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The usage and safety of the Boost and Ease-off features in the CamAPS FX hybrid closed-loop system was analysed in a retrospective analysis of real-world data from 7,464 users over a 12-month period. Boost was used more frequently than Ease-off, but for a shorter duration per use. Mean starting glucose was above range for Boost (229±51 mg/dL), and within range for Ease-off (114±29 mg/dL). Time spent below 70 mg/dL was low during Boost periods [median (IQR) 0.0% (0.0, 0.5%)], and lower than during no Boost periods [2.1% (1.2, 3.4%)], while time spent above 180 mg/dL was lower during Ease-off periods (15±14%) compared to no Ease-off periods (25±12%). There were no episodes of severe hypoglycaemia or DKA attributed to Boost or Ease-off use. Boost and Ease-off allow users to engage safely with CamAPS FX to manage their glucose levels during periods of more-than-usual and less-than-usual insulin needs.
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OBJECTIVE: To compare postprandial glucose excursions following a bolus with inhaled technosphere insulin (TI) or subcutaneous rapid-acting analog (RAA) insulin. RESEARCH DESIGN AND METHODS: A meal challenge was completed by 122 adults with type 1 diabetes who were using multiple daily injections (MDI), a nonautomated pump, or automated insulin delivery (AID) and who were randomized to bolus with their usual RAA insulin (n = 61) or TI (n = 61). RESULTS: The primary outcome, the treatment group difference in area under the curve for glucose >180 mg/dL over 2 h, was less with TI versus RAA (adjusted difference -12 mg/dL, 95% CI -22 to -2, P = 0.02). With TI, the glucose excursion was smaller (P = 0.01), peak glucose lower (P = 0.01), and time to peak glucose shorter (P = 0.006). Blood glucose <70 mg/dL occurred in one participant in each group. CONCLUSIONS: Postmeal glucose excursion was smaller with TI than with RAA insulin in a cohort that included both AID and MDI users.
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Objective: To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. Research Methods: In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. Results: The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. Conclusion: CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24-34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT.
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OBJECTIVE: To evaluate whether continuous glucose monitoring (CGM)-derived glycemic patterns observed throughout pregnancy were associated with adverse perinatal outcomes, specifically fetal growth disorders and hypertensive disorders of pregnancy (HDP). METHODS: We conducted a prospective observational study of individuals with viable singleton pregnancies and screening hemoglobin A1c levels less than 6.5%. Those with preexisting diabetes were excluded. Enrollment occurred at the earliest gestational age before 17 weeks. Participants wore blinded continuous glucose monitors consecutively as willing until delivery. Those with at least 14 days of CGM data were included in analysis. Rates of large-for-gestational-age (LGA) neonates, small-for-gestational age (SGA) neonates, and HDP were assessed. Continuous glucose monitoring-derived glycemic metrics were calculated, including mean glucose level and percent time above and below thresholds. Two-sample t tests were used to compare glycemic metrics between participants with and without adverse perinatal outcomes. RESULTS: Of 937 participants enrolled, 760 met inclusion criteria. Those delivering LGA neonates or who were diagnosed with HDP had higher mean glucose levels (102±9 vs 100±8, P=.01 and 103±8 vs 99±8, P<.001) and spent more time above 120 mg/dL (median 16% vs 12%, P=.006, and 16% vs 12%, P<.001, respectively) and above 140 mg/dL (median 3.9% vs 2.8%, P=.006, and 3.5% vs 2.8%, P<.001, respectively) throughout gestation than those without these outcomes. These findings were present regardless of gestational diabetes mellitus status. Participants with SGA neonates had lower mean glucose levels (97±7 vs 101±8, P=.01) and spent less time above 140 mg/dL (median 1.6% vs 2.3%, P=.01) and more time below 63 mg/dL (median 3.0% vs 2.3%, P=.02) than those without SGA neonates. CONCLUSION: Individuals with LGA neonates or HDP exhibit a slightly higher mean glucose levels and spend more time hyperglycemic in early pregnancy than those who do not experience these outcomes.
