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Dive into the research topics where Maria Roselle Abraham is active.

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Featured researches published by Maria Roselle Abraham.


Hepatology | 2012

Association between diabetes, family history of diabetes, and risk of nonalcoholic steatohepatitis and fibrosis.

Rohit Loomba; Maria Roselle Abraham; Aynur Unalp; Laura Wilson; Joel E. Lavine; Ed Doo; Nathan M. Bass

Previous studies have shown familial aggregation of insulin resistance and nonalcoholic fatty liver disease (NAFLD). Therefore, we aimed to examine whether family history of diabetes mellitus (DM) is associated with nonalcoholic steatohepatitis (NASH) and fibrosis in patients with NAFLD. This was a cross‐sectional analysis in participants of the NAFLD Database study and PIVENS trial who had available data on family history of DM. One thousand and sixty‐nine patients (63% women), with mean age of 49.6 (± 11.8) years and body mass index (BMI) of 34.2 (± 6.4) kg/m2, were included. Thirty percent had DM, and 56% had a family history of DM. Both personal history of DM and family history of DM were significantly associated with NASH, with an odds ratio (OR) of 1.93 (95% confidence interval [CI]: 1.37‐2.73; P <0.001) and 1.48 (95% CI: 1.11‐1.97; P = 0.01) and any fibrosis with an OR of 3.31 (95% CI: 2.26‐4.85; P < 0.001) and 1.66 (95% CI: 1.25‐2.20; P < 0.001), respectively. When the models were adjusted for age, sex, BMI, ethnicity, and metabolic traits, the association between diabetes and family history of DM with NASH showed an increased adjusted OR of 1.76 (95% CI: 1.13‐2.72; P < 0.001) and 1.34 (95% CI: 0.99‐1.81; P = 0.06), respectively, and with any fibrosis with a significant adjusted OR of 2.57 (95% CI: 1.61‐4.11; P < 0.0001) and 1.38 (95% CI: 1.02‐1.87; P = 0.04), respectively. After excluding patients with personal history of diabetes, family history of DM was significantly associated with the presence of NASH and any fibrosis with an adjusted OR of 1.51 (95% CI: 1.01‐2.25; P = 0.04) and 1.49 (95% CI: 1.01‐2.20; P = 0.04), respectively. Conclusions: Diabetes is strongly associated with risk of NASH, fibrosis, and advanced fibrosis. Family history of diabetes, especially among nondiabetics, is associated with NASH and fibrosis in NAFLD. (HEPATOLOGY 2012;56:943–951)


Hepatology | 2012

Ethnicity and Nonalcoholic Fatty Liver Disease

Kiran Bambha; Patricia Belt; Maria Roselle Abraham; Laura Wilson; Mark Pabst; Linda D. Ferrell; Aynur Unalp-Arida; Nathan M. Bass

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disorder in the United States; however, few data are available about racial and ethnic variation. We investigated relationships between ethnicity, NAFLD severity, metabolic derangements, and sociodemographic characteristics in a well‐characterized cohort of adults with biopsy‐proven NAFLD. Data were analyzed from 1,026 adults (≥18 years) in the Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) from 2004 to 2008, for whom liver histology data were available within 6 months of enrollment. Associations between ethnicity (i.e., Latino versus non‐Latino white) and NAFLD severity (i.e., NASH versus non‐NASH histology and mild versus advanced fibrosis) were explored with multiple logistic regression analysis. We also investigated effect modification of ethnicity on metabolic derangements for NAFLD severity. Within the NASH CRN, 77% (N = 785) were non‐Latino white and 12% (N = 118) were Latino. Sixty‐one percent (N = 628) had NASH histology and 28% (N = 291) had advanced fibrosis. Latinos with NASH were younger, performed less physical activity, and had higher carbohydrate intake, compared to non‐Latino whites with NASH. Gender, diabetes, hypertension, hypertriglyceridemia, aspartate aminotransferase (AST), platelets, and the homeostasis model assessment of insulin resistance (HOMA‐IR) were significantly associated with NASH. Age, gender, AST, alanine aminotransferase, alkaline phosphatase, platelets, total cholesterol, hypertension, and HOMA‐IR, but not ethnicity, were significantly associated with advanced fibrosis. The effect of HOMA‐IR on the risk of NASH was modified by ethnicity: HOMA‐IR was not a significant risk factor for NASH among Latinos (odds ratio [OR] = 0.93; 95% confidence interval [CI]: 0.85‐1.02), but was significant among non‐Latino whites (OR, 1.06; 95% CI: 1.01‐1.11). Conclusion: Metabolic risk factors and sociodemographic characteristics associated with NASH differ by ethnicity. Additional insights into NASH pathogenesis may come from further studies focused on understanding ethnic differences in this disease. (HEPATOLOGY 2012)


STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges | 2011

Matching sparse sets of cardiac image cross-sections using large deformation diffeomorphic metric mapping algorithm

Siamak Ardekani; Aastha Jain; Saurabh Jain; Theodore P. Abraham; Maria Roselle Abraham; Stefan L. Zimmerman; Raimond L. Winslow; Michael I. Miller; Laurent Younes

The purpose of this study is to illustrate the application of large deformation diffeomorphic metric mapping to perform registration among sparsely sampled cardiac magnetic resonance imaging (MRI) data. To evaluate the performance of this method, we use two sets of data: 1) contours that are generated from sparsely sampled left ventricular sections and extracted from short axis cardiac MRI of patients with hypertrophic cardiomyopathy and 2) left ventricular surface mesh that is generated from higher resolution cardiac computed tomography image. We present two different discrepancy criteria, one based on a measure that is embedded in the dual of a reproducing kernel Hilbert space of functions for curves and the other is based on a geometric soft matching distance between a surface and a curve.


International Journal of Environmental Research and Public Health | 2016

The Relation of Moderate Alcohol Consumption to Hyperuricemia in a Rural General Population

Zhao Li; Xiaofan Guo; Yamin Liu; Ye Chang; Yingxian Sun; Guangshuo Zhu; Maria Roselle Abraham

Background: although alcohol abuse is known to increase serum uric acid, the relation between moderate drinking and uric acid have remained poorly understood. We performed this study to evaluate whether different alcohol consumption level has different effects on the risk of hyperuricemia based on a rural general population. Method: multi-stage cluster sampling method was used to select a representative sample of individuals aged 35 years or older. Participants were asked to provide information about their alcohol consumption. Data regarding the demographic and lifestyle characteristics and the blood biochemical indexes of these participants were collected by well-trained personnel. Results: in total, 11,039 participants aged 35 years or older were included (4997 men and 6042 women). The prevalence of hyperuricemia in the different male alcohol consumption groups was 11.9% in non-drinkers, 12.6% in moderate drinkers, and 16.3% in heavy drinkers (p < 0.001). In females, the rates were 6.3% in non-drinkers, 8.1% in moderate drinkers, and 6.6% for heavy drinkers (p = 0.818). In males, multivariate logistic regression analyses shows heavy drinkers had an approximately 1.7-fold higher risk of hyperuricemia (OR: 1.657, 95% CI: 1.368 to 2.007, p < 0.001) than non-drinkers; moderate drinkers did not experience a significant increase in risk (OR: 1.232, 95% CI: 0.951 to 1.596, p = 0.114)). Multivariate logistic regression analyses of females showed that, compared with non-drinkers, neither moderate nor heavy drinkers had a significantly increased risk of hyperuricemia (OR: 1.565, 95% CI: 0.521 to 4.695, p = 0.425 for heavy drinkers; OR: 0.897, 95% CI: 0.117 to 6.855, p = 0.916 for moderate drinkers). Conclusions: heavy alcohol consumption increased the risk of hyperuricemia for males but not for females. Among both males and females, moderate alcohol consumption did not increase the risk of hyperuricemia.


International Journal of Environmental Research and Public Health | 2016

Diagnosed but Not Undiagnosed Diabetes Is Associated with Depression in Rural Areas

Zhao Li; Xiaofan Guo; Hongkun Jiang; Guozhe Sun; Yingxian Sun; Maria Roselle Abraham

Background: There is a lack of study on the relation between undiagnosed diabetes and depression in the general population. Methods: A total of 11,531 adults were examined using a multistage cluster sampling method to select a representative sample of individuals who were at least 35 years old. Subjects were classified into three groups: no diabetes (ND), diagnosed diabetes (DD), and undiagnosed diabetes (UD). The participants were surveyed with the Patient Health Questionnaire-9 (PHQ-9). Results: Of all the 11,531 participants, the prevalence of depression was higher in the DD group than in the other two groups. Multi variable logistic regression analyses show that the DD group had significantly higher odds for depression compared with the ND group (p < 0.01), while the UD group showed no significant differences compared to the ND group. Subgroup analyses show that diagnosed diabetes in subjects with a lower educational level, compared with subjects with an educational level of high school or above, had higher odds for a PHQ-9 score ≥5 (p < 0.01). Conclusion: In this general population, diagnosed but not undiagnosed diabetes was significantly associated with depression. Much higher odds for depression were found among diagnosed diabetic individuals with a lower level of education.


