Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Modifiable causes of premature death in middle-age in Western Europe: results from the EPIC cohort study

  • David C. Muller1,
  • Neil Murphy2,
  • Mattias Johansson1,
  • Pietro Ferrari1,
  • Konstantinos K. Tsilidis3, 4,
  • Marie-Christine Boutron-Ruault5, 6, 7,
  • Francoise Clavel6, 7, 8,
  • Laureen Dartois6, 7, 8,
  • Kuanrong Li9,
  • Rudolf Kaaks9,
  • Cornelia Weikert10,
  • Manuela Bergmann10,
  • Heiner Boeing10,
  • Anne Tjønneland11,
  • Kim Overvad12,
  • M. Luisa Redondo13,
  • Antonio Agudo14,
  • Elena Molina-Portillo15, 16,
  • Jone M. Altzibar16, 17,
  • Lluís Cirera16, 18, 19,
  • Eva Ardanaz16, 20, 21,
  • Kay-Tee Khaw22,
  • Nicholas J. Wareham23,
  • Timothy J. Key24,
  • Ruth C. Travis24,
  • Christina Bamia25, 26,
  • Philippos Orfanos25, 26,
  • Antonia Trichopoulou25, 26,
  • Domenico Palli27,
  • Valeria Pala28,
  • Rosario Tumino29,
  • Paolo Vineis2, 30,
  • Salvatore Panico31,
  • H. Bas Bueno-de-Mesquita2, 32, 33, 34,
  • W. M. Monique Verschuren32, 35,
  • Ellen A. Struijk35,
  • Petra H. Peeters2, 35,
  • Gunnar Engström36,
  • Olle Melander37,
  • Malin Sund38,
  • Elisabete Weiderpass39, 40, 41, 42,
  • Guri Skeie39, 43,
  • Eiliv Lund39, 43,
  • Teresa Norat2,
  • Marc Gunter2,
  • Elio Riboli2 and
  • Paul Brennan1Email author
Contributed equally
BMC Medicine201614:87

https://doi.org/10.1186/s12916-016-0630-6

Received: 4 March 2016

Accepted: 27 May 2016

Published: 14 June 2016

Abstract

Background

Life expectancy is increasing in Europe, yet a substantial proportion of adults still die prematurely before the age of 70 years. We sought to estimate the joint and relative contributions of tobacco smoking, hypertension, obesity, physical inactivity, alcohol and poor diet towards risk of premature death.

Methods

We analysed data from 264,906 European adults from the EPIC prospective cohort study, aged between 40 and 70 years at the time of recruitment. Flexible parametric survival models were used to model risk of death conditional on risk factors, and survival functions and attributable fractions (AF) for deaths prior to age 70 years were calculated based on the fitted models.

Results

We identified 11,930 deaths which occurred before the age of 70. The AF for premature mortality for smoking was 31 % (95 % confidence interval (CI), 31–32 %) and 14 % (95 % CI, 12–16 %) for poor diet. Important contributions were also observed for overweight and obesity measured by waist-hip ratio (10 %; 95 % CI, 8–12 %) and high blood pressure (9 %; 95 % CI, 7–11 %). AFs for physical inactivity and excessive alcohol intake were 7 % and 4 %, respectively. Collectively, the AF for all six risk factors was 57 % (95 % CI, 55–59 %), being 35 % (95 % CI, 32–37 %) among never smokers and 74 % (95 % CI, 73–75 %) among current smokers.

Conclusions

While smoking remains the predominant risk factor for premature death in Europe, poor diet, overweight and obesity, hypertension, physical inactivity, and excessive alcohol consumption also contribute substantially. Any attempt to minimise premature deaths will ultimately require all six factors to be addressed.

Keywords

Premature death Mortality Modifiable risk factors Attributable fraction Absolute risk

Background

Life expectancy in all countries of Western Europe has increased substantially over recent decades, primarily due to important decreases in mortality rates for death in middle age [1]. Within the European Union (EU27, 27 countries in the European Union), male age expectancy at age 40 ranges from 71 in the Baltic countries to around 80 in Mediterranean Europe, UK, and Sweden [2]. For women at age 40, the range is from 79 years in Bulgaria and Romania, to 86 years in France and Spain. If we define the age range 40–69 as ‘middle age’ and death occurring in this range as being premature, then about 20 % of men and 11 % of women in Europe (15 countries in the European Union, EU15) who reach the age of 40 can be expected to die prematurely based on current mortality rates [2].

Studies of premature death require the analysis of very large population cohorts or intervention studies with extensive baseline exposure information on major risk factors and complete mortality data. An alternative is to use a modelling approach, such as that taken by the Global Burden of Diseases (GBD) initiative, whereby estimates of exposure and disease risk from multiple sources are combined and used to approximate the contribution of different exposures to overall mortality and morbidity [1]. The GBD has estimated that, for Europe, the primary causes of premature mortality, in order of importance, are smoking, dietary risks, high blood pressure, high body mass index (BMI), physical inactivity, and high alcohol consumption. Other important factors are thought to include high cholesterol, high fasting plasma glucose, ambient air pollution, and occupational risks [3, 4]. While this initiative has resulted in extremely important information, and is the only option for most populations, it has a number of limitations such as the inclusion of data of variable quality from many different sources, and the inability to adjust comprehensively and consistently for other mortality risk factors that may confound the observed relationships. The GBD modelling approach is also usually not able to provide estimates of absolute risk and attributable fractions for important subgroups, e.g. for smokers and non-smokers separately, or for sets of risk factors combined.

To overcome these limitations, we have calculated estimates of the contribution of primary risk factors for premature death in Europe based on individual level data using the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study, which includes extensive exposure data on all primary risk factors, as well as complete follow-up for vital status, from 265,000 adults in 10 European countries.

Methods

Selection of risk factors

Our primary focus was on risk factors that are modifiable at a personal level, which have all been consistently associated with elevated risks of major chronic diseases and subsequent premature death, namely smoking [49], unhealthy diet [4, 1012], high blood pressure [4, 9, 13], overweight and obesity [4, 1418], physical inactivity [4, 6, 16, 1922], and alcohol intake [2327]. Additionally, we considered total cholesterol to HDL ratio (TC:HDL) [28, 29] and glycated haemoglobin (HbA1c) [4, 3032].

