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Wang LX, Zhang X, Guan LJ, Pen Y. What role do extracellular vesicles play in developing physical frailty and sarcopenia? : A systematic review. Z Gerontol Geriatr 2023; 56:697-702. [PMID: 36580105 DOI: 10.1007/s00391-022-02150-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/23/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Frailty and sarcopenia are typical geriatric conditions with a complex pathophysiology. Extracellular vesicles (EVs) are key regulators of age-related diseases, but the mechanisms underlying physical frailty, sarcopenia, and EVs are not well understood. METHODS A systematic literature review was conducted to examine the evidence supporting an association between EVs and physical frailty and/or sarcopenia by searching the electronic databases, including the Cochrane Library, PubMed, and Embase, from January 2000 to January 2021. RESULTS A total of 216 cross-sectional studies were retrieved, and after the removal of 43 duplicate records, the title and abstract of 167 articles were screened, identifying 6 relevant articles for full-text review. Of the studies five met the inclusion criteria, and heterogeneity among studies was high. There is controversy regarding whether frailty and/or sarcopenia are related to circulating EV levels; however, the cargo of EVs has been associated with frailty and sarcopenia in various ways, such as microRNAs, mitochondrial-derived vesicles (MDVs), and protein cargoes. CONCLUSION Recent studies, although limited, depicted that EVs could be one of the underlying mechanisms of frailty and/or sarcopenia. There is a possibility that physical frailty and sarcopenia may have specific EV concentrations and cargo profiles; however, further research is required to fully understand the mechanisms and identify potential biomarkers and early preventative strategies for physical frailty and sarcopenia.
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Affiliation(s)
- Ling-Xiao Wang
- Geriatric Diseases Institute of Chengdu, Department of gerontology and geriatrics, Chengdu Fifth People's Hospital, 611137, Chengdu, China.
| | - Xia Zhang
- Geriatric Diseases Institute of Chengdu, Department of gerontology and geriatrics, Chengdu Fifth People's Hospital, 611137, Chengdu, China
| | - Li-Juan Guan
- Geriatric Diseases Institute of Chengdu, Department of gerontology and geriatrics, Chengdu Fifth People's Hospital, 611137, Chengdu, China
| | - Yang Pen
- Geriatric Diseases Institute of Chengdu, Department of gerontology and geriatrics, Chengdu Fifth People's Hospital, 611137, Chengdu, China
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Hoekstra C, Swart M, Bautmans I, Melis R, Peeters G. Association between Muscle Fatigability, Self-Perceived Fatigue and C-Reactive Protein at Admission in Hospitalized Geriatric Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6582. [PMID: 37623168 PMCID: PMC10454850 DOI: 10.3390/ijerph20166582] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/28/2023] [Accepted: 08/11/2023] [Indexed: 08/26/2023]
Abstract
Background: The capacity to perceived vitality (CPV) ratio is a novel measure for intrinsic capacity or resilience based on grip work and self-perceived fatigue. CPV has been associated with pre-frailty in older adults and post-surgery inflammation in adults. To better understand the utility of this measure in a frail population, we examined the association between CPV and inflammation in geriatric inpatients. Methods: Data were obtained from 104 hospitalized geriatric patients. The average age of participants was 83.3 ± 7.5 years, and 55.8% of participants were women. In the cross-sectional analyses, associations between C-reactive protein (CRP), grip work (GW), self-perceived fatigue (SPF) and the CPV ratio (higher values indicate better capacity) were examined using linear regression adjusted for confounders. Results: The adjusted association between CRP (abnormal vs. normal) and the CPV ratio was not statistically significant (B = -0.33, 95% CI = -4.00 to 3.34). Associations between CRP and GW (B = 25.53, 95% CI = -478.23 to 529.30) and SPF (B = 0.57, 95% CI = -0.64 to 1.77) were also not statistically significant. Similar results were found in unadjusted models and analyses of cases with complete data. Conclusions: In this frail group of geriatric inpatients, inflammation, routinely assessed with CRP, was not associated with CPV or its components, GW and SPF. Further research is needed to explore whether CPV is a useful indicator of frailty or recovery capacity in hospitalized geriatric patients.
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Affiliation(s)
- Carmen Hoekstra
- Department of Geriatric Medicine, Radboud University Medical Centre, Geert Grooteplein Zuid 10 (Route 696), Postbus 9101, 6500 HB Nijmegen, The Netherlands; (C.H.)
| | - Myrthe Swart
- Department of Geriatric Medicine, Radboud University Medical Centre, Geert Grooteplein Zuid 10 (Route 696), Postbus 9101, 6500 HB Nijmegen, The Netherlands; (C.H.)
| | - Ivan Bautmans
- Gerontology Department, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, 1090 Brussels, Belgium
- Frailty in Ageing Research (FRIA) Group, Vrije Universiteit Brussel, 1090 Brussels, Belgium
- Department of Geriatrics, Universitair Ziekenhuis Brussel, 1090 Brussels, Belgium
| | - René Melis
- Department of Geriatric Medicine, Radboud University Medical Centre, Geert Grooteplein Zuid 10 (Route 696), Postbus 9101, 6500 HB Nijmegen, The Netherlands; (C.H.)
| | - Geeske Peeters
- Department of Geriatric Medicine, Radboud University Medical Centre, Geert Grooteplein Zuid 10 (Route 696), Postbus 9101, 6500 HB Nijmegen, The Netherlands; (C.H.)