Journal of Cardiovascular Translational Research | 2008

Stem Cell: ``KANT`` You See It?

Michael Bonios; John Terrovitis; Maria Roselle Abraham

Immanuel Kant proposed that “it is the representation that makes the object possible rather than the object that makes the representation possible” in an effort to introduce the concept of the human mind as an active originator of experience. But if the mind actively generates perception, then this begs the question of how closely the result reflects reality? In vivo imaging of transplanted stem cells has substantially increased our knowledge of stem cell fate and biology. But how closely does the representation of the transplanted cells using current imaging modalities reflect their true status? In this editorial, we provide a brief description of the imaging modalities that have been employed to image stem cells in the heart following transplantation. The ideal imaging modality should provide specific information about the number and location of engrafted cells, their viability and differentiation. Several methods have been used to track stem cells in vivo and can be divided into two broad categories: direct labeling and reporter gene labeling. In direct labeling, cells uptake agents such as radionuclides or particles prior to transplantation. An important disadvantage of direct labeling is the release of label after cell death with persistence in tissue, resulting in false-positive signals and consequently, overestimation of engraftment. An attractive alternative to direct labeling is genetic labeling using a reporter gene. The basic principle of this modality is genetic modification using a gene which is not normally expressed in the target organ, and which encodes a protein that mediates bioluminescence, fluorescence, enzyme activity, or transport of a radiolabeled reporter probe. The reporter gene is transferred to cells prior to transplantation and can be detected after administration of the substrate or radiolabeled reporter probe which is specific for the reporter gene and which accumulates in the transduced, transplanted cells [1]. Importantly, since accumulation of the reporter probe requires expression of the reporter gene and activity of the reporter gene product, the imaging signal will depend on transplanted cell viability. Furthermore, reporter gene imaging can be performed repeatedly and is not limited by radioactive decay or loss of the label; the main downside to reporter gene labeling is silencing of viral promoters that are often used to express the reporter genes [2]. Several groups, including ours, have studied both direct labeling and reporter gene labeling for stem cell tracking after transplantation into the heart [3, 4]. Magnetic resonance imaging (MRI) is the most commonly used direct labeling technique in both experimental and clinical studies of stem cell transplantation [5–7]. MRI is an attractive imaging modality because it does not involve the use of ionizing radiation and can simultaneously provide accurate information about cardiac structure and function [8]. For this purpose, cells are labeled with nanometeror micrometer-sized iron particles prior to transplantation and the heart is then imaged in vivo by MRI [9, 10]. The principle of this technique is as follows: iron present in the cells distorts magnetization locally and consequently produces a signal void, which represents the injected cells [11]. The Achilles’ heel of this technique is the persistence of signal in tissue after labeled stem cell death due to uptake of the iron oxide particles by macroJ. of Cardiovasc. Trans. Res. (2008) 1:103–105 DOI 10.1007/s12265-008-9023-3


Kardiologia Polska | 2005

Cardiac stem cells

Eduardo Marbán; Maria Roselle Abraham; Rachel R. Smith


Journal of Cardiovascular Translational Research | 2011

Constitutive HIF-1α Expression Blunts the Beneficial Effects of Cardiosphere-Derived Cell Therapy in the Heart by Altering Paracrine Factor Balance

Michael Bonios; Connie Y. Chang; John Terrovitis; Aurelio Pinheiro; Andreas S. Barth; Peihong Dong; Miguel Santaularia; D. Brian Foster; Venu Raman; Theodore P. Abraham; Maria Roselle Abraham


International Journal of Cardiovascular Imaging | 2016

Left ventricular wall thickness in patients with hypertrophic cardiomyopathy: a comparison between cardiac magnetic resonance imaging and echocardiography

Celia P. Corona-Villalobos; Lars Sorensen; Iraklis Pozios; Linda Chu; John Eng; Maria Roselle Abraham; Theodore P. Abraham; Ihab R. Kamel; Stefan L. Zimmerman


American Journal of Cardiology | 2016

Relation of Heavy Alcohol Consumption to QTc Interval Prolongation.

Zhao Li; Xiaofan Guo; Yamin Liu; Guozhe Sun; Yingxian Sun; Yufan Guan; Guangshuo Zhu; Maria Roselle Abraham

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Iraklis Pozios

Johns Hopkins University

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Stefan L. Zimmerman

Johns Hopkins University School of Medicine

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Lars Sorensen

Johns Hopkins University

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Eduardo Marbán

Cedars-Sinai Medical Center

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Hongchang Luo

Johns Hopkins University

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John Terrovitis

National and Kapodistrian University of Athens

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