Study population

EPIC is an on-going multicentre prospective cohort study that recruited approximately 520,000 participants in 10 European countries (Denmark, France, Germany, Greece, Italy, The Netherlands, Norway, Spain, Sweden, and the United Kingdom) from 1992 to 2000. Approximately 70 % were women and most were aged between 35 and 70 years at baseline. A detailed description of the methods employed has previously been described [33, 34]. Blood samples were collected at baseline according to standardised procedures, and written informed consent for the baseline data collection and follow-up for vital status was provided by all study participants. For this analysis we excluded participants who were ≥ 75 or < 40 years old when recruited (n = 62,775); participants with missing questionnaire information (n = 30,048) or blood pressure measurements (n = 161,693); and participants with missing follow-up information (n = 1908). This analysis therefore included 264,906 participants (172,119 women and 92,787 men) from nine countries (as participants from Norway did not have their blood pressure measured). We also conducted sensitivity analyses including all participants with missing blood pressure to ensure that the estimates for the other covariates remained similar.

Diet, lifestyle and anthropometric information

At recruitment, lifestyle and dietary questionnaires were used to obtain detailed information on all risk factors. Participants were classified as being never, former or current smokers at the time of interview. Diet and alcohol intake over the previous 12 months were assessed at study baseline using validated country-/centre-specific dietary questionnaires [33, 34], and alcohol intake was converted into grams of alcohol per day (g/day) by applying empirically derived definitions of standard drinks for each beverage and country. Both occupational and recreational physical activity were assessed via questionnaire using a validated scoring system [35].

Weight was measured with participants not wearing shoes to the nearest 0.1 kg; while height was measured – dependent on the study centre – to the nearest 0.1, 0.5, or 1.0 cm. Waist circumference was measured either at the smallest torso circumference or at the midpoint between the lower ribs and iliac crest. Hip circumference was measured horizontally at the level of the largest lateral extension of the hips or over the buttocks.

Systolic (SBP) and diastolic (DBP) blood pressure was measured on the right arms of participants while in sitting positions by trained personnel at baseline. Two separate readings were performed for each participant using a standard mercury manometer or oscillometric device, except in Denmark and Sweden, where one single measurement was taken in the supine position. To avoid any possible “white-coat” effect, if available, the second reading was used.

Finally, the EPIC Biomarkers sub-cohort of 16,775 randomly-selected participants (10,524 with complete risk factor data for the present analysis) was used to measure levels of circulating cholesterol (TC:HDL ratio) and HbA1c as a marker of average plasma glucose concentration. Total cholesterol and HDL-C levels were measured from serum (plasma for Umea, Sweden) using the Cobas® homogeneous enzymatic colorimetric test assay. HbA1c was measured from red blood cell fraction using the Tosoh (HLC-723G8) ion exchange high-performance liquid chromatography assay.

Assessment of mortality

Data on vital status and the cause and date of death were collected at the EPIC study centres using record linkages with cancer registries, boards of health and death indices, or through active follow-up. End of follow-up was defined as the latest date of complete follow-up for vital status, which was between 2008 and 2010 dependent on study centre.

Definition of premature mortality

We provide estimates of the contribution for various risk factors to the risk of dying before 70 years of age, conditional on surviving to age 40 years. We additionally provide these estimates for death prior to ages 65 and 75 years in Additional file 1: Table S3 and Additional file 2: Table S4.

Statistical methods

Participants were classified as never, former, or current smokers. Alcohol consumption was modelled using the following categories (standard drinks/day, with a standard drink defined as 10 g of alcohol): 0, 0–0.5, 0.5–1, 1–2, 2–6, 6–10, and > 12 (men only). The dietary score used was an adapted version of the WCRF/AICR score [11], including the intakes of (1) energy dense foods/sugary drinks, (2) plant foods (fruits/vegetables/dietary fibre), and (3) animal foods (red and processed meat). The derived dietary score was categorised into four groups: unhealthy, moderately unhealthy, moderately healthy, and healthy. Full details of dietary score derivation can be found in the Additional file 3: Methods. Occupational and recreational physical activity were combined and categorised into four groups (“The Cambridge Index”) – inactive, moderately inactive, moderately active and active. BMI was calculated as kg/m2 and categorised as < 20, 20–21.9, 22–24.9 (reference), 25–29.9, 30–34.9, and ≥ 35. We constructed five categories of waist-hip ratio (WHR) by splitting the distribution at its sex-specific quintiles. Blood pressure was categorised into clinical cut-points: normal (SBP < 120 mm Hg and DBP < 80 mm Hg); pre-hypertension (SBP ≥ 120 mm Hg or DBP ≥ 80 mm Hg); hypertension 1 (SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg); and hypertension 2 (SBP ≥ 160 mm Hg or DBP ≥ 100 mm Hg) [36]. Sex-specific fourths were created for both TC:HDL and HbA1c.

Hazard ratios (HR) and 95 % confidence intervals (CI) for all-cause mortality were estimated using flexible parametric survival models on the cumulative hazards scale [37, 38], which – in addition to the HR – allow direct estimation of the conditional cumulative hazard function, and thus absolute risks of death. Within these models we employed restricted cubic splines with three internal knots to model the baseline hazard using attained age as the time-scale. Separate models were fitted for men and women, as well as for both sexes combined. All models included age at baseline, smoking status, dietary score, alcohol intake, physical activity, blood pressure, and either BMI or WHR. Finally, we considered models that also included the TC:HDL ratio or HbA1c in the sub-cohort which had these measures available. We present HRs and CIs for premature mortality estimated using models fit to follow-up data censored at 70 years of age. We investigated non-proportional hazards by fitting interactions between covariates and the time-scale (attained age), and the final models allowed the parameters for smoking status to vary over time. We also examined whether there were any important interactions between pairs of covariates by comparing models with and without interaction terms using the likelihood-ratio test.

Model-based survival functions and their CI were obtained from fitted models at specific combinations of covariate values. These survival functions were conditional on surviving until age 40 years. We also calculated attributable fractions (AFs) for each covariate based on the predicted survival functions evaluated at age 70. These AFs rely on the comparison between the expected survival at age 70 under the following scenarios: (1) the observed distribution of risk factors in the cohort and (2) an alternative, reference (counterfactual) distribution of risk factors corresponding to the removal of a specific risk factor or set of risk factors. For most risk factors, the reference distribution was simply set so each member of the cohort was “unexposed” to the risk factor, or in the category of the risk factor associated with the lowest risk of death. These attributable risks thus represent a “best-case”, in that they are calculated based on a hypothetical reference population with risk factors removed entirely. For smoking, the reference distribution was a cohort of never smokers. For most risk factors the reference distribution was similarly defined, i.e. normal blood pressure, most healthy diet, physically active, and lowest category of WHR. For BMI, the reference distribution was set to 22–25 for all participants with a BMI above 22, and left unchanged for those with a BMI < 22. The reference distribution for alcohol intake was a population that drinks no more than one or two standard drinks per day. Thus, the reference distribution removes the excess risk associated with drinking more than two drinks per day, without removing the apparent excess risk from consuming less than one drink per day or abstaining. See Additional file 3: Methods for technical details of the AF calculations. All statistical analyses were conducted using Stata version 12.1 and R version 3.1.2.

