scholarly journals Patterns of Dietary Iron Intake, Iron Status, and Predictors of Haemoglobin Levels among Early Adolescents in a Rural Ghanaian District

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Michael Akenteng Wiafe ◽  
Charles Apprey ◽  
Reginald Adjetey Annan

Introduction. Early adolescents are vulnerable to anaemia due to lean body mass and menarche. The study assessed patterns of dietary iron intake, iron status, and predictors of anaemia among early adolescents. Method. One hundred and thirty-seven early adolescents were randomly selected in a rural district in Ghana. Multiple-pass 24-hour recall, iron food frequency questionnaire consisting of 27 food items, and semistructured questionnaire were administered. Variables include sociodemographics, dietary factors, and laboratory investigation including haemoglobin, ferritin, and C-reactive protein examination. Statistical Package for the Social Sciences (SPSS) software was used to calculate odds ratio and perform Mann–Whitney U test, chi-square (X2) test, exploratory factor analysis, and partial correlation (r) tests. Results. Participants had mean age of 11.5 years. Three iron dietary patterns explaining 28.7% of the total variance were identified: iron dietary pattern 1 (11%) composed of iron-rich, iron-enhancing, and iron-inhibiting foods; iron dietary pattern 2 (9.9%) comprised of iron-rich, iron-enhancing, and non-iron-inhibiting foods; and iron dietary pattern 3 (7.1%) consisting of stinging nettle, iron-inhibiting foods, non-iron-enhancing foods, non-cocoyam leaves, and non-turkey berries. Meal skipping (X2 = 5.7, p < 0.05 ), times of eating a day (X2 = 12.6, p < 0.05 ), and guardian educational status (X2 = 6.7, p < 0.05 ) significantly affected dietary iron intake. Anaemia was associated with meal skipping (β = 0.367, p > 0.05 ), snacking (β = 0.484, p > 0.05 ), and junior high school (JHS) education (β = 0.544, p > 0.05 ). Partial correlation showed statistically significant relationship between iron dietary pattern 1 and dietary iron (r = −0.234, p < 0.01 ), iron dietary pattern 2 and dietary iron (r = -0.198, p < 0.05 ), iron dietary pattern 2 and vitamin C (r = -0.201, p < 0.05 ), and haemoglobin and ferritin (r = −0.178, p < 0.05 ). Conclusion. Meal skipping, guardian educational status, and number of times of eating a day were significantly associated with dietary iron intake. Meal skipping, snacking, and adolescents with JHS education were positively associated with anaemia.

Transfusion ◽  
2013 ◽  
Vol 54 (3pt2) ◽  
pp. 770-774 ◽  
Author(s):  
Alison O. Booth ◽  
Karen Lim ◽  
Hugh Capper ◽  
David Irving ◽  
Jenny Fisher ◽  
...  

Nutrients ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1127 ◽  
Author(s):  
Pei Lin ◽  
Chun-Chao Chang ◽  
Kuo-Ching Yuan ◽  
Hsing-Jung Yeh ◽  
Sheng-Uei Fang ◽  
...  

Red blood cell (RBC) aggregation and iron status are interrelated and strongly influenced by dietary factors, and their alterations pose a great risk of dyslipidemia and metabolic syndrome (MetS). Currently, RBC aggregation-related dietary patterns remain unclear. This study investigated the dietary patterns that were associated with RBC aggregation and their predictive effects on hyperlipidemia and MetS. Anthropometric and blood biochemical data and food frequency questionnaires were collected from 212 adults. Dietary patterns were derived using reduced rank regression from 32 food groups. Adjusted linear regression showed that hepcidin, soluble CD163, and serum transferrin saturation (%TS) independently predicted RBC aggregation (all p < 0.01). Age-, sex-, and log-transformed body mass index (BMI)-adjusted prevalence rate ratio (PRR) showed a significant positive correlation between RBC aggregation and hyperlipidemia (p-trend < 0.05). RBC aggregation and iron-related dietary pattern scores (high consumption of noodles and deep-fried foods and low intake of steamed, boiled, and raw food, dairy products, orange, red, and purple vegetables, white and light-green vegetables, seafood, and rice) were also significantly associated with hyperlipidemia (p-trend < 0.05) and MetS (p-trend = 0.01) after adjusting for age, sex, and log-transformed BMI. Our results may help dieticians develop dietary strategies for preventing dyslipidemia and MetS.


Author(s):  
Joanna Gajewska ◽  
Jadwiga Ambroszkiewicz ◽  
Witold Klemarczyk ◽  
Ewa Głąb-Jabłońska ◽  
Halina Weker ◽  
...  

Iron metabolism may be disrupted in obesity, therefore, the present study assessed the iron status, especially ferroportin and hepcidin concentrations, as well as associations between the ferroportin-hepcidin axis and other iron markers in prepubertal obese children. The following were determined: serum ferroportin, hepcidin, ferritin, soluble transferrin receptor (sTfR), iron concentrations and values of hematological parameters as well as the daily dietary intake in 40 obese and 40 normal-weight children. The ferroportin/hepcidin and ferritin/hepcidin ratios were almost two-fold lower in obese children (p = 0.001; p = 0.026, respectively). Similar iron concentrations (13.2 vs. 15.2 µmol/L, p = 0.324), the sTfR/ferritin index (0.033 vs. 0.041, p = 0.384) and values of hematological parameters were found in obese and control groups, respectively. Iron daily intake in the obese children examined was consistent with recommendations. In this group, the ferroportin/hepcidin ratio positively correlated with energy intake (p = 0.012), dietary iron (p = 0.003) and vitamin B12 (p = 0.024). In the multivariate regression model an association between the ferroportin/hepcidin ratio and the sTfR/ferritin index in obese children (β = 0.399, p = 0.017) was found. These associations did not exist in the controls. The results obtained suggest that in obese children with sufficient iron intake, the altered ferroportin-hepcidin axis may occur without signs of iron deficiency or iron deficiency anemia. The role of other micronutrients, besides dietary iron, may also be considered in the iron status of these children.


2004 ◽  
Vol 39 (Supplement 1) ◽  
pp. S481
Author(s):  
T. Lind ◽  
O. Hernell ◽  
B. L??nnerdal ◽  
H. Stenlund ◽  
M. Domell??f ◽  
...  

2017 ◽  
Vol 106 (Supplement 6) ◽  
pp. 1672S-1680S ◽  
Author(s):  
Cuilin Zhang ◽  
Shristi Rawal

2011 ◽  
Vol 25 (S1) ◽  
Author(s):  
James P. McClung ◽  
J. Philip Karl ◽  
Laura J. Bass ◽  
Jennifer C. Rood ◽  
Bryan C. Wiley ◽  
...  

2010 ◽  
Vol 6 (4) ◽  
pp. 262-267
Author(s):  
Katharina Dube ◽  
Hermann Kalhoff ◽  
Mathilde Kersting

1996 ◽  
Vol 85 (9) ◽  
pp. 1033-1038 ◽  
Author(s):  
G Samuelson ◽  
L-E Bratteby ◽  
K Berggren ◽  
J-E Elverby ◽  
B Kempe
Keyword(s):  

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