Monoacylglycerol as a metabolic coupling factor in glucose-stimulated insulin secretion

Les cellules beta pancréatiques sécrètent l’insuline lors d’une augmentation post-prandiale du glucose dans le sang. Ce processus essentiel est contrôlé par des facteurs physiologiques, nutritionnels et pathologiques. D’autres sources d’énergie, comme les acides aminés (leucine et glutamine) ou les acides gras potentialisent la sécrétion d’insuline. Une sécrétion d’insuline insuffisante au besoin du corps déclanche le diabète. Le rôle que joue l’augmentation du calcium intracellulaire et les canaux K+/ATP dans la sécrétion d’insuline est bien connu. Bien que le mécanisme exact de la potentialisation de la sécrétion d’insuline par les lipides est inconnu, le cycle Glycérolipides/Acides gras (GL/FFA) et son segment lipolytique ont été reconnu comme un composant essentiel de la potentialisation lipidique de la sécrétion d’insuline. Le diacylglycérol, provenant de la lipolyse, a été proposé comme un signal lipidique important d’amplification. Cependant, l’hydrolyse des triglycérides et des diacylglycérides a été démontrée essentielle pour la sécrétion d’insuline stimulée par le glucose, en suggérant un rôle du monoacylglycérol (MAG) dans ce processus. Dans cette étude, on démontre que la réduction de la sécrétion d’insuline stimulée par le glucose, lors d’une inhibition de la lipolyse, est restaurée par l’addition de MAG. Dans les cellules beta pancréatiques, le niveau de MAG augmente en présence des concentrations élevées du glucose, et également lorsqu’on inhibe l’enzyme MAG hydrolase abhydrolase-6 (ABHD6) avec l’inhibiteur spécifique WWL70. L’analyse lipidomique a démontré qu’après la stimulation des cellules beta pancréatiques avec le glucose et aussi avec le WWL70, l’espèce la plus accumulée de MAG était le 1-stearoylglycérol (1-SG). L’addition de 1-SG, de 1-palmitoylglycérol (1-PG) ou de WWL70 augmente la sécrétion d’insuline stimulée par le glucose, et cette augmentation est indépendante de la génération de acides gras à partir de MAG. Cela suggère que le MAG est un signal lipidique pour la potentialisation de la sécrétion d’insuline stimulée par le glucose. De plus, la surexpression du gène d’ABHD6 dans les cellules INS832/13 cause une réduction de la sécrétion d’insuline, due probablement à la diminution des niveaux intracellulaire de MAG. Avec le but de comprendre le mécanisme moléculaire impliqué dans la potentialisation de la sécrétion d’insuline par le MAG, on a bloqué l’action du récepteur vanilloid-1 (TRPV1) liant le MAG par l’agent pharmacologiste, AMG9810. Le traitement des cellules beta pancréatique par AMG9810 entraîne une diminution de la potentialisation de la sécrétion de l’insuline induite par le MAG. Il est a noter que le MAG pourrait activer TRPV1 par une liaison physique dans la membrane cellulaire interne; ce qui entraînerai l’entrée du calcium dans la cellule, et ensuite la stimulation de l’exocytose des granules à insuline. En soutien de cette hypothèse, on a trouvé une diminution du calcium intracellulaire lorsqu’on traite au AMG9810 des cellules beta pancréatique de rat (provenant des îlots dispersés) stimulées au glucose et au WWL70. L’ensemble des résultats suggère que le MAG est un médiateur de la potentialisation lipidique de la sécrétion d’insuline stimulée par le glucose. Vu que l’inhibition pharmacologique d’ABHD6 augmente la sécrétion d’insuline, on pourra conclure que cette enzyme représente une cible thérapeutique potentielle dans le développement des médicaments anti-diabétiques, visant une augmentation de la sécrétion d’insuline.

Body composition and its relationship to metabolic risk factors in Asian children

