Vitamin B12, folate, homocysteine, and bone health in adults and elderly people: a systematic review with meta-analyses.

Elevated homocysteine levels and low vitamin B12 and folate levels have been associated with deteriorated bone health. This systematic literature review with dose-response meta-analyses summarizes the available scientific evidence on associations of vitamin B12, folate, and homocysteine status with fractures and bone mineral density (BMD). Twenty-seven eligible cross-sectional (n = 14) and prospective (n = 13) observational studies and one RCT were identified. Meta-analysis on four prospective studies including 7475 people showed a modest decrease in fracture risk of 4% per 50 pmol/L increase in vitamin B12 levels, which was borderline significant (RR = 0.96, 95% CI = 0.92 to 1.00). Meta-analysis of eight studies including 11511 people showed an increased fracture risk of 4% per µ mol/L increase in homocysteine concentration (RR = 1.04, 95% CI = 1.02 to 1.07). We could not draw a conclusion regarding folate levels and fracture risk, as too few studies investigated this association. Meta-analyses regarding vitamin B12, folate and homocysteine levels, and BMD were possible in female populations only and showed no associations. Results from studies regarding BMD that could not be included in the meta-analyses were not univocal.

Biomarker-based evaluation of two 24-h recalls for comparing usual fish, fruit and vegetable intakes across European centers in the EFCOVAL Study.

BACKGROUND/OBJECTIVES: A standardized methodology is important to enable consistent monitoring of dietary intake across European countries. For this reason, we evaluated the comparability of the assessment of usual food intake collected with two non-consecutive computerized 24-h dietary recalls (24-HDRs) and a food propensity questionnaire (FPQ) among five European centers. SUBJECTS/METHODS: Two 24-HDRs using EPIC-Soft (the software developed to conduct 24-HDRs in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study) were performed to determine fish, fruit and vegetable (FV) consumed by 600 adults in Belgium (BE), the Czech Republic (CZ), France (FR), the Netherlands (NL) and Norway (NO) in a validation study. An FPQ was used to identify non-consumers. Information from the 24-HDRs and FPQ were used to estimate individual usual food intake by the Multiple Source Method (MSM). Blood samples were drawn to determine fatty acids in phospholipids and serum carotenoids as biomarkers of fish, and FV intake, respectively. RESULTS: The pooled correlation between usual fish intake and eicosapentaenoic acid plus docosahexaenoic acid in phospholipids was 0.19 in men and 0.31 in women (P for heterogeneity >0.50) and center-specific correlations ranged between 0.08 (CZ) and 0.28 (BE and NO) in men, and between 0.19 (BE) and 0.55 (FR) in women. For usual FV intake, the pooled correlation with serum carotenoids was 0.31 in men and 0.40 in women (P for heterogeneity >0.10); the center-specific correlations varied between 0.07 (NO) and 0.52 (FR) in men, and between 0.25 (NL) and 0.45 (NO) in women. CONCLUSION: Two standardized 24-HDRs using EPIC-Soft and an FPQ appeared to be appropriate to rank individuals according to their fish and FV intake in a comparable way among five European centers.

Comparing four methods to estimate usual intake distributions.

