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Ekholm J, Ohukainen P, Kangas AJ, Kettunen J, Wang Q, Karsikas M, Khan AA, Kingwell BA, Kähönen M, Lehtimäki T, Raitakari OT, Järvelin MR, Meikle PJ, Ala-Korpela M. EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics. Int J Epidemiol 2020; 49:1075-1081. [PMID: 31943015 PMCID: PMC7660139 DOI: 10.1093/ije/dyz244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 11/11/2019] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming. IMPLEMENTATION EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser. GENERAL FEATURES Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000). AVAILABILITY The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].
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Affiliation(s)
- Jussi Ekholm
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | | | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- THL: National Institute for Health and Welfare, Helsinki, Finland
| | - Qin Wang
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mari Karsikas
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Solita Ltd, Tampere, Finland
| | - Anmar A Khan
- Metabolomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Laboratory Medicine Department, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia
| | - Bronwyn A Kingwell
- Metabolic and Vascular Physiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Marjo-Riitta Järvelin
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Peter J Meikle
- Metabolomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Alfred Hospital, Monash University, Melbourne, VIC, Australia
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Ohukainen P, Kuusisto S, Kettunen J, Perola M, Järvelin MR, Mäkinen VP, Ala-Korpela M. Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease. Atherosclerosis 2019; 294:10-15. [PMID: 31931463 DOI: 10.1016/j.atherosclerosis.2019.12.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND AIMS Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts. METHODS We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles. RESULTS The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour. CONCLUSIONS These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment.
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Affiliation(s)
- Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Sanna Kuusisto
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; National Institute for Health and Welfare, Helsinki, Finland
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland; Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland; Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
| | - Ville-Petteri Mäkinen
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, SAHMRI, Australia
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
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Gandía-Aguiló V, Cibrián R, Soria E, Serrano AJ, Aguiló L, Paredes V, Gandía JL. Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment. Med Oral Patol Oral Cir Bucal 2017; 22:e233-e241. [PMID: 28160587 PMCID: PMC5359714 DOI: 10.4317/medoral.21509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 10/01/2016] [Indexed: 11/16/2022] Open
Abstract
Background Displaced maxillary permanent canine is one of the more frequent findings in canine eruption process and it’s easy to be outlined and early diagnosed by means of x-ray images. Late diagnosis frequently needs surgery to rescue the impacted permanent canine.
In many cases, interceptive treatment to redirect canine eruption is needed. However, some patients treated by interceptive means end up requiring fenestration to orthodontically guide the canine to its normal occlusal position.
It would be interesting, therefore, to discover the dental characteristics of patients who will need additional surgical treatment to interceptive treatment. Material and Methods To study the dental characteristics associated with canine impaction, conventional statistics have traditionally been used. This approach, although serving to illustrate many features of this problem, has not provided a satisfactory response or not provided an overall idea of the characteristics of these types of patients, each one of them with their own particular set of variables.
Faced with this situation, and in order to analyze the problem of impaction despite interceptive treatment, we have used an alternative method for representing the variables that have an influence on this syndrome. This method is known as Self-Organizing Maps (SOM), a method used for analyzing problems with multiple variables. Results We analyzed 78 patients with a PMC angulation higher than 100º. All of them were subject to interceptive treatment and in 21 cases it was necessary to undertake the above-mentioned fenestration to achieve the final eruption of the canine. Conclusions In this study, we describe the process of debugging variables and selecting the appropriate number of cells in SOM so as to adequately visualize the problem posed and the dental characteristics of patients with regard to a greater or lesser probability of the need for fenestration. Key words:Interceptive orthodontic treatment, altered eruption, impacted canines, neuronal networks, self-organizing maps.
