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Method Article

The Trends and Prospects of Research on Caenorhabditis elegans in the Aging Area: a Bibliometric and Visualization Analysis

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DOI:

10.3791/70364

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May 22nd, 2026

 ,  ,  ,  ,  ,  ,  , 
1Department of General Surgery, Tianjin Medical University General Hospital, 2Ambulatory surgery center, Tianjin Medical University General Hospital, 3Tianjin Key Laboratory of Precise Vascular Reconstruction and Organ Function Repair, 4Tianjin General Surgery Institute

* These authors contributed equally

In This Article

Summary

Here, we present a protocol for retrieving, validating, and analyzing Web of Science Core Collection records on Caenorhabditis elegans and aging using CiteSpace, VOSviewer, Bibliometrix, and Scimago Graphica for reproducible bibliometric mapping.

Abstract

Aging is a complex biological phenomenon characterized by the progressive deterioration of physiological functions and increased vulnerability to age-associated diseases. At the cellular level, this process is exemplified by senescence, in which cells experience an irreversible halt in division and enter a state of permanent growth arrest, without progressing to apoptosis. However, the underlying mechanisms remain incompletely understood. The nematode Caenorhabditis elegans (C. elegans), with a short lifespan and well-characterized genetics, is a powerful model for deciphering the molecular basis of aging. Nevertheless, a comprehensive bibliometric mapping of research at the intersection of C. elegans and aging is still lacking. Here, we present a protocol to retrieve records from the Web of Science Core Collection, perform data processing, and conduct reproducible bibliometric visualization using CiteSpace, Bibliometrix (R), VOSviewer, and Pajek, aiming to map the knowledge structure and identify emerging trends in C. elegans aging research. Using 6,117 articles published from 1970 through June 5, 2025, we analyzed country and institutional contributions, authorship, journal impact, citation patterns, and thematic evolution. The dataset comprised 6,117 records from 19,864 authors across 3,798 institutes in 91 countries. Annual publications increased from 312 in 2020 to 341 in 2021 and remained high thereafter. The United States led global output, followed by China, Germany, and the United Kingdom, with collaboration networks concentrated among these countries. The University of California system ranked first in productivity, while the Chinese Academy of Sciences ranked third. Topic evolution indicated a shift from foundational studies of insulin/insulin-like growth factor 1 signaling (IIS) pathways and stress responses toward emerging themes such as thermal control and mitophagy in C. elegans aging models. Overall, this protocol enables transparent, updatable bibliometric analyses and supports future studies of key directions, including caloric restriction and mitophagy.

Introduction

Aging is a multifaceted biological process that refers to the loss of the proliferative capacity of normal somatic cells after a limited number of divisions. During the aging process, the physiological functions of the organism gradually decline, and susceptibility to age-related diseases increases1. At the cellular level, this process manifests as DNA damage, such as the accumulation of double-strand breaks and oxidative damage, and these damages exceed the handling capacity of the repair mechanisms. These cellular-level changes are reflected at the organismal level as the gradual deterioration of physiological functions. Furthermore, some studies have further demonstrated that aged cells drive age-related pathological processes by secreting senescence-associated secretory phenotype (SASP). These factors can disrupt the tissue microenvironment and promote fibrosis, leading to chronic inflammation and even cancer progression2. With the deepening of research on aging, it has been recognized that aging plays a significant role in the occurrence and development of various diseases, including cardiovascular diseases, type 2 diabetes, Alzheimer's disease, and cancer, among others3. With the acceleration of global aging, understanding the mechanisms of aging is crucial for extending healthy lifespan.

