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Papers referenced in the video:
Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

Age-related changes in clinical parameters and their associations with common complex diseases
https://pubmed.ncbi.nlm.nih.gov/26623014/

Variation in serum creatinine level is correlated to risk of type 2 diabetes
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3811691/

Age and cystatin C in healthy adults: a collaborative study
https://pubmed.ncbi.nlm.nih.gov/19749145/

Cystatin C and long-term mortality among subjects with normal creatinine-based estimated glomerular filtration rates: NHANES III (Third National Health and Nutrition Examination Survey)
https://pubmed.ncbi.nlm.nih.gov/21109116/

Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors
https://pubmed.ncbi.nlm.nih.gov/37118062/

The Intestinal Microbiota and Metabolites in the Gut-Kidney-Heart Axis of Chronic Kidney Disease
https://pubmed.ncbi.nlm.nih.gov/35370631/

Characteristics of Colon-Derived Uremic Solutes
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140561/