Sabina Bijlsma

3.5k total citations · 1 hit paper
48 papers, 2.8k citations indexed

About

Sabina Bijlsma is a scholar working on Molecular Biology, Analytical Chemistry and Physiology. According to data from OpenAlex, Sabina Bijlsma has authored 48 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 13 papers in Analytical Chemistry and 8 papers in Physiology. Recurrent topics in Sabina Bijlsma's work include Spectroscopy and Chemometric Analyses (13 papers), Metabolomics and Mass Spectrometry Studies (10 papers) and Analytical Chemistry and Chromatography (7 papers). Sabina Bijlsma is often cited by papers focused on Spectroscopy and Chemometric Analyses (13 papers), Metabolomics and Mass Spectrometry Studies (10 papers) and Analytical Chemistry and Chromatography (7 papers). Sabina Bijlsma collaborates with scholars based in Netherlands, United States and United Kingdom. Sabina Bijlsma's co-authors include Age K. Smilde, Ben van Ommen, Elwin Verheij, I. Bobeldijk, Sunil Kochhar, Carina M. Rubingh, Raymond Ramaker, Ian Macdonald, Renger H. Jellema and Mariët J. van der Werf and has published in prestigious journals such as Bioinformatics, PLoS ONE and Analytical Chemistry.

In The Last Decade

Sabina Bijlsma

45 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation

2005 720 citations Sabina Bijlsma, I. Bobeldijk et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sabina Bijlsma Netherlands	27	1.4k	501	471	333	276	48	2.8k
Johan Gottfries Sweden	34	2.5k 1.7×	616 1.2×	638 1.4×	625 1.9×	385 1.4×	87	4.8k
Aurélien Thomas Switzerland	37	1.4k 0.9×	205 0.4×	963 2.0×	409 1.2×	334 1.2×	129	3.7k
Götz Schlotterbeck Switzerland	25	1.8k 1.3×	247 0.5×	663 1.4×	285 0.9×	513 1.9×	54	3.1k
Francesco Savorani Italy	25	1.3k 0.9×	627 1.3×	242 0.5×	265 0.8×	502 1.8×	56	2.4k
Ewoud J. J. van Velzen Netherlands	16	1.5k 1.1×	352 0.7×	213 0.5×	313 0.9×	324 1.2×	19	2.7k
Frank Dieterle Switzerland	22	1.7k 1.2×	175 0.3×	301 0.6×	294 0.9×	449 1.6×	34	3.4k
Muireann Coen United Kingdom	30	2.1k 1.5×	215 0.4×	564 1.2×	246 0.7×	281 1.0×	63	3.0k
I. Bobeldijk Netherlands	19	1.1k 0.8×	214 0.4×	388 0.8×	216 0.6×	212 0.8×	34	2.0k
Elwin Verheij Netherlands	29	2.3k 1.6×	210 0.4×	961 2.0×	436 1.3×	608 2.2×	65	3.7k
Olaf Beckonert United Kingdom	23	3.0k 2.0×	306 0.6×	645 1.4×	448 1.3×	426 1.5×	27	3.8k

Countries citing papers authored by Sabina Bijlsma

Since Specialization

Citations

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

Fields of papers citing papers by Sabina Bijlsma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sabina Bijlsma

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

All Works

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20 of 20 papers shown

Westerhout, Joost, et al.. (2025). Food Compounds Associated With Disease Activity in Inflammatory Bowel Disease. Inflammatory Bowel Diseases. 31(11). 3051–3059.

Eggink, Hannah M., Ines Warnke, Sabina Bijlsma, et al.. (2024). Impact of fibre supplementation on microbiome and resilience in healthy participants: A randomized, placebo-controlled clinical trial. Nutrition Metabolism and Cardiovascular Diseases. 34(6). 1416–1426. 5 indexed citations

Meima, Marie Y., Joost Westerhout, Sabina Bijlsma, Marjolein Meijerink, & Geert F. Houben. (2023). Coupling food compounds data from FooDB and Phenol‐Explorer to the Dutch food coding system NEVO: towards a novel approach to studying the role of food in health and disease. Journal of Food Composition and Analysis. 123. 105550–105550. 3 indexed citations

Hoogh, Iris M. de, Barbara Winters, Kristin M. Nieman, et al.. (2021). A Novel Personalized Systems Nutrition Program Improves Dietary Patterns, Lifestyle Behaviors and Health-Related Outcomes: Results from the Habit Study. Nutrients. 13(6). 1763–1763. 21 indexed citations

