Philip Schroeder

911 total citations
10 papers, 99 citations indexed

About

Philip Schroeder is a scholar working on Genetics, Epidemiology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Philip Schroeder has authored 10 papers receiving a total of 99 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Genetics, 4 papers in Epidemiology and 4 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Philip Schroeder's work include Genetic Associations and Epidemiology (5 papers), Liver Disease Diagnosis and Treatment (3 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers). Philip Schroeder is often cited by papers focused on Genetic Associations and Epidemiology (5 papers), Liver Disease Diagnosis and Treatment (3 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers). Philip Schroeder collaborates with scholars based in United States, Denmark and Poland. Philip Schroeder's co-authors include José C. Florez, Josep M. Mercader, Ravi Mandla, Miriam S. Udler, Laura E. Barnes, Christopher C. Moore, Alicia Huerta‐Chagoya, Aaron J. Deutsch, Varinderpal Kaur and Alisa K. Manning and has published in prestigious journals such as Nature Medicine, Diabetes Care and Diabetes.

In The Last Decade

Philip Schroeder

10 papers receiving 98 citations

Peers

Philip Schroeder

GENET

EPIDE

EDM

SURGE

Timothy D. Majarian United States

Claudia H. T. Tam Hong Kong

И. А. Ефимова Russia

Yayoi Segura-Kato Mexico

Jason Schairer United States

Venla Kurra Finland

Ulf Landmesser Germany

Alice E. Hughes United Kingdom

Kristen Kulasa United States

Timothy D. Majarian United States

Philip Schroeder

79 ×1.8

GENET

24 ×0.9

EPIDE

36 ×1.3

EDM

20 ×1.0

SURGE

57 ×3.0

Citations per year, relative to Philip Schroeder Philip Schroeder (= 1×) peers Timothy D. Majarian

Countries citing papers authored by Philip Schroeder

Since Specialization

Citations

This map shows the geographic impact of Philip Schroeder's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Philip Schroeder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Schroeder more than expected).

Fields of papers citing papers by Philip Schroeder

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip Schroeder. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip Schroeder. The network helps show where Philip Schroeder may publish in the future.

Co-authorship network of co-authors of Philip Schroeder

This figure shows the co-authorship network connecting the top 25 collaborators of Philip Schroeder. A scholar is included among the top collaborators of Philip Schroeder based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Philip Schroeder. Philip Schroeder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Mandla, Ravi, Philip Schroeder, Bianca Porneala, et al.. (2024). Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study. Genome Medicine. 16(1). 63–63. 4 indexed citations

Smith, Kirk, Aaron J. Deutsch, Sarah Hsu, et al.. (2024). Multi-ancestry polygenic mechanisms of type 2 diabetes. Nature Medicine. 30(4). 1065–1074. 39 indexed citations

Mandla, Ravi, Philip Schroeder, José C. Florez, Josep M. Mercader, & Aaron Leong. (2024). Hemoglobin A1c Genetics and Disparities in Risk of Diabetic Retinopathy in Individuals of Genetically Inferred African American/African British and European Ancestries. Diabetes Care. 47(10). 1731–1739. 1 indexed citations

Szczerbiński, Łukasz, Ravi Mandla, Philip Schroeder, et al.. (2024). Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores. Scientific Reports. 14(1). 26895–26895. 1 indexed citations

Cromer, Sara J., Josep M. Mercader, Timothy D. Majarian, et al.. (2023). Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity. Diabetes Care. 46(5). 944–952. 9 indexed citations

Deutsch, Aaron J., Philip Schroeder, Ravi Mandla, et al.. (2023). Type 2 Diabetes Polygenic Score Predicts the Risk of Glucocorticoid-Induced Hyperglycemia in Patients Without Diabetes. Diabetes Care. 46(8). 1541–1545. 9 indexed citations

Schroeder, Philip, Laura Brenner, Varinderpal Kaur, et al.. (2022). Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19. Cardiovascular Diabetology. 21(1). 136–136. 2 indexed citations

O’Connor, Mark J., Philip Schroeder, Alicia Huerta‐Chagoya, et al.. (2021). Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes. 71(3). 554–565. 12 indexed citations

Schroeder, Philip, et al.. (2018). Predictive Models of Sepsis in Adult ICU Patients. 390–391. 15 indexed citations

10.

Schroeder, Philip, et al.. (2018). Relative Mortality Analysis Of The “Golden Hour”: A Comprehensive Acuity Stratification Approach To Address Disagreement In Current Literature. Prehospital Emergency Care. 23(2). 254–262. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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