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Modelling the Interplay between Lifestyle Factors and Genetic Predisposition on Markers of Type 2 Diabetes Mellitus Risk.

Walker CG, Solis-Trapala I, Holzapfel C, Ambrosini GL, Fuller NR, Loos RJ, Hauner H, Caterson ID, Jebb SA - PLoS ONE (2015)

Bottom Line: A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c).We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits.Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors.

View Article: PubMed Central - PubMed

Affiliation: MRC Human Nutrition Research, Elsie Widdowson Laboratory, Cambridge, United Kingdom.

ABSTRACT
The risk of developing type 2 diabetes mellitus (T2DM) is determined by a complex interplay involving lifestyle factors and genetic predisposition. Despite this, many studies do not consider the relative contributions of this complex array of factors to identify relationships which are important in progression or prevention of complex diseases. We aimed to describe the integrated effect of a number of lifestyle changes (weight, diet and physical activity) in the context of genetic susceptibility, on changes in glycaemic traits in overweight or obese participants following 12-months of a weight management programme. A sample of 353 participants from a behavioural weight management intervention were included in this study. A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c). We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits. Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors. This analysis shows that modifiable factors relating to body weight, diet, and physical activity are more likely to impact on glycaemic traits than genetic predisposition during a behavioural intervention.

No MeSH data available.


Related in: MedlinePlus

Interactions between weight loss, dietary saturated fat and treatment group on changes in traits.Plots of interactions identified in the models represented in Fig 2 are shown for a) the interaction of changes in weight and saturated fat on the changes in HOMA-IR b) the interaction of changes in weight and saturated fat on the changes in glucose c) the interaction of treatment group and weight changes on the change in HOMA-B d) the interaction of treatment group and change in saturated fat on changes in HbA1c. Each outcome measure was logged for analysis, therefore the change is represented as the geometric mean of the outcome ratio (final/baseline) where a result >1 indicates the variable was higher at the 12 month measure than the baseline measure, conversely a result <1 indicates the variable was lower at the 12 month measure than at baseline. Change in outcome is indicated on the y-axis (a-d) and the scale of the y-axis was chosen to represent a wide range of the outcome values between the lower and the upper quintiles. Changes in weight are shown along the x-axis (a-c). Changes in the proportion of dietary saturated fat are represented by the 3 curves which approximate to tertile levels in a & b and by the x-axis in d. The two treatment arms are represented by separate curves in c & d.
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pone.0131681.g003: Interactions between weight loss, dietary saturated fat and treatment group on changes in traits.Plots of interactions identified in the models represented in Fig 2 are shown for a) the interaction of changes in weight and saturated fat on the changes in HOMA-IR b) the interaction of changes in weight and saturated fat on the changes in glucose c) the interaction of treatment group and weight changes on the change in HOMA-B d) the interaction of treatment group and change in saturated fat on changes in HbA1c. Each outcome measure was logged for analysis, therefore the change is represented as the geometric mean of the outcome ratio (final/baseline) where a result >1 indicates the variable was higher at the 12 month measure than the baseline measure, conversely a result <1 indicates the variable was lower at the 12 month measure than at baseline. Change in outcome is indicated on the y-axis (a-d) and the scale of the y-axis was chosen to represent a wide range of the outcome values between the lower and the upper quintiles. Changes in weight are shown along the x-axis (a-c). Changes in the proportion of dietary saturated fat are represented by the 3 curves which approximate to tertile levels in a & b and by the x-axis in d. The two treatment arms are represented by separate curves in c & d.

Mentions: Paths of associations were estimated for changes at 12 months from baseline in HOMA-IR (shown in blue), plasma glucose (shown in green), HOMA-B (shown in purple) and plasma HbA1c (shown in brown). Paths common to all outcomes are shown in black. The arrows represent strong or moderate associations at a significance level of 10%. The strengths of these associations are shown as partial regression coefficients (SE) and P-value for each pair of variables. The partial regression coefficients correspond to changes in the ratio (final/baseline) of the outcome variables in the logarithmic scale. Absence of a line between two variables indicates that they are not associated after conditioning out their combined set of explanatory variables. Significant interactions which were identified are also indicated with dot-dash lines and are detailed in Fig 3. Differences by countries were found, but the effects are not presented for clarity of main findings in the figure. The German centre had significant differences in mean measurements compared to the UK centre for the change in proportion of dietary saturated fat (P<0.1) and physical activity (P<0.1). There were also important differences between the Australian and UK centres in changes of plasma glucose (P = 0.001). Δ Weight (kg), Δ dietary saturated fat (% of total energy), Δ physical activity (1000 000 steps), genetic predisposition (T2DM-GPS, per increased risk allele), age (years), Δ glucose (mM) and Δ HbA1c (mmol/mol).


