To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Currently it is estimated that about 1 billion people globally have non-alcoholic fatty liver disease (NAFLD), a condition in which liver fat exceeds 5 % of liver weight in the absence of significant alcohol intake. Due to the central role of the liver in metabolism, the prevalence of NAFLD is increasing in parallel with the prevalence of obesity, insulin resistance and other risk factors of metabolic diseases. However, the contribution of liver fat to the risk of type 2 diabetes mellitus and CVD, relative to other ectopic fat depots and to other risk markers, is unclear. Various studies have suggested that the accumulation of liver fat can be reduced or prevented via dietary changes. However, the amount of liver fat reduction that would be physiologically relevant, and the timeframes and dose–effect relationships for achieving this through different diet-based approaches, are unclear. Also, it is still uncertain whether the changes in liver fat per se or the associated metabolic changes are relevant. Furthermore, the methods available to measure liver fat, or even individual fatty acids, differ in sensitivity and reliability. The present report summarises key messages of presentations from different experts and related discussions from a workshop intended to capture current views and research gaps relating to the points above.
Consumption of carbohydrate-containing foods leads to transient postprandial rises in blood glucose concentrations that vary between food types. Higher postprandial glycaemic exposures have particularly been implicated in the development of chronic cardiometabolic diseases. Reducing such diet-related exposures may be beneficial not only for diabetic patients but also for the general population. A variety of markers have been used to track different aspects of glycaemic exposures, with most of the relevant knowledge derived from diabetic patients. The assessment of glycaemic exposures among the non-diabetic population may require other, more sensitive markers. The present report summarises key messages of presentations and related discussions from a workshop organised by Unilever intended to consider currently applied markers of glycaemic exposure. The particular focus of the meeting was to identify the potential applicability of glycaemic exposure markers for studying dietary effects in the non-diabetic population. Workshop participants concluded that markers of glycaemic exposures are sparsely used in intervention studies among non-diabetic populations. Continuous glucose monitoring remains the optimal approach to directly assess glycaemic exposure. Markers of glycaemic exposure such as glycated Hb, fructosamine, glycated albumin, 1,5-anhydroglucitol and advanced glycation end products can be preferred dependent on the aspect of interest (period of exposure and glucose variability). For all the markers of glycaemia, the responsiveness to interventions will probably be smaller among the non-diabetic than among the diabetic population. Further validation and acceptance of existing glycaemic exposure markers applied among the non-diabetic population would aid food innovation and better design of dietary interventions targeting glycaemic exposure.
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.
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.
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.
Weight gain and risk of type 2 diabetes are inversely associated with a high intake of insoluble cereal fibres. Because nutrient-induced changes of ‘satiety hormones’ from the gut may play a role in this process, we evaluated the effects of purified insoluble fibres on postprandial responses of plasma peptide YY (PYY), serum ghrelin and satiety as secondary outcome measures of a study investigating effects of cereal fibres on parameters of glucose metabolism. Fourteen healthy women were studied on six occasions in a randomized, single-blind, controlled crossover design. After 24 h run-in periods and 10 h overnight fasts, subjects ingested isoenergetic and macronutrient matched portions of control white bread or fibre-enriched bread (wheat-fibre or oat-fibre) at 08.15 hours. Gut hormones and hunger scores were measured for 300 min. Basal PYY and ghrelin concentrations were not different between the test meals (P>0·15). Postprandial responses of PYY and ghrelin were blunted after the intake of wheat-fibre (total area under the curve (AUC) PYY, 177·9 (sem 8·1) (pmol/l) min; P=0·016; ghrelin 51·0 (sem 2·5) (pmol/l) min; P=0·003), but not after oat-fibre (PYY 226·7 (sem 25·7) (pmol/l) min; P>0·15; ghrelin 46·2 (sem 1·6) (pmol/l) min; P=0·127), compared to control (PYY 247·5 (sem 25·6) (pmol/l) min; ghrelin 42·5 (sem 1·3) (pmol/l) min). Postprandial hunger scores were unaffected by the different test meals (P>0·15). Thus, oat- and wheat-fibre consumption result in different postprandial responses of PYY and ghrelin, but interestingly do not differ in satiety effects.
Email your librarian or administrator to recommend adding this to your organisation's collection.