Blood tests are widely known to be clinically and biologically informative, to help diagnose numerous diseases such as anaemia, infection, cancer and autoimmune diseases. Gaining an understanding of an individual’s immune system over time using a blood test result has the potential to transform personalised healthcare. If we can better understand a patient’s blood results this could potentially transform our ability to make earlier diagnoses and develop more ways to effectively prevent diseases. Currently the widely accepted ‘normal’ range for a blood test result is the same range for all patients of the same gender. However immune systems vary between patients and what is classed as within the ‘normal’ range for one patient may be an ‘abnormal’ result based on their previous blood results. This widely accepted assessment of a result as ‘normal’ or ‘abnormal’ seems to contradict the use of a blood test as a personalised measure of the individual’s immune system due to the broad ‘normal’ range.
We aim to select patients who have had a blood test in CPRD and use the values to calculate an ‘immune age score’ across different diseases of interest. We will also validate the immune age scores previously published in CPRD patients. These validated risk scores can then be used in precise healthcare and predictive medicine to learn a patient’s optimal blood health over time.
The Full blood count (FBC) is the most commonly performed medical diagnostic test worldwide. It is estimated there are more than 4 billion FBC tests per year globally. Common haematological markers such as those from the FBC are clinically and biologically informative and are made up of component tests which indicate numerous medical issues. Using a learning adaptive algorithm, we will create risk scores comparing a patients chronological age from birth to an estimation of a biological immune age to calculate a patient’s optimal blood health over time. The risk scores will consist of numerical and colour-coded versions and will be calculated from the components of the FBC, and other relevant blood markers available, and the score effectivity tested and validated against the patient clinical data (e.g. diagnosis, medication), including lifestyle (e.g. drinking or smoking). The resulting risk score values will be grouped as belonging to healthy individuals or to a selected list of common diseases. In addition, we will validate previous findings related to compounding markers into risk assessment scores. Patient data will be selected for the project only if they have a record of a blood test recorded. The start of Clinical Practice Research Datalink (CPRD) follow-up will be defined as the later of the patient’s registration date and, in CPRD GOLD, their practice’s up-to-standard date; the end of CPRD data follow-up will be defined as the earliest of the patient’s transfer-out date, date of death (if applicable), and the last data-collection date for their practice. The presentation date will be defined as that of the patient’s first ever record with a code indicative of a blood test. The objective is to better understand the role of the immune system by monitoring common measured blood marker variations over time using personally-adaptive risk scores and assessing longitudinal disease signatures.
The study will seek to determine associations between blood counts and the following conditions.
Primary outcomes: Non-Hodgkin’s lymphoma, Hodgkin lymphoma, Multiple myeloma, Acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL), chronic myeloid leukaemia (CML), Haemolytic anaemias, Thalassemia, Sickle cell disorders, Aplastic anaemias, Coagulation defects, Factor VIII deficiency, Factor IX deficiency, Von Willebrand’s disease, Haemorrhagic conditions, Allergic purpura/platelet disorders , Influenza, Common cold, Bronchiolitis, Herpes zoster, Enteroviruses, Ebola, Poliomyelitis, Measles, Smallpox, Varicella, Norovirus, Mumps, Rubella, Infectious mononucleosis, Hepatitis A/B/C, Rabies, Human papilloma virus, HIV/AIDS, SARS/COVID, Campylobacter, Chlamydia, Cholera, Clostridium Difficile, Diphtheria, E. Coli, Gonorrhoea, Legionella, Listeria, Lyme disease, Salmonella, Shigella, Staphylococcus, Streptococcus, Typhoid, Tuberculosis, Syphilis, Tetanus, Malaria, Cryptosporidium, Giardiasis, Helminths, Toxoplasmosis, Trichomoniasis, Leishmaniasis, Chaga’s disease, Trypanosomiasis, Schistosomiasis
