Ignite Creation Date:
2025-12-26 @ 11:07 AM
Ignite Modification Date:
2026-02-25 @ 9:47 PM
Study NCT ID:
NCT02973412
Status:
RECRUITING
Last Update Posted:
2024-02-15
First Post:
2016-10-26
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Chronotype of Patients With Diabetes and Effect on Glycaemic Control
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}, {'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Collection of whole blood for genetic analysis'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3447}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2016-12-07', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-02', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-02-14', 'studyFirstSubmitDate': '2016-10-26', 'studyFirstSubmitQcDate': '2016-11-21', 'lastUpdatePostDateStruct': {'date': '2024-02-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2016-11-25', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'HBA1C level (%) measured from a blood sample', 'timeFrame': 'Baseline (1 time point)'}], 'secondaryOutcomes': [{'measure': 'Mid-Sleep Time (MSF) - on both free and work days', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Glucose (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Insulin (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'C-Peptide (ng/mL (conventional units), or nmol/L (SI))', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Total cholesterol levels (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'HDL-cholesterol levels (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'LDL-cholesterol levels (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Triglyceride levels (mmol/L)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Liver function test (including AST, ALT, ALP and albumin)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Weight (Kg)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Body composition via bioimpedance', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Height (cm)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Blood pressure (mmHg)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'hsCRP (mg/L)', 'timeFrame': 'Baseline (1 time point)', 'description': 'Biomarker of inflammation'}, {'measure': 'Levels of physical activity (Recall Physical Activity Questionnaire,RPAQ)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Duration of diabetes', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Consumption of Pathogen Associated Molecular Patterns (PAMPs)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Sleep duration (self-report)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Physical function (self - report)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Physical performance (Short Physical Performance Battery (SPPB) plus hand grip)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Objective measures of physical activity and sleep duration (GENEActiv)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Energy intake (24-hour dietary recall (DR))', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Clock genes (whole blood sample)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Temporal distribution of calorie intake (determined by 24-hr food recall)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Prevalence of each chronotype category', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'IL-6 (pg/ml)', 'timeFrame': 'Baseline (1 time point)', 'description': 'Biomarker of inflammation'}, {'measure': 'Leptin (ng/L)', 'timeFrame': 'Baseline (1 time point)', 'description': 'Biomarker of inflammation'}, {'measure': 'Adiponectin (pg/ml)', 'timeFrame': 'Baseline (1 time point)', 'description': 'Biomarker of inflammation'}, {'measure': 'Age of onset', 'timeFrame': 'Baseline (1 time point)', 'description': 'Age at which the participant was diagnosed with Type 2 Diabetes'}, {'measure': 'Symptoms of depressive disorder (PHQ-9)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Level of diabetes specific distress (DDS-17)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Level of self-compassion (SCS)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'The Hypoglycaemic Confidence Scale (HCS)', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Gold Score', 'timeFrame': 'Baseline (1 time point)'}, {'measure': 'Continuous Glucose Monitoring (CGM)', 'timeFrame': 'Baseline (1 time point)'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'conditions': ['Diabetes Mellitus, Type 1', 'Diabetes Mellitus, Type 2']}, 'referencesModule': {'references': [{'pmid': '36171016', 'type': 'DERIVED', 'citation': 'Gunn S, Henson J, Robertson N, Maltby J, Brady EM, Henderson S, Hadjiconstantinou M, Hall AP, Rowlands AV, Yates T, Davies MJ. Self-compassion, sleep quality and psychological well-being in type 2 diabetes: a cross-sectional study. BMJ Open Diabetes Res Care. 2022 Sep;10(5):e002927. doi: 10.1136/bmjdrc-2022-002927.'