Dietary and lifestyle interventions in the management of the metabolic syndrome: present status and future perspective.

OBJECTIVE: To review the mechanisms underlying the metabolic syndrome, or syndrome X, in humans, and to delineate dietary and environmental strategies for its prevention. DESIGN: Review of selected papers of the literature. RESULTS: Hyperinsulinemia and insulin resistance play a key role in the development of the metabolic syndrome. Strategies aimed at reducing insulin resistance may be effective in improving the metabolic syndrome. They include low saturated fat intake, consumption of low-glycemic-index foods, physical exercise and prevention of obesity. CONCLUSIONS: Future research, in particular the genetic basis of the metabolic syndrome and the interorgan interactions responsible for insulin resistance, is needed to improve therapeutic strategies for the metabolic syndrome.

Substrate metabolism, nutrient balance and obesity development in children and adolescents: a target for intervention?

Obesity results from the organism's inability to maintain energy balance over a long term. Childhood obesity and its related factors and pathological consequences tend to persist into adulthood. A cluster of factors, including high energy density in the diet (high fat intake), low energy expenditure, and disturbed substrate oxidation, favour the increase in fat mass. Oxidation of three major macronutrients and their roles in the regulation of energy balance, particularly in children and adolescents, are discussed. Total glucose oxidation is not different between obese and lean children; exogenous glucose utilization is higher whereas endogenous glucose utilization is lower in obese compared with lean children. Carbohydrate composition of the diet determines carbohydrate oxidation regardless of fat content of the diet. Both exogenous and endogenous fat oxidation are higher in obese than in lean subjects. The influence of high fat intake on accumulation of fat mass is operative rather over a long term. Several future directions are addressed, such that a combination of increased physical activity and modification in diet composition, in terms of energy density and glycemic index, is recommended for children and adolescents.

Role of dietary carbohydrates and macronutrients in the pathogenesis of nonalcoholic fatty liver disease.

PURPOSE OF REVIEW: The prevalence of nonalcoholic fatty liver disease is increasing worldwide and there is strong evidence that dietary factors play a role in its pathogenesis. The present review aims to provide a better understanding of how carbohydrates and other macronutrients may affect the disease. RECENT FINDINGS: The effects of carbohydrates on the development of nonalcoholic fatty liver disease differ depending upon the carbohydrate type; high-glycemic index foods are related to increased hepatic fat in both rodents and humans. Similarly, simple carbohydrates, such as fructose, stimulate hepatic de-novo lipogenesis and decrease lipid oxidation, thus leading to increased fat deposition. The underlying mechanisms may involve the activation of transcription factors. Fat intake broadly leads to hepatic fat deposition in rodents but few data are available on humans. Both carbohydrates and fat trigger inflammatory factors, which are closely related to metabolic disorders and nonalcoholic fatty liver disease. Lifestyle interventions appear to be the most appropriate first-line treatment for nonalcoholic fatty liver disease. SUMMARY: There is strong evidence that the diet may affect the development of nonalcoholic fatty liver disease. Although simple carbohydrates are clearly shown to have deleterious effects in humans, the role of fat remains controversial. Further studies will be required to evaluate the effects of macronutrient composition on the development of nonalcoholic fatty liver disease.

Metabolic effects of fructose and the worldwide increase in obesity.

While virtually absent in our diet a few hundred years ago, fructose has now become a major constituent of our modern diet. Our main sources of fructose are sucrose from beet or cane, high fructose corn syrup, fruits, and honey. Fructose has the same chemical formula as glucose (C(6)H(12)O(6)), but its metabolism differs markedly from that of glucose due to its almost complete hepatic extraction and rapid hepatic conversion into glucose, glycogen, lactate, and fat. Fructose was initially thought to be advisable for patients with diabetes due to its low glycemic index. However, chronically high consumption of fructose in rodents leads to hepatic and extrahepatic insulin resistance, obesity, type 2 diabetes mellitus, and high blood pressure. The evidence is less compelling in humans, but high fructose intake has indeed been shown to cause dyslipidemia and to impair hepatic insulin sensitivity. Hepatic de novo lipogenesis and lipotoxicity, oxidative stress, and hyperuricemia have all been proposed as mechanisms responsible for these adverse metabolic effects of fructose. Although there is compelling evidence that very high fructose intake can have deleterious metabolic effects in humans as in rodents, the role of fructose in the development of the current epidemic of metabolic disorders remains controversial. Epidemiological studies show growing evidence that consumption of sweetened beverages (containing either sucrose or a mixture of glucose and fructose) is associated with a high energy intake, increased body weight, and the occurrence of metabolic and cardiovascular disorders. There is, however, no unequivocal evidence that fructose intake at moderate doses is directly related with adverse metabolic effects. There has also been much concern that consumption of free fructose, as provided in high fructose corn syrup, may cause more adverse effects than consumption of fructose consumed with sucrose. There is, however, no direct evidence for more serious metabolic consequences of high fructose corn syrup versus sucrose consumption.

