Effects of a multi-component nutritional telemonitoring intervention on nutritional status, diet quality, physical functioning and quality of life of community-dwelling older adults.

This study aimed to evaluate the effects of an intervention including nutritional telemonitoring, nutrition education, and follow-up by a nurse on nutritional status, diet quality, appetite, physical functioning and quality of life of Dutch community-dwelling elderly. We used a parallel arm pre-test post-test design with 214 older adults (average age 80 years) who were allocated to the intervention group (n 97) or control group (n 107), based on the municipality. The intervention group received a 6-month intervention including telemonitoring measurements, nutrition education and follow-up by a nurse. Effect measurements took place at baseline, after 4·5 months, and at the end of the study. The intervention improved nutritional status of participants at risk of undernutrition (ß (T1)=2·55; 95 % CI 1·41, 3·68; ß (T2)=1·77; 95 % CI 0·60, 2·94) and scores for compliance with Dutch guidelines for the intake of vegetables (ß=1·27; 95 % CI 0·49, 2·05), fruit (ß=1·24; 95 % CI 0·60, 1·88), dietary fibre (ß=1·13; 95 % CI 0·70, 1·57), protein (ß=1·20; 95 % CI 0·15, 2·24) and physical activity (ß=2·13; 95 % CI 0·98, 3·29). The intervention did not have an effect on body weight, appetite, physical functioning and quality of life. In conclusion, this intervention leads to improved nutritional status in older adults at risk of undernutrition, and to improved diet quality and physical activity levels of community-dwelling elderly. Future studies with a longer duration should focus on older adults at higher risk of undernutrition than this study population to investigate whether the impact of the intervention on nutritional and functional outcomes can be improved.

Dietary reporting errors on 24 h recalls and dietary questionnaires are associated with BMI across six European countries as evaluated with recovery biomarkers for protein and potassium intake.

Whether there are differences between countries in the validity of self-reported diet in relation to BMI, as evaluated using recovery biomarkers, is not well understood. We aimed to evaluate BMI-related reporting errors on 24 h dietary recalls (24-HDR) and on dietary questionnaires (DQ) using biomarkers for protein and K intake and whether the BMI effect differs between six European countries. Between 1995 and 1999, 1086 men and women participating in the European Prospective Investigation into Cancer and Nutrition completed a single 24-HDR, a DQ and one 24 h urine collection. In regression analysis, controlling for age, sex, education and country, each unit (1 kg/m²) increase in BMI predicted an approximately 1·7 and 1·3 % increase in protein under-reporting on 24-HDR and DQ, respectively (both P < 0·0001). Exclusion of individuals who probably misreported energy intake attenuated BMI-related bias on both instruments. The BMI effect on protein under-reporting did not differ for men and women and neither between countries on both instruments as tested by interaction (all P>0·15). In women, but not in men, the DQ yielded higher mean intakes of protein that were closer to the biomarker-based measurements across BMI groups when compared with 24-HDR. Results for K were similar to those of protein, although BMI-related under-reporting of K was of a smaller magnitude, suggesting differential misreporting of foods. Under-reporting of protein and K appears to be predicted by BMI, but this effect may be driven by 'low-energy reporters'. The BMI effect on under-reporting seems to be the same across countries.

Reproducibility and relative validity of dietary glycaemic index and glycaemic load assessed by the food-frequency questionnaire used in the Dutch cohorts of the European Prospective Investigation into Cancer and Nutrition.

