Emmanuel Stamatakis, 1 Mark Hamer, 1 Gita Mishra 1 1 University College London

51
Emmanuel Stamatakis, 1 Mark Hamer, 1 Gita Mishra 1 1 University College London Department of Epidemiology & Public Health, London, UK Adulthood TV Viewing Relates Independently to Cardiometabolic Risk Profile in Early Middle Age. The 1958 British birth cohort (1981 & 2003 waves) 03/03/2010. AHA 50th Joint Cardiovascula r Disease Epidemiology and Prevention - & - Nutrition, Physical Activity and Metabolism Conference, San Francisco, CA

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Adulthood TV Viewing Relates Independently to Cardiometabolic Risk Profile in Early Middle Age. The 1958 British birth cohort (1981 & 2003 waves). 03/03/2010. AHA - PowerPoint PPT Presentation

Transcript of Emmanuel Stamatakis, 1 Mark Hamer, 1 Gita Mishra 1 1 University College London

Page 1: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Emmanuel Stamatakis,1 Mark Hamer,1 Gita Mishra1

1University College LondonDepartment of Epidemiology & Public Health,

London, UK

Adulthood TV Viewing Relates Independently

to Cardiometabolic Risk Profile in Early

Middle Age.The 1958 British birth cohort (1981 & 2003 waves)

03/03/2010. AHA

50th Joint Cardiovascular

Disease Epidemiology

and Prevention - & - Nutrition,

Physical Activity and Metabolism Conference,

San Francisco, CA

Page 2: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Page 3: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

?

Page 4: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

?

Page 5: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sedentary behaviour as an “independent” risk marker:

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Page 6: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sedentary behaviour as an “independent” risk marker:

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Even if you do “enough” of this...

Page 7: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sedentary behaviour as an “independent” risk marker:

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Even if you do “enough” of this......you may still be at risk if you do “too much” of this

Page 8: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Page 9: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Page 10: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sleeping (0.9 MET)

Sedentary behaviour(1 - 1.5 MET)

Light-intensity physical activity

(1.5-3 MET )

Moderate -intensity physical

activity (3-6 MET)

Vigorous-intensity physical activity

(>6 MET)

Page 11: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London
Page 12: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

To investigate the relationship between TV viewing in early adulthood and cardiometabolic risk profile in early middle age

Is this relationship independent of physical activity participation?

Study aims

Page 13: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Data source: the 1958 British birth cohort

Page 14: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Data source: the 1958 British birth cohort

Page 15: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:1981

(age 23yrs)

Page 16: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

1981 (age 23yrs)

Exposure

Page 17: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

1981 (age 23yrs)

2002 (age 44yrs)

Exposure

Page 18: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

Cardiometabolic risk markers:

•Triglycerides •Total cholesterol •HDL cholesterol •LDL cholesterol •D-dimer •Fibrinogen •vonWillebrand antigen factor •C-reactive protein •BMI •Waist circumference •Systolic blood pressure•Diastolic blood pressure•Glycated haemoglobin (HbA1c) •Insulin Growth Factor 1•Resting Heart Rate

1981

2002 [N=9,377]

Outcomes

Exposure

Page 19: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

Cardiometabolic risk markers:

•Triglycerides •Total cholesterol •HDL cholesterol •LDL cholesterol •D-dimer •Fibrinogen •vonWillebrand antigen factor •C-reactive protein •BMI •Waist circumference •Systolic blood pressure•Diastolic blood pressure•Glycated haemoglobin (HbA1c) •Insulin Growth Factor 1•Resting Heart Rate

1981 Outcomes

Exposure?

2002 [N=9,377]

Page 20: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

Cardiometabolic risk markers:

•Triglycerides •Total cholesterol •HDL cholesterol •LDL cholesterol •D-dimer •Fibrinogen •vonWillebrand antigen factor •C-reactive protein •BMI •Waist circumference •Systolic blood pressure•Diastolic blood pressure•Glycated haemoglobin (HbA1c) •Insulin Growth Factor 1•Resting Heart Rate

1981

Covariables

Outcomes

Exposure

Covariable

2002 [N=9,377]

•Weekly exercise frequency

•Social class

•Daily TV time•Daily physical activity time (EPAQ2)•CVD medication•Smoking *•Alcohol intake•Social class*

Page 21: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Study Design:

Weekly TV Frequency

Cardiometabolic risk markers:

•Triglycerides •Total cholesterol •HDL cholesterol •LDL cholesterol •D-dimer •Fibrinogen •vonWillebrand antigen factor •C-reactive protein •BMI •Waist circumference •Systolic blood pressure•Diastolic blood pressure•Glycated haemoglobin (HbA1c) •Insulin Growth Factor 1•Resting Heart Rate

1981

Covariables

Outcomes

Exposure

Covariables

•Weekly exercise frequency

•Social class

2002 [N=9,377]

N=5,629•Daily TV time•Daily physical activity time (EPAQ2)•CVD medication•Smoking *•Alcohol intake•Social class*

Page 22: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS:

COMPONENT 1 •Triglycerides •HDL cholesterol •BMI •Waist circumference •Systolic BP•Diastolic BP

COMPONENT 2 •vonWillebrand antigen factor •Fibrinogen•D-dimer •C-reactive protein

COMPONENT 3 •Total cholesterol •LDL cholesterol --------------EXCLUDED---------------------Glycated haemoglobin (HbA1c) -Insulin Growth Factor 1 -Resting Heart Rate

Study Design:

Weekly TV Frequency

•Weekly exercise frequency

•Social class

1981

Covariables

Outcomes

Exposure

Covariables

2002 [N=9,377]

N=5,629•Daily TV time•Daily physical activity time (EPAQ2)•CVD medication•Smoking *•Alcohol intake•Social class*

Page 23: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS* COMPONENTS:C1 (27% V.E.)• Triglycerides

• HDL cholesterol • BMI

• Waist circumference • Systolic BP• Diastolic BP

C2 (16% V.E.)• vonWillebrand antigen

factor • Fibrinogen• D-dimer

• C-reactive protein

C3 (13% V.E.)• Total cholesterol • LDL cholesterol

*Principal Component Analysis; factor loading criterion: ≥ 0.35

Page 24: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS* COMPONENTS:C1 (27% V.E.)• Triglycerides

• HDL cholesterol • BMI

• Waist circumference • Systolic BP• Diastolic BP

C2 (16% V.E.)• vonWillebrand antigen

factor • Fibrinogen• D-dimer

• C-reactive protein

C3 (13% V.E.)• Total cholesterol • LDL cholesterol

Metabolic

*Principal Component Analysis; factor loading criterion: ≥ 0.35

Page 25: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS* COMPONENTS:C1 (27% V.E.)• Triglycerides

• HDL cholesterol • BMI

• Waist circumference • Systolic BP• Diastolic BP

C2 (16% V.E.)• vonWillebrand antigen

factor • Fibrinogen• D-dimer

• C-reactive protein

C3 (13% V.E.)• Total cholesterol • LDL cholesterol

Metabolic

Haemostatic/inflammatory

*Principal Component Analysis; factor loading criterion: ≥ 0.35

Page 26: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS* COMPONENTS:C1 (27% V.E.)• Triglycerides

• HDL cholesterol • BMI

• Waist circumference • Systolic BP• Diastolic BP

C2 (16% V.E.)• vonWillebrand antigen

factor • Fibrinogen• D-dimer

• C-reactive protein

C3 (13% V.E.)• Total cholesterol • LDL cholesterol

Metabolic

Haemostatic/inflammatory

Cholesterol component

*Principal Component Analysis; factor loading criterion: ≥ 0.35

Page 27: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

FACTOR ANALYSIS COMPONENTS:C1 (27% V.E.)• Triglycerides

• HDL cholesterol • BMI

• Waist circumference • Systolic BP• Diastolic BP

C2 (16% V.E.)• vonWillebrand antigen

factor • Fibrinogen• D-dimer

• C-reactive protein

C3 (13% V.E.)• Total cholesterol • LDL cholesterol

EXCLUDED: -Glycated haemoglobin

-Insulin Growth Factor 1 -Resting Heart Rate

Metabolic

Cholesterol component

Not fitting in any component

Haemostatic/inflammatory

Page 28: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 29: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 30: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 31: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 32: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 0.3 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 33: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Page 34: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Adjusted for: sex

Page 35: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Adjusted for: sex, smoking, social class, alcohol, CVD medication

Page 36: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44)

Page 37: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 38: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 39: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 40: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 41: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 42: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 3.0 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 43: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Generalized linear model coefficients (95% CI)