Over the past few decades, model organisms have played an indispensable role in revealing the genetic, molecular, and environmental determinants of aging. Model organisms exhibit distinct advantages, including short generation times, well-characterized genetic backgrounds, and high experimental tractability. Common model organisms include yeast, fruit flies, and mice4. In the 1960s, Caenorhabditis elegans (C. elegans) was first established as a novel model organism. Its core advantages for selection include a short life cycle and ease of cultivation, complemented by a suite of mature genetic tools. Moreover, due to its tiny size, it can be cultivated and manipulated on a large scale in a standardized manner, like microorganisms5. Most importantly, it effectively fills a significant gap in previous research, as single-celled organisms, despite being simple and easy to operate, are challenging to utilize for genetics research on complex life mechanisms. In addition, traditional model animals are limited in the efficiency of large-scale screening due to their complex operation. The application of C. elegans precisely solves the above problems6.

The use of C. elegans in aging research dates back to the 1980s7. It was not until 1993 that the field of C. elegans in aging research took a transformative leap. Mutagenesis screens led by Thomas Johnson and colleagues identified the first long-lived mutant, the age-1 gene8,9. This finding changed the prevailing view that aging was a stochastic, unregulated process, instead implicating specific genetic pathways in lifespan determination. Subsequent studies built on this foundation, revealing a conserved network of signaling pathways that coordinate aging. The IIS pathway has emerged as a central regulator. The mutation in daf-2, which encodes the C. elegans ortholog of the insulin/ insulin-like growth factor (IIGF-1) receptor, could reduce IIS activity, activating the FOXO transcription factor DAF-16, causing it to translocate from the cytoplasm to the nucleus, thereby promoting the expression of stress response genes, molecular chaperones, and detoxification enzymes, thus delaying the aging process and extending healthy lifespan10. DAF-16 translocates from the cytoplasm to the nucleus, promoting the expression of stress-resistance genes, molecular chaperones, and detoxification enzymes, thereby extending organismal health span and delaying the progression of aging and extending organismal healthspan11. Furthermore, C. elegans has illuminated other conserved mechanisms of aging. For example, the target of rapamycin (TOR) pathway, a key nutrient-sensing axis, was shown to modulate lifespan through interactions with IIS12.

In recent years, bibliometric methods have been widely applied to many fields. Bibliometric network analysis employs statistical methods to uncover development patterns, disciplinary structures, research hotspots, and trends in a field by examining the quantity, structure, distribution, and citation relationships of literature13. Despite extensive research on C. elegans in aging, no study has provided a comprehensive bibliometric analysis of this interdisciplinary field. In this study, we present a comprehensive, multi-method, and reproducible analysis that elucidates the collaborative framework, intellectual foundation, and dynamic thematic developments within the field, thereby identifying emerging trajectories for future research endeavors. This article reviews the landscape of recent aging research achievements in the C. elegans field. Furthermore, this study aims to: (1) analyze publication and citation trends in C. elegans aging research from 1970 to June 5, 2025; (2) assess contributions by countries and institutions and explore collaboration networks; (3) identify top authors and key journals by productivity and citation impact; (4) uncover the field's intellectual foundation through co-citation and citation-burst analysis; and (5) track major research themes and emerging trends using keyword co-occurrence and temporal analysis. Ultimately, these insights will facilitate a deeper comprehension of the field’s evolving trajectory.

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Protocol

This study does not refer to ethical approval and consent to participate. The data used in this study were obtained from the Web of Science Core Collection (WoSCC).

1. Database selection

  1. Access the Web of Science Core Collection (WoSCC) database via https://webofscience. clarivate.cn/wos/author/author-search.
  2. Construct a search strategy using targeted keywords, specifically "C. elegans" and "aging," to identify relevant literature.
  3. Relevance screening and data validation
    1. Screen each retrieved record for topical relevance by reviewing the title. Include records that use C. elegans as the primary study organism and address aging-related concepts or outcomes.
    2. If relevance cannot be determined from the title alone, review the Abstract to confirm eligibility.
    3. Exclude records that do not focus on C. elegans and/or do not address aging-related outcomes or mechanisms.
    4. For borderline records, document the reason for uncertainty and resolve inclusion through a secondary check by a second author or by consensus between two reviewers.
    5. Exclude any record labeled Retracted in the database metadata.
    6. Record the number of excluded records and the reason for exclusion and report the final dataset size used for bibliometric analysis.
      ​NOTE: Refer to Supplementary material 1 for a complete list of keywords employed in the search strategy to enhance accuracy and inclusivity.

