Status
Ongoing
Title
Defining the relationship between lipids, statins, and the risk of amyotrophic lateral sclerosis and frontotemporal dementia using primary health records
What is the aim of the study and why is it important?
Amyotrophic lateral sclerosis (ALS, also known as motor neuron disease, MND) is a fatal disease affecting the brain and nervous system that causes progressive weakness and death, typically within 3 years of symptoms starting. Frontotemporal dementia (FTD) is a form of dementia affecting mostly younger people, beginning with behavioural change or speech problems and progressing to severe brain dysfunction and death. We cannot predict who will get ALS or FTD, but we know that some people have a high risk of ALS or FTD due to genetic factors. Understanding things that increase the risk of ALS and FTD, particularly to prevent or delay their onset in people at high risk of developing these diseases, is a major goal.
There is growing evidence that cholesterol influences the risk of developing ALS and FTD. Higher levels of low-density lipoprotein cholesterol ([LDL-c], so-called “bad” cholesterol) and lower levels of high-density lipoprotein cholesterol ([HDL-c], so-called “good” cholesterol) are associated with a higher risk of ALS and some forms of dementia (i.e., FTD). It is not known whether drugs that lower LDL-c (such as statins) reduce the risk of developing ALS or FTD. Recent evidence also suggests changes in cholesterol and the results of other routine blood tests taken in general practice happen before people get symptoms of ALS.
It is not known whether the relationships between cholesterol and risk hold for people who carry genes that put them at high risk of developing ALS or FTD in later life, in whom tailored treatments to prevent ALS or FTD could have the greatest impact.
We will use anonymised data collected through routine health care of millions of people in general practice to:
1) see whether the relationship between cholesterol and ALS risk is also seen in primary care data, and exists for FTD and for other biomarkers, and how this relates to age at diagnosis
2) explore the influence of cholesterol-lowering medication on the risk of ALS and FTD.
3) look for changes in the levels of cholesterol and other blood test measurements before the onset of ALS and FTD that could be used to predict when the disease will begin
Chief Investigator
Dr Alexander Thompson
Lead Applicant Organisation Name
Sponsor
Oxford
Location of research
University of Oxford
Date on which research approved
04-Mar-2024
Project reference ID
OX166
Generic ethics approval reference
23/EM/0166
Are all data accessed are in anonymised form?
Yes
Brief summary of the dataset to be released (including any sensitive data)
Data required:
- Diagnosis of amyotrophic lateral sclerosis, frontotemporal dementia
- Diagnosis of vascular diseases (angina, myocardial infarction, stroke, peripheral vascular disease)
- History of diabetes
- Family history of angina in a first-degree relative age <60
- History of chronic kidney disease
- History of atrial fibrillation
- Prescription data: specifically lipid-lowering medications and antihypertensives
- Blood test result data: LDL-c, HDL-c, total cholesterol, triglyceride, creatinine, creatine kinase, HbA1c
- Family history of motor neuron disease or frontotemporal dementia
- Lipid-lowering medications
- Antihypertensive medications
Data required:
- Diagnosis of amyotrophic lateral sclerosis, frontotemporal dementia
- Diagnosis of vascular diseases (angina, myocardial infarction, stroke, peripheral vascular disease)
- History of diabetes
- Family history of angina in a first-degree relative age <60
- History of chronic kidney disease
- History of atrial fibrillation
Data required:
- Diagnosis of amyotrophic lateral sclerosis, frontotemporal dementia
- Diagnosis of vascular diseases (angina, myocardial infarction, stroke, peripheral vascular disease)
- History of diabetes
- Family history of angina in a first-degree relative age <60
- History of chronic kidney disease
- History of atrial fibrillation
Public Benefit Statement
Research Team
Prof Julia Hippisley-Cox (University of Oxford)
Prof Carol Coupland (University of Oxford)
Prof Martin Turner (University of Oxford)
Dr Christos Chalitsios (University of Oxford)
Approval Letter
Access Type
Trusted Research Environment (TRE)