Progression of brain lesions in migraine. The Population-Based CAMERA MRI follow-up study

Progression of brain lesions in migraine. The Population-Based CAMERA MRI follow-up study - Brain lesions in migraine

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON30636

Enrollment

300

Registered

2007-10-11

Start date

2007-09-01

Completion date

Unknown

Last updated

2024-05-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

migraine

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: Participants in CAMERA I MRI study Males and females Age between 40 and 70 yrs Signed informed consent in 1999-2000 Cases: Diagnosed with migraine Controls: Screened negative

Exclusion criteria

Exclusion criteria: Will-unabled MRI contra indicated Claustrophobia Unable to sign informed consent

Design outcomes

Primary

Measure	Time frame
Differences in descriptive statistics between the migraine cases and controls will be examined with *2 test, unpaired t tests, and one-way analyses of variance. Volume of white matter lesions and number of infarcts will be analyzed cross-sectionally as well as longitudinally. Cross-sectional analyses will concentrate on prevalence of participants with a high WML load (similar criteria as to CAMERA 1) and on the number of (posterior circulation) infarcts prevalent. The same methods will be used as was reported for CAMERA 1. Longitudinal analyses will concentrate on progression of lesions (=primary endpoint): either increase in number and/or total volume of WMLs, and/or increase in size of existing infarcts and/or the presence of new infarcts together. Progression of brain lesions over a period of 6 years in relation to migraine diagnosis and characteristics will be tested using multivariate models, in which will be controlled for relevant confounding factors. This follow-up data will be analyzed using both logistic regression, as well as regression based on a Cox proportional hazard model. Confounders will be entered into final models. Analyses of the PFO measurements will be categorical. Multi-variate logistic regression, adjusting for confounders will be to analyze the association of PFO to migraine diagnose, MRI-characteristics, and combined. All parameters of the BST are continuous, and will be analyzed in relation to migraine status and presence of cerebellar lesions. Results of the cognitive testing will be analyzed cross-sectional as well as longitudinal, also in relationship to migraine characteristics and/or brain lesions. For both the cerebellar function tests and the cognitive outcomes, the analyses will be based on linear regression, after ascertaining the normality of their distribution. Statistical analyses and appropriate regression diagnostics will be conducted with current SPSS statistical software (SPSS Inc, Chicago, Ill).	—

Measure

Time frame

Differences in descriptive statistics between the migraine cases and controls will be examined with *2 test, unpaired t tests, and one-way analyses of variance. Volume of white matter lesions and number of infarcts will be analyzed cross-sectionally as well as longitudinally. Cross-sectional analyses will concentrate on prevalence of participants with a high WML load (similar criteria as to CAMERA 1) and on the number of (posterior circulation) infarcts prevalent. The same methods will be used as was reported for CAMERA 1. Longitudinal analyses will concentrate on progression of lesions (=primary endpoint): either increase in number and/or total volume of WMLs, and/or increase in size of existing infarcts and/or the presence of new infarcts together. Progression of brain lesions over a period of 6 years in relation to migraine diagnosis and characteristics will be tested using multivariate models, in which will be controlled for relevant confounding factors. This follow-up data will be analyzed using both logistic regression, as well as regression based on a Cox proportional hazard model. Confounders will be entered into final models. Analyses of the PFO measurements will be categorical. Multi-variate logistic regression, adjusting for confounders will be to analyze the association of PFO to migraine diagnose, MRI-characteristics, and combined. All parameters of the BST are continuous, and will be analyzed in relation to migraine status and presence of cerebellar lesions. Results of the cognitive testing will be analyzed cross-sectional as well as longitudinal, also in relationship to migraine characteristics and/or brain lesions. For both the cerebellar function tests and the cognitive outcomes, the analyses will be based on linear regression, after ascertaining the normality of their distribution. Statistical analyses and appropriate regression diagnostics will be conducted with current SPSS statistical software (SPSS Inc, Chicago, Ill).

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Secondary

Measure	Time frame
nvt	—

Countries

Netherlands

Outcome results