Imperial College London
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ProfessorMarjo-RiittaJarvelin

Faculty of Medicine, School of Public Health

Chair in Lifecourse Epidemiology
 
 
 
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Contact

 

+44 (0)20 7594 3345m.jarvelin

 
 
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Location

 

156Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lowry:2018:10.1038/s41366-018-0175-1,
author = {Lowry, E and Rautio, N and Karhunen, V and Miettunen, J and Ala-Mursula, L and Auvinen, J and Keinänen-Kiukaanniemi, S and Puukka, K and Prokopenko, I and Herzig, K and Lewin, A and Sebert, S and Jarvelin, M},
doi = {10.1038/s41366-018-0175-1},
journal = {International Journal of Obesity},
pages = {1181--1192},
title = {Understanding the complexity of glycaemic health - Systematic bio-psychosocial modelling of fasting glucose in middle-age adults; a DynaHEALTH study},
url = {http://dx.doi.org/10.1038/s41366-018-0175-1},
volume = {43},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Abstract BackgroundThe prevention of the risk of type 2 diabetes (T2D) is complicated by multidimensional interplays between biological and psychosocial factors acting at the individual level. To address the challenge we took a systematic approach, to explore the bio-psychosocial predictors of blood glucose in mid-age.MethodsBased on the 31- and 46-year follow-ups (5,078 participants, 43% male) of Northern Finland Birth Cohort 1966, we used a systematic strategy to select bio-psychosocial variables at 31 years to enable a data-driven approach. As selection criteria, the variable must be i) a component of the metabolic syndrome or an indicator of psychosocial health using WHO guidelines, ii) easily obtainable in general health check-ups and iii) associated with fasting blood glucose at 46 years (p<0.10). Exploratory and confirmatory factor analysis were used to derive latent factors, and stepwise linear regression allowed exploration of relationships between factors and fasting glucose.ResultsOf all 26 variables originally considered, 19 met selection criteria and were included in an exploratory factor analysis. Two variables were further excluded due to low loading (<0.3). We derived four latent factors, which we named as socioeconomic, metabolic, psychosocial and blood pressure status. The combination of metabolic and psychosocial factors, adjusted for sex, provided best prediction of fasting glucose at 46 years (explaining 10.7% of variation in glucose; P<0.001). Regarding different bio-psychosocial pathways and relationships, the importance of psychosocial factors in addition to established metabolic risk factors was highlighted.ConclusionsThe present study supports evidence for the bio-psychosocial nature of adult glycemic health and exemplifies an evidence-based approach to model the bio-psychosocial relationships. The factorial model may help further research and public health practice in focusing also on psychosocial aspects in maintaining normoglyca
AU  - Lowry,E
AU  - Rautio,N
AU  - Karhunen,V
AU  - Miettunen,J
AU  - Ala-Mursula,L
AU  - Auvinen,J
AU  - Keinänen-Kiukaanniemi,S
AU  - Puukka,K
AU  - Prokopenko,I
AU  - Herzig,K
AU  - Lewin,A
AU  - Sebert,S
AU  - Jarvelin,M
DO  - 10.1038/s41366-018-0175-1
EP  - 1192
PY  - 2018///
SN  - 0307-0565
SP  - 1181
TI  - Understanding the complexity of glycaemic health - Systematic bio-psychosocial modelling of fasting glucose in middle-age adults; a DynaHEALTH study
T2  - International Journal of Obesity
UR  - http://dx.doi.org/10.1038/s41366-018-0175-1
UR  - https://www.nature.com/articles/s41366-018-0175-1
UR  - http://hdl.handle.net/10044/1/61808
VL  - 43
ER  -
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