Reduction in Glycated Hemoglobin and Daily Insulin Dose Alongside Circadian Clock Upregulation in Patients With Type 2 Diabetes Consuming a Three-Meal Diet: A Randomized Clinical Trial.

OBJECTIVE: In type 2 diabetes, insulin resistance and progressive ß-cell failure require treatment with high insulin doses, leading to weight gain. Our aim was to study whether a three-meal diet (3Mdiet) with a carbohydrate-rich breakfast may upregulate clock gene expression and, as a result, allow dose reduction of insulin, leading to weight loss and better glycemic control compared with an isocaloric six-meal diet (6Mdiet). RESEARCH DESIGN AND METHODS: Twenty-eight volunteers with diabetes (BMI 32.4 ± 5.2 kg/m2 and HbA1c 8.1 ± 1.1% [64.5 ± 11.9 mmol/mol]) were randomly assigned to 3Mdiet or 6Mdiet. Body weight, glycemic control, continuous glucose monitoring (CGM), appetite, and clock gene expression were assessed at baseline, after 2 weeks, and after 12 weeks. RESULTS: 3Mdiet, but not 6Mdiet, led to a significant weight loss (-5.4 ± 0.9 kg) (P < 0.01) and decreased HbA1c (-12 mmol/mol [-1.2%]) (P < 0.0001) after 12 weeks. Fasting glucose and daily and nocturnal glucose levels were significantly lower on the 3Mdiet. CGM showed a significant decrease in the time spent in hyperglycemia only on the 3Mdiet. Total daily insulin dose was significantly reduced by 26 ± 7 units only on the 3Mdiet. There was a significant decrease in the hunger and cravings only in the 3Mdiet group. Clock genes exhibited oscillation, increased expression, and higher amplitude on the 3Mdiet compared with the 6Mdiet. CONCLUSIONS: A 3Mdiet, in contrast to an isocaloric 6Mdiet, leads to weight loss and significant reduction in HbA1c, appetite, and overall glycemia, with a decrease in daily insulin. Upregulation of clock genes seen in this diet intervention could contribute to the improved glucose metabolism.

Lifestyle intervention program benefits children with overweigh compared to children with obesity.

BACKGROUND: Lifestyle weight loss interventions represent the mainstay of treating children with obesity. Long-term follow-up studies of intervention programs are scarce. This study assessed the long-term effects and identified factors associated with significant weight loss. METHODS: This prospective, observational study involved 165 children with body mass index (BMI) z-score ≥1.5 who participated in after-school intervention program. MAIN OUTCOME MEASURE: change in BMI z-scores; decreased BMI z-score ≥0.5 units was defined as clinically significant. RESULTS: At baseline, 55/165 (33.3%) had BMI z-score 1.5-2 and 98 (59.4%) had BMI z-score >2. At follow-up (mean 5.4±1.4 years), 80 (48.5%) had a clinically significant reduction in BMI z-score, while 56 (33.9%) reported a mildly decrease in BMI z-score of 0-0.5 and 29 (17.6%) reported increased BMI z-score. Lower BMI z-score at baseline and participation in sport activity at follow-up were associated with long-term decrease in BMI z-score. Participation in additional post-intervention weight control programs was correlated with weight gain. CONCLUSIONS: Intervention program was associated with long-term improvement in weight control, especially in children with mild obesity. Physical activity was related to long-term success. Participation in an additional intervention program was associated with failure of weight control.

Detection of Lung Cancer and EGFR Mutation by Electronic Nose System.