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OBJECTIVES: Evidence suggests that glucose levels in menstruating females with type 1 diabetes change throughout the menstrual cycle, reaching a peak during the luteal phase. The Type 1 Diabetes Exercise Initiative (T1DEXI) study provided the opportunity to assess glycemic metrics between early and late phases of the menstrual cycle, and whether differences could be explained by exercise, insulin, and carbohydrate intake. METHODS: One hundred seventy-nine women were included in our analysis. Glycemic metrics, carbohydrate intake, insulin requirements, and exercise habits during the early vs late phases of their menstrual cycles (i.e. 2 to 4 days after vs 2 to 4 days before reported menstruation start date) were compared. RESULTS: Mean glucose increased from 8.2±1.5 mmol/L (148±27 mg/dL) during the early follicular phase to 8.6±1.6 mmol/L (155±29 mg/dL) during the late luteal phase (p<0.001). Mean percent time-in-range (3.9 to 10.0 mmol/L [70 to 180 mg/dL]) decreased from 73±17% to 70±18% (p=0.002), and median percent time >10.0 mmol/L (>180 mg/dL) increased from 21% to 23% (p<0.001). Median total daily insulin requirements increased from 37.4 units during the early follicular phase to 38.5 units during the late luteal phase (p=0.02) and mean daily carbohydrate consumption increased slightly from 127±47 g to 133±47 g (p=0.05); however, the difference in mean glucose during early follicular vs late luteal phase was not explained by differences in exercise duration, total daily insulin units, or reported carbohydrate intake. CONCLUSIONS: Glucose levels during the late luteal phase were higher than those of the early follicular phase of the menstrual cycle. These glycemic changes suggest that glucose management for women with type 1 diabetes may need to be fine-tuned within the context of their menstrual cycles.
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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.
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OBJECTIVE: We evaluated the effect of long-term intensive metabolic control with hybrid closed-loop (CL) on residual C-peptide secretion and glucose control compared with standard insulin therapy in youth with type 1 diabetes over 48 months. RESEARCH DESIGN AND METHODS: Following the 24-month primary phase of a multicenter, randomized, parallel trial of 96 newly diagnosed youth aged 10 to 16.9 years, participants were invited to an extension phase using treatment allocated at randomization. They continued with hybrid CL using the Cambridge algorithm or standard insulin therapy (control) until 48 months after diagnosis. Analysis was by intention-to-treat. RESULTS: At 24 months after diagnosis, 81 participants (mean ± SD age 14 ± 2 years) continued in the extension phase (47 CL, 34 control). There was no difference in fasting C-peptide corrected for fasting glucose at 48 months between groups (CL: 5 ± 9 vs. control: 6 ± 14 pmol/L per mmol/L; mean adjusted difference -2 [95% CI -7, 4; P = 0.54]). Central laboratory HbA1c remained lower in the CL group by 0.9% (10 mmol/mol [95% CI 0.2, 1.5; 3, 17 mmol/mol); P = 0.009). Time in target range of 3.9 to 10.0 mmol/L was 12 percentage points (95% CI 3, 20; P = 0.008) higher in the CL group compared with control. There were 11 severe hypoglycemic events (6 CL, 5 control) and 7 diabetic ketoacidosis events (3 CL, 4 control) during the extension phase. CONCLUSIONS: Improved glycemic control was sustained over 48 months after diagnosis with CL insulin delivery compared with standard therapy in youth with type 1 diabetes. This did not appear to confer a protective effect on residual C-peptide secretion.
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Glicemia , Peptídeo C , Diabetes Mellitus Tipo 1 , Sistemas de Infusão de Insulina , Insulina , Humanos , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/metabolismo , Adolescente , Peptídeo C/sangue , Insulina/uso terapêutico , Insulina/administração & dosagem , Masculino , Criança , Feminino , Glicemia/metabolismo , Glicemia/efeitos dos fármacos , Controle Glicêmico/métodos , Hipoglicemiantes/uso terapêutico , Hipoglicemiantes/administração & dosagem , Hemoglobinas Glicadas/metabolismoRESUMO
INTRODUCTION: To characterize glucose levels during uncomplicated pregnancies, defined as pregnancy with a hemoglobin A1c <5.7% (<39 mmol/mol) in early pregnancy, and without a large-for-gestational-age birth, hypertensive disorders of pregnancy, or gestational diabetes mellitus (ie, abnormal oral glucose tolerance test). RESEARCH DESIGN AND METHODS: Two sites enrolled 937 pregnant individuals aged 18 years and older prior to reaching 17 gestational weeks; 413 had an uncomplicated pregnancy (mean±SD body mass index (BMI) of 25.3±5.0 kg/m2) and wore Dexcom G6 continuous glucose monitoring (CGM) devices throughout the observed gestational period. Mealtimes were voluntarily recorded. Glycemic levels during gestation were characterized using CGM-measured glycemic metrics. RESULTS: Participants wore CGM for a median of 123 days each. Glucose levels were nearly stable throughout all three trimesters in uncomplicated pregnancies. Overall mean±SD glucose during gestation was 98±7 mg/dL (5.4±0.4 mmol/L), median per cent time 63-120 mg/dL (3.5-6.7 mmol/L) was 86% (IQR: 82-89%), median per cent time <63 mg/dL (3.5 mmol/L) was 1.8%, median per cent time >120 mg/dL (6.7 mmol/L) was 11%, and median per cent time >140 mg/dL (7.8 mmol/L) was 2.5%. Mean post-prandial peak glucose was 126±22 mg/dL (7.0±1.2 mmol/L), and mean post-prandial glycemic excursion was 36±22 mg/dL (2.0±1.2 mmol/L). Higher mean glucose levels were low to moderately associated with pregnant individuals with higher BMIs (103±6 mg/dL (5.7±0.3 mmol/L) for BMI ≥30.0 kg/m2 vs 96±7 mg/dL (5.3±0.4 mmol/L) for BMI 18.5-<25 kg/m2, r=0.35). CONCLUSIONS: Mean glucose levels and time 63-120 mg/dL (3.5-6.7 mmol/L) remained nearly stable throughout pregnancy and values above 140 mg/dL (7.8 mmol/L) were rare. Mean glucose levels in pregnancy trend higher as BMI increases into the overweight/obesity range. The glycemic metrics reported during uncomplicated pregnancies represent treatment targets for pregnant individuals.
