MRC Elsie Widdowson Laboratory

RISCK study

Scientific background

Metabolic syndrome affects an increasing proportion of the UK population and describes a clustering of metabolic abnormalities that predispose individuals to an increased risk of Type 2 diabetes (T2D) and cardiovascular disease (CVD). Obesity and a sedentary lifestyle are established causative risk factors contributing to metabolic syndrome, and many of its characteristics are ameliorated by weight loss and increased physical activity. Since insulin resistance is a key feature of metabolic syndrome, it follows that improvements in insulin sensitivity (Si) would also reduce cardiovascular risk factors associated with this condition. The role of changes in the macronutrient composition of the diet on metabolic syndrome and its associated CVD risks is uncertain, and there is a need to establish the impact of changes in diet composition on Si independent of weight loss.

A reduction in saturated fatty acids (SFA) is a key dietary target to reduce the risk of CVD. In the absence of weight loss, reductions in SFA are usually offset by increases in energy from other nutrients. The optimal strategy to replace energy from SFA is unclear. Debate has focused on the relative impact on blood lipid concentrations when SFA are replaced with monounsaturated fats (MUFA) or with carbohydrate (CHO). The dyslipidaemia associated with metabolic syndrome is characterised by raised plasma triglycerides (TG), low HDL-cholesterol (HDL-C) concentration and a predominance of small, dense low-density lipoprotein (sdLDL). There is uncertainty with regard to the effects of dietary fat modification on this dyslipidaemic profile and other CVD risk factors, not least insulin resistance. The type of CHO consumed may also influence both plasma lipids and Si. Observational studies suggest that diets rich in CHO-containing foods with a low glycaemic index (GI) are associated with a lower risk of T2D and CVD than those with a preponderance of high GI foods, however, evidence from intervention studies is limited and equivocal.

Purpose of project

The RISCK (Reading, Imperial, Surrey, Cambridge, King’s) project was established to address these issues, among subjects at risk of metabolic syndrome. RISCK tests the impact on CVD risk, with a change in insulin resistance as the primary outcome, of 4 different diets relative to a high SFA, high GI reference diet, in a suitably powered intervention. The 4 treatments were diets high in MUFA or low fat, each subdivided into a high or low GI group. The aim was to provide new data, on the effects of reducing SFA intake on cardiometabolic risk, to inform public health nutrition policy for the prevention of metabolic syndrome.

Approach

The first objective was to identify and recruit participants predisposed to the development of metabolic syndrome. A screening scheme was developed based on a combination of existing guidelines for the identification of metabolic syndrome and clinical cut-offs associated with increased CVD risk. This was successfully employed to identify participants across all five centres. Participants who expressed a minimum of two features of metabolic syndrome were selected. These features included central adiposity/obesity, insulin resistance (elevated fasting glucose or insulin), dyslipidaemia (elevated fasting TG and HDL-C) and high blood pressure; but excluded those requiring acute clinical intervention for these or other conditions. A total of 1316 individuals were screened for metabolic features and 858 (65%) were identified as at increased risk and recruited to the study.

The second objective was to develop a strategy to attain five isoenergetic dietary groups, differing only in the amount and composition of fat (Diet A [reference]: High SFA/High GI; Diet B: High MUFA/High GI; Diet C High MUFA/Low GI; Diet D: Low Fat/High GI; Diet E: Low Fat/Low GI). A food exchange strategy was developed from data from the National Diet and Nutrition Survey (NDNS). ‘Exchangeable’ dietary sources of fat and CHO were removed from the diet and replaced by study foods with either a specific fatty acid profile or GI to achieve the fat and CHO modification respectively. Low fat substitutions were also included in Diets D and E. Using this model 42% of dietary fat and 56% of total dietary CHO could be manipulated, sufficient to achieve the target intervention.

The third objective was to test the impact of these specific dietary changes on cardiovascular risk factors associated with metabolic syndrome. Baseline measurements were taken after a 4-week run-in period (wk –4 to 0) during which all participants followed Diet A. Participants were allocated to one of the five diets (A-E) using a minimisation procedure, controlling for gender, age, waist circumference and HDL-C. They were asked to consume the prescribed diet for 24 weeks before measurements taken at baseline were repeated. The primary outcome was the impact of dietary change on insulin resistance. An intravenous glucose tolerance test (IVGTT), with a reduced sampling schedule, was used to study both Si and glucose effectiveness (Sg). Fasting measures of Si and other cardiovascular risk factors associated with metabolic syndrome were measured at baseline and after the 24-week dietary intervention. Diet diaries were collected at 4 time points (-4, 0, 12 and 24 weeks) and measures of plasma fatty acid status taken at baseline and at the end of the study to assess compliance to the fat manipulation aspects of the dietary intervention protocol. There is no established biomarker for the GI manipulation.

Results

Diet diary data indicated that target intakes within each dietary prescription were broadly achieved, with clear segregation between treatments with respect to the amount and type of fat (38 vs. 28%E fat, 18 vs. 10%E SFA) and amount of CHO (45%E vs. 55%E). However, MUFA intake in Diets B and C and the differential in dietary GI between the high and low groups were slightly less than modelled. In both instances the changes are likely to represent realistic, achievable intakes using a household food substitution strategy based on current dietary habits. Importantly for this experimental study, the GI intervention was not associated with differences in fibre intake. Compliance to the changes in the type of fat was confirmed by the plasma fatty acid analysis.

