Endpoint definition ↥ FinnGen phenotype data 321302 individuals Apply sex-specific rule None 321302 Check conditions None 321302 Filter registries None 0 Check pre-conditions, main-only, mode, ICD version None 0 Check minimum number of events None 0 Include endpoints T1D T2D DM_OTHER_WIDE E4_DMNAS 55191 DIABETES_FG Extra metadata First used in FinnGen datafreeze DF2
FinnGen phenotype data 321302 individuals Apply sex-specific rule None 321302 Check conditions None 321302 Filter registries None 0 Check pre-conditions, main-only, mode, ICD version None 0 Check minimum number of events None 0 Include endpoints T1D T2D DM_OTHER_WIDE E4_DMNAS 55191 DIABETES_FG Extra metadata First used in FinnGen datafreeze DF2
Similar endpoints ↥ List of similar endpoints to Diabetes, varying definitions based on the number of shared cases. Broader endpoints: None Narrower endpoints: Other diabetes, wide definition Type 2 diabetes, definitions combined Type 2 diabetes, strict (exclude DM1) Unspecified diabetes Operations, diabetic retinopathy Show all endpoint correlations
List of similar endpoints to Diabetes, varying definitions based on the number of shared cases. Broader endpoints: None Narrower endpoints: Other diabetes, wide definition Type 2 diabetes, definitions combined Type 2 diabetes, strict (exclude DM1) Unspecified diabetes Operations, diabetic retinopathy Show all endpoint correlations
Summary Statistics ↥ Key figures All Female Male ? X Number of individuals Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the number of individuals is 2, since only 2 unique individuals (FG1 and FG2) have EXAMPLE_ENDPOINT events. Number of individuals 53388 24164 29224 ? X Unadjusted prevalence Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the unadjusted prevalence is 66 %. The unadjusted prevalence is the number of individuals having an endpoint divided by the total number of individuals. Here: 2 unique individuals (FG1 and FG2) have EXAMPLE_ENDPOINT events 1 individual (FG3) has no EXAMPLE_ENDPOINT event So the unadjusted prevalence is 2 / 3 = 66 %. Unadjusted prevalence (%) 17.30 13.93 21.64 ? X Mean age at first event Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the mean age at first event is 37.5. The mean age at first event for EXAMPLE_ENDPOINT is computed by: selecting individuals having EXAMPLE_ENDPOINT: FG1 and FG2 for these individuals, taking the age of their first event of EXAMPLE_ENDPOINT: 45 for FG1 and 30 for FG2 computing the mean of these values So the mean age at first event is Mean(45, 30) = 37.5. Mean age at first event (years) 54.77 53.16 56.10 ? X Follow-up Amount of time to look for the endpoint since entering the study. This is either either 1, 5, 15 years or the full study time from 1998 to 2019. Absolute risk Estimates the probability of dying from the current endpoint. Check the Documentation page for Mortality: absolute risk to get more details. Hazard Ratio (HR) and 95% Confidence Interval (CI) Measures how much the risk of dying increases (HR > 1) or decreases (HR < 1). Number of individuals (N) Number of individuals having the current endpoint and died during the follow-up time. Mortality Follow-up Absolute risk HR [95% CI] p N 1998–2019 0.03 2.55 [2.31, 2.81] 4.0e-79 8048 15 years 0.01 1.75 [1.61, 1.90] 1.9e-38 4051 5 years 0.00 2.73 [2.52, 2.95] <1e-100 1983 1 year 0.00 4.75 [4.19, 5.39] <1e-100 677 Age distribution of first events 167221112139324364941361914789761916168601020304050607080900k2k4k6k8k10k12k14kageindividuals Year distribution of first events 2966893511051343167775076369113921123011133197019751980198519901995200020052010201520210k1k2k3k4k5k6k7k8k9k10k11kyearindividuals Cumulative Incidence
Key figures All Female Male ? X Number of individuals Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the number of individuals is 2, since only 2 unique individuals (FG1 and FG2) have EXAMPLE_ENDPOINT events. Number of individuals 53388 24164 29224 ? X Unadjusted prevalence Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the unadjusted prevalence is 66 %. The unadjusted prevalence is the number of individuals having an endpoint divided by the total number of individuals. Here: 2 unique individuals (FG1 and FG2) have EXAMPLE_ENDPOINT events 1 individual (FG3) has no EXAMPLE_ENDPOINT event So the unadjusted prevalence is 2 / 3 = 66 %. Unadjusted prevalence (%) 17.30 13.93 21.64 ? X Mean age at first event Using example data: ID phenocode age FG1 EXAMPLE_ENDPOINT 45 FG1 ENDPOINT_XYZ 46 FG1 DEATH 47 FG2 EXAMPLE_ENDPOINT 30 FG2 EXAMPLE_ENDPOINT 30.1 FG3 ENDPOINT_XYZ 50 In this example the mean age at first event is 37.5. The mean age at first event for EXAMPLE_ENDPOINT is computed by: selecting individuals having EXAMPLE_ENDPOINT: FG1 and FG2 for these individuals, taking the age of their first event of EXAMPLE_ENDPOINT: 45 for FG1 and 30 for FG2 computing the mean of these values So the mean age at first event is Mean(45, 30) = 37.5. Mean age at first event (years) 54.77 53.16 56.10
? X Follow-up Amount of time to look for the endpoint since entering the study. This is either either 1, 5, 15 years or the full study time from 1998 to 2019. Absolute risk Estimates the probability of dying from the current endpoint. Check the Documentation page for Mortality: absolute risk to get more details. Hazard Ratio (HR) and 95% Confidence Interval (CI) Measures how much the risk of dying increases (HR > 1) or decreases (HR < 1). Number of individuals (N) Number of individuals having the current endpoint and died during the follow-up time. Mortality Follow-up Absolute risk HR [95% CI] p N 1998–2019 0.03 2.55 [2.31, 2.81] 4.0e-79 8048 15 years 0.01 1.75 [1.61, 1.90] 1.9e-38 4051 5 years 0.00 2.73 [2.52, 2.95] <1e-100 1983 1 year 0.00 4.75 [4.19, 5.39] <1e-100 677
Follow-up Amount of time to look for the endpoint since entering the study. This is either either 1, 5, 15 years or the full study time from 1998 to 2019.
Absolute risk Estimates the probability of dying from the current endpoint. Check the Documentation page for Mortality: absolute risk to get more details.
Hazard Ratio (HR) and 95% Confidence Interval (CI) Measures how much the risk of dying increases (HR > 1) or decreases (HR < 1).
Number of individuals (N) Number of individuals having the current endpoint and died during the follow-up time.
Age distribution of first events 167221112139324364941361914789761916168601020304050607080900k2k4k6k8k10k12k14kageindividuals
Year distribution of first events 2966893511051343167775076369113921123011133197019751980198519901995200020052010201520210k1k2k3k4k5k6k7k8k9k10k11kyearindividuals
Survival analyses between endpoints ↥ Plot before Diabetes, varying definitions after Diabetes, varying definitions Loading survival analyses plot Table Loading survival analyses table Download CSV
Plot before Diabetes, varying definitions after Diabetes, varying definitions Loading survival analyses plot