2.1 Data
We used the national 11-digit personal identity number linking individual data from the three following sources: baseline information on participants in the SAMINOR 1 Survey (the first survey of the Population-based Study on Health and Living Conditions in Regions with Sami and Norwegian Populations—the SAMINOR Study), mortality data from the Norwegian Cause of Death Registry, and information on emigration from Statistics Norway.
The population of Northern Norway includes people of Norwegian, Sami and Kven (descendants of Finnish immigrants in the 18th and 19th Century) ethnicity. The Sami is an ethnic minority and acknowledged as an indigenous people. Traditionally, the Sami inhabited Northern parts of Norway, Sweden, Finland and the Kola Peninsula in the Russian Federation.
The SAMINOR Study is a population-based study that was originally designed to investigate the health and living conditions in regions of Norway with an assumed proportion of at least 5―10% Sami inhabitants. The Centre for Sami Health Research at UiT The Arctic University of Norway and the Norwegian Institute of Public Health conducted the SAMINOR 1 Survey in 2003―2004 in 24 rural municipalities mainly in northern parts of Norway. Clinical measurements, blood samples and self-administered questionnaire data were collected on men and women aged 36―79 years. Of 27,151 invited individuals, 16,455 (60.6%) participated and consented to have their data linked to medical and national registries. Survey details have been reported previously (19).
2.2 Clinical measurements
The following measurements of each participant were made by trained personnel: waist circumference, recorded to the nearest centimetre at the umbilicus, the participant standing and breathing normally; height and weight, measured to the nearest 0.1 cm and 100 g, respectively, using an electronic scale with participants wearing light clothing and no shoes; and blood pressure, measured with a Dinamap‐R automatic device (Critikon, Tampa, Florida, USA). Blood pressure was measured after a 2‐minute seated rest, and three measurements with 1‐minute intervals were recorded. The first measurement was discarded and the average of the second and third was used. Trained personnel performed venepuncture with the participant in a seated position and non-fasting blood samples were centrifuged within 1.5 hours. Serum was sent by overnight post to the laboratory at Ullevål University Hospital, Oslo. Lipids and glucose were measured by an enzymatic method (Hitachi 917 autoanalyzer, Roche Diagnostic, Switzerland)
2.3 Lifestyle and disease variables
Participants were asked to fill in a questionnaire from which we obtained the following information (answer options in parenthesis): education (total number of school years); diabetes (yes/no); angina pectoris (yes/no); previous stroke (yes/no); previous heart attack (yes/no); use of blood pressure-lowering drug (currently/previously, but not now/never); use of cholesterol‐lowering drug (currently/previously, but not now/never); use of insulin (currently/previously, but not now/never); use of glucose‐lowering drug in tablet format (currently/previously, but not now/never); smoking (currently/previously/never); leisure‐time physical activity by a modified Saltin-Grimsby Physical Activity Level scale (reading, watching television, or engaging in sedentary activities/at least 4 hours a week of walking, bicycling, or other types of physical activity/at least 4 hours a week of participating in recreational athletics or heavy gardening/regular, vigorous training or participating in competitive sports several times a week) (20); alcohol consumption (never/not this year/a few times during this year/1 time per month/2‐3 times per month/1 time per week/2‐3 times per week/4‐7 times per week). Leisure-time physical activity was categorised into “sedentary” (the first option), “light” (the second option) and “moderate-hard” (the last two options merged). Alcohol consumption was categorised into “weekly alcohol consumption”, “less than weekly alcohol consumption” and “never/not last year”. Participants were also asked to list any medication they had used within the last four weeks and the information was combined with information from drug-specific questions, details are found elsewhere (21).
The questionnaire also included questions (11 in total) on use of language at home by grandparents, parents and participants, ethnic background for parents and participants, and the participants’ self‐perceived ethnicity (one or more of these alternatives were allowed: Norwegian, Sami, Kven, and other). Participants were categorised as Sami if they answered Sami as 1) their self-perceived ethnicity or 2) their own ethnic background. All others were categorised as non-Sami.
2.4 Independent variables
We defined MetS according to the ‘harmonised’ Adult Treatment Panel-III definition, with some adaptations (22). At least three of the following five components had to be present:
- hypertension, defined as systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or current use of antihypertensive drug;
- elevated random glucose, defined as random plasma glucose ≥7.8 mmol/L or self-reported diabetes;
- increased waist circumference, defined as waist circumference ≥80 cm in women and ≥94 cm in men;
- hypertriglyceridemia, defined as random plasma triglycerides ≥1.7 mmol/L; and
- lowered HDL cholesterol, defined as random plasma HDL cholesterol <1.3 mmol/L in women and <1.0 mmol/L in men.
Participants were categorised as metabolically unhealthy if they had any of the following, as recommended by Smith et al. (23):
- MetS (for abdominal obesity phenotypes, the MetS definition was modified to the presence of any given two or more components excluding increased waist circumference),
- self-reported diabetes, stroke, angina pectoris, or myocardial infarction,
- self-reported current treatment for high blood pressure, hyperglycaemia or dyslipidaemia.
