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Lack of tools to evaluate the quality of diet impedes dietary counselling in healthcare. We constructed a scoring for a validated food intake questionnaire, to measure the adherence to a healthy diet that prevents type 2 diabetes (T2D). The Healthy Diet Index (HDI) consists of seven weighted domains (meal pattern, grains, fruit and vegetables, fats, fish and meat, dairy, snacks and treats). We studied the correlations of the HDI with nutrient intakes calculated from 7-day food records among 52 men and 25 women, and associations of HDI with biomarkers and anthropometrics among 645 men and 2455 women. The HDI correlated inversely with total fat (Pearson’s r = −0.37), saturated fat (r = −0.37), monounsaturated fat (r = −0.37), and the glycaemic index of diet (r = −0.32) and positively with carbohydrates (r = 0.23), protein (r = 0.25), fibre (r = 0.66), magnesium (r = 0.26), iron (r = 0.25), and vitamin D (r = 0.27), (p &lt, 0.05 for all). In the linear regression model adjusted for BMI and age, HDI is associated inversely with waist circumference, concentrations of fasting and 2-h glucose and triglycerides in men and women, total and LDL cholesterol in women, and fasting insulin in men (p &lt, 0.05 for all). The HDI proved to be a valid tool to measure adherence to a health-promoting diet and to support individualised dietary counselling.

Our aim was to validate a 16-item food intake questionnaire (16-FIQ) and create an easy to use method to estimate patients’ nutrient intake in primary health care. Participants (52 men, 25 women) completed a 7-day food record and a 16-FIQ. Food and nutrient intakes were calculated and compared using Spearman correlation. Further, nutrient intakes were compared using kappa-statistics and exact and opposite agreement of intake tertiles. The results indicated that the 16-FIQ reliably categorized individuals according to their nutrient intakes. Methods to estimate nutrient intake based on the answers given in 16-FIQ were created. In linear regression models nutrient intake estimates from the food records were used as the dependent variables and sum variables derived from the 16-FIQ were used as the independent variables. Valid regression models were created for the energy proportion of fat, saturated fat, and sucrose and the amount of fibre (g), vitamin C (mg), iron (mg), and vitamin D (μg) intake. The 16-FIQ is a valid method for estimating nutrient intakes in group level. In addition, the 16-FIQ could be a useful tool to facilitate identification of people in need of dietary counselling and to monitor the effect of counselling in primary health care.

Twenty-nine hemodialysis patients were studied to evaluate different laboratory measures in assessing muscle protein stores and detecting protein malnutrition. Arm muscle circumference (AMC) was used as a reference for somatic protein stores. AMC correlated with serum complement C3, plasma histidine, isoleucine, leucine, methionine and threonine concentrations as well as with body mass index. The lowest quartile of AMC was found most reliably by measuring plasma methionine, histidine, leucine and isoleucine concentrations. Protein malnutrition was detected in five patients (17%). They had significantly lower serum prealbumin and plasma leucine concentrations than the others. In the follow-up of 3 years every malnourished patient died, three from septic infection. Of the others only seven died, none from infection. The analysis of plasma essential amino acid and serum prealbumin concentrations had an important role in assessing muscle protein stores as well as the protein nutrition status in hemodialysis patients. Patients with malnutrition should be detected in view of their unfavourable prognosis.