D Garrel

Relationship between insulin sensitivity and the triglyceride-HDL-C ratio in overweight and obese postmenopausal women : a MONET study.

The objective of this cross-sectional study was to examine the relationship between the triglyceride-HDL-cholesterol ratio (TG:HDL-C) and insulin sensitivity in overweight and obese sedentary postmenopausal women. The study population consisted of 131 non-diabetic overweight and obese sedentary postmenopausal women (age; 57.7+/-5.0 y; body mass index (BMI), 32.2+/-4.3 kg/m2). Subjects were characterized by dividing the entire cohort into tertiles based on the TG:HDL-C (T1<0.86 vs. T2=0.86 to 1.35 vs. T3>1.35, respectively). We measured (i) insulin sensitivity (using the hyperinsulinenic-euglycemic clamp and homeostasis model assessment (HOMA)), (ii) body composition (using dual-energy X-ray absorptiometry), (iii) visceral fat (using computed tomography), (iv) plasma lipids, C-reactive protein, 2 h glucose concentration during an oral glucose tolerance test (2 h glucose), as well as fasting glucose and insulin, (v) peak oxygen consumption, and (vi) lower-body muscle strength (using weight training equipment). Significant correlations were observed between the TG:HDL-C and the hyperinsulinemic-euglycemic clamp (r=-0.45; p<0.0001), as well as with HOMA (r=0.42; p<0.0001). Moreover, the TG:HDL-C significantly correlated with lean body mass, visceral fat, 2 h glucose, C-reactive protein, and muscle strength. Stepwise regression analysis showed that the TG:HDL-C explained 16.4% of the variation in glucose disposal in our cohort, which accounted for the greatest source of unique variance. Other independent predictors of glucose disposal were 2 h glucose (10.1%), C-reactive protein (CRP; 7.6%), and peak oxygen consumption (5.8%), collectively (including the TG:HDL-C) explaining 39.9% of the unique variance. In addition, the TG:HDL-C was the second predictor for HOMA, accounting for 11.7% of the variation. High levels of insulin sensitivity were associated with low levels of the TG:HDL-C. In addition, the TG:HDL-C was a predictor for glucose disposal rates and HOMA values in our cohort of overweight and obese postmenopausal women.

Surrogate indexes vs. euglycaemic-hyperinsulinemic clamp as an indicator of insulin resistance and cardiovascular risk factors in overweight and obese postmenopausal women

BACKGROUND There is considerable interest in validating the most convenient method to estimate insulin sensitivity in clinical research protocols that could best indicate cardiovascular risk factors. To address this issue we examined the interrelationships of several cardiovascular risk factors with surrogate indexes such as fasting insulin, the homeostasis model assessment (HOMA), the quantitative insulin sensitivity check index (QUICKI) and the revised QUICKI vs the euglycaemic-hyperinsulinemic (EH) clamp in a non-diabetic overweight or obese postmenopausal female population. DESIGN Cross-sectional study involving 88 obese postmenopausal women (age: 57.5+/-5.0 yrs; body mass index: 32.52+/-4.4 kg/m2; percent body fat: 46.35+/-4.9%). METHODS Insulin sensitivity was determined by the EH clamp technique as well as by surrogate indexes such as fasting insulin, HOMA, log HOMA, QUICKI and revised QUICKI. Body composition and body fat distribution were measured using dual energy x-ray absorptiometry and computed tomography, respectively. RESULTS Correlations between insulin resistance indexes (fasting insulin, revised QUICKI, QUICKI, log HOMA, HOMA) vs glucose disposal were similar (range of rs=0.40 to 0.49), suggesting that no index was superior to another with respect to its relationship with the EH clamp. Correlations between the insulin resistance indexes with plasma lipids were comparable among all indexes, however, systolic blood pressure, visceral fat and C-reactive protein were moderately superior with index vs the EH clamp. CONCLUSION Surrogate measures of insulin resistance, in particular fasting insulin, are simple tools appropriate for epidemiological studies that can be used as substitutes for the EH clamp to estimate glucose disposal and cardiovascular risk factors in overweight and obese postmenopausal women.

