Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06563401
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-19
Brief Title:
Mortality and Cardiovascular Diseases in Adult-onset Type 1 Diabetes
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Cardiovascular Disease Mortality and Prognostic Factors in Adult-onset Type 1 Diabetes
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Scarce evidence is available for prognosis in adult-onset type 1 diabetes T1D The aim of this study is to investigate the risk of all-cause mortality cause-specific mortality and incident CVD in adult-onset T1D as compared to type 2 diabetes with comparable age at diagnosis and population controls We will explore potential modifiable factors including lifestyle and clinical characteristics that contribute to T1D prognosis In addition we will estimate the life expectancy associated with different age at T1D diagnosis as compared to population controls This study will be based on people with diabetes recorded in Swedish National Diabetes Registers with linkages to other nationwide registers to retrieve data on lifestyle factors biomarkers treatment and outcome information
Detailed Description:
Introduction Type 1 diabetes T1D is traditionally considered as childhood-onset disease while it occurs more often in adults than in children with a median onset-age of 29 years1 There is a scarcity of evidence on mortality and causes2 and cardiovascular diseases CVD in adult-onset T1D Previous studies involving adult-onset T1D mostly included individuals diagnosed before age 30 years3-8 Nationwide studies in Sweden found that T1D diagnosed at any age between 0-30 have shorter life expectancy higher risks of all-cause mortality CVD mortality and incident CVD than population controls and age of T1D onset and HbA1c levels are important risk stratifiers45
Age at diagnosis is an important factor affecting prognosis of diabetes but evidence on T1D diagnosed after age 30 years is relatively scarce A study of 35355 incident T1D cases in England and Wales found that the hazard for all-cause mortality in people with T1D was higher than that in people with T2D across all groups of age at diagnosis from 20-39 years to 60 years but such excess risk was observed in men and not in women9 A small Swedish study of 112 adult-onset T1D cases diagnosed between age 18 and 100 years indicated no excess mortality in T1D as compared to population controls10 In comparison a recent study of 1573 people with newly diagnosed at age 35 years adult-onset T1D in Sweden found that these individuals had excess mortality as compared to people with T2D while the CVD incidence was close to that in population controls11 It is unclear how mortality and CVD incidence in adult-onset T1D vary by diabetes duration lifestyle factors and clinical characteristics HbA1c blood pressure and lipids etc
This study aims to investigate the risk of all-cause mortality cause-specific mortality and incident CVD in adult-onset T1D as compared to T2D with comparable age at diagnosis and population controls We will explore potential modifiable factors including lifestyle and clinical characteristics that contribute to T1D prognosis
Methods Study population Adult-onset 18 years T1D cases diagnosed in 2006-2020 will be identified from the Swedish National Diabetes Register NDR 1996-2020 without conflicting types of diabetes diagnosis primary diagnosis in the National Patient Register NPR 1995-2021 and with exclusive insulin prescription initiated within the first 6 months of diagnosis and recorded in the National Prescribed Drug Register NPDR 2005-2022 We will also include all T2D cases diagnosed at age 18 years in NDR in 2006-2020 and without conflicting types of diabetes diagnosis primary diagnosis in NPR The date of diabetes diagnosis will be defined as the date of first diabetes record in NDR visiting date or self-reported date of diagnosis whichever comes first NPR or the date of first prescription of glucose-lowering drugs in NPDR whichever comes first Individuals whose self-reported year of diabetes diagnosis debutar in NDR was earlier than the year of diagnosis as defined above will be excluded Population controls will be selected from participants who have not any record of diabetes diagnosis in NDR or NPR before the end of follow-up 2021 Each T1D case will be matched for age sex county5 with 50 the ratio depends on the minimum number of available population controls in each matched stratum population controls who are still alive at the year of diagnosis in their matched T1D cases The analysis of CVD and MACE major adverse cardiovascular events incidence will exclude individuals with corresponding diagnosis at baseline