A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

Lack of metabolic and behavioral adaptations in rural Gambian men with low body mass index.

Energy expenditure was measured by means of a respiratory chamber in two groups of adult rural Gambian men. The first group (n = 29) had a low body mass index (BMI; in kg/m2) < 18.5), whereas the control group (n = 29) had a higher BMI (> 22). This study shows that the energy expenditure of Gambian men with low BMI is not different from that of Gambian men with normal BMI when the results are normalized for fat-free mass or for weight by analysis of covariance. In Gambian men the nutritional status thus does not seem to affect energy metabolism notably. No differences in respiratory quotient, diet-induced thermogenesis, net work efficiency, spontaneous physical activity, heart rate, or urinary catecholamine excretion were observed between the two groups. It is, however, interesting to note that the basal metabolic rate of Gambian men, regardless of their nutritional status, is approximately 10% (range 4-12% depending on the reference value used) lower than that predicted for individuals living in industrialized countries.

Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index.

OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m(2)), a body fat mass index (BFMI; kg/m(2)), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI. METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated. RESULTS: FFMI values were 16.7 to 19.8 kg/m(2) for men and 14.6 to 16.8 kg/m(2) for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m(2) for men and 3.9 to 8.2 kg/m(2) for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m(2) in men and women, respectively, for obese BMI (>30 kg/m(2)). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively. CONCLUSION: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.

Measured and predicted resting metabolic rate in obese and nonobese adolescents.

OBJECTIVES: The validity of equations for the calculation of resting metabolic rate (RMR) were studied and new predictive equations were developed. STUDY DESIGN: The RMR was measured in a sample of 371 10- to 16-year-old prepubertal and postpubertal children. The study group included 193 male (116 nonobese and 77 obese) and 178 female (119 nonobese and 59 obese) subjects; for each group the RMRs predicted from five equations recommended for this age group were compared. The RMR was assessed by indirect calorimetry with a ventilated hood system for 45 minutes after an overnight fast. Body composition was estimated from skin-fold measurements. RESULTS: The mean +/- SD RMR was found to be 5600 +/- 972 kJ/24 hr and 7223 +/- 1220 kJ/24 hr in nonobese and obese boys, and 5112 +/- 632 kJ/24 hr and 6665 +/- 1106 kJ/24 hr in nonobese and obese girls, respectively. All five equations applicable to 10- to 16-year-old children overestimated RMR by 7.5% to 18.1% (p < 0.001 for each equation). Stepwise regression analysis, with independent variables such as age, weight, height, and gender, allowed development of new predictive equations for the calculation of RMR in 10- to 16-year-old boys (RMR = 50.9 Weight (kg) + 25.3 Height (cm) -50.3 Age (yr) + 26.9; R2 = 0.884, p < 0.0001) and girls (RMR = 51.2 Weight (kg) + 24.5 Height (cm) - 207.5 Age (yr) + 1629.8; R2 = 0.824, p < 0.0001). These predictive equations were tested in a second, independent cohort of children (80 male and 61 female subject) and were found to give a reliable estimate of RMR in 10- to 16-year-old obese and nonobese adolescents. CONCLUSIONS: The currently used predictive equations overestimate RMR in 10- to 16-year-old children. The use of the newly developed equations is recommended.

Body mass index and health-related quality of life among young Swiss men.

