Evaluating the influence of sleep quality and quantity on glycemic control in adults with type 1 diabetes

BackgroundSleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control.Materials and methodsAn observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality. ResultsA total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure. ConclusionsThis study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.2022-2

Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools

Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood andstress influence blood glucose levels and variability.Material and Methods: Continuous glucosemonitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucosevalues were recorded every 15 min. A daily survey was conducted through Google Forms, collectinginformation on mood and stress. The day was divided into six slots of 4-h each, asking the patientto assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous).Different measures of glycemic control (arithmetic mean and percentage of time below/above thetarget range) and variability (standard deviation, percentage coefficient of variation, mean amplitudeof glycemic excursions and mean of daily differences) were calculated to relate the mood and stressperceived by patients with blood glucose levels and glycemic variability. A hypothesis test wascarried out to quantitatively compare the data groups of the different measures using the Student’st-test.Results: Statistically significant differences (p-value < 0.05) were found between differentlevels of stress. In general, average glucose and variability decrease when the patient is calm. Thereare statistically significant differences (p-value < 0.05) between different levels of mood. Variabilityincreases when the mood changes from sad to happy. However, the patient’s average glucosedecreases as the mood improves.Conclusions: Variations in mood and stress significantly influenceblood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus.Therefore, they are factors to consider for improving glycemic control. The mean of daily differencesdoes not seem to be a good indicator for variability.2021-2

Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain : Large-Scale Epidemiological Study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD-Crohn's disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)-during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100,000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31-56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery

Correction : Chaparro et al. Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain: Large-Scale Epidemiological Study. J. Clin. Med. 2021, 10, 2885

The authors wish to make the following corrections to this paper [...]

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Evaluating the influence of sleep quality and auqntity con glycemic control in patients with DM1 using Machine Learning

Background: Sleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control. Materials and methods: An observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality. Results: A total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure. Conclusions: This study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.2022-2

Implementation of user driven innovation methodology to estimate Origin-Destination Matrices and to deploy tailored bus routes

WHAT IS B_us? As a new solution to estimate OD-M of transport and to design tailored bus routes, the project B_us (commercial name of the project FitYourBus, funded by the European Commision H2020 programme frontierCities) proposes a new way of collecting and treating mobility pattern data in order to reduce about 36% the cost of data acquisition and 41% the cost of exploiting data, allowing the deployment of user-driven transport services. METHODOLOGY The proposed methodology includes the following stages: 1) Platform. Deployment of a back-end service and its administration interfaces. The data collection setup is based on a client-server architecture using J2EE and Docker technologies. 2) Data collection. Users provide their basic commuting data-origin, destination, work hours, etc-using our cross-platform smartphone app, which communicates with the back-end service.FrontierCities de la Comisión Europea (H2020)No data 2018UE

Evaluating the Influence of Mood and Stress on Glycemic Variability in People with T1DM Using Glucose Monitoring Sensors and Pools

Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucose values were recorded every 15 min. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into six slots of 4-h each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student’s t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient’s average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability