Type 1 diabetes mellitus and exercise in competitive athletes

AIMS

The number of patients with type 1 diabetes mellitus who are actively participating in competitive sports is increasing. Here, we aimed to assess individual experiences of competitive athletes with type 1 diabetes and to compare these experiences with current recommendations.

METHODS

A survey of 20 competitive athletes with type 1 diabetes, categorized as endurance (n=10) and non-endurance (n=10) athletes, was performed.

RESULTS

Endurance and non-endurance athletes did not differ in gender distribution, age, body mass index, and known diabetes duration. Self-reported target blood glucose values prior to exercise were lower in non-endurance than in endurance athletes (195±34 vs. 137±28 mg/dl, P=0.001). The majority of all athletes experienced activity-induced hypo- and hyperglycemic events, independently of exercise type. However, endurance athletes used additional carbohydrate units to prevent activity-induced hypoglycemic events more frequently without monitoring their blood glucose levels than non-endurance athletes (50% vs. 0%, P=0.01). The reduction of the insulin dose on training and competition days compared to days without exercise was similar for endurance and non-endurance athletes.

CONCLUSION

These results point to a very individual adaption of the athlete's therapy during training and competition. However, there are distinct differences in diabetes management between endurance and non-endurance athletes.

A clinical screening score for diabetic polyneuropathy: KORA F4 and AusDiab studies

AIMS

Since screening for distal sensorimotor polyneuropathy (DSPN) in individuals with diabetes is being underused, our aim was to develop a clinical screening score for identifying individuals with DSPN.

METHODS

All participants with type 2 diabetes and aged 61-82 years from the German population-based KORA F4 Study (n=177) and the Australian population-based AusDiab Study (n=244) were combined into one study sample. Risk indicators of DSPN were identified and entered into a stepwise model-selection procedure, constructing two consecutive scores with increasing complexity (a base and clinical model).

RESULTS

The prevalence of DSPN was 18.2% (95% confidence interval (CI): 14.7-22.3). The base model comprised age (years), height (cm), weight (kg), pain or discomfort in the feet and/or legs (yes/no), and duration of diabetes (years), yielding an area under the receiver operating characteristics curve (AUC) of 0.80 (95% CI: 0.76-0.85). The clinical model additionally included diastolic blood pressure (mmHg) and serum creatinine levels (mmol/l). The AUC increased only marginally to 0.82 (0.77-0.87) (p for AUC comparison=0.188). The internal validation of the scores produced similar AUCs.

CONCLUSIONS

The screening scores developed in this study are a simple tool to differentiate between a high and low likelihood of having DSPN among individuals with type 2 diabetes.

Metabolically healthy and unhealthy obese--the 2013 Stock Conference report

Obesity is closely associated with cardiovascular diseases and type 2 diabetes, but some obese individuals, despite having excessive body fat, exhibit metabolic health that is comparable with that of lean individuals. The 'healthy obese' phenotype was described in the 1980s, but major advancements in its characterization were only made in the past five years. During this time, several new mechanisms that may be involved in health preservation in obesity were proposed through the use of transgenic animal models, use of sophisticated imaging techniques and in vivo measurements of insulin sensitivity. However, the main obstacle in advancing our understanding of the metabolically healthy obese phenotype and its related long-term health risks is the lack of a standardized definition. Here, we summarize the proceedings of the 13th Stock Conference of the International Association of the Study of Obesity. We describe the current research and highlight the unanswered questions and gaps in the field. Better understanding of metabolic health in obesity will assist in therapeutic decision-making and help identify therapeutic targets to improve metabolic health in obesity.