title: Malnutrition predicting factors in hemodialysis patients.
abstract: Malnutrition is a predictor of increased mortality in chronic hemodialysis (HD) patients. Various factors may contribute to malnutrition in these patients including energy and protein intake, inflammation, and comorbidity. To determine the importance of these factors in malnutrition of chronic HD patients, we studied 112 chronic HD patients in two centers was evaluated with the Dialysis Malnutrition Score (DMS) and anthropometric and biochemical indices. Seventy six (67.8%) patients were classified as malnourished. According to DMS score, poor protein intake (r= -0.34, P< 0.01), comorbidities (r= -0.24, P< 0.05), poor energy intake (r= - 0.18, P< 0.005), and inflammation (r= -0.16, P< 0.05) were significant predictors of malnutrition in descending order of importance. Multiple regression analysis showed that only poor protein intake was the explanatory variable of anthropometric measurements decline including body mass index, triceps skin fold thick-ness, mid arm circumference, mid arm muscle circumference, fat free mass, fat mass, albumin, creatinine and transferrine. None of the mentioned factors predicted the decrease of biochemical markers. We conclude that the frequency of malnutrition is high in our population and poor protein intake is the primary contributing factor for this condition. Therefore, providing enough protein may be a simple and effective way in preventing malnutrition in these patients.
authors.affiliation: Shefa Neuroscience Research Center, Tehran, Iran. Razeghi@razi.tums.ac.ir
authors.forename: Soodeh Razeghi
authors.initials: SR
authors.lastname: Jahromi
authors.forename: Saeed
authors.initials: S
authors.lastname: Hosseini
authors.forename: Effat
authors.initials: E
authors.lastname: Razeghi
authors.forename: Ali pasha
authors.initials: Ap
authors.lastname: Meysamie
authors.forename: Haleh
authors.initials: H
authors.lastname: Sadrzadeh
mesh_terms: D050154:Adiposity; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D016009:Chi-Square Distribution; D015897:Comorbidity; D003404:Creatinine / Q000097:blood; D004044:Dietary Proteins / Q000008:administration & dosage*; D002149:Energy Intake; D005260:Female; D006801:Humans; D007249:Inflammation / Q000150:complications / Q000453:epidemiology; D007492:Iran / Q000453:epidemiology; D008297:Male; D044342:Malnutrition / Q000097:blood / Q000453:epidemiology* / Q000209:etiology* / Q000503:physiopathology; D008875:Middle Aged; D009752:Nutritional Status; D011502:Protein-Energy Malnutrition / Q000097:blood / Q000453:epidemiology / Q000209:etiology* / Q000503:physiopathology; D012044:Regression Analysis; D006435:Renal Dialysis / Q000009:adverse effects*; D018570:Risk Assessment; D012307:Risk Factors; D012709:Serum Albumin / Q000378:metabolism; D012885:Skinfold Thickness; D013997:Time Factors; D014168:Transferrin / Q000378:metabolism; D055815:Young Adult
chemical_list: D015415:Biomarkers; D004044:Dietary Proteins; D012709:Serum Albumin; D014168:Transferrin; D003404:Creatinine