Study design and population

Data were sourced from the cross-sectional “Nituuchischaayihtitaau Aschii: A Multi-Community Environment-and-Health Study in Eeyou Istchee”, a collaboration of the Cree Board of Health and Social Services of James Bay (CBHSSJB) with Laval, McGill and McMaster Universities. The study design has been described previously [15]. Briefly, a random sample of participants was recruited from seven communities of Eeyou Istchee (latitude > 49.6° N). Data were collected during the spring and/or summer of 2005, 2007, 2008 and 2009. Participants were advised to fast overnight and, during the next day’s appointment, a research nurse measured anthropometric data and collected venous blood samples which were kept frozen at −80 °C and transported to the CHUQ Research Centre, Québec (Canada), for biological analysis. Inclusion criteria for the present analysis were age 8–17 years and blood samples collected under fasting conditions (≥ 8 h). The study population also included participants aged between 0 and 7 years old. However, anthropometric and clinical measurements were not assessed in this age group. Two of the initial 290 participants selected were excluded because of missing BMI data, leaving 288 participants for analysis.

Participation was voluntary, and written informed consent was given by one of the child’s parents or guardian. Ethics approval was obtained from all participating institutions.

Anthropometric data

Weight without shoes was measured with a bioelectrical impedance scale (Tanita Corp., Arlington Heights, IL, USA). Height without shoes was quantified using a measuring tape with patients standing barefoot on a hard surface. Waist circumference (WC) was assessed at the end of exhalation by tape located midway between the lower margin of the last floating rib and the top of the iliac crest [1]. Height was recorded to the nearest cm, and WC, to the nearest 0.5 cm. BMI was calculated by dividing weight (kg) by squared height (m2).

Weight status was defined according to 2005 IOTF [7], 2000 CDC [8], and 2007 WHO criteria [9]. IOTF cut-off values are age- and sex-specific extrapolations of adult overweight (BMI ≥ 25 kg/m2) and obesity (BMI ≥ 30 kg/m2) definitions at 18 years. CDC BMI age- and sex-specific growth references classify overweight as 85th percentile ≤ BMI < 95th percentile, and obesity, as BMI ≥ 95th percentile. WHO BMI-for-age categorizes overweight as BMI > +1 standard deviation above the WHO growth standard median while BMI > +2 standard deviations is considered as obesity. Participants who were neither overweight nor obese were defined as normal weight. BMI z-scores were calculated by the CDC SAS program [16]. Body fat percentage (%) was assessed by bioelectrical impedance analyzer (Tanita TBF-300, GHT Canada, Laval, QC, Canada).

Cardiometabolic risk factors

Systolic (SBP) and diastolic blood pressure (DBP) were measured according to Canadian Hypertensive Education Program recommendations [17]. Fasting plasma glucose was quantified by spectrophotometric assay (Vitros 950 Chemistry Station, Ortho-Clinical Diagnostics, Raritan, NJ, USA), and fasting plasma insulin, by immunoassay with chemiluminescent detection (Advia Centaur, Siemens, Washington, DC, USA). Homeostatic model assessment 2 of insulin resistance (HOMA2-IR) was calculated from fasting plasma glucose and insulin levels [18]. Triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were evaluated by enzymatic methods (Vitros 950 Chemistry Station, Ortho-Clinical Diagnostics). TG/HDL-C ratio was ascertained by dividing TG by HDL-C concentrations. Lipids were not assessed in participants aged 8 to 14 years during the 2005 (1 community) and 2007 (2 communities) surveys, which corresponded to missing values for 91 participants.

Statistical analyses

The characteristics of study participants per gender were compared by t-tests and reported as arithmetic means ± standard deviations. Overweight and obesity prevalence estimates (%) according to the three BMI classification systems were presented graphically, and differences between prevalence rates were compared by chi-square tests. Agreements between BMI classification systems were evaluated by weighted kappa (κw) coefficients according to Landis and Koch’s guiding principles (19). κw coefficients between 0 and 0.20 are considered as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect [19]. Analysis of variance was used to investigate whether means of body fat and cardiometabolic risk factors were different according to weight status. Tests for trend were assessed by assigning the median BMI value to each weight status category and modelling this value as a continuous variable using the contrast statement of the SAS PROC GLM procedure. Mean differences in body fat and cardiometabolic risk factors according to agreement and or non-agreement between weight status based on IOTF/CDC and IOTF/WHO classification systems were calculated. All statistical analyses were performed with SAS software (version 9.4, SAS Institute Inc., Cary, NC, USA). Two-sided P < 0.05 values were considered to be statistically significant.