Metabolically healthy and unhealthy obesity phenotypes in the general population: the FIN-D2D Survey
- Equal contributors
1 Diabetes Prevention Unit, Division of Welfare and Health Promotion, National Institute for Health and Welfare, Helsinki, Finland
2 Department of Medicine, Division of Diabetes, University of Helsinki, Helsinki, Finland
3 Minerva Medical Research Institute, Helsinki, Finland
4 Department of Internal Medicine, South Ostrobothnia Central Hospital, Seinäjoki, Finland
5 Institute of Health Sciences (General Practice), University of Oulu, Finland
6 Unit of General Practice, Oulu University Hospital and Health Centre of Oulu, Oulu, Finland
7 Tampere University Hospital, Tampere, Finland
8 Department of Medicine/Diabetology and Endocrinology, Kuopio University Hospital, Kuopio, Finland
9 Finnish Diabetes Association, Tampere, Finland
10 Department of Medicine, Central Finland Central Hospital, Jyväskylä, Finland
11 Disease Risk Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
12 School of Medicine, Unit of Primary Health Care, University of Eastern Finland, Kuopio, Finland
13 Unit of Family Practice, Central Hospital of Central Finland, Jyväskylä, Finland
14 Institute of Public Health and Clinical Nutrition, Clinical Nutrition, University of Eastern Finland, and Research Unit, Kuopio University Hospital, Kuopio, Finland
BMC Public Health 2011, 11:754 doi:10.1186/1471-2458-11-754Published: 1 October 2011
The aim of this work was to examine the prevalence of different metabolical phenotypes of obesity, and to analyze, by using different risk scores, how the metabolic syndrome (MetS) definition discriminates between unhealthy and healthy metabolic phenotypes in different obesity classes.
The Finnish type 2 diabetes (FIN-D2D) survey, a part of the larger implementation study, was carried out in 2007. The present cross-sectional analysis comprises 2,849 individuals aged 45-74 years. The MetS was defined with the new Harmonization definition. Cardiovascular risk was estimated with the Framingham and SCORE risk scores. Diabetes risk was assessed with the FINDRISK score. Non-alcoholic fatty liver disease (NAFLD) was estimated with the NAFLD score. Participants with and without MetS were classified in different weight categories and analysis of regression models were used to test the linear trend between body mass index (BMI) and various characteristics in individuals with and without MetS; and interaction between BMI and MetS.
A metabolically healthy but obese phenotype was observed in 9.2% of obese men and in 16.4% of obese women. The MetS-BMI interaction was significant for fasting glucose, 2-hour plasma glucose, fasting plasma insulin and insulin resistance (HOMA-IR)(p < 0.001 for all). The prevalence of total diabetes (detected prior to or during survey) was 37.0% in obese individuals with MetS and 4.3% in obese individuals without MetS (p < 0.001). MetS-BMI interaction was significant (p < 0.001) also for the Framingham 10 year CVD risk score, NAFLD score and estimated liver fat %, indicating greater effect of increasing BMI in participants with MetS compared to participants without MetS. The metabolically healthy but obese individuals had lower 2-hour postload glucose levels (p = 0.0030), lower NAFLD scores (p < 0.001) and lower CVD risk scores (Framingham, p < 0.001; SCORE, p = 0.002) than normal weight individuals with MetS.
Undetected Type 2 diabetes was more prevalent among those with MetS irrespective of the BMI class and increasing BMI had a significantly greater effect on estimates of liver fat and future CVD risk among those with MetS compared with participants without MetS. A healthy obese phenotype was associated with a better metabolic profile than observed in normal weight individuals with MetS.