C. Perlemoine

1.1k total citations
25 papers, 704 citations indexed

About

C. Perlemoine is a scholar working on Nephrology, Endocrinology, Diabetes and Metabolism and Physiology. According to data from OpenAlex, C. Perlemoine has authored 25 papers receiving a total of 704 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Nephrology, 7 papers in Endocrinology, Diabetes and Metabolism and 6 papers in Physiology. Recurrent topics in C. Perlemoine's work include Chronic Kidney Disease and Diabetes (9 papers), Dialysis and Renal Disease Management (8 papers) and Liver Disease Diagnosis and Treatment (4 papers). C. Perlemoine is often cited by papers focused on Chronic Kidney Disease and Diabetes (9 papers), Dialysis and Renal Disease Management (8 papers) and Liver Disease Diagnosis and Treatment (4 papers). C. Perlemoine collaborates with scholars based in France, Belize and United States. C. Perlemoine's co-authors include Vincent Rigalleau, H. Gin, Christian Combe, C. Raffaitin, Nicole Barthe, Philippe Chauveau, Catherine Lasseur, Marie-Christine Beauvieux, C. Lasseur and Pierre Burbaud and has published in prestigious journals such as Diabetes Care, British Journal Of Nutrition and Movement Disorders.

In The Last Decade

C. Perlemoine

23 papers receiving 678 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
C. Perlemoine France 13 374 137 130 127 79 25 704
Nobuyuki Kawazoe Japan 12 316 0.8× 37 0.3× 219 1.7× 72 0.6× 89 1.1× 30 655
Emanuel Fritschka Germany 11 65 0.2× 108 0.8× 113 0.9× 44 0.3× 51 0.6× 55 512
Per Ederoth Sweden 15 152 0.4× 42 0.3× 84 0.6× 26 0.2× 35 0.4× 30 516
J. A. O’Hare Ireland 9 50 0.1× 77 0.6× 105 0.8× 137 1.1× 78 1.0× 17 463
Brown Jj United Kingdom 12 166 0.4× 41 0.3× 178 1.4× 126 1.0× 36 0.5× 28 563
Alain Roman Belgium 12 84 0.2× 111 0.8× 61 0.5× 32 0.3× 85 1.1× 29 626
Kjell Arne Arntzen Norway 14 78 0.2× 36 0.3× 372 2.9× 82 0.6× 66 0.8× 29 718
Geneviève Boulet Canada 15 72 0.2× 57 0.4× 76 0.6× 198 1.6× 142 1.8× 31 542
Taisuke Kitano Japan 10 114 0.3× 44 0.3× 68 0.5× 20 0.2× 30 0.4× 53 323
Sibel Benli Türkiye 14 51 0.1× 102 0.7× 21 0.2× 76 0.6× 53 0.7× 46 554

Countries citing papers authored by C. Perlemoine

Since Specialization
Citations

This map shows the geographic impact of C. Perlemoine'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 C. Perlemoine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Perlemoine more than expected).

Fields of papers citing papers by C. Perlemoine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Perlemoine. 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 C. Perlemoine. The network helps show where C. Perlemoine may publish in the future.