2. Search parameters

  1. Define the publication period from 1970 through June 5, 2025, to capture the most recent and comprehensive research trends.
  2. Limit search results to include only articles and review articles published in English to ensure data consistency and facilitate comparative analysis.

3. Data retrieval and format

  1. Compile selected publications in "Full Record and Cited References" format to preserve detailed metadata.
  2. Save the collected data as "Plain Text" files for subsequent analysis using bibliometric tools.
  3. Verify that each record contains complete metadata, including citation and co-authorship information, to enable a thorough bibliometric analysis.

4. Data preprocessing

  1. Data collection and import
    1. Open R (version 4.5.2) and load bibliometrix (version 4.5.2). Run the following commands to launch Biblioshiny:
      1. library(bibliometrix)
      2. packageVersion("bibliometrix")
      3. biblioshiny()
    2. Open the import panel.
      1. In the biblioshiny interface, click “Data” in the left sidebar.
      2. Under Data, click “Import or Load”.
    3. Configure import settings.
      1. Please, choose what to do, select “Import raw file(s)”.
      2. In Database, select “Web of Science (WoS/WoK)”.
      3. In Author Name Format, select “Surname and Initials”.
    4. Select the WoSCC file and import.
      1. Under Choose a file, click “Browse”… and select the merged WoSCC Plain Text file exported with Full Record and Cited References.
      2. Click “Start” to import the dataset.
      3. In the left sidebar, click “Overview”, then select the required module (e.g., Annual Scientific Production or Average Citations per Year) to display the results.
      4. To export figures, click Export Plot as PNG. To export tables, click “Excel” and save the downloaded file for subsequent analysis.
  2. Annual growth trend of publications and citations
    1. Annual Growth Trend of Publications and Citations Analysis in CiteSpace.
      1. Open CiteSpace, select “Date”, and then “import/export”.
      2. Browse the Input Directory and Output Directory.
      3. Click “Remove Duplicates”, select “Article” and “Review”, and finally click “Start”. Then you will be able to obtain the specific annual publication and citation statistics.
      4. Use the Word software to convert the exported results into chart format.
  3. National publication and collaboration analysis
    1. National Publication and Collaboration Analysis in Vosview.
      1. Open VOSviewer, select “Create”, and then “Create a map based on bibliographic data”.
      2. Import the relevant plain text files using the “Read data from bibliographic database files” option.
      3. Set the analysis type to “Co-authorship”, using the “Full counting” method, with “Countries” as the unit of analysis.
      4. Apply a number of Sources to be selected, 50, and click “Analysis”. Set the number of sources to be selected to 20 and click “Export selected Sources” to export as a CSV file for viewing.
      5. Execute the analysis by clicking “GO” to generate a reference co-citation map, adjusting font and color settings for improved readability.
    2. Geographical distribution of national publications: Analysis in Scimago Graphica.
      1. Open Scimago Graphica, click “LOAD DATE FILE”, and then import the relevant plain text files.
      2. Create a new view, select “documents” for size, “link_strength” for color, “label” for label, “disks” for marks, and “map” for layout.
      3. Country with the highest average citation count Analysis
        1. Launch the Bibliometrix package's Biblioshiny interface in R.
        2. Access the WoSCC database and select a merged Plain Text file containing bibliometric data.
        3. Import the data and export it in txt Format for subsequent analysis.
        4. Select “Author”, and then click “Most Cited Countries”
          NOTE: The specific code is provided as follows:
          1. library(bibliometrix)
          2. packageVersion("bibliometrix")
          3. biblioshiny()
  4. Distribution of universities and institutions analysis
    1. Co-authorship analysis
      1. Analysis Setup in VOSviewer: Launch VOSviewer, choose “Create,” and then “Create a map based on bibliographic data”. Select “Read data from bibliographic database files” and import the Plain Text files containing institutional information.
      2. Configuration and Parameters: Set the analysis type to “Co-authorship,” choose “Full counting”, and use “Organizations” as the unit of analysis. Configure the parameters to allow up to 3,798 organizations per document and set a minimum inclusion threshold for the first 50 institutions to build a representative collaboration network.
      3. Visualization and Interpretation: Click “Finish” to generate the institutional collaboration map. Examine the clusters and connections to identify core institutions, collaboration intensity, and major cooperation patterns among universities and research organizations.
    2. Analysis of the Institutions with the most publications
      1. Launch the Bibliometrix package's Biblioshiny interface in R.
      2. Access the WoSCC database and select a merged Plain Text file containing bibliometric data.
      3. Import the data and export it in txt Format for subsequent analysis.
