Markus Perola

93.8k total citations · 1 hit paper
280 papers, 11.3k citations indexed

About

Markus Perola is a scholar working on Genetics, Molecular Biology and Epidemiology. According to data from OpenAlex, Markus Perola has authored 280 papers receiving a total of 11.3k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Genetics, 70 papers in Molecular Biology and 53 papers in Epidemiology. Recurrent topics in Markus Perola's work include Genetic Associations and Epidemiology (68 papers), Liver Disease Diagnosis and Treatment (34 papers) and Alcohol Consumption and Health Effects (25 papers). Markus Perola is often cited by papers focused on Genetic Associations and Epidemiology (68 papers), Liver Disease Diagnosis and Treatment (34 papers) and Alcohol Consumption and Health Effects (25 papers). Markus Perola collaborates with scholars based in Finland, United States and United Kingdom. Markus Perola's co-authors include Veikko Salomaa, Jaakko Kaprio, Pekka J. Karhunen, Antti Penttilä, Leena Peltonen, Karri Silventoinen, Hely Tuorila, Aarno Palotie, Samuli Ripatti and Erkki Vartiainen and has published in prestigious journals such as The Lancet, Circulation and Nature Communications.

In The Last Decade

Markus Perola

270 papers receiving 11.0k 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.

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

2023 216 citations Mika Tiainen, Kati Kristiansson et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Markus Perola Finland	58	3.1k	3.1k	1.6k	1.5k	1.4k	280	11.3k
Claudia Langenberg United Kingdom	54	2.8k 0.9×	2.7k 0.9×	1.7k 1.1×	1.9k 1.3×	2.0k 1.4×	144	11.8k
Adam S. Butterworth United Kingdom	35	4.2k 1.4×	2.7k 0.9×	1.4k 0.9×	1.4k 0.9×	1.6k 1.1×	78	12.0k
Charles N. Rotimi United States	55	5.2k 1.7×	3.5k 1.1×	1.6k 1.0×	1.6k 1.1×	1.4k 1.0×	240	13.3k
Harold Snieder Netherlands	65	3.1k 1.0×	2.9k 0.9×	3.2k 2.1×	1.8k 1.2×	1.5k 1.1×	404	15.4k
Daniel I. Chasman United States	59	4.3k 1.4×	4.1k 1.3×	1.7k 1.1×	1.6k 1.1×	1.2k 0.8×	185	12.5k
Adebowale Adeyemo United States	46	4.4k 1.4×	3.2k 1.1×	986 0.6×	1.1k 0.7×	1.3k 0.9×	266	11.6k
Erik Ingelsson Sweden	63	2.3k 0.8×	2.9k 1.0×	3.3k 2.1×	1.7k 1.1×	2.1k 1.5×	209	12.8k
Abbas Dehghan Netherlands	55	1.8k 0.6×	2.4k 0.8×	1.3k 0.9×	1.9k 1.2×	1.5k 1.1×	201	9.3k
Paul McKeigue United Kingdom	62	4.0k 1.3×	2.4k 0.8×	2.1k 1.3×	3.3k 2.2×	2.1k 1.5×	187	16.3k
Marie‐Claude Vohl Canada	50	2.3k 0.8×	3.8k 1.2×	965 0.6×	1.8k 1.2×	1.6k 1.1×	327	10.2k

Countries citing papers authored by Markus Perola

Since Specialization

Citations

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

Fields of papers citing papers by Markus Perola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Markus Perola

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Perola. A scholar is included among the top collaborators of Markus Perola 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 Markus Perola. Markus Perola 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

1.

Perola, Markus, et al.. (2025). Electronic health records reveal variations in the use of blood units by hour and medical specialty. Vox Sanguinis. 120(6). 584–596. 1 indexed citations

2.

Dalmasso, Carolina, Jenni Lehtisalo, Mikko Hiltunen, et al.. (2025). Alzheimer and cardiovascular genetic scores and cognition: the FINGER randomized controlled trial. Brain. 149(2). 644–652.

3.

Kristiansson, Kati, E. Katriina Tarkiainen, Liisa Ukkola‐Vuoti, et al.. (2024). Value of Pharmacogenetic Testing Assessed with Real‐World Drug Utilization and Genotype Data. Clinical Pharmacology & Therapeutics. 117(1). 278–288. 1 indexed citations

4.

Jauhiainen, Matti, et al.. (2024). Effects of fat loss and low energy availability on the serum cardiometabolic profile of physique athletes. Scandinavian Journal of Medicine and Science in Sports. 34(1). e14553–e14553. 4 indexed citations

5.

