Determinants of serum manganese levels in an Italian population

Filippini, Tommaso; Michalke, Bernhard; Grill, Peter; Malagoli, Carlotta; Malavolti, Marcella; Vescovi, Luciano; Sieri, Sabina; Cherubini, Andrea; Maffeis, Giuseppe; Lucchini, Roberto; Ferrante, Margherita

doi:10.3892/mmr.2017.6379

Cited by 17 publications

(15 citation statements)

References 57 publications

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“…Food pattern modeling has been used to estimate community level nutrient intake through modeling methods like the present approach elsewhere [ 29 , 30 , 31 , 32 ]. However, this is the first study, to our knowledge, that has compared the effectiveness of food pattern modeling to repeated multiple-pass memory-based dietary assessment by comparing acute and chronic measurements of nutrient status by hematological outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Despite its definition in the Dietary Guidelines for Americans [ 2 ], there is no standardized approach to food pattern modeling as a form of dietary or nutrient assessment. In addition to the methodology from the present study, other studies reviewed suggested a variety of models and methods that were employed with characteristics that met the definition of food pattern modeling [ 3 , 4 , 6 , 7 , 8 , 30 , 31 , 32 ]. To use food pattern modeling broadly, methodology and further validation should be considered for research and programming moving forward.…”

Section: Discussionmentioning

confidence: 99%

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Food Pattern Modeling as an Alternative Assessment Method to Multiday Dietary Recalls for Iron-Related Nutrients: A Proof-of-Concept Study

Delimont

Nickel

2020

Nutrients

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There are barriers to in-depth memory-based dietary assessment techniques in community-based research. Food pattern modeling may be an alternative method to traditional assessment techniques. The objective of this study was to pilot a comparison of food pattern modeling to 24 h diet recalls for predicting hematological outcomes of iron status. Data from 3–24 h dietary recalls in 27 women were analyzed by two methods: mean dietary intake estimates or food pattern modeling. Food pattern modeling was used to determine the total inventory of foods consumed with iron, phytate, or ascorbic acid or iron–phytate ratios. Each variable was analyzed for its relationship to hemoglobin, ferritin, and acute iron absorption from a meal challenge study by creating receiver operating characteristic (ROC) curves. There were no differences in ROC curves or diagnostic accuracies between food pattern modeling or mean dietary intake estimates for iron, vitamin C, phytate, or phytate–iron ratios for estimating hemoglobin or ferritin values (p > 0.05). Food pattern modeling was inferior to mean dietary estimates for acute iron absorption, suggesting that more detailed methods may be necessary for studies with sensitive or acute dietary measurement outcomes. Food pattern modeling for total iron, vitamin C, phytate, and phytate–iron ratios may be comparable to detailed memory-based recalls for larger studies assessing the impact of foods on iron status.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Food Pattern Modeling as an Alternative Assessment Method to Multiday Dietary Recalls for Iron-Related Nutrients: A Proof-of-Concept Study

Delimont

Nickel

2020

Nutrients

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“…The limit of quantification of the determination method was 18 ng/L related to native serum. Regular laboratory intercomparing studies (GEQUAS quality controls scheme and IRMM/EU certification campaign) as well as regular analysis of certificate material, namely ERM-BD-150 (skimmed milk powder from European Reference Materials-BD150), ERM-CD281 (rye grass from European Reference Materials-CD281), and Recipe RM ClinCal Plasma, were implemented for quality control [24][25][26]. The selection of CRMs was adopted according to sample matrix (e.g., plasma RM from Recipe Chemicals + Instruments GmbH, Munich, Germany) and to comparable (low) concentrations to samples (e.g., Cd in CRM CD281 or ERM-BD-150).…”

Section: Laboratory Analysismentioning

confidence: 99%

Comparison of Methodologies to Estimate Dietary Cadmium Intake in an Italian Population

