From ageing clocks to human digital twins in personalising healthcare through biological age analysis
Abstract Age is the most important risk factor for the majority human diseases, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (BA). These predictors offer promising insights into the ageing process and age-related diseases. Wi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01911-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849225905090592768 |
|---|---|
| author | Murih Pusparum Olivier Thas Stephan Beck Simone Ecker Gökhan Ertaylan |
| author_facet | Murih Pusparum Olivier Thas Stephan Beck Simone Ecker Gökhan Ertaylan |
| author_sort | Murih Pusparum |
| collection | DOAJ |
| description | Abstract Age is the most important risk factor for the majority human diseases, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (BA). These predictors offer promising insights into the ageing process and age-related diseases. With real-time, multi-modal data streams and continuous patient monitoring, these BA can also inform the construction of ‘human digital twins’, quantifying how age-related changes impact health trajectories. This study highlights the significance of BA within a deeply phenotyped longitudinal cohort, using omics-based approaches alongside gold-standard clinical risk predictors. BA and health traits predictions were computed from 29 epigenetics, 4 clinical-biochemistry, 2 proteomics, and 3 metabolomics clocks. The study reveals that ageing is different between individuals but relatively stable within individuals. We suggest that BA should be considered crucial biomarkers complementing routine clinical tests. Regular updates of BA predictions within digital twin frameworks can also help guiding individualised treatment plans. |
| format | Article |
| id | doaj-art-49e16fcedbbe4f0386f9ff4ffcec8588 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-49e16fcedbbe4f0386f9ff4ffcec85882025-08-24T11:52:00ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111310.1038/s41746-025-01911-9From ageing clocks to human digital twins in personalising healthcare through biological age analysisMurih Pusparum0Olivier Thas1Stephan Beck2Simone Ecker3Gökhan Ertaylan4Environmental Intelligence, Flemish Institute for Technological Research (VITO)Data Science Institute, Hasselt UniversityUCL Cancer Institute, University College LondonUCL Cancer Institute, University College LondonEnvironmental Intelligence, Flemish Institute for Technological Research (VITO)Abstract Age is the most important risk factor for the majority human diseases, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (BA). These predictors offer promising insights into the ageing process and age-related diseases. With real-time, multi-modal data streams and continuous patient monitoring, these BA can also inform the construction of ‘human digital twins’, quantifying how age-related changes impact health trajectories. This study highlights the significance of BA within a deeply phenotyped longitudinal cohort, using omics-based approaches alongside gold-standard clinical risk predictors. BA and health traits predictions were computed from 29 epigenetics, 4 clinical-biochemistry, 2 proteomics, and 3 metabolomics clocks. The study reveals that ageing is different between individuals but relatively stable within individuals. We suggest that BA should be considered crucial biomarkers complementing routine clinical tests. Regular updates of BA predictions within digital twin frameworks can also help guiding individualised treatment plans.https://doi.org/10.1038/s41746-025-01911-9 |
| spellingShingle | Murih Pusparum Olivier Thas Stephan Beck Simone Ecker Gökhan Ertaylan From ageing clocks to human digital twins in personalising healthcare through biological age analysis npj Digital Medicine |
| title | From ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| title_full | From ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| title_fullStr | From ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| title_full_unstemmed | From ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| title_short | From ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| title_sort | from ageing clocks to human digital twins in personalising healthcare through biological age analysis |
| url | https://doi.org/10.1038/s41746-025-01911-9 |
| work_keys_str_mv | AT murihpusparum fromageingclockstohumandigitaltwinsinpersonalisinghealthcarethroughbiologicalageanalysis AT olivierthas fromageingclockstohumandigitaltwinsinpersonalisinghealthcarethroughbiologicalageanalysis AT stephanbeck fromageingclockstohumandigitaltwinsinpersonalisinghealthcarethroughbiologicalageanalysis AT simoneecker fromageingclockstohumandigitaltwinsinpersonalisinghealthcarethroughbiologicalageanalysis AT gokhanertaylan fromageingclockstohumandigitaltwinsinpersonalisinghealthcarethroughbiologicalageanalysis |