Relationship between the metabolic syndrome and physical activity energy expenditure : a MONET study.

The purpose of this cross-sectional study was to examine the association between the metabolic syndrome (MetS) and physical activity energy expenditure (PAEE) in overweight and obese sedentary postmenopausal women. The study population consisted of 137 overweight and obese sedentary postmenopausal women (age, 57.7 +/- 4.8 years; BMI, 32.4 +/- 4.6 kg.m(-2)). Subjects had the MetS if 3 out of the following 5 criteria were met: visceral fat > 130 cm2, high-density lipoprotein (HDL) cholesterol < 1.29 mmol.L(-1), fasting triglycerides > or = 1.7 mmol.L(-1), blood pressure > or = 130/85 mmHg, and fasting glucose > or =5.6 mmol.L(-1). We measured (i) body composition (by dual-energy X-ray absorptiometry); (ii) visceral fat (by computed tomography); (iii) insulin sensitivity (using the hyperinsulinemic-euglycemic clamp); (iv) plasma lipids, fasting glucose, and insulin, as well as 2 h glucose during an oral glucose tolerance test; (v) resting blood pressure; (vi) peak oxygen consumption (VO2 peak); (vii) PAEE (using doubly labeled water); and (viii) lower-body muscle strength (using weight-training equipment). Forty-two women (30.7%) had the MetS in our cohort. Individuals without the MetS had significantly higher levels of PAEE (962 +/- 296 vs. 837 +/- 271 kcal.d(-1); p < 0.05), VO2 peak (18.2 +/- 3.0 vs. 16.7 +/- 3.2 mL.min(-1).kg(-1); p < 0.05), and insulin sensitivity, as well as significantly lower levels of 2 h glucose and central lean body mass. No differences in total energy expenditure, resting metabolic rate, and muscle strength between groups were observed. Logistic regression analysis showed that 2 h glucose (odds ratio (OR): 1.50 (95% CI 1.17-1.92)), central lean body mass (OR: 1.17 (95% CI 1.05-1.31)), and PAEE (OR: 0.998 (95% CI 0.997-1.000)), but not VO2 peak and (or) muscle strength, were independent predictors of the MetS. Lower levels of PAEE and higher levels of 2 h glucose, as well as central lean body mass, are independent determinants of the MetS in our cohort of overweight and obese postmenopausal women.

Metabolic syndrome in three ethnic groups using current definitions

According to two current definitions, the prevalence of the metabolic syndrome (MetS) among black Haitians of Montreal was <20%, 30%-36% in Algonquin Indians of Quebec, and >45% in Mexicans of Oaxaca (all aged 35-60 y). Although phenotypes were different, high triglycerides and fasting dysglycemia were good predictors of MetS in all three groups using both definitions. The international cut-offs for abdominal obesity were not predictive of MetS in the Haitian subjects.