Filippini

Upson

Adani

et al. 2020

IJERPH

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Cadmium is a metal that is toxic to humans, and the major source of cadmium exposure in the non-smoking general population is diet. To identify major food sources and lower exposure from diet, an accurate estimate of dietary cadmium intake is needed. Hence, the objectives of this study are to develop a method to assess dietary cadmium intake using a biomarker measurement and to improve the estimation of dietary cadmium intake when using a food frequency questionnaire (FFQ). In a random sample of an Italian population, we collected dietary habits by FFQ and measured cadmium in foods and beverages. These data were used to compute the estimated weekly dietary intake (WDI) of cadmium (µg) by kilogram (kg) of body weight (bw) (WDIFFQ). We also measured fasting serum cadmium levels by inductively-coupled plasma mass spectrometry. We used these data to develop a model for the estimation of the biomarker-derived dietary cadmium intake (WDIbio). In the 51 subjects recruited, the median level of serum cadmium was 0.041 µg/L (interquartile range (IQR): 0.030–0.054). The median WDIFFQ and WDIbio were 1.34 µg/kg bw/week (IQR: 0.86–1.70) and 0.72 µg/kg bw/week (IQR: 0.55–1.11), respectively. The correlation between the two estimates was low-to-moderate (r = 0.291). In exploratory analyses, the correlation was slightly higher in women and participants ages <50 years, and markedly higher in participants with body mass index <25 kg/m2 and smokers. Our approach allows for the dietary contribution to be isolated from the overall cadmium exposure measured with a biomarker; the estimated dietary cadmium intake was roughly similar to that estimated using the FFQ, especially in select subgroups. Future refinements to the biomarker-derived dietary cadmium intake approach should take into consideration additional sources of cadmium exposure, as well as factors affecting its absorption and metabolism.

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“…Sensitivity analyses adding smoking habits (coded as 0, 1 and 2 for never, former and current smokers, respectively) and BMI were tested. Finally, the environmental source of Se through air was tested using levels of particulate matter <10 µm (PM 10 ) estimated with a methodology previously reported for other trace elements [i.e., cadmium (28) and manganese in this issue (41)].…”

Section: Laboratory Analysismentioning

confidence: 99%

Toenail selenium as an indicator of environmental exposure: A cross-sectional study

Filippini

Ferrari

Michalke

et al. 2017

Molecular Medicine Reports

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The relation between toxicity and essentiality of selenium (Se) is of growing interest in human health, as the effects may widely differ depending of its different chemical species and the exposure levels. Toenail Se has been proposed as a reliable biomarker of long-term Se exposure, but few studies investigated the correlation between its toenail content and environmental determinants (i.e., dietary food intake). We aimed to determine the relation of toenail Se levels with serum Se species as well as food items. We recruited a random sample of Modena (Northern Italy) municipal residents, from whom we collected detailed personal information, dietary habits, toenail specimen for Se determination and a blood sample for serum Se speciation analysis. Toenail Se mean value was 0.96 µg/g (range, 0.47‑1.60), with slightly higher levels in females, in non-obese subjects and in Se supplements users, while it was lower in current smokers. Toenail Se positively correlated with organic Se forms, mainly selenoprotein P and selenocysteine, and inversely with the inorganic forms (selenite and selenate). Toenail Se was not associated with meat, cereals and dairy products consumption, positively correlated with fruit and slightly with vegetable intake, and negatively with fish and seafood consumption. Finally, no clear association emerged with estimated air Se exposure.

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Determinants of serum manganese levels in an Italian population

Cited by 17 publications

References 57 publications

Food Pattern Modeling as an Alternative Assessment Method to Multiday Dietary Recalls for Iron-Related Nutrients: A Proof-of-Concept Study

Food Pattern Modeling as an Alternative Assessment Method to Multiday Dietary Recalls for Iron-Related Nutrients: A Proof-of-Concept Study

Comparison of Methodologies to Estimate Dietary Cadmium Intake in an Italian Population

Toenail selenium as an indicator of environmental exposure: A cross-sectional study

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