1型糖尿病青少年患者血糖控制和糖尿病微血管并发症

为了解1型糖尿病青少年患者的血糖控制情况、糖尿病微血管并发症的发生率及其危险因素 ,调查病程5年以上的1型糖尿病患者76例 ,平均年龄17.2±3.9岁 ,平均病程9.0±3.4年 ,检测餐后2小时血糖、糖化血红蛋白 (HbA1C)、尿微量白蛋白 ,眼底荧光造影等。结果显示 ,HbA1C 平均水平为 (9.7±1.7) % ;糖尿病肾病发生率为9.2% ,微量白蛋白尿 (UAER :20～200μg/min)和大量蛋白尿 (UAER>200μg/min)分别占7.9 %和1.3 %;糖尿病视网膜病变发生率为23.7% ,非增殖型16例占21.1% ,增殖型2例占2.6 % ,其中3例合并白内障。提示目前1型糖尿病青少年患者的血糖控制不理想 ,仅有18.4 %的患者HbA1C <8 % ;长病程、女性、血糖控制差是1型糖尿病青少年发生糖尿病视网膜病变、糖尿病肾病的危险因素     

Correlates of glycemic control and quality of life outcomes in adolescents with type 1 diabetes

Ingerski LM, Laffel L, Drotar D, Repaske D, Hood KK. Correlates of glycemic control and quality of life outcomes in adolescents with type 1 diabetes. Background: A major focus of pediatric multidisciplinary diabetes care is promoting glycemic control (A1c) while ensuring high quality of life (QOL). The current study investigated factors associated with A1c and QOL using a methodology that considered these variables as simultaneous outcomes. Method: A total of 261 adolescents (aged 13–18) with type 1 diabetes completed measures of blood glucose monitoring (BGM) frequency, diabetes‐specific QOL, negative affect, and depression. Caregivers completed measures of demographic and disease characteristics, depression, and family conflict. Results: A1c was negatively correlated with QOL (r = −0.18 to −0.29, p < 0.01) across all subscales. Based on clinical A1c goals and median QOL scores, adolescents fell into four glycemic control–QOL groups. Multinomial logistic regression determined correlates of group membership utilizing adolescents with suboptimal glycemic control–low QOL as the referent group. Adolescents with optimal glycemic control–high QOL reported increased BGM frequency (OR = 1.87), less negative affect (OR = 1.32), and were more likely to use CSII (OR = 5.41). Adolescents with optimal A1c–low QOL reported greater BGM frequency (OR = 1.91) and shorter disease duration (OR = 1.09). Adolescents with suboptimal glycemic control–high QOL reported greater BGM frequency (OR = 1.41), fewer depressive symptoms (OR = 1.13), and less negative affect (OR = 1.31). Conclusions: Results reveal disease, management, and psychosocial characteristics that differentiate glycemic control–QOL outcome groups and identify risk factors related to this relationship. Further appreciation of these characteristics may increase clinicians' understanding and attention to these important clinical outcomes and help tailor the most appropriate interventions (e.g., individual psychotherapy vs. family problem‐solving interventions) to help adolescents achieve glycemic control without sacrificing QOL.     

Influence of plasma glucagon levels on glycemic control in children with type 1 diabetes

Objective: The aim of this study was to investigate the association between plasma glucose (PG), HbA1c and plasma glucagons levels in children with type 1 diabetes to determine the influence of plasma glucagon on their glycemic control. Methods: The study was conducted in 60 Japanese children, aged 13.3 ± 4.6 years, with type 1 diabetes for at least 3 years of diabetes. Most of the subjects had absent pancreatic β‐cell function. We compared the glucagon levels among patient groups stratified according to the 2‐hour postprandial levels (<50, 50–99, 100–199, 200–299, and ≥300 mg/dL), and the HbA1c levels (<7.0, 7.0–7.9, 8.0–8.9, and ≥9%). Results: The mean 2‐hour postprandial PG, HbA1c and plasma glucagon levels were 174 ± 97 mg/dL, 7.7 ± 1.3% and 84.0 ± 32.6 pg/mL, respectively. The glucagon levels were highly correlated with the PG levels (r = 0.553, P < 0.0001) and mildly correlated with the HbA1c levels (r = 0.301, P = 0.0192). Patients with high PG levels had significantly higher levels of glucagon as compared with those with lower PG levels (139.4 ± 47.2, 78.4 ± 17.3, 82.4 ± 21.0, 98.3 ± 29.2 and 93.8 ± 18.3 pg/mL, P = 0.0009). On the other hand, there were no significant differences in plasma glucagon levels among patient groups stratified according to HbA1c levels (P = 0.1566), however, patients with HbA1c levels ≥ 9% had significantly higher levels of glucagon than those with HbA1c levels < 7% (113.3 ± 53.4 vs 80.8 ± 18.4 pg/mL, P = 0.0291). Conclusion: These results suggest that patients with high PG are likely to have high concentrations of plasma glucagon, which may aggravate glycemic control progressively, leading to elevation of HbA1c levels.     