Results

Among the 264,906 EPIC participants (172,119 women and 92,787 men) with complete baseline and follow-up data who were included in this analysis, we identified 11,930 premature deaths (i.e. before the age of 70) during a median follow-up of 11.5 years. These deaths were predominantly due to cancer (5907, 50 %) and circulatory diseases (2580, 22 %). Survival functions for men and women in our study sample are presented in Additional file 4: Figure S1. Survival to age 70 was somewhat higher in EPIC (93 % for women and 86 % for men) than that expected based on the general European population (89 % for women and 80 % for men, calculated based on mortality rates from the EU15) [2]. After accounting for sex and smoking status, the survival functions were similar across all countries, with the exception of Italy and France, which had higher survival in each of the sex/smoking strata (Additional file 5: Figure S2). The distributions of the risk factors are presented in Table 1. The number of participants by country and sex are presented in Additional file 6: Table S1 and the distribution of covariates by country is presented in Additional file 7: Table S2.
Table 1

Baseline and covariate distributions in the EPIC cohort: overall, for premature deaths (prior to age 70 years), and by sex

 

All

 

Sex

Total

Premature deaths

Male

Female

n = 264,906

%

n = 11,930

%

n = 92,787

%

n = 172,119

%

Age at baseline, years

40–50

67,229

25

1597

13

21,270

23

45,959

27

 

50–60

124,557

47

7302

61

44,441

48

80,116

47

 

60–70

64,640

24

3031

25

23,492

25

41,148

24

Smoking

Never smoker

121,743

46

3365

28

26,462

29

95,281

55

 

Former smoker

79,391

30

3489

29

37,967

41

41,424

24

 

Current smoker

63,772

24

5076

43

28,358

31

35,414

21

Blood pressure

Normal

54,935

21

1793

15

11,253

12

43,682

25

 

Pre-hypertension

102,263

39

4023

34

35,844

39

66,419

39

 

Hypertension 1

72,429

27

3711

31

30,481

33

41,948

24

 

Hypertension 2

35,279

13

2403

20

15,209

16

20,070

12

BMI (kg/m2)

< 20

12,001

5

665

6

1447

2

10,554

6

 

20–21.9

30,376

11

1244

10

5443

6

24,933

14

 

22–24.9 (reference)

77,680

29

3127

26

24,984

27

52,696

31

 

25–29.9 (overweight)

103,379

39

4602

39

46,392

50

56,987

33

 

30–34.9 (obese)

32,270

12

1672

14

12,391

13

19,879

12

 

35+ (very obese)

9200

3

620

5

2130

2

7070

4

Waist-to-hip ratio

(sex-specific fifths)

1

57,336

22

1960

16

19,000

20

38,336

22

2

52,688

20

2102

18

21,985

24

30,703

18

 

3

59,966

23

2372

20

18,517

20

41,449

24

 

4

45,066

17

2200

18

14,844

16

30,222

18

 

5

49,850

19

3296

28

18,441

20

31,409

18

Alcohol intake (drinks/day)

0

32,826

12

1598

13

5941

6

26,885

16

 

0–0.5

78,871

30

3054

26

16,748

18

62,123

36

 

0.5–1

42,158

16

1580

13

13,255

14

28,903

17

 

1–2

50,904

19

2073

17

20,550

22

30,354

18

 

2–6

52,468

20

2741

23

29,860

32

22,608

13

 

> 6 (women), 6–10 (men)

6683

3

690

6

5437

6

1246

1

 

> 10 (men)

996

0

194

2

996

1

0

0

Diet

Unhealthy

22,346

8

1746

15

12,617

14

9729

6

 

Moderately unhealthy

112,965

43

5792

49

48,238

52

64,727

38

 

Moderately healthy

77,110

29

2800

23

20,515

22

56,595

33

 

Healthy

52,485

20

1592

13

11,417

12

41,068

24

Physical activity

Inactive

61,771

23

3155

26

18,658

20

43,113

25

 

Moderately inactive

88,907

34

3753

31

29,062

31

59,845

35

 

Moderately active

60,548

23

2531

21

22,015

24

38,533

22

 

Active

53,680

20

2491

21

23,052

25

30,628

18

 

All

10,524

100

469

100

3899

100

6625

100

Total to HDL cholesterol ratio

(sex-specific fourths)

< 3.79 (men), < 3.09 (women)

2530

24

73

16

933

24

1597

24

3.79–4.64 (men), 3.09–3.76 (women)

2531

24

96

20

921

24

1610

24

 

4.64–5.73 (men), 3.76–4.69 (women)

2517

24

118

25

920

24

1597

24

 

5.73 + (men), 4.69 + (women)

2503

24

159

34

923

24

1580

24

 

missing

443

4

23

5

202

5

241

4

Glycated haemoglobin (%) (sex-specific fourths)

< 5.26

3121

30

120

26

1154

30

1967

30

5.26–5.54 (men), 5.26–5.44 (women)

2543

24

100

21

1156

30

1387

21

 

5.54–5.72 (men), 5.44–5.72 (women)

2354

22

96

20

619

16

1735

26

 

5.72 +

2399

23

142

30

934

24

1465

22

 

missing

107

1

11

2

36

1

71

1

Hazard ratios

The HRs for premature mortality are presented in Table 2. After mutual adjustment for all risk factors, over a two-fold greater risk was observed for current smokers when compared with never smokers for both men (HR, 2.54; 95 % CI, 2.36–2.73) and women (HR, 2.14; 95 % CI, 2.01–2.29), with former smokers having an intermediate risk. When compared with the physically inactive group, being physically very active was associated with substantially lower mortality rates for men (HR, 0.73; 95 % CI, 0.68–0.78) and women (HR, 0.69; 95 % CI, 0.64–0.76). Men and women with a ‘healthy’ diet had 22 % (HR, 0.78; 95 % CI, 0.69–0.88) and 25 % (HR, 0.75; 95 % CI, 0.67–0.83) lower mortality rates, respectively, when compared with the ‘unhealthy’ diet groups. J-shaped relationships between alcohol consumption at baseline and mortality were observed for men and women, with the highest mortality rates found for the highest consumers (>6 drinks/day for women; HR, 1.63; 95 % CI, 1.31–2.04 and > 10 drinks/day for men; HR, 2.38; 95 % CI, 2.02–2.79) when compared with the moderate alcohol consumption reference group (1–2 drinks per day). For BMI (unadjusted for WHR), when compared with the 22–24.9 reference group, higher mortality rates were observed for the extreme high (35+: men, HR, 1.77; 95 % CI, 1.50–2.09; women, HR, 1.40; 95 % CI, 1.20–1.63) and low (<20: men, HR, 1.76; 95 % CI, 1.54–2.01; women, HR, 1.55; 95 % CI, 1.38–1.75) BMI groups. For WHR (unadjusted for BMI), when the highest and lowest fifths were compared, 39 % (HR, 1.39; 95 % CI, 1.28–1.51) and 52 % (HR, 1.52; 95 % CI, 1.39–1.65) higher premature mortality rates were observed for women and men, respectively. Participants with hypertension also had higher premature mortality rates than those with normal blood pressure levels, with an increasing gradient for hypertension level 1 (HR, 1.17; 95 % CI, 1.09–1.25) and level 2 (HR, 1.52; 95 % CI, 1.40–1.64).
Table 2

