In de American Academy of Neurology is een toelichting gegeven op een 5-jarige studie onder 412.691 volwassenen met een gemiddelde leeftijd van 56 jaar. Uit de analyse kwam naar voren dat minder buikvet en meer spiermassa het risico vermindert op het ontwikkelen van zogeheten neurodegeneratieve ziekten zoals Alzheimer - dementie en ziekte van Parkinson.
Aan het onderzoek namen 412.691 mensen deel met een gemiddelde leeftijd van 56 jaar. Ze werden gemiddeld negen jaar gevolgd. Aan het begin van het onderzoek werden metingen gedaan voor de lichaamssamenstelling, zoals taille- en heupomvang, grijpkracht, botdichtheid en vet- en vetvrije massa. Tijdens het onderzoek ontwikkelden 8.224 van hen neurodegeneratieve ziekten, voornamelijk Alzheimer - dementie, andere vormen van dementie en de ziekte van Parkinson.
De belangrijkste resultaten:
- Mannen met veel lichaamsvet in hun buik ontwikkelden de neurodegeneratieve ziekten met een frequentie van 3,38 per 1.000 persoonsjaren, vergeleken met 1,82 gevallen per 1.000 persoonsjaren voor degenen met weinig lichaamsvet in die regio. Dus een behoorlijk groot verschil.
- Vrouwen hadden een frequentie van 2,55 gevallen per 1.000 persoonsjaren voor degenen met meer lichaamsvet in de buik en 1,39 voor minder. En ook hier was het verschil procentueel ongeveer hetzelfde als bij mannen.
- Na aanpassing voor andere factoren die het ziektecijfer zouden kunnen beïnvloeden, zoals hoge bloeddruk, rook- en drinkgedrag en diabetes, ontdekten de onderzoekers dat mensen met veel buikvet 13 procent meer risico liepen om deze ziekten te ontwikkelen dan mensen met weinig buikvet.
- Mensen met veel armvet liepen 18 procent meer risico om deze ziekten te ontwikkelen dan mensen met weinig armvet.
- En mensen met veel spierkracht liepen 26 procent minder risico om de ziekten te ontwikkelen dan mensen met weinig spierkracht.
Hier grafisch weergegeven wat de verschillen waren:
Exposure levels were categorized into low, moderate (md), and high groups based on sex-specific tertile distribution (low: <first tertile; moderate: first-second tertile; and high: >second tertile). HRs are indicated by solid lines, and 95% CIs are indicated by shaded areas. The vertical dashed line represents the sex-specific tertile level of each pattern of body composition. Restricted cubic splines were constructed with 4 knots located at the 5th, 35th, 65th, and 95th percentiles of each pattern. We tested the associations between identified patterns of body composition and risk of incident neurodegenerative diseases by treating exposures as continuous variables, using Cox models with attained age as the timescale after adjusting for annual household income; Townsend deprivation index; educational level; smoking and drinking status; physical activity; fruit and vegetable intake; history of hypertension, diabetes, or dyslipidemia; baseline cognition function; family history of neurodegenerative diseases; height; and weight (results denoted as “P overall”). We tested the potential nonlinearity by using the likelihood-ratio test comparing models with only a linear term against models with linear and cubic terms (results denoted as “P nonlinearity”). HR = hazard ratio.
EOS wetenschap heeft de originele tekst van het persbericht,
DOES YOUR BODY COMPOSITION AFFECT YOUR RISK OF DEMENTIA OR PARKINSON’S?
vertaald in het Nederlands en ik voeg deze tekst gedeeltelijk hier ongewijzigd toe.Onder de Nederlandse vertaling staat het abstract met een link naar het volledige studierapport dat in het Engels is.
Minder dementie door meer spieren en minder vet
Kan je je risico op het ontwikkelen van neurodegeneratieve ziekten zoals alzheimer en parkinson verminderen door je lichaamssamenstelling te verbeteren?
