Periodic Reporting for period 3 - DYNAHEALTH (Understanding the dynamic determinants of glucose homeostasis and social capability to promote Healthy and active aging)
Reporting period: 2018-04-01 to 2019-03-31
The DynaHEALTH Action was set to address the challenge of healthy and active ageing through reducing the risks of obesity and type 2 diabetes (T2D), acknowledging the following statistics:
• Life expectancy at birth for males is expected to increase by 7.8 years over the projection period, from 78.3 in 2016 to 86.1 in 2070. For females, life expectancy at birth is projected to increase by 6.6 years, from 83.7 in 2013 to 90.3 in 2070, implying a convergence of life expectancy between males and females.
• According to estimates from the WHO’s Childhood Obesity Surveillance Initiative (COSI), around 1 in 3 children in the EU aged 6–9 years old were overweight or obese in 2010. This is a worrying increase on 2008, when estimates were 1 in 4.
• Obesity is estimated to cost Europe €70 billion annually in healthcare costs and lost productivity.
• The OECD estimates that average healthcare expenditure for a person with obesity can be 25% higher than for someone of normal weight.
• According to the International Diabetes Federation, in the Europe region 66 million adults are estimated to have diabetes and this figure is predicted to rise to 81 million by 2045. For 2017, this equates to 1 in 11 adults with diabetes. There were estimated to be approximately 690,000 deaths due to diabetes in 2017.
• 1 in 6 births is affected by hyperglycaemia in pregnancy.
• Over one third of diabetes cases have not been diagnosed and are at a higher risk of developing harmful and costly complications.
Scientific evidence from observational epidemiology supports a syndemic hypothesis i.e. synergy of epidemics acting on each other. It is further proposed that biological determinants and social factors may interplay with one-another through the longitudinal patterns finding their origins in early life. From a societal point of view this may translate into the aggravation of the social divide and the reduction of health equality.
To this end, DynaHEALTH has tested the concept of a Gluco-Psychosocial Axis (GPA), to account simultaneously for the biological components of health and the psychosocial capacity to impact actively upon them. In order to establish a GPA measure, the project studied the cardiometabolic and ageing patterns related to social adversity, family demographics, gestational weight gain and obesity, and diseases such as gestational diabetes mellitus.
• Life expectancy at birth for males is expected to increase by 7.8 years over the projection period, from 78.3 in 2016 to 86.1 in 2070. For females, life expectancy at birth is projected to increase by 6.6 years, from 83.7 in 2013 to 90.3 in 2070, implying a convergence of life expectancy between males and females.
• According to estimates from the WHO’s Childhood Obesity Surveillance Initiative (COSI), around 1 in 3 children in the EU aged 6–9 years old were overweight or obese in 2010. This is a worrying increase on 2008, when estimates were 1 in 4.
• Obesity is estimated to cost Europe €70 billion annually in healthcare costs and lost productivity.
• The OECD estimates that average healthcare expenditure for a person with obesity can be 25% higher than for someone of normal weight.
• According to the International Diabetes Federation, in the Europe region 66 million adults are estimated to have diabetes and this figure is predicted to rise to 81 million by 2045. For 2017, this equates to 1 in 11 adults with diabetes. There were estimated to be approximately 690,000 deaths due to diabetes in 2017.
• 1 in 6 births is affected by hyperglycaemia in pregnancy.
• Over one third of diabetes cases have not been diagnosed and are at a higher risk of developing harmful and costly complications.
Scientific evidence from observational epidemiology supports a syndemic hypothesis i.e. synergy of epidemics acting on each other. It is further proposed that biological determinants and social factors may interplay with one-another through the longitudinal patterns finding their origins in early life. From a societal point of view this may translate into the aggravation of the social divide and the reduction of health equality.
To this end, DynaHEALTH has tested the concept of a Gluco-Psychosocial Axis (GPA), to account simultaneously for the biological components of health and the psychosocial capacity to impact actively upon them. In order to establish a GPA measure, the project studied the cardiometabolic and ageing patterns related to social adversity, family demographics, gestational weight gain and obesity, and diseases such as gestational diabetes mellitus.
During the four-year project, the DynaHEALTH project participants have published over 230 peer-reviewed papers accessible via open-access to understand the link between unhealthy ageing, T2D and related conditions and individual inequalities in terms of psychological and social well-being (Cohort profile in International Journal of Epidemiology 2019; doi: 10.1093/ije/dyz056). One key aspect of DynaHEALTH was to develop a life-course approach linking data from longitudinal birth cohorts and randomised controlled trials in early life to help identify the ways to intervene, the health and cost benefit of intervention strategies as well as the optimal period(s) to intervene.
As also supported by the review by the DynaHEALTH external scientific and ethics advisory board, it is important to understand the pathways through which the associations between an exposure during the life-course and the risk for T2D have developed and what were the causative mechanisms.
Different life-course models testing the paradigm of the Gluco-Psychosocial Axis (GPA) have helped identify the role of child BMI at school entry (varying from age 4 to 7 in European countries) on the long-term disease risk.
Importantly we support the consistent observation that maternal obesity during pregnancy was robustly, and with a large effect size, associated with the BMI of her child. However, we reported consistently by triangulating RCTs, epigenetic wide-association studies, observations and Mendelian randomisation techniques that the association was not likely to be caused by foetal programming (i.e. epigenetic changes induced through the mother to the foetus during the foetal period) but is more likely explained by a shared genetic component (7 to 10% of the risk) and environmental factors. This suggests that clinical intervention during pregnancy, aiming to target foetal programming, is less likely to have long term effect, as is observed from our results.
