Mitsuhiko Noda
- Endocrinology, Diabetes and Metabolism top 0.1%
- Physiology top 0.2%
- Molecular Biology top 1%
- Epidemiology top 0.5%
- Surgery top 1%
- Co-authors
- Atsushi GotoHiroshi NotoShoichiro TsuganeTakashi KadowakiTetsuya MizoueManami InoueTetsuro TsujimotoKazuhiro Eto
- Topics
- Diabetes, Cardiovascular Risks, and Lipoproteins (64 papers)Metabolism, Diabetes, and Cancer (61 papers)Diabetes Management and Research (56 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Mitsuhiko Noda
322 papers receiving 15.1k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Endocrinology, Diabetes and Metabolism 5.1k
- Physiology 4.2k
- Molecular Biology 4.2k
- Epidemiology 4.0k
- Surgery 2.8k
Countries citing papers authored by Mitsuhiko Noda
This map shows the geographic impact of Mitsuhiko Noda'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 Mitsuhiko Noda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitsuhiko Noda more than expected).
Fields of papers citing papers by Mitsuhiko Noda
This network shows the impact of papers produced by Mitsuhiko Noda. 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 Mitsuhiko Noda. The network helps show where Mitsuhiko Noda may publish in the future.
Co-authorship network of co-authors of Mitsuhiko Noda
This figure shows the co-authorship network connecting the top 25 collaborators of Mitsuhiko Noda. A scholar is included among the top collaborators of Mitsuhiko Noda 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 Mitsuhiko Noda. Mitsuhiko Noda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 1 | |
| 9 | 6 | |
| 10 | 8 | |
| 11 | 23 | |
| 12 | 7 | |
| 13 | 7 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | 4 | |
| 17 | 15 | |
| 18 | 44 | |
| 19 | Alcohol consumption and risk of type 2 diabetes mellitus in Japanese: a systematic review. | 58 |
| 20 | Comparisons between anthropometric indices for predicting the metabolic syndrome in Japanese. | 13 |
About Mitsuhiko Noda
Mitsuhiko Noda is a scholar working on Endocrinology, Diabetes and Metabolism, Physiology and Public Health, Environmental and Occupational Health, having authored 332 papers that have together received 15.6k indexed citations. Recurring topics across this work include Diabetes, Cardiovascular Risks, and Lipoproteins (64 papers), Metabolism, Diabetes, and Cancer (61 papers) and Diabetes Management and Research (56 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (5.1k citations), Physiology (4.2k citations) and Endocrine and Autonomic Systems (824 citations). Mitsuhiko Noda has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Atsushi Goto, Hiroshi Noto, Shoichiro Tsugane, Takashi Kadowaki, Tetsuya Mizoue, Manami Inoue, Tetsuro Tsujimoto, Kazuhiro Eto, Kohjiro Ueki and Toshimasa Yamauchi. Their work appears in journals such as The Lancet, Journal of Biological Chemistry and Journal of Clinical Investigation.
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.