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

BACKGROUND: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures. METHOD: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively. RESULTS: In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia. CONCLUSIONS: In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.

BACKGROUND AND OBJECTIVE: Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden. METHODS: We modeled quadrennial prostate-specific antigen (PSA) screening for men between the ages of 50 and 74 yr over a lifetime horizon using a health care perspective. Men with PSA ≥3 ng/ml were referred for standard biopsy (SBx), for which cores were either examined via AI followed by a pathologist for AI-labeled positive cores, or a pathologist alone. The AI performance characteristics were estimated using an internal STHLM3 validation data set. Outcome measures included the number of tests, PCa incidence and mortality, overdiagnosis, quality-adjusted life years (QALYs), and the potential reduction in pathologist-evaluated biopsy cores if AI were used. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio. KEY FINDINGS AND LIMITATIONS: In comparison to a pathologist alone, the AI-assisted workflow increased the number of PSA tests, SBx procedures, and PCa deaths by ≤0.03%, and slightly reduced PCa incidence and overdiagnosis. AI would reduce the proportion of biopsy cores evaluated by a pathologist by 80%. At a cost of €10 per case, the AI-assisted workflow would cost less and result in <0.001% lower QALYs in comparison to a pathologist alone. The results were sensitive to the AI cost. CONCLUSIONS AND CLINICAL IMPLICATIONS: According to our model, AI-assisted pathology would significantly decrease the workload of pathologists, would not affect patient quality of life, and would yield cost savings in Sweden when compared to a human pathologist alone. PATIENT SUMMARY: We compared outcomes for prostate cancer patients and relevant costs for two methods of assessing prostate biopsies in Sweden: (1) artificial intelligence (AI) technology and review of positive biopsies by a human pathologist; and (2) a human pathologist alone for all biopsies. We found that addition of AI would reduce the pathology workload and save money, and would not affect patient outcomes when compared to a human pathologist alone. The results suggest that adding AI to prostate pathology in Sweden would save costs.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.

Objective: To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Methods: Adolescents with T1D (n = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. Results: RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; P < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. Conclusions: Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative.

AIMS: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS: This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS: A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h. CONCLUSIONS: In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

Repeated Prostate Cancer Screening Using Prostate-Specific Antigen Testing and Magnetic Resonance Imaging: A Secondary Analysis of the STHLM3-MRI Randomized Clinical Trial.

Importance: Magnetic resonance imaging (MRI) has been proposed to enhance the benefit-to-harm ratio of prostate cancer screening, but data on repeated screening outcomes are lacking. Objective: To describe outcomes of prostate-specific antigen (PSA)-based screening with MRI and prostate biopsies at repeat screening. Design, Setting, and Participants: This secondary analysis examined the population-based, screen-by-invitation STHLM3-MRI randomized clinical trial, which recruited Swedish men aged 50 to 74 years. Men were eligible for repeat screening at 2 to 3 years if they had PSA levels of 1.5 ng/mL or greater at trial inclusion, were randomized to the MRI-targeted group (including screening using biomarkers and MRI), and were not diagnosed with prostate cancer after the first screening round. Repeat screening was performed between November 10, 2021, and February 20, 2023. Data analysis was performed between May and August 2023. Intervention: Participants underwent blood sampling, including PSA testing. A biparametric MRI scan was performed if PSA levels were 3 ng/mL or greater, and men with lesions with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3 or greater were referred for targeted and systematic biopsies. Main Outcomes and Measures: The primary outcome was clinically significant prostate cancer (Gleason score of ≥3 + 4). Secondary outcomes included the proportion of men with clinically insignificant cancer (Gleason score of 6), the number of elevated PSA tests, MRI scans, and biopsy procedures. Results: Of 7609 men from the first screening round, 2078 (27.3%) were eligible for and were invited for rescreening. Among the invitees, 1500 (72.2%) participated. Their median age was 67 (IQR, 61-72) years. Of 1094 men with PSA levels between 1.5 and 2.9 ng/mL in the first screening round, 326 (29.8%) had levels of 3 ng/mL or greater in the second round. Overall, 667 men (44.5%) had PSA levels of 3 ng/mL or greater: 617 underwent MRI (92.5%), revealing 51 (7.6%) with equivocal lesions (PI-RADS score of 3) and 33 (4.9%) with suspicious lesions (PI-RADS score of ≥4). Only 10 of 383 men (2.6%) with a prior negative MRI result had a lesion with a PI-RADS score of 4 or greater. Among the 1500 rescreened men, 48 (3.2%) had a Gleason score of 3 + 4 or greater, including 19 (1.3%) with a score of 4 + 3 or greater and 11 (0.7%) with a score of 6. Conclusions and Relevance: In this secondary analysis of the STHLM3-MRI randomized clinical trial, cancer detection during the second screening round in biennial PSA and MRI-based prostate cancer screening was limited, and the detection of low-grade tumors remained low. A substantial proportion of men exhibited elevated PSA levels during rescreening, and a considerable portion of MRI scans performed lacked lesions suggestive of cancer. Future studies should explore strategies to reduce MRI-related resource use. Trial Registration: ClinicalTrials.gov Identifier: NCT03377881.