On the other hand, we observed that the determinants of obesity (a key causal factor to T2D and the ageing process), including the genetic factors, are very stable from the age at adiposity rebound (5–7 years) onwards. We also found the age at adiposity rebound, a period when we observed that the child BMI starts increasing again after a phase of natural loss of adiposity (fig. child BMI development), to be associated with multiple age-related factors including the risk of T2D.
From an economic point of view, we simulated the effects that suggested a 6-month younger age at adiposity rebound could result in a 2 to 3% higher BMI as an adult with subsequent reduction of the QALY (quality-adjusted life year) accounting for a total a €46 billion over 35 years in Europe.
Importantly we found the risk of T2D to be preventable and reversible if the child obesity can be intervened. This is nicely demonstrated in a large Danish data as a part of DynaHEALTH project.
As also supported by the review by the DynaHEALTH external scientific and ethics advisory board, it is important to understand the pathways through which the associations between an exposure during the life-course and the risk for T2D have developed and what were the causative mechanisms.
Different life-course models testing the paradigm of the Gluco-Psychosocial Axis (GPA) have helped identify the role of child BMI at school entry (varying from age 4 to 7 in European countries) on the long-term disease risk.
Importantly we support the consistent observation that maternal obesity during pregnancy was robustly, and with a large effect size, associated with the BMI of her child. However, we reported consistently by triangulating RCTs, epigenetic wide-association studies, observations and Mendelian randomisation techniques that the association was not likely to be caused by foetal programming (i.e. epigenetic changes induced through the mother to the foetus during the foetal period) but is more likely explained by a shared genetic component (7 to 10% of the risk) and environmental factors. This suggests that clinical intervention during pregnancy, aiming to target foetal programming, is less likely to have long term effect, as is observed from our results.
On the other hand, we observed that the determinants of obesity (a key causal factor to T2D and the ageing process), including the genetic factors, are very stable from the age at adiposity rebound (5–7 years) onwards. We also found the age at adiposity rebound, a period when we observed that the child BMI starts increasing again after a phase of natural loss of adiposity (fig. child BMI development), to be associated with multiple age-related factors including the risk of T2D.
From an economic point of view, we simulated the effects that suggested a 6-month younger age at adiposity rebound could result in a 2 to 3% higher BMI as an adult with subsequent reduction of the QALY (quality-adjusted life year) accounting for a total a €46 billion over 35 years in Europe.
Importantly we found the risk of T2D to be preventable and reversible if the child obesity can be intervened. This is nicely demonstrated in a large Danish data as a part of DynaHEALTH project.
The role of critical periods in a child’s development during pregnancy and infancy are well studied, however, the project identified that there does seem to be a potential ‘missed’ opportunity for interventions between 18 months and 5 years of age. It is during this pre-school age range that children across Europe, are most likely to fall into gaps between local healthcare and school monitoring. The policy recommendations presented in this document therefore focus mainly on this window of opportunity supported by:
• The risk of early adiposity rebound (AR) can be identified during this age range. The trajectory of AR is an established indicator of a child’s risk of obesity in later childhood and adulthood;
• Children are already at risk (of T2D) by age 7, so early intervention to maintain a normal BMI is critical up to this age.
There are a range of EU and national policies addressing childhood obesity, but we have identified that 18 months to 5 years is under-represented as a target age range for interventions, despite the well-established link between delaying AR and reducing the risks of obesity. There are many interventions targeted at the first 1000 days of life (pregnancy, breast feeding etc.), but the role of AR in the context of the life-course and reducing the risks of obesity and T2D is not widely appreciated.
Health policies should recognise the heterogeneous origins of obesity; genetics, socioeconomic position, stress etc. The approach should be based on: detection (using AR as an indicator of future risks), informed healthcare professionals, carers and parents on the significance of AR, prevention as early as possible, using AR to track the trajectory of BMI increase in children, personalized approach (psychosocial approach), precision (best point in the life-course, genetic predisposition), participation/engagement, especially that of hard to reach groups.
A policy briefing will be edited by the consortium to alert and propose solutions to address the under-representation of system-based actions in toddlers and pre-school children.
• The risk of early adiposity rebound (AR) can be identified during this age range. The trajectory of AR is an established indicator of a child’s risk of obesity in later childhood and adulthood;
• Children are already at risk (of T2D) by age 7, so early intervention to maintain a normal BMI is critical up to this age.
There are a range of EU and national policies addressing childhood obesity, but we have identified that 18 months to 5 years is under-represented as a target age range for interventions, despite the well-established link between delaying AR and reducing the risks of obesity. There are many interventions targeted at the first 1000 days of life (pregnancy, breast feeding etc.), but the role of AR in the context of the life-course and reducing the risks of obesity and T2D is not widely appreciated.
Health policies should recognise the heterogeneous origins of obesity; genetics, socioeconomic position, stress etc. The approach should be based on: detection (using AR as an indicator of future risks), informed healthcare professionals, carers and parents on the significance of AR, prevention as early as possible, using AR to track the trajectory of BMI increase in children, personalized approach (psychosocial approach), precision (best point in the life-course, genetic predisposition), participation/engagement, especially that of hard to reach groups.
A policy briefing will be edited by the consortium to alert and propose solutions to address the under-representation of system-based actions in toddlers and pre-school children.