Understanding the complexity of glycaemic health: systematic bio-psychosocial modelling of fasting glucose in middle-age adults; a DynaHEALTH study

Abstract

Background The 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. Methods Based on the 31-year 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. Results Of all 26 variables originally considered, 19 met the 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. Conclusions The 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 normoglycaemia in the prevention of cardiometabolic diseases.

Early exposure to social disadvantages and later life body mass index beyond genetic predisposition in three generations of Finnish birth cohorts

“…In order to create a composite measure of social disadvantage during early life and adulthood, we used a systematic approach for variable selection as described previously in [19]. Following an inventory of available variables in each dataset, we selected all those, which were an indicator of social disadvantage (a lack of social and economic resources) based on previous literature and a priori knowledge.…”

Cohort Profile: The DynaHEALTH consortium – a European consortium for a life-course bio-psychosocial model of healthy ageing of glucose homeostasis

“…Essentially, we must operate data to move beyond association studies and explore how the psychosocial factors, usually classified as confounders in medicine (Figure 2), can be analysed to help their operationalization and integration into healthcare programmes. Despite no apparent consensus in the literature on a single definition of psychosocial health, DynaHEALTH is referring to the following WHO definition 3 as a guiding principle: ‘a state of wellbeing in which every individual realises their own potential, can cope with the normal stresses of everyday life, can work productively and fruitfully, and is able to make a contribution to their community’ 4 , 5 …”

Gender in Endocrine Diseases: Role of Sex Gonadal Hormones

“…As a result of the use of thiazolidinediones, the risk of bone fractures in postmenopausal women increases [ 15 ]. Finally, recent studies have observed a marked influence of socioeconomic and psychosocial aspects on glycemic health control and a significant association between sociodemographic profile and absolute control of T1DM risk factors [ 59 , 60 ]. Furthermore, T2DM women and men with lower income and education level show poor food choice revealing higher carbohydrate and lower fat intake.…”