BACKGROUND: Studies about the association between body mass index (BMI) and health-related quality of life (HRQOL) are often limited, because they 1) did not include a broad range of health-risk behaviors as covariates; 2) relied on clinical samples, which might lead to biased results; and 3) did not incorporate underweight individuals. Hence, this study aims to examine associations between BMI (from being underweight through obesity) and HRQOL in a population-based sample, while considering multiple health-risk behaviors (low physical activity, risky alcohol consumption, daily cigarette smoking, frequent cannabis use) as well as socio-demographic characteristics. METHODS: A total of 5 387 young Swiss men (mean age = 19.99; standard deviation = 1.24) of a cross-sectional population-based study were included. BMI was calculated (kg/m²) based on self-reported height and weight and divided into 'underweight' (<18.5), 'normal weight' (18.5-24.9), 'overweight' (25.0-29.9) and 'obese' (≥30.0). Mental and physical HRQOL was assessed via the SF-12v2. Self-reported information on physical activity, substance use (alcohol, cigarettes, and cannabis) and socio-demographic characteristics also was collected. Logistic regression analyses were conducted to study the associations between BMI categories and below average mental or physical HRQOL. Substance use variables and socio-demographic variables were used as covariates. RESULTS: Altogether, 76.3% were normal weight, whereas 3.3% were underweight, 16.5% overweight and 3.9% obese. Being overweight or obese was associated with reduced physical HRQOL (adjusted OR [95% CI] = 1.58 [1.18-2.13] and 2.45 [1.57-3.83], respectively), whereas being underweight predicted reduced mental HRQOL (adjusted OR [95% CI] = 1.49 [1.08-2.05]). Surprisingly, obesity decreased the likelihood of experiencing below average mental HRQOL (adjusted OR [95% CI] = 0.66 [0.46-0.94]). Besides BMI, expressed as a categorical variable, all health-risk behaviors and socio-demographic variables were associated with reduced physical and/or mental HRQOL. CONCLUSIONS: Deviations from normal weight are, even after controlling for important health-risk behaviors and socio-demographic characteristics, associated with compromised physical or mental HRQOL among young men. Hence, preventive programs should aim to preserve or re-establish normal weight. The self-appraised positive mental well-being of obese men noted here, which possibly reflects a response shift, might complicate such efforts.

New reference tables and user-friendly Internet application for predicted heart weights.

BACKGROUND: Knowledge of normal heart weight ranges is important information for pathologists. Comparing the measured heart weight to reference values is one of the key elements used to determine if the heart is pathological, as heart weight increases in many cardiac pathologies. The current reference tables are old and in need of an update. AIMS: The purposes of this study are to establish new reference tables for normal heart weights in the local population and to determine the best predictive factor for normal heart weight. We also aim to provide technical support to calculate the predictive normal heart weight. METHODS: The reference values are based on retrospective analysis of adult Caucasian autopsy cases without any obvious pathology that were collected at the University Centre of Legal Medicine in Lausanne from 2007 to 2011. We selected 288 cases. The mean age was 39.2 years. There were 118 men and 170 women. Regression analyses were performed to assess the relationship of heart weight to body weight, body height, body mass index (BMI) and body surface area (BSA). RESULTS: The heart weight increased along with an increase in all the parameters studied. The mean heart weight was greater in men than in women at a similar body weight. BSA was determined to be the best predictor for normal heart weight. New reference tables for predicted heart weights are presented as a web application that enable the comparison of heart weights observed at autopsy with the reference values. CONCLUSIONS: The reference tables for heart weight and other organs should be systematically updated and adapted for the local population. Web access and smartphone applications for the predicted heart weight represent important investigational tools.

Impact of tight glycemic control on cerebral glucose metabolism after severe brain injury: a microdialysis study.