Of the 615 participants with baseline measures, 548 (89%) completed the dietary intervention study. Despite efforts to maintain energy balance, reducing the total fat content of the diet was associated with small, but statistically significant reductions (P=0.001) in body weight. The mean % changes in body weight on Diets A, B, C, D and E were 0.4 (95% CI -0.3, 1.0), -0.5 (-1.0, 0.0), 0.2 (-0.2, 0.6), -1.1 (-1.6, -0.6), -1.1 (-1.7, -0.5). There was no effect of GI on body weight. There were no significant changes in waist circumference or % body fat or in plasma leptin concentration.

There were no significant differences between the diets on Si, glucose effectiveness or other simpler indices of glucose homeostasis. In post-hoc analysis there was a weak, but significant, positive association between weight loss and improvements in Si (r=-0.16), suggesting that body weight is a more important determinant of Si than diet composition.

All diets designed to reduce SFA, with the exception of the high MUFA/high GI diet, were associated with significant improvements in total serum cholesterol (TC) and LDL-cholesterol (LDL-C) compared to the reference group. There is some evidence to suggest that reductions in the GI of the diet were associated with greater reductions in TC (P=0.011) and LDL-C (P=0.028) relative to the higher GI dietary prescription. The mean % changes in median cholesterol were: Diet A –1.2 (95% CI -3.0,0.6), Diets B & D (high GI) –4.8 (-6.0, -3.5) Diets C & E (low GI) –6.8 (-8.2, -5.5). The mean % changes in LDL-C were: Diet A –0.7 (-3.4, 2.0), Diets B & D –5.9 (–7.5, -4.2), Diets C & E –7.7 (-9.4, -5.9). Reductions in HDL-C were attenuated when SFA were replaced primarily with MUFA, relative to the low fat dietary prescription (Diet A –1.9 (-4.2, 0.4), Diets B & C (high MUFA) –3.5 (-4.9, -2.1) Diets D & E (low fat) –6.5 (-7.8, -5.3)). Accordingly, the high MUFA, but not the low fat, diets showed a significant reduction in TC:HDL-C compared to the reference diet. This difference was only marginally statistically significant (P=0.027), but significance was strengthened when adjusted for changes in body weight (P=0.0033). This suggests that the effects of weight reduction may have offset the HDL-C-lowering effect of a low fat/high CHO diet.

There were no significant differences between treatment groups in a range of other metabolic risk factors including specialised lipids (LDL and HDL subclasses), haemostatic and clotting factors, inflammatory markers and adipokines (leptin and adiponectin).

The findings of the study do not lend support to the hypothesis that dietary fat composition influences Si. However, the results do confirm the well-established LDL-C lowering effect of replacing SFA with either MUFA or CHO. A novel finding of the present study is that a reduction in GI resulted in a further reduction in LDL-C. The lack of any effect of the dietary intervention, on other markers of CVD risk associated with metabolic syndrome, is consistent with no change in Si. Future research should consider low fat or other diets that promote moderate weight loss, and include the replacement of SFA with MUFA, and high GI CHO-rich foods with low GI alternatives. This dietary prescription is estimated to be associated with reductions in CVD risk of approximately 5%.

Collaborators

Professor Gary Frost, Imperial College London

Professor Bruce Griffin, University of Surrey

Professor Julie Lovegrove, University of Reading

Professor Tom Sanders, Kings College London

Publications

Alsaleh A, O’Dell SD, Frost GS, Griffin BA, Lovegrove JA, Jebb SA, Sanders TA; RISCK Study investigators. Interaction of PPARG Pro12Ala with dietary fat influences plasma lipids in subjects at cardiometabolic risk. J Lipid Res. 2011 Dec;52(12):2298-303.

AlSaleh A, O’Dell SD, Frost GS, Griffin BA, Lovegrove JA, Jebb SA, Sanders TA; RISCK Study Group. Single nucleotide polymorphisms at the ADIPOQ gene locus interact with age and dietary intake of fat to determine serum adiponectin in subjects at risk of the metabolic syndrome. Am J Clin Nutr. 2011 Jul;94(1):262-9.

Walker CG, Goff L, Bluck LJ, Griffin BA, Jebb SA, Lovegrove JA, Sanders TA, Frost GS; RISCK Study Group. Variation in the FFAR1 gene modifies BMI, body composition and beta-cell function in overweight subjects: an exploratory analysis. PLoS One. 2011 Apr 28;6(4):e19146.

Walker CG, Loos RJ, Olson AD, Frost GS, Griffin BA, Lovegrove JA, Sanders TA, Jebb SA. Genetic predisposition influences plasma lipids of participants on habitual diet, but not the response to reductions in dietary intake of saturated fatty acids. Atherosclerosis. 2011 Apr;215(2):421-7.

Jebb SA, Lovegrove JA, Griffin BA, Frost GS, Moore CS, Chatfield MD, Bluck LJ, Williams CM, Sanders TA; RISCK Study Group. Effect of changing the amount and type of fat and carbohydrate on insulin sensitivity and cardiovascular risk: the RISCK (Reading, Imperial, Surrey, Cambridge, and Kings) trial. Am J Clin Nutr. 2010 Oct;92(4):748-58.

Moore C, Gitau R, Goff L, Lewis FJ, Griffin MD, Chatfield MD, Jebb SA, Frost GS, Sanders TA, Griffin BA, Lovegrove JA; RISCK Study Group. Successful manipulation of the quality and quantity of fat and carbohydrate consumed by free-living individuals using a food exchange model. J Nutr. 2009 Aug;139(8):1534-40.