BACKGROUND/OBJECTIVES: The aim of this paper was to compare methods to estimate usual intake distributions of nutrients and foods. As 'true' usual intake distributions are not known in practice, the comparison was carried out through a simulation study, as well as empirically, by application to data from the European Food Consumption Validation (EFCOVAL) Study in which two 24-h dietary recalls (24-HDRs) and food frequency data were collected. The methods being compared were the Iowa State University Method (ISU), National Cancer Institute Method (NCI), Multiple Source Method (MSM) and Statistical Program for Age-adjusted Dietary Assessment (SPADE). SUBJECTS/METHODS: Simulation data were constructed with varying numbers of subjects (n), different values for the Box-Cox transformation parameter (λ(BC)) and different values for the ratio of the within- and between-person variance (r(var)). All data were analyzed with the four different methods and the estimated usual mean intake and selected percentiles were obtained. Moreover, the 2-day within-person mean was estimated as an additional 'method'. These five methods were compared in terms of the mean bias, which was calculated as the mean of the differences between the estimated value and the known true value. The application of data from the EFCOVAL Project included calculations of nutrients (that is, protein, potassium, protein density) and foods (that is, vegetables, fruit and fish). RESULTS: Overall, the mean bias of the ISU, NCI, MSM and SPADE Methods was small. However, for all methods, the mean bias and the variation of the bias increased with smaller sample size, higher variance ratios and with more pronounced departures from normality. Serious mean bias (especially in the 95th percentile) was seen using the NCI Method when r(var) = 9, λ(BC) = 0 and n = 1000. The ISU Method and MSM showed a somewhat higher s.d. of the bias compared with NCI and SPADE Methods, indicating a larger method uncertainty. Furthermore, whereas the ISU, NCI and SPADE Methods produced unimodal density functions by definition, MSM produced distributions with 'peaks', when sample size was small, because of the fact that the population's usual intake distribution was based on estimated individual usual intakes. The application to the EFCOVAL data showed that all estimates of the percentiles and mean were within 5% of each other for the three nutrients analyzed. For vegetables, fruit and fish, the differences were larger than that for nutrients, but overall the sample mean was estimated reasonably. CONCLUSIONS: The four methods that were compared seem to provide good estimates of the usual intake distribution of nutrients. Nevertheless, care needs to be taken when a nutrient has a high within-person variation or has a highly skewed distribution, and when the sample size is small. As the methods offer different features, practical reasons may exist to prefer one method over the other.

Multiple imputation of missing genotype data for unrelated individuals.

The objective of this study was to investigate the performance of multiple imputation of missing genotype data for unrelated individuals using the polytomous logistic regression model, focusing on different missingness mechanisms, percentages of missing data, and imputation models. A complete dataset of 581 individuals, each analysed for eight biallelic polymorphisms and the quantitative phenotype HDL-C, was used. From this dataset one hundred replicates with missing data were created, in different ways for different scenarios. The performance was assessed by comparing the mean bias in parameter estimates, the root mean squared standard errors, and the genotype-imputation error rates. Overall, the mean bias was small in all scenarios, and in most scenarios the mean did not differ significantly from 'no bias'. Including polymorphisms that are highly correlated in the imputation model reduced the genotype-imputation error rate and increased precision of the parameter estimates. The method works well for data that are missing completely at random, and for data that are missing at random. In conclusion, our results indicate that multiple imputation with the polytomous logistic regression model can be used for association studies to deal with the problem of missing genotype data, when attention is paid to the imputation model and the percentage of missing data.

Common variants of multiple genes that control reverse cholesterol transport together explain only a minor part of the variation of HDL cholesterol levels.

It is assumed that the combined effects of multiple common genetic variants explain a large part of variation of high-density lipoprotein cholesterol (HDL-C) plasma levels, but little evidence exists to corroborate this assumption. It was our objective to study the contribution of multiple common genetic variants of HDL-C-related genes to variation of HDL-C plasma levels. A well-characterized cohort of 546 Caucasian men with documented coronary artery disease was genotyped for common functional variants in genes that control reverse cholesterol transport: ATP-binding cassette transporter A1, apolipoprotein A-I and apolipoprotein-E, cholesteryl ester transfer protein, hepatic lipase, lecithin : cholesterol-acyl transferase, lipoprotein lipase, and scavenger receptor class B type 1. Multivariate linear regression showed that these variants, in conjunction, explain 12.4% (95% confidence interval: 6.9-17.9%) of variation in HDL-C plasma levels. When the covariates smoking and body mass index were taken into account, the explained variation increased to 15.3% (9.4-21.2%), and when 10 two-way interactions were incorporated, this percentage rose to 25.2% (18.9-31.5%). This study supports the hypothesis that multiple, mildly penetrant, but highly prevalent genetic variants explain part of the variation of HDL-C plasma levels, albeit to a very modest extent. Multiple environmental and genetic influences on HDL-C plasma levels still have to be elucidated.