6 results
478 Magnetic Resonance Biomarkers of Metabolic Dysfunction-Associated Steatotic Liver Disease
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- Marissa Brown, Alexander Moody, Juan Vasquez, John Blangero, Luke Norton, Geoffrey Clarke
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- Journal:
- Journal of Clinical and Translational Science / Volume 8 / Issue s1 / April 2024
- Published online by Cambridge University Press:
- 03 April 2024, p. 141
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OBJECTIVES/GOALS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major public health concern due to its increasing prevalence and association with type 2 diabetes mellitus. Non-invasive magnetic resonance-based biomarkers can aid in the monitoring of disease progression and identification of patients at risk for chronic liver disease. METHODS/STUDY POPULATION: Over 600 subjects will be recruited from the San Antonio Mexican American Family Study and from a second study, which consists of (i) T2DM patients diagnosed with either MASLD or metabolic dysfunction-associated steatohepatitis (MASH) or (ii) metabolically healthy controls. Hydrogen-1 MRS and diffusion-weighted MRI (DW-MRI) will be used to measure liver fat fraction and liver stiffness biomarkers, respectively. Several potential biomarkers of liver stiffness will be evaluated in vivo using the intravoxel incoherent motion (IVIM) model for DW-MRI. To further improve the diagnostic accuracy of patients with liver fibrosis, we will integrate MRI/MRS data with relevant clinical indicators of hepatic metabolism. Results will be compared to biopsy samples to evaluate the model’s diagnostic accuracy. RESULTS/ANTICIPATED RESULTS: Based on preliminary data, we predict that IVIM will be able to accurately diagnose hepatic fibrosis in patients with MASLD, allowing it to be implemented in clinics with high-field MRI units easily. Previous studies have shown correlations between IVIM estimates and fibrosis stages, however, none included additional clinical indicators of liver disease in their models. We have already found significant differences in metabolic measurements such as fasting plasma glucose and HbA1c levels. Additionally, the use of machine learning in developing these models has shown improvements in the ability to extract features from the data. The aim is to achieve high accuracy and robustness in the staging of liver fibrosis. DISCUSSION/SIGNIFICANCE: Over 100 million people in the US are affected by MASLD. Without treatment, it progresses from hepatic steatosis to MASH, fibrosis (liver stiffening), and ultimately to hepatic cirrhosis and hepatocellular carcinoma (HCC). Continued research efforts and clinical implementation of MRI and MRS are vital in combating the growing burden of MASLD.
Genetic influences on externalizing psychopathology overlap with cognitive functioning and show developmental variation
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- Josephine Mollon, Emma E. M. Knowles, Samuel R. Mathias, Amanda Rodrigue, Tyler M. Moore, Monica E. Calkins, Ruben C. Gur, Juan Manuel Peralta, Daniel J. Weiner, Elise B. Robinson, Raquel E. Gur, John Blangero, Laura Almasy, David C. Glahn
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- Journal:
- European Psychiatry / Volume 64 / Issue 1 / 2021
- Published online by Cambridge University Press:
- 31 March 2021, e29
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Background
Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation.
MethodsUsing data from 9,421 individuals aged 8–21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation.
ResultsExternalizing was heritable (h2 = 0.46, p = 1 × 10−6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = −0.412, p = 0.004), verbal reasoning (ρg = −0.485, p = 0.001), spatial reasoning (ρg = −0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10−4), verbal knowledge (ρg = −0.314, p = 0.002), and general cognitive ability (g)(ρg = −0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = −0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing.
ConclusionsCognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults.