Lundgren, Sara, Kirsi H. Pietiläinen, Mikko Hurme, et al.. (2022). BMI is positively associated with accelerated epigenetic aging in twin pairs discordant for body mass index. Journal of Internal Medicine. 292(4). 627–640. 36 indexed citations

6.

Birukov, Anna, Fabian Eichelmann, Olga Kuxhaus, et al.. (2022). Immunoglobulin G N-Glycosylation Signatures in Incident Type 2 Diabetes and Cardiovascular Disease. Diabetes Care. 45(11). 2729–2736. 30 indexed citations

7.

Männistö, Ville, Tuija Jääskeläinen, Martti Färkkilâ, et al.. (2021). Low serum vitamin D level associated with incident advanced liver disease in the general population – a prospective study. Scandinavian Journal of Gastroenterology. 56(3). 299–303. 4 indexed citations

8.

Tikkanen, Emmi, Michael V. Holmes, Naveed Sattar, et al.. (2021). Metabolic biomarker discovery for risk of peripheral artery disease compared with coronary artery disease:lipoprotein and metabolite profiling of 31 657 individuals from 5 prospective cohorts. University of Oulu Repository (University of Oulu). 43 indexed citations

9.

Männistö, Ville, Veikko Salomaa, Martti Färkkilâ, et al.. (2021). Incidence of liver‐related morbidity and mortality in a population cohort of non‐alcoholic fatty liver disease. Liver International. 41(11). 2590–2600. 19 indexed citations

10.

Nuotio, Marja-Liisa, Natalia Pervjakova, Anni Joensuu, et al.. (2020). An epigenome-wide association study of metabolic syndrome and its components. Scientific Reports. 10(1). 20567–20567. 30 indexed citations

11.

Holmes, Michael V., Pauli Ohukainen, Antti J. Kangas, et al.. (2019). Direct Estimation of HDL-Mediated Cholesterol Efflux Capacity from Serum. Clinical Chemistry. 65(8). 1042–1050. 14 indexed citations

12.

Sliz, Eeva, Marita Kalaoja, Ari Ahola‐Olli, et al.. (2019). Genome-wide association study identifies seven novel loci associating with circulating cytokines and cell adhesion molecules in Finns. Journal of Medical Genetics. 56(9). 607–616. 37 indexed citations

13.

Åberg, Fredrik, Pauli Puukka, Markku Nissinen, et al.. (2019). Metabolic risk factors for advanced liver disease among alcohol risk users in the general population. STM:n Hallinnonalan avoin julkaisuarkisto (Julkari). 1 indexed citations

14.

Kettunen, Johannes, Scott C. Ritchie, Leo‐Pekka Lyytikäinen, et al.. (2018). Biomarker Glycoprotein Acetyls Is Associated With the Risk of a Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients. Circulation Genomic and Precision Medicine. 11(11). e002234–e002234. 30 indexed citations

15.

Rodríguez‐Girondo, Mar, Perttu Salo, Tomasz Burzykowski, et al.. (2018). Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 2 indexed citations

16.

Vučković, Frano, Evropi Τheodoratou, Maria Timofeeva, et al.. (2016). IgG Glycome in Colorectal Cancer. Clinical Cancer Research. 22(12). 3078–3086. 109 indexed citations

17.

Goebeler, Sirkka, et al.. (2010). Birthplace in area with high coronary heart disease mortality predicts the severity of coronary atherosclerosis among middle-aged Finnish men who had migrated to capital area: The Helsinki Sudden Death Study. Annals of Medicine. 42(4). 286–295. 5 indexed citations

18.

Hiekkalinna, Tero, Joseph D. Terwilliger, Sampo Sammalisto, Leena Peltonen, & Markus Perola. (2005). AUTOGSCAN: Powerful Tools for Automated Genome-Wide Linkage and Linkage Disequilibrium Analysis. Twin Research and Human Genetics. 8(1). 16–21. 21 indexed citations

19.

Silander, Kaisa, Pekka Ellonen, Mervi Alanne, et al.. (2005). Evaluating Whole Genome Amplification via Multiply-Primed Rolling Circle Amplification for SNP Genotyping of Samples With Low DNA Yield. Twin Research and Human Genetics. 8(4). 368–375. 16 indexed citations

20.

Hiekkalinna, Tero, Joseph D. Terwilliger, Sampo Sammalisto, Leena Peltonen, & Markus Perola. (2005). AUTOGSCAN: Powerful Tools for Automated Genome-Wide Linkage and Linkage Disequilibrium Analysis. Twin Research and Human Genetics. 8(1). 16–21. 26 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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