Population
We used data from The Maastricht Study, an observational prospective population-based cohort study. The rationale and methodology have been described previously [17]. In brief, the study focuses on the aetiology, pathophysiology, complications and comorbidities of type 2 diabetes and is characterised by an extensive phenotyping approach. Individuals were eligible to participate in the study if they were aged between 40 and 75 years and living in the southern part of the Netherlands. Participants were recruited through mass media campaigns and from the municipal registries and the regional Diabetes Patient Registry via regular mail. Recruitment was stratified according to known type 2 diabetes status, with an oversampling of individuals with type 2 diabetes, for reasons of efficiency. The present report includes cross-sectional data from a selection of the first 3451 participants, who completed the baseline survey between November 2010 and September 2013. The study complies with the Declaration of Helsinki and has been approved by the institutional medical ethical committee (NL31329.068.10) and the Minister of Health, Welfare and Sports of the Netherlands (Permit 131088-105234-PG). All participants gave written informed consent.
Cardiometabolic outcomes
The metabolic syndrome and diabetes status were used as main outcomes. In addition, the following individual outcome measures were used: waist circumference, blood pressure, plasma levels of HDL-cholesterol, triacylglycerol and glucose and homeostatic model assessment insulin resistance (HOMA2-IR). Details of assessment have been described previously [17]. The metabolic syndrome was defined according to the Adult Treatment Panel (ATP) III guidelines [18]. To determine diabetes status according to the WHO 2006 criteria [19], all participants (except those who used insulin) underwent an OGTT after an overnight fast as described elsewhere [17]. Participants were categorised as having normal glucose, prediabetes (impaired fasting glucose and/or impaired glucose tolerance; fasting plasma glucose 6.1–6.9 mmol/l and/or 2 h plasma glucose ≥7.8 to- <11.1 mmol/l), or type 2 diabetes (fasting plasma glucose ≥7.0 mmol/l and/or 2 h plasma glucose ≥11.1 mmol/l). Participants taking glucose-lowering medication were also considered as having type 2 diabetes. Medication use was assessed during a medication interview where generic name, dose and frequency were registered. HOMA2-IR was calculated using the HOMA calculator, available from https://www.dtu.ox.ac.uk (accessed 31 March 2016).
Accelerometry
Daily activity levels were measured using the activPAL3 physical activity monitor (PAL Technologies, Glasgow, UK). The activPAL3 is a small (53 × 35 × 7 mm), lightweight (15 g) triaxial accelerometer that determines posture (sitting/lying, standing, stepping) based on acceleration information. Participants were asked to wear the accelerometer on the right thigh for 8 consecutive days without removing it at any time. Data were uploaded using the activPAL software and processed using customised software written in MATLAB R2013b (MathWorks, Natick, MA, USA). Data from the first day were excluded from the analysis. In addition, data from the final wear day providing ≤14 wear h of data were excluded from the analysis. Participants were included if they provided at least one valid day (≥10 h of waking data).
ST was calculated as the mean time spent in a sedentary position during waking time per day. The total amount of physical activity was calculated as the mean time stepping during waking time per day. Further, physical activity (stepping time) was classified as higher intensity physical activity (HPA) when step frequency >110 steps/min, and was used as a proxy for MVPA [20]. Details and the method used to determine waking time have been described previously [21].
CRF
As a measure of CRF, estimated maximum power output (Wmax) adjusted for body weight (Wmax kg−1) was used. Wmax was estimated from a graded submaximal exercise protocol performed on a cycle–ergometer system (CASETM version 6.6 in combination with e-bike; GE Healthcare, Milwaukee, WI, USA). For safety reasons, participants with recent or manifest cardiovascular complications were excluded from the exercise test. The protocol has been described in detail elsewhere [12]. Wmax kg−1 was transformed into oxygen consumption (\( \overset{\cdot }{V}{\mathrm{O}}_{2\mathrm{max}} \)) using the following formula from the American College of Sports Medicine [22]: \( \overset{\cdot }{V}{\mathrm{O}}_{2\mathrm{max}} \)(ml kg−1 min−1) = (10.8 × Wmax kg−1) + 7.
Covariates
Questionnaires were used to collect information on age (in years), sex, educational level (highest completed education, subsequently classified as low, middle and high), smoking behaviour (non-smoker, former smoker and current smoker), alcohol consumption (non-consumer, low-consumer [women ≤7 glasses per week, men ≤14 glasses per week] and high-consumer [women >7 glasses per week, men >14 glasses per week]), CVD history (derived from the Rose questionnaire and defined as a self-reported history of any of the following conditions: myocardial infarction, cerebrovascular infarction or haemorrhage and percutaneous artery angioplasty of, or vascular surgery on, the coronary, abdominal, peripheral or carotid arteries) [23], mobility limitations (defined as having difficulty walking 500 m or climbing stairs) and energy intake (calculated as the mean energy intake per day from a frequent food questionnaire). Percentage of body fat was calculated with the Siri equation [24] after estimating body density from skinfold thickness at four sites (suprailiac, subscapula, biceps and triceps) according to Durnin and Womersley [25].
Statistical analyses
First, population characteristics were provided as means (± SD), median (25–75%) or percentages as appropriate.
Second, (multinomial) logistic regression analyses were performed for the outcomes metabolic syndrome and diabetes status. Associations in model 1 were adjusted for age, sex, waking time, education level, smoking status, alcohol consumption, mobility limitation, CVD history and energy intake. Glucose metabolism was additionally adjusted for body fat percentage. Associations in models 2a, 2b and 2c were additionally adjusted for ST, HPA and CRF, respectively. To examine their relative importance in cardiometabolic outcomes, ST, HPA and CRF were expressed per 1 SD.
Third, combined associations of ST–CRF and HPA–CRF with the metabolic syndrome and diabetes status were analysed. For this, CRF was categorised into tertiles (CRFlow, CRFmedium and CRFhigh) based on sex and age (40–49, 50–59, 60–69 and >70 years). CRF values for each age- and sex-specific tertiles are provided in electronic supplemental material (ESM) Table 1. Further, proportions of daily ST and HPA were categorised into sex-specific tertiles (SThigh STmedium and STlow and HPAlow, HPAmedium, and HPAhigh, respectively). For men, tertile cut points were 59% and 67% for ST and 1.0% and 2.3% for HPA. For women, tertile cut points were 52% and 60% for ST and 1.9% and 3.3% for HPA. Tertiles of CRF and HPA and tertiles of CRF and ST were combined into nine subgroups. For each subgroup, the odds for the metabolic syndrome and prediabetes and type 2 diabetes were calculated. These analyses were adjusted for the same covariates as described in model 1 above.
Fourth, in additional analyses, linear regression analyses were performed to assess the independent association of ST, HPA and CRF with individual cardiometabolic outcome measures. Adjustments were similar to those for model 1 (described above), with the addition of antihypertensive and lipid-modifying medication use.
Fifth, the combined effects of ST–CRF and HPA–CRF on individual markers of cardiometabolic health were examined by calculating adjusted means for all subgroups of ST–CRF and HPA–CRF using general linear models. The adjusted means of subgroups based on CRF and HPA were additionally adjusted for ST. The adjusted means of subgroups based on CRF and ST were additionally adjusted for HPA.
In all analyses men and women were analysed together, as no interaction effect of sex was observed. In sensitivity analyses, all analyses were repeated after excluding participants with mobility limitations (n = 341).