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Risk stratification is central
On September 22, 2022, the Roland Eils team at the Charite Medical School in Berlin and Duo published an article
Study population and neural network models for predicting diseases based on metabolic profiles
The authors first extracted valid information on metabolic profiles and disease development risk based on the UK Biobank cohort of 22 central 117981 participants, trained a neural network model based on metabolic profiles to predict 24 disease risks, and validated
Metabolic profiles stratify disease risk at the beginning of the disease
The key to prevention is to identify high-risk people
Predictive effect of metabolic spectroscopy in combination with clinical predictors
Information such as gender and age are clinical predictors that are readily available in primary care and are often used for risk stratification
The authors used the Cox proportional risk model to predict the risk of disease development by predicting the risk of disease alone or in combination with metabolic profile, such as age and sex, ASCVD (set of cardiovascular disease predictors) and PANEL (set of clinical predictors such as blood tests), and quantitatively compared the stratification ability of different indicators with Harrell's C index, and found that the disease prediction stratification of metabolic spectrogram indicators was significantly better than that of age and sex, ASCVD indicators, and the treatment of kidney disease, The prediction effect of liver disease and type II diabetes was significantly better than that of clinical indicators, and the prediction effect of cataracts, glaucoma, skin cancer, colon cancer, rectal cancer and prostate cancer was weaker than that of traditional clinical indicators
In the comparison of metabolic and clinical indicators, the authors found that metabolic profiles predicted kidney disease, liver disease, type II diabetes, COPD, and heart failure better than metabolic profiles combined with age and sex
Manifestations of model stratification capabilities in clinical practice
While the stratification capability of the model is critical, the clinical utility of any risk model depends on calibration and selection of appropriate intervention thresholds
The disadvantage of neural networks in practical applications is that they are very poorly
The authors found that glutamine, glycine, tyrosine, metabolites of sugar metabolism, albumin, creatinine, acetylated glycoproteins, ketone bodies, fatty acids, and lipoproteins all contributed to
Next, the authors looked at type II diabetes mellitus and all-cause dementia, where metabolic states make a significant contribution