Nephrotic range proteinuria associated with focal segmental glomerulosclerosis reversed with pioglitazone therapy in a patient with Dunnigan type lipodystrophy.

Familial partiallipodystrophy (FPLD)is a rare disorder associated withsevere insulin resistance, hypertriglyceridemia, lowserumHDLcholesterol and proteinuricrenaldisease. Although proteinuric renal disease is not common among in patients with partial lipodystrophy, we report a patient with Dunnigan type FPLD complicated by nephrotic syndrome which resolved following treatment with thePPARγagonist pioglitazone, CPAP, diet, and exercise.

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.

Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose. METHODS: A feed-forward NNM was designed for real-time prediction of glucose in patients with diabetes implementing a prediction horizon of 75 min. Inputs to the NNM included CGM values, insulin dosages, metered glucose values, nutritional intake, lifestyle, and emotional factors. Performance of the NNM was assessed in 10 patients not included in the model training set. RESULTS: The NNM had a root mean squared error of 43.9 mg/dL and a mean absolute difference percentage of 22.1. The NNM routinely overestimates hypoglycemic extremes, which can be attributed to the limited number of hypoglycemic reactions in the model training set. The model predicts 88.6% of normal glucose concentrations (> 70 and < 180 mg/dL), 72.6% of hyperglycemia (≥ 180 mg/dL), and 2.1% of hypoglycemia (≤ 70 mg/dL). Clarke Error Grid Analysis of model predictions indicated that 92.3% of predictions could be regarded as clinically acceptable and not leading to adverse therapeutic direction. Of these predicted values, 62.3% and 30.0% were located within Zones A and B, respectively, of the error grid. CONCLUSIONS: Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.

Primary menopausal insomnia: definition, review, and practical approach.

OBJECTIVE: To present a case of primary menopausal insomnia with hot flashes to introduce recent changes in technology and nomenclature of sleep medicine and to review presentation, diagnosis, and therapies for menopausal insomnia. METHODS: Clinical findings and results of sleep evaluation in the menopausal study patient are presented with details about polysomnography performed before and after therapy with pregabalin. RESULTS: A 56.5-year-old female athlete with severe hot flashes and insomnia of 12 years' duration was treated with pregabalin, which ameliorated the hot flashes and sweats and improved sleep quality and architecture. Menopause is associated with hormonal and metabolic changes that disrupt sleep. Disruption of sleep can in turn lead to morbidity and metabolic sequelae. Hormonal treatment, although effective, carries risks unacceptable to many patients and physicians. To date, nonhormonal therapies of symptomatic menopause have not been objectively studied for effects on sleep efficiency and architecture. Primary menopausal insomnia is insomnia associated with menopause and not attributable to secondary causes. Polysomnographically, it seems characterized by a high percentage of slow-wave (N3) sleep, decreased rapid eye movement sleep, cyclic alternating pattern, and arousals. CONCLUSIONS: Primary menopausal insomnia is probably mediated through a mechanism separate from hot flashes, and one can occur without the other. Thermal dys-regulation and sleep abnormalities of menopause are probably related to more general changes mediated through loss of estrogenic effects on neuronal modulation of energy metabolism, and more clinical direction is expected as this research field develops. Identification of sleep disorders in menopausal women is important, and polysomnographic evaluation is underused in both clinical and research evaluations of metabolic disturbances.

Caloric restriction reverses hepatic insulin resistance and steatosis in rats with low aerobic capacity.

Rats selectively bred for low aerobic running capacity exhibit the metabolic syndrome, including hyperinsulinemia, insulin resistance, visceral obesity, and dyslipidemia. They also exhibit features of nonalcoholic steatohepatitis, including chicken-wire fibrosis, inflammation, and oxidative stress. Hyperinsulinemia in these rats is associated with impaired hepatic insulin clearance. The current studies aimed to determine whether these metabolic abnormalities could be reversed by caloric restriction (CR). CR by 30% over a period of 2-3 months improved insulin clearance in parallel to inducing the protein content and activation of the carcinoembryonic antigen-related cell adhesion molecule 1, a main player in hepatic insulin extraction. It also reduced glucose and insulin intolerance and serum and tissue (liver and muscle) triglyceride levels. Additionally, CR reversed inflammation, oxidative stress, and fibrosis in liver. The data support a significant role of CR in the normalization of insulin and lipid metabolism in liver.

Development of a neural network model for predicting glucose levels in a surgical critical care setting.

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia.

Mice with null mutation of Ceacam I develop nonalcoholic steatohepatitis.

Transgenic liver-specific inactivation of the carcinoembryonic antigen-related cell adhesion molecule (CEACAM1) impairs hepatic insulin clearance and causes hyperinsuline-mia, insulin resistance, elevation in hepatic and serum triglyceride levels, and visceral obesity. It also predisposes to nonalchoholic steatohepatitis (NASH) in response to a high-fat diet. To discern whether this phenotype reflects a physiological function of CEACAM1 rather than the effect of the dominant-negative transgene, we investigated whether Ceacam1 (gene encoding CEACAM1 protein) null mice with impaired insulin clearance also develop a NASH-like phenotype on a prolonged high-fat diet. Three-month-old male null and wild-type mice were fed a high-fat diet for 3 months and their NASH phenotype was examined. While high-fat feeding elevated hepatic triglyceride content in both strains of mice, it exacerbated macrosteatosis and caused NASH-characteristic fibrogenic changes and inflammatory responses more intensely in the null mouse. This demonstrates that CEACAM1-dependent insulin clearance pathways are linked with NASH pathogenesis.