Obesity is a major public health problem in both developed and developing countries. The body mass index (BMI) is the most common index used to define obesity. The universal application of the same BMI classification across different ethnic groups is being challenged due to the inability of the index to differentiate fat mass (FM) and fat�]free mass (FFM) and the recognized ethnic differences in body composition. A better understanding of the body composition of Asian children from different backgrounds would help to better understand the obesity�]related health risks of people in this region. Moreover, the limitations of the BMI underscore the necessity to use where possible, more accurate measures of body fat assessment in research and clinical settings in addition to BMI, particularly in relation to the monitoring of prevention and treatment efforts. The aim of the first study was to determine the ethnic difference in the relationship between BMI and percent body fat (%BF) in pre�]pubertal Asian children from China, Lebanon, Malaysia, the Philippines, and Thailand. A total of 1039 children aged 8�]10 y were recruited using a non�]random purposive sampling approach aiming to encompass a wide BMI range from the five countries. Percent body fat (%BF) was determined using the deuterium dilution technique to quantify total body water (TBW) and subsequently derive proportions of FM and FFM. The study highlighted the sex and ethnic differences between BMI and %BF in Asian children from different countries. Girls had approximately 4.0% higher %BF compared with boys at a given BMI. Filipino boys tended to have a lower %BF than their Chinese, Lebanese, Malay and Thai counterparts at the same age and BMI level (corrected mean %BF was 25.7�}0.8%, 27.4�}0.4%, 27.1�}0.6%, 27.7�}0.5%, 28.1�}0.5% for Filipino, Chinese, Lebanese, Malay and Thai boys, respectively), although they differed significantly from Thai and Malay boys. Thai girls had approximately 2.0% higher %BF values than Chinese, Lebanese, Filipino and Malay counterparts (however no significant difference was seen among the four ethnic groups) at a given BMI (corrected mean %BF was 31.1�}0.5%, 28.6�}0.4%, 29.2�}0.6%, 29.5�}0.6%, 29.5�}0.5% for Thai, Chinese, Lebanese, Malay and Filipino girls, respectively). However, the ethnic difference in BMI�]%BF relationship varied by BMI. Compared with Caucasians, Asian children had a BMI 3�]6 units lower for a given %BF. More than one third of obese Asian children in the study were not identified using the WHO classification and more than half were not identified using the International Obesity Task Force (IOTF) classification. However, use of the Chinese classification increased the sensitivity by 19.7%, 18.1%, 2.3%, 2.3%, and 11.3% for Chinese, Lebanese, Malay, Filipino and Thai girls, respectively. A further aim of the first study was to determine the ethnic difference in body fat distribution in pre�]pubertal Asian children from China, Lebanon, Malaysia, and Thailand. The skin fold thicknesses, height, weight, waist circumference (WC) and total adiposity (as determined by deuterium dilution technique) of 922 children from the four countries was assessed. Chinese boys and girls had a similar trunk�]to�]extremity skin fold thickness ratio to Thai counterparts and both groups had higher ratios than the Malays and Lebanese at a given total FM. At a given BMI, both Chinese and Thai boys and girls had a higher WC than Malays and Lebanese (corrected mean WC was 68.1�}0.2 cm, 67.8�}0.3 cm, 65.8�}0.4 cm, 64.1�}0.3 cm for Chinese, Thai, Lebanese and Malay boys, respectively; 64.2�}0.2 cm, 65.0�}0.3 cm, 62.9�}0.4 cm, 60.6�}0.3 cm for Chinese, Thai, Lebanese and Malay girls, respectively). Chinese boys and girls had lower trunk fat adjusted subscapular/suprailiac skinfold ratio compared with Lebanese and Malay counterparts. The second study aimed to develop and cross�]validate bioelectrical impedance analysis (BIA) prediction equations of TBW and FFM for Asian pre�]pubertal children from China, Lebanon, Malaysia, the Philippines, and Thailand. Data on height, weight, age, gender, resistance and reactance measured by BIA were collected from 948 Asian children (492 boys and 456 girls) aged 8�]10 y from the five countries. The deuterium dilution technique was used as the criterion method for the estimation of TBW and FFM. The BIA equations were developed from the validation group (630 children randomly selected from the total sample) using stepwise multiple regression analysis and cross�]validated in a separate group (318 children) using the Bland�]Altman approach. Age, gender and ethnicity influenced the relationship between the resistance index (RI = height2/resistance), TBW and FFM. The BIA prediction equation for the estimation of TBW was: TBW (kg) = 0.231�~Height2 (cm)/resistance (ƒ¶) + 0.066�~Height (cm) + 0.188�~Weight (kg) + 0.128�~Age (yr) + 0.500�~Sex (male=1, female=0) . 