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Prenatal undernutrition and low birth weight are associated with risk of type 2 diabetes and obesity. Prenatal caloric restriction results in low birth weight, glucose intolerance, obesity, and reduced plasma bile acids (BAs) in offspring mice. Because BAs can regulate systemic metabolism and glucose homeostasis, we hypothesized that BA supplementation could prevent diet-induced obesity and glucose intolerance in this model of developmental programming. Pregnant dams were food restricted by 50% from gestational days 12.5 to 18.5. Offspring of both undernourished (UN) and control (C) dams given unrestricted diets were weaned to high-fat diets with or without supplementation with 0.25% w/w ursodeoxycholic acid (UDCA), yielding four experimental groups: C, UN, C + UDCA, and UN + UDCA. Glucose homeostasis, BA composition, liver and intestinal gene expression, and microbiota composition were analyzed in the four groups. Although UDCA supplementation ameliorated diet-induced obesity in C mice, there was no effect in UN mice. UDCA similarly lowered fasting insulin, and improved glucose tolerance, pyruvate tolerance, and liver steatosis in C, but not UN, animals. BA composition differed significantly, and liver and ileal expression of genes involved in BA metabolism (Cyp7b1, Shp) were differentially induced by UDCA in C vs UN animals. Bacterial taxa in fecal microbiota correlated with treatment groups and metabolic parameters. In conclusion, prenatal undernutrition alters responsiveness to the metabolic benefits of BA supplementation, with resistance to the weight-lowering and insulin-sensitizing effects of UDCA supplementation. Our findings suggest that BA metabolism may be a previously unrecognized contributor to developmentally programmed diabetes risk.

Background— Associating a patient’s profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Methods and Results— Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier–based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. Conclusions— This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.

Rapid postnatal weight gain is a potentially modifiable risk factor for obesity and metabolic syndrome. To identify markers of rapid infancy weight gain and childhood obesity, we analyzed the metabolome in cord blood from infants differing in their postnatal weight trajectories.We performed a nested case-control study within Project Viva, a longitudinal cohort of mothers and children. We selected cases (n=26) based on top quartile of change in weight-for-age 0-6 months and body mass index (BMI)85th percentile in mid-childhood (median 7.7 years). Controls (n=26) were age and sex matched, had normal postnatal weight gain (2nd or 3rd quartile of change in weight-for-age 0-6 months) and normal mid-childhood weight (BMI 25th-75th percentile). Cord blood metabolites were measured using untargeted liquid chromatography-mass spectrometry; individual metabolites and pathways differing between cases and controls were compared in categorical analyses. We adjusted metabolites for maternal age, maternal BMI and breastfeeding duration (linear regression), and assessed whether metabolites improved the ability to predict case-control status (logistic regression).Of 415 detected metabolites, 16 were altered in cases versus controls (t-test, nominal P0.05). Three metabolites were related to tryptophan: serotonin, tryptophan betaine and tryptophyl leucine (46%, 48% and 26% lower in cases, respectively, P0.05). Mean levels of two methyl donors, dimethylglycine and N-acetylmethionine, were also lower in cases (18% and 16%, respectively, P=0.01). Moreover, the glutamine:glutamate ratio was reduced by 33% (P0.05) in cases. Levels of serotonin, tryptophyl leucine and N-acetylmethionine remained significantly different after adjustment for maternal BMI, age and breastfeeding. Adding metabolite levels to logistic regression models including only clinical covariates improved the ability to predict case versus control status.Several cord blood metabolites are associated with rapid postnatal weight gain. Whether these patterns are causally linked to childhood obesity is not clear from this cross-sectional analysis, but will require further study.

Identifying markers of human insulin resistance may permit development of new approaches for treatment and prevention of type 2 diabetes. To this end, we analyzed the fasting plasma metabolome in metabolically characterized human volunteers across a spectrum of insulin resistance. We demonstrate that plasma betaine levels are reduced in insulin-resistant humans and correlate closely with insulin sensitivity. Moreover, betaine administration to mice with diet-induced obesity prevents the development of impaired glucose homeostasis, reduces hepatic lipid accumulation, increases white adipose oxidative capacity, and enhances whole-body energy expenditure. In parallel with these beneficial metabolic effects, betaine supplementation robustly increased hepatic and circulating fibroblast growth factor (Fgf)21 levels. Betaine administration failed to improve glucose homeostasis and liver fat content in Fgf21−/− mice, demonstrating that Fgf21 is necessary for betaine’s beneficial effects. Together, these data indicate that dietary betaine increases Fgf21 levels to improve metabolic health in mice and suggest that betaine supplementation merits further investigation as a supplement for treatment or prevention of type 2 diabetes in humans.