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Chew J, Lim JP, Yew S, Yeo A, Ismail NH, Ding YY, Lim WS. Disentangling the Relationship between Frailty and Intrinsic Capacity in Healthy Community-Dwelling Older Adults: A Cluster Analysis. J Nutr Health Aging 2021; 25:1112-1118. [PMID: 34725670 DOI: 10.1007/s12603-021-1679-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Frailty and intrinsic capacity (IC) are distinct but interrelated constructs. Uncertainty remains regarding how they are related and interact to influence health outcomes. We aim to understand the relationship between frailty and IC by identifying subgroups based on frailty criteria and IC domains and studying one-year outcomes. METHODS We studied 200 independent community-dwelling older adults (mean age 67.9±7.9 years, Modified Barthel Index (MBI) score 99±2.6). Frailty was defined by modified Fried criteria. Scores (range: 0-2) were assigned to individual IC domains (cognition, psychological, locomotion, and vitality) to yield a total IC score of 8. To identify subgroups, two-step cluster analysis was performed with age, frailty and IC domains. Cluster associations with one-year outcomes (frailty, muscle strength (grip strength, repeated chair stand test), physical performance (gait speed, Short Physical Performance Battery), function (MBI) and quality-of-life (EuroQol (EQ)-5D)) were examined using multiple linear regression adjusted for age, gender and education. RESULTS Three distinct clusters were identified - Cluster 1: High IC/Robust (N=74, 37%); Cluster 2: Intermediate IC/Prefrail (N=73, 36.5%); and Cluster 3: Low IC/Prefrail-Frail (53, 26.5%). Comparing between clusters, IC domains, cognition, depressive symptoms, nutrition, strength and physical performance were least impaired in Cluster 1, intermediate in Cluster 2 and most impaired in Cluster 3. At one year, the proportion transitioning to frailty or remaining frail was highest in Cluster 3 compared to Cluster 2 and Cluster 1 (39% vs 6.9% vs 2.8%, P<0.001). Compared to Cluster 1, Cluster 3 experienced greatest declines in grip strength (β=-4.1, P<.001), MBI (β=-1.24, P=0.045) and EQ-5D utility scores (β=-0.053, P=0.005), with Cluster 2 intermediate between Cluster 1 and Cluster 3. CONCLUSIONS Amongst independent community-dwelling older adults, IC is complementary to frailty measures through better risk-profiling of one-year outcomes amongst prefrail individuals into intermediate and high-risk groups. The intermediate group merits follow-up to ascertain longer-term prognosis.
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Affiliation(s)
- J Chew
- Justin Chew, Tan Tock Seng Hospital, Singapore,
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6
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Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, Aman Y, Frank J, Liao J, Zou H, Wang NY, Wu J, Liu X, Li T, Fang Y, Niu Z, Yang G, Hong J, Wang Q, Chen G, Li J, Chen HZ, Kang L, Su H, Gilmour BC, Zhu X, Jiang H, He N, Tao J, Leng SX, Tong T, Woo J. A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev 2020; 64:101174. [PMID: 32971255 PMCID: PMC7505078 DOI: 10.1016/j.arr.2020.101174] [Citation(s) in RCA: 252] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/13/2020] [Accepted: 09/03/2020] [Indexed: 12/18/2022]
Abstract
One of the key issues facing public healthcare is the global trend of an increasingly ageing society which continues to present policy makers and caregivers with formidable healthcare and socio-economic challenges. Ageing is the primary contributor to a broad spectrum of chronic disorders all associated with a lower quality of life in the elderly. In 2019, the Chinese population constituted 18 % of the world population, with 164.5 million Chinese citizens aged 65 and above (65+), and 26 million aged 80 or above (80+). China has become an ageing society, and as it continues to age it will continue to exacerbate the burden borne by current family and public healthcare systems. Major healthcare challenges involved with caring for the elderly in China include the management of chronic non-communicable diseases (CNCDs), physical frailty, neurodegenerative diseases, cardiovascular diseases, with emerging challenges such as providing sufficient dental care, combating the rising prevalence of sexually transmitted diseases among nursing home communities, providing support for increased incidences of immune diseases, and the growing necessity to provide palliative care for the elderly. At the governmental level, it is necessary to make long-term strategic plans to respond to the pressures of an ageing society, especially to establish a nationwide, affordable, annual health check system to facilitate early diagnosis and provide access to affordable treatments. China has begun work on several activities to address these issues including the recent completion of the of the Ten-year Health-Care Reform project, the implementation of the Healthy China 2030 Action Plan, and the opening of the National Clinical Research Center for Geriatric Disorders. There are also societal challenges, namely the shift from an extended family system in which the younger provide home care for their elderly family members, to the current trend in which young people are increasingly migrating towards major cities for work, increasing reliance on nursing homes to compensate, especially following the outcomes of the 'one child policy' and the 'empty-nest elderly' phenomenon. At the individual level, it is important to provide avenues for people to seek and improve their own knowledge of health and disease, to encourage them to seek medical check-ups to prevent/manage illness, and to find ways to promote modifiable health-related behaviors (social activity, exercise, healthy diets, reasonable diet supplements) to enable healthier, happier, longer, and more productive lives in the elderly. Finally, at the technological or treatment level, there is a focus on modern technologies to counteract the negative effects of ageing. Researchers are striving to produce drugs that can mimic the effects of 'exercising more, eating less', while other anti-ageing molecules from molecular gerontologists could help to improve 'healthspan' in the elderly. Machine learning, 'Big Data', and other novel technologies can also be used to monitor disease patterns at the population level and may be used to inform policy design in the future. Collectively, synergies across disciplines on policies, geriatric care, drug development, personal awareness, the use of big data, machine learning and personalized medicine will transform China into a country that enables the most for its elderly, maximizing and celebrating their longevity in the coming decades. This is the 2nd edition of the review paper (Fang EF et al., Ageing Re. Rev. 2015).