Mensen met veel lichaamsvet in hun buik en armen lopen mogelijk meer risico om ziekten zoals alzheimer en parkinson te ontwikkelen dan mensen met weinig vet in deze gebieden. En mensen met veel spierkracht lopen minder risico op deze aandoeningen dan mensen met weinig spierkracht. Dat blijkt uit een onderzoek van Huan Song van de Sichuan Universiteit in Chengdu in China dat op 24 juli is gepubliceerd in het online nummer van Neurology, het medische tijdschrift van de American Academy of Neurology.
Neurodegeneratieve ziekten zoals alzheimer en parkinson treffen wereldwijd meer dan 60 miljoen mensen en verwacht wordt dat dat aantal nog zal toenemen naarmate de bevolking ouder wordt. Het is volgens Song dan ook van cruciaal belang dat we manieren vinden om die risicofactoren aan te pakken.
Zijn onderzoek laat zien dat het mogelijk is om je risico op het ontwikkelen van deze ziekten te verminderen door je lichaamssamenstelling te verbeteren. Gerichte interventies om romp- en armvet te verminderen en tegelijkertijd een gezonde spierontwikkeling te bevorderen, zijn volgens hem mogelijk effectiever voor de bescherming tegen deze ziekten dan een algemene gewichtscontrole.>>>>>>>>lees verder
Het originele studierapport is gratis in te zien. Hier het abstract van de studie:
Association Between Body Composition Patterns, Cardiovascular Disease, and Risk of Neurodegenerative Disease in the UK Biobank
Shishi Xu, MD, PhD, Shu Wen, MD, PhD, Yao Yang, MSc, Junhui He, MSc, Huazhen Yang, MSc, Yuanyuan Qu, MSc, Yu Zeng, MSc, Jianwei Zhu, MD, PhD https://orcid.org/0000-0002-6236-6628, Fang Fang, PhD https://orcid.org/0000-0002-3310-6456, and Huan Song, MD, PhD https://orcid.org/0000-0003-3845-8079AUTHORS INFO & AFFILIATIONS
Abstract
Background and Objectives
Accumulating evidence connects diverse components of body composition (e.g., fat, muscle, and bone) to neurodegenerative disease risk, yet their interplay remains underexplored. This study examines the associations between patterns of body composition and the risk of neurodegenerative diseases, exploring the mediating role of cardiovascular diseases (CVDs).
Methods
This retrospective analysis used data from the UK Biobank, a prospective community-based cohort study. We included participants free of neurodegenerative diseases and with requisite body composition measurements at recruitment, who were followed from 5 years after recruitment until April 1, 2023, to identify incident neurodegenerative diseases. We assessed the associations between different components and major patterns of body composition (identified by principal component analysis) with the risk of neurodegenerative diseases, using multivariable Cox models. Analyses were stratified by disease susceptibility, indexed by polygenetic risk scores for Alzheimer and Parkinson diseases, APOE genotype, and family history of neurodegenerative diseases. Furthermore, we performed mediation analysis to estimate the contribution of CVDs to these associations. In addition, in a subcohort of 40,790 participants, we examined the relationship between body composition patterns and brain aging biomarkers (i.e., brain atrophy and cerebral small vessel disease).
Results
Among 412,691 participants (mean age 56.0 years, 55.1% female), 8,224 new cases of neurodegenerative diseases were identified over an average follow-up of 9.1 years. Patterns identified as “fat-to-lean mass,” “muscle strength,” “bone density,” and “leg-dominant fat distribution” were associated with a lower rate of neurodegenerative diseases (hazard ratio = 0.74–0.94) while “central obesity” and “arm-dominant fat distribution” patterns were associated with a higher rate (HR = 1.13–1.18). Stratification analysis yielded comparable risk estimates across different susceptibility groups. Notably, 10.7%–35.3% of the observed associations were mediated by CVDs, particularly cerebrovascular diseases. The subcohort analysis of brain aging biomarkers corroborated the findings for “central obesity,” “muscle strength,” and “arm-dominant fat distribution” patterns.
