It is widely accepted that cardiometabolic risk has its origins in childhood, although clinical symptoms may not become apparent until later in life( Reference Williams, WHayman, Daniels, Robinson, Steinberger and Paridon 1 ). A growing body of evidence suggests that diet in childhood plays a key role in cardiometabolic risk profile in adulthood( Reference Sharma, Roberts, Hudes, Lustig and Fleming 2 ). It is proposed that proportions of macronutrient intake may be more pivotal in the development of cardiometabolic risk than total energy intake( Reference Sharma, Roberts, Hudes, Lustig and Fleming 2 ). The aims of this cross sectional study were to investigate the relationships between cardiometabolic risk and macronutrient intake in 10 to 11 year old children.
Participants (n=55) were recruited from 11 primary schools from a North West English town, of those 27 provided complete data sets for all measures (mean age=10.6, sd=0.28 years). Children completed 7 day food dairies and Microdiet employed to estimate mean daily total energy intake (KCal) and the following macronutrients, as a percentage of total energy; protein, carbohydrate (CHO), starch and sugars, fructose, maltose, sucrose, and glucose, fat, mono unsaturated fatty acids (MUFA), poly unsaturated fatty acids (PUFA), and saturated fatty acids (SFA). Cardiometabolic risk markers measured included fasting capillary blood total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), triglycerides (TRG), and glucose; body composition (DEXA); resting blood pressure (sBP and dBP) and resting heart rate (RHR), carotid intima media thickness (CIMT), left ventricular diastolic filling (E/A); septal myocardial tissue velocities (E'/A'), and left ventricular mass (LV mass). LV mass index was calculated to account for body size. A clustered risk score (CRS) was calculated using TC: HDL-C, glucose, systolic BP, LV Mass Index, and trunk fat mass (g). The following outcome measures were not normally distributed and were log10 transformed: TRG, fructose, dietary glucose, trunk fat mass and capillary glucose. Pearson's correlation coefficients, controlling for gender and somatic maturation, were completed to assess the relationships between total energy, CHO, sugars and starch and cardiometabolic risk markers.
Sucrose had a weak to moderate negative correlation with TRG (log) [r=−0.42, p=0.029], TC: HDL-C [r=−0.487, p=0.001] and CRS [r=−0.625, p=0.001], and a moderate positive correlation with HDL-C [r=0.530, p=0.004]. Total CHO was also negatively correlated with CIMT [r=−0.375, p=0.029]. PUFA had a weak positive correlation with BMI [r=0.343, p=0.033] and RHR [r=0.336, p=0.039]. There were no other correlations between macronutrients and cardiometabolic risk markers.
In conclusion, a higher intake of PUFA was associated with a higher RHR, and BMI. A higher intake of carbohydrates and sucrose was associated with a favourable cardiometabolic risk profile. This evidence is contradictory to conventional wisdom and further investigation into the contribution of foods to the nutritional values is required, to explain this finding.
