Biological Age: Difference between revisions
Line 407: | Line 407: | ||
The common aging biomarkers of four methods in different systems. | The common aging biomarkers of four methods in different systems. | ||
{| class="wikitable" | {| class="wikitable" | ||
! | ! System | ||
! | ! MLR | ||
! | ! PCA | ||
! | ! KDM | ||
! | ! Deep learning | ||
|- | |- | ||
| colspan="1" rowspan="17" |Cardiovascular system | | colspan="1" rowspan="17" |Cardiovascular system | ||
| | | SBP{{pmid|23213031}}{{pmid|5841151}}{{pmid|3226152}}{{pmid|950448}}{{pmid|6667707}}{{pmid|17889950}}{{pmid|23642770}}{{pmid|28110151}}{{pmid|30899733}} | ||
| | | SBP{{pmid|23213031}}{{pmid|3226152}}{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}{{pmid|12634284}}{{pmid|12672981}}{{pmid|17921421}}{{pmid|18840798}}{{pmid|18597867}}{{pmid|22433233}}{{pmid|28110151}}{{pmid|30899733}} | ||
| | | SBP{{pmid|23213031}}{{pmid|26150497}}{{pmid|28110151}}{{pmid|28958059}}{{pmid|28977464}}{{pmid|30999227}}{{pmid|30899733}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|34038024}} | ||
| | |||
|- | |- | ||
| | | DBP{{pmid|17889950}}{{pmid|23642770}}{{pmid|31179487}} | ||
| | | DBP{{pmid|31179487}} | ||
| | | DBP{{pmid|27216811}}{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | | Pulse pressure{{pmid|23642770}} | ||
| | | Pulse pressure{{pmid|29188884}}{{pmid|24522464}}{{pmid|19940465}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Mean arterial pressure{{pmid|28203066}} | ||
| | |||
| | |||
|- | |- | ||
| | | Pulse{{pmid|3226152}} | ||
| | | Pulse{{pmid|3226152}}{{pmid|2737197}} | ||
| | |||
| | |||
|- | |- | ||
| | | Pulse wave velocity{{pmid|6667707}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Heart rate{{pmid|2282902}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Intima-media thickness{{pmid|24659482}}{{pmid|19940465}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| Minimum intima-media | |||
thickness | thickness{{pmid|29188884}}{{pmid|24522464}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | End diastolic velocity{{pmid|19940465}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | mitral valve E/A peak{{pmid|29188884}}{{pmid|19940465}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | MVEL{{pmid|19940465}}, MVES{{pmid|24522464}}, MVEA{{pmid|24659482}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Atherosclerosis index{{pmid|2737197}}{{pmid|2282902}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| | | NT-proBNP{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Cardiac troponin I{{pmid|34453631}} | ||
|- | |- | ||
| | | Creatine phosphokinase{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Homocysteine{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| colspan="1" rowspan="6" |Respiratory | | colspan="1" rowspan="6" |Respiratory | ||
system | system | ||
| | | FVC{{pmid|3226152}}{{pmid|17889950}}{{pmid|23642770}}{{pmid|20005245}} | ||
| | | FVC{{pmid|3226152}}{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}{{pmid|12634284}} | ||
| | | FVC{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | FEV1{{pmid|23213031}}{{pmid|950448}}{{pmid|7162237}}{{pmid|17889950}}{{pmid|23642770}}{{pmid|28110151}}{{pmid|30899733}}{{pmid|31179487}} | ||
| | | FEV1{{pmid|23213031}}{{pmid|12634284}}{{pmid|12672981}}{{pmid|17921421}}{{pmid|18840798}}{{pmid|18597867}}{{pmid|22433233}}{{pmid|28110151}}{{pmid|30899733}}{{pmid|31179487}} | ||
| | | FEV1{{pmid|23213031}}{{pmid|26150497}}{{pmid|28110151}}{{pmid|28958059}}{{pmid|28977464}}{{pmid|30899733}}{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | | Vital capacity{{pmid|5841151}}{{pmid|6667707}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | |||
| Maximal midexpiratory | |||
flow rate 75/25 | flow rate 75/25{{pmid|24522464}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | VO2 max{{pmid|18597867}}{{pmid|22433233}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Chest radiography{{pmid|33744131}} | ||