0.316�~Ethnicity (Thai ethnicity=1, others=0) �] 4.574, and for the estimation of FFM: FFM (kg) = 0.299�~Height2 (cm)/resistance (ƒ¶) + 0.086�~Height (cm) + 0.245�~Weight (kg) + 0.260�~Age (yr) + 0.901�~Sex (male=1, female=0) �] 0.415�~Ethnicity (Thai ethnicity=1, others=0) �] 6.952. The R2 was 88.0% (root mean square error, RSME = 1.3 kg), 88.3% (RSME = 1.7 kg) for TBW and FFM equation, respectively. No significant difference between measured and predicted TBW and between measured and predicted FFM for the whole cross�]validation sample was found (bias = �]0.1�}1.4 kg, pure error = 1.4�}2.0 kg for TBW and bias = �]0.2�}1.9 kg, pure error = 1.8�}2.6 kg for FFM). However, the prediction equation for estimation of TBW/FFM tended to overestimate TBW/FFM at lower levels while underestimate at higher levels of TBW/FFM. Accuracy of the general equation for TBW and FFM compared favorably with both BMI�]specific and ethnic�]specific equations. There were significant differences between predicted TBW and FFM from external BIA equations derived from Caucasian populations and measured values in Asian children. There were three specific aims of the third study. The first was to explore the relationship between obesity and metabolic syndrome and abnormalities in Chinese children. A total of 608 boys and 800 girls aged 6�]12 y were recruited from four cities in China. Three definitions of pediatric metabolic syndrome and abnormalities were used, including the International Diabetes Federation (IDF) and National Cholesterol Education Program (NCEP) definition for adults modified by Cook et al. and de Ferranti et al. The prevalence of metabolic syndrome varied with different definitions, was highest using the de Ferranti definition (5.4%, 24.6% and 42.0%, respectively for normal�]weight, overweight and obese children), followed by the Cook definition (1.5%, 8.1%, and 25.1%, respectively), and the IDF definition (0.5%, 1.8% and 8.3%, respectively). Overweight and obese children had a higher risk of developing the metabolic syndrome compared to normal�]weight children (odds ratio varied with different definitions from 3.958 to 6.866 for overweight children, and 12.640�]26.007 for obese children). Overweight and obesity also increased the risk of developing metabolic abnormalities. Central obesity and high triglycerides (TG) were the most common while hyperglycemia was the least frequent in Chinese children regardless of different definitions. The second purpose was to determine the best obesity index for the prediction of cardiovascular (CV) risk factor clustering across a 2�]y follow�]up among BMI, %BF, WC and waist�]to�]height ratio (WHtR) in Chinese children. Height, weight, WC, %BF as determined by BIA, blood pressure, TG, high�]density lipoprotein cholesterol (HDL�]C), and fasting glucose were collected at baseline and 2 years later in 292 boys and 277 girls aged 8�]10 y. The results showed the percentage of children who remained overweight/obese defined on the basis of BMI, WC, WHtR and %BF was 89.7%, 93.5%, 84.5%, and 80.4%, respectively after 2 years. Obesity indices at baseline significantly correlated with TG, HDL�]C, and blood pressure at both baseline and 2 years later with a similar strength of correlations. BMI at baseline explained the greatest variance of later blood pressure. WC at baseline explained the greatest variance of later HDL�]C and glucose, while WHtR at baseline was the main predictor of later TG. Receiver�]operating characteristic (ROC) analysis explored the ability of the four indices to identify the later presence of CV risk. The overweight/obese children defined on the basis of BMI, WC, WHtR or %BF were more likely to develop CV risk 2 years later with relative risk (RR) scores of 3.670, 3.762, 2.767, and 2.804, respectively. The final purpose of the third study was to develop age�] and gender�]specific percentiles of WC and WHtR and cut�]off points of WC and WHtR for the prediction of CV risk in Chinese children. Smoothed percentile curves of WC and WHtR were produced in 2830 boys and 2699 girls aged 6�]12 y randomly selected from southern and northern China using the LMS method. The optimal age�] and gender�]specific thresholds of WC and WHtR for the prediction of cardiovascular risk factors clustering were derived in a sub�]sample (n=1845) by ROC analysis. Age�] and gender�]specific WC and WHtR percentiles were constructed. The WC thresholds were at the 90th and 84th percentiles for Chinese boys and girls, respectively, with sensitivity and specificity ranging from 67.2% to 83.3%. The WHtR thresholds were at the 91st and 94th percentiles for Chinese boys and girls, respectively, with sensitivity and specificity ranging from 78.6% to 88.9%. The cut�]offs of both WC and WHtR were age�] and gender�]dependent. In conclusion, the current thesis quantifies the ethnic differences in the BMI�]%BF relationship and body fat distribution between Asian children from different origins and confirms the necessity to consider ethnic differences in body composition when developing BMI and other obesity index criteria for obesity in Asian children. Moreover, ethnicity is also important in BIA prediction equations. In addition, WC and WHtR percentiles and thresholds for the prediction of CV risk in Chinese children differ from other populations. Although there was no advantage of WC or WHtR over BMI or %BF in the prediction of CV risk, obese children had a higher risk of developing the metabolic syndrome and abnormalities than normal�]weight children regardless of the obesity index used.