Predictors of glucose control in children and adolescents with type 1 diabetes mellitus

AIMS: To evaluate the glucose control [(as measured by hemoglobin A1c (HbA1c)], the factors associated with glycemic control, and possible explanations for these associations in a sample of children and adolescents with type 1 diabetes. METHODS: Data were collected on 155 children and adolescents, with type 1 diabetes mellitus, attending a multidisciplinary diabetes clinic in Portland, OR. Patients' hospital charts were reviewed to determine demographic factors, disease-related characteristics, and HbA1c level. RESULTS: Mean percent HbA1c was 9.3. Adolescents between the ages of 14 and 18 yr were in poorer metabolic control (adjusted mean percent HbA1c 0.56 higher than children 2-8 yr). Children who attended the clinic three to four times in the previous year were in better control (adjusted mean percent HbA1c 0.46 lower than those who visited two or fewer times and 1.11 lower than those who attended five or more times). Children with married parents were in better glycemic control than those of single, separated, or divorced parents (adjusted mean percent HbA1c 0.47 lower for children of married parents). This effect appeared to be mediated, in part, by the number of glucose checks performed per day. CONCLUSIONS: This study suggests that adolescents should be targeted for improved metabolic control. Diabetes team members need to be aware of changing family situations and provide extra support during stressful times. Regular clinic attendance is an important component of intensive diabetes management. Strategies must be developed to improve accessibility to the clinic and to identify patients who frequently miss appointments.     

Feeding problems reported by parents of young children with type 1 diabetes on insulin pump therapy and their associations with children's glycemic control

Objective: Previous research demonstrated high rates of perceived mealtime behavior problems in families of young children with type 1 diabetes who were managed with conventional therapy. Because of new insulin regimens that offer greater flexibility, reexamination of mealtime behaviors is required. We assessed parent-reported mealtime behaviors in a sample of young children using an insulin pump. An additional aim was to evaluate the associations of two measures of parental feeding behavior with children's glycemic control.
Methods: Primary caregivers of 31 young children (mean age = 5.0 ± 1.3 yr) completed the Child Feeding Questionnaire (CFQ) and the Behavioral Pediatric Feeding Assessment Scale (BPFAS). Hemoglobin A1c (HbA1c) was used as a surrogate marker for children's glycemic control.
Results: Children had a mean HbA1c of 7.8 ± 0.64%. Mean CFQ – Restriction and Pressure to Eat scores were 3.1 ± 0.94 and 2.0 ± 0.88, respectively (range = 1–5). Mean BPFAS – Parent and Child scores were 16.0 ± 4.3 (range = 10–50) and 44.9 ± 9.3 (range = 25–125), respectively. Positive correlations were found between children's HbA1c levels and caregivers' reporting of frequency of child mealtime behavior problems.
Conclusions: Caregivers of young children on pump therapy report relatively low rates of mealtime behavior problems. However, correlations with children's HbA1c suggest that parent–child mealtime behaviors continue to relate to children's health outcomes. Research is needed to determine if changing mealtime interactions can improve children's glycemic control; items from the BPFAS and CFQ can offer targets to guide interventions.     

Telemedical support to improve glycemic control in adolescents with type 1 diabetes mellitus

Introduction In this paper, we evaluated the feasibility of a telemedical (TM) support program and its effect on glycemic control in adolescents with type 1 diabetes mellitus (T1DM). Thirty-six adolescents (m=20, median age at the start of the study: 15.3 years (range: 10.7–19.3 years), median age at diagnosis: 9.3 years (2.1–13.8 years), median duration of disease: 6.4 years (1.0–12.8 years), HbA1c>8%, all on intensified insulin therapy) were randomized in a crossover trial over 6 months (3 months with TM, 3 months with conventional support and paper diary (PD)). During the TM phase, the patients sent their data (date, time, blood glucose, carbohydrate intake, insulin dosage) via mobile phone, at least daily, to our server and diabetologists sent back their advice via short message service (SMS) once a week.Results Glycemic control improved during the TM phase, while it deteriorated during the PD phase: TM-PD group HbA1c (%, median (range)): 9.05 (8–11.3) (at 0 months), 8.9 (6.9–11.3) (at 3 months), and 9.2 (7.4–12.6) (at 6 months), and PD-TM group: 8.9 (8.3–11.6), 9.9 (8.1–11), and 8.85 (7.3–11.7) (p<0.05). Patients rated the TM support program to be a good idea. Technical problems with General Packet Radio Service (GPRS) data transmission led to data loss and decreased patient satisfaction.Conclusion Our telemedical support program, VIE-DIAB, proved to be feasible in adolescents and helped to improve glycemic control.