Model 1 Model 2 Model 3 Model 4*Component 1 (metabolic)≤ 2 Referent3-4 0.07

( -.009,1.54)0.07 (-0.008,15.2)

0.06 (-0.01, 0.15) 0.04 (-0.03,0.12)

≥ 5 0.19 (0.13 ,0.26)

0.17 (0.10, 0.23)

0.16 (0.09, 0.22) 0.10 (0.03,0.16)

Trend P <0.001 <0.001 <0.001 0.02Component 2 (haemostatic/inflammatory)≤ 2 Referent3-4 0.06 (-0.03,

0.15)0.05 ( -.04,0.13)

0.04 (-.05,0.13) 0.02(-0.07, 0.10)

≥ 5 0.23 (0.15, 0.30)

0.14 ( 0.07,0.21)

0.13 ( 0.06,0.20) 0.07 (-0.003,0.14)

Trend P <0.001 <0.001 0.03 0.03

Component 3 (Cholesterol)≤ 2 Referent3-4 0.04 ( -

0.05,0.10)0.0 (-0.5, 0.13) 0.04 (-0.05, 0.13) 0.04 (-0.05,0.13)

≥ 5 0.03 (-0.04 ,0.10)

0.03 (-0.04,0.10)

0.04 (-0.04,0.11) 0.03 ( -0.05,0.10)

Trend P 0.57 0.54 0.44 0.57

Results: TV at 23yrs Risk factor components at 44yrs

N.S.

*Adjusted for: sex, smoking, social class, alcohol, CVD medication, physical activity (age 23 & 44), daily TV time (age 44 )

Page 44: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sensitivity analysis: active1 participants (N=1228)

1≥1/wk sport at age 23 AND meeting the moderate to vigorous physical activity guidelines at age 44 yrs

Page 45: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sensitivity analysis: Non-overweight at baseline (23yrs)(N=4783)

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Component 1 Component 2 Component 3

-.300

-.200

-.100

.000

.100

.200

Sco

re

p<0.001

p=0.156

p<0.001

p<0.001

p=0.156

p<0.001

Page 46: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Sensitivity analysis: Overweight at baseline (23yrs)(N=772)

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Low

TV

23

Hig

hTV

23/L

ow T

V44

Hig

hTV

23/H

igh

TV44

Component 1 Component 2 Component 3

-0.50

-0.30

-0.10

0.10

0.30

0.50

0.70

0.90

1.10

Sco

re

p=0.09

p=0.009

p=0.34

Page 47: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Crude TV measurement at baseline: what does it capture exactly? ◦ Frequency only?◦ Increased tendency to be sedentary in general?◦ Larger volumes of TV viewing?◦ Dietary confounding? (see e.g. Cleland VJ, et al. Am J Clin Nutr

2008; 87,:1148-55] TV is only a partial (i.e. incomplete)

indicator of sedentary behaviour Self-reported physical activity measures

Study limitations

Page 48: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

Multivariable-adjusted∫ odds ratios for >2hrs/day of TV watching at age 44, by TV watching frequency at age 23. O.R. 95% C.I.TV frequency at age 23yrs≤ 2 (reference) 13-4 1.37 1.12 - 1.67≥ 5 2.67 2.27 - 3.15

(p<0.001) ∫Adjusted for sex, social class, recreational physical activity and recreational PC use time at age 44.

Does TV frequency at 23yrs predict TV time at 44yrs?

Page 49: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

TV habits in early adulthood may predict an adverse cardiometabolic risk profile in early middle age independently of physical activity

Sedentary behaviour affects CV health through metabolic & haemostatic pathways?

Conclusions

Page 50: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

The extent to which certain biological risk factors explain the association between TV & other screen-based entertainment time and CVD fatal/nonfatal events. Prospective study (4.5yrs follow up), N=1928 Scottish adults >35yrs

[Stamatakis E, Hamer M, Dunstan DW. Under revision]

Page 51: Emmanuel Stamatakis, 1  Mark Hamer, 1 Gita  Mishra 1 1 University College London

This analysis was funded by a National Institute for Health Research (UK) personal Fellowship.

The 1958 British Birth Cohort was funded by:

◦ Wellcome Trust ◦ Department of Health and Social Security◦ Department of Education and Science◦ Department of Employment◦ Manpower Services Commission◦ Department of the Environment◦ Medical Research Council

Acknowledgments & Funding