Discussion
Our analyses demonstrated robust associations of body composition patterns featured by “central obesity,” “muscle strength,” and “arm-dominant fat distribution” with both neurodegenerative diseases and brain aging, which were partially mediated by CVDs. These findings underscore the potential of improving body composition and early CVD management in mitigating risk of neurodegenerative diseases.
OPEN IN VIEWER
Acknowledgment
This work uses data provided by patients and collected by the NHS as part of their care and support. In addition, the authors thank the team members involved in West China Biomedical Big Data Center for their support.
Appendix Authors
| Name | Location | Contribution |
|---|---|---|
| Shishi Xu, MD, PhD | West China Hospital of Sichuan University, Chengdu, China | Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data |
| Shu Wen, MD, PhD | West China Hospital of Sichuan University, Chengdu, China | Analysis or interpretation of data |
| Yao Yang, MSc | West China Hospital of Sichuan University, Chengdu, China | Analysis or interpretation of data |
| Junhui He, MSc | West China Hospital of Sichuan University, Chengdu, China | Analysis or interpretation of data |
| Huazhen Yang, MSc | West China Hospital of Sichuan University, Chengdu, China | Major role in the acquisition of data |
| Yuanyuan Qu, MSc | West China Hospital of Sichuan University, Chengdu, China | Major role in the acquisition of data |
| Yu Zeng, MSc | West China Hospital of Sichuan University, Chengdu, China | Major role in the acquisition of data |
| Jianwei Zhu, MD, PhD | West China Hospital of Sichuan University, Chengdu, China | Analysis or interpretation of data |
| Fang Fang, PhD | Karolinska Institutet, Solna, Sweden | Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data |
| Huan Song, MD, PhD | West China Hospital of Sichuan University, Chengdu, China | Drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data |
Supplementary Material
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References
1.
Global Status Report on the Public Health Response to Dementia - Exec Summary. World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.
2.
Parkinson Disease: A Public Health Approach. Technical Brief. World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO.
3.
Livingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413-446.
4.
Kivimäki M, Luukkonen R, Batty GD, et al. Body mass index and risk of dementia: analysis of individual-level data from 1.3 million individuals. Alzheimers Dement. 2018;14(5):601-609.
5.
Kivimäki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol. 2022;10(4):253-263.
6.
Duchowny KA, Ackley SF, Brenowitz WD, et al. Associations between handgrip strength and dementia risk, cognition, and neuroimaging outcomes in the UK biobank cohort study. JAMA Netw Open. 2022;5(6):e2218314.
7.
Beeri MS, Leugrans SE, Delbono O, Bennett DA, Buchman AS. Sarcopenia is associated with incident Alzheimer's dementia, mild cognitive impairment, and cognitive decline. J Am Geriatr Soc. 2021;69(7):1826-1835.
8.
Portugal B, Artaud F, Domenighetti C, et al. Body mass index, abdominal adiposity, and incidence of Parkinson disease in French women from the E3N cohort study. Neurology. 2023;100(3):e324–e335.
9.
Mina T, Yew YW, Ng HK, et al. Adiposity impacts cognitive function in Asian populations: an epidemiological and mendelian randomization study. Lancet Reg Health West Pac. 2023;33:100710.
10.
Andrews JS, Gold LS, Reed MJ, et al. Appendicular lean mass, grip strength, and the incidence of dementia among older adults in the health ABC study. J Gerontol A Biol Sci Med Sci. 2023;78(11):2070-2076.
11.
Cui C, Mackey RH, Shaaban CE, Kuller LH, Lopez OL, Sekikawa A. Associations of body composition with incident dementia in older adults: cardiovascular Health Study-Cognition Study. Alzheimers Dement. 2020;16(10):1402-1411.
12.