Covariates and prognostic factors Information on sex year and month of birth country of birth and marital status will be obtained from the Total Population Register while information on education will be obtained from the Longitudinal integration database for health insurance and labor market studies LISA NDR provides information on smoking yes or no body mass index BMI physical activity HbA1c blood pressure lipids profiles eGFR and albuminuria after diabetes diagnosis HbA1c within control target will be defined as an HbA1c of 7012 Blood pressure within control target will be defined as systolic blood pressure 140 mmHg and diastolic blood pressure 90 mmHg13 A favorable HDL cholesterol level will be defined as HDL 10 mmoll in men and 13 mmoll in women14 A low-risk LDL cholesterol level will be defined as LDL 26 mmoll14 We will categorize eGFR levels into two groups namely eGFR 60 or 60 mLmin173 m2 Microalbuminuria will be defined as two positive tests from three samples taken within 1 year with an albumincreatinine ratio of 3-30 mgmmol 30-300 mgg or U-albumin of 20-200 μg min 20-300 mgL and macroalbuminuria as albumincreatinine ratio 30 mgmmol 300 mgg or U-albumin 200 μg min 300 mgL4 Physical activity is recorded as never 1 timeweek 1-2 timesweek 3-5 timesweek or daily Individuals with physical activity 1 timeweek will be defined as having regular physical activity We will obtain information on use of anti-hypertensive drugs ATC codes C02 C03 C04 C07 C08 C09 and statins ATC codes C10A C10B from NPDR Information on insulin regimens insulin pump or short-acting insulin combined with long-acting insulin will be retrieved from NDR and NPDR
Outcome assessment Outcomes include all-cause mortality cause-specific mortality diabetes-related deaths ICD-10 E10-E14 CVD-related deaths ICD-10 I00-I99 cancer-related death ICD-10 C00-C97 incident CVD and MACE Information on vital status and causes of death will be retrieved from the Causes-of-Death Register 2006-2021 We will define the composite CVD outcome as the first inpatient record primary diagnosis and up to 7 contributory diagnoses3 of ischemic heart disease ICD-10 codes I20-I25 stroke I60-I64 or heart failure I50 in NPR or the record of corresponding CVD events as the underlying causes of death in Causes-of-Death Register MACE will be defined as cardiovascular deaths ICD-10 I70-I7715 recorded in Causes-of-Death Register or the first inpatient record primary diagnosis and up to 7 contributory diagnoses3 of nonfatal myocardial infarction ICD-10 I21 or nonfatal stroke ICD-10 I60-I6415-18 in NPR
Statistical analysis Basic characteristics Basic characteristics will be presented as means or medians for continuous variables age at diabetes diagnosisbaseline BMI HbA1c blood pressure HDL LDL total cholesterol eGFR and as proportions for categorical variables sex country of birth marital status education CVD diagnosis at baseline smoking physical activity albuminuria insulin regimen in individuals with T1D T2D and population controls if feasible Differences across groups will be tested using Students t-test Kruskal-Wallis test or χ2 test
Duration of follow-up will be calculated from the date of diabetes diagnosis date of diabetes diagnosis of their matched T1D cases for population controls to the date of the occurrence of outcomes death or December of 2021 for CVD June of 2022 for mortality depending on the availability of different registers
Risks of mortality and CVD as compared to population controls We will estimate the cumulative probability 95 CI of all-cause mortality cause-specific mortality incident CVD and MACE in T1D and population controls over diabetesfollow-up duration according to age at diagnosis if power allows by plotting Kaplan-Meier curves R package survfit and ggsurvfit We will estimate the hazard ratio HR for these outcomes in T1D overall onset-age 18-29 years 30-39 years and 40 years as compared to population controls in Cox models We will also estimate the diabetes duration-specific HR 95 CI in T1D as compared to population controls by splitting participants according to duration of diabetesfollow-up 0-5 years 5-10 years 10-15 years and 15 years
To explore the benefit of having modifiable factors within control level we will estimate the HR for different outcomes in different T1D subgroups separated according to modifiable factors the first record before the occurrence of outcomes including smoking status level of physical activity control of HbA1c blood pressure lipids eGFR or albuminuria status with population controls as the reference group
All the Cox models estimating HR for T1D vs population controls will be fitted with diabetesfollow-up duration as the time scale with adjustment for education country of birth and marital status and with stratification by matching groups
Risks of mortality and CVD as compared to T2D We will estimate the HR for all-cause mortality cause-specific mortality incident CVD and MACE in T1D as compared to T2D in Cox models with attained age as the time scale with adjustment for age and calendar year at diabetes diagnosis sex education marital status country of birth We will further adjust for smoking BMI physical activity HbA1c continuous blood pressure continuous lipids continuous eGFR continuous albuminuria continuous anti-hypertensive drugs and statins to explore potential factors leading to the excess or reduced risks of different outcomes in T1D as compared to T2D We will also estimate the HR in T1D vs T2D according to different subgroups of age at diabetes diagnosis separately 18-29 years 30-39 years and 40 years Individuals with missing data for categorical covariates will be treated as a separate group and those with missing values on continuous covariates were assigned the median value with a binary variable indicating whether the values are imputed or not A previous NDR study used linear mixed models to impute missing data for biomarkers such as HbA1c19 Not sure if we can also perform such imputation