3069 Characterizing the Neural Signature of Metabolic Syndrome
- Eithan Kotkowski, Larry R. Price, Crystal G. Franklin, Maximino Salazar, Ralph A. DeFronzo, David Glahn, John Blangero, Peter T. Fox
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, p. 4
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OBJECTIVES/SPECIFIC AIMS: Our objective is to understand the influence of the features comprising metabolic syndrome (central obesity, raised fasting plasma glucose, triglycerides, blood pressure, and decreased HDL cholesterol) on brain structure in men and women. With the understanding that MetS is a strong predictor of gray matter volume loss in specific brain regions, in this study we sought to quantify the influence of each of the metabolic syndrome biometric variables on the structures involved in the neural signature of metabolic syndrome. METHODS/STUDY POPULATION: We conducted multiple linear regression analyses on a cross-sectional sample of 800 individuals from the Genetics of Brian Structure (GOBS) image archive (352 men and 448 women). GOBS is an offshoot of the San Antonio Heart Study involving an extended pedigree of Mexican Americans from the greater San Antonio area. Its goal is to localize, identify, and characterize genes/quantitative trait loci associated with variations in brain structure and function (Winkler, 2010). The archive has continuously added participants from approximately 40 families since 2006. Neuroanatomic (T1-weighted MRI scans obtained on a Siemens 3T scanner and processed using FSL), neurocognitive, and biometric phenotypes have been obtained for each subject (including blood lipids). Linear regressions were run using SPSS and incorporated biometric and gray matter volume values obtained from 800 GOBS participants. RESULTS/ANTICIPATED RESULTS: Linear regressions incorporating metabolic syndrome variables as dependent variables and gray matter volume from regions involved in the neural signature of metabolic syndrome as predictors show significant predictive patterns that are largely similar between men and women, with some differences. Another linear regression conducted with gray matter volume from the neural signature of metabolic syndrome as the dependent variable and metabolic syndrome variables as predictors show that waist circumference and triglycerides are the greatest predictors of gray matter volume loss in men, and fasting plasma glucose and waist circumference are the greatest predictors of gray matter volume loss in women. DISCUSSION/SIGNIFICANCE OF IMPACT: Significant sex differences in the relationships between metabolic syndrome variables and gray matter volume changes between brain regions comprising the neural signature of metabolic syndrome were identified. waist circumference, fasting plasma glucose, and triglycerides are the most reliable predictors of gray matter volume loss. The variance in gray matter volume of the neural signature of metabolic syndrome in men is more significantly explained by waist circumference and triglycerides (when accounting for age) and in women is more significantly explained by waist circumference and fasting plasma glucose (when accounting for age). A model of metabolic syndrome that emphasizes a risk of neurodegeneration should focus on waist circumference for both men and women and weigh the remaining variables accordingly by sex (triglycerides in men and fasting plasma glucose in women).
Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders
- Josephine Mollon, Samuel R. Mathias, Emma E. M. Knowles, Amanda Rodrigue, Marinka M. G. Koenis, Godfrey D. Pearlson, Abraham Reichenberg, Jennifer Barrett, Dominique Denbow, Katrina Aberizk, Molly Zatony, Russell A. Poldrack, John Blangero, David C. Glahn
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- Journal:
- Psychological Medicine / Volume 50 / Issue 1 / January 2020
- Published online by Cambridge University Press:
- 04 January 2019, pp. 48-57
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Background
Cognitive impairment is a core feature of psychotic disorders, but the profile of impairment across adulthood, particularly in African-American populations, remains unclear.
MethodsUsing cross-sectional data from a case–control study of African-American adults with affective (n = 59) and nonaffective (n = 68) psychotic disorders, we examined cognitive functioning between early and middle adulthood (ages 20–60) on measures of general cognitive ability, language, abstract reasoning, processing speed, executive function, verbal memory, and working memory.
ResultsBoth affective and nonaffective psychosis patients showed substantial and widespread cognitive impairments. However, comparison of cognitive functioning between controls and psychosis groups throughout early (ages 20–40) and middle (ages 40–60) adulthood also revealed age-associated group differences. During early adulthood, the nonaffective psychosis group showed increasing impairments with age on measures of general cognitive ability and executive function, while the affective psychosis group showed increasing impairment on a measure of language ability. Impairments on other cognitive measures remained mostly stable, although decreasing impairments on measures of processing speed, memory and working memory were also observed.