Blom, W. Marty, Harmieke van Os‐Medendorp, Sabina Bijlsma, et al.. (2020). Allergen risk assessment: Food intake levels of the general population represent those of food allergic patients. Food and Chemical Toxicology. 146. 111781–111781. 15 indexed citations

Winters, Barbara, Suzan Wopereis, Sabina Bijlsma, et al.. (2019). A Novel Personalized Nutrition Program Improves Health and Lifestyle Behaviors: Subgroup Analysis Results from Habit’s Personalized Approaches to Health (PATH) Study (P15-019-19). Current Developments in Nutrition. 3. nzz037.P15–19. 1 indexed citations

Broek, Tim J. van den, G.C.M. Bakker, Carina M. Rubingh, et al.. (2017). Ranges of phenotypic flexibility in healthy subjects. Genes & Nutrition. 12(1). 32–32. 38 indexed citations

Wopereis, Suzan, Coby Eelderink, Roel J. Vonk, et al.. (2016). Metabolic Profiling Reveals Differences in Plasma Concentrations of Arabinose and Xylose after Consumption of Fiber-Rich Pasta and Wheat Bread with Differential Rates of Systemic Appearance of Exogenous Glucose in Healthy Men. Journal of Nutrition. 147(2). 152–160. 13 indexed citations

Bijlsma, Sabina, et al.. (2014). Raman Spectroscopy as a Promising Tool for Noninvasive Point-of-Care Glucose Monitoring. Journal of Diabetes Science and Technology. 8(5). 974–979. 34 indexed citations

10.

Kelder, Thomas, Johanna H. M. Stroeve, Sabina Bijlsma, Marijana Radonjić, & Guus Roeselers. (2014). Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health. Nutrition and Diabetes. 4(6). e122–e122. 76 indexed citations

11.

Kuper, C. Frieke, Mariska Gröllers‐Mulderij, Nicole Meulendijks, et al.. (2014). Toxicity assessment of aggregated/agglomerated cerium oxide nanoparticles in an in vitro 3D airway model: The influence of mucociliary clearance. Toxicology in Vitro. 29(2). 389–397. 50 indexed citations

12.

Kuil, Wieke Altorf-van der, Elizabeth J. Brink, Els Siebelink, et al.. (2013). Identification of biomarkers for intake of protein from meat, dairy products and grains: a controlled dietary intervention study. British Journal Of Nutrition. 110(5). 810–822. 48 indexed citations

13.

Pasman, Wilrike J., Marjan J. van Erk, Linette Pellis, et al.. (2013). Nutrigenomics approach elucidates health-promoting effects of high vegetable intake in lean and obese men. Genes & Nutrition. 8(5). 507–521. 8 indexed citations

14.

Balvers, Michiel G.J., Kitty Verhoeckx, Jocelijn Meijerink, et al.. (2012). Time-dependent effect of in vivo inflammation on eicosanoid and endocannabinoid levels in plasma, liver, ileum and adipose tissue in C57BL/6 mice fed a fish-oil diet. International Immunopharmacology. 13(2). 204–214. 49 indexed citations

15.

Ven, Peter van de, et al.. (2011). A Framework for Efficient Process Development Using Optimal Experimental Designs. Journal of Pharmaceutical Innovation. 6(1). 24–31. 1 indexed citations

16.

Rubingh, Carina M., Sabina Bijlsma, Renger H. Jellema, et al.. (2009). Analyzing Longitudinal Microbial Metabolomics Data. Journal of Proteome Research. 8(9). 4319–4327. 30 indexed citations

17.

Zanden, Jelmer J. van, Heleen M. Wortelboer, Sabina Bijlsma, et al.. (2004). Quantitative structure activity relationship studies on the flavonoid mediated inhibition of multidrug resistance proteins 1 and 2. Biochemical Pharmacology. 69(4). 699–708. 146 indexed citations

18.

Verhoeckx, Kitty, Sabina Bijlsma, Els M. de Groene, et al.. (2004). A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line. PROTEOMICS. 4(4). 1014–1028. 111 indexed citations

19.

Verhoeckx, Kitty, Sabina Bijlsma, Sonja Jespersen, et al.. (2004). Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis. International Immunopharmacology. 4(12). 1499–1514. 55 indexed citations

20.

Windig, Willem, et al.. (1999). Applications and new developments of the direct exponential curve resolution algorithm (DECRA). Examples of spectra and magnetic resonance images. Journal of Chemometrics. 13(2). 95–110. 31 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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