Hazard ratios and confidence intervals for death prior to age 70 years in the EPIC cohort

  

Overall

Women

Men

  

Model 1

Model 2

Model 1

Model 2

Model 1

Model 2

 

HR

(95 % CI)

HR

(95 % CI)

HR

(95 % CI)

HR

(95 % CI)

HR

(95 % CI)

HR

(95 % CI)

Smoking status

Never

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Former

1.39

(1.32–1.46)

1.37

(1.30–1.43)

1.30

(1.21–1.39)

1.29

(1.20–1.38)

1.47

(1.36–1.58)

1.43

(1.32–1.54)

 

Smoker

2.38

(2.27–2.50)

2.36

(2.25–2.48)

2.16

(2.03–2.30)

2.14

(2.01–2.29)

2.57

(2.39–2.77)

2.54

(2.36–2.73)

Physical activity

Inactive

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Moderately inactive

0.78

(0.74–0.82)

0.79

(0.75–0.82)

0.78

(0.73–0.84)

0.79

(0.73–0.84)

0.77

(0.72–0.82)

0.77

(0.72–0.83)

 

Moderately active

0.75

(0.71–0.79)

0.75

(0.71–0.79)

0.72

(0.66–0.78)

0.72

(0.67–0.78)

0.76

(0.70–0.82)

0.77

(0.71–0.83)

 

Active

0.71

(0.67–0.75)

0.72

(0.68–0.76)

0.69

(0.64–0.75)

0.69

(0.64–0.76)

0.72

(0.67–0.77)

0.73

(0.68–0.78)

Diet

Unhealthy

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Moderately unhealthy

0.87

(0.82–0.92)

0.88

(0.83–0.93)

0.85

(0.77–0.93)

0.85

(0.77–0.93)

0.88

(0.82–0.94)

0.89

(0.83–0.95)

 

Moderately healthy

0.80

(0.75–0.85)

0.81

(0.76–0.87)

0.78

(0.70–0.86)

0.79

(0.71–0.87)

0.80

(0.74–0.88)

0.82

(0.75–0.90)

 

Healthy

0.75

(0.70–0.81)

0.77

(0.72–0.83)

0.73

(0.66–0.82)

0.75

(0.67–0.83)

0.76

(0.67–0.85)

0.78

(0.69–0.88)

Alcohol intake (drinks/day)

0

1.58

(1.47–1.70)

1.58

(1.47–1.70)

1.46

(1.33–1.61)

1.46

(1.32–1.60)

1.90

(1.70–2.12)

1.91

(1.71–2.14)

0–0.5

1.17

(1.10–1.25)

1.18

(1.11–1.25)

1.13

(1.04–1.22)

1.13

(1.04–1.22)

1.26

(1.14–1.38)

1.27

(1.15–1.39)

 

0.5–1

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

1–2

0.97

(0.91–1.04)

0.97

(0.91–1.04)

0.98

(0.90–1.08)

0.98

(0.89–1.08)

0.97

(0.89–1.07)

0.97

(0.88–1.07)

 

2–6

1.07

(1.00–1.14)

1.06

(1.00–1.13)

1.16

(1.05–1.27)

1.15

(1.04–1.26)

1.04

(0.96–1.14)

1.03

(0.95–1.13)

 

> 6 (women), 6–10 (men)

1.51

(1.38–1.66)

1.47

(1.34–1.62)

1.72

(1.38–2.15)

1.63

(1.31–2.04)

1.48

(1.32–1.65)

1.45

(1.29–1.62)

 

> 10 (men)

2.47

(2.12–2.88)

2.40

(2.06–2.79)

    

2.43

(2.07–2.85)

2.38

(2.02–2.79)

Blood pressure

Normal

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 

1.00

 
 

Pre-hypertension

1.03

(0.97–1.09)

0.99

(0.93–1.04)

1.04

(0.96–1.11)

1.00

(0.93–1.07)

1.04

(0.95–1.14)

0.99

(0.90–1.08)

 

Hypertension 1

1.19

(1.12–1.26)

1.12

(1.06–1.19)

1.16

(1.07–1.26)

1.10

(1.02–1.19)

1.24

(1.13–1.37)

1.15

(1.05–1.27)

 

Hypertension. 2

1.56

(1.46–1.67)

1.45

(1.36–1.55)

1.50

(1.37–1.65)

1.42

(1.29–1.55)

1.63

(1.47–1.81)

1.50

(1.36–1.66)

BMI (kg/m2)

< 20

1.62

(1.48–1.76)

  

1.47

(1.33–1.63)

  

1.98

(1.70–2.30)

  
 

20–21.9

1.17

(1.09–1.25)

  

1.10

(1.01–1.20)

  

1.25

(1.13–1.40)

  
 

22–24.9 (reference)

1.00

   

1.00

   

1.00

   
 

25–29.9 (overweight)

0.98

(0.94–1.03)

  

1.03

(0.96–1.10)

  

0.97

(0.91–1.03)

  
 

30–34.9 (obese)

1.16

(1.09–1.23)

  

1.05

(0.95–1.15)

  

1.26

(1.16–1.37)

  
 

35+ (very obese)

1.61

(1.47–1.76)

  

1.55

(1.38–1.75)

  

1.76

(1.54–2.01)

  

Waist-to-hip ratio (fifths)

1

  

1.00

   

1.00

   

1.00

 

2

  

1.01

(0.95–1.07)

  

1.00

(0.91–1.09)

  

1.03

(0.95–1.13)

 

3

  

1.02

(0.96–1.08)

  

0.99

(0.92–1.08)

  

1.06

(0.97–1.15)

 

4

  

1.19

(1.11–1.26)

  

1.15

(1.05–1.25)

  

1.24

(1.13–1.36)

 

5

  

1.44

(1.36–1.53)

  

1.39

(1.28–1.51)

  

1.52

(1.39–1.65)

