Sarah Huang

All Publications

• Distinct Mechanistic Features of Atrial Fibrillation in Hypertrophic Cardiomyopathy. Heart rhythm Liu, M., Huang, S., Kawana, M., Wheeler, M. T., Parikh, V., Perez, M. V. 2026

  Abstract

  Atrial fibrillation in hypertrophic cardiomyopathy (AF-HCM) is increasingly recognized as a distinct clinical entity, characterized by early onset, high prevalence, and uniquely poor outcomes compared to AF in the general population. This review synthesizes emerging evidence on the genetic, molecular, and hemodynamic mechanisms underlying AF-HCM. Patients with AF-HCM face a dramatically increased risk of stroke and heart failure, independent of conventional risk stratification tools, necessitating universal anticoagulation and aggressive rhythm control. Current therapies including cardiac ablation and antiarrhythmic drugs demonstrate limited efficacy and/or safety in this population. Recent clinical trials and real-world studies reveal persistent AF incidence despite advances in HCM management, underscoring the need for novel, disease-specific therapeutic strategies. AF-HCM exemplifies the importance of genotype-driven precision medicine in cardiology and highlights ongoing gaps in treatment for complex inherited arrhythmias.

  View details for DOI 10.1016/j.hrthm.2026.03.1958

  View details for PubMedID 41956264

• Genetic exceptionalism and genomic contextualism among Asian Americans: a qualitative study. Journal of community genetics Huang, S. D., Martschenko, D., Scherer, C. R., Chiang, K. F., Chang, K., Naik, H. 2025; 17 (1): 2

  Abstract

  Views on genetic information - and how it compares to other health information - play a key role in shaping policy surrounding its treatment, management, and communication. Genetic exceptionalism and genomic contextualism are important frameworks to better understand how communities view genetic information in comparison to other medical data. This study aimed to explore how Asian Americans view genetic information and what factors influence their views. Using a qualitative study design guided by reflexive thematic analysis, we interviewed 20 ostensibly healthy Asian American adults about their attitudes toward and experiences with genetic information. We developed four themes: first, participants discussed diverse potential uses and qualities of genetic information that distinguished it from other types of health information, such as providing insight into future health conditions. However, they underscored the continued importance of other types of health information depending on context, and did not view genetic information as deterministic or the essence, giving weight to environmental contributors in molding who they are. Views on genetic information were shaped by complex, interacting factors at individual, family, and community or cultural levels, such as stigma, intersectional identities, and family dynamics. Participants had overall limited awareness of clinical genetics services and indications genetic testing could be offered for, despite high self-reported health literacy. Our participants' views on genetic information were complex and context-dependent, in line with genomic contextualism. This should be considered in providing culturally-engaged genetics education and developing genomics policies that reflect how diverse communities truly feel about genetic information.

  View details for DOI 10.1007/s12687-025-00838-8

  View details for PubMedID 41252082

  View details for PubMedCentralID PMC12627301

• Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation. Journal of proteome research Yan, Y., Sankar, B. S., Mirza, B., Ng, D. C., Pelletier, A. R., Huang, S. D., Wang, W., Watson, K., Wang, D., Ping, P. 2024; 23 (9): 4151-4162

  Abstract

  Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

  View details for DOI 10.1021/acs.jproteome.4c00263

  View details for PubMedID 39189460

  View details for PubMedCentralID PMC11385379

• Exploring the role of digital tools in rare disease management: An interview-based study. Journal of genetic counseling Chang, A., Huang, S. D., Benjamin, D. J., Schmidt, J. L., Palmer, C. G., Garrison, N. A. 2024

  Abstract

  While digital tools, such as the Internet, smartphones, and social media, are an important part of modern society, little is known about the specific role they play in the healthcare management of individuals and caregivers affected by rare disease. Collectively, rare diseases directly affect up to 10% of the global population, suggesting that a significant number of individuals might benefit from the use of digital tools. The purpose of this qualitative interview-based study was to explore: (a) the ways in which digital tools help the rare disease community; (b) the healthcare gaps not addressed by current digital tools; and (c) recommended digital tool features. Individuals and caregivers affected by rare disease who were comfortable using a smartphone and at least 18 years old were eligible to participate. We recruited from rare disease organizations using purposive sampling in order to achieve a diverse and information rich sample. Interviews took place over Zoom and reflexive thematic analysis was utilized to conceptualize themes. Eight semistructured interviews took place with four individuals and four caregivers. Three themes were conceptualized which elucidated key aspects of how digital tools were utilized in disease management: (1) digital tools should lessen the burden of managing a rare disease condition; (2) digital tools should foster community building and promote trust; and (3) digital tools should provide trusted and personalized information to understand the condition and what the future may hold. These results suggest that digital tools play a central role in the lives of individuals with rare disease and their caregivers. Digital tools that centralize trustworthy information, and that bring the relevant community together to interact and promote trust are needed. Genetic counselors can consider these ideal attributes of digital tools when providing resources to individuals and caretakers of rare disease.

  View details for DOI 10.1002/jgc4.1908

  View details for PubMedID 38741243

• Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network. American journal of medical genetics. Part A Huang, S. D., Bamba, V., Bothwell, S., Fechner, P. Y., Furniss, A., Ikomi, C., Nahata, L., Nokoff, N. J., Pyle, L., Seyoum, H., Davis, S. M. 2023

  Abstract

  Turner syndrome (TS) is a genetic condition occurring in ~1 in 2000 females characterized by the complete or partial absence of the second sex chromosome. TS research faces similar challenges to many other pediatric rare disease conditions, with homogenous, single-center, underpowered studies. Secondary data analyses utilizing electronic health record (EHR) have the potential to address these limitations; however, an algorithm to accurately identify TS cases in EHR data is needed. We developed a computable phenotype to identify patients with TS using PEDSnet, a pediatric research network. This computable phenotype was validated through chart review; true positives and negatives and false positives and negatives were used to assess accuracy at both primary and external validation sites. The optimal algorithm consisted of the following criteria: female sex, ≥1 outpatient encounter, and ≥3 encounters with a diagnosis code that maps to TS, yielding an average sensitivity of 0.97, specificity of 0.88, and C-statistic of 0.93 across all sites. The accuracy of any estradiol prescriptions yielded an average C-statistic of 0.91 across sites and 0.80 for transdermal and oral formulations separately. PEDSnet and computable phenotyping are powerful tools in providing large, diverse samples to pragmatically study rare pediatric conditions like TS.

  View details for DOI 10.1002/ajmg.a.63495

  View details for PubMedID 38066696