Predicting iron and folate deficiency anaemias from standard blood testing: the mechanism and implications for clinical medicine and public health in developing countries

通过标准血液检测预测铁和叶酸缺乏性贫血:机制及其对发展中国家临床医学和公共卫生的意义

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Abstract

BACKGROUND: Developing countries have high prevalence of diseases, but facilities to diagnose and treat them are limited. We must use available resources in ways not needed where there are sophisticated equipment and trained staff. Anaemia is common; iron deficiency affects health and productivity; folate deficiency in pregnant women causes foetal abnormalities. Few developing countries can measure serum folate or ferritin, but standard automated blood analyses are widely available and can help predict folate and iron deficiency. The RDW-CV% (coefficient of variation of the red cell width) measures the variability in the size of red blood cells (RBC) in routine automated analysis of blood cells, but is seldom reported. Levels of RDW-CV% and haemoglobin (Hb) can predict iron deficiency anaemia. METHOD AND RESULTS: I have written a computer model based on the standard mechanism for blood formation and destruction. This shows that before anaemia develops and during recovery, there are both normal and abnormal RBC (small in iron deficiency and large in folate deficiency) in the circulation. The model calculates the abnormality in the RDW-CV% in standard automated blood analyses. In early iron deficiency and during recovery the full blood count shows the Hb near the lower limit of normal, a low MCV and a high RDW-CV%. A similar pattern, but with a higher MCV, develops in folate deficiency. Folate deficiency is often brief and may not cause anaemia. The high RDW-CV% may persist for three months. CONCLUSION: This long footprint could be medically useful for detecting folate deficiency and so limiting foetal damage in individuals and communities. Few clinicians or public health workers know about RDW-CV%. Standard blood reports for clinical use should include the RDW-CV% and note the possible significance of abnormal values.

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