Levine, James A.

Objective: The objective is to report a contemporary population-based estimate of hypoglycemia requiring emergency medical services (EMS), its burden on medical resources, and its associated mortality in patients with or without diabetes mellitus (DM, non-DM), which will enable development of prospective strategies that will capture hypoglycemia promptly and provide an integrated approach for prevention of such episodes. Methods: We retrieved all ambulance calls activated for hypoglycemia in Olmsted County, Minnesota, between January 1, 2003 and December 31, 2009. Results: A total of 1473 calls were made by 914 people (DM 8%, non-DM 16%, unknown DM status 3%). Mean age was 60 ± 16 years with 49% being female. A higher percentage of calls were made by DM patients (87%) with proportionally fewer calls coming from non-DM patients (11%) (chi-square test, p < .001), and the remaining 2% calls by people with unknown DM status. Emergency room transportation and hospitalization were significantly higher in non-DM patients compared to DM patients ( p < .001) and type 2 diabetes mellitus compared to type 1 diabetes mellitus ( p < .001). Sulphonylureas alone or in combination with insulin varied during the study period ( p = .01). The change in incidence of EMS for hypoglycemia was tracked during this period. However, causality has not been established. Death occurred in 240 people, 1.2 (interquartile range 0.2–2.7) years after their first event. After adjusting for age, mortality was higher in non-DM patients compared with DM patients ( p < .001) but was not different between the two types of DM. Conclusions: The population burden of EMS requiring hypoglycemia is high in both DM and non-DM patients, and imposes significant burden on medical resources. It is associated with long-term mortality.

Administration of leptin to rodents results in weight loss through decreased food intake and increased energy expenditure that occurs in part through increased spontaneous activity. In humans, low levels of spontaneous physical activity and below normal plasma leptin concentrations predict subsequent excess weight gain. We recently found that failure to increase nonexercise activity thermogenesis (NEAT) with overfeeding results in greater fat gain in humans, and subsequently evaluated whether changes in leptin are related to NEAT activation. We measured plasma leptin concentrations and adipose tissue leptin messenger ribonucleic acid together with the components of energy expenditure in 16 nonobese humans before and after overfeeding to assess the relationship between leptin responses to overfeeding and the changes in NEAT. Adipocyte leptin expression was up-regulated with overfeeding, and leptin concentrations increased. Leptin concentrations correlated with body fat before and after overfeeding. Changes in leptin with overfeeding were strongly related to changes in body fat, but not to changes in NEAT. Changes in NEAT correlated inversely with fat gain. It is, therefore, unlikely that leptin mediates activation of NEAT with overfeeding in nonobese humans; rather, leptin directly reflects body fat mass and fat mass gain.

Hyperthyroidism causes increased energy intake and expenditure, although anorexia and higher weight loss have been reported in elderly individuals with hyperthyroidism. To determine the effect of age on energy homeostasis in response to experimental hyperthyroidism, we administered 200 μg triiodothyronine (T3) in 7‐ and 27‐mo‐old rats for 14 d. T3 increased energy expenditure (EE) in both the young and the old rats, although the old rats lost more weight (147 g) than the young rats (58 g) because of the discordant effect of T3 on food intake, with a 40% increase in the young rats, but a 40% decrease in the old ones. The increased food intake in the young rats corresponded with a T3‐mediated increase in the appetite‐regulating proteins agouti‐related peptide, neuropeptide Y, and uncoupling protein 2 in the hypothalamus, but no increase occurred in the old rats. Evidence of mitochondrial biogenesis in response to T3 was similar in the soleus muscle and heart of the young and old animals, but less consistent in old plantaris muscle and liver. Despite the comparable increase in EE, T3's effect on mitochondrial function was modulated by age in a tissue‐specific manner. We conclude that older rats lack compensatory mechanisms to increase caloric intake in response to a T3‐induced increase in EE, demonstrating a detrimental effect of age on energy homeostasis.—Walrand, S., Short, K. R., Heemstra, L. A., Novak, C. M., Levine, J. A., Coenen‐Schimke, J. M., Nair, K. S. Altered regulation of energy homeostasis in older rats in response to thyroid hormone administration. FASEB J. 28, 1499–1510 (2014). www.fasebj.org

Background: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. Subjects and Methods: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. Results: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40–45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35–40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. Conclusions: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose–insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Background: Currently, patients with type 1 diabetes decide on the amount of insulin to administer based on several factors, including current plasma glucose value, expected meal input, and physical activity (PA). One future therapeutic modality for patients with type 1 diabetes is the artificial endocrine pancreas (AEP). Incorporation of PA could enhance the efficacy of AEP significantly. We compared the main technologies used for PA quantitation. Subjects and Methods: Data were collected during inpatient studies involving healthy control subjects and type 1 diabetes. We report PA quantified from accelerometers (acceleration units [AU]) and heart rate (HR) monitors during a standardized activity protocol performed after a dinner meal at 7 p.m. from nine control subjects (four were males, 37.4±12.7 years old, body mass index of 24.8±3.8 kg/m 2 , and fasting plasma glucose of 4.71±0.63 mmol/L) and eight with type 1 diabetes (six were males, 45.2±13.4 years old, body mass index of 25.1±2.9 kg/m 2 , and fasting plasma glucose of 8.44±2.31 mmol/L). Results: The patient-to-patient variability was considerably less when examining AU compared with HR monitors. Furthermore, the exercise bouts and rest periods were more evident from the data streams when AUs were used to quantify activity. Unlike the AU, the HR measurements provided little insight for active and rest stages, and HR data required patient-specific standardizations to discern any meaningful pattern in the data. Conclusions: Our results indicated that AU provides a reliable signal in response to PA, including low-intensity activity. Correlation of this signal with continuous glucose monitoring data would be the next step before exploring inclusion as input for AEP control.

