Katharina Wirth1, Jochen Klenk2, Simone Brefka3, Dhayana Dallmeier3, Kathrin Faehling4, Marta Roqué I Figuls5, Mark A Tully6, Maria Giné-Garriga7, Paolo Caserotti8, Antoni Salvà5, Dietrich Rothenbacher9, Michael Denkinger4, Brendon Stubbs10. 1. Agaplesion Bethesda Hospital, Geriatric Medicine Ulm University, Ulm, Germany; Geriatric Center Ulm/Alb-Donau, Ulm, Germany; Department of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany. Electronic address: katharina.wirth@bethesda-ulm.de. 2. Department of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany; Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany. 3. Agaplesion Bethesda Hospital, Geriatric Medicine Ulm University, Ulm, Germany; Geriatric Center Ulm/Alb-Donau, Ulm, Germany. 4. Agaplesion Bethesda Hospital, Geriatric Medicine Ulm University, Ulm, Germany; Geriatric Center Ulm/Alb-Donau, Ulm, Germany; Department of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany. 5. Fundació Salut i Envelliment - Universitat Autònoma de Barcelona, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Barcelona, Spain. 6. UKCRC Centre of Excellence for Public Health (NI), Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, United Kingdom. 7. Faculty of Psychology, Education and Sport Sciences Blanquerna, Ramon Llull University, Barcelona, Spain. 8. Department of Sports Science and Clinical Biomechanics, SDU Muscle Research Cluster (SMRC), University of Southern Denmark, Odense, Denmark; National Institutes of Health, National Institute on Aging, Laboratory of Epidemiology and Population Sciences (LPES), Bethesda, MD, USA. 9. Department of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany. 10. Physiotherapy Department, South London and Maudsley NHS Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, Box SE5 8AF, United Kingdom; Faculty of Health, Social Care and Education, Anglia Ruskin University, Chelmsford, United Kingdom.
Abstract
OBJECTIVE: Pathomechanisms of sedentary behaviour (SB) are unclear. We conducted a systematic review to investigate the associations between SB and various biomarkers in older adults. METHODS: Electronic databases were searched (MEDLINE, EMBASE, CINAHL, AMED) up to July 2015 to identify studies with objective or subjective measures of SB, sample size ≥50, mean age ≥60years and accelerometer wear time ≥3days. Methodological quality was appraised with the CASP tool. The protocol was pre-specified (PROSPERO CRD42015023731). RESULTS: 12701 abstracts were retrieved, 275 full text articles further explored, from which 249 were excluded. In the final sample (26 articles) a total of 63 biomarkers were detected. Most investigated markers were: body mass index (BMI, n=15), waist circumference (WC, n=15), blood pressure (n=11), triglycerides (n=12) and high density lipoprotein (HDL, n=15). Some inflammation markers were identified such as interleukin-6, C-reactive protein or tumor necrosis factor alpha. There was a lack of renal, muscle or bone biomarkers. Randomized controlled trials found a positive correlation for SB with BMI, neck circumference, fat mass, HbA1C, cholesterol and insulin levels, cohort studies additionally for WC, leptin, C-peptide, ApoA1 and Low density lipoprotein and a negative correlation for HDL. CONCLUSION: Most studied biomarkers associated with SB were of cardiovascular or metabolic origin. There is a suggestion of a negative impact of SB on biomarkers but still a paucity of high quality investigations exist. Longitudinal studies with objectively measured SB are needed to further elucidate the pathophysiological pathways and possible associations of unexplored biomarkers.
OBJECTIVE: Pathomechanisms of sedentary behaviour (SB) are unclear. We conducted a systematic review to investigate the associations between SB and various biomarkers in older adults. METHODS: Electronic databases were searched (MEDLINE, EMBASE, CINAHL, AMED) up to July 2015 to identify studies with objective or subjective measures of SB, sample size ≥50, mean age ≥60years and accelerometer wear time ≥3days. Methodological quality was appraised with the CASP tool. The protocol was pre-specified (PROSPERO CRD42015023731). RESULTS: 12701 abstracts were retrieved, 275 full text articles further explored, from which 249 were excluded. In the final sample (26 articles) a total of 63 biomarkers were detected. Most investigated markers were: body mass index (BMI, n=15), waist circumference (WC, n=15), blood pressure (n=11), triglycerides (n=12) and high density lipoprotein (HDL, n=15). Some inflammation markers were identified such as interleukin-6, C-reactive protein or tumornecrosis factor alpha. There was a lack of renal, muscle or bone biomarkers. Randomized controlled trials found a positive correlation for SB with BMI, neck circumference, fat mass, HbA1C, cholesterol and insulin levels, cohort studies additionally for WC, leptin, C-peptide, ApoA1 and Low density lipoprotein and a negative correlation for HDL. CONCLUSION: Most studied biomarkers associated with SB were of cardiovascular or metabolic origin. There is a suggestion of a negative impact of SB on biomarkers but still a paucity of high quality investigations exist. Longitudinal studies with objectively measured SB are needed to further elucidate the pathophysiological pathways and possible associations of unexplored biomarkers.
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