OBJECTIVES: To analyze the effect of tight glycemic control with the use of intensive insulin therapy on cerebral glucose metabolism in patients with severe brain injury. DESIGN: Retrospective analysis of a prospective observational cohort. SETTING: University hospital neurologic intensive care unit. PATIENTS: Twenty patients (median age 59 yrs) monitored with cerebral microdialysis as part of their clinical care. INTERVENTIONS: Intensive insulin therapy (systemic glucose target: 4.4-6.7 mmol/L [80-120 mg/dL]). MEASUREMENTS AND MAIN RESULTS: Brain tissue markers of glucose metabolism (cerebral microdialysis glucose and lactate/pyruvate ratio) and systemic glucose were collected hourly. Systemic glucose levels were categorized as within the target "tight" (4.4-6.7 mmol/L [80-120 mg/dL]) vs. "intermediate" (6.8-10.0 mmol/L [121-180 mg/dL]) range. Brain energy crisis was defined as a cerebral microdialysis glucose <0.7 mmol/L with a lactate/pyruvate ratio >40. We analyzed 2131 cerebral microdialysis samples: tight systemic glucose levels were associated with a greater prevalence of low cerebral microdialysis glucose (65% vs. 36%, p < 0.01) and brain energy crisis (25% vs.17%, p < 0.01) than intermediate levels. Using multivariable analysis, and adjusting for intracranial pressure and cerebral perfusion pressure, systemic glucose concentration (adjusted odds ratio 1.23, 95% confidence interval [CI] 1.10-1.37, for each 1 mmol/L decrease, p < 0.001) and insulin dose (adjusted odds ratio 1.10, 95% CI 1.04-1.17, for each 1 U/hr increase, p = 0.02) independently predicted brain energy crisis. Cerebral microdialysis glucose was lower in nonsurvivors than in survivors (0.46 +/- 0.23 vs. 1.04 +/- 0.56 mmol/L, p < 0.05). Brain energy crisis was associated with increased mortality at hospital discharge (adjusted odds ratio 7.36, 95% CI 1.37-39.51, p = 0.02). CONCLUSIONS: In patients with severe brain injury, tight systemic glucose control is associated with reduced cerebral extracellular glucose availability and increased prevalence of brain energy crisis, which in turn correlates with increased mortality. Intensive insulin therapy may impair cerebral glucose metabolism after severe brain injury.

Expression of MYC, IgM, As Well As Non-Germinal Centre B-Cell Like Immunophenotype and Positive Immunofish Index Predict a Worse Progression Free Survival and Overall Survival in a Series of 670 De Novo Diffuse Large B-Cell Lymphomas Included in Clinical Trials: A GELA Study of the 2003 Program

Introduction: Diffuse large B-cell lymphomas (DLBCL) represent a heterogeneous disease with variable clinical outcome. Identifying phenotypic biomarkers of tumor cells on paraffin sections that predict different clinical outcome remain an important goal that may also help to better understand the biology of this lymphoma. Differentiating non-germinal centre B-cell-like (non-GCB) from Germinal Centre B-cell-like (GCB) DLBCL according to Hans algorithm has been considered as an important immunohistochemical biomarker with prognostic value among patients treated with R-CHOP although not reproducibly found by all groups. Gene expression studies have also shown that IgM expression might be used as a surrogate for the GCB and ABC subtypes with a strong preferential expression of IgM in ABC DLBCL subtype. ImmunoFISH index based on the differential expression of MUM-1, FOXP1 by immunohistochemistry and on the BCL6 rearrangement by FISH has been previously reported (C Copie-Bergman, J Clin Oncol. 2009;27:5573-9) as prognostic in an homogeneous series of DLBCL treated with R-CHOP. In addition, oncogenic MYC protein overexpression by immunohistochemistry may represent an easy tool to identify the consequences of MYC deregulation in DLBCL. Our aim was to analyse by immunohistochemistry the prognostic relevance of MYC, IgM, GCB/nonGCB subtype and ImmunoFISH index in a large series of de novo DLBCL treated with Rituximab (R)-chemotherapy (anthracyclin based) included in the 2003 program of the Groupe d'Etude des Lymphomes de l'Adulte (GELA) trials. Methods: The 2003 program included patients with de novo CD20+ DLBCL enrolled in 6 different LNH-03 GELA trials (LNH-03-1B, -B, -3B, 39B, -6B, 7B) stratifying patients according to age and age-adjusted IPI. Tumor samples were analyzed by immunohistochemistry using CD10, BCL6, MUM1, FOXP1 (according to Barrans threshold), MYC, IgM antibodies on tissue microarrays and by FISH using BCL6 split signal DNA probes. Considering evaluable Hans score, 670 patients were included in the study with 237 (35.4%) receiving intensive R-ACVBP regimen and 433 (64.6%) R-CHOP/R-mini-CHOP. Results: 304 (45.4%) DLBCL were classified as GCB and 366 (54.6%) as non-GCB according to Hans algorithm. 337/567 cases (59.4%) were positive for the ImmunoFISH index (i.e. two out of the three markers positive: MUM1 protein positive, FOXP1 protein Variable or Strong, BCL6 rearrangement). Immunofish index was preferentially positive in the non-GCB subtype (81.3%) compared to the GCB subtype (31.2%), (p<0.001). IgM was recorded as positive in tumor cells in 351/637 (52.4%) DLBCL cases with a preferential expression in non-GCB 195 (53.3%) vs GCB subtype 100(32.9%), p<0.001). MYC was positive in 170/577 (29.5%) cases with a 40% cut-off and in 44/577 (14.2%) cases with a cut-off of 70%. There was no preferential expression of MYC among GCB or non-GCB subtype (p>0.4) for both cut-offs. Progression-free Survival (PFS) was significantly worse among patients with high IPI score (p<0.0001), IgM positive tumor (p<0.0001), MYC positive tumor with a 40% threshold (p<0.001), ImmunoFISH positive index (p<0.002), non-GCB DLBCL subtype (p<0.0001). Overall Survival (OS) was also significantly worse among patients with high IPI score (p<0.0001), IgM positive tumor (p=0.02), MYC positive tumor with a 40% threshold (p<0.01), ImmunoFISH positive index (p=0.02), non-GCB DLBCL subtype (p<0.0001). All significant parameters were included in a multivariate analysis using Cox Model and in addition to IPI, only the GCB/non-GCB subtype according to Hans algorithm predicted significantly a worse PFS among non-GCB subgroup (HR 1.9 [1.3-2.8] p=0.002) as well as a worse OS (HR 2.0 [1.3-3.2], p=0.003). This strong prognostic value of non-GCB subtyping was confirmed considering only patients treated with R- CHOP for PFS (HR 2.1 [1.4-3.3], p=0.001) and for OS (HR 2.3 [1.3-3.8], p=0.002). Conclusion: Our study on a large series of patients included in trials confirmed the relevance of immunohistochemistry as a useful tool to identify significant prognostic biomarkers for clinical use. We show here that IgM and MYC might be useful prognostic biomarkers. In addition, we confirmed in this series the prognostic value of the ImmunoFISH index. Above all, we fully validated the strong and independent prognostic value of the Hans algorithm, daily used by the pathologists to subtype DLBCL.