Trajectory analysis The first step of analysis Finally we will estimate the trajectories of smoking BMI physical activity HbA1c blood pressure lipids eGFR albuminuria and insulin regimens over diabetes duration in individuals with T1D and T2D according to age at diabetes diagnosis 18-29 years 30-39 years and 40 years The trajectories will be estimated using generalized linear model GLM glm package in Stata 170 adjusted for sex and calendar year at diabetes diagnosis with logit link function and binomial distribution and with cluster robust standard errors SEs20 to account for the dependence among measurements in the same individual diabetes duration Such trajectory analysis will provide clues to factors contributing to the potential difference in mortalityCVD risks between T1D and T2D with comparable age at diagnosis
Age at diagnosis is an important factor affecting prognosis of diabetes but evidence on T1D diagnosed after age 30 years is relatively scarce A study of 35355 incident T1D cases in England and Wales found that the hazard for all-cause mortality in people with T1D was higher than that in people with T2D across all groups of age at diagnosis from 20-39 years to 60 years but such excess risk was observed in men and not in women9 A small Swedish study of 112 adult-onset T1D cases diagnosed between age 18 and 100 years indicated no excess mortality in T1D as compared to population controls10 In comparison a recent study of 1573 people with newly diagnosed at age 35 years adult-onset T1D in Sweden found that these individuals had excess mortality as compared to people with T2D while the CVD incidence was close to that in population controls11 It is unclear how mortality and CVD incidence in adult-onset T1D vary by diabetes duration lifestyle factors and clinical characteristics HbA1c blood pressure and lipids etc
This study aims to investigate the risk of all-cause mortality cause-specific mortality and incident CVD in adult-onset T1D as compared to T2D with comparable age at diagnosis and population controls We will explore potential modifiable factors including lifestyle and clinical characteristics that contribute to T1D prognosis
Methods Study population Adult-onset 18 years T1D cases diagnosed in 2006-2020 will be identified from the Swedish National Diabetes Register NDR 1996-2020 without conflicting types of diabetes diagnosis primary diagnosis in the National Patient Register NPR 1995-2021 and with exclusive insulin prescription initiated within the first 6 months of diagnosis and recorded in the National Prescribed Drug Register NPDR 2005-2022 We will also include all T2D cases diagnosed at age 18 years in NDR in 2006-2020 and without conflicting types of diabetes diagnosis primary diagnosis in NPR The date of diabetes diagnosis will be defined as the date of first diabetes record in NDR visiting date or self-reported date of diagnosis whichever comes first NPR or the date of first prescription of glucose-lowering drugs in NPDR whichever comes first Individuals whose self-reported year of diabetes diagnosis debutar in NDR was earlier than the year of diagnosis as defined above will be excluded Population controls will be selected from participants who have not any record of diabetes diagnosis in NDR or NPR before the end of follow-up 2021 Each T1D case will be matched for age sex county5 with 50 the ratio depends on the minimum number of available population controls in each matched stratum population controls who are still alive at the year of diagnosis in their matched T1D cases The analysis of CVD and MACE major adverse cardiovascular events incidence will exclude individuals with corresponding diagnosis at baseline
Covariates and prognostic factors Information on sex year and month of birth country of birth and marital status will be obtained from the Total Population Register while information on education will be obtained from the Longitudinal integration database for health insurance and labor market studies LISA NDR provides information on smoking yes or no body mass index BMI physical activity HbA1c blood pressure lipids profiles eGFR and albuminuria after diabetes diagnosis HbA1c within control target will be defined as an HbA1c of 7012 Blood pressure within control target will be defined as systolic blood pressure 140 mmHg and diastolic blood pressure 90 mmHg13 A favorable HDL cholesterol level will be defined as HDL 10 mmoll in men and 13 mmoll in women14 A low-risk LDL cholesterol level will be defined as LDL 26 mmoll14 We will categorize eGFR levels into two groups namely eGFR 60 or 60 mLmin173 m2 Microalbuminuria will be defined as two positive tests from three samples taken within 1 year with an albumincreatinine ratio of 3-30 mgmmol 30-300 mgg or U-albumin of 20-200 μg min 20-300 mgL and macroalbuminuria as albumincreatinine ratio 30 mgmmol 300 mgg or U-albumin 200 μg min 300 mgL4 Physical activity is recorded as never 1 timeweek 1-2 timesweek 3-5 timesweek or daily Individuals with physical activity 1 timeweek will be defined as having regular physical activity We will obtain information on use of anti-hypertensive drugs ATC codes C02 C03 C04 C07 C08 C09 and statins ATC codes C10A C10B from NPDR Information on insulin regimens insulin pump or short-acting insulin combined with long-acting insulin will be retrieved from NDR and NPDR
