Eiichi Maeda
Impact in
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- Diabetes, Cardiovascular Risks, and Lipoproteins
- Diet, Metabolism, and Disease
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- Adipose Tissue and Metabolism
Papers in
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- Peroxisome Proliferator-Activated Receptors 4
- Surgery 10
- Cholesterol and Lipid Metabolism 5
- Lipoproteins and Cardiovascular Health 4
- Co-authors
- Gen Yoshino (18 shared papers)Masato Kasuga (12 shared papers)Tsutomu Kazumi (10 shared papers)Koh‐ichi Nagata (8 shared papers)Yukio Murata (7 shared papers)Tsutomu Hirano (5 shared papers)Mitsuru Hashiramoto (1 shared paper)Kazuya Iwamoto (1 shared paper)
In The Last Decade
Eiichi Maeda
33 papers receiving 289 citations
Peers
Comparison fields: 5 of 67
- Endocrinology, Diabetes and Metabolism 71
- Physiology 81
- Clinical Biochemistry 17
- Surgery 89
- Biochemistry 15
Countries citing papers authored by Eiichi Maeda
This map shows the geographic impact of Eiichi Maeda'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 Eiichi Maeda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eiichi Maeda more than expected).
Fields of papers citing papers by Eiichi Maeda
This network shows the impact of papers produced by Eiichi Maeda. 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 Eiichi Maeda. The network helps show where Eiichi Maeda may publish in the future.
Co-authors
The 25 scholars most cited alongside Eiichi Maeda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 39 | |
| 2 | 1991 | 31 | |
| 3 | 2001 | 31 | |
| 4 | 1995 | 22 | |
| 5 | 1993 | 18 | |
| 6 | 1992 | 18 | |
| 7 | 2007 | 16 | |
| 8 | 2005 | 14 | |
| 9 | 1993 | 11 | |
| 10 | 1992 | 9 | |
| 11 | 1992 | 9 | |
| 12 | 1991 | 8 | |
| 13 | Effect of microsomal triglyceride transfer protein gene polymorphism in the promoter region on dyslipidemia in type 2 diabetic subjects. | 2003 | 8 |
| 14 | 1996 | 8 | |
| 15 | 1998 | 7 | |
| 16 | 1997 | 7 | |
| 17 | 2013 | 6 | |
| 18 | Noninvasive temperature measurement during moxibustion using MRI | 2011 | 5 |
| 19 | 1993 | 4 | |
| 20 | 1989 | 4 |
About Eiichi Maeda
Eiichi Maeda is a scholar working on Molecular Biology, Surgery, Physiology, Endocrinology, Diabetes and Metabolism and Epidemiology, having authored 34 papers that have together received 302 indexed citations. Recurring topics across this work include Cholesterol and Lipid Metabolism (5 papers), Adipose Tissue and Metabolism (5 papers), Diet, Metabolism, and Disease (5 papers), Peroxisome Proliferator-Activated Receptors (4 papers), Lipoproteins and Cardiovascular Health (4 papers), Liver Disease Diagnosis and Treatment (3 papers), Cancer, Lipids, and Metabolism (3 papers) and Thermoregulation and physiological responses (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (71 citations), Physiology (81 citations), Clinical Biochemistry (17 citations), Surgery (89 citations) and Biochemistry (15 citations). Eiichi Maeda has collaborated with scholars based in Japan, Italy and China. Frequent co-authors include Gen Yoshino, Masato Kasuga, Tsutomu Kazumi, Koh‐ichi Nagata, Yukio Murata, Tsutomu Hirano, Mitsuru Hashiramoto, Kazuya Iwamoto, Hideki Okazawa and Hiroyuki Mori. Their work appears in journals such as Atherosclerosis, Journal of Lipid Research, Metabolism, Biochemical and Biophysical Research Communications and Hormone and Metabolic Research.
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.