Carbohydrate-dense snacks are a key feature of the nutrition transition among Ghanaian adults - findings from the RODAM study.

BACKGROUND: African populations in sub-Saharan Africa and African migrants in Europe are facing a rapid upsurge in obesity. This trend has been related to urbanization, migration and associated shifts in lifestyle, including dietary habits. Whether changes in eating patterns contribute to the rising burden of obesity among African populations is currently unknown. OBJECTIVE: Our aims in conducting this study were to characterize eating patterns among Ghanaian adults living in their country of origin and in Europe and to explore associations of meal patterns with body mass index (BMI). DESIGN: Within the cross-sectional RODAM (Research on Obesity and Diabetes among African Migrants) study, data of single 24-h dietary recalls from Ghanaian adults in rural Ghana (n = 20), urban Ghana (n = 42), and Europe (n = 172) were recorded. Eating frequencies, energy intake, and macronutrient composition of eating occasions (EOs, i.e. meals or snacks) were compared between study sites based on descriptive statistics and χ 2-/Kruskal-Wallis tests. RESULTS: A rising gradient of EO frequencies from rural Ghana through urban Ghana to Europe was observed, mainly reflecting the differences in snacking frequencies (≥1 snack per day: 20 vs. 48 vs. 52%, P = 0.008). Meal frequencies were similar across study sites (≥3 meals per day: 30 vs. 33 vs. 38%, P = 0.80). Meals were rich in carbohydrates (median 54.5, interquartile range (IQR): 43.2-64.0 energy%) and total fats (median: 27.0, IQR: 19.9-34.4 energy %); their protein content was lowest in rural Ghana, followed by urban Ghana and Europe (P = 0.0005). Snacks mainly contained carbohydrates (median: 75.7, IQR: 61.0-89.2 energy%). In linear regression analyses, there was a non-significant trend for an inverse association between snacking frequencies and BMI. DISCUSSION AND CONCLUSIONS: The observed integration of carbohydrate-dense snacks into the diet supports the growing evidence for a nutrition transition among African populations undergoing socioeconomic development. This analysis constitutes a starting point to further investigate the nutritional implications of increased snacking frequencies on obesity and metabolic health in these African populations

Estimating usual intake in the 2nd Bavarian food consumption survey: comparison of the results derived by the national cancer institute method and a basic individual means approach

BACKGROUND: The valid estimation of the usual dietary intake remains a challenge till date. We applied the method suggested by the National Cancer Institute (NCI) to data from the 2nd Bavarian Food Consumption Survey (BVS II) and compared it to an individual means approach. METHODS: Within the cross-sectional BVS II, 1,050 Bavarian residents aged 13-80 years participated in a personal interview and completed three 24-h dietary recalls by telephone interview. For the 13 main food groups and 23 subgroups the usual intake was calculated by (1) an individual means approach and (2) by the NCI method. RESULTS: The distributions derived by the individual means approach are wider than those derived from the NCI approach. For a majority of food groups and subgroups, the proportion of participants who meet the dietary recommendations published by the German Nutrition Society is higher when the NCI approach is applied. The proportions of participants above or below recommended amounts differ greatly for "meat and meat products" and "cheese." CONCLUSION: The mean intake at the groups level can easily be derived from the individual means approach. Since only the NCI method accounts for intra-personal variation, this method provides more valid intake estimates at the individual level and should be applied when, for example, individual intakes are compared with dietary recommendations

Evaluating the effect of measurement error when using one or two 24 h dietary recalls to assess eating out: a study in the context of the HECTOR project

Eating out is often recorded through short-term measurements and the large within-person variability in intakes may not be adequately captured. The present study aimed to understand the effect of measurement error when using eating-out data from one or two 24 h dietary recalls (24hDR), in order to describe intakes and assess associations between eating out and personal characteristics. In a sample of 366 adults from Potsdam, Germany, two 24hDR and a FFQ were collected. Out-of-home intakes were estimated based on either one 24hDR or two 24hDR or the Multiple Source Method (MSM) combining the two 24hDR and the questionnaire. The distribution of out-of-home intakes of energy, macronutrients and selected foods was described. Multiple linear regression and partial correlation coefficients were estimated to assess associations between out-of-home energy intake and participants’ characteristics. The mean daily out-of-home intakes estimated from the two 24hDR were similar to the usual intakes estimated through the MSM. The out-of-home energy intake, estimated through either one or two 24hDR, was positively associated with total energy intake, inversely with age and associations were stronger when using the two 24hDR. A marginally significant inverse association between out-of-home energy intake and physical activity at work was observed only on the basis of the two 24hDR. After applying the MSM, all significant associations remained and were more precise. Data on eating out collected through one or two 24hDR may not adequately describe intake distributions, but significant associations between eating out and participants’ characteristics are highly unlikely to appear when in reality these do not exist

