| Literature DB >> 35938103 |
Mansura Khanam1, Kazi Istiaque Sanin1, Gulshan Ara1, Razia Sultana Rita1, Anika Bushra Boitchi1, Fahmida Dil Farzana1, Md Ahshanul Haque1, Tahmeed Ahmed1,2,3.
Abstract
Objectives: Moringa oleifera has been used for centuries due to its medicinal properties and health benefits. The plant has antifungal, anti-viral, and anti-inflammatory properties. We aimed to evaluate the effect of consumption of Moringa leaves, along with a regular diet on serum hemoglobin and retinol and underweight status among rural Bangladeshi adolescent girls.Entities:
Keywords: Moringa; adolescent girls; hemoglobin; underweight; vitamin A
Year: 2022 PMID: 35938103 PMCID: PMC9353109 DOI: 10.3389/fnut.2022.959890
Source DB: PubMed Journal: Front Nutr ISSN: 2296-861X
Figure 1Study profile.
Household characteristics of the participants by group.
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| Male | 79.7 (90) | 85.8 (97) |
| Female | 20.3 (23) | 14.2 (16) |
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| Islam | 111 (98.2) | 108 (95.6) |
| Hinduism | 2 (1.8) | 5 (4.4) |
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| 1–2 | 65 (57.5) | 66 (58.4) |
| >2 | 48 (42.5) | 47 (41.6) |
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| Service holder | 14 (12.4) | 11 (9.7) |
| Housewife | 99 (87.6) | 102 (90.3) |
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| Agricultural laborer | 21 (18.5) | 27 (23.9) |
| Service | 14 (12.4) | 9 (7.9) |
| Business | 14 (12.4) | 21 (18.6) |
| Factory worker | 9 (8.0) | 7 (6.2) |
| Construction laborer | 7 (6.2) | 9 (8.0) |
| Unemployed | 48 (42.5) | 40 (35.4) |
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| Illiterate | 15 (13.3) | 8 (7.08) |
| Primary completed | 49 (43.36) | 70 (61.9) |
| Secondary and higher | 49 (43.36) | 35 (30.9) |
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| Illiterate | 27 (23.9) | 8 (7.08) |
| Primary completed | 42 (37.1) | 81 (71.7) |
| Secondary and higher | 44 (38.9) | 24 (21.2) |
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| Own tube well | 83 (73.4) | 82 (72.6) |
| Other's tube well | 2 (1.8) | 6 (5.3) |
| Community tube well | 1 (0.9) | 0 (0) |
| Supply water (piped) | 0 (0) | 1 (0.9) |
| Deep tube well | 27 (23.9) | 24 (21.2) |
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| Sanitary with flush | 4 (3.6) | 17 (15.1) |
| Sanitary without flush | 47 (41.6) | 58 (51.3) |
| Pucca/pit | 57 (50.4) | 37 (32.7) |
| Kutcha/Hanging | 5 (4.4) | 1 (0.9) |
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| Sanitary with flush | 4 (3.6) | 17 (15.1) |
| Sanitary without flush | 47 (41.6) | 58 (51.3) |
| Pucca/pit | 55 (48.7) | 34 (30.1) |
| Kutcha/Hanging | 6 (5.2) | 1 (0.9) |
| Others | 1 (0.9) | 3 (2.6) |
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| Yes | 11 (9.7) | 27 (23.9) |
| No | 102 (90.3) | 86 (76.1) |
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| Owns the house | 102 (90.3) | 110 (97.3) |
| Rented | 2 (1.8) | 1 (0.9) |
| In kind | 9 (7.9) | 2 (1.8) |
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| Poor | 20 (17.7) | 26 (23.0) |
| Poorer | 24 (21.2) | 21 (18.6) |
| Middle | 20 (17.7) | 25 (22.1) |
| Richer | 22 (19.5) | 23 (20.4) |
| Richest | 27 (23.9) | 18 (15.9) |
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| Age (Y) (mean ± SD) | 12.7 ± 0.7 | 13.3 ± 0.8 |
| Year of schooling (mean ± SD) | 6.52 ± 0.50 | 7.43 ± 1.09 |
| Weight (kg) | 42.4 ± 9.7 | 42.6 ± 8.3 |
| Height (cm) | 148.8 ± 7.5 | 149.7 ± 5.7 |
| Hemoglobin (g/dL) | 12.0 ± 0.7 | 11.8 ± 0.9 |
| Serum retinol (μmol/l) | 1.3 ± 0.3 | 1.4 ± 0.9 |
Wealth quintile: The household wealth quintile was constructed using household asset data obtained from the Socioeconomic Status questionnaire. From these asset-related dichotomous variables, a common factor score for each household was generated using polychoric principal components analysis in STATA software. After ranking based on their score, we divided first principal component score into quintiles to create five categories where the first category represents the poorest household, and the fifth category represents the wealthiest household.
Figure 2(A,B) Changes in nutritional status among the study participants.
Changes in biomarkers between the intervention and control groups.
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| Haemoglobin | 12.04 | 11.78 | 0.31 | 13.31 | 12.59 | 0.72 | 0.41 (0.14, 0.76) | 0.009 |
| Retinol (μmol/l) | 1.32 | 1.40 | −0.07 | 1.38 | 1.19 | 0.19 | 0.27 (0.14, 0.36) | 0.000 |
| Weight (kg) | 42.39 | 42.64 | 0.25 | 46.64 | 45.28 | −1.36 | 1.41 (−1.91, 4.72) | 0.406 |
| BMI for age | −0.09 | −0.24 | −0.16 | 0.20 | −0.05 | −0.25 | 0.09 (−0.34, 0.51) | 0.674 |
Difference between control and Intervention at baseline and endline.
Effects of the intervention over 6 months of follow-up using GLM with adjustment for maternal age, sex of household head, maternal education, BMI for age, wealth index, and absentism of school.
Effects of the intervention over 6 months of follow-up using GLM with adjustment for maternal age, sex of household head, maternal education, wealth index, and absentism of school.