}, {'pmid': '32675292', 'type': 'DERIVED', 'citation': 'Henson J, Rowlands AV, Baldry E, Brady EM, Davies MJ, Edwardson CL, Yates T, Hall AP; CODEC Investigators. Physical behaviors and chronotype in people with type 2 diabetes. BMJ Open Diabetes Res Care. 2020 Jul;8(1):e001375. doi: 10.1136/bmjdrc-2020-001375.'}, {'pmid': '31719069', 'type': 'DERIVED', 'citation': 'Brady EM, Hall AP, Baldry E, Chatterjee S, Daniels LJ, Edwardson C, Khunti K, Patel MI, Henson JJ, Rowlands A, Smith AC, Yates T, Davies MJ. Rationale and design of a cross-sectional study to investigate and describe the chronotype of patients with type 2 diabetes and the effect on glycaemic control: the CODEC study. BMJ Open. 2019 Nov 11;9(11):e027773. doi: 10.1136/bmjopen-2018-027773.'}]}, 'descriptionModule': {'briefSummary': 'The aim of this study is to explore the associations between chronotype and glycaemic control, cardiometabolic health and other lifestyle factors.', 'detailedDescription': 'The incidence and prevalence of diabetes mellitus has now reached over 5 million in the United Kingdom (UK). Type 2 diabetes mellitus (T2DM) accounts for approximately 90% of the UK population with diabetes and is a condition characterised by hyperglycaemia, resulting from defects in hepatic and peripheral glucose uptake, insulin secretion, or both. Conversely, around 8% of those diagnosed with diabetes, have Type 1 diabetes mellitus (T1DM). This equates to \\~400,000 people in the UK. The incidence of new diagnoses of T1DM is also increasing by about 4% each year, with around half of these being in people over the age of 18. T1DM is caused by an autoimmune reaction where the body\'s defence system attacks the cells that produce insulin. As such, T1DM requires life-long treatment with exogenous insulin therapy accompanied by blood glucose monitoring.\n\nRegardless of type, there is an increased vulnerability to microvascular (nephropathy, neuropathy, and retinopathy) and macrovascular complications (coronary artery disease, peripheral arterial disease, and stroke). As a result, new paradigms for characterising and treating these patients could enhance current and future diabetes management.\n\nRecently, there has been considerable interest in the association between quantity and quality of sleep and circadian rhythms and the development and management of cardiometabolic disease especially metabolic syndrome, diabetes and Cardiovascular Disease (CVD). A "U"-shaped relationship related to both short and long sleep duration exists between sleep duration and diabetes, obesity, CVD, hypertension and stroke. A meta-analysis of nearly 500,000 individuals (\\~4% T2DM) identified a relative risk (RR) of 1.14 (95% CI 1.03-1.26) for every additional hour of sleep and RR 1.09 (95% CI 1.04-1.15) with each hour of shorter sleep compared to 7-hours sleep per day for the development of T2DM. Despite this many individuals do not consider sleep essential for good health but instead consider it to be rather more of an inconvenience. Subsequently, lifestyle choices, societal pressures and shift-work render the population chronically sleep deprived and thus at increased risk of metabolic dysfunction.\n\nSleep is regulated, in part, by a homeostatic drive and is therefore unavoidable in humans (without sleep disorders). The circadian system, our internal clock, is also responsible for the regulation of sleep. Sleep is a multidimensional behaviour (and biological process) where we need to not only consider duration and quality but timing also. A person\'s sleep pattern, in relation to the 24-hour clock, i.e. timing, is individual to them and referred to as their chronotype. We can quantitatively characterise these individual differences in daily timing using a number of questionnaire based tools.\n\nFive different chronotypes have been identified using the \'Morningness-Eveningness\' Questionnaire i.e. definite evening type, moderate evening type, intermediate, moderate morning type and definite morning type. The identification of these different chronotypes, which describes preferred circadian phases, into, at the two extremes, "morning type" and "evening type" has led to further research confirming that "evening types" are at greater risk of cardiometabolic disease and metabolic dysfunction. The underlying causes have not been clearly defined but appear to be related to circadian mal-alignment causing chronic sleep deprivation and leading to dysregulation of metabolic, immune and hormonal processes that govern energy regulation and glycaemic control.