Predicting therapeutic weight loss

Effect of liraglutide 3.0mg treatment on weight reduction in obese antipsychotic-treated patients

BACKGROUND

Patients treated with antipsychotics experience significant weight gain and accompanying metabolic disorders. We investigated the efficacy of liraglutide 3.0 mg in reducing the weight of antipsychotic-treated obese patients.

METHOD

We retrospectively reviewed 16 obese patients with schizophrenia or bipolar disorder who were treated with 3.0 mg of liraglutide each. During the 16 weeks of treatment, changes in body weight and Clinical Global Impression-Severity scale (CGI-S) were analyzed. The participants were divided into responders (lost at least 5% of body weight) and non-responders for analysis.

RESULTS

Treatment with liraglutide 3.0 mg significantly decreased body weight (estimated marginal mean, 93.2 kg at baseline and 88.9 kg at 16 weeks; p < 0.001) as well as waist circumference, BMI and plasma glucose levels. Six of 16 patients (37.5%) complained of a modest degree of nausea. Six of the 12 subjects (50%) completing 16 weeks of treatment were responders. There were no significant differences in baseline characteristics between responders and non-responders. There was no worsening of CGI-S scores.

CONCLUSION

Liraglutide 3.0 mg significantly decreased body weight in obese patients treated with antipsychotics without altering the status of psychiatric diseases. A randomized controlled study is required to corroborate the results of this study.

Breath Sensors for Health Monitoring

Breath sensors can revolutionize medical diagnostics by on-demand detection and monitoring of health parameters in a noninvasive and personalized fashion. Despite extensive research for more than two decades, however, only a few breath sensors have been translated into clinical practice. Actually, most never even left the scientific laboratories. Here, we describe key challenges that currently impede realization of breath sensors and highlight strategies to overcome them. Specifically, we start with breath marker selection (with emphasis on metabolic and inflammatory markers) and breath sampling. Next, the sensitivity, stability, and selectivity requirements for breath sensors are described. Concepts are elaborated to systematically address these requirements by material design (focusing on chemoresistive metal oxides), orthogonal arrays, and filters. Finally, aspects of portable device integration, user communication, and clinical applicability are discussed.

Computational Approaches Toward Integrating Quantified Self Sensing and Social Media

The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user's behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user's diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.