Limited information is available on the reproducibility and validity of dietary glycaemic index (GI) and glycaemic load (GL) estimated by habitual diet assessment methods such as FFQ, including the FFQ used in the Dutch cohorts of the European Prospective Investigation into Cancer and Nutrition study. To examine the reproducibility and relative validity of GI and GL, we used data from 121 Dutch men and women aged 23-72 years. They completed the FFQ three times at intervals of 6 months and twelve 24-h dietary recalls (24HDR) monthly during 1991-2. GI and GL were calculated using published values. Intra-class correlation coefficients of the three repeated FFQ were 0.78 for GI and 0.74 for GL. Pearson correlation coefficients between the first FFQ and the weighted average of the 24HDR were 0.63 for both GI and GL. Weighted kappa values between the first FFQ and the average of the 24HDR (in quintiles) were 0.40 for GI and 0.41 for GL. Bland-Altman plots showed a proportional bias in GI (beta = 0.46), but not in GL (beta = 0.06). In conclusion, this FFQ can be used in epidemiological studies to investigate the relationship of GI and GL with disease risks, but the proportional bias should be taken into account when using this FFQ to assess the absolute GI values.

Methodological challenges in the application of the glycemic index in epidemiological studies using data from the European Prospective Investigation into Cancer and Nutrition.

Associations between the glycemic index (GI) or glycemic load (GL) and diseases are heterogeneous in epidemiological studies. Differences in assigning GI values to food items may contribute to this inconsistency. Our objective was to address methodological issues related to the use of current GI and GL values in epidemiological studies. We performed ecological comparison and correlation studies by calculating dietary GI and GL from country-specific dietary questionnaires (DQ) from 422,837 participants from 9 countries participating in the European Prospective Investigation into Cancer and Nutrition study and single standardized 24-h dietary recalls (24-HDR) obtained from a representative sample (n = 33,404) using mainly Foster Powell's international table as a reference source. Further, 2 inter-rater and 1 inter-method comparison were conducted, comparing DQ GI values assigned by independent groups with values linked by us. The ecological correlation between DQ and 24-HDR was good for GL (overall r = 0.76; P < 0.005) and moderate for GI (r = 0.57; P < 0.05). Mean GI/GL differences between DQ and 24-HDR were significant for most centers. GL but not GI from DQ was highly correlated with total carbohydrate (r = 0.98 and 0.15, respectively; P < 0.0001) and this was higher for starch (r = 0.72; P < 0.0001) than for sugars (r = 0.36; P < 0.0001). The inter-rater and inter-method variations were considerable for GI (weighted kappa coefficients of 0.49 and 0.65 for inter-rater and 0.25 for inter-method variation, respectively) but only mild for GL (weighted kappa coefficients > 0.80). A more consistent methodology to attribute GI values to foods and validated DQ is needed to derive meaningful GI/GL estimates for nutritional epidemiology.

Glycemic index and glycemic load in relation to food and nutrient intake and metabolic risk factors in a Dutch population.

BACKGROUND: Previous studies on the glycemic index (GI) and glycemic load (GL) reported inconsistent findings on their association with metabolic risk factors. This may partly have been due to differences in underlying dietary patterns. OBJECTIVE: We aimed to examine the association of GI and GL with food and nutrient intake and with metabolic risk factors including blood glucose, insulin, lipids, and high-sensitivity C-reactive protein (CRP). DESIGN: The study entailed cross-sectional analyses of data from 2 joint observational studies, the CoDAM Study and the Hoorn Study. RESULTS: In total, 974 subjects aged 42-87 y were included in the study. The mean (+/-SD) GI was 57 +/- 4 and the mean GL was 130 +/- 39. Dairy products, potatoes and other tubers, cereal products, and fruit were the main predictive food groups for GI. GL was closely correlated with intake of total carbohydrates (r(s) = 0.97), which explained >95% of the variation in GL. After adjustment for potential confounders, GI was significantly inversely associated with HDL cholesterol and positively associated with fasting insulin, the homeostasis model assessment index of insulin resistance, the ratio of total to HDL cholesterol, and CRP. No association was observed between GL and any of the metabolic risk factors, except for a borderline significant positive association with CRP. CONCLUSIONS: In this population, a low-GI diet, which is high in dairy and fruit but low in potatoes and cereals, is associated with improved insulin sensitivity and lipid metabolism and reduced chronic inflammation. GL is highly correlated with carbohydrate intake and is not clearly associated with the investigated metabolic risk factors.