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Automonitorização da Glicemia , Glicemia , Humanos , Feminino , Gravidez , Glicemia/análise , Adulto , Automonitorização da Glicemia/métodos , Hemoglobinas Glicadas/análise , Diabetes Gestacional/sangue , Diabetes Gestacional/diagnóstico , Teste de Tolerância a Glucose , Adulto Jovem , Seguimentos , Biomarcadores/sangue , Biomarcadores/análise , Monitoramento Contínuo da GlicoseRESUMO
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.
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OBJECTIVE: To determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24-28 weeks' gestation compared with those without GDM. RESEARCH DESIGN AND METHODS: The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics. RESULTS: Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63-120 mg/dL (3.5-6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13-14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT. CONCLUSIONS: Prior to OGTT at 24-34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM.
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Automonitorização da Glicemia , Glicemia , Diabetes Gestacional , Teste de Tolerância a Glucose , Humanos , Diabetes Gestacional/sangue , Diabetes Gestacional/diagnóstico , Gravidez , Feminino , Glicemia/análise , Glicemia/metabolismo , Adulto , Estudos Prospectivos , Monitoramento Contínuo da GlicoseRESUMO
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.
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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.
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Aim: The impact of weight gain on insulin dosage and glycemic control in adults with type 1 diabetes (T1D) aged 25 years and older was investigated in the T1D Exchange Registry participants. Methods: Participants were categorized into four groups based on their change in weight from T1D Exchange registry enrollment to year 5: stable weight (-5 to <5 lb), gained 5 to <10 lb, gained 10 to <20 lb, or gained ≥20 lb. Those who lost >5 lb were excluded. The primary outcomes were glucose control, as measured by glycosylated hemoglobin (HbA1c), and total daily insulin dose (TDD) at year 5. Linear regression models were used to evaluate the association between weight gain, HbA1c, and TDD. Results: There were 1969 participants included in the analyses. The mean ± standard deviation age was 45 ± 13 years, 57% were female, and 92% were White non-Hispanic. For those with an enrollment HbA1c <8.0%, the mean HbA1c at year 5 was higher for those who gained ≥20 lb compared to those with a stable weight of -5 to <5 lb (7.4% ± 1.1% vs. 7.2% ± 0.8%, respectively; P = 0.005). For this cohort, the mean TDD at year 5 increased from 49 ± 25 to 61 ± 29 U for those who gained ≥20 lb, while decreased from 45 ± 27 to 44 ± 25 U for those with stable weight of -5 to <5 lb (P < 0.001). Among participants with an enrollment HbA1c ≥9.0%, the mean HbA1c at year 5 was statistically insignificant at 8.4% ± 1.3% for those who gained ≥20 lb compared to 9.2% ± 1.7% for those with a stable weight of -5 to <5 lb (P = 0.09). Conclusion: Significant weight gain in adults with T1D who had good to adequate glycemic control was associated with modest deterioration in glucose control despite an increase in TDD. Worsening glucose control may indicate insulin resistance related to weight gain despite significantly increased insulin dosage which was insufficient to maintain adequate glycemic control.
