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Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine

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Abstract

Aims

Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia.

Results

In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study.

Conclusion

Sarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical settings to improve the diagnosis of sarcopenia.

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Abbreviations

ASM :

appendicular skeletal muscle mass

AUC :

areas under the receiver operating characteristic curve

BMI :

body mass index

CT :

computed tomography

DEXA :

dual-energy X-ray absorptiometry

KNHANES :

Korean National Health and Nutrition Examination Survey

KOS :

Korean Ophthalmological Society

MRD1 :

marginal reflex distance 1

MRI :

magnetic resonance imaging

PPPM/3PM :

predictive, preventive, and personalized medicine

SHAP :

SHapley Additive exPlanations

TabNet :

deep learning network for tubular data

XGBoost :

eXtreme Gradient Boosting

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Acknowledgements

We would like to thank the Editage for English language editing.

Data availability

The raw data used for model training, validation, and testing are publicly available at https://knhanes.kdca.go.kr/knhanes/eng. The development data for this study is available at http://data.mendeley.com/datasets/h547645876. The sarcopenia risk calculator is accessible at https://knhanesoculomics.github.io/sarcopenia.

Code availability

The XGBoost and TabNet codes used as the backbone of our architecture are available at http://data.mendeley.com/datasets/h547645876. The codes that support the findings of this study are available from the corresponding authors, upon reasonable request.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, Hangil Eye Hospital, Incheon, Republic of Korea

    Bo Ram Kim

  2. B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, Republic of Korea

    Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim & In Sik Lee

  3. VISUWORKS, Seoul, Republic of Korea

    Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim & Jung Soo Kim

  4. Department of Ophthalmology, Dankook University College of Medicine, Dankook University Hospital, Cheonan, Republic of Korea

    Hong Kyu Kim

  5. Malgeun Seoul Family Clinic, Seoul, Republic of Korea

    Dong-Hyeok Shin

  6. Department of Family Medicine, CHA Bundang Medical Centre, CHA University, Seongnam, Republic of Korea

    Young-Sang Kim

  7. Department of Family Practice & Community Health, Ajou University School of Medicine, Suwon, Gyeonggi-do, 16499, Republic of Korea

    Bom Taeck Kim

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  1. Bo Ram Kim
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Contributions

Bo Ram Kim, Tae Keun Yoo, and Bom Taeck Kim had full access to all the data in the study and take responsibility for the integrity and accuracy of its analysis. All authors met the following criteria: (1) substantial contributions to the conception or design of the work or the acquisition, analysis, or interpretation of the data; (2) drafting the work or revising it critically for important intellectual content; (3) approval of the final version; and (4) accountability for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding authors

Correspondence to Tae Keun Yoo or Bom Taeck Kim.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of the Korean Center for Disease Control and Prevention (No. 2008–04EXP-01-C, 2009–01CON-03-C, 2010–02CON-21-C, and 2011–02CON-06-C). The raw data sets are publicly available through the KNHANES website, and data collection from the KNHANES dataset was approved by the Institutional Review Board of the Korean National Institute for Bioethics Policy, which waived the requirement for informed consent for this study. The study adhered to the tenets of the Declaration of Helsinki.

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Not applicable.

Competing interests

Jin Kuk Kim, Ik Hee Ryu, and Tae Keun Yoo are executives of VISUWORKS, Inc., a Korean artificial intelligence company providing medical machine learning solutions. Jin Kuk Kim is an executive of the Korea Intelligent Medical Industry Association. They received salaries or stocks as part of their standard compensation packages. The remaining authors declare no conflicts of interest.

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Kim, B.R., Yoo, T.K., Kim, H.K. et al. Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine. EPMA Journal 13, 367–382 (2022). https://doi.org/10.1007/s13167-022-00292-3

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