Application of systematic evidence mapping to identify available data on the potential human health hazards of selected market-relevant azo dyes

Background: Azo dyes are used in textiles and leather clothing. Human exposure can occur from wearing textiles containing azo dyes. Since the body’s enzymes and microbiome can cleave azo dyes, potentially resulting in mutagenic or carcinogenic metabolites, there is also an indirect health concern on the parent compounds. While several hazardous azo dyes are banned, many more are still in use that have not been evaluated systematically for potential health concerns. This systematic evidence map (SEM) aims to compile and categorize the available toxicological evidence on the potential human health risks of a set of 30 market-relevant azo dyes. Methods: Peer-reviewed and gray literature was searched and over 20,000 studies were identified. These were filtered using Sciome Workbench for Interactive computer-Facilitated Text-mining (SWIFT) Review software with evidence stream tags (human, animal, in vitro) yielding 12,800 unique records. SWIFT Active (a machine-learning software) further facilitated title/abstract screening. DistillerSR software was used for additional title/abstract, full-text screening, and data extraction. Results: 187 studies were identified that met populations, exposures, comparators, and outcomes (PECO) criteria. From this pool, 54 human, 78 animal, and 61 genotoxicity studies were extracted into a literature inventory. Toxicological evidence was abundant for three azo dyes (also used as food additives) and sparse for five of the remaining 27 compounds. Complementary search in ECHA’s REACH database for summaries of unpublished study reports revealed evidence for all 30 dyes. The question arose of how this information can be fed into an SEM process. Proper identification of prioritized dyes from various databases (including U.S. EPA’s CompTox Chemicals Dashboard) turned out to be a challenge. Evidence compiled by this SEM project can be evaluated for subsequent use in problem formulation efforts to inform potential regulatory needs and prepare for a more efficient and targeted evaluation in the future for human health assessments

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design : EPIC Case Study

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted twopart regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model

Carbohydrate-dense snacks are a key feature of the nutrition transition among ghanaian adults – findings from the rodam study

Background: African populations in sub-Saharan Africa and African migrants in Europe are facing a rapid upsurge in obesity. This trend has been related to urbanization, migration and associated shifts in lifestyle, including dietary habits. Whether changes in eating patterns contribute to the rising burden of obesity among African populations is currently unknown. Objective: Our aims in conducting this study were to characterize eating patterns among Ghanaian adults living in their country of origin and in Europe and to explore associations of meal patterns with body mass index (BMI). Design: Within the cross-sectional RODAM (Research on Obesity and Diabetes among African Migrants) study, data of single 24-h dietary recalls from Ghanaian adults in rural Ghana (n = 20), urban Ghana (n = 42), and Europe (n = 172) were recorded. Eating frequencies, energy intake, and macronutrient composition of eating occasions (EOs, i.e. meals or snacks) were compared between study sites based on descriptive statistics and χ2-/Kruskal–Wallis tests. Results: A rising gradient of EO frequencies from rural Ghana through urban Ghana to Europe was observed, mainly reflecting the differences in snacking frequencies (≥1 snack per day: 20 vs. 48 vs. 52%, P = 0.008). Meal frequencies were similar across study sites (≥3 meals per day: 30 vs. 33 vs. 38%, P = 0.80). Meals were rich in carbohydrates (median 54.5, interquartile range (IQR): 43.2–64.0 energy%) and total fats (median: 27.0, IQR: 19.9–34.4 energy %); their protein content was lowest in rural Ghana, followed by urban Ghana and Europe (P = 0.0005). Snacks mainly contained carbohydrates (median: 75.7, IQR: 61.0–89.2 energy%). In linear regression analyses, there was a non-significant trend for an inverse association between snacking frequencies and BMI. Discussion and conclusions: The observed integration of carbohydrate-dense snacks into the diet supports the growing evidence for a nutrition transition among African populations undergoing socioeconomic development. This analysis constitutes a starting point to further investigate the nutritional implications of increased snacking frequencies on obesity and metabolic health in these African populations

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the ‘meeting-in-the-middle’ concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum H-1 nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the ‘predictors’, including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the ‘responses’). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the ‘meeting-in-the-middle’ approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation

Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women

Background: Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. Methods: We used lifestyle and nutritional data from 53 degrees 758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining >= 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). Results: Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model’s discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of >= 200 points were 9% and 96%, respectively. Conclusion: The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score