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To evaluate whether the lifestyle intervention MetSLIM targeting individuals of low socio-economic status of Turkish, Moroccan and Dutch origin was successful in improving waist circumference and other cardiometabolic risk factors, lifestyle behaviour and quality of life.
A quasi-experimental intervention study (Netherlands Trial Register NTR3721). The intervention group participated in a 12-month combined dietary and physical activity programme. Examinations were performed at baseline and after 12 months. Participants underwent anthropometric measurements and blood withdrawal, and completed questionnaires on dietary intake, physical activity and quality of life.
Socio-economically deprived neighbourhoods in two Dutch cities, involving non-blinded ethnicity-matched and gender-matched research assistants, dietitians and sports instructors.
Mainly Turkish (49 %) and Dutch (36 %) subjects, aged 30–70 years, with a waist-to-height ratio of >0·5 (intervention, n 117; control, n 103). Dropout was 31 %.
At 12 months, the intervention group showed greater improvements than the control group in waist circumference (β=−3·3 cm, 95 % CI −4·7, −1·8, P<0·001) and other obesity measures. Additionally, greater reductions were observed for total cholesterol (β=−0·33 mmol/l, 95 % CI −0·56, −0·10, P=0·005) and LDL cholesterol (β=−0·35 mmol/l, 95 % CI −0·56, −0·14, P=0·001). Dietary changes were significant for fibre intake (β=1·5 g/4184 kJ (1000 kcal), 95 % CI 0·3, 2·7, P=0·016). Compared with the control group, the intervention group reported a decrease in total minutes of physical activity (β=−573 min/week, 95 % CI −1126, −21, P=0·042) and showed improvements in the quality-of-life domains ‘health transition’ and ‘general health’.
MetSLIM was shown to be effective in improving waist circumference, total and LDL cholesterol, and quality of life among Dutch and Turkish individuals living in deprived neighbourhoods.
Primary cilia are organelles that are present on many different cell types, either transiently or permanently. They play a crucial role in receiving signals from the environment and passing these signals to other parts of the cell. In that way, they are involved in diverse processes such as adipocyte differentiation and olfactory sensation. Mutations in genes coding for ciliary proteins often have pleiotropic effects and lead to clinical conditions, ciliopathies, with multiple symptoms. In this study, we reviewed observations from ciliopathies with obesity as one of the symptoms. It shows that variation in cilia-related genes is itself not a major cause of obesity in the population but may be a part of the multifactorial aetiology of this complex condition. Both common polymorphisms and rare deleterious variants may contribute to the obesity risk. Genotype–phenotype relationships have been noticed. Among the ciliary genes, obesity differs with regard to severity and age of onset, which may relate to the influence of each gene on the balance between pro- and anti-adipogenic processes. Analysis of the function and location of the proteins encoded by these ciliary genes suggests that obesity is more linked to activities at the basal area of the cilium, including initiation of the intraflagellar transport, but less to the intraflagellar transport itself. Regarding the role of cilia, three possible mechanistic processes underlying obesity are described: adipogenesis, neuronal food intake regulation and food odour perception.
Endothelial dysfunction (ED) and low-grade inflammation (LGI) have a role in the development of CVD. The two studies reported here explored the effects of dietary proteins and carbohydrates on markers of ED and LGI in overweight/obese individuals with untreated elevated blood pressure. In the first study, fifty-two participants consumed a protein mix or maltodextrin (3×20 g/d) for 4 weeks. Fasting levels and 12 h postprandial responses of markers of ED (soluble intercellular adhesion molecule 1 (sICAM), soluble vascular cell adhesion molecule 1 (sVCAM), soluble endothelial selectin and von Willebrand factor) and markers of LGI (serum amyloid A, C-reactive protein and sICAM) were evaluated before and after intervention. Biomarkers were also combined into mean Z-scores of ED and LGI. The second study compared 4 h postprandial responses of ED and LGI markers in forty-eight participants after ingestion of 0·6 g/kg pea protein, milk protein and egg-white protein. In addition, postprandial responses after maltodextrin intake were compared with a protein mix and sucrose. The first study showed significantly lower fasting ED Z-scores and sICAM after 4 weeks on the high-protein diet (P≤0·02). The postprandial studies found no clear differences of ED and LGI between test meals. However, postprandial sVCAM decreased more after the protein mix compared with maltodextrin in both studies (P≤0·04). In conclusion, dietary protein is beneficial for fasting ED, but not for fasting LGI, after 4 weeks of supplementation. On the basis of Z-scores, postprandial ED and LGI were not differentially affected by protein sources or carbohydrates.