Akt, AS160, metabolic risk factors and aerobic fitness in middle-aged women

Aims: This study investigated the association between the basal (rest) insulin-signaling proteins, Akt, and the Akt substrate AS160, metabolic risk factors, inflammatory markers and aerobic fitness, in middle-aged women with varying numbers of metabolic risk factors for type 2 diabetes. Methods: Sixteen women (n = 16) aged 51.3+/-5.1 (mean +/-SD) years provided muscle biopsies and blood samples at rest. In addition, anthropometric characteristics and aerobic power were assessed and the number of metabolic risk factors for each participant was determined (IDF criteria). Results: The mean number of metabolic risk factors was 1.6+/-1.2. Total Akt was negatively correlated with IL-1 beta (r = -0.45, p = 0.046), IL-6 (r = -0.44, p = 0.052) and TNF-alpha (r = -0.51, p = 0.025). Phosphorylated AS160 was positively correlated with HDL (r = 0.58, p = 0.024) and aerobic fitness (r = 0.51, p = 0.047). Furthermore, a multiple regression analysis revealed that both HDL (t = 2.5, p = 0.032) and VO(2peak) (t = 2.4, p = 0.037) were better predictors for phosphorylated AS160 than TNF-alpha or IL-6 (p>0.05). Conclusions: Elevated inflammatory markers and increased metabolic risk factors may inhibit insulin-signaling protein phosphorylation in middle-aged women, thereby increasing insulin resistance under basal conditions. Furthermore, higher HDL and fitness levels are associated with an increased AS160 phosphorylation, which may in turn reduce insulin resistance.

Inflammation, hepatic enzymes and resistance training in individuals with metabolic risk factors

AIMS: Increases in inflammatory markers, hepatic enzymes and physical inactivity are associated with the development of the metabolic syndrome (MetS). We examined whether inflammatory markers and hepatic enzymes are correlated with traditional risk factors for MetS and studied the effects of resistance training (RT) on these emerging risk factors in individuals with a high number of metabolic risk factors (HiMF, 2.9 +/- 0.8) and those with a low number of metabolic risk factors (LoMF, 0.5 +/- 0.5). METHODS: Twenty-eight men and 27 women aged 50.8 +/- 6.5 years (mean +/- sd) participated in the study. Participants were randomized to four groups, HiMF training (HiMFT), HiMF control (HiMFC), LoMF training (LoMFT) and LoMF control (LoMFC). Before and after 10 weeks of RT [3 days/week, seven exercises, three sets with intensity gradually increased from 40-50% of one repetition maximum (1RM) to 75-85% of 1RM], blood samples were obtained for the measurement of pro-inflammatory cytokines, C-reactive protein (CRP), gamma-glutamyltransferase (GGT) and alanine aminotransferase (ALT). RESULTS: At baseline, HiMF had higher interleukin-6 (33.9%), CRP (57.1%), GGT (45.2%) and ALT (40.6%) levels, compared with LoMF (all P < 0.05). CRP, GGT and ALT correlated with the number of risk factors (r = 0.48, 0.51 and 0.57, respectively, all P < 0.01) and with other anthropometric and clinical measures (r range from 0.26 to 0.60, P < 0.05). RT did not significantly alter inflammatory markers or hepatic enzymes (all P > 0.05). CONCLUSIONS: HiMF was associated with increased inflammatory markers and hepatic enzyme concentrations. RT did not reduce inflammatory markers and hepatic enzymes in individuals with HiMF.

The influence of family history of hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults

Background Hypertension is a major contributor to the global non-communicable disease burden. Family history is an important non-modifiable risk factor for hypertension. The present study aims to describe the influence of family history (FH) on hypertension prevalence and associated metabolic risk factors in a large cohort of South Asian adults, from a nationally representative sample from Sri Lanka. Methods A cross-sectional survey among 5,000 Sri Lankan adults, evaluating FH at the levels of parents, grandparents, siblings and children. A binary logistic regression analysis was performed in all patients with ‘presence of hypertension’ as dichotomous dependent variable and using family history in parents, grandparents, siblings and children as binary independent variables. The adjusted odds ratio controlling for confounders (age, gender, body mass index, diabetes, hyperlipidemia and physical activity) are presented below. Results In all adults the prevalence of hypertension was significantly higher in patients with a FH (29.3 %, n = 572/1951) than those without (24.4 %, n = 616/2530) (p < 0.001). Presence of a FH significantly increased the risk of hypertension (OR:1.29; 95 % CI:1.13-1.47), obesity (OR:1.36; 95 % CI: 1.27–1.45), central obesity (OR:1.30; 95 % CI 1.22–1.40) and metabolic syndrome (OR:1.19; 95 % CI: 1.08–1.30). In all adults presence of family history in parents (OR:1.28; 95 % CI: 1.12–1.48), grandparents (OR:1.34; 95 % CI: 1.20–1.50) and siblings (OR:1.27; 95 % CI: 1.21–1.33) all were associated with significantly increased risk of developing hypertension. Conclusions Our results show that the prevalence of hypertension was significantly higher in those with a FH of hypertension. FH of hypertension was also associated with the prevalence of obesity, central obesity and metabolic syndrome. Individuals with a FH of hypertension form an easily identifiable group who may benefit from targeted interventions.