Xiao T, Ghatan S, Mooldijk SS, et al. Association of bone mineral density and dementia: the Rotterdam study. Neurology. 2023;100(20):e2125-e2133.
13.
Zamboni M, Mazzali G, Brunelli A, et al. The role of crosstalk between adipose cells and myocytes in the pathogenesis of sarcopenic obesity in the elderly. Cells. 2022;11(21):3361.
14.
Kirk B, Feehan J, Lombardi G, Duque G. Muscle, bone, and fat crosstalk: the biological role of myokines, osteokines, and adipokines. Curr Osteoporos Rep. 2020;18(4):388-400.
15.
Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203-209.
16.
Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.
17.
Thygesen SK, Christiansen CF, Christensen S, Lash TL, Sørensen HT. The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of Patients. BMC Med Res Methodol. 2011;11(1):83.
18.
UK Biobank Algorithmically-Defined Outcomes. Accessed July 1, 2024. biobank.ctsu.ox.ac.uk/crystal/ukb/docs/alg_outcome_main.pdf
19.
Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523-1536.
20.
Harshfield EL, Markus HS. Association of baseline metabolomic profiles with incident stroke and dementia and with imaging markers of cerebral small vessel disease. Neurology. 2023;101(5):e489-e501.
21.
Quan M, Cao S, Wang Q, Wang S, Jia J. Genetic phenotypes of Alzheimer's disease: mechanisms and potential therapy. Phenomics. 2023;3(4):333-349.
22.
Thompson DJ, Wells D, Selzam S, et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits. medRxiv. Preprint posted online June 16, 2022.
23.
Shi B, Choirat C, Coull BA, VanderWeele TJ, Valeri L. CMAverse: a suite of functions for reproducible causal mediation analyses. Epidemiology. 2021;32(5):e20-e22.
24.
Park KY, Nam GE, Han K, Park HK, Hwang HS. Waist circumference and risk of Parkinson's disease. NPJ Parkinsons Dis. 2022;8(1):89.
25.
Tang X, Zhao W, Lu M, et al. Relationship between central obesity and the incidence of cognitive impairment and dementia from cohort studies involving 5,060,687 participants. Neurosci Biobehav Rev. 2021;130:301-313.
26.
Wang W, Luo Y, Zhuang Z, et al. Total and regional fat-to-muscle mass ratio and risks of incident all-cause dementia, Alzheimer's disease, and vascular dementia. J Cachexia, Sarcopenia Muscle. 2022;13(5):2447-2455.
27.
Mooldijk SS, de Crom TOE, Ikram MK, Ikram MA, Voortman T. Adiposity in the older population and the risk of dementia: the Rotterdam Study. Alzheimers Dement. 2023;19(5):2047-2055.
28.
Alser M, Elrayess MA. From an apple to a pear: moving fat around for reversing insulin resistance. Int J Environ Res Public Health. 2022;19(21):14251.
29.
Custodia A, Ouro A, Romaus-Sanjurjo D, et al. Endothelial progenitor cells and vascular alterations in Alzheimer's disease. Front Aging Neurosci. 2022;13:811210.
30.
Fang YC, Hsieh YC, Hu CJ, Tu YK. Endothelial dysfunction in neurodegenerative diseases. Int J Mol Sci. 2023;24(3):2909.
31.
Favaretto F, Bettini S, Busetto L, Milan G, Vettor R. Adipogenic progenitors in different organs: pathophysiological implications. Rev Endocr Metab Disord. 2022;23(1):71-85.
32.
Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Cardiovasc Med. 2020;7:22.
33.
Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM. Evaluation of the novel Tanita body-fat analyser to measure body composition by comparison with a four-compartment model. Br J Nutr. 2000;83(2):115-122.
34.
Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of Sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026-1034.
35.
Lear SA, Humphries KH, Kohli S, Chockalingam A, Frohlich JJ, Birmingham CL. Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). Am J Clin Nutr. 2007;86(2):353-359.
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