DISEASE RISK
In substudy III, the membership in PA and sleep Profiles was associated with cardiometabolic risk factors among women, but very little among men, when information on smoking, alcohol consumption, and socioeconomic status also was considered. Both the “Lightly active, normal range sleepers”
and the “Physically inactive, short sleepers” in women showed many associations with high cardiometabolic risk factor levels. However, in both genders the Profiles were different in their Framingham 10-year Risk Score, indicating a different total CVD risk between the Profiles. Looking at CVD mortality (substudy IV), neither sleep or PA alone, but jointly they showed a significant connection with higher all-cause and CVD mortality independent of a history of sports.
Full understanding is still wanting regarding the interaction between PA and sleep with cardiometabolic risk factors, even though there are several large-scale studies that have studied PA, sleep and sedentary behaviors as predictors of cardiometabolic risk factors (Pepin et al., 2014). The studies have mainly controlled for either PA or sleep as a covariate for the other in modelling, and only few have also studied the interaction of the behaviors.
Nevertheless, the main conclusion to be made based upon these previous studies is that PA is associated with improved cardiometabolic risk factor profile even when adjusting for sleep, and that sleep duration can associate with cardiometabolic risk factors differently at different PA levels (Pepin et al., 2014). The findings in substudy III are in line with the previously reported independent associations between PA, sleep and cardiometabolic risk factors, but also provide new insight to the issue as actual clustering of the behaviors and the joint association with cardiometabolic risk factors was observed from a person-oriented perspective.
In substudy III, the first step in modelling was a variable-centered approach where the association of each PA and sleep variable with each cardiometabolic risk factor was studied. This resulted in a large contingency table with a number of associations to be interpreted. Most associations occurred as expected and described in previous literature, but taken together, the generalization and interpretation of the information would have been problematic. Importantly, as the PA and sleep Profiles identify inter-individual variation, having membership in the Profiles as the predictor of cardiometabolic risk factors and total CVD risk, made the results generalizable to persons instead of combination of variables. It was, based on the first variable-centered analyses, expected that having a Profile where likelihoods of unfavorable behaviors occur, would be more prominently associated with cardiometabolic risk factors.
In women, profiling by PA and sleep distinguishes those with unfavorable levels in many cardiometabolic risk factors. The observed associations between the “Physically inactive, short sleepers” in women and high blood pressure, high triglycerides, high HbA1c, high CRP, high BMI, large waist circumference and lower OR for high HDL cholesterol, does reflect the same associations that have been observed in previous variable-centered studies for the independent behaviors. The impact of PA is convincing for higher HDL cholesterol and lower triglyceride levels (Ahmed et al., 2012) and improved glucose metabolism (Roberts et al., 2013). Cross-sectional and longitudinal evidence also support an association between PA and lower inflammation (Ahmed et al., 2012; Roberts et al., 2013), smaller BMI and lower waist circumference (Glazer et al., 2013; Waller et al., 2008). The risks associated with short sleep include obesity, impaired glucose metabolism, and hypertension (Knutson and Van Cauter, 2008; Knutson, 2010; Spiegel et al., 2009). The results for women in Profile 4 support the idea that PA and sleep are synergistic in relation to cardiometabolic risk factors. This has also been shown earlier by substitution modelling (Buman et al., 2014b; Pepin et al., 2014).
Membership in Profile 2 showed associations with high total cholesterol, high LDL cholesterol, and high triglycerides as compared to membership in Profile 1 among women. As the dichotomized variables included information on medication use, this can be a cause for the observed associations considering these risk factors. Members of Profile 2 were on average 62 year
olds and can have been prescribed with lipid lowerers earlier in life which, if in current use, places them in the high risk category for total cholesterol, LDL cholesterol and triglycerides. Being on medication for high cholesterol or blood pressure indicates a less ideal level of the respective risk factor (Lloyd-Jones et al., 2010; Working group set up by the Finnish Medical Society Duodecim and Finnish Society of Internal Medicine, 2013) further related with an substantial lifetime CVD risk (Lloyd-Jones, 2010).
Whereas the cardiometabolic risk factors describe one dimension of risk each, the Framingham Risk Score indicates an estimated risk of total CVD, including also information about age, smoking, and medications. In men, smoking and alcohol consumption and the demographic background related with the PA and sleep Profiles attenuated the independent association of membership in the PA and sleep Profiles with cardiometabolic risk factors.