Co-authorship network of co-authors of C. Perlemoine

This figure shows the co-authorship network connecting the top 25 collaborators of C. Perlemoine. A scholar is included among the top collaborators of C. Perlemoine 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 C. Perlemoine. C. Perlemoine is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Saulnier, Pierre‐Jean, Claire Briet, Elise Gand, et al.. (2019). No association between fear of hypoglycemia and blood glucose variability in type 1 diabetes: The cross-sectional VARDIA study. Journal of Diabetes and its Complications. 33(8). 554–560. 8 indexed citations
2.
Rigalleau, Vincent, C. Lasseur, Philippe Chauveau, et al.. (2008). Cystatin C improves the diagnosis and stratification of chronic kidney disease, and the estimation of glomerular filtration rate in diabetes. Diabetes & Metabolism. 34(5). 482–489. 44 indexed citations
3.
Fagour, C., et al.. (2007). Body Composition of Obese Subjects by Air Displacement Plethysmography: The Influence of Hydration. Obesity. 15(1). 78–84. 24 indexed citations
4.
Rigalleau, Vincent, C. Lasseur, C. Raffaitin, et al.. (2007). Bone loss in diabetic patients with chronic kidney disease. Diabetic Medicine. 24(1). 91–93. 12 indexed citations
5.
Beauvieux, Marie-Christine, Catherine Lasseur, C. Raffaitin, et al.. (2007). New Predictive Equations Improve Monitoring of Kidney Function in Patients With Diabetes. Diabetes Care. 30(8). 1988–1994. 53 indexed citations
6.
Perlemoine, C., F. Macia, François Tison, et al.. (2006). Subthalamic nucleus stimulation in parkinsonian patients does not increase serum ghrelin levels. 11.
7.
Fagour, C., et al.. (2006). Air displacement plethysmography can detect moderate changes in body composition. European Journal of Clinical Nutrition. 61(1). 25–29. 6 indexed citations
8.
Gin, H., C. Perlemoine, & Vincent Rigalleau. (2006). How to better systematize the diagnosis of neuropathy?. Diabetes & Metabolism. 32(4). 367–372. 5 indexed citations
9.
Rigalleau, Vincent, C. Lasseur, C. Perlemoine, et al.. (2006). A simplified Cockcroft-Gault formula to improve the prediction of the glomerular filtration rate in diabetic patients. Diabetes & Metabolism. 32(1). 56–62. 17 indexed citations
10.
Rigalleau, Vincent, C. Lasseur, C. Raffaitin, et al.. (2006). The Mayo Clinic quadratic equation improves the prediction of glomerular filtration rate in diabetic subjects. Nephrology Dialysis Transplantation. 22(3). 813–818. 50 indexed citations
11.
Rigalleau, Vincent, Laurence Baillet‐Blanco, C. Perlemoine, Louis‐Rachid Salmi, & H. Gin. (2005). Lower plasma triglycerides are associated with increased need for insulin requirement in poorly controlled Type 2 diabetic patients. Diabetic Medicine. 22(7). 877–881. 1 indexed citations
12.
Perlemoine, C., F. Macia, François Tison, et al.. (2005). Effects of subthalamic nucleus deep brain stimulation and levodopa on energy production rate and substrate oxidation in Parkinson's disease. British Journal Of Nutrition. 93(2). 191–198. 43 indexed citations
13.
Rigalleau, Vincent, Catherine Lasseur, C. Perlemoine, et al.. (2005). Estimation of Glomerular Filtration Rate in Diabetic Subjects. Diabetes Care. 28(4). 838–843. 146 indexed citations
14.
Rigalleau, Vincent, Catherine Lasseur, C. Perlemoine, et al.. (2005). Cockcroft-Gault formula is biased by body weight in diabetic patients with renal impairment. Metabolism. 55(1). 108–112. 66 indexed citations
15.
Camou, Fabrice, et al.. (2005). Liens physiopathologiques entre maladie de Kikuchi et lupus : à propos de trois nouvelles observations. La Revue de Médecine Interne. 26(8). 651–655. 12 indexed citations
16.
Perlemoine, C., et al.. (2004). Emphysematous cystitis. Diabetes & Metabolism. 30(4). 377–379. 12 indexed citations
17.
Rigalleau, Vincent, C. Lasseur, Philippe Chauveau, et al.. (2004). Resting energy expenditure in uremic, diabetic, and uremic diabetic subjects. Journal of Diabetes and its Complications. 18(4). 237–241. 14 indexed citations
18.
Rigalleau, Vincent, C. Lasseur, Philippe Chauveau, et al.. (2004). Body Composition in Diabetic Subjects with Chronic Kidney Disease: Interest of Bio-Impedance Analysis, and Anthropometry. Annals of Nutrition and Metabolism. 48(6). 409–413. 7 indexed citations
19.
Macia, F., C. Perlemoine, Irène Coman, et al.. (2003). Parkinson's disease patients with bilateral subthalamic deep brain stimulation gain weight. Movement Disorders. 19(2). 206–212. 97 indexed citations
20.
Perlemoine, C., et al.. (2003). Interest of cystatin C in screening diabetic patients for early impairment of renal function. Metabolism. 52(10). 1258–1264. 37 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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