Abstract

Today, one of the healthcare challenges is to detect diseases before they occur so they can be prevented. It is well known the high frequency of iron deficiency (ID) with anemia (IDA) or without anemia, Latent Iron deficiency (LID) in fertile women, due to menstruation and sometimes due to poor diet. Also well known is the relatively high frequency of anemia in chronic diseases (ACD) or the previous clinical stage without anemia that we will call Latent Functional Iron Deficiency (LFID) and hemochromatosis (HEM) in the general population. In this study we were trying to see how frequently iron metabolism disturbances were present: LFID, LID and HEM in fertile, non-anemic women.

Patients and Methods: We collected 211 consecutive random women with an age between 12 and 45 years old with a hemoglobin (Hb) between 12 g/dL and 13 g/dL. We have tested all of them for the complete blood count, reticulocyte count and reticulocyte derived parameters (MRV, IRF, etc.), iron profile: serum iron (Fe), Transferrin (Tf), Ferritin (Fer), Transferrin Saturation (TfS%). We analyzed the most common genetic abnormalities related to Hereditary Hemochromatosis in all the women with Ferritin higher than 100 with normal or high iron, and we froze serum for further analysis: Soluble Transferrin receptor (sTfR), Hepcidin, CRP, etc. We then studied what are the best possible parameters and cut-offs to help in the detection of suspect iron metabolism disturbances.

RESULTS: we found 31 LID (14.7%), 7 Latent Functional Iron deficiency (LFID)(3.3%), 9 Hemochromatosis (HH) (4.3%) and 164 with normal Iron profiles (77.7%). From the 9 Hemochromatosis the genetic studies we studied, shown three heterozygotic and two homozygotic mutation for the H 63 D and one heterozygotic mutation for the gen C 282 T. We found differences statistically significant to differentiate the abnormalities analyzed as compared to normals (N) with these parameters or combination of them: LID vs N: MCV, MCH, MCHC, RDW. LFID vs N: MCV, MCH, MCHC, RDW and combined parameters, like Maf (Hb x MCV). HH vs N: MCV, MCH, MCHC and MRV. Some of the discriminant functions we found increase significantly the sensitivity and specificity of the parameters used alone

Stats Latent Iron deficiency vs Normal Female

Stat / ParameterHb x MCV/100 (R)MCVMCHMCHCRDW
Mean LID 10,8 86,8 29,0 33,3 14,5 
Mean Normal 11,2 88,9 30,0 33,7 14,1 
p Student test (*U Mann) p 0,0209 p 0,05 p 0,0095 p 0,0029 p 0,0054 * 
Cut off < 10,5 <82,77 <29,5 <33,66 >13,99 
Stat / ParameterHb x MCV/100 (R)MCVMCHMCHCRDW
Mean LID 10,8 86,8 29,0 33,3 14,5 
Mean Normal 11,2 88,9 30,0 33,7 14,1 
p Student test (*U Mann) p 0,0209 p 0,05 p 0,0095 p 0,0029 p 0,0054 * 
Cut off < 10,5 <82,77 <29,5 <33,66 >13,99 

Stats Latent Functional Iron Deficiency and HH

/ DiseaseLFIDLFIDLFIDHHHHHH
Stat / Parameter HbxMCV/100 (R) HbxMCVx(1/RDW)(R) RDW MRVxMCV/100 (R) MCH MRV 
Mean 10,7 7,2 14,9 104,0 31,3 113,2 
Mean Normal 11,2 8,0 14,1 93,0 30,0 104.7 
p Student test (*U Mann) p 0,075 NS p 0,036 p 0,0291 * p 0,0069 p 0,0016 p 0,0097 
Cut off <10,5 <7,49 >13,61 >92,4 >30,26 >106,4 
/ DiseaseLFIDLFIDLFIDHHHHHH
Stat / Parameter HbxMCV/100 (R) HbxMCVx(1/RDW)(R) RDW MRVxMCV/100 (R) MCH MRV 
Mean 10,7 7,2 14,9 104,0 31,3 113,2 
Mean Normal 11,2 8,0 14,1 93,0 30,0 104.7 
p Student test (*U Mann) p 0,075 NS p 0,036 p 0,0291 * p 0,0069 p 0,0016 p 0,0097 
Cut off <10,5 <7,49 >13,61 >92,4 >30,26 >106,4 

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