Estimates from flexible parametric survival models with attained age as the time-scale. Models included all listed covariates, as well as age at baseline attendance and country of recruitment. Overall estimates are also adjusted for sex. Model 1 includes BMI, whereas Model 2 includes waist-to-hip ratio as a measure of adiposity. Model 1 and Model 2 are otherwise identical

Attributable fractions

Attributable fractions (AF) for premature mortality were calculated overall and by sex, and also for never smokers and current smokers separately (Table 3). Given the monotonic association between mortality and WHR, this was used as the primary measure of overweight and obesity, although all results were calculated using BMI also. The AF calculations represent “best-case” estimates based on the hypothetical removal of risk-elevating factors from the population entirely.
Table 3

Population attributable fractions of deaths prior to age 70 given the distribution of covariates in the EPIC cohort, using waist-to-hip ratio to assess for obesity

  

All participants

Never smokers

Current smokers

 

Covariatea

Overallb

Cumulativec

Overallb

Cumulativec

Overallb

Cumulativec

Women and Men

Smoking

0.31 (0.31–0.32)

0.31 (0.31–0.32)

0.56 (0.55–0.56)

0.56 (0.55–0.56)

 

Diet

0.14 (0.12–0.16)

0.41 (0.39–0.43)

0.12 (0.10–0.14)

0.12 (0.10–0.14)

0.16 (0.13–0.18)

0.63 (0.62–0.64)

 

Overweight and obesity (WHR)

0.10 (0.08–0.12)

0.47 (0.45–0.49)

0.10 (0.08–0.11)

0.21 (0.18–0.23)

0.10 (0.08–0.12)

0.67 (0.66–0.68)

 

High blood pressure

0.09 (0.07–0.11)

0.52 (0.50–0.54)

0.09 (0.08–0.11)

0.28 (0.25–0.31)

0.08 (0.07–0.10)

0.70 (0.69–0.71)

 

Physical inactivity

0.07 (0.05–0.09)

0.56 (0.54–0.57)

0.08 (0.06–0.10)

0.34 (0.31–0.36)

0.07 (0.05–0.09)

0.72 (0.71–0.74)

 

Alcohol intake

0.04 (0.03–0.04)

0.57 (0.55–0.59)

0.02 (0.01–0.02)

0.35 (0.32–0.37)

0.05 (0.04–0.06)

0.74 (0.73–0.75)

 

Combined

0.57 (0.55–0.59)

 

0.35 (0.32–0.37)

 

0.74 (0.73–0.75)

 

Women

Smoking

0.26 (0.25–0.26)

0.26 (0.25–0.26)

0.55 (0.54–0.55)

0.55 (0.54–0.55)

 

Diet

0.14 (0.11–0.16)

0.36 (0.33–0.38)

0.12 (0.09–0.15)

0.12 (0.09–0.15)

0.16 (0.13–0.19)

0.62 (0.60–0.63)

 

Overweight and obesity (WHR)

0.07 (0.05–0.09)

0.40 (0.38–0.43)

0.07 (0.05–0.09)

0.18 (0.15–0.22)

0.07 (0.05–0.10)

0.65 (0.63–0.66)

 

High blood pressure

0.10 (0.07–0.12)

0.46 (0.43–0.48)

0.11 (0.08–0.13)

0.27 (0.23–0.30)

0.09 (0.07–0.11)

0.68 (0.66–0.70)

 

Physical inactivity

0.06 (0.03–0.09)

0.49 (0.46–0.52)

0.07 (0.03–0.10)

0.32 (0.28–0.35)

0.06 (0.03–0.09)

0.70 (0.68–0.72)

 

Alcohol intake

0.02 (0.01–0.03)

0.50 (0.47–0.53)

0.01 (0.01–0.02)

0.32 (0.28–0.36)

0.03 (0.02–0.04)

0.71 (0.69–0.72)

 

Combined

0.50 (0.47–0.53)

 

0.32 (0.28–0.36)

 

0.71 (0.69–0.72)

 

Men

Smoking

0.37 (0.35–0.38)

0.37 (0.35–0.38)

0.57 (0.56–0.57)

0.57 (0.56–0.57)

 

Diet

0.13 (0.09–0.17)

0.45 (0.43–0.48)

0.13 (0.09–0.17)

0.13 (0.09–0.17)

0.14 (0.10–0.18)

0.63 (0.61–0.65)

 

Overweight and obesity (WHR)

0.13 (0.11–0.16)

0.53 (0.50–0.55)

0.12 (0.09–0.14)

0.23 (0.19–0.27)

0.13 (0.11–0.16)

0.69 (0.67–0.70)

 

High blood pressure

0.08 (0.05–0.12)

0.57 (0.54–0.60)

0.08 (0.05–0.12)

0.29 (0.25–0.34)

0.07 (0.04–0.11)

0.71 (0.69–0.73)

 

Physical inactivity

0.07 (0.05–0.10)

0.60 (0.57–0.63)

0.07 (0.05–0.10)

0.35 (0.30–0.39)

0.07 (0.05–0.10)

0.74 (0.72–0.75)

 

Alcohol intake

0.06 (0.05–0.07)

0.63 (0.60–0.65)

0.04 (0.02–0.05)

0.37 (0.32–0.41)

0.07 (0.06–0.09)

0.76 (0.74–0.77)

 

Combined

0.63 (0.60–0.65)

 

0.37 (0.32–0.41)

 

0.76 (0.74–0.77)

 

aAttributable fractions were calculated based on the difference in expected cumulative risk given the observed covariate distributions in EPIC and the expected cumulative risk under the following scenarios. Smoking: A population of never smokers. Diet: A population of people in the healthy category. Blood pressure: A population of people with normal blood pressure. High alcohol intake: A population who drink at most 1–2 drinks per day. Physical activity: A population of people in the active category. Overweight and obesity: A population of people with WHR below the lowest sex-specific quintile. These attributable risks thus represent a “best-case”, in that they are calculated based on a hypothetical reference population with risk factors removed entirely

bEstimated using predictions from a model mutually adjusted for all listed covariates as well as age at baseline. Attributable fractions are based on modifying one covariate at a time, with the distribution of the remaining covariates left as observed in EPIC

cThe cumulative attributable fraction after the sequential addition of each covariate

The AF for smoking was 31 % (26 % among women, 37 % among men, and 56 % among current smokers overall; Table 3). For diet and WHR the AF was 14 % and 10 %, respectively. Overall, 9 % of premature deaths were attributed to high blood pressure, and the AFs for physical inactivity and high alcohol intake were 7 % and 4 %, respectively. The role of alcohol was more important among men than women (6 % vs. 2 %). When BMI was used instead of WHR, the effect of being overweight/obese was less apparent, with the AF estimated to be 3 % (Additional file 1: Table S3).