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Affiliation(s)
- V Gandía-Aguiló
- Avenida Maria Cristina n 12- 2 , CP: 46001, Valencia, Spain,
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Paavola T, Kuusisto S, Jauhiainen M, Kakko S, Kangas-Kontio T, Metso J, Soininen P, Ala-Korpela M, Bloigu R, Hannuksela ML, Savolainen MJ, Salonurmi T. Impaired HDL2-mediated cholesterol efflux is associated with metabolic syndrome in families with early onset coronary heart disease and low HDL-cholesterol level. PLoS One 2017; 12:e0171993. [PMID: 28207870 PMCID: PMC5313225 DOI: 10.1371/journal.pone.0171993] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 01/30/2017] [Indexed: 12/18/2022] Open
Abstract
Objective The potential of high-density lipoproteins (HDL) to facilitate cholesterol removal from arterial foam cells is a key function of HDL. We studied whether cholesterol efflux to serum and HDL subfractions is impaired in subjects with early coronary heart disease (CHD) or metabolic syndrome (MetS) in families where a low HDL-cholesterol level (HDL-C) predisposes to early CHD. Methods HDL subfractions were isolated from plasma by sequential ultracentrifugation. THP-1 macrophages loaded with acetyl-LDL were used in the assay of cholesterol efflux to total HDL, HDL2, HDL3 or serum. Results While cholesterol efflux to serum, total HDL and HDL3 was unchanged, the efflux to HDL2 was 14% lower in subjects with MetS than in subjects without MetS (p<0.001). The efflux to HDL2 was associated with components of MetS such as plasma HDL-C (r = 0.76 in men and r = 0.56 in women, p<0.001 for both). The efflux to HDL2 was reduced in men with early CHD (p<0.01) only in conjunction with their low HDL-C. The phospholipid content of HDL2 particles was a major correlate with the efflux to HDL2 (r = 0.70, p<0.001). A low ratio of HDL2 to total HDL was associated with MetS (p<0.001). Conclusion Our results indicate that impaired efflux to HDL2 is a functional feature of the low HDL-C state and MetS in families where these risk factors predispose to early CHD. The efflux to HDL2 related to the phospholipid content of HDL2 particles but the phospholipid content did not account for the impaired efflux in cardiometabolic disease, where a combination of low level and poor quality of HDL2 was observed.
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Affiliation(s)
- Timo Paavola
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sanna Kuusisto
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Matti Jauhiainen
- Genomics and Biomarkers Unit, National Institute for Health and Welfare, Biomedicum, Helsinki, Finland
| | - Sakari Kakko
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Tiia Kangas-Kontio
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Jari Metso
- Genomics and Biomarkers Unit, National Institute for Health and Welfare, Biomedicum, Helsinki, Finland
| | - Pasi Soininen
- Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Oulu University Hospital, Oulu, Finland
- Computational Medicine, School of Social and Community Medicine & Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Risto Bloigu
- Medical Informatics and Statistics Research Group, University of Oulu, Oulu, Finland
| | - Minna L. Hannuksela
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
- Department of Clinical Chemistry, Institute of Diagnostics, University of Oulu, Oulu, Finland
| | - Markku J. Savolainen
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Tuire Salonurmi
- Department of Internal Medicine, Institute of Clinical Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland and Medical Research Center, Oulu University Hospital, Oulu, Finland
- * E-mail:
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Zou X, Holmes E, Nicholson JK, Loo RL. Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection. Anal Chem 2014; 86:5308-15. [PMID: 24773160 PMCID: PMC4110102 DOI: 10.1021/ac500161k] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Accepted: 04/28/2014] [Indexed: 12/24/2022]
Abstract
We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.
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Affiliation(s)
- Xin Zou
- Medway
School of Pharmacy, Universities of Kent
and Greenwich, Anson
Building, Central Avenue, Chatham, Kent ME4 4TB, U.K.
| | - Elaine Holmes
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
- MRC-HPA
Centre for Environment and Health, Imperial
College London, 150 Stamford
Street, London SE1 9NH, U.K.
| | - Jeremy K. Nicholson
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
- MRC-HPA
Centre for Environment and Health, Imperial
College London, 150 Stamford
Street, London SE1 9NH, U.K.
| | - Ruey Leng Loo
- Medway
School of Pharmacy, Universities of Kent
and Greenwich, Anson
Building, Central Avenue, Chatham, Kent ME4 4TB, U.K.