The origin and speciation of orchids.

Orchids constitute one of the most spectacular radiations of flowering plants. However, their origin, spread across the globe, and hotspots of speciation remain uncertain due to the lack of an up-to-date phylogeographic analysis. We present a new Orchidaceae phylogeny based on combined high-throughput and Sanger sequencing data, covering all five subfamilies, 17/22 tribes, 40/49 subtribes, 285/736 genera, and c. 7% (1921) of the 29 524 accepted species, and use it to infer geographic range evolution, diversity, and speciation patterns by adding curated geographical distributions from the World Checklist of Vascular Plants. The orchids' most recent common ancestor is inferred to have lived in Late Cretaceous Laurasia. The modern range of Apostasioideae, which comprises two genera with 16 species from India to northern Australia, is interpreted as relictual, similar to that of numerous other groups that went extinct at higher latitudes following the global climate cooling during the Oligocene. Despite their ancient origin, modern orchid species diversity mainly originated over the last 5 Ma, with the highest speciation rates in Panama and Costa Rica. These results alter our understanding of the geographic origin of orchids, previously proposed as Australian, and pinpoint Central America as a region of recent, explosive speciation.

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry.

BACKGROUND: In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework. AIM: To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks. METHODS: From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales). RESULTS: For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models. CONCLUSIONS: For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models. HIGHLIGHTS: For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes.

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

Improving Planning for Transition to Adult Care in a Pediatric Diabetes Clinic.

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes efforts at a large midwestern pediatric health system to improve planning for patients' transition from pediatric to adult diabetes care settings.

Benchmarking Diabetes Technology Use Among 21 U.S. Pediatric Diabetes Centers.

The American Diabetes Association's Standards of Care in Diabetes recommends the use of diabetes technology such as continuous glucose monitoring systems and insulin pumps for people living with type 1 diabetes. Unfortunately, there are multiple barriers to uptake of these devices, including local diabetes center practices. This study aimed to examine overall change and center-to-center variation in uptake of diabetes technology across 21 pediatric centers in the T1D Exchange Quality Improvement Collaborative. It found an overall increase in diabetes technology use for most centers from 2021 to 2022 with significant variation.

The Relationship Between Parent Fear of Hypoglycemia and Youth Glycemic Control Across the Recent-Onset Period in Families of Youth with Type 1 Diabetes.

BACKGROUND: This study aims to examine the relationship between parents' fear of hypoglycemia (FH) over a 1-year period and child glucose metrics in 126 families of youth recently diagnosed with type 1 diabetes (T1D). METHODS: Parents completed the Hypoglycemia Fear Survey for Parents (HFS-P) and uploaded 14 days of glucose data at a baseline, 6-month, and 12-month assessment. RESULTS: Parents' HFS-P total and worry scores increased to a clinically meaningful degree from baseline to 6-month assessment, while multilevel models revealed within- and between-person variability in parents' HFS-P worry and behavior scores over time associated with child glycemia. Specifically, a significant negative relationship for within-person worry scores suggested that when parents reported higher than their average worry scores, their children recorded fewer glucose values in the target range, while within-person behavior scores suggested that when parents reported lower than their average behavior scores, their children recorded more values above the target range. There was also a negative relationship for between-person behavior scores with child glycated hemoglobin and a positive relationship for between-person behavior scores with child glucose values in the target range. CONCLUSIONS: In the recent-onset period of T1D, parental FH worry and behavior associated with child glycemia possibly due to changes in parents' perceptions of their child's hypoglycemia risk. The clinically meaningful increases in parent FH in the recent-onset period and the negative association for between-person behavior scores with child glycated hemoglobin suggest that clinics should consider screening parents for FH, especially among parents of children with lower glycemic levels.