Haren et al. BMC Public Health 2010, 10:454 http://www.biomedcentral.com/1471-2458/10/454 Metabolic syndrome was defined according to the IDF definition of waist circumference (Europid cut-offs: ≥94 cm for men or ≥80 cm for women) plus any two of the following: raised triglycerides (≥1.7 mmol/L); reduced HDL cholesterol (< 1.03 mmol/L for men or < 1.29 mmol/L for women); raised blood pressure (≥130 mmHg for systolic or ≥85 mmHg for diastolic); and raised fasting plasma glucose (≥5.6 mmol/L) [26]. Specific treatment for lipid abnormalities or previously diagnosed hypertension and previously diagnosed T2DM were not used in classification. Waist circumference was used as a continuous variable and also to classify abdominal overweight and obesity, using World Health Organization (WHO) gen- der-specific criteria: overweight was ≥80 cm in women and ≥94 cm in men; obesity was ≥88 cm in women and ≥102 cm in men; < 80 cm in women and < 94 cm in men was classified as normal. BMI was used as a contin- uous variable and also to classify generalized overweight and obesity: < 25 Kg/m2 (normal); 25-29.9 Kg/m2 (over- weight) and ≥30 Kg/m2 (obesity) [27]. Diabetes was determined by clinical diagnosis of diabetes, verified by participants’ medical records, or 2 hour glucose toler- ance test or fasting blood glucose ≥7.0 mmol/L [28]. Blood pressure, fasting triglycerides, total cholesterol, HDL and glucose were used as continuous variables in all analyses. Derivation of the metabolic risk factor cluster variable (MR cluster) Cluster Analysis is the process of assigning members of a population into groups such that the members of each group share common characteristics. The method is an example of unsupervised learning. There is no a priori assumption made about what the groups represent or indeed the number of groups. Expectation Maximisation (EM) cluster analysis was applied to standardized (mean centred) variables: waist circumference, TG, HDL, systo- lic and diastolic blood pressure and fasting glucose, and was performed in STATISTICA (data analysis software system), version 8.0 (StatSoft, Inc. (2007) http://www. statsoft.com). To determine the major contributing vari- ables to cluster membership, partial least squares (PLS) was applied since the predictors (the original variables) are highly correlated and so ordinary multinomial regression is inaccurate due to multicollinearity. Detail on both EM cluster analysis and PLS can be found in Additional file 1. Statistical Analysis A priori it was expected that Aboriginal and TSI people would differ significantly in their anthropometric and metabolic characteristics, so comparisons for all vari- ables were made across Aboriginal versus TSI ethnicity. Comparisons were also made for all variables across Page 3 of 10 normal versus elevated GGT. Comparative analyses were by Chi-squared tests for categorical variables, independent t-tests for normally distributed continuous variables or Mann-Whitney rank-sum tests for non- normally distributed continuous variables. Unadjusted comparisons between MR clusters were analyzed using independent samples t-tests for continuous variables and Chi-squared analysis with Fisher’s exact test for categorical variables. Logistic regression models were built to determine the associations (log odds) of drink- ing, metabolic syndrome and MR cluster with elevated GGT (≥50 U/L) for the whole population and stratified by ethnicity and sex. Similarly, odds ratios for the asso- ciation of abdominal obesity, BMI categories, metabolic syndrome and MR cluster with elevated GGT, were cal- culated for the whole population and stratified by drink- ing status. Separate logistic regression models were built to examine the effect on elevated GGT of interactions between drinking and: sex; ethnicity; overweight and obesity defined by BMI and waist circumference; and metabolic risk defined by IDF MetS criteria and MR cluster. Models were adjusted for age, sex, ethnicity, BMI, smoking and physical activity levels where appro- priate. Data analyses were performed using STATA ver- sion 10.1 (STATA Corp, College Station, Texas, USA). Results Participants with missing data on serum GGT (N = 76) were not different in age, sex, BMI, metabolic profile or lifestyle behaviours than those with GGT data. Those missing information regarding metabolic syndrome (N = 224) were not different in age, sex, BMI, and lifestyle behaviours but had lower mean BP than those with complete metabolic syndrome information (125.5 ± 18.4 v 130.4 ± 19.6 mmHg, P < 0.001). Those missing records of drinking (N = 83) tended to be older (43.2 ± 17.6 v 37.2 ± 15.3 years, P < 0.001) and more likely to be female (65.1% v 51.4%, P = 0.014). Table 1 describes the metabolic and behavioral char- acteristics of the cohort by Aboriginal and TSI status and sex. In both sexes, the TSI population had a signifi- cantly lower prevalence of elevated GGT, a higher pre- valence of obesity, type 2 diabetes, metabolic syndrome and physically active people, and a lower proportion of smokers and drinkers, when compared with the Aborigi- nal population (all Ps < 0.05). Waist circumference, BMI, SBP and fasting glucose were higher and GGT and triglycerides were lower in TSI when compared with Aboriginal people (all Ps < 0.05). The prevalence of adverse MR cluster was significantly greater in TSI when compared to Aboriginal men, but was similar in women. Table 2 describes the demographic, behavioral and metabolic characteristics of the cohort by normal and