Because these risk factors co-vary to a large degree, it is not possible to sum up the AF estimates to come to an overall estimate. We can derive such an estimate by removing the effect of each risk factor consecutively, yielding a cumulative AF for a combination of risk factors. After accounting for the fraction of premature deaths attributable to smoking, an additional 10 % can be attributed to poor diet. Beyond this, an additional 6 % were attributed to overweight and obesity (WHR), and 5 % to high blood pressure (Table 3). Overall, 52 % of all premature deaths can be attributed to these four factors. The two remaining factors (physical activity and alcohol intake) added an additional 5 %, resulting in a total AF of 57 %. The AF for premature mortality for all six exposures was 74 % among current smokers (56 % smoking) and 35 % among never smokers. The attributable fractions were similar for deaths prior to age 65 (Additional file 2: Table S4) and 75 years (Additional file 8: Table S5), and remained similar in sensitivity analyses including all participants with missing blood pressure (Additional file 9: Table S6).

Survival curves

To estimate the effect that these risk factors can have on expected survival at an individual level, we compared survival curves for current smokers and non-smokers after further stratifying by whether they had otherwise “healthy” or “unhealthy” characteristics (Fig. 1). ‘Healthy’ was defined as a BMI of 22–25, having normal blood pressure, being moderately physically active, eating a healthy diet, and drinking one to two drinks per day. “Unhealthy” was defined as a BMI of 30–35, being physically inactive, eating an unhealthy diet, being hypertensive, and consuming more than two drinks per day. Comparison of the four groups indicated that 96 % of “healthy” non-smoking women (95 % CI, 96–97 %) and 95 % of “healthy” non-smoking men (95 % CI, 94–96 %) could be expected to survive to 70. Conversely, only 64 % (95 % CI, 60–67 %) and 79 % (95 % CI, 76–82 %) of smoking men and women with additional unhealthy characteristics, respectively, could be expected to live to this age. The two intermediate groups, smokers with otherwise healthy characteristics and ‘unhealthy’ non-smokers, had similar expected survival.
Fig. 1

Model-based survival curves by smoking status and other individual factors. “Healthy” participants are those with a body mass index (BMI) of 22–25 who are moderately active, have normal blood pressure, eat a healthy diet, and drink one to two drinks per day. “Unhealthy” participants are those with a BMI of 30–35 who are inactive, hypertensive, eat an unhealthy diet, and consume more than two drinks per day

Cholesterol and glycated haemoglobin

Based on the EPIC-Biomarkers sub-cohort of 10,524 individuals, including 469 premature deaths, an increased risk of premature mortality was seen across all four categories of TC:HDL (Additional file 10: Table S7), reaching a 63 % increase in the fourth (highest) compared with the first (lowest) quartile. Based on this modest sample size, the AF for TC:HDL was estimated to be 15 % (95 % CI, 7–23 %). Conversely, no apparent increase in risk was observed for higher levels of HbA1c.

Discussion

Our analysis of the primary causes of premature death among more than 250,000 European adults indicates that the four major risk factors are tobacco smoking, poor diet, obesity, and high blood pressure, which together account for over 50 % of premature deaths. Two other risk factors, physical inactivity and excessive alcohol consumption, have AFs of 7 % and 4 % of premature deaths, respectively. Our study also provides preliminary evidence for an important AF for high cholesterol levels, although the sample size was limited.

The GBD has derived similar estimates for the role of each exposure using an alternative modelling approach, relying largely on published estimates of effect for each exposure and estimates of exposure prevalence in each population [1]. Their estimates of AFs for premature mortality for the European population provide broadly comparable estimates for smoking, poor diet and high blood pressure (Table 4). However, the GBD estimates are approximately twice as high for excessive alcohol consumption (8 % vs. 4 %), and substantially higher for excessive body mass (14 % vs. 3 % based on BMI). Using WHR as a measure of obesity made the estimates more comparable (10 % based on WHR).
Table 4

Comparison of population attributable fractions (%, 95 % CI) from the Global Burden of Diseases analysis with those from the present EPIC analysis

 

GBD

EPIC

Tobacco smoking

25 (22–27)

31 (31–32)

Dietary risks

23 (21–26)

14 (12–16)

High blood pressure

15 (13–17)

9 (7–11)

High body mass index

14 (12–15)

3 (2–5)

High waist-to-hip ratio

10 (8–12)

Physical inactivity and low physical activity

9 (8–11)

7 (5–9)

High alcohol use

8 (7–9)

4 (3–4)

Estimates from the GBD are taken from the website http://vizhub.healthdata.org/gbd-compare. They are the estimated attributable fractions for death in Western Europe for the age range 50–69 years for each risk factor

There are three possible explanations for these differences. Firstly, the estimates of relative risk used in the calculations might differ – indeed, we estimated modest relative risks for overweight and obesity and physical inactivity. Secondly, the distribution of the risk factors used for the GBD computations might differ from the distribution in EPIC which, for example, includes relatively few very heavy consumers of alcohol or very obese participants. This is a well-known phenomenon in prospective cohort studies, also called “healthy volunteer” effect. Finally, the reference or counterfactual distributions used for the AF calculations might differ. For instance, the GBD used a “theoretical minimum-risk exposure distribution”. On the other hand, we have chosen to not necessarily use a theoretically “optimal” exposure distribution in all cases. For instance, using the lowest risk category for alcohol intake or BMI would involve an increase in alcohol intake or BMI in a proportion of the participants. Instead, we have focused on the AF for high alcohol intake and overweight and obesity per se. Low reported alcohol intake in particular is associated with substantially higher risk of death in EPIC, possibly due to the influence of former drinkers who quit for health reasons or misclassification of heavy drinkers [23, 27, 39]. This misclassification would lead to an underestimation of the AF for alcohol in the EPIC study, and – assuming that the GBD estimates do not suffer from the same problems – may explain the difference between the EPIC and GBD estimates in this case.

On an individual level, the estimated conditional survival curves suggest smoking could have a similar effect on survival to age 70 to that of all other factors combined. Men who were smokers but possessed otherwise healthy characteristics had expected survival of 86 %, similar to the 83 % expected survival for men with unhealthy characteristics but who never smoked. For women, both smokers with otherwise healthy characteristics and unhealthy non-smokers also had similar expected survival (92 % and 90 %, respectively). These estimates reinforce the critical importance of smoking in terms of preventing premature death, and suggest it is as important as the other five major risk factors combined.

A strength of our approach is the ability to estimate AFs for important sub-groups, such as smokers and never smokers, and also the direct estimation of AFs for combinations of risk allowing for their interdependence. Interestingly, our results indicate that, for both current and never smokers, an equivalent proportion of premature deaths can be attributed to poor diet, hypertension, overweight and obesity, and physically inactivity.

