Abstract
Manganese deficiency may cause severe health disorders and is becoming prevalent
in societies that are rapidly urbanising. Determining the Manganese contents and its
relationship with the intake of protein, fibre and fats is important; which may allow
people to select Manganese rich foods based on their major macronutrients. A crosssectional study including detailed dietary assessment for two weeks, followed by
proximate analysis of regularly consumed diets was conducted using 1200 adults from
three different social segments as; lecturers/teachers (400), managerial employees in
the private sector (400), and houses wives (400). Each social segment was assumed to
elicit different lifestyles and different daily Manganese and Macronutrient intake
levels. Most frequently consumed food items by them and their respective portion
sizes were identified. Those were prepared using mostly practiced cooking methods
and chemically analysed for proximate compositions of fat, protein, dietary fibre and
Manganese contents. Regression and general liner models were used to estimate the
association between protein, fibre and fat intake and Mn levels. The average daily
protein, fibre and fat intakes were 53.51, 36.85 and 41.85 grams respectively. The
average Manganese intake was 1.87 grams. There were significantly negative
association between dietary Manganese levels and fat intake (β=-0.041, p<0.00).
Increased dietary fat intake was associated with low levels of Manganese (β=-0.041,
p<0.00) for all social segments. Higher protein (β=0.019, p=0.01) or fibre intake
(β=0.013 p=0.002) reduced the risk of Manganese deficiency. Overall, dietary
Manganese elicited a positive correlation with proteins and fibre in foods, but a
negative correlation with dietary fat.
Madhura Jayasinghe, Binosha Fernando, Subhashinie Senadheera, Pubudu Gunawardene, Somathilaka Ranaweera. (2020) Investigation of the association between dietary fibre, protein and fat with Manganese content in food, Asian Journal of Agriculture and Biology, Volume 8, Issue 1.
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