Within the 34.8 years of follow-up (median time 27.8 years), 1395 out of the 2235 study participants died. The median age of death was 75 years with a minimum of 45 years. The oldest study participants were 92 years old at the end of follow-up, December 31, 2018. At baseline, those who survived the entire follow-up period were younger, less frequently smokers, less frequently obese, had a healthier diet, and consumed less alcohol (p-value < 0.001 in each case) (Table 1).
Table 1
Baseline characteristics of the study population
| Survivors | Non-Survivors | Total | P-values* |
Number of participants (%) | 840 (38) | 1395 (62) | 2235 | |
Age a | 50.4 (5.6) | 54.5 (4.3) | 52.9 (5.2) | < 0.001 |
Age category (%) 42–47 47–52 52–57 57–62 | 219 (26.1) 191 (22.7) 380 (45.2) 50 (6.0) | 80 (5.7) 116 (8.3) 920 (65.9) 279 (20.0) | 299 (13.4) 307 (13.7) 1300 (58.2) 329 (14.7) | < 0.001 |
BMI category (%) normal weight ≤ 25 slight overweight 25–27.5 overweight 27.5–30 obese ≥ 30 | 322 (38.3) 268 (31.9) 143 (17.0) 107 (12.7) | 392 (28.1) 427 (30.6) 299 (21.4) 277 (19.9) | 714 (31.9) 695 (31.1) 442 (19.8) 384 (17.2) | < 0.001 |
Smokers (%) | 187 (22.3) | 581 (41.6) | 768 (34.4) | < 0.001 |
Healthy Nordic Diet index a | 12.6 (3.8) | 11.6 (4.0) | 12.0 (4.0) | < 0.001 |
Alcohol consumption in g/week a | 68.8 (95.2) | 97.5 (163.5) | 86.7 (142.4) | < 0.001 |
Leisure-time physical activity in MET-hours/day a | 4.7 (3.6) | 4.8 (4.3) | 4.8 (4.0) | 0.094 |
Charlson Comorbidity Index a | 0.5 (1.0) | 0.9 (1.2) | 0.8 (1.1) | < 0.001 |
Follow-up time in years a | 31.6 (1.6) | 19.5 (8.8) | 24.1 (9.1) | < 0.001 |
a results presented as mean (SD) * Chi-square and Mann Whitney U tests to compare survived and dead participants |
The Cox model found associations of tobacco smoking, unhealthy diet, and alcohol consumption with mortality (Fig. 1). Tobacco smokers had the HR of 2.72 (95% CI 2.22–3.33) in contrast to nonsmokers. Each 5 units increase in healthy HND index was associated with 12% lower risk of mortality (HR 0.88, 95% CI 0.82–0.94). Each increase of 100 grams per week in alcohol intake associated with a 13% increase in mortality risk (HR 1.13, 95% CI 1.10–1.17). BMI categories “slight overweight”, “overweight”, and “obese” were also linked to a higher risk of mortality in contrast to “normal weight” with HRs of 1.29 (95% CI 1.06–1.57), 1.63 (95% CI 1.33–2.00), and 1.96 (95% CI 1.59–2.40), respectively. The interaction of BMI categories with smoking associated with a protective effect on mortality risk with HRs of 0.67 (95% CI 0.51–0.88), 0.59 (95% CI 0.43–0.80), and 0.57 (95% CI 0.41–0.80) for the respective interaction of “slight overweight”, “overweight”, and “obese” with smoking. In other words, when smoking and increased BMI co-existed in the same study participant, as they interacted with each other, their individual effects on mortality risk were lower than when they did not co-exist. In term of goodness of fit and accuracy, the model had a Likelihood ratio test of 598.8 (p-value < 0.001), a concordance index of 0.69 (SE 0.007), and an AUC of 72.8 (95% CI 70.8–74.9) (supplementary material 2).
To better illustrate the effect of different combinations of health behaviors on mortality risk we computed a relative risk score of mortality (RRSM) for a generated series of profiles with predefined parameters. We used the model to estimate a prediction of the absolute mortality risk for each of the theoretical profiles.[27] The RRSM of each dummy profile was then computed as the relative risk to the absolute mortality risk of the profile with the ideal health behavior. Therefore, the RRSM represents a relative measure of the mortality risk associated to the combined effects of unhealthy behaviors regardless of follow-up time.
The predefined parameters represent a variety of health behavioral profiles that were determined by different levels of the model’s variables. The chosen levels of HND index correspond to minimum, 1st quartile, 3rd quartile, and maximum values of HND index in the cohort population. The chosen levels for alcohol consumption correspond to 2nd percentile, median, and 98th percentile values in the cohort population. We focused on these values to illustrate contrasts. The ideal health behavioral profile, which is attributed an RRSM of 1, corresponds to a normal weight 42–47 years old person who does not smoke, has a HND index of 24, and consumes 1 gram of alcohol per week. RRSMs of other health behavioral profiles represent risks that are relative to this reference ideal profile. Age-adjusted RRSMs are reported in supplementary material 3.
Rounded RRSMs at different ages are presented in Fig. 2. Throughout age categories, the chart shows an inverse relationship between HND index and RRSM, and a direct relationship between alcohol consumption and RRSM. Smoking was clearly associated with higher RRSMs across the profiles. The chart also shows a trend of risk increase with higher values of BMI. Moreover, the risk associated to combined smoking and high BMI was lower than the product of risks associated to smoking and BMI separately, illustrating a dysergic smoking-BMI interaction.
An overweight smoker aged 42–47 who consumes 475 g of alcohol per week and has a poor-quality diet (HND 1), has nearly the same RRSM as a normal weight nonsmoker aged 57–62 who has a healthy diet (HND 24) and drinks less heavily (≤ 43 g of alcohol per week). Irrespective of BMI category, a 42–47 years old smoker with a low-quality diet (HND 1) has nearly the same RRSM as a 52–57 years old person who has a better-quality diet and who does not smoke. These examples conveyed, in terms of age, the risk associated to the combined effects of different health behaviors. Also, if a person for example has a low quality diet (HND 1), they can be shown that the risk associated to their diet is equivalent to the risk associated to drinking 475 g of alcohol per week, or close to the risk associated to obesity (in nonsmokers), in comparison to the ideal health behavioral profile (supplementary material 3 for more precise estimates).
The mortality risk associated to unhealthy behaviors directly translates into life-years lost as illustrated in Fig. 3, which presents estimated median ages of survival for different combinations of predictors’ parameters. The estimates are valid for the age category 57–62 as estimated median survivals are not available for all health behavioral profiles in other age categories.
The ideal health behavioral profile had an estimated median age of survival of 90.72 years (95% CI 88.49–93.38). The interaction BMI-smoking also appeared in median ages of survival; the deleterious effect of smoking was clearly more marked in normal BMI category than in all others. The lowest estimates of median survival age (around 70 years) were found in profiles of heavy drinking (475 g of alcohol per week) smokers who had a low-quality diet (HND 1), with little variation across BMI categories.
The combined effects of obesity, smoking, poor quality diet (HND 1), and high level of alcohol consumption (475 g/week) might be associated with nearly 10 folds the mortality risk of a normal weight individual engaged in healthy behavior regardless of age (Fig. 2), and a loss of about 20 years in median survival (Fig. 3).