Outcome assessment Outcomes include all-cause mortality cause-specific mortality diabetes-related deaths ICD-10 E10-E14 CVD-related deaths ICD-10 I00-I99 cancer-related death ICD-10 C00-C97 incident CVD and MACE Information on vital status and causes of death will be retrieved from the Causes-of-Death Register 2006-2021 We will define the composite CVD outcome as the first inpatient record primary diagnosis and up to 7 contributory diagnoses3 of ischemic heart disease ICD-10 codes I20-I25 stroke I60-I64 or heart failure I50 in NPR or the record of corresponding CVD events as the underlying causes of death in Causes-of-Death Register MACE will be defined as cardiovascular deaths ICD-10 I70-I7715 recorded in Causes-of-Death Register or the first inpatient record primary diagnosis and up to 7 contributory diagnoses3 of nonfatal myocardial infarction ICD-10 I21 or nonfatal stroke ICD-10 I60-I6415-18 in NPR
Statistical analysis Basic characteristics Basic characteristics will be presented as means or medians for continuous variables age at diabetes diagnosisbaseline BMI HbA1c blood pressure HDL LDL total cholesterol eGFR and as proportions for categorical variables sex country of birth marital status education CVD diagnosis at baseline smoking physical activity albuminuria insulin regimen in individuals with T1D T2D and population controls if feasible Differences across groups will be tested using Students t-test Kruskal-Wallis test or χ2 test
Duration of follow-up will be calculated from the date of diabetes diagnosis date of diabetes diagnosis of their matched T1D cases for population controls to the date of the occurrence of outcomes death or December of 2021 for CVD June of 2022 for mortality depending on the availability of different registers
Risks of mortality and CVD as compared to population controls We will estimate the cumulative probability 95 CI of all-cause mortality cause-specific mortality incident CVD and MACE in T1D and population controls over diabetesfollow-up duration according to age at diagnosis if power allows by plotting Kaplan-Meier curves R package survfit and ggsurvfit We will estimate the hazard ratio HR for these outcomes in T1D overall onset-age 18-29 years 30-39 years and 40 years as compared to population controls in Cox models We will also estimate the diabetes duration-specific HR 95 CI in T1D as compared to population controls by splitting participants according to duration of diabetesfollow-up 0-5 years 5-10 years 10-15 years and 15 years
To explore the benefit of having modifiable factors within control level we will estimate the HR for different outcomes in different T1D subgroups separated according to modifiable factors the first record before the occurrence of outcomes including smoking status level of physical activity control of HbA1c blood pressure lipids eGFR or albuminuria status with population controls as the reference group
All the Cox models estimating HR for T1D vs population controls will be fitted with diabetesfollow-up duration as the time scale with adjustment for education country of birth and marital status and with stratification by matching groups
Risks of mortality and CVD as compared to T2D We will estimate the HR for all-cause mortality cause-specific mortality incident CVD and MACE in T1D as compared to T2D in Cox models with attained age as the time scale with adjustment for age and calendar year at diabetes diagnosis sex education marital status country of birth We will further adjust for smoking BMI physical activity HbA1c continuous blood pressure continuous lipids continuous eGFR continuous albuminuria continuous anti-hypertensive drugs and statins to explore potential factors leading to the excess or reduced risks of different outcomes in T1D as compared to T2D We will also estimate the HR in T1D vs T2D according to different subgroups of age at diabetes diagnosis separately 18-29 years 30-39 years and 40 years Individuals with missing data for categorical covariates will be treated as a separate group and those with missing values on continuous covariates were assigned the median value with a binary variable indicating whether the values are imputed or not A previous NDR study used linear mixed models to impute missing data for biomarkers such as HbA1c19 Not sure if we can also perform such imputation
Trajectory analysis The first step of analysis Finally we will estimate the trajectories of smoking BMI physical activity HbA1c blood pressure lipids eGFR albuminuria and insulin regimens over diabetes duration in individuals with T1D and T2D according to age at diabetes diagnosis 18-29 years 30-39 years and 40 years The trajectories will be estimated using generalized linear model GLM glm package in Stata 170 adjusted for sex and calendar year at diabetes diagnosis with logit link function and binomial distribution and with cluster robust standard errors SEs20 to account for the dependence among measurements in the same individual diabetes duration Such trajectory analysis will provide clues to factors contributing to the potential difference in mortalityCVD risks between T1D and T2D with comparable age at diagnosis
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None