\n\nSeveral clock genes have been identified in the control of circadian rhythms including Clock (Circadian locomotor output cycles protein kaput), Npas2 (Neuronal PAS domain protein2), Bmal1 (Brain and muscle ARNT-like protein), Per1 (Period), Per2, Per3, Cry1 (Cryptochrome), Cry2, Rev-Erbs (Reverse erythroblastosis virus) and CkI (Casein kinase). However their role if any, in progression in diabetes remains to be elucidated.\n\nThe concept of "social jetlag" has been developed to describe the deleterious effects of chronic sleep deprivation related to weekday occupational obligations on "evening types" and weekend social demands on "morning types". For example, a large epidemiological study in Germany has shown that social jetlag is associated with obesity. Several public health questions are raised by these associations, not least whether chronotyping of all individuals should be considered and whether the individual chronotype can be adjusted by sleep hygiene and training (which requires discipline for maintenance) and/or exogenous treatment with melatonin.\n\nIn this cross-sectional observational study, we therefore propose to extensively chronotype a sample of patients with T1DM and T2DM to determine the impact of chronotype on glycaemic control, insulin resistance, biochemical profile, and inflammatory, adipocytokine and genetic markers using a validated questionnaire and blood tests.\n\nIn an optional sub-study, we will explore the association between diabetes, chronotype and objectively measured physical activity, energy intake, clock genes and well-being. We will also offer participants the opportunity to wear a continuous glucose monitor. Those that have consented to take part in the optional sub-study, and to be contacted about future ethically approved research, will also be offered the opportunity to have these measurements repeated at a later date.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Participants with established (\\>6months since diagnosis) T1DM or T2DM, between the age of 18-75 years inclusive, who do not currently have a known sleep disorder (excluding OSA).', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Participant is willing and able to give informed consent for participation in the study\n2. T1DM or established T2DM (\\>6months since diagnosis)\n3. Male or Female\n4. Aged 18-75 years inclusive\n5. BMI less than or equal to 45kg/m² inclusive\n6. No known sleep disorders except Obstructive Sleep Apnoea (OSA)\n7. On any glucose-lowering therapy (T1DM or T2DM) or lifestyle modification for management of T2DM\n8. Good command of the English language\n\nExclusion Criteria:\n\n1. Participant is unwilling or unable to give informed consent\n2. Anyone without a good command of the English language\n3. Anyone \\<18 years of age and \\>75 years of age\n4. BMI greater than 45 kg/m²\n5. A regular cannabis user i.e. weekly use\n6. Have a terminal illness\n7. A known sleep disorder that is not OSA\n8. Regular use of the following medicines i.e. Weekly use: (Wakefulness promoting agents Modafinil, Amphetamine derivatives, Methylphenidate; Sedatives including benzodiazepines, Z-drugs (zopiclone, zolpidem \\& zaleplon); Melatonin, including Circadin and melatonin analogues; Clonazepam and other drugs for nocturnal movement disorders).'}, 'identificationModule': {'nctId': 'NCT02973412', 'acronym': 'CODEC', 'briefTitle': 'Chronotype of Patients With Diabetes and Effect on Glycaemic Control', 'organization': {'class': 'OTHER', 'fullName': 'University of Leicester'}, 'officialTitle': 'Chronotype of Patients With Diabetes and Effect on Glycaemic Control: The CODEC Study', 'orgStudyIdInfo': {'id': '0590'}}, 'contactsLocationsModule': {'locations': [{'zip': 'LE5 4PW', 'city': 'Leicester', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'Sahar Khodabakhsh, PhD', 'role': 'CONTACT', 'email': 'sahar.khodabakhsh@uhl-tr.nhs.uk', 'phone': '0116 258 8070'}], 'facility': 'Leicester Diabetes Centre, Leicester General Hospital', 'geoPoint': {'lat': 52.6386, 'lon': -1.13169}}], 'centralContacts': [{'name': 'Sahar Khodabakhsh, PhD', 'role': 'CONTACT', 'email': 'sahar.khodabakhsh@uhl-tr.nhs.uk', 'phone': '0116258', 'phoneExt': '8070'}], 'overallOfficials': [{'name': 'Andrew Hall', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Sleep Consultant'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Leicester', 'class': 'OTHER'}, 'collaborators': [{'name': 'University Hospitals, Leicester', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}