General and abdominal obesity were defined as BMI ≥30 kg/m2 and waist circumference ≥88 cm in women and ≥102 cm in men, respectively. The following general obesity phenotypes were created: metabolically healthy non-obesity (MHNO); metabolically unhealthy non-obesity (MUNO); metabolically healthy obesity (MHO); and metabolically unhealthy obesity (MUO). The following abdominal obesity phenotypes were created: metabolically healthy non-abdominal-obesity (MHNAO); metabolically unhealthy non-abdominal-obesity (MUNAO); metabolically healthy abdominal obesity (MHAO); and metabolically unhealthy abdominal obesity (MUAO).
2.5 Outcome variables
Mortality data comprised date of death and underlying cause of death, coded using the International Statistical Classification of Diseases and Related Health Problems, 10th revision. The study period started at the date of study entry (between 14th January 2003 and 5th March 2004) and ended at date of death (the event), date of emigration (censored) or the end of follow-up 31st December 2018 (censored), whichever occurred first. The outcome variables of interest were all-cause mortality and CVD mortality (death from causes I00-I99).
2.6 Missing data and exclusions
Figure 1 shows a flow chart describing the cohort selection. We excluded 497 participants who died within the first 5 years of follow-up and 90 participants with a BMI ≤18.5 kg/m2 to avoid the potential for reverse causality (14). Because information on pre-existing disease or prescribed drugs was not necessary for the categorisation, we did not exclude participants with missing data for these variables. However, most participants with missing data for these variables were categorised into a metabolically unhealthy group by other determinants (Table 1). After exclusions, the complete case analytical sample comprised 12,815 participants, 47.2% of the invited sample.
2.7 Statistical analysis
Sample characteristics were described in strata of sex and metabolic–obesity phenotype and reported as mean (SD) and frequency (percentage) as appropriate. One-way analysis of variance and Pearson’s χ2 test were used to compare characteristics across the phenotypes. We calculated age-standardised mortality rates using the direct method and the 2013 European standard population.
In separate models for each pair of outcome and exposure, we modelled the relationships between all-cause mortality and CVD mortality (outcomes) and MetS, general obesity phenotypes and abdominal obesity phenotypes (exposures) using Cox proportional hazard regression. We tested interactions between exposures and sex, and between exposures and ethnicity, and compared models with and without interaction terms using the likelihood ratio test. Interaction was considered present if p<0.05. There were no significant interactions with ethnicity, but we found evidence of interactions between sex and general (p=0.02) and abdominal (p=0.05) obesity phenotypes for CVD mortality. Therefore, all models were stratified by sex. Attained age was set as the time-scale as recommended in observational studies (24), hence, all models were inherently and non-parametrically controlled for age (model 1). Further adjustments were made for smoking (model 2), plus leisure-time physical activity, education and alcohol consumption (model 3). The proportional hazard assumption was evaluated using Schoenfeld residuals. In models with all-cause mortality, non-proportional hazards for smoking status were handled by allowing separate baseline hazards for subgroups of the data, i.e. stratified Cox models. We reported adjusted hazard ratios (HR) with 95% confidence intervals (CI) for each pair of outcome and exposure.
Next, in separate models, we fitted continuous BMI and waist circumference using restricted cubic splines against all-cause and CVD mortality, respectively, while adjusting for the same covariates as in model 3 above, in addition to metabolic health. Fitting three knots provided the lowest Akaike information criterion and were thus sufficient, as recommended by Harrell (25). We assessed non-linearity by testing models with the linear term against models with both linear and a cubic spline term using likelihood ratio test. Non-linearity was considered present if p<0.05. We also assessed interaction between metabolic health status and BMI/waist circumference using likelihood ratio tests. If there was a significant interaction, we kept the interaction term in the model; if there was no interaction, metabolic health status was kept in the model as a covariate. Adjusted HR (95% CI) of all-cause and CVD mortality, respectively, were plotted against BMI and waist circumference, respectively, with separate curves for metabolically healthy and unhealthy, using the sex-specific sample median of BMI or waist circumference as reference values. In models with a significant interaction, metabolically healthy with the sex-specific sample median of BMI or waist circumference were used as reference.
We used R software for statistical computing (26).
2.8 Sensitivity analysis
We excluded 1) ever-smokers and 2) participants with pre-existing diseases (or prescribed drugs for cardiometabolic disease) in sensitivity analyses. Furthermore, we analysed data with more conservative cut-offs for MetS-components: waist circumference (≥88/102 cm in women/men), random triglycerides (≥2.1 mmol/L), and random glucose (≥11.1 mmol/L). We also repeated the analyses in the full sample, adjusting for sex. Finally, we used multiple imputation to address missing data on at least one variable for 2030 participants (13.7%). The variables with the largest proportion of missing data were found for leisure-time physical activity (n=1322, 8.9%) and education (n=881, 5.9%). Characteristics differed between participants with complete and missing data (Supplementary Table 1). The mechanism for missing information was assumed to be missing-at-random (27). We used a rich set of relevant variables, performed 20 imputations, and pooled the data according to Rubin’s rules using the ‘mice’ package in R (28). Because metabolic health is a known mediator of the relationship between obesity and mortality, we also ran the analyses of continuous BMI/waist circumference vs mortality without adjusting for metabolic health.