Modelling the Interplay between Lifestyle Factors and Genetic Predisposition on Markers of Type 2 Diabetes Mellitus Risk.

Walker CG, Solis-Trapala I, Holzapfel C, Ambrosini GL, Fuller NR, Loos RJ, Hauner H, Caterson ID, Jebb SA - PLoS ONE (2015)

Interactions between weight loss, dietary saturated fat and treatment group on changes in traits.Plots of interactions identified in the models represented in Fig 2 are shown for a) the interaction of changes in weight and saturated fat on the changes in HOMA-IR b) the interaction of changes in weight and saturated fat on the changes in glucose c) the interaction of treatment group and weight changes on the change in HOMA-B d) the interaction of treatment group and change in saturated fat on changes in HbA1c. Each outcome measure was logged for analysis, therefore the change is represented as the geometric mean of the outcome ratio (final/baseline) where a result >1 indicates the variable was higher at the 12 month measure than the baseline measure, conversely a result <1 indicates the variable was lower at the 12 month measure than at baseline. Change in outcome is indicated on the y-axis (a-d) and the scale of the y-axis was chosen to represent a wide range of the outcome values between the lower and the upper quintiles. Changes in weight are shown along the x-axis (a-c). Changes in the proportion of dietary saturated fat are represented by the 3 curves which approximate to tertile levels in a & b and by the x-axis in d. The two treatment arms are represented by separate curves in c & d.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496090&req=5

pone.0131681.g003: Interactions between weight loss, dietary saturated fat and treatment group on changes in traits.Plots of interactions identified in the models represented in Fig 2 are shown for a) the interaction of changes in weight and saturated fat on the changes in HOMA-IR b) the interaction of changes in weight and saturated fat on the changes in glucose c) the interaction of treatment group and weight changes on the change in HOMA-B d) the interaction of treatment group and change in saturated fat on changes in HbA1c. Each outcome measure was logged for analysis, therefore the change is represented as the geometric mean of the outcome ratio (final/baseline) where a result >1 indicates the variable was higher at the 12 month measure than the baseline measure, conversely a result <1 indicates the variable was lower at the 12 month measure than at baseline. Change in outcome is indicated on the y-axis (a-d) and the scale of the y-axis was chosen to represent a wide range of the outcome values between the lower and the upper quintiles. Changes in weight are shown along the x-axis (a-c). Changes in the proportion of dietary saturated fat are represented by the 3 curves which approximate to tertile levels in a & b and by the x-axis in d. The two treatment arms are represented by separate curves in c & d.
Mentions: Paths of associations were estimated for changes at 12 months from baseline in HOMA-IR (shown in blue), plasma glucose (shown in green), HOMA-B (shown in purple) and plasma HbA1c (shown in brown). Paths common to all outcomes are shown in black. The arrows represent strong or moderate associations at a significance level of 10%. The strengths of these associations are shown as partial regression coefficients (SE) and P-value for each pair of variables. The partial regression coefficients correspond to changes in the ratio (final/baseline) of the outcome variables in the logarithmic scale. Absence of a line between two variables indicates that they are not associated after conditioning out their combined set of explanatory variables. Significant interactions which were identified are also indicated with dot-dash lines and are detailed in Fig 3. Differences by countries were found, but the effects are not presented for clarity of main findings in the figure. The German centre had significant differences in mean measurements compared to the UK centre for the change in proportion of dietary saturated fat (P<0.1) and physical activity (P<0.1). There were also important differences between the Australian and UK centres in changes of plasma glucose (P = 0.001). Δ Weight (kg), Δ dietary saturated fat (% of total energy), Δ physical activity (1000 000 steps), genetic predisposition (T2DM-GPS, per increased risk allele), age (years), Δ glucose (mM) and Δ HbA1c (mmol/mol).

Bottom Line: A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c).We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits.Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors.

View Article: PubMed Central - PubMed

Affiliation: MRC Human Nutrition Research, Elsie Widdowson Laboratory, Cambridge, United Kingdom.

ABSTRACT
The risk of developing type 2 diabetes mellitus (T2DM) is determined by a complex interplay involving lifestyle factors and genetic predisposition. Despite this, many studies do not consider the relative contributions of this complex array of factors to identify relationships which are important in progression or prevention of complex diseases. We aimed to describe the integrated effect of a number of lifestyle changes (weight, diet and physical activity) in the context of genetic susceptibility, on changes in glycaemic traits in overweight or obese participants following 12-months of a weight management programme. A sample of 353 participants from a behavioural weight management intervention were included in this study. A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c). We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits. Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors. This analysis shows that modifiable factors relating to body weight, diet, and physical activity are more likely to impact on glycaemic traits than genetic predisposition during a behavioural intervention.

No MeSH data available.


Related in: MedlinePlus