Psychosocial profile of the metabolically healthy but obese postmenopausal woman

unique subset of obese individuals has been identified that appears to have a normal metabolic profile [1,2]. These individuals, now known as “Metabolically healthy but obese” (MHO) individuals, despite having excessive body fatness, display a favorable metabolic profile characterized by normal levels of insulin sensitivity, a favorable lipid and inflammation profile as well as no hypertension. It is presently unclear as to why MHO individuals appear to be “protected” to the development of metabolic disturbances associated with obesity. It seems only two studies have examined several metabolic characteristics associated with the normal metabolic profile of the MHO individual including some potential causal factors [1,2]. However, no data appear to be currently available on the psychosocial profile of the MHO individual. Several studies have shown that psychosocial factors are associated with insulin resistance and cardiovascular disease [3-5]. These studies provide tantalizing evidence that several psychosocial factors may be implicated in the normal metabolic profile of some obese individuals. Therefore, the purpose of this study was to investigate the psychosocial profile of the MHO individual in obese postmenopausal women. We hypothesized that the MHO phenotype will be associated with a favorable psychosocial profile. We examined the psychosocial characteristics in a sample of 81 obese (BMI ≥ 27 kg/m), non-diabetic, non-smoking, sedentary postmenopausal women with stable weight. As previously described and analyzed [2], subjects were classified as MHO or as “at risk” based on the upper and lower quartiles (M ≥ 12.62 vs. M ≤ 9.29 mg/min/FFM, respectively) of insulin sensitivity as measured by the hyperinsulinemic/euglycaemic clamp technique. Thereafter, we determined 1) body composition (by dual energy x-ray absorptiometry), and 2) psychosocial factors using validated self-administered questionnaires that measured quality of life (Medical Outcomes Study General Health Survey), perceived stress (Cohen et al. Perceived Stress Scale), selfesteem (Rosenberg Self-Esteem Scale), body-esteem (Mendelson et al. Body-Esteem Scale), perceived benefits of controlling weight (six item scale), perceived risks for developing heart disease and diabetes (two item scale), and self-efficacy that is perceived capacity for controlling body weight (six item scale). The Cronbach alpha coefficients, internal consistency reliability, for each measure varied between 0.73 to 0.91. It should be noted that higher scores for the psychosocial measures signify a more favourable profile, except for perceived stress, self-esteem and perceived risk. Level of education was also measured. The physical and psychosocial characteristics of MHO (n = 21) and “at risk” (n = 20) individuals are described as follows. Both groups of women were comparable for age (57.4 ± 6.3 vs. 59.6 ± 5.1 yrs), body mass index (32.6 ± 4.0 vs. 34.8 ± 4.1 kg/m), fat mass (40.9 ± 8.6 vs. 41.3 ± 8.3 kg), level of education (48 vs. 42% less than University) as well as all psychosocial factors, which included quality of life (80.7 ± 12.7 vs. 78.8 ± 10.2), perceived stress (19.5 ± 7.2 vs. 19.7 ± 9.0), self-esteem (1.9 ± 0.4 vs. 1.8 ± 0.5), body esteem (1.5 ± 0.6 vs. 1.1 ± 0.5), perceived benefits (3.7 ± 0.4 vs. 3.9 ± 0.2), perceived risk (2.8 ± 0.9 vs. 3.3 ± 0.9), self-efficacy (2.8 ± 0.4 vs. 2.7 ± 0.4) and number of meals (2.8 ± 0.4 vs. 2.9 ± 0.3), respectively. By design, insulin sensitivity was significantly higher in MHO individuals compared to “at risk” subjects (15.4 ± 2.4 vs. 7.5 ± 1.2 mg/min/FFM, respectively) (P < 0.001).

Effects of a weight-loss program on body composition and the metabolic profile in obese individuals displaying various obesity phenotypes

Introduction: A network is a group of people or organizations that are closely connected and work with one another. Reasons to build networks and multiple-disciplinary teamwork include: finding solutions to real-world complex problems, such as obesity and chronic diseases and their risk factors, and pooling resources and expertise. Methods: A literature review was undertaken to identify promotors and barriers for multiple-disciplinary teamwork success. results: To build good teamwork, there are eight themes to consider, as summarized by the acronym “TEAMWORK”: Team, Enthusiasm, Accessibility, Motivation, Workplace, Objective, Role, and Kinship. A number of examples are used from national and international riskfactor surveillance networks, including Rapid Risk Factor Surveillance System (RRFSS, Ontario), Canadian Alliance for Regional Risk Factor Surveillance (CARRFS, Canada), Americas’ Network for Chronic Disease Surveillance (AMNET, Latin America), and World Alliance for Risk Factor Surveillance (WARFS, global). Discussion: The proposed eight themes should help build efficient networks, which, in turn, should enhance capacity in risk factor surveillance and other public health files, by providing multipledisciplinary perspectives, and pooling resources and expertise.