Dietary and exercise interventions to improve quality of life, metabolic risk factors and androgen deficiency symptoms in men with prostate cancer undergoing androgen deprivation therapy

Introduction Lifestyle interventions might be useful in the management of adverse effects of androgen deprivation therapy (ADT) in men with prostate cancer. Objectives To examine the effects of dietary and exercise interventions on quality of life (QoL), metabolic risk factors and androgen deficiency symptoms in men with prostate cancer undergoing ADT. Methods CINAHL, Cochrane library, Medline and PsychINFO were searched to identify randomised controlled trials published from January, 2004 to October, 2014. Data extraction and methodological quality assessment was independently conducted by two reviewers. Meta-analysis was conducted using RevMan® 5.3.5. Results Of 2183 articles retrieved, 11 studies met the inclusion criteria and had low risk of bias.Nine studies evaluated exercise (resistance and/or aerobic and/or counselling) and three evaluated dietary supplementation. Median sample size =79 (33–121) and median intervention duration was 12 weeks (12–24). Exercise improved QoL measures (SMD 0.26, 95%CI −0.01 to 0.53) but not body composition, metabolic risk or vasomotor symptoms. Qualitative analysis indicated soy (or isoflavone) supplementation did not improve vasomotor symptoms; however, may improve QoL. Conclusions Few studies have evaluated the efficacy of lifestyle interventions in the management of adverse effects of ADT. We found inconclusive results for exercise in improving QoL and negative results for other outcomes. For soy-based products, we found negative results for modifying vasomotor symptoms and inconclusive results for improving QoL. Future work should investigate the best mode of exercise for improving QoL and other interventions such as dietary counselling should be investigated for their potential to modify these outcomes.

Serum uric acid and metabolic risk factors in three ethnic groups: Asian Indians and Creoles in Mauritius and Chinese in Qingdao, China

In humans with a loss of uricase the final oxidation product of purine catabolism is uric acid (UA). The prevalence of hyperuricemia has been increasing around the world accompanied by a rapid increase in obesity and diabetes. Since hyperuricemia was first described as being associated with hyperglycemia and hypertension by Kylin in 1923, there has been a growing interest in the association between elevated UA and other metabolic abnormalities of hyperglycemia, abdominal obesity, dyslipidemia, and hypertension. The direction of causality between hyperuricemia and metabolic disorders, however, is unceartain. The association of UA with metabolic abnormalities still needs to be delineated in population samples. Our overall aims were to study the prevalence of hyperuricemia and the metabolic factors clustering with hyperuricemia, to explore the dynamical changes in blood UA levels with the deterioration in glucose metabolism and to estimate the predictive capability of UA in the development of diabetes. Four population-based surveys for diabetes and other non-communicable diseases were conducted in 1987, 1992, and 1998 in Mauritius, and in 2001-2002 in Qingdao, China. The Qingdao study comprised 1 288 Chinese men and 2 344 women between 20-74, and the Mauritius study consisted of 3 784 Mauritian Indian and Mauritian Creole men and 4 442 women between 25-74. In Mauritius, re-exams were made in 1992 and/or 1998 for 1 941 men (1 409 Indians and 532 Creoles) and 2 318 non pregnant women (1 645 Indians and 673 Creoles), free of diabetes, cardiovascular diseases, and gout at baseline examinations in 1987 or 1992, using the same study protocol. The questionnaire was designed to collect demographic details, physical examinations and standard 75g oral glucose tolerance tests were performed in all cohorts. Fasting blood UA and lipid profiles were also determined. The age-standardized prevalence in Chinese living in Qingdao was 25.3% for hyperuricemia (defined as fasting serum UA > 420 μmol/l in men and > 360 μmol/l in women) and 0.36% for gout in adults between 20-74. Hyperuricemia was more prevalent in men than in women. One standard deviation increase in UA concentration was associated with the clustering of metabolic risk factors for both men and women in three ethnic groups. Waist circumference, body mass index, and serum triglycerides appeared to be independently associated with hyperuricemia in both sexes and in all ethnic groups except in Chinese women, in whom triglycerides, high-density lipoprotein cholesterol, and total cholesterol were associated with hyperuricemia. Serum UA increased with increasing fasting plasma glucose levels up to a value of 7.0 mmol/l, but significantly decreased thereafter in mainland Chinese. An inverse relationship occurred between 2-h plasma glucose and serum UA when 2-h plasma glucose higher than 8.0 mmol/l. In the prospective study in Mauritius, 337 (17.4%) men and 379 (16.4%) women developed diabetes during the follow-up. Elevated UA levels at baseline increased 1.14-fold in risk of incident diabetes in Indian men and 1.37-fold in Creole men, but no significant risk was observed in women. In conclusion, the prevalence of hyperuricemia was high in Chinese in Qingdao, blood UA was associated with the clustering of metabolic risk factors in Mauritian Indian, Mauritian Creole, and Chinese living in Qingdao, and a high baseline UA level independently predicted the development of diabetes in Mauritian men. The clinical use of UA as a marker of hyperglycemia and other metabolic disorders needs to be further studied. Keywords: Uric acid, Hyperuricemia, Risk factors, Type 2 Diabetes, Incidence, Mauritius, Chinese