      4. Select “Author”, and then click “Affiliations’ Production over Time”
        NOTE: The specific code is provided as follows:
        library(bibliometrix)
        packageVersion("bibliometrix")
        biblioshiny()
    3. The universities with the strongest citation bursts Analysis
      1. Open CiteSpace, navigate to the “Data” menu, and select “Import/Export”.
      2. Import data from the WoSCC database in the appropriate format, configuring the analysis time slice from January 1972 to June 5, 2025, with a one-year interval for temporal detail.
      3. Set the node type to “Reference” and apply pruning using the “Pathfinder” and “Pruning Sliced Networks” options to streamline the network, focusing on the most influential co-cited references.
      4. Execute the analysis by clicking “GO” to generate a reference co-citation map, adjusting font and color settings for improved readability.
      5. Select the “Burstness” option and set the parameter γ to [0, 1].
      6. Refresh the data to generate the top 25 institutions with the most significant increase in citation counts, highlight those with a substantial increase in citations, and indicate that their influence in the field is continuously growing.
  5. Author collaboration networks
    1. Open CiteSpace, select the “Data” menu, and click “Import/Export” to import data from WoSCC in the required format.
      1. Define the time slice from January 1970 to June 5, 2025, with a one-year step to capture the temporal evolution of collaborations.
    2. Network Configuration and Pruning
      1. Set the node type to “Author”, and apply “Pathfinder” and “Pruning Sliced Networks” to reduce redundant links while preserving key structures.
      2. Click “GO” to execute the analysis and generate the co-citation/co-authorship map, which reveals the development, structure, and growth dynamics of author collaboration networks over time.
  6. Analysis of co-cited references and clustering network
    1. Data import and setup in CiteSpace
      1. Open CiteSpace, go to “Data”, and select “Import/Export” to load the WoSCC data.
      2. Set the analysis time span from January 1970 to June 5, 2025, using a one-year interval to obtain detailed temporal information.
    2. Network configuration and pruning
      1. Choose “Reference” as the node type and use “Pathfinder” together with “Pruning Sliced Networks” to retain the core co-citation links and remove minor or redundant connections.
      2. Click “GO” to run the analysis and generate the reference co-citation network, then refine fonts, colors, and layout to improve visual clarity.
    3. Identifying high-impact references
      1. Enable the “Burstness” option and set γ in the interval [0, 1].
      2. Refresh the data to obtain the top 25 references with the strongest citation bursts, highlighting those works that have experienced rapid growth in citations and have become highly influential in the research area.
  7. Analysis of journals and co-cited journals
    1. Journals Analysis in Vosview
      1. Open VOSviewer, select “Create”, and then “Create a map based on bibliographic data”.
      2. Import the relevant plain-text files through the “Read data from bibliographic database files” option.
      3. Set the analysis type to “Citation”, choose the “Full counting” method, and specify “Sources” as the unit of analysis.
      4. Set the minimum number of sources to 20, run the analysis, and use “Export selected Sources” to save the selected journals as a CSV file for further examination.
    2. Co-cited journals analysis in Vosview.
      1. Open VOSviewer again, select “Create”, and then “Create a map based on bibliographic data”.
      2. Import the same plain-text files with “Read data from bibliographic database files”.
      3. Set the analysis type to “Co-citation”, choose “Full counting”, and use “Cited sources” as the unit of analysis.
      4. Set the minimum number of cited sources to 20, run the analysis, and click “Export selected Sources” to output a CSV file for detailed co-cited journal analysis.
  8. Analysis of keyword co-occurrence
    1. Keyword Co-Occurrence Network Analysis in VOSviewer.
      1. Open VOSviewer, select “Create”, and then “Create a map based on bibliographic data”.
      2. Import the relevant plain text files using the “Read data from bibliographic database files” option.
      3. Set the analysis type to “co-occurrence”, using the “Full counting” method, with “all keywords” as the unit of analysis.
      4. Apply a number of keywords to be selected 100 and click “Finish” to complete the analysis, producing a co-occurrence network that visualizes keyword relationships within the field.
      5. Click on “Overlay Visualization” to generate a visual analytics representation of the evolving keyword co-occurrence clusters.
      6. Click on “Density Visualization” and the keyword distribution chart will be generated.
    2. Keyword burst analysis in CiteSpace.
      1. Use CiteSpace to perform keyword burst analysis by selecting the “Burstness"option and setting the parameter γ to [0, 1].
      2. After refreshing the data, select “View” to generate a list of the top 25 keywords with the strongest citation bursts, indicating keywords that have drawn notable attention over time.