The principal limitation of our study is the relatively small number of participants with available cholesterol and HbA1c measurements, leading to imprecise estimates of relative risks, prevalence, and AF for these factors. Further, given that all exposures were assessed only once at recruitment to the study, we could not assess the potential effects of changing exposures over time, such as quitting smoking, gaining or losing weight, or increasing physical activity. As such, our estimated AFs and survival functions cannot be interpreted as the expected effects on mortality if individuals were to change their lifestyle or diet, but rather reflect comparisons of individuals with a given, constant pattern of exposures, or hypothetical scenarios in which no-one in the population is exposed to a given risk factor. Similarly, with only one assessment of exposure we cannot assess the potential effects of measurement error, which are unlikely to be equal across the six factors (e.g. BMI and blood pressure are subject to only modest measurement error, especially compared with self-reported diet and physical activity), and can lead to under- or overestimates of the risk associated with specific risk factors. Finally, the participants in EPIC are not representative of the general European population and may have a different distribution of risk factors than other target populations. In addition to the participants not being a representative sample of any population at baseline, during the follow-up period, the participants have aged, so estimates presented here do not strictly represent the age group 40 to 70.

We choose to include individuals who reported a prevalent chronic disease condition at baseline (e.g. diabetes, heart disease, angina, or a previous diagnosis of cancer), as such a sample would be more representative of the underlying population. Excluding the 7 % of the cohort who did report a prevalent chronic disease at baseline did not have any substantial effect on our results. As an additional sensitivity analysis to evaluate the possibility of sick individuals changing their lifestyle habits relatively recently prior to interview, we also excluded the first 4 years of follow-up. Again, this exclusion did not substantially affect the results.

Conclusions

In summary, we used individual level data from a large European prospective cohort study to estimate the relative contributions of various factors to premature mortality both on the population level and the individual level. While smoking remains the predominant risk factor for premature mortality in Europe, poor diet, obesity, and hypertension also have a substantial additional effect. We also provided an estimate of the incidence of death prior to age 70 years that could be expected among an otherwise healthy non-smoking population (about 4 %). Our results indicate that it is of public health importance to persist and extend the fight against smoking as well as to promote healthy behaviour, including better diet, avoidance of overweight/obesity, physical activity, and blood pressure and blood lipid control with the aim of minimising premature mortality.

Abbreviations

AF, attributable fraction; BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; EPIC, European Prospective Investigation into Cancer and Nutrition; GBD, Global Burden of Diseases; HR, hazard ratio; SBP, systolic blood pressure; WHR, waist-to-hip ratio

Declarations

Availability of data and materials

EPIC data are available for investigators who seek to answer important questions on health and disease in the context of research projects that are consistent with the legal and ethical standard practices of IARC/WHO and the EPIC Centres. The primary responsibility for accessing the data belongs to the EPIC centres that provided them. The use of a random sample of anonymised data from the EPIC study can be requested by contacting Dr Paul Brennan, gep@iarc.fr, who will issue the request to members of the EPIC Steering Committee for deliberation.

Authors’ contributions

Conception, design, analysis, and interpretation of data: DCM, NM, ER, PB. Acquisition of data: M-CB-R, RK, HB, KO, AA, K-TK, NJW, TJK, AT, CB, DP, RT, PV, SP, HBBdeM, PHP, EW, EL, ER. Writing or review of the manuscript: DCM, NM, ER, PB. MJ, PF, KKT, M-CB-R, FC-C, LD, KL, RK, CW, MB, HB, AT, KO, MLR, AA, EM-P, JMA, LC, EA, K-TK, NJW, TJK, RCT, CB, PO, AT, DP, VP, RT, PV, SP, HBB-d-M, EAS, PHP, GE, OM, MS, EW, GS, EL, TN, MG. All authors read and approved the final manuscript.

Competing interests

The authors declare that they no competing interests.

Ethics approval and consent to participate

This study complies with the Declaration of Helsinki. Ethical review boards of IARC and all local institutions where participants had been recruited gave approval for the study, and all participants gave written informed consent for data collection and storage, as well as individual follow-up.

Financial support

This work was supported by the French Social Affairs & Health Ministry, Department of Health (Direction Générale de la Santé). The work undertaken by David C Muller for this project was performed during the tenure of an IARC-Australia fellowship supported by Cancer Council Australia. Elio Riboli was supported by the Imperial College Biomedical Research Centre funded by the National Institute of Health Research of UK. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Nordic Centre of Excellence programme on Food, Nutrition and Health. (Norway); Health Research Fund (FIS), PI13/00061 to Granada, Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk) (United Kingdom).

The sponsors did not have any input into study design, study conduct, data collection, analysis, or interpretation nor did they influence the preparation, review, or approval of the manuscript.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
International Agency for Research on Cancer
(2)
School of Public Health, Imperial College London
(3)
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine
(4)
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London
(5)
Centre for Research in Epidemiology and Population Health (CESP)
(6)
Université Paris Sud
(7)
Institut Gustave Roussy
(8)
Inserm, Centre for Research in Epidemiology and Population Health (CESP)
(9)
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ)
(10)
German Institute of Human Nutrition Potsdam-Rehbrücke (DifE)
(11)
Danish Cancer Society Research Center
(12)
Department of Public Health, Section for Epidemiology, Aarhus University
(13)
Public Health Directorate
(14)
Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology
(15)
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria GRANADA, Hospitales Universitarios de Granada/Universidad de Granada
(16)
Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP)
(17)
Public Health Division of Gipuzkoa-BIODONOSTIA, Basque Regional Health Department
(18)
Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca
(19)
Department of Health and Social Sciences, Universidad de Murcia
(20)
Navarra Public Health Institute
(21)
Navarra Institute for Health Research (IdiSNA) Pamplona
(22)
School of Clinical Medicine, University of Cambridge
(23)
MRC Epidemiology Unit, University of Cambridge
(24)
Cancer Epidemiology Unit, University of Oxford
(25)
Hellenic Health Foundation
(26)
Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School
(27)
Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO)
(28)
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori
(29)
Cancer Registry and Histopathology Unit, “Civic - M. P. Arezzo” Hospital, ASP Ragusa
(30)
Human Genetics Foundation (HuGeF)
(31)
Dipartimento di Medicina Clinica e Sperimentale, Federico II University
(32)
Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM)
(33)
Department of Gastroenterology and Hepatology, University Medical Centre
(34)
Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya
(35)
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht
(36)
Department of Clinical Science, Malmö Lund University
(37)
Department of Clinical Sciences, Hypertension & Cardiovascular Disease, Clinical Research Centre, Malmö University Hospital
(38)
Department of Surgical and Perioperative Sciences, Umea University
(39)
Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway
(40)
Department of Research, Cancer Registry of Norway
(41)
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
(42)
Samfundet Folkhälsan
(43)
The Arctic University of Norway