ConclusionsThese findings suggest similarities, but also differences in the profile of cognitive dysfunction in adults with affective and nonaffective psychotic disorders. Both affective and nonaffective patients showed substantial and relatively stable impairments across adulthood. The nonaffective group also showed increasing impairments with age in general and executive functions, and the affective group showed an increasing impairment in verbal functions, possibly suggesting different underlying etiopathogenic mechanisms.
Genetic Influences on Type 2 Diabetes and Metabolic Syndrome Related Quantitative Traits in Mauritius
- Jeremy B. Jowett, Vincent P. Diego, Navaratnam Kotea, Sudhir Kowlessur, Pierrot Chitson, Thomas D. Dyer, Paul Zimmet, John Blangero
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- Journal:
- Twin Research and Human Genetics / Volume 12 / Issue 1 / 01 February 2009
- Published online by Cambridge University Press:
- 21 February 2012, pp. 44-52
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Epidemiological studies report a high prevalence of type 2 diabetes and metabolic syndrome in the island nation of Mauritius. The Mauritius Family Study was initiated to examine heritable factors that contribute to these high rates of prevalence and consists of 400 individuals in 24 large extended multigenerational pedigrees. Anthropometric and biochemical measurements relating to the metabolic syndrome were undertaken in addition to family and lifestyle based information for each individual. Variance components methods were used to determine the heritability of the type 2 diabetes and metabolic syndrome related quantitative traits. The cohort was made up of 218 females (55%) and 182 males with 22% diagnosed with type 2 diabetes and a further 30% having impaired glucose tolerance or impaired fasting glucose. Notably BMI was not significantly increased in those with type 2 diabetes (P = .12), however a significant increase in waist circumference was observed in these groups (P = .02). The heritable proportion of trait variance was substantial and greater than values previously published for hip circumference, LDL and total cholesterol, diastolic and systolic blood pressure and serum creatinine. Height, weight and BMI heritabilities were all in the upper range of those previously reported. The phenotypic characteristics of the Mauritius family cohort are similar to those previously reported in the Mauritian population with a high observed prevalence rate of type 2 diabetes. A high heritability for key type 2 diabetes and metabolic syndrome related phenotypes (range 0.23 to 0.68), suggest the cohort will have utility in identifying genes that influence these quantitative traits.
Chapter 11 - Mapping Genes Influencing Human Quantitative Trait Variation
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- By John Blangero, Southwest Foundation for Biomedical Research, Jeff Williams, Southwest Foundation for Biomedical Research, Laura Almasy, Southwest Foundation for Biomedical Research, Sarah Williams-Blangero, Southwest Foundation for Biomedical Research
- Edited by Michael H. Crawford, University of Kansas
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- Book:
- Anthropological Genetics
- Published online:
- 05 June 2012
- Print publication:
- 30 November 2006, pp 306-334
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Summary
Introduction
In the post-genomic era, the genetic analysis of common diseases will be one of the most critically important areas of biomedical science. Over the past two decades, it has become clear that many of the diseases that constitute the major public health burden in the United States – diseases such as diabetes, atherosclerosis, obesity, hypertension, depression, alcoholism, osteoporosis, and cancer – have a substantial genetic component. The genetic architecture of such diseases is complex, however, involving multiple genetic and environmental components and their interactions. The specific quantitative trait loci (QTLs) that are involved in the biological pathways of these diseases, and the individual effects of these QTLs in the general population, are still largely unknown. The stochastic complexity of the genotype-phenotype relationship of a common disease requires that statistical inference plays a prominent role in the dissection of the underlying genetic architecture. However, statistical genetic methods suitable for this immense task are still in their infancy. The genomic localization and identification of QTLs and characterization of their causal functional polymorphisms will require new advanced statistical genetic tools.
Over the past decade, we have been successful in developing the theoretical and empirical foundation requisite to a thorough understanding of the strengths and weaknesses of variance component-based quantitative trait linkage methods. We have incorporated many of our statistical genetic developments into our freely available computer package, SOLAR (Sequential Oligogenic Linkage Analysis Routines) (Almasy and Blangero, 1998).