GWAS of butyrylcholinesterase activity identifies four novel loci, independent effects within BCHE and secondary associations with metabolic risk factors

Serum butyrylcholinesterase (BCHE) activity is associated with obesity, blood pressure and biomarkers of cardiovascular and diabetes risk. We have conducted a genome-wide association scan to discover genetic variants affecting BCHE activity, and to clarify whether the associations between BCHE activity and cardiometabolic risk factors are caused by variation in BCHE or whether BCHE variation is secondary to the metabolic abnormalities. We measured serum BCHE in adolescents and adults from three cohorts of Australian twin and family studies. The genotypes from approximately 2.4 million single-nucleotide polymorphisms (SNPs) were available in 8791 participants with BCHE measurements. We detected significant associations with BCHE activity at three independent groups of SNPs at the BCHE locus (P = 5.8 x 10(-262), 7.8 x 10(-47), 2.9 x 10(-12)) and at four other loci: RNPEP (P = 9.4 x 10(-16)), RAPH1-ABI2 (P = 4.1 x 10(-18)), UGT1A1 (P = 4.0 x 10(-8)) and an intergenic region on chromosome 8 (P = 1.4 x 10(-8)). These loci affecting BCHE activity were not associated with metabolic risk factors. On the other hand, SNPs in genes previously associated with metabolic risk had effects on BCHE activity more often than can be explained by chance. In particular, SNPs within FTO and GCKR were associated with BCHE activity, but their effects were partly mediated by body mass index and triglycerides, respectively. We conclude that variation in BCHE activity is due to multiple variants across the spectrum from uncommon/large effect to common/small effect, and partly results from (rather than causes) metabolic abnormalities.

Effects of Metabolic Risk Factors and Type 1 Diabetes on Brain Glucose and Brain Metabolites

The metabolic syndrome and type 1 diabetes are associated with brain alterations such as cognitive decline brain infarctions, atrophy, and white matter lesions. Despite the importance of these alterations, their pathomechanism is still poorly understood. This study was conducted to investigate brain glucose and metabolites in healthy individuals with an increased cardiovascular risk and in patients with type 1 diabetes in order to discover more information on the nature of the known brain alterations. We studied 43 20- to 45-year-old men. Study I compared two groups of non-diabetic men, one with an accumulation of cardiovascular risk factors and another without. Studies II to IV compared men with type 1 diabetes (duration of diabetes 6.7 ± 5.2 years, no microvascular complications) with non-diabetic men. Brain glucose, N-acetylaspartate (NAA), total creatine (tCr), choline, and myo-inositol (mI) were quantified with proton magnetic resonance spectroscopy in three cerebral regions: frontal cortex, frontal white matter, thalamus, and in cerebellar white matter. Data collection was performed for all participants during fasting glycemia and in a subgroup (Studies III and IV), also during a hyperglycemic clamp that increased plasma glucose concentration by 12 mmol/l. In non-diabetic men, the brain glucose concentration correlated linearly with plasma glucose concentration. The cardiovascular risk group (Study I) had a 13% higher plasma glucose concentration than the control group, but no difference in thalamic glucose content. The risk group thus had lower thalamic glucose content than expected. They also had 17% increased tCr (marker of oxidative metabolism). In the control group, tCr correlated with thalamic glucose content, but in the risk group, tCr correlated instead with fasting plasma glucose and 2-h plasma glucose concentration in the oral glucose tolerance test. Risk factors of the metabolic syndrome, most importantly insulin resistance, may thus influence brain metabolism. During fasting glycemia (Study II), regional variation in the cerebral glucose levels appeared in the non-diabetic subjects but not in those with diabetes. In diabetic patients, excess glucose had accumulated predominantly in the white matter where the metabolite alterations were also the most pronounced. Compared to the controls values, the white matter NAA (marker of neuronal metabolism) was 6% lower and mI (glia cell marker) 20% higher. Hyperglycemia is therefore a potent risk factor for diabetic brain disease and the metabolic brain alterations may appear even before any peripheral microvascular complications are detectable. During acute hyperglycemia (Study III), the increase in cerebral glucose content in the patients with type 1 diabetes was, dependent on brain region, between 1.1 and 2.0 mmol/l. An every-day hyperglycemic episode in a diabetic patient may therefore as much as double brain glucose concentration. While chronic hyperglycemia had led to accumulation of glucose in the white matter, acute hyperglycemia burdened predominantly the gray matter. Acute hyperglycemia also revealed that chronic fluctuation in blood glucose may be associated with alterations in glucose uptake or in metabolism in the thalamus. The cerebellar white matter appeared very differently from the cerebral (Study IV). In the non-diabetic men it contained twice as much glucose as the cerebrum. Diabetes had altered neither its glucose content nor the brain metabolites. The cerebellum seems therefore more resistant to the effects of hyperglycemia than is the cerebrum.

Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern

Background and aim: Cardiorespiratory fitness (CRF) and diet have been involved as significant factors towards the prevention of cardio-metabolic diseases. This study aimed to assess the impact of the combined associations of CRF and adherence to the Southern European Atlantic Diet (SEADiet) on the clustering of metabolic risk factors in adolescents. Methods and Results: A cross-sectional school-based study was conducted on 468 adolescents aged 15-18, from the Azorean Islands, Portugal. We measured fasting glucose, insulin, total cholesterol (TC), HDL-cholesterol, triglycerides, systolic blood pressure, waits circumference and height. HOMA, TC/HDL-C ratio and waist-to-height ratio were calculated. For each of these variables, a Z-score was computed by age and sex. A metabolic risk score (MRS) was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had 1SD of this score. CRF was measured with the 20 m-Shuttle-Run- Test. Adherence to SEADiet was assessed with a semi-quantitative food frequency questionnaire. Logistic regression showed that, after adjusting for potential confounders, unfit adolescents with low adherence to SEADiet had the highest odds of having MRS (OR Z 9.4; 95%CI:2.6e33.3) followed by the unfit ones with high adherence to the SEADiet (OR Z 6.6; 95% CI: 1.9e22.5) when compared to those who were fit and had higher adherence to SEADiet.

The effect of resistance training on functional capacity and quality of life in individuals with high and low number of metabolic risk factors

OBJECTIVE—There are limited data on the effects of resistance training on the capacity to perform activities of daily living (ADLs) and quality of life (QoL) for individuals with a high number of metabolic risk factors (HiMF). In this study, we examined the effect of resistance training on the capacity to perform ADLs and QoL in individuals with HiMF and compared any benefits with individuals with a low number of metabolic risk factors (LoMF).

RESEARCH DESIGN AND METHODS—Fifty-five untrained individuals, aged 50.8 ± 6.5 years, were randomized to four groups: HiMF training (HiMFT), HiMF control, LoMF training (LoMFT), and LoMF control. At baseline and after 10 weeks of resistance training, participants underwent anthropometric measurements and assessments of aerobic power (Vo2peak), muscle strength, capacity to perform ADLs, and a self-perceived QoL questionnaire. A repeated-measures ANOVA was used to examine the effect of training over time among groups.

RESULTS—Training increased lean body mass in both HiMFT (P = 0.03) and LoMFT (P = 0.03) groups. Total fat content and Vo2peak improved in the LoMFT group only. Muscle strength improved in both training groups (P < 0.01). Time to complete ADLs was reduced by 8.8% in the LoMFT group (P < 0.01) and 9.7% in the HiMF group (P < 0.01). Only the HiMFT group reported improvement in QoL.

CONCLUSIONS— Resistance training improved muscle strength and the capacity to perform ADLs in individuals with HiMF and LoMF. Resistance training improved QoL for the HiMF group, and this result was independent of changes in body fat content or aerobic power. Longer training regimens may be needed to improve QoL in individuals with LoMF.

Functional capacity and quality of life in middle-age men and women with high and low number of metabolic risk factors

It is not clear if men and women with high numbers of risk factors (HiMF) for metabolic syndrome (MetS) have impaired capacities to perform activities of daily living (ADL's) and lower quality of life (QoL) in the absence of symptomatic heart disease. Our results indicate that in women there is a correlation between the number of risk factors and the capacity to perform ADL's and QoL. This was not evident for men. These findings may partly explain why women tend to consult healthcare practitioners earlier in the disease process than men.

