Study design and population
The Tromsø Study is a multipurpose single-centre population based prospective study with repeated health surveys among inhabitants of the Tromsø municipality, Northern Norway . All residents born 1969 or earlier were invited to the fourth Tromsø survey in 1994/95. Altogether 27,158 (77%) out of an eligible population of 35,420 subjects, participated. Subjects not consenting to medical research (n = 201), subjects with a prior diagnosis of type 2 diabetes (n = 336), unknown type 2 diabetes (45), type 1 diabetes (n = 81), uncertain type of diabetes during follow up (n = 91), uncertain year of diagnosis (n = 142) and subjects with missing values for any metabolic syndrome criteria and other relevant risk factors (n = 169), were excluded, leaving 26,093 to be included in this study. Subjects were followed from the date of enrolment (1994/95) through December 31st, 2005. The median follow up time was 10.8 years.
A subgroup of 7,160 comprising those aged 55–74 years and 5–10% of those 25–54 and 75–85 years of age were invited to a second visit a few weeks after the main survey; this gave additional baseline information on waist circumference, non-fasting glucose and HbA1c and the possibility to estimate the prevalence of unknown diabetes. In 2001, 7,064 of the original cohort of the 1994/95 survey participated in the fifth Tromsø survey. This enabled us to evaluate the change in risk factors from 1994 to 2001.
The screening included a self administered questionnaire concerning treatment for hypertension, smoking habits and level of leisure-time physical activity (LTPA). The questionnaire was checked by trained nurses. Physical inactivity was defined as less than 3 h per week of light activity in leisure time without sweating or dyspnoea. Moderate LTPA was defined as 3 h or more of light activity or 1–2 h of hard LTPA which caused sweating or dyspnoea per week. Hard LTPA was defined as hard activity with sweating or becoming out of breath for 3 h or more per week .
Educational level was defined having completed 1: primary and secondary-school, 2: high school or vocational school 1–4 years, 3: university less than 4 years and 4: 4 years or more.
Family history of diabetes was reported as first degree family members, i.e. parents or siblings, with a history of diabetes. Smoking status was ascertained as current, previous or never smoker.
Height and weight were measured at screening with light clothing, and body mass index (BMI) was computed as kg/m2. Waist circumference was measured in centimetres. Blood pressure was recorded in the sitting position after 2 min’ rest by the use of an automatic blood pressure measurement device (Dinamap Vital Signs Monitor, Waukesha, WI, US). Three recordings were taken at 2-min intervals, and the mean of the two last readings were used in this analysis. The participant was considered to have hypertension if he or she had systolic blood pressure (BP) ≥ 130 mmHg or diastolic BP ≥ 85 mmHg at screening, or reported being on antihypertensive medication.
Non-fasting blood samples were collected from an antecubital vein, serum prepared by centrifugation after one hour respite at room temperature, and analyzed at the Department of Clinical Chemistry, University Hospital of North Norway. Serum total cholesterol and triglycerides were analyzed by enzymatic colorimetric methods and commercially available kits (CHOD-PAP for cholesterol and GPO-PAP for triglycerides: Boeringer Mannheim). Serum HDL-cholesterol was measured after precipitation of lower-density lipoproteins with heparin and manganese chloride. Determination of glycosylated haemoglobin (HbA1c) in EDTA whole blood was based on an immunoturbidometric assay (UNIMATES, F. Hoffmann-La Roche AG: Basel, Switzerland).
Definition of metabolic score
A metabolic score was defined according to a modified version of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III , in which the metabolic syndrome is present when three or more of the following criteria are fulfilled.
Hypertension; BP ≥ 130/85 mmHg and/or antihypertensive medication.
Hypertriglyceridemia; fasting serum triglycerides > 1.70 mmol/L.
Low high-density lipoprotein (HDL) cholesterol; below 1.03 mmol/L in men and < 1.29 mmol/L in women.
Central obesity; waist circumference > 102 cm (men), > 88 cm (women).
Fasting plasma glucose ≥ 6.1 mmol/L.
Because waist circumference measurements were available only for a subset of participants, BMI was used instead of waist circumferences in the metabolic score, as suggested as a possible alternative in other studies [10, 11]. In our study, the cut off values for BMI were calculated as the mean BMI values in men and women with waist circumference of 102 and 88 cm, respectively. Accordingly, BMI > 28.3 kg/m2 for men and > 27.0 kg/m2 for women were used. The last criterion; fasting plasma glucose was not available and not included in the analysis.
Subjects were given a score from 0 to 4 for each fulfilled feature of the metabolic syndrome (based on the modified NCEP definition) and grouped according to number of fulfilled features. A score of ≥ 3 fulfilled criteria was labelled “high metabolic score”. Conversely, those with 2 or less fulfilled criteria were labelled “low metabolic score”.
Follow-up and case identification
Possible cases of diabetes mellitus were identified through self-reported diabetes in questionnaires or HbA1c > 6.5% in the health surveys 1994/95 or 2001, and through linkage of the Tromsø Study participant list to diabetes related discharge diagnoses in the digital patient records at the only local hospital (ICD- 9 codes 250, 357.2, 362.0, 583.8, 648.0, 648.8, 790.2, ICD-10 codes E10 -E14, O24 and R73). Some cases of hospital confirmed diabetes, but with no diabetes-related discharge diagnosis, were detected through our adjudication process for cardiovascular diseases. We validated all possible cases of diabetes by checking their medical records. Cases were classified as having no diabetes, type 1 or type 2 diabetes, based on glucose measurements if they had non-fasting glucose ≥ 11.1 mmol/L, fasting glucose > 7.0 mmol/L, 2 h glucose load ≥ 11.1 mmol/L or HbA1c ≥ 7.0% in the hospital laboratory database or recorded use of insulin or oral anti-diabetic drugs [12, 13]. C-peptide measurement was the common method at the hospital during the follow-up period to differentiate between type 1 and type 2 diabetes, while Glutamic acid decarboxylase antibody (anti-GAD) measurements were performed in a minority of cases. Follow up ended December 31st, 2005.
This study has been approved by the Regional Committee for Medical and Health Research Ethics, Northern Norway and all participants included have given written informed consent to scientific use of data and linkage to health registries.
Data are presented stratified by gender. Differences in means between groups were tested using age adjusted general linear models. Categorical variables were tested with logistic regression. As cases were diagnosed with diabetes throughout the follow up period, hazard ratios (HR) for diabetes were calculated using Cox proportional hazard models adjusting for age and all the other variables. To evaluate differences in HR estimates between subgroups, interaction terms between risk factor and subgroups were tested. The proportional hazard assumption was examined using log minus log plots and evidence of non-proportionality were not found. Preliminary analyses revealed significant interactions between sex and BMI and between sex and triglycerides. Accordingly, all analyses were sex stratified. ROC areas were computed for presence of high metabolic score univariably, in multivariable analysis with the metabolic syndrome factors as continuous variables, and in a full model with all significant predictors entered in a logistic regression model. Significance tests were two-tailed, and the significance level was chosen at 5%. Estimations of differences between ROC areas were performed in SAS statistical software (SAS Institute, Cary, NC, USA) version 9.2 using the roc and roccontrast statement in proc logistic. All other analyses were performed using SPSS version 17 (Statistical Package for Social Sciences, Chicago, IL, USA, 2009).