Clinical node: Obesity, Diabetes and Metabolism Research Group (ODIM)
- Prediction of weight response after BC: This line has been active since 2016, and has obtained funding in 3 competitive projects at national [PI18/00964, PI21/00462] and regional level [PERIS 2016-2020, SLT002/16/00497].
- Predicting metabolic changes caused by BC: Data on genetic susceptibility and resolution of comorbidities after BC are still scarce. However, the last decade has seen the rise of "metabolic surgery" for the possibility of achieving remission of type 2 diabetes. In addition, approximately half of the genetic variation associated with sleep apnoea-hypopnoea syndrome is associated with obesity, and some SNPs have also been associated with fatty liver associated with metabolic dysfunction.
- Diabetes and lung function: The lung is not usually considered a target organ in diabetes complications. Our Group, based mainly on the study of the BC waiting list population, has provided sufficient evidence to contradict this concept.
- Diabetes and sleep breathing: Evidence that type 2 DM has a negative effect on sleep breathing has been provided by our Group over the last few years.
- Chronobiology, sleep breathing and nocturnal hypoxia: Alterations in nocturnal breathing and circadian rhythms both influence human metabolism. Our group has initiated the study of genes in patients with obesity and type 2 DM.
- Computer-assisted telephone surveys and the use of self-reported anthropometric data: On 2 occasions we have used representative samples of the Spanish adult population, consisting of 1,000 subjects who underwent a computer-assisted telephone interview.
- Utility of the study of advanced glycation end-products (AGEs) by skin autofluorescence (SAF): We delved into the role of AGEs determination in obesity, metabolic diseases and cardiovascular disease.
- Study of body composition using mathematical formulae: Reference techniques to directly measure total and abdominal adipose tissue are complex, costly and time-consuming. Therefore, their widespread use in clinical practice is limited and various mathematical indices combining anthropometric data to assess total and abdominal fat mass have been proposed.