Diet composition may affect blood pressure (BP), but the mechanisms are unclear. The aim of the present study was to compare postprandial BP-related responses to the ingestion of pea protein, milk protein and egg-white protein. In addition, postprandial BP-related responses to the ingestion of maltodextrin were compared with those to the ingestion of sucrose and a protein mix. We hypothesised that lower postprandial total peripheral resistance (TPR) and BP levels would be accompanied by higher plasma concentrations of nitric oxide, insulin, glucagon-like peptide 1 (GLP-1) and glucagon. On separate occasions, six meals were tested in a randomised order in forty-eight overweight or obese adults with untreated elevated BP. Postprandial responses of TPR, BP and plasma concentrations of insulin, glucagon, GLP-1 and nitrite, nitroso compounds (RXNO) and S-nitrosothiols (NOx) were measured for 4 h. No differences were observed in TPR responses. Postprandial BP levels were higher after the ingestion of the egg-white-protein meal than after that of meals containing the other two proteins (P≤ 0·01). The ingestion of the pea-protein meal induced the highest NOx response (P≤ 0·006). Insulin and glucagon concentrations were lowest after the ingestion of the egg-white-protein meal (P≤ 0·009). Postprandial BP levels were lower after the ingestion of the maltodextrin meal than after that of the protein mix and sucrose meals (P≤ 0·004), while postprandial insulin concentrations were higher after the ingestion of the maltodextrin meal than after that of the sucrose and protein mix meals after 1–2 h (P≤ 0·0001). Postprandial NOx, GLP-1 and glucagon concentrations were lower after the ingestion of the maltodextrin meal than after that of the protein mix meal (P≤ 0·008). In conclusion, different protein and carbohydrate sources induce different postprandial BP-related responses, which may be important for BP management. Lower postprandial BP levels are not necessarily accompanied by higher NOx, insulin, glucagon or GLP-1 responses.
Dairy products have previously been reported to be associated with beneficial effects on body weight and metabolic risk markers. Moreover, primary data from the Diet, Obesity and Genes (DiOGenes) study indicate a weight-maintaining effect of a high-protein–low-glycaemic index diet. The objective of the present study was to examine putative associations between consumption of dairy proteins and changes in body weight and metabolic risk markers after weight loss in obese and overweight adults. Results were based on secondary analyses of data obtained from overweight and obese adults who completed the DiOGenes study. The study consisted of an 8-week weight-loss phase and a 6-month weight-maintenance (WM) phase, where the subjects were given five different diets varying in protein content and glycaemic index. In the present study, data obtained from all the subjects were pooled. Dairy protein intake was estimated from 3 d dietary records at two time points (week 4 and week 26) during the WM phase. Body weight and metabolic risk markers were determined at baseline (week − 9 to − 11) and before and at the end of the WM phase (week 0 and week 26). Overall, no significant associations were found between consumption of dairy proteins and changes in body weight and metabolic risk markers. However, dairy protein intake tended to be negatively associated with body weight gain (P= 0·08; β = − 0·17), but this was not persistent when controlled for total protein intake, which indicates that dairy protein adds no additional effect to the effect of total protein. Therefore, the present study does not report that dairy proteins are more favourable than other proteins for body weight regulation.
