Se Status Prediction by Food Intake as Compared to Circulating Biomarkers in a West Algerian Population
Abstract
:1. Introduction
2. Materials and Methods
2.1. Human Samples
2.2. Dietary Intake Assessment
2.3. Selenium Status Assessment
2.4. Statistical Analysis
3. Results
3.1. Characterisation of the Study Cohort
3.2. Assessment of Se Intake via the Food Categories Using Reference Composition Data
3.3. Comparison of Intake-Deduced Se Status with Plasma Se Status Biomarkers
3.4. Interrelation of Plasma Se and SELENOP Concentrations in Se-Deficient vs. Se-Replete Subjects
3.5. Interrelation of GPX3 Activity with Se Intake, Plasma Se and SELENOP
3.6. Deducing a Model of Food Intake according to Food Categories Predicting Se Status
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Se-Deficient | Se-Replete | Total | p Value | |
---|---|---|---|---|
Total number (n) | 12 | 146 | 158 | |
Age | 0.728 | |||
median (IQR) | 45 (30, 69) | 46 (19, 90) | 46 (19, 90) | |
mean (95% CI) | 47 (38, 56) | 49 (46, 51) | 49 (46, 51) | |
BMI | 0.208 | |||
median (IQR) | 25.4 (19.5, 32.5) | 27.2 (16.0, 43.2) | 26.8 (16.0, 43.2) | |
mean (95% CI) | 25.8 (23.0, 28.5) | 27.8 (27.0, 28.7) | 27.7 (26.9, 28.5) | |
Educational level | 0.151 | |||
<high school | 6 (54.5%) | 68 (50.0%) | 74 (50.3%) | |
>high school | 1 (9.1%) | 43 (31.6%) | 44 (29.9%) | |
high school | 4 (36.4%) | 25 (18.4%) | 29 (19.7%) | |
Family income (k DZD) * | 0.130 | |||
median (IQR) | 28.0 (6.0, 200.0) | 50.0 (8.0, 200.0) | 47.5 (6.0, 200.0) | |
mean (95% CI) | 48.9 (13.7, 111.4) | 54.7 (46.1, 63.4) | 54.3 (45.6, 63.0) | |
Gender | 0.416 | |||
Female | 9 (75.0%) | 119 (84.4%) | 128 (83.7%) | |
Male | 3 (25.0%) | 22 (15.6%) | 25 (16.3%) | |
Hypertension | 0.469 | |||
No | 7 (63.6%) | 103 (75.7%) | 110 (74.8%) | |
Yes | 4 (36.4%) | 33 (24.3%) | 37 (25.2%) | |
Marital status | 0.626 | |||
Married | 9 (81.8%) | 120 (88.2%) | 129 (87.8%) | |
Single | 2 (18.2%) | 16 (11.8%) | 18 (12.2%) | |
Smoking | 0.510 | |||
No | 9 (81.8%) | 95 (69.9%) | 104 (70.7%) | |
Yes | 2 (18.2%) | 41 (30.1%) | 43 (29.3%) | |
SELENOP (mg/L) | 0.010 | |||
median (IQR) | 3.84 (2.21, 7.17) | 5.20 (1.19, 14.21) | 5.11 (1.19, 14.21) | |
mean (95% CI) | 4.23 (3.37, 5.10) | 5.56 (5.22, 5.89) | 5.46 (5.14, 5.78) |
Se-Deficient | Se-Replete | Total | p-Value | |
---|---|---|---|---|
Bread (g/day) | 0.307 | |||
mean (95% CI) | 114 (45, 184) | 154 (136, 171) | 151 (134, 168) | |
Cereals (g/day) | 0.176 | |||
mean (95% CI) | 152 (96, 208) | 204 (184, 224) | 201 (181, 220) | |
Eggs (g/day) | 0.344 | |||
mean (95% CI) | 0.3 (−0.4, 1.1) | 23.0 (13.4, 32.6) | 21.5 (12.5, 30.5) | |
Fish & Seafood (g/day) | 0.382 | |||
mean (95% CI) | 0.0 (0.0, 0.0) | 9.1 (2.8, 15.4) | 8.4 (2.6, 14.3) | |
Fresh fruits (g/day) | 0.263 | |||
mean (95% CI) | 122.2 (24.0, 220.4) | 80.4 (58.0, 102.8) | 83.2 (61.5, 104.8) | |
Legumes (g/day) | 0.800 | |||
mean (95% CI) | 53.4 (48.6, 155.3) | 41.9 (24.1, 59.7) | 42.6 (25.2, 60.1) | |
Meat (g/day) | 0.073 | |||
mean (95% CI) | 67.8 (17.1, 118.4) | 39.1 (28.3, 49.9) | 41.0 (30.5, 51.6) | |
Milk (g/day) | 0.814 | |||
mean (95% CI) | 56 (1.9, 109) | 85 (57, 113) | 83 (57, 110) | |
Potatoes (g/day) | 0.054 | |||
mean (95% CI) | 22.2 (−5.7, 50.1) | 85.9 (67.5, 104.4) | 81.7 (64.2, 99.1) | |
Vegetables (g/day) | 0.443 | |||
mean (95% CI) | 201.1 (84.3, 318.0) | 253.4 (222.8, 284.0) | 249.9 (220.6, 279.3) | |
Calculated Se Intake (µg/day) | 0.669 | |||
median (IQR) | 77.0 (36.6, 84.8) | 54.5 (17.8, 247.3) | 55.2 (17.8, 247.3) | |
mean (95% CI) | 62.5 (48.6, 76.4) | 62.7 (57.0, 68.3) | 62.7 (57.4, 68.0) |
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Belhadj, M.; Kazi Tani, L.S.; Dennouni Medjati, N.; Harek, Y.; Dali Sahi, M.; Sun, Q.; Heller, R.; Behar, A.; Charlet, L.; Schomburg, L. Se Status Prediction by Food Intake as Compared to Circulating Biomarkers in a West Algerian Population. Nutrients 2020, 12, 3599. https://doi.org/10.3390/nu12123599
Belhadj M, Kazi Tani LS, Dennouni Medjati N, Harek Y, Dali Sahi M, Sun Q, Heller R, Behar A, Charlet L, Schomburg L. Se Status Prediction by Food Intake as Compared to Circulating Biomarkers in a West Algerian Population. Nutrients. 2020; 12(12):3599. https://doi.org/10.3390/nu12123599
Chicago/Turabian StyleBelhadj, Moussa, Latifa Sarra Kazi Tani, Nouria Dennouni Medjati, Yahia Harek, Majda Dali Sahi, Qian Sun, Raban Heller, Ammaria Behar, Laurent Charlet, and Lutz Schomburg. 2020. "Se Status Prediction by Food Intake as Compared to Circulating Biomarkers in a West Algerian Population" Nutrients 12, no. 12: 3599. https://doi.org/10.3390/nu12123599