FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4290653018> ?p ?o ?g. }

• W4290653018 endingPage "382" @default.
• W4290653018 startingPage "367" @default.
• W4290653018 abstract "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).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.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.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.The online version contains supplementary material available at 10.1007/s13167-022-00292-3." @default.
• W4290653018 created "2022-08-08" @default.
• W4290653018 creator A5025106192 @default.
• W4290653018 creator A5032014402 @default.
• W4290653018 creator A5045180104 @default.
• W4290653018 creator A5045286653 @default.
• W4290653018 creator A5046574247 @default.
• W4290653018 creator A5055962186 @default.
• W4290653018 creator A5059329731 @default.
• W4290653018 creator A5072848026 @default.
• W4290653018 creator A5077328685 @default.
• W4290653018 creator A5091796975 @default.
• W4290653018 date "2022-08-08" @default.
• W4290653018 modified "2023-10-09" @default.
• W4290653018 title "Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine" @default.
• W4290653018 cites W1537158318 @default.
• W4290653018 cites W2007063403 @default.
• W4290653018 cites W2020852889 @default.
• W4290653018 cites W2058368576 @default.
• W4290653018 cites W2067825692 @default.
• W4290653018 cites W2082119880 @default.
• W4290653018 cites W2131735754 @default.
• W4290653018 cites W2136289855 @default.
• W4290653018 cites W2143445490 @default.
• W4290653018 cites W2148428406 @default.
• W4290653018 cites W2156791190 @default.
• W4290653018 cites W2171838635 @default.
• W4290653018 cites W2252143047 @default.
• W4290653018 cites W2325218589 @default.
• W4290653018 cites W2593764183 @default.
• W4290653018 cites W2597964501 @default.
• W4290653018 cites W2610001125 @default.
• W4290653018 cites W2734377645 @default.
• W4290653018 cites W2752747624 @default.
• W4290653018 cites W2806496241 @default.
• W4290653018 cites W2888929939 @default.
• W4290653018 cites W2892741787 @default.
• W4290653018 cites W2901059465 @default.
• W4290653018 cites W2908411629 @default.
• W4290653018 cites W2981626889 @default.
• W4290653018 cites W3006354677 @default.
• W4290653018 cites W3012071733 @default.
• W4290653018 cites W3013773890 @default.
• W4290653018 cites W3016526532 @default.
• W4290653018 cites W3026757513 @default.
• W4290653018 cites W3046241697 @default.
• W4290653018 cites W3087670447 @default.
• W4290653018 cites W3087740606 @default.
• W4290653018 cites W3088248772 @default.
• W4290653018 cites W3090859437 @default.
• W4290653018 cites W3102476541 @default.
• W4290653018 cites W3124963373 @default.
• W4290653018 cites W3133610231 @default.
• W4290653018 cites W3152552104 @default.
• W4290653018 cites W3163071830 @default.
• W4290653018 cites W3174086521 @default.
• W4290653018 cites W3193353606 @default.
• W4290653018 cites W3196663128 @default.
• W4290653018 cites W3200151149 @default.
• W4290653018 cites W3202688695 @default.
• W4290653018 cites W3216660278 @default.
• W4290653018 cites W4200103701 @default.
• W4290653018 cites W4200517423 @default.
• W4290653018 cites W4205123661 @default.
• W4290653018 cites W4211144975 @default.
• W4290653018 cites W4212774806 @default.
• W4290653018 cites W4220776988 @default.
• W4290653018 cites W4281702821 @default.
• W4290653018 doi "https://doi.org/10.1007/s13167-022-00292-3" @default.
• W4290653018 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36061832" @default.
• W4290653018 hasPublicationYear "2022" @default.
• W4290653018 type Work @default.
• W4290653018 citedByCount "8" @default.
• W4290653018 countsByYear W42906530182022 @default.
• W4290653018 countsByYear W42906530182023 @default.
• W4290653018 crossrefType "journal-article" @default.
• W4290653018 hasAuthorship W4290653018A5025106192 @default.
• W4290653018 hasAuthorship W4290653018A5032014402 @default.
• W4290653018 hasAuthorship W4290653018A5045180104 @default.
• W4290653018 hasAuthorship W4290653018A5045286653 @default.
• W4290653018 hasAuthorship W4290653018A5046574247 @default.
• W4290653018 hasAuthorship W4290653018A5055962186 @default.
• W4290653018 hasAuthorship W4290653018A5059329731 @default.
• W4290653018 hasAuthorship W4290653018A5072848026 @default.
• W4290653018 hasAuthorship W4290653018A5077328685 @default.
• W4290653018 hasAuthorship W4290653018A5091796975 @default.
• W4290653018 hasBestOaLocation W42906530182 @default.
• W4290653018 hasConcept C118487528 @default.
• W4290653018 hasConcept C119857082 @default.
• W4290653018 hasConcept C126322002 @default.
• W4290653018 hasConcept C151730666 @default.
• W4290653018 hasConcept C156957248 @default.
• W4290653018 hasConcept C1862650 @default.
• W4290653018 hasConcept C2776214593 @default.
• W4290653018 hasConcept C2779343474 @default.
• W4290653018 hasConcept C2779874844 @default.
• W4290653018 hasConcept C2780225610 @default.
• W4290653018 hasConcept C2908647359 @default.

• next