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Affiliation(s)
- Evandro F Fang
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Department of Hypertension and Vascular Disease, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China; Institute of Geriatric Immunology, School of Medicine, Jinan University, 510632, Guangzhou, China; Department of Geriatrics, The First Affiliated Hospital, Zhengzhou University, 450052, Zhengzhou, China.
| | - Chenglong Xie
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway; Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Joseph A Schenkel
- Durham University Department of Sports and Exercise Sciences, Durham, United Kingdom.
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, 215316, Kunshan, China; Duke Global Health Institute, Duke University, Durham, 27710, North Carolina, USA.
| | - Qian Long
- Global Health Research Center, Duke Kunshan University, 215316, Kunshan, China.
| | - Honghua Cui
- Department of Endodontics, Shanghai Stomatological Hospital, Fudan University, China; Oral Biomedical Engineering Laboratory, Shanghai Stomatological Hospital, Fudan University, China.
| | - Yahyah Aman
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway.
| | - Johannes Frank
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway.
| | - Jing Liao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, 510275, Guangzhou, China; Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, 510275, Guangzhou, China.
| | - Huachun Zou
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China; Kirby Institute, University of New South Wales, Sydney, Australia.
| | - Ninie Y Wang
- Pinetree Care Group, 515 Tower A, Guomen Plaza, Chaoyang District, 100028, Beijing, China.
| | - Jing Wu
- Department of Sociology and Work Science, University of Gothenburg, SE-405 30, Gothenburg, Sweden.
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
| | - Tao Li
- BGI-Shenzhen, Beishan Industrial Zone, 518083, Shenzhen, China; China National GeneBank, BGI-Shenzhen, 518120, Shenzhen, China.
| | - Yuan Fang
- Department of Public Health, Erasmus University Medical Centre, Rotterdam, the Netherlands.
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., 25 City Rd, Shoreditch, London EC1Y 1AA, UK.
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; and National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, United Kingdom.
| | | | - Qian Wang
- Department of Geriatrics, The First Affiliated Hospital, Zhengzhou University, 450052, Zhengzhou, China.
| | - Guobing Chen
- Institute of Geriatric Immunology, School of Medicine, Jinan University, 510632, Guangzhou, China.
| | - Jun Li
- Department of Biochemistry and Molecular Biology, The Institute of Basic Medical Sciences, The Chinese Academy of Medical Sciences (CAMS)& Peking Union Medical University (PUMC), 5 Dondan Santiao Road, Beijing, 100730, China.
| | - Hou-Zao Chen
- Department of Biochemistry and Molecular Biology, The Institute of Basic Medical Sciences, The Chinese Academy of Medical Sciences (CAMS)& Peking Union Medical University (PUMC), 5 Dondan Santiao Road, Beijing, 100730, China.
| | - Lin Kang
- Department of Geriatrics, Peking Union Medical College Hospital, Beijing, 100730, China.
| | - Huanxing Su
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao.
| | - Brian C Gilmour
- The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway.
| | - Xinqiang Zhu
- Department of Toxicology, Zhejiang University School of Public Health, Hangzhou, 310058, Zhejiang, China; The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China.
| | - Hong Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China; Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
| | - Na He
- School of Public Health, Fudan University, 200032, Shanghai, China; Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, 200032, Shanghai, China; Key Laboratory of Health Technology Assessment of Ministry of Health, Fudan University, 200032, Shanghai, China.
| | - Jun Tao
- Department of Hypertension and Vascular Disease, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China.
| | - Sean Xiao Leng
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, 5505 Hopkins Bayview Circle, Baltimore, MD 21224, USA.
| | - Tanjun Tong
- Research Center on Ageing, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Beijing, China.
| | - Jean Woo
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
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