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Background: measuring the prevalence of malnutrition, frailty and sarcopenia in same group of older adults is effective in understanding the relation between these conditions. This could support diagnosing, treatment and prevention in future practice. The research is aiming to measure the estimate prevalence of malnutrition, frailty, sarcopenia and their overlap in older adults, using the UK Biobank. In addition, it will aim to compare the two models of frailty the phenotype and deficit accumulation using the UK Biobank database, as data comparing these models is limited.
Methods/design: This is a cross-sectional study design that will use the UK Biobank database, which includes 381,000 participants males and females, aged 50 years and above, who completed the UK Biobank baseline assessments were included that is a subset from the main sample size from the UK Biobank. For baseline, details of participant's characteristics will be included. All three conditions will be identified as malnutrition by using GLIM criteria, while frailty by using two models; the first model will be the 36 deficits model and phenotype model. Finally, sarcopenia condition will be judge according to EWGSOP standard. All these models will be determining the feasibility to apply it using the available database in the UK Biobank.
Discussion: This proposed study will help in understanding the relation between malnutrition, frailty and sarcopenia. As in worldwide, there is little published research on the overlap between malnutrition, frailty and sarcopenia. Despite definitions and diagnostic criteria were developed for these conditions. There is conflict extend to the definitions and identification criteria's. This study will use UK Biobank database to measuring the estimate prevalence in older people and determine the overlap between three conditions.
The UK Biobank is a population-based study of a large prospective longitudinal cohort with information on 500,000 people, who were aged 40-69 when recruited in 2006-2010 from England, Scotland and Wales. The database includes demographic data, online questionnaires, X-rays and scan image of (brain, heart, abdomen, bones and carotid artery), as well as urine and saliva samples and blood biochemistry including: hormones and blood lipid. The online questionnaires (about: diet, cognitive function, work history and digestive health). It aims to improve the following: prevention, diagnosis and treatment for a broad number of diseases including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. This detailed information on participants provide a resource for investigators to conduct research related a particular diseases.
There are four phases of assessment in UK Biobank. The first phase was the UK Biobank Pilot assessment that included 3798 participants from Stockport only in 2006. Then, the second phase was the initial assessment visit that started in 2007 until 2010. This was the baseline assessment and included approximately 500000 participants. After that, the third phase was conducted and called the first repeat assessment visit which took place between 2012-2013 and included 20346 participants. Lastly, the imaging visit which is considered the fourth phase started in 2014 until present.
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| Measure | Description | Time Frame |
|---|---|---|
| Determine the feasibility of obtaining variables to be able to determine malnutrition, frailty and sarcopenia in the UK Biobank. | Determine if the variables in the UK biobank would be able to use different models to investigating malnutrition by mapping GLIM criteria, frailty by mapping it with two models 36 deficits and phenotype model. while sarcopenia will be matched with EWGSOP standard. | cross sectional, 2007 |
| Measuring the prevalence of the three conditions by applying the models in UK Biobank. | Measuring the estimate prevalence of malnutrition, frailty and sarcopenia in older people using UK Biobank database. | cross sectional, 2007 |
| Measure | Description | Time Frame |
|---|---|---|
| Determine the overlap between three conditions in the baseline assessment | Frailty overlaps with sarcopenia and malnutrition due to similarities of outcome related to body weight. In addition, frailty and sarcopenia have recently had set definitions and diagnostic criteria | cross sectional, 2007 |
| Compare prevalence results between different models |
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Inclusion Criteria:
The inclusion criteria will be both genders, age more than or equal to 50 years old, who completed the touchscreen questionnaire, 24-hour recall and physical measurements to enable the identification of malnutrition, frailty and sarcopenia.
Exclusion Criteria:
Any participant who is under 50 or with incomplete information will be excluded.
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Virtually all members of the general population are registered with a general practitioner through the NHS. Subsequently, recruitment was through NHS and all people aged 40-69 were eligible to participate. An invitation by mail was sent to those people who were living within reasonable travelling distance from assessment centres of UK Biobank. Additional information was collected prior sending an invitation; title, forename, gender, address, date of birth, name and address of General Practitioner and NHS number.
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| Name | Affiliation | Role |
|---|---|---|
| Sorrel Burden | University of Manchester | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Nada Almohaisen | Manchester | M1 5GB | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36472992 | Derived | AlMohaisen N, Gittins M, Todd C, Burden S. What is the overlap between malnutrition, frailty and sarcopenia in the older population? Study protocol for cross-sectional study using UK Biobank. PLoS One. 2022 Dec 6;17(12):e0278371. doi: 10.1371/journal.pone.0278371. eCollection 2022. |
| Label | URL |
|---|---|
| UK biobank, which is the database will be use in this study | View source |
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Estimating the frailty prevalence using two different measurement techniques the phenotype model with cumulative deficits model. In order to draw conclusion by comparing the results from each model. |
| cross sectional, 2007 |
| Hereditary Hemochromatosis Associations with Frailty, SarcopThis is the title of a study used UK Biobank for two conditions frailty and sarcopenia. We are aiming to have similar method in measuring the conditions | View source |
| This study used UK Biobank in measuring the frailty condition for different age groups. We aimed using similar method in measuring frailty | View source |
| This study point out the overlap and evidence gaps in the diagnosis and treatment of frailty and malnutrition. | View source |
| This is evidence that shows differences between two models in measuring frailty. As this study has one object in comparing the results from both models. | View source |
| In this study we used the definition of sarcopenia and criteria of diagnosed in measuring the prevalence. | View source |
| This study we used their definition of malnutrition and criteria of diagnosed. | View source |
| ID | Term |
|---|---|
| D044342 | Malnutrition |
| D000073496 | Frailty |
| D055948 | Sarcopenia |
| ID | Term |
|---|---|
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D009133 | Muscular Atrophy |
| D020879 | Neuromuscular Manifestations |
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
| D001284 | Atrophy |
| D020763 | Pathological Conditions, Anatomical |
| D012816 | Signs and Symptoms |
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