Comparison of Equations Estimating Resting Metabolic Rate in Older Adults with Type 2 Diabetes.

Measuring resting metabolic rate (RMR) is time-consuming and expensive, and thus various equations for estimating RMR have been developed. This study's objective was to compare five equations in elderly people with type 2 diabetes (T2DM). RMR was measured in 90 older adults (≥65 years) with T2DM (mean body mass index (BMI) of 31.5 kg/m2), using indirect calorimetry. Results were compared to four frequently used equations (those of Cunningham, Harris and Benedict, and Gougeon developed for young adults with T2DM, and that of Lührmann, which was developed for the elderly), in addition to a new equation developed recently at the Academic College at Wingate (Nachmani) for overweight individuals. Estimation accuracy was defined as the percentage of subjects with calculated RMR within ±10% of measured RMR. Measured RMR was significantly underestimated by all equations. The equations of Nachmani and Lührmann had the best estimation accuracy: 71.4% in males and 50.9% in females. Skeletal muscle mass, fat mass, hemoglobin A1c (HbA1c), and the use of insulin explained 70.6% of the variability in measured RMR. RMR in elderly participants with T2DM was higher than that calculated using existing equations. The most accurate equations for this specific population were those developed for obesity or the elderly. Unbalanced T2DM may increase caloric demands in the elderly. It is recommended to adjust the RMR equations used for the target population.

Improving blood glucose level predictability using machine learning.

This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-monitoring system (CGM) that needs no human intervention, and minimises false-positive alerts. The CGM data over 7 to 50 non-consecutive days from 11 type-1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best-fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root-mean-square-error was 20.48 mg/dL and the average absolute-mean-error was 15.36 mg/dL when the best-fit model was selected for each patient. Using the best-fit-model, the true-positive-hypoglycemia-prediction-rate was 64%, whereas the false-positive- rate was 4.0%, and the false-negative-rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true-positive-hyperglycemia-prediction-rate was 61%. State-of-the-art SML tools are effective in predicting the glucose level values of patients with type-1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop "artificial pancreas" system. The algorithm provides a personalised medical solution that can successfully identify the best-fit method for each patient.

Sexual lifestyle among young adults with type 1 diabetes.

BACKGROUND: Sexual lifestyles including sexual activity, problems, satisfaction, and the formation and maintenance of relationships are greatly affected by physical health. Data are limited regarding the sexual lifestyle of adolescents and young adults with type 1 diabetes mellitus (T1DM). Fear of hypoglycemic episodes during sexual intercourse and intimacy issues can impact individuals with T1DM. The aim of this study was to assess sexual lifestyles of individuals with T1DM. METHODS: Fifty-three patients with T1DM, 27 (51%) males, mean ± SD age 27.9 ± 8.3 years completed the Hypoglycemia Fear Survey-II and the Sex Practices and Concerns questionnaire. RESULTS: Thirty-seven (70%) reported they never or almost never had concerns in their sexual lifestyles that were related to their diabetes. None experienced severe hypoglycemia during sex, but 21 (40%) reported occasional mild hypoglycemic events. More than two-thirds do not take any measures to prevent hypoglycemia before sex (decreasing insulin dose, snacks, and measuring blood glucose levels). Fear of hypoglycemia during sex was reported by 18 (35%); those who reported increased fear experienced mild hypoglycemic events during sex (61.1% vs 26.5%, P = .01), were singles (94.4% vs 64.7%, P = .02), and had higher scores on the Worries subscale of the Hypoglycemia Fear Survey-II (42.8 ± 12.8 vs 34.9 ± 10.5, P = .04) compared with those who did not. CONCLUSIONS: Among young people with T1DM, most do not have concerns regarding sex that are related to their diabetes, and most do not take specific measures before or after sex. One-third, however, fear of hypoglycemia during sex, mostly singles and those who experienced hypoglycemia in the past. Caregivers should be aware and address these concerns. Copyright © 2016 John Wiley & Sons, Ltd.

Ghrelin secretion is modulated in a nutrient- and gender-specific manner.

BACKGROUND: Ghrelin is a potent GH secretagogue that also plays an important role in appetite and weight regulation. Ghrelin increases hunger and food intake, and its levels decrease after a standard meal or glucose. OBJECTIVE: To examine the effects of standard oral glucose, lipid and protein loads on ghrelin levels, investigating the possibility that these responses may be modulated by several anthropometric and metabolic factors. SUBJECTS AND METHODS: There were 24 adult nondiabetic subjects (13 men/11 women; mean age 55.3 +/- 2.9 years, range 26-74 years). Each participant underwent one or more of the following nutrient loads: (i) a standard oral glucose (75 g) load (n = 18); (ii) an oral lipid load (40 g, with 24 g saturated fat; n = 13); (iii) an oral protein load (40 g; n = 11). RESULTS: Fasting ghrelin levels were negatively related to body mass index (BMI; r =-0.47; P = 0.02), waist circumference (r = -0.58; P = 0.0028), waist/hip ratio (r = -0.56; P = 0.0046), fasting insulin (r = -0.44, P = 0.03), and homeostasis model assessment insulin resistance index (HOMA-R; r = -0.43, P = 0.034). Glucose load induced a decrease in ghrelin levels (P < 0.0001), and this response was modulated by sex (P < 0.0001), in that levels were significantly higher in females. The presence of obesity affected ghrelin response to glucose (< 0.0217), in that log-transformed ghrelin levels started to increase back to baseline after its initial decline earlier in obese than in lean subjects. Ghrelin levels after a glucose load were lower over time in subjects with more pronounced insulin resistance (P < 0.0001). Similarly, ghrelin levels decreased significantly following the lipid meal (P = 0.035), and were modulated by HOMA-R (P = 0.027) and gender (P = 0.029). Protein did not affect ghrelin levels. CONCLUSIONS: This study demonstrates that ghrelin levels respond in a different manner to glucose, lipid and protein loads, and are subject to modulation according to gender, obesity and insulin sensitivity.