BDNF, metabolic risk factors, and resistance training in middle-aged individuals

Introduction and Purpose: Brain-derived neurotrophic factor (BDNF) and physical inactivity contribute to the development of the metabolic syndrome (MetS). There appears to be an association between BDNF and risk factors for MetS, and the effects of resistance training (RT) on BDNF and metabolic risk in middle-aged individuals with high and low numbers of metabolic risk factors (HiMF and LoMF, respectively) are unclear and are the focus of this research.

Methods: Forty-nine men (N = 25) and women (N = 24) aged 50.9 ± 6.2 yr were randomized to four groups, HiMF training (HiMFT), HiMF control (HiMFC), LoMF training (LoMFT), and LoMF control (LoMFC). Before and after 10 wk of RT, participants underwent tests for muscle strength and anthropometry, and a fasting blood sample was taken. Data were analyzed using Spearman correlations and repeated-measures ANOVA.

Results: BDNF was positively correlated with plasma triglycerides, glucose, HbA1C, and insulin resistance. BDNF was elevated in HiMF compared with LoMF (904.9 ± 270.6 vs 709.6 ± 239.8 respectively, P = 0.01). Training increased muscle strength and lean body mass but had no effect on BDNF levels or any examined risk factors.

Conclusion: BDNF levels correlated with risk factors for MetS and were elevated in individuals with HiMF. RT had no effect on BDNF levels or other risk factors for MetS. As RT has an effect on muscle strength and lean body mass, it should be added to other nonpharmacological interventions for middle-aged individuals with HiMF such as aerobic and/or diet.

Inflammation, hepatic enzymes and resistance training in individuals with metabolic risk factors

Aims Increases in inflammatory markers, hepatic enzymes and physical inactivity are associated with the development of the metabolic syndrome (MetS). We examined whether inflammatory markers and hepatic enzymes are correlated with traditional risk factors for MetS and studied the effects of resistance training (RT) on these emerging risk factors in individuals with a high number of metabolic risk factors (HiMF, 2.9 ± 0.8) and those with a low number of metabolic risk factors (LoMF, 0.5 ± 0.5).

Methods Twenty-eight men and 27 women aged 50.8 ± 6.5 years (mean ± sd) participated in the study. Participants were randomized to four groups, HiMF training (HiMFT), HiMF control (HiMFC), LoMF training (LoMFT) and LoMF control (LoMFC). Before and after 10 weeks of RT [3 days/week, seven exercises, three sets with intensity gradually increased from 40–50% of one repetition maximum (1RM) to 75–85% of 1RM], blood samples were obtained for the measurement of pro-inflammatory cytokines, C-reactive protein (CRP), -glutamyltransferase (GGT) and alanine aminotransferase (ALT).

Results At baseline, HiMF had higher interleukin-6 (33.9%), CRP (57.1%), GGT (45.2%) and ALT (40.6%) levels, compared with LoMF (all P < 0.05). CRP, GGT and ALT correlated with the number of risk factors (r = 0.48, 0.51 and 0.57, respectively, all P < 0.01) and with other anthropometric and clinical measures (r range from 0.26 to 0.60, P < 0.05). RT did not significantly alter inflammatory markers or hepatic enzymes (all P > 0.05).

Conclusions HiMF was associated with increased inflammatory markers and hepatic enzyme concentrations. RT did not reduce inflammatory markers and hepatic enzymes in individuals with HiMF.

Akt, AS160, metabolic risk factors and aerobic fitness in middle-aged women

This study investigated the association between the basal (rest) insulin-signaling proteins, Akt, and the Akt substrate AS160, metabolic risk factors, inflammatory markers and aerobic fitness, in middle-aged women with varying numbers of metabolic risk factors for type 2 diabetes. Methods: Sixteen women (n=16) aged 51.3±5.1 (mean ±SD) years provided muscle biopsies and blood samples at rest. In addition, anthropometric characteristics and aerobic power were assessed and the number of metabolic risk factors for each participant was determined (IDF criteria). Results: The mean number of metabolic risk factors was 1.6±1.2. Total Akt was negatively correlated with IL-1β (r = -0.45, p = 0.046), IL-6 (r = -0.44, p = 0.052) and TNF-α (r = -0.51, p = 0.025). Phosphorylated AS160 was positively correlated with HDL (r = 0.58, p= 0.024) and aerobic fitness (r = 0.51, p=0.047). Furthermore, a multiple regression analysis revealed that both HDL (t=2.5, p=0.032) and VO_2peak (t=2.4, p=0.037) were better predictor for phosphorylated AS160 than TNF-α or IL-6 (p>0.05). Conclusions: Elevated inflammatory markers and increased metabolic risk factors may inhibit insulin-signaling protein phosphorylation in middle-aged women, thereby increasing insulin resistance under basal conditions. Furthermore, higher HDL and fitness levels are associated with an increase AS160 phosphorylation, which may in turn reduce insulin resistance.