Nutrient density, but not cost of diet, is associated with anemia and iron deficiency in school-age children in South Africa.

OBJECTIVES: This study aimed to investigate the relationship of nutrient density and diet cost with anemia and iron deficiency (ID) in children. METHODS: Dietary intake data of 5- to 12-y-old children (n = 578) from three independent studies in low-income communities were pooled. Nutrient densities were calculated using the Nutrient Rich Foods index and Nutrient Rich Diet index, with higher scores indicating more nutrient-dense foods and diets. Food prices and food intake data were used to calculate ratios of nutrient density to price for foods and diets. Descriptive and correlation analyses examined associations of nutrient density and diet cost with anemia and ID. RESULTS: Most children (>50%) consumed starchy staples (100%), vegetables that are not vitamin A rich (63.9%), and legumes (58.1%), with mean NRF9.3 scores ranging from 31.9 to 56.3. Cheese, eggs, organ meat, fish, dark-green leafy vegetables, and vitamin A-rich vegetables and fruits had mean NRF9.3 scores ranging from 112.6 to 184.7, but each was consumed by less than a third of the children. Children with anemia or ID had lower NRD9.3 scores than children without (P < 0.001 and P = 0.039, respectively). Diet cost did not differ according to anemia and iron status, but nutrient-density-to-price ratio was lower in children with anemia than without (P = 0.001). CONCLUSIONS: Careful selection of nutrient-dense foods as substitutes for foods with lower nutrient density could make it possible for children to consume a diet richer in specific nutrients and help prevent anemia and ID without affecting diet cost.

Food or nutrient pattern assessment using the principal component analysis applied to food questionnaires. Pitfalls, tips and tricks.

We considered the Blom's transformation, a statistical method aimed to normalise and standardise food intakes before principal component analysis. A simulation study was performed to evaluate the eigenvalue distribution of a correlation matrix under common conditions in food questionnaire analysis. The scree plot visual inspection and the Guttman-Kaiser (GK) criterion were compared to Horn's parallel analysis to evaluate their efficacy in food pattern identification. The scree plot results as a monotone continuous series when no food patterns are present. In this situation, about 50% of the eigenvalues assume a value higher than one, showing a first fallacy of the GK. When three food patterns are simulated a clear discontinuity appears after the third eigenvalue, showing that the scree-plot visual inspection is a suitable method to identify food patterns. Finally, according to the present work it appears that the GK generates a number of false-positive food patterns.

Iodine Status Assessment in South African Adults According to Spot Urinary Iodine Concentrations, Prediction Equations, and Measured 24-h Iodine Excretion.

The iodine status of populations is conventionally assessed using spot urinary samples to obtain a median urinary iodine concentration (UIC) value, which is assessed against standard reference cut-offs. The assumption that spot UIC reflects daily iodine intake may be flawed because of high day-to-day variability and variable urinary volume outputs. This study aimed to compare iodine status in a sample of South African adults when determined by different approaches using a spot urine sample (median UIC (MUIC), predicted 24 h urinary iodine excretion (PrUIE) using different prediction equations) against measured 24 h urinary iodine excretion (mUIE). Both 24 h and spot urine samples were collected in a subsample of participants (n = 457; median age 55 year; range 18⁻90 year) in the World Health Organization Study on global AGEing and adult health (SAGE) Wave 2 in South Africa, in 2015. Kawasaki, Tanaka, and Mage equations were applied to assess PrUIE from predicted urinary creatinine (PrCr) and spot UIC values. Adequacy of iodine intake was assessed by comparing PrUIE and mUIE to the Estimated Average Requirement of 95 µg/day, while the MUIC cut-off was.

Optimization of a New Mass Spectrometry Method for Measurement of Breast Milk Iodine Concentrations and an Assessment of the Effect of Analytic Method and Timing of Within-Feed Sample Collection on Breast Milk Iodine Concentrations.

BACKGROUND: Breast milk iodine concentration (BMIC) may be an indicator of iodine status during lactation, but there are few data comparing different analytical methods or timing of sampling. The aims of this study were: (i) to optimize a new inductively coupled plasma mass spectrometry (ICP-MS) method; and (ii) to evaluate the effect of analytical method and timing of within-feed sample collection on BMIC. METHODS: The colorimetric Sandell-Kolthoff method was evaluated with (a) or without (b) alkaline ashing, and ICP-MS was evaluated using a new (129)I isotope ratio approach including Tellurium (Te) for mass bias correction (c) or external standard curve (d). From iodine-sufficient lactating women (n = 97), three samples were collected within one breast-feeding session (fore-, mid-, and hind-feed samples) and BMIC was analyzed using (c) and (d). RESULTS: Iodine recovery from NIST SRM1549a whole milk powder for methods (a)-(d) was 67%, 24%, 105%, and 102%, respectively. Intra- and inter-assay coefficients of variation for ICP-MS comparing (c) and (d) were 1.3% versus 5.6% (p = 0.04) and 1.1% versus 2.4% (p = 0.33). The limit of detection (LOD) was lower for (c) (0.26 µg/kg) than it was for (d) (2.54 µg/kg; p = 0.02). Using (c), the median [95% confidence interval (CI) obtained by bootstrap] BMIC (µg/kg) in foremilk (179 [CI 161-206]) and in mid-feed milk (184 [CI 160-220]) were not significantly different (p = 0.017), but were higher than in hindmilk (175 [CI 153-216]; p < 0.001). In foremilk using (d), BMIC was 199 ([CI 182-257]; p < 0.001 vs. (c)). The variation in BMIC comparing (c) and (d) (13%) was greater than variation within feeding (5%; p < 0.001). CONCLUSIONS: Because of poor recoveries, (a) and (b) should not be used to measure BMIC. Compared with (d), (c) has the advantages of higher precision and a lower LOD. In iodine-sufficient women, BMIC shows low variation within a breast-feeding session, so timing of sampling is not a major determinant of BMIC.