bioRxiv ScienceSearch

bioRxiv · 10.1101/2020.09.27.315770

Incidence of canine dilated cardiomyopathy, breed and age distributions, and grain-free diet sales in the United States from 2000-2019: A retrospective survey.

Abstract

BackgroundDilated cardiomyopathy (DCM) is considered a predominantly inherited disease in dogs. Recent reports suggest an increased incidence of DCM in atypical breeds eating grain-free and/or legume-rich diets. However, little data regarding incidence of DCM within the US is available; and no existing data quantifies DCM among breeds over time. HypothesisWe hypothesized that DCM incidence among breeds could be estimated by retrospective polling of veterinary cardiologists. Further, if a correlation existed between grain-free diets and DCM, an increase in DCM would be on trend with increased grain-free pet food sales. Materials and MethodsThirty-six U.S. cardiology specialty practices were asked for all initial canine and DCM cases evaluated from 2000-2019; fourteen cardiology practices participated. DCM signalment data was provided by three hospitals over 15 years; representing 68 breeds. Age distribution of DCM cases upon diagnosis were compared to other cardio cases and general hospital population from one hospital. All data were evaluated using linear regression models. Grain-free pet food sales data was evaluated from 2011-2019. ResultsFourteen hospitals participated and reported 67,243 unique canine patients. Nationally, data did not support a significant change in percent DCM over time (p=0.85). The overall average incidence rate of DCM during the study period was 3.83% (range 2.41-5.65%), while grain-free diet sales increased 500% from 2011-2019. No correlation between overall DCM incidence and grain free diet sales was discovered. A significant upward trend in mixed breeds diagnosed with DCM, with no significant trend in other breeds was appreciated. An upward trend in age at DCM diagnosis was identified, correlating with trends from overall hospital populations. ConclusionsThese data do not support overall increased DCM incidence, or a correlation with grain-free pet food sales. Additional data are necessary to understand whether regional factors contribute to increased DCM within smaller cohorts.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Quest, B., Clark, S. D., Garimella, S., Konie, A., Leach, S. B., Oxford, E. M.. 2020-09-28. Incidence of canine dilated cardiomyopathy, breed and age distributions, and grain-free diet sales in the United States from 2000-2019: A retrospective survey.. https://doi.org/10.1101/2020.09.27.315770

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education

The Mismatch Between Neuroscience Graduate Training and Professional Skill Sets

Understanding the skill sets required for career paths is a prerequisite for preparing students for those careers. Neuroscience career paths are rapidly changing as the field expands and increasingly overlaps with computational and data-heavy job sectors. With the steady growth in neuroscience trainees and the diversification of jobs for those trainees, it is important to assess whether or not our training is matching the skill sets required in the workforce. Here, we surveyed hundreds of neuroscience professionals and graduate students to assess their use and valuation of a range of skills, from bench skills to communication and management. We find that professionals with neuroscience degrees can be clustered into three main groups based on their skill sets: academic research, industry research and technical work, and non-research. Further, we find that while graduate students do not use or highly value management and communication skills, almost all neuroscience professionals report strongly needing those skills. Finally, coding and data analysis skills are widely used in academic and industry research and predict higher salaries. Our findings can help trainees assess their own skill sets as well as encourage educational leaders to offer training in management and communication-skills which may help catapult trainees into the next stages of their careers.

scientific communication and education

Is the genomics cart before the restoration ecology horse? Insights from qualitative interviews and trends from the literature

Harnessing new technologies is vital to achieve global imperatives to restore degraded ecosystems. We explored the potential of genomics as one such tool. We aimed to understand barriers hindering the uptake of genomics, and how to overcome them, via exploratory interviews with leading scholars in both restoration and its sister discipline of conservation - a discipline that has successfully leveraged genomics. We also conducted an examination of research trends to explore some insights that emerged from the interviews, including publication trends that have used genomics to address restoration and conservation questions. Our qualitative findings revealed varied perspectives in harnessing genomics. For example, scholars in restoration without genomics experience felt genomics was over-hyped. Scholars with genomics experience emphatically emphasised the need to proceed cautiously in using genomics in restoration. Both genomics-experienced and less-experienced scholars called for case studies to demonstrate the benefits of genomics in restoration. These qualitative data contrasted with our examination of research trends, which revealed 70 restoration genomics studies, particularly studies using environmental DNA as a monitoring tool. We provide a roadmap to facilitate the uptake of genomics into restoration, to help the restoration sector meet the monumental task of restoring huge areas to biodiverse and functional ecosystems.

scientific communication and education