Biology subjects

Huang, C.-C.

Publications and source records attributed to Huang, C.-C..

3 recordsLinked to original sources

bioRxiv2018

Topographic diversity of structural connectivity in schizophrenia

The neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging (DTI) study, we recruited 77 healthy controls (HC), and 71 patients with DSM-IV diagnosis of schizophrenia (SCZ), and reconstructed the structural connectivity of 90 brain regions covering entire cerebral cortex. We first confirmed the heterogeneity in structural connectivity by showing a reduced inter-individual similarity in SCZ compared with HC. Moreover, we found it was not possible to cluster patients into subgroups with shared patterns of dysconnectivity, indicating a high degree of mechanistic divergence in schizophrenia. Instead of the strength of connectivity between any particular brain regions, we investigated the diversity (or statistically, the variance) of the topographic distribution of the strength was reduced. HC had higher topographic diversity in whole brain structural connectivity compared to the patient group\n\n(P = 2 x 10-6, T = 4.96, Cohen'S d = 0.87). In 62 of the 90 brain regions, the topographic diversity was significantly reduced in patients compared to controls after FDR correction (<0.05). When topographic diversity was used as a discriminant feature for classification between patients and controls, we significantly (P = 4.29 x 10-24) improved the classification accuracy to 79.6% (sensitivity 78.3%, specificity 81.3%). This finding suggests highly individualized pattern of structural dysconnectivity underlying the heterogeneity of schizophrenia converges to a convergent common pathway as reduced topographic diversity for the clinical construct of the disease.

neuroscience↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗

bioRxiv2017

The Impact of Vitamin A and Carotenoids on the Risk of Tuberculosis Progression

BackgroundLow and deficient levels of vitamin A are common in low and middle income countries where tuberculosis burden is high. We assessed the impact of baseline levels of vitamins A and carotenoids on TB disease risk.\n\nMethods and FindingsWe conducted a case-control study nested within a longitudinal cohort of household contacts of pulmonary TB cases in Lima, Peru. We screened all contacts for TB disease at 2, 6, and 12 months after enrollment. We defined cases as HIV-negative household contacts with blood samples who developed TB disease at least 15 days after enrollment of the index patient. For each case, we randomly selected 4 controls from among contacts who did not develop TB disease, matching on gender and year of age. We used conditional logistic regression to estimate odds ratios (ORs) for incident TB disease by vitamin A and carotenoids levels, controlling for other nutritional and socioeconomic factors.\n\nAmong 6751 HIV-negative household contacts with baseline blood samples, 192 developed secondary TB disease during follow-up. We analyzed 180 cases with viable samples and 709 matched controls. After controlling for possible confounders, we found that baseline vitamin A deficiency was associated with a 10-fold increase in risk of TB disease among household contacts (aOR 10.42; 95% CI 4.01-27.05; p < 0.001). This association was dose-dependent with stepwise increases in TB disease risk with each decreasing quartile of vitamin A level. Carotenoid levels were also inversely associated with TB risk among adolescents.\n\nOur study is limited by the one year duration of follow up and by the relatively few blood samples available from household contacts under ten years of age.\n\nConclusionsVitamin A deficiency strongly predicted risk of incident TB disease among household contacts of TB patients. Vitamin A supplementation among individuals at high risk of TB may provide an effective means of preventing TB disease.

epidemiology↗