In the present controlled, randomised, multiple cross-over dietary intervention study, we aimed to identify potential biomarkers for dietary protein from dairy products, meat and grain, which could be useful to estimate intake of these protein types in epidemiological studies. After 9 d run-in, thirty men and seventeen women (22 (sd 4) years) received three high-protein diets (aimed at approximately 18 % of energy (en%)) in random order for 1 week each, with approximately 14 en% originating from either meat, dairy products or grain. We used a two-step approach to identify biomarkers in urine and plasma. With principal component discriminant analysis, we identified amino acids (AA) from the plasma or urinary AA profile that were distinctive between diets. Subsequently, after pooling total study data, we applied mixed models to estimate the predictive value of those AA for intake of protein types. A very good prediction could be made for the intake of meat protein by a regression model that included urinary carnosine, 1-methylhistidine and 3-methylhistidine (98 % of variation in intake explained). Furthermore, for dietary grain protein, a model that included seven AA (plasma lysine, valine, threonine, α-aminobutyric acid, proline, ornithine and arginine) made a good prediction (75 % of variation explained). We could not identify biomarkers for dairy protein intake. In conclusion, specific combinations of urinary and plasma AA may be potentially useful biomarkers for meat and grain protein intake, respectively. These findings need to be cross-validated in other dietary intervention studies.
Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene–diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile after weight loss and in response to a given diet, among overweight European adults participating in the Diet Obesity and Genes study. By multiple linear regressions, 240 SNPs in twenty-four candidate genes were investigated for SNP main and SNP–diet interaction effects on total cholesterol, LDL-cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect), and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP–dietary protein interaction effect on TAG was identified for lipin 1 (LPIN1) rs4315495, with a decrease in TAG of − 0·26 mmol/l per A-allele/protein unit (95 % CI − 0·38, − 0·14, P= 0·000043). In conclusion, we investigated SNP–diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration.
Hypertension is highly prevalent among renal transplant recipients (RTR) and a risk factor for graft failure and cardiovascular events. Protein intake has been claimed to affect blood pressure (BP) in the general population and may affect renal function. We examined the association of dietary protein with BP and renal function in RTR. We included 625 RTR (age 53 (sd 13) years; 57 % male). Protein intake was assessed with a FFQ, differentiating between animal and plant protein. BP was measured according to a strict protocol. Creatinine clearance and albuminuria were measured as renal parameters. Protein intake was 83 (sd 12) g/d, of which 63 % derived from animal sources. BP was 136 (sd 17) mmHg systolic (SBP) and 83 (sd 11) mmHg diastolic (DBP). Creatinine clearance was 66 (sd 26) ml/min; albuminuria 41 (10–178) mg/24 h. An inverse, though statistically insignificant, association was found between the total protein intake and both SBP (β = − 2·22 mmHg per sd, P= 0·07) and DBP (β = − 0·48 mmHg per sd, P= 0·5). Protein intake was not associated with creatinine clearance. Although albuminuria was slightly higher in the highest tertile of animal protein intake compared with the lowest tertile (66 v. 33 mg/d, respectively, P= 0·03), linear regression analyses did not reveal significant associations between dietary protein and albuminuria. Protein intake exceeded the current recommendations. Nevertheless, within the range of protein intake in our RTR population, we found no evidence for an association of dietary protein with BP and renal function. Intervention studies focusing on different protein types are warranted to clarify their effect on BP and renal function in RTR.
Weight regain after weight loss is common. In the Diogenes dietary intervention study, a high-protein and low-glycaemic index (GI) diet improved weight maintenance. The objective of the present study was to identify (1) blood profiles associated with continued weight loss and weight regain (2) blood biomarkers of dietary protein and GI levels during the weight-maintenance phase. Blood samples were collected at baseline, after 8 weeks of low-energy diet-induced weight loss and after a 6-month dietary intervention period from female continued weight losers (n 48) and weight regainers (n 48), evenly selected from four dietary groups that varied in protein and GI levels. The blood concentrations of twenty-nine proteins and three steroid hormones were measured. The changes in analytes during weight maintenance largely correlated negatively with the changes during weight loss, with some differences between continued weight losers and weight regainers. Increases in leptin (LEP) and C-reactive protein (CRP) were significantly associated with weight regain (P < 0·001 and P = 0·005, respectively), and these relationships were influenced by the diet. Consuming a high-protein and high-GI diet dissociated the positive relationship between the change in LEP concentration and weight regain. CRP increased during the weight-maintenance period only in weight regainers with a high-protein diet (P < 0·001). In addition, testosterone, luteinising hormone, angiotensinogen, plasminogen activator inhibitor-1, resistin, retinol-binding protein 4, insulin, glucagon, haptoglobin and growth hormone were also affected by the dietary intervention. The blood profile reflects not only the weight change during the maintenance period, but also the macronutrient composition of the dietary intervention, especially the protein level.