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Results

Overview of trends and evolution of C. elegans in the aging area
The bibliometric analysis included 6,117 publication records from 1,123 sources, selected from an initial 6,205 publications from WOSCC that met eligibility criteria. This included 45 early-access reviews and 145 proceedings papers. After excluding 87 irrelevant and one retracted publication, the data were analyzed for temporal distribution and document types. Over the past five decades, 4,949 articles (80.90%) and 1,168 review...

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Discussion

To ensure reproducibility and interpretability, key steps in this workflow include: thoroughly reporting the retrieval strategy (database source, query, document types, language limits, retrieval date), conducting relevance screening and data validation to minimize off-topic records, consistently reporting tool parameters (time slicing, node types, pruning, thresholds, clustering), and exporting outputs in standard formats while documenting software versions for transparency and future updates. This protocol can be adjus...

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Disclosures

The authors have nothing to disclose.

Acknowledgements

The authors wish to express their appreciation to all the participants and researchers who contributed to this work. Furthermore, they are grateful for the analytical tools provided by CiteSpace, VOSviewer, Scimago Graphica, and R, which were instrumental in conducting this research.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
LEGION Savior Y7000 2019 laptop (SerialNumber PF1CGZJU)Lenovohttps://pcsupport.lenovo.com/us/en/products/laptops-and-netbooks/legion-series/legion-y7000-2019Computer used for analyses
R (version 4.5.2)R Foundation for Statistical Computing / R Core Teamhttps://www.r-project.org/Statistical computing environment
CiteSpace (version 6.3.R3)CiteSpace / Chaomei Chen (Drexel University)https://citespace.podia.com/Bibliometric visualization and analysis software
VOSviewer (version 1.6.20)Leiden University CWTS (Nees Jan van Eck, Ludo Waltman)https://www.vosviewer.com/Bibliometric mapping software
Pajek (version 64.6.01)University of Ljubljana (Vladimir Batagelj, Andrej Mrvar)http://mrvar.fdv.uni-lj.si/pajek/Network analysis and visualization software