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
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Kuusisto SM, Peltola T, Laitinen M, Kumpula LS, Mäkinen VP, Salonurmi T, Hedberg P, Jauhiainen M, Savolainen MJ, Hannuksela ML, Ala-Korpela M. The interplay between lipoprotein phenotypes, adiponectin, and alcohol consumption. Ann Med 2012; 44:513-22. [PMID: 22077217 DOI: 10.3109/07853890.2011.611529] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
CONTEXT AND OBJECTIVE Lipoproteins are involved in the pathophysiology of several metabolic diseases. Here we focus on the interplay between lipoprotein metabolism and adiponectin with the extension of alcohol intake. DESIGN AND SUBJECTS Eighty-three low-to-moderate and 80 heavy alcohol drinkers were studied. Plasma adiponectin, other biochemical and extensive lipoprotein data were measured. Self-organizing maps were applied to characterize lipoprotein phenotypes and their interrelationships with biochemical measures and alcohol consumption. RESULTS Alcohol consumption and plasma adiponectin had a strong positive association. Heavy alcohol consumption was associated with decreased low-density lipoprotein cholesterol (LDL-C). Nevertheless, two distinct lipoprotein phenotypes were identified, one with elevated high-density lipoprotein cholesterol (HDL-C) and decreased very-low-density lipoprotein triglycerides (VLDL-TG) together with low prevalence of metabolic syndrome, and the other vice versa. The HDL particles were enlarged in both phenotypes related to the heavy drinkers. The low-to-moderate alcohol drinkers were characterized with high LDL-C and C-enriched LDL particles. CONCLUSIONS The analyses per se illustrated the multi-faceted and non-linear nature of lipoprotein metabolism. The heavy alcohol drinkers were characterized either by an anti-atherogenic lipoprotein phenotype (with also the highest adiponectin concentrations) or by a phenotype with pro-atherogenic and metabolic syndrome-like features. Clinically this underlines the need to distinguish the differing individual risk for lipid-related metabolic disturbances also in heavy alcohol drinkers.
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Affiliation(s)
- Sanna M Kuusisto
- Institute of Clinical Medicine, Department of Internal Medicine, Biocenter Oulu and Clinical Research Center, University of Oulu, Oulu, Finland
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Finni T, Sääkslahti A, Laukkanen A, Pesola A, Sipilä S. A family based tailored counselling to increase non-exercise physical activity in adults with a sedentary job and physical activity in their young children: design and methods of a year-long randomized controlled trial. BMC Public Health 2011; 11:944. [PMID: 22185647 PMCID: PMC3271995 DOI: 10.1186/1471-2458-11-944] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2011] [Accepted: 12/20/2011] [Indexed: 12/02/2022] Open
Abstract
Background Epidemiological evidence suggests that decrease in sedentary behaviour is beneficial for health. This family based randomized controlled trial examines whether face-to-face delivered counselling is effective in reducing sedentary time and improving health in adults and increasing moderate-to-vigorous activities in children. Methods The families are randomized after balancing socioeconomic and environmental factors in the Jyväskylä region, Finland. Inclusion criteria are: healthy men and women with children 3-8 years old, and having an occupation where they self-reportedly sit more than 50% of their work time and children in all-day day-care in kindergarten or in the first grade in primary school. Exclusion criteria are: body mass index > 35 kg/m2, self-reported chronic, long-term diseases, families with pregnant mother at baseline and children with disorders delaying motor development. From both adults and children accelerometer data is collected five times a year in one week periods. In addition, fasting blood samples for whole blood count and serum metabonomics, and diurnal heart rate variability for 3 days are assessed at baseline, 3, 6, 9, and 12 months follow-up from adults. Quadriceps and hamstring muscle activities providing detailed information on muscle inactivity will be used to realize the maximum potential effect of the intervention. Fundamental motor skills from children and body composition from adults will be measured at baseline, and at 6 and 12 months follow-up. Questionnaires of family-influence-model, health and physical activity, and dietary records are assessed. After the baseline measurements the intervention group will receive tailored counselling targeted to decrease sitting time by focusing on commute and work time. The counselling regarding leisure time is especially targeted to encourage toward family physical activities such as visiting playgrounds and non-built environments, where children can get diversified stimulation for play and practice fundamental of motor skills. The counselling will be reinforced during the first 6 months followed by a 6-month maintenance period. Discussion If shown to be effective, this unique family based intervention to improve lifestyle behaviours in both adults and children can provide translational model for community use. This study can also provide knowledge whether the lifestyle changes are transformed into relevant biomarkers and self-reported health. Trial registration number ISRCTN: ISRCTN28668090
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Affiliation(s)
- Taija Finni
- Neuromuscular Research Center, Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland.
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