Ingestion of dietary protein is known to induce both insulin and glucagon secretion. These responses may be affected by the dose and the form (intact or hydrolysed) in which protein is ingested. The aim of the study was to investigate the effect of different amounts of intact protein and protein hydrolysate of a vegetable (soya) and animal (whey) protein on insulin and glucagon responses and to study the effect of increasing protein loads for both intact protein and protein hydrolysate in man. The study employed a repeated-measures design with Latin-square randomisation and single-blind trials. Twelve healthy non-obese males ingested three doses (0·3, 0·4 and 0·6 g/kg body weight) of intact soya protein (SPI) and soya protein hydrolysate (SPH). Another group of twelve healthy male subjects ingested three doses (0·3, 0·4 and 0·6 g/kg body weight) of intact whey protein (WPI) and whey protein hydrolysate (WPH). Blood was sampled before (t = 0) and 15, 30, 60, 90 and 120 min after protein ingestion for insulin, glucagon and glucose determination. SPI induced a higher total area under the curve for insulin and glucagon than SPH while no difference between WPI and WPH was found. Insulin and glucagon responses increased with increasing protein load for SPI, SPH, WPI and WPH, but the effect was more pronounced for glucagon. A higher dose of protein or its hydrolysate will result in a lower insulin:glucagon ratio, an important parameter for the control of postprandial substrate metabolism. In conclusion, insulin and glucagon responses were protein and hydrolysate specific.
The present paper is the introductory paper to a series of brief reviews representing the proceedings of a recent conference on ‘The biochemical basis for the health effects of exercise’ organized by the International Research Group on the Biochemistry of Exercise in conjunction with the Nutrition Society. Here the aim is to briefly review and highlight the main innovations presented during this meeting. The following topics were covered during the meeting: exercise signalling pathways controlling fuel oxidation during and after exercise; the fatty acid transporters of skeletal muscle; mechanisms involved in exercise-induced mitochondrial biogenesis in skeletal muscle; new methodologies and insights in the regulation of fat metabolism during exercise; muscle hypertrophy: the signals of insulin, amino acids and exercise; adipose tissue–liver–muscle interactions leading to insulin resistance. In these symposia state-of-the-art knowledge on how physical exercise exerts its effects on health was presented. The fast-growing number of identified pathways and processes involved in the health effects of physical exercise, which were discussed during the meeting, will help to develop tailored physical-activity regimens in the prevention of inactivity-induced deterioration of health.
Energy expenditure rises above resting energy expenditure when physical activity is performed. The activity-induced energy expenditure varies with the muscle mass involved and the intensity at which the activity is performed: it ranges between 2 and 18 METs approximately. Differences in duration, frequency and intensity of physical activities may create considerable variations in total energy expenditure. The Physical Activity Level (= total energy expenditure divided by resting energy expenditure) varies between 1.2 and 2.2–2.5 in healthy adults. Increases in activity-induced energy expenditure have been shown to result in increases in total energy expenditure, which are usually greater than the increase in activity-induced energy expenditure itself. No evidence for increased spontaneous physical activity, measured by diary, interview or accelerometer, was found. However, this does not exclude increased physical activity that can not be measured by these methods. Part of the difference may also be explained by the post-exercise elevation of metabolic rate.
If changes in the level of physical activity affect energy balance, this should result in changes in body mass or body composition. Modest decreases of body mass and fat mass are found in response to increases in physical activity, induced by exercise training, which are usually smaller than predicted from the increase in energy expenditure. This indicates that the training-induced increase in total energy expenditure is at least partly compensated for by an increase in energy intake. There is some evidence that the coupling between energy expenditure and energy intake is less at low levels of physical activity. Increasing the level of physical activity for weight loss may therefore be most effective in the most sedentary individuals.