References

  1. McHugh, D., Durán, I., Gil, J. Senescence as a therapeutic target in cancer and age-related diseases. Nat Rev Drug Discov. 24 (1), 57-71 (2025).
  2. Young, A. R. J., Narita, M. SASP reflects senescence. EMBO Rep. 10 (3), 228-230 (2019).
  3. Suda, M., Tchkonia, T., Kirkland, J. L., Minamino, T. Targeting senescent cells for the treatment of age-associated diseases. J Biochem. 177 (3), 177-187 (2025).
  4. Irion, U., Nüsslein-Volhard, C. Developmental genetics with model organisms. Proc Natl Acad Sci U S A. 119 (30), e2122148119(2022).
  5. Wu, C. Y., Davis, S., Saudagar, N., et al. Caenorhabditis elegans as a convenient animal model for microbiome studies. Int J Mol Sci. 25 (12), 6670(2024).
  6. Kimble, J., Nüsslein-Volhard, C. The great small organisms of developmental genetics: Caenorhabditis elegans and Drosophila melanogaster. Dev Biol. 485, 93-122 (2022).
  7. Riddle, D. L., Swanson, M. M., Albert, P. S. Interacting genes in nematode dauer larva formation. Nature. 290 (5808), 668-671 (1981).
  8. Friedman, D. B., Johnson, T. E. A mutation in the age-1 gene in Caenorhabditis elegans lengthens life and reduces hermaphrodite fertility. Genetics. 118 (1), 75-86 (1988).
  9. Shmookler Reis, R. J., Ayyadevara, S., Crow, W. A., Lee, T., Delongchamp, R. R. Gene categories differentially expressed in C. elegans age-1 mutants of extraordinary longevity: new insights from novel data-mining procedures. J Gerontol A Biol Sci Med Sci. 67 (4), 366-375 (2012).
  10. Guarente, L., Kenyon, C. Genetic pathways that regulate ageing in model organisms. Nature. 408 (6809), 255-262 (2000).
  11. Berk, Ş, Cetin, A., Özdemir, ÖÜ, Pektaş, A. N., Yurtcu, N., Dastan, S. D. The combination of metformin and high glucose increased longevity of Caenorhabditis elegans in a DAF-16/FOXO-independent manner: cancer/diabetic model via C. elegans. Front Endocrinol. 15, 1435098(2024).
  12. Pandey, T., Wang, B., Wang, C., et al. LPD-3 as a megaprotein brake for aging and insulin-mTOR signaling in C. elegans. Cell Rep. 43 (3), 113899(2024).
  13. Chen, C., Dubin, R., Kim, M. C. Emerging trends and new developments in regenerative medicine: a scientometric update (2000-2014). Expert Opin Biol Ther. 14 (9), 1295-1317 (2014).
  14. Beguet, B., Brun, J. L. Influence of parental aging on the reproduction of the F1 generation in a hermaphrodite nematode Caenorhabditis elegans. Exp Gerontol. 7 (3), 195-206 (1972).
  15. Beguet, B. The persistence of processes regulating the level of reproduction in the hermaphrodite nematode Caenorhabditis elegans, despite the influence of parental aging, over several consecutive generations. Exp Gerontol. 7 (3), 207-218 (1972).
  16. Ogg, S., Paradis, S., Gottlieb, S., et al. The forkhead transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature. 389 (6654), 994-999 (1997).
  17. Lin, K., Dorman, J. B., Rodan, A., Kenyon, C. Daf-16: An HNF-3/forkhead family member that can function to double the life-span of Caenorhabditis elegans. Science. 278 (5341), 1319-1322 (1997).
  18. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., Kroemer, G. The hallmarks of aging. Cell. 153 (6), 1194-1217 (2013).
  19. Olshansky, S. J., Willcox, B. J., Demetrius, L., Beltrán-Sánchez, H. Implausibility of radical life extension in humans in the twenty-first century. Nat Aging. 4 (11), 1635-1642 (2024).
  20. Chen, C., Lim, J., Koh, J., Beard, J., Rowe, J. W. Research network on an aging society: a global analysis of adaptation to societal aging across low-, middle-, and high-income countries using the Global Aging Society Index. Nat Aging. 5 (1), 113-121 (2025).