Nevertheless, in the Framingham Risk Score, differences between the Profiles were observed. The estimated total CVD risk was higher in the
“Physically inactive, poor sleepers” as compared to the “Physically active, normal range sleepers”. Both PA (Myers et al., 2015) and good quality sleep (Jackson et al., 2015) are related with a lower CVD risk, and furthermore, low socioeconomic status correlates with health behavior clustering (Berrigan et al., 2003; Poortinga, 2007) and CVD risk (Kestilä et al., 2012; Laaksonen et al., 2008). It has also been shown that health behaviors account for a great proportion of the association between educational level and CVD mortality in men (Laaksonen et al., 2008). Members in the “Physically inactive, poor sleepers” were likely unemployed and had lower mean educational years compared to “Physically active, normal range sleepers”. Consequently, in men the observed association between Profile 4 and Framingham Risk Score can likely reflect general clustering of poor health behaviors and thereby a high CVD risk for members of this Profile.
The mean age of members in the Profiles in both men and women is very different. Age is an important correlate of both PA and sleep, where younger age correlates with higher PA, less insomnia-like problems, but not necessarily lower dissatisfaction with sleep (Bauman et al., 2012; Ohayon, 2002). The mean age in different Profiles in men and women varies greatly and a higher age clearly associates with Profiles 2 and 4, but age does not make up a characteristic of the Profiles. Age is also a significant risk factor for CVD and it is possible that the different age distribution in the Profiles account for some of the observed differences, particularly in the Framingham Risk Score between the Profiles. However, age was adjusted for in all final models, attenuating both the results for individual risk factors and general CVD risk particularly in men, but not fully explaining all associations.
Gender differences in the associations between membership in Profiles and cardiometabolic risk factors can be due to the small differences in the behavioral characteristics of the Profiles in men and women (discussed in chapter 6.1.). It has for example been observed that the dose-response association between total PA and CVD risk is stronger in women than in men
(Sattelmair et al., 2011). In a previous Finnish population-based study, Kronholm et al. (2011) concluded on an independent association between sleep duration and CVD risk among women but not men. Variation in the same self-reported behaviors between the genders can result in different observed relationships between the behavior and the outcome. It is also possible that physiological differences such as women having lower muscle-mass and resting energy expenditure than men, hormonal functions and the genetic variance account for gender differences and the effects of PA and sleep behaviors. Furthermore, even if the results for men were not statistically significant, the OR for many risk factors were in the same direction and of similar magnitude as in women, suggesting that some of these associations can have been affected by the lower sample size in men.
6.3.1 INTERACTION BETWEEN PHYSICAL ACTIVITY AND SLEEP FOR CARDIOVASCULAR MORTALITY
Substudy IV shows that being physically inactive in leisure time and having concomitant short or poor sleep, one is at higher risk of CVD mortality, regardless a history of sport.
Few longitudinal studies have assessed the interaction between PA and sleep with mortality (Pepin et al., 2014). Previously Xiao et al. (2014) and Bellavia et al. (2014) have studied the interaction between sleep and PA with mortality. The findings in these two studies were contradictory where one found a significant interaction but the other did not. These two studies were conducted in samples more representative of the general population including both men and women, and therefore are not directly comparable to the findings in substudy IV. Furthermore, in some large population-based studies, the clustering of behavioral risk factors, including also PA and sleep duration has strongly been associated with fatal CVD (Eguchi et al., 2012;
Hoevenaar-Blom et al., 2014; Odegaard et al., 2011).
The interaction of LTPA and sleep duration in predicting CVD mortality was evident on both a multiplicative and additive scale. The RERI was calculated for the synergistic association of insufficient PA and short sleep with mortality. The RERI symbolizes the additive risk i.e. whether the risk associated with two behaviors is higher than the sum of the two behaviors separately (Knol and VanderWeele, 2011). Additive interaction was confirmed by the analysis, further strengthening the idea of a joint, synergistic association between low PA and short sleep with CVD risk.
There was no significantly increased risk of mortality for long sleep and low PA in the current study. Bellavia et al. (2014) who particularly observed that long sleep associate with mortality among subjects with low physical activity, did not control for depression. In substudy IV, life satisfaction that previously has been shown to correlate with depression (Bäckmand et al., 2001) was not found to impact the non-significant association between long
sleep and mortality. Adjusting the Cox proportional hazards models for life satisfaction score also did not significantly impact on the hazard ratios and the interpretational outcome of the models, and was left out from the final models.
The interaction of sleep quality and LTPA with all-cause mortality was also significant, but not as strong as the interaction of sleep duration and PA with mortality. Sleep quality and sleep duration are correlated yet distinct characteristics of sleep (Altman et al., 2012; Grandner and Drummond, 2007) and both deserve to be acknowledged. Nevertheless, results of the substudy IV indicate that in regards of CVD mortality, sleep duration is a stronger predictor. Similar conclusions were drawn by Hublin et al. (2007).
The role of sleep quality for cardiovascular health should, however, not be neglected because occasional sleep problems are continuingly increasing among the working aged population (Kronholm et al., 2016).