Citations

Objective Little is known about long‐term metabolic (energy expenditure) adaptation after bariatric surgery. Methods Resting metabolic rate under basal conditions (RMR), total daily energy expenditure (TDEE), and body composition were measured in 25 participants in the Longitudinal Assessment of Bariatric Surgery‐2. Results Six months after surgery, BMI (±SD) decreased (47 ± 6 kg/m 2 to 37 ± 5 kg/m 2 ), body fat went from 48% ± 6% to 40% ± 6% fat, and fat‐free mass went from 67 ± 9 kg to 60 ± 9 kg. In absolute terms, RMR and TDEE both decreased significantly (1,730 ± 278 kcal/d vs. 1,430 ± 200 kcal/d and 2,879 ± 544 kcal/d vs. 2,369 ± 304 kcal/d), and the achieved energy balance was −1,293 ± 355 kcal/d. Sixteen of these participants underwent repeated measures at ∼24 months; TDEE decreased 6 months postoperatively (2,957 ± 540 kcal/d to 2,423 ± 324 kcal/d; P = 0.0003), but at ∼24 months, TDEE (2,602 ± 471 kcal/d) was not significantly different compared with month 6. The average negative energy balance from baseline to month 24 was −379 ± 131 kcal/d. Conclusions RMR and TDEE fall precipitously in the first 6 months after bariatric surgery, but these adaptive changes were no longer significant after 2 years.

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Physical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second half of 2015. Studies (1) involving patients with other disorders that have lasted 12 weeks or longer and tracked physical activity and (2) including both aerobic and resistance activity may offer insights about the user experience and device optimization even as single input CLC heads into real-world clinical trials over the next few years and nonglucose input is introduced as the next advance.

Adipose tissue macrophage (ATM) recruitment and activation play a critical role in obesity-induced inflammation and insulin resistance (IR). The mechanism regulating ATM activation and infiltration remains unclear. In this study, we found receptor interacting protein 140 (RIP140) can regulate the dynamics of ATM that contribute to adipose tissue remodeling. A high-fat diet (HFD) elevates RIP140 expression in macrophages. We generated mice with RIP140 knockdown in macrophages using transgenic and bone marrow transplantation procedures to blunt HFD-induced elevation in RIP140. We detected significant white adipose tissue (WAT) browning and improved systemic insulin sensitivity in these mice, particularly under an HFD feeding. These mice have decreased circulating monocyte population and altered ATM profile in WAT (a dramatic reduction in inflammatory classically activated macrophages [M1] and expansion in alternatively activated macrophages [M2]), which could improve HFD-induced IR. These studies suggest that reducing RIP140 expression in monocytes/macrophages can be a new therapeutic strategy in treating HFD-induced and inflammation-related diseases.

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.

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 < 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.

The goal of health care is to provide high-quality care at an affordable cost for its patients. However, the population it serves has changed dramatically since the popularization of hospital-based health care. With available new technology, alternative health care delivery methods can be designed and tested. This study examines scalable office-based health care for small business, where health care is delivered to the office floor. This delivery was tested in 18 individuals at a small business in Minneapolis, Minnesota. The goal was to deliver modular health care and mitigate conditions such as diabetes, hyperlipidaemia, obesity, sedentariness and metabolic disease. The modular health care system was welcomed by employees – 70% of those eligible enrolled. The findings showed that the modular health care deliverable was feasible and effective. The data demonstrated significant improvements in weight loss, fat loss and blood variables for at risk participants. This study leaves room for improvement and further innovation. Expansion to include offerings such as physicals, diabetes management, smoking cessation and prenatal treatment would improve its utility. Future studies could include testing the adaptability of delivery method, as it should adapt to reach rural and under-served populations.