We recently demonstrated a diurnal pattern to insulin action (i.e., insulin sensitivity [SI]) in healthy individuals with higher SI at breakfast than at dinner. To determine whether such a pattern exists in type 1 diabetes, we studied 19 subjects with C-peptide–negative diabetes (HbA1c 7.1 ± 0.6%) on insulin pump therapy with normal gastric emptying. Identical mixed meals were ingested during breakfast, lunch, and dinner at 0700, 1300, and 1900 h in randomized Latin square of order on 3 consecutive days when measured daily physical activity was equal. The triple tracer technique enabled measurement of glucose fluxes. Insulin was administered according to the customary insulin:carbohydrate ratio for each participant. Although postprandial glucose excursions did not differ among meals, insulin concentration was higher (P &lt; 0.01) and endogenous glucose production less suppressed (P &lt; 0.049) at breakfast than at lunch. There were no differences in meal glucose appearance or in glucose disappearance between meals. Although there was no statistical difference (P = 0.34) in SI between meals in type 1 diabetic subjects, the diurnal pattern of SI taken across the three meals in its entirety differed (P = 0.016) from that of healthy subjects. Although the pattern in healthy subjects showed decreasing SI between breakfast and lunch, the reverse SI pattern was observed in type 1 diabetic subjects. The results suggest that in contrast to healthy subjects, SI diurnal pattern in type 1 diabetes is specific to the individual and cannot be extrapolated to the type 1 diabetic population as a whole, implying that artificial pancreas algorithms may need to be personalized.

OBJECTIVE Physical activity (PA), even at low intensity, promotes health and improves hyperglycemia. However, the effect of low-intensity PA captured with accelerometery on glucose variability in healthy individuals and patients with type 1 diabetes has not been examined. Quantifying the effects of PA on glycemic variability would improve artificial endocrine pancreas (AEP) algorithms. RESEARCH DESIGN AND METHODS We studied 12 healthy control subjects (five males, 37.7 ± 13.7 years of age) and 12 patients with type 1 diabetes (five males, 37.4 ± 14.2 years of age) for 88 h. Participants performed PA approximating a threefold increase over their basal metabolic rate. PA was captured using a PA-monitoring system, and interstitial fluid glucose concentrations were captured with continuous glucose monitors. In random order, one meal per day was followed by inactivity, and the other meals were followed by walking. Glucose and PA data for a total of 216 meals were analyzed from 30 min prior to meal ingestion to 270 min postmeal. RESULTS In healthy subjects, the incremental glucose area under the curve was 4.5 mmol/L/270 min for meals followed by walking, whereas it was 9.6 mmol/L/270 min (P = 0.022) for meals followed by inactivity. The corresponding glucose excursions for those with type 1 diabetes were 7.5 mmol/L/270 min and 18.4 mmol/L/270 min, respectively (P &lt; 0.001). CONCLUSIONS Walking significantly impacts postprandial glucose excursions in healthy populations and in those with type 1 diabetes. AEP algorithms incorporating PA may enhance tight glycemic control end points.

Background. Physical activity is important for multiple aspects of health, for example, cancer prevention, metabolic disease treatment, cardiovascular health, and obesity management. Despite the improved capability of measuring physical activity in the research arena, the options are scarce and less reliable for measurements in free-living people. In this article, the authors tested the validity of a robust stand-alone patient-operated Accelerometer System that can measure physical activity and sedentariness in active people. Methods. Thirty subjects wore the Accelerometer System along with the validated physical activity monitoring system (PAMS) with different body postures and during graded walking at 7 velocities. Energy expenditure was measured using indirect calorimetry. Results. In all the 30 subjects, the Accelerometer System distinguished sedentary and walking activity reliably even with ½ mph increments in walking and was accurate and precise compared with PAMS, with an intraclass correlation coefficient ( r2 > .98). The Accelerometer System showed excellent sequential increases with increase in walking velocity and energy expenditure ( r2 > .9). Conclusion. A stand-alone Accelerometer System was accurate and reliable in measuring and quantifying physical activity in the laboratory setting.

Background. In this pilot study, the authors aimed to examine the implementation of standing desks on classroom performance and behavior. They also examined how the standing desks affected in-class physical activity and body mass index. Methods. Eight sixth graders from Hope Lutheran elementary school participated in the study (age 11.3 ± 0.5 years). Baseline and 8-month postintervention measures were step counts using (W4L Classic pedometers), height, weight, and behavioral markers. Results. Data showed that there were no statistically significant changes in the participants’ body mass index (19.4 kg/m2 vs 19.3 kg/m2), step counts (1886 steps vs 2248 steps), and behavioral markers including classroom management, concentration, and discomfort. Height and weight changes were significant and are attributed to the normal growth rate associated with the age of the participants (146.8 cm vs 151.8 cm, P < .0001; 41.4 kg vs 44.5 kg, P > .0007). Conclusions. It is feasible to integrate standing desks into a classroom without negative effects. Although the authors observed over a 19% increase in pedometer activity, it was not statistically significant. This highlights the importance for larger experimental groups and the use of more advanced physical activity tracking and body composition technologies.