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- Hagström, Hannes, et al.
(author)
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Body composition measurements and risk of hematological malignancies : A population-based cohort study during 20 years of follow-up
- 2018
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:8
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Journal article (peer-reviewed)abstract
- High body mass index (BMI) is associated with development of hematological malignancies (HMs). However, although BMI is a well-established measurement of excess weight, it does not fully reflect body composition and can sometimes misclassify individuals. This study aimed at investigating what body composition measurements had highest association with development of HM. Body composition measurements on 27,557 individuals recorded by healthcare professionals as part of the Malmo Diet and Cancer study conducted in Sweden between 1991-1996 were matched with data from national registers on cancer incidence and causes of death. Cox regression models adjusted for age and sex were used to test the association between one standard deviation increments in body composition measurements and risk of HM. During a median follow-up of 20 years, 564 persons developed an HM. Several body composition measurements were associated with risk of developing an HM, but the strongest association was found for multiple myeloma (MM). Waist circumference (HR 1.31, p = 0.04) and waist-hip ratio (HR 1.61, p = 0.05) had higher risk estimates than BMI (HR 1.18, p = 0.07) for MM. In conclusion, our study shows that measurements of abdominal adiposity better predict the risk of developing HM, particularly MM, compared to BMI.
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| 2. |
- Lykiardopoulos, Byron, et al.
(author)
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Development of Serum Marker Models to Increase Diagnostic Accuracy of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: The New LINKI Algorithm Compared with Established Algorithms
- 2016
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In: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 11:12
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Journal article (peer-reviewed)abstract
- Background and Aim Detection of advanced fibrosis (F3-F4) in nonalcoholic fatty liver disease (NAFLD) is important for ascertaining prognosis. Serum markers have been proposed as alternatives to biopsy. We attempted to develop a novel algorithm for detection of advanced fibrosis based on a more efficient combination of serological markers and to compare this with established algorithms. Methods We included 158 patients with biopsy-proven NAFLD. Of these, 38 had advanced fibrosis. The following fibrosis algorithms were calculated: NAFLD fibrosis score, BARD, NIKEI, NASH-CRN regression score, APRI, FIB-4, Kings score, GUCI, Lok index, Forns score, and ELF. Study population was randomly divided in a training and a validation group. A multiple logistic regression analysis using bootstrapping methods was applied to the training group. Among many variables analyzed age, fasting glucose, hyaluronic acid and AST were included, and a model (LINKI-1) for predicting advanced fibrosis was created. Moreover, these variables were combined with platelet count in a mathematical way exaggerating the opposing effects, and alternative models (LINKI-2) were also created. Models were compared using area under the receiver operator characteristic curves (AUROC). Results Of established algorithms FIB-4 and Kings score had the best diagnostic accuracy with AUROCs 0.84 and 0.83, respectively. Higher accuracy was achieved with the novel LINKI algorithms. AUROCs in the total cohort for LINKI-1 was 0.91 and for LINKI-2 models 0.89. Conclusion The LINKI algorithms for detection of advanced fibrosis in NAFLD showed better accuracy than established algorithms and should be validated in further studies including larger cohorts.
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