INTRODUCTION: Early detection of lung cancer (LC) has been well established as a significant key point in patient survival and prognosis. New highly sensitive nanoarray sensors for exhaled volatile organic compounds that have been developed and coupled with powerful statistical programs may be used when diseases such as LC are suspected. Detection of genetic aberration mutation by nanoarray sensors is the next target. METHODS: Breath samples were taken from patients who were evaluated for suspicious pulmonary lesions. Patients were classified as those with benign nodules, as patients with LC with or without the EGFR mutation, and according to their smoking status. Breath prints were recognized by nanomaterial-based sensor array, and pattern recognition methods were used. RESULTS: A total of 119 patients participated in this study, 30 patients with benign nodules and 89 patients with LC (16 with early disease and 73 with advanced disease). Patients with LC who harbored the EGFR mutation (n = 19) could be discriminated from those with wild-type EGFR (n = 34) with an accuracy of 83%, sensitivity of 79%, and specificity of 85%. Discrimination of early LC from benign nodules had 87% accuracy and positive and negative predictive values of 87.7 and 87.5% respectively. Moderate discrimination (accuracy of 76%) was found between LC of heavy smokers and that of never-smokers or distant past light smokers. CONCLUSIONS: Breath analysis could discriminate patients with LC who harbor the EGFR mutation from those with wild-type EGFR and those with benign pulmonary nodules from those patients with early LC. A positive breath print for the EGFR mutation may be used in treatment decisions if tissue sampling does not provide adequate material for definitive mutation analysis.

Cancerous glucose metabolism in lung cancer-evidence from exhaled breath analysis.

Cancer cells prefer hyperglycolysis versus oxidative phosphorylation, even in the presence of oxygen. This phenomenon is used through the FDG-PET scans, and may affect the exhaled volatile signature. This study investigates the volatile signature in lung cancer (LC) before and after an oral glucose tolerance test (OGTT) to determine if tumor cells' hyperglycolysis would affect the volatile signature. Blood glucose levels and exhaled breath samples were analyzed before the OGTT, and 90 min after, in both LC patients and controls. The volatile signature was measured by proton transfer reaction mass spectrometry (PTR-MS). Twenty-two LC patients (age 66.6 ± 12.7) with adenocarcinoma (n = 14), squamous (n = 6), small cell carcinoma (n = 2), and twenty-one controls (age 54.4 ± 13.7; 10 non-smokers and 11 smokers) were included. All LC patients showed a hyperglycolytic state in their FDG-PET scans. Both baseline and post OGTT volatile signatures discriminate between the groups. The OGTT has a minimal effect in LC (a decrease in m/z 54 by 39%, p v = 0.0499); whereas in the control group, five masses (m/z 64, 87,88, 142 and 161) changed by -13%, -49%, -40% and -29% and 46% respectively. To conclude, OGTT has a minimal effect on the VOC signature in LC patients, where a hyperglycolytic state already exists. In contrast, in the control group the OGTT has a profound effect in which induced hyperglycolysis significantly changed the VOC pattern. We hypothesized that a ceiling effect in cancerous patients is responsible for this discrepancy.

Exhaled Breath Analysis for Monitoring Response to Treatment in Advanced Lung Cancer.

INTRODUCTION: The Response Evaluation Criteria in Solid Tumors (RECIST) serve as the accepted standard to monitor treatment efficacy in lung cancer. However, the time intervals between consecutive computerized tomography scans might be too long to allow early identification of treatment failure. This study examines the use of breath sampling to monitor responses to anticancer treatments in patients with advanced lung cancer. METHODS: A total of 143 breath samples were collected from 39 patients with advanced lung cancer. The exhaled breath signature, determined by gas chromatography/mass spectrometry and a nanomaterial-based array of sensors, was correlated with the response to therapy assessed by RECIST: complete response, partial response, stable disease, or progressive disease. RESULTS: Gas chromatography/mass spectrometry analysis identified three volatile organic compounds as significantly indicating disease control (PR/stable disease), with one of them also significantly discriminating PR/stable disease from progressive disease. The nanoarray had the ability to monitor changes in tumor response across therapy, also indicating any lack of further response to therapy. When one-sensor analysis was used, 59% of the follow-up samples were identified correctly. There was 85% success in monitoring disease control (stable disease/partial response). CONCLUSION: Breath analysis, using mainly the nanoarray, may serve as a surrogate marker for the response to systemic therapy in lung cancer. As a monitoring tool, it can provide the oncologist with a quick bedside method of identifying a lack of response to an anticancer treatment. This may allow quicker recognition than does the current RECIST analysis. Early recognition of treatment failure could improve patient care.