References

  1. GBD 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385(9963):117–71.Google Scholar
  2. Life expectancy by age and sex - Eurostat. http://ec.europa.eu/eurostat/web/products-datasets/-/proj_13nalexp. 29 April 2015.
  3. GBD Data. http://www.healthdata.org/gbd/data. 29 April 2015.
  4. Lim SS, Vos T, Flaxman AD, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224–60.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Peto R, Boreham J, Lopez AD, Thun M, Heath C. Mortality from tobacco in developed countries: indirect estimation from national vital statistics. Lancet. 1992;339(8804):1268–78.View ArticlePubMedGoogle Scholar
  6. Danaei G, Ding EL, Mozaffarian D, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009;6(4):e1000058.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Doll R, Peto R, Boreham J, Sutherland I. Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ. 2004;328(7455):1519.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Pirie K, Peto R, Reeves GK, Green J, Beral V. The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. Lancet. 2013;381(9861):133–41.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Ikeda N, Inoue M, Iso H, et al. Adult mortality attributable to preventable risk factors for non-communicable diseases and injuries in Japan: a comparative risk assessment. PLoS Med. 2012;9(1):e1001160.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Vergnaud A-C, Romaguera D, Peeters PH, et al. Adherence to the World Cancer Research Fund/American Institute for Cancer Research guidelines and risk of death in Europe: results from the European Prospective Investigation into Nutrition and Cancer cohort study. Am J Clin Nutr. 2013;97(5):1107–20.View ArticlePubMedGoogle Scholar
  11. Romaguera D, Vergnaud A-C, Peeters PH, et al. Is concordance with World Cancer Research Fund/American Institute for Cancer Research guidelines for cancer prevention related to subsequent risk of cancer? Results from the EPIC study. Am J Clin Nutr. 2012;96(1):150–63.View ArticlePubMedGoogle Scholar
  12. Heidemann C, Schulze MB, Franco OH, van Dam RM, Mantzoros CS, Hu FB. Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women. Circulation. 2008;118(3):230–7.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360(9349):1903–13.View ArticleGoogle Scholar
  14. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569–78.View ArticlePubMedGoogle Scholar
  15. Prospective Studies Collaboration, Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373(9669):1083–96.View ArticlePubMed CentralGoogle Scholar
  16. Katzmarzyk PT, Janssen I, Ardern CI. Physical inactivity, excess adiposity and premature mortality. Obes Rev. 2003;4(4):257–90.View ArticlePubMedGoogle Scholar
  17. Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363(23):2211–9.Google Scholar
  18. Pischon T, Boeing H, Hoffmann K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359(20):2105–20.View ArticlePubMedGoogle Scholar
  19. Pekkanen J, Nissinen A, Marti B, Tuomilehto J, Punsar S, Karvonen M. Reduction of premature mortality by high physical activity: A 20-year follow-up of middle-aged Finnish men. Lancet. 1987;329(8548):1473–7.View ArticleGoogle Scholar
  20. Loef M, Walach H. The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis. Prev Med. 2012;55(3):163–70.View ArticlePubMedGoogle Scholar
  21. Byberg L, Melhus H, Gedeborg R, et al. Total mortality after changes in leisure time physical activity in 50 year old men: 35 year follow-up of population based cohort. BMJ. 2009;338:b688.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Ekelund U, Ward HA, Norat T, et al. Physical activity and all-cause mortality across levels of overall and abdominal adiposity in European men and women: the European Prospective Investigation into Cancer and Nutrition Study (EPIC). Am J Clin Nutr. 2015;101:613–21.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Ferrari P, Licaj I, Muller DC, et al. Lifetime alcohol use and overall and cause-specific mortality in the European Prospective Investigation into Cancer and nutrition (EPIC) study. BMJ Open. 2014;4(7):e005245.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Duffy JC. Alcohol consumption and all-cause mortality. Int J Epidemiol. 1995;24(1):100–5.View ArticlePubMedGoogle Scholar
  25. Corrao G, Bagnardi V, Zambon A, La Vecchia C. A meta-analysis of alcohol consumption and the risk of 15 diseases. Prev Med. 2004;38(5):613–9.View ArticlePubMedGoogle Scholar
  26. Di Castelnuovo A, Costanzo S, Bagnardi V, Donati M, Iacoviello L, de Gaetano G. Alcohol dosing and total mortality in men and women: An updated meta-analysis of 34 prospective studies. Arch Intern Med. 2006;166(22):2437–45.View ArticlePubMedGoogle Scholar
  27. Bergmann MM, Rehm J, Klipstein-Grobusch K, et al. The association of pattern of lifetime alcohol use and cause of death in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Int J Epidemiol. 2013;42(6):1772–90.View ArticlePubMedGoogle Scholar
  28. Prospective Studies Collaboration. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet. 2007;370(9602):1829–39.View ArticleGoogle Scholar
  29. Stamler J, Daviglus ML, Garside DB, Dyer AR, Greenland P, Neaton JD. Relationship of baseline serum cholesterol levels in 3 large cohorts of younger men to long-term coronary, cardiovascular, and all-cause mortality and to longevity. JAMA. 2000;284(3):311–8.View ArticlePubMedGoogle Scholar
  30. Khaw K-T, Wareham N, Luben R, et al. Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of European Prospective Investigation of Cancer and Nutrition (EPIC-Norfolk). BMJ. 2001;322(7277):15.View ArticlePubMedPubMed CentralGoogle Scholar
  31. The Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.View ArticlePubMed CentralGoogle Scholar
  32. Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362(9):800–11.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Riboli E, Kaaks R. The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol. 1997;26 Suppl 1:S6.Google Scholar
  34. Riboli E, Hunt K, Slimani N, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5(6b):1113–24.View ArticlePubMedGoogle Scholar
  35. Wareham NJ, Jakes RW, Rennie KL, et al. Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public Health Nutr. 2003;6(04):407–13.View ArticlePubMedGoogle Scholar
  36. National High Blood Pressure Education Program. Classification of Blood Pressure - The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. U.S. Department of Health And Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute. 2004. http://www.nhlbi.nih.gov/files/docs/guidelines/jnc7full.pdf.
  37. Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–97.View ArticlePubMedGoogle Scholar
  38. Royston P, Lambert PC. Flexible parametric survival analysis using Stata: beyond the Cox model. College Station, TX: StataCorp LP; 2011.Google Scholar
  39. Fillmore KM, Stockwell T, Chikritzhs T, Bostrom A, Kerr W. Moderate alcohol use and reduced mortality risk: systematic error in prospective studies and new hypotheses. Ann Epidemiol. 2007;17(5 Suppl):S16–23.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement