Inferring Individual Chronotypes with Transcriptomics for Personalised Chrononutrition – PHRT
Inferring Individual Chronotypes with Transcriptomics for Personalised Chrononutrition
The global epidemic of obesity and the related metabolic syndrome presents a major risk to both the global and Swiss populations. Personalised medicine has the opportunity to tackle obesity and metabolic syndrome by harnessing new advances in diagnostic technology and cutting-edge machine learning techniques. We will collect clinical and molecular data from human subjects and use Bayesian statistics to investigate the links between the internal state of an individual’s circadian clock (also termed the chronotype or circadian phase), eating rhythm, control of body weight and possible reversal of metabolic syndrome.
The core aim of this proposal is to collect suitable clinical and molecular data from human subjects in order to investigate the relationship between individual circadian phase, eating habits, changes of weight and possible reversal of metabolic syndrome. To achieve this, we will integrate information from clinical data on the health/disease state of a cohort of volunteers in an ongoing chrononutrition study led by Dr. Collet at CHUV, which includes smartphone app recordings of subjects’ eating patterns. We will add to this a transcriptome analysis (RNA-seq) of monocytes isolated from blood samples to infer circadian phase.
The long-term goal is to find possible treatment avenues to curtail the obesity epidemic, where information on circadian metabolism is leveraged to design nutritional advices that optimize the timing of energy intake within the 24-hour cycle. To this end, the fellowship will generate new analysis tools for generating personalised eating profiles from app data and probabilistic estimation of chronotype from blood samples using RNA-seq. In addition, we will quantify the association between chronotype and eating patterns from human clinical data, and in a pilot intervention phase we will test whether responses to dietary advice are predictable from covariates including chronotype at baseline. In summary, our proposal represents a first step to develop personalised dietary plans based on individual circadian phase.
One of the current major healthcare challenges is the global epidemic of obesity and the related metabolic syndrome, which is a group of conditions including central obesity, elevated blood pressure and impaired glucose tolerance. The cardiovascular and metabolic diseases associated with metabolic syndrome, such as myocardial infarction, stroke and diabetes, are major health risks. There is therefore a major drive to find new solutions to the obesity epidemic, but programs targeting physical activity and food intake have experienced limited success, as lifestyle programs can be difficult to apply for some. Basic chronobiology research has demonstrated that many metabolic processes are influenced by the 24-hour circadian body clock, so clinicians may be able to devise more effective dietary regimes using personalised parameters including individual circadian phase together with sleeping and eating habits.