  21. Johnson, T. E., Conley, W. L., Keller, M. L. Long-lived lines of Caenorhabditis elegans can be used to establish predictive biomarkers of aging. Exp Gerontol. 23 (4-5), 281-295 (1988).
  22. Johnson, T. E. Increased lifespan of age-1 mutants in Caenorhabditis elegans and lower Gompertz rate of aging. Science. 249 (4971), 908-912 (1990).
  23. Kimura, K. D., Tissenbaum, H. A., Liu, Y., Ruvkun, G. Daf-2, an insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science. 277 (5328), 942-946 (1997).
  24. Dorman, J. B., Albinder, B., Shroyer, T., Kenyon, C. The age-1 and daf-2 genes function in a common pathway to control the lifespan of Caenorhabditis elegans. Genetics. 141 (4), 1399-1406 (1995).
  25. Barzilai, N., Huffman, D. M., Muzumdar, R. H., Bartke, A. The critical role of metabolic pathways in aging. Diabetes. 61 (6), 1315-1322 (2012).
  26. Lee, H., Lee, S. V. Recent progress in regulation of aging by insulin/IGF-1 signaling in Caenorhabditis elegans. Mol Cells. 45 (11), 763-770 (2022).
  27. Martins, R., Lithgow, G. J., Link, W. Long live FOXO: unraveling the role of FOXO proteins in aging and longevity. Aging Cell. 15 (2), 196-207 (2016).
  28. Fontana, L., Partridge, L. Promoting health and longevity through diet: from model organisms to humans. Cell. 161 (1), 106-118 (2015).
  29. Calorie restriction increases muscle mitochondrial biogenesis in healthy humans. PLoS Med. Civitarese, A. E., Carling, S., Heilbronn, L. K., et al. 4 (3), e76(2007).
  30. Palikaras, K., Lionaki, E., Tavernarakis, N. Coordination of mitophagy and mitochondrial biogenesis during ageing in C. elegans. Nature. 521 (7553), 525-528 (2015).
  31. Jang, J. Y., Blum, A., Liu, J., Finkel, T. The role of mitochondria in aging. J Clin Invest. 128 (9), 3662-3670 (2018).
  32. Picca, A., Faitg, J., Auwerx, J., Ferrucci, L. D'Amico D. Mitophagy in human health, ageing and disease. Nat Metab. 5 (12), 2047-2061 (2023).
  33. Ryu, D., Mouchiroud, L., Andreux, P. A., et al. Urolithin A induces mitophagy and prolongs lifespan in C. elegans and increases muscle function in rodents. Nat Med. 22 (8), 879-888 (2016).
  34. Hong, X., Isern, J., Campanario, S., et al. Mitochondrial dynamics maintain muscle stem cell regenerative competence throughout adult life by regulating metabolism and mitophagy. Cell Stem Cell. 29 (9), 1298-1314.e10 (2022).
  35. Rappe, A., McWilliams, T. G. Dynamic mitophagy trajectories hallmark brain aging. Autophagy. 21 (2), 487-489 (2025).
  36. Kerr, J. S., Adriaanse, B. A., Greig, N. H., et al. Mitophagy and Alzheimer's disease: cellular and molecular mechanisms. Trends Neurosci. 40 (3), 151-166 (2017).
  37. Tarpey, M. D., Davy, K. P., McMillan, R. P., et al. Skeletal muscle autophagy and mitophagy in endurance-trained runners before and after a high-fat meal. Mol Metab. 6 (12), 1597-1609 (2017).
  38. Estébanez, B., Moreira, O. C., Almar, M., de Paz, J. A., Gonzalez-Gallego, J., Cuevas, M. J. Effects of a resistance-training programme on endoplasmic reticulum unfolded protein response and mitochondrial functions in PBMCs from elderly subjects. Eur J Sport Sci. 19 (7), 931-940 (2019).

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Aging ResearchBibliometric AnalysisInsulin SignalingStress ResponseCaloric RestrictionMitophagyKnowledge MappingModel Organism
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