Secondary outcomes: Achalasia, Addison’s disease, Anaphylaxis, Amyloidosis, Ankylosing spondylitis, Antiphospholipid syndrome, Autoimmune hepatitis, Coeliac disease, Chaga’s disease, Crohn’s disease, Dermatomyositis, Giant cell arteritis, Glomerulonephritis, Grave’s thyroiditis, Guillain-Barre Syndrome, Henoch-Schonlein Purpura, IgA nephropathy, Idiopathic (immune) thrombocytopenic purpura, Type 1 diabetes, Lichen planus, Lichen sclerosis, Lupus, Meniere’s disease, Multiple sclerosis, Myasthenia gravis, Myositis, Paroxysmal nocturnal haemoglobinuria, Polyarteritis nodosa, Polymyalgia rheumatica, Primary biliary cholangitis, Primary sclerosing cholangitis, Psoriasis, Psoriatic arthritis, Reactive arthritis, Reynaud’s disease, Rheumatic arthritis, Rheumatic fever, Sarcoidosis, Scleroderma, Systemic Lupus erythematosus, Thrombocytopenic purpura, Thyroid eye disease, Ulcerative colitis, Vasculitis, Vitiligo, Cancers (Tongue, Mouth , Pharynx, Oesophagus, Stomach, Small intestine, Colon, Rectum, Anal canal, Liver, Gallbladder and biliary tree, Pancreas, Larynx, Lungs and bronchus, Bone, Skin, Cervix, Ovary, Prostate, Testis, Bladder, Kidney, Ureter, Brain, Thyroid, Parathyroid) Obesity, Coronary artery disease, Angina, Peripheral vascular disease, Hypertension, Congestive cardiac failure, Arrhythmias, Congenital heart defects, Polycystic kidney disease, Kidney stones, Acute kidney injury, Cerebral aneurysm, Varicose veins, Aortic aneurysms, Autism, Alzheimer’s disease, Ataxia, Dementia, Parkinson’s disease, Huntingdon’s chorea, Epilepsy, Stroke, Spina bifida, Muscular dystrophy, Neuropathy, Migraine, Motor neurone disease, Cerebral palsy, Amnesia, Vertigo, COPD, Asthma, Bronchitis, Pulmonary fibrosis, Emphysema, Alpha-1 antitrypsin deficiency, Cystic fibrosis, Sarcoidosis, Pneumothorax, Osteoporosis, Osteoarthritis, Scoliosis, Gout, Bursitis, Acne, Ankylosing spondylitis, Tendonitis, Fibromyalgia, Carpal tunnel syndrome, Bone fractures, Urticaria, Dermatitis, Gallstones, Gastroesophageal reflux disease, Irritable bowel syndrome, Constipation, Pancreatitis, Peptic ulcer disease, Chronic diarrhoea, Faecal incontinence, Diverticulitis, Lactose intolerance, Urinary incontinence, Benign prostatic hyperplasia, Type 2 diabetes, Hypercholesterolemia, Gaucher disease, Hunter syndrome, Porphyria, Wilson’s disease, Phenylketonuria, Tay-Sachs disease, Hemochromatosis, Anorexia nervosa, Bulimia nervosa, Depression, Schizophrenia, Anxiety disorder, Obsessive compulsive disorder, ADHD, Bipolar disorder, PTSD, Insomnia, Narcolepsy, Sleep apnoea, Addiction, Personality disorders, Endometriosis, Uterine prolapse, Menorrhagia, Dysmenorrhea, Fibroids, Subfertility, Glaucoma, Abnormal visual acuity, Cataracts, Optic atrophy, Optic neuritis, Hearing loss and Anosmia.
ICD10 and medcode lists will be created as part of the study process.
Craig Currie - Chief Investigator - Pharmatelligence Limited t/a Human Data Sciences
Bethan Jones - Corresponding Applicant - Pharmatelligence Limited t/a Human Data Sciences
Abicumaran Uthamacumaran - Collaborator - Oxford Immune Algorithmics
Hector Zenil - Collaborator - Oxford Immune Algorithmics
Kourosh Saeb-Parsy - Collaborator - Oxford Immune Algorithmics
Luan Ozelim - Collaborator - Oxford Immune Algorithmics
Riya Nagar - Collaborator - Oxford Immune Algorithmics
Sultan Khan - Collaborator - Oxford Immune Algorithmics
HES Admitted Patient Care;ONS Death Registration Data;Practice Level Index of Multiple Deprivation