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Diabetes Mellitus Tipo 1 , Adulto , Feminino , Humanos , Masculino , Diabetes Mellitus Tipo 1/tratamento farmacológico , Glicemia , Hemoglobinas Glicadas , Controle Glicêmico , Sistema de Registros , Insulina Regular Humana , Insulina/uso terapêutico , Aumento de PesoRESUMO
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 ).
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Automonitorização da Glicemia , Glicemia , Diabetes Mellitus Tipo 1 , Exercício Físico , Humanos , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/terapia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Adulto , Feminino , Masculino , Automonitorização da Glicemia/métodos , Glicemia/metabolismo , Glicemia/análise , Pessoa de Meia-Idade , Exercício Físico/fisiologia , Hemoglobinas Glicadas/metabolismo , Hemoglobinas Glicadas/análise , Insulina/uso terapêutico , Insulina/administração & dosagem , Estudos de Coortes , Monitoramento Contínuo da GlicoseRESUMO
CONTEXT: Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. OBJECTIVE: This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. METHODS: We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 "aware" adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. RESULTS: Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to "aware" (-21 ± 44 vs -19 ± 43â mg/dL [-1.17 ± 2.44 vs -1.05 ± 2.39 mmol/L], adjusted group difference of -4.2 [95% CI, -8.4 to 0.05] mg/dL [-0.23 95% CI, -.47 to 0.003â mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70â mg/dL [3.89â mmol/L] (≥15â minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89â mmol/L) for individuals with IAH compared to "aware" did not differ significantly between exercise and sedentary days (interaction P = .36). CONCLUSION: Individuals with IAH have a higher underlying risk of hypoglycemia than "aware" individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.
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Automonitorização da Glicemia , Glicemia , Diabetes Mellitus Tipo 1 , Exercício Físico , Hipoglicemia , Humanos , Hipoglicemia/sangue , Masculino , Feminino , Exercício Físico/fisiologia , Adulto , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/complicações , Diabetes Mellitus Tipo 1/tratamento farmacológico , Pessoa de Meia-Idade , Automonitorização da Glicemia/métodos , Hipoglicemiantes/uso terapêutico , Hipoglicemiantes/administração & dosagem , Conscientização , Hemoglobinas Glicadas/análise , Insulina/administração & dosagemRESUMO
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.
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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 â¼10 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.
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Diabetes Mellitus Tipo 1 , Hipoglicemia , Adolescente , Criança , Feminino , Humanos , Masculino , Glicemia , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Exercício Físico/fisiologia , Glucose , Hemoglobinas Glicadas , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Insulina Regular HumanaRESUMO
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.
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Diabetes Mellitus Tipo 1 , Glucose , Adolescente , Feminino , Humanos , Criança , Masculino , Automonitorização da Glicemia , Glicemia , Refeições , InsulinaRESUMO
OBJECTIVE: Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS: In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS: Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over â¼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS: Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.
Assuntos
Diabetes Mellitus Tipo 1 , Humanos , Adolescente , Feminino , Criança , Masculino , Glicemia , Hemoglobinas Glicadas , Automonitorização da Glicemia , Insulina/uso terapêutico , Insulina Regular Humana , Exercício Físico/fisiologia , Hipoglicemiantes/uso terapêuticoRESUMO
Objective: To evaluate the relationship between continuous glucose monitoring (CGM)-measured time-in-range 70-180 mg/dL (TIR) and time-in-tight-range 70-140 mg/dL (TITR). Methods: TIR and TITR were calculated from CGM data collected using blinded or unblinded Dexcom sensors from 9 studies with 912 participants with type 1 diabetes (T1D) and 2 studies with 184 participants with type 2 diabetes (T2D). The TIR-TITR relationship was assessed overall and stratified by coefficient of variation (CV) and by time below range <70 mg/dL (TBR). Results: The correlation between TIR and TITR was 0.94. TITR was higher for a given TIR for T2D compared with T1D. However, after adjusting for the differences in CV or TBR, both of which were higher with T1D than T2D, the differences were minimized. The TIR-TITR relationship was nonlinear, with a higher ratio of TITR:TIR observed as TIR increased ranging from 0.42 when TIR was 20% to 0.66 when TIR was 80%. Similarly, as TITR increased, the ratio of TIR:TITR decreased, varying from 2.6 with TITR of 10% to 1.3 for TITR of 70%. The TIR-TITR relationship varied according to CV and TBR, such that the higher the CV or higher the amount of TBR the greater was TITR for a given TIR. Conclusions: TIR and TITR are highly correlated, although the relationship is nonlinear. With knowledge of TIR, TITR can be estimated with reasonable precision.