New reference tables and user-friendly Internet application for predicted heart weights.

BACKGROUND: Knowledge of normal heart weight ranges is important information for pathologists. Comparing the measured heart weight to reference values is one of the key elements used to determine if the heart is pathological, as heart weight increases in many cardiac pathologies. The current reference tables are old and in need of an update. AIMS: The purposes of this study are to establish new reference tables for normal heart weights in the local population and to determine the best predictive factor for normal heart weight. We also aim to provide technical support to calculate the predictive normal heart weight. METHODS: The reference values are based on retrospective analysis of adult Caucasian autopsy cases without any obvious pathology that were collected at the University Centre of Legal Medicine in Lausanne from 2007 to 2011. We selected 288 cases. The mean age was 39.2 years. There were 118 men and 170 women. Regression analyses were performed to assess the relationship of heart weight to body weight, body height, body mass index (BMI) and body surface area (BSA). RESULTS: The heart weight increased along with an increase in all the parameters studied. The mean heart weight was greater in men than in women at a similar body weight. BSA was determined to be the best predictor for normal heart weight. New reference tables for predicted heart weights are presented as a web application that enable the comparison of heart weights observed at autopsy with the reference values. CONCLUSIONS: The reference tables for heart weight and other organs should be systematically updated and adapted for the local population. Web access and smartphone applications for the predicted heart weight represent important investigational tools.

Cross-Sectional and Longitudinal Associations between Body Mass Index and Cardiometabolic Risk Factors in Adolescents in a Country of the African Region.

We assessed the association between several cardiometabolic risk factors (CRFs) (blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, uric acid, and glucose) in 390 young adults aged 19-20 years in Seychelles (Indian Ocean, Africa) and body mass index (BMI) measured either at the same time (cross-sectional analysis) or at the age of 12-15 years (longitudinal analysis). BMI tracked markedly between age of 12-15 and age of 19-20. BMI was strongly associated with all considered CRFs in both cross-sectional and longitudinal analyses, with some exceptions. Comparing overweight participants with those having a BMI below the age-specific median, the odds ratios for high blood pressure were 5.4/4.7 (male/female) cross-sectionally and 2.5/3.9 longitudinally (P < 0.05). Significant associations were also found for most other CRFs, with some exceptions. In linear regression analysis including both BMI at age of 12-15 and BMI at age of 19-20, only BMI at age of 19-20 remained significantly associated with most CRFs. We conclude that CRFs are predicted strongly by either current or past BMI levels in adolescents and young adults in this population. The observation that only current BMI remained associated with CRFs when including past and current levels together suggests that weight control at a later age may be effective in reducing CRFs in overweight children irrespective of past weight status.