|- | |- | ||
| colspan="1" rowspan="9" |Nervous system | | colspan="1" rowspan="9" |Nervous system | ||
| | | MMSE{{pmid|31179487}} | ||
| | | MMSE{{pmid|31179487}} | ||
| | | MMSE{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | | Digital symbol test{{pmid|6667707}} | ||
| | | Digital symbol test{{pmid|24659482}} | ||
| | |||
| | |||
|- | |- | ||
| | | Numeric memory{{pmid|20005245}} | ||
| | |||
| | | Numeric memory{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Associated memory{{pmid|20005245}} | ||
| | |||
| | | Associated memory{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Topological memory{{pmid|20005245}} | ||
| | |||
| | | Topological memory{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Short-time memory{{pmid|6610563}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Concentration{{pmid|20005245}} | ||
| | |||
| | | Concentration{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Intellectuality -mental defect{{pmid|6667707}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Trail making test{{pmid|29188884}}{{pmid|24522464}} | ||
| | |||
| | |||
|- | |- | ||
| colspan="1" rowspan="15" |Endocrine metabolic system | | colspan="1" rowspan="15" |Endocrine metabolic system | ||
| | | Glucose{{pmid|17889950}}{{pmid|23642770}} | ||
| | | Glucose{{pmid|8803500}}{{pmid|12672981}}{{pmid|28203066}} | ||
| | | Glucose{{pmid|28977464}} | ||
| | | Glucose{{pmid|27191382}}{{pmid|29340580}}{{pmid|30644411}}{{pmid|34453631}} | ||
|- | |- | ||
| | | HBA1C{{pmid|23213031}}{{pmid|23642770}} | ||
| | | HBA1C{{pmid|23213031}}{{pmid|18597867}} | ||
| | | HBA1C{{pmid|23213031}}{{pmid|26150497}}{{pmid|28958059}}{{pmid|30999227}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|31693736}}{{pmid|34038024}} | ||
| | | HBA1C{{pmid|30644411}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | C-peptide{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Insulin{{pmid|34453631}} | ||
|- | |- | ||
| | | Triglyceride{{pmid|950448}}{{pmid|17889950}}{{pmid|23642770}} | ||
| | | Triglyceride{{pmid|2282902}}{{pmid|28203066}} | ||
| | | Triglyceride{{pmid|32946548}} | ||
| | | Triglyceride{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | | TC{{pmid|23213031}}{{pmid|5841151}}{{pmid|3226152}}{{pmid|950448}}{{pmid|7162237}}{{pmid|17889950}}{{pmid|23642770}}{{pmid|28110151}} | ||
| | | TC{{pmid|23213031}}{{pmid|3226152}}{{pmid|8803500}}{{pmid|28110151}} | ||
| | | TC{{pmid|23213031}}{{pmid|26150497}}{{pmid|28110151}}{{pmid|28958059}}{{pmid|28977464}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|34038024}} | ||
| | | TC{{pmid|27191382}}{{pmid|29340580}} | ||
|- | |- | ||
| | | HDL{{pmid|17889950}}, LDL{{pmid|23642770}} | ||
| | | HDL{{pmid|28203066}}, LDL{{pmid|18597867}} | ||
| | |||
| | | HDL{{pmid|29340580}}{{pmid|34453631}},LDL{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Apolipoprotein A1 and B{{pmid|34453631}} | ||
|- | |- | ||
| | | TSH{{pmid|17889950}} | ||
| | |||
| | | TSH{{pmid|27216811}} | ||
| | |||
|- | |- | ||
| | | Testosterone{{pmid|17889950}} | ||
| | |||
| | |||
| | | Testosterone{{pmid|34453631}} | ||
|- | |- | ||
| | | Vitamin D{{pmid|30899733}} | ||
| | | Vitamin D{{pmid|30899733}} | ||
| | | Vitamin D{{pmid|30899733}} | ||
| | | Vitamin D{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | | Calcium{{pmid|27216811}} | ||
| | | Calcium{{pmid|29340580}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Potassium{{pmid|29340580}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Sodium{{pmid|29340580}} | ||
|- | |- | ||
| | |||
| | |||
| | | Inorganic phosphorus{{pmid|27216811}} | ||
| | |||
|- | |- | ||
| colspan="1" rowspan="8" |Urinary system | | colspan="1" rowspan="8" |Urinary system | ||
| | | Urea{{pmid|23213031}}{{pmid|3226152}}{{pmid|950448}}{{pmid|23642770}}{{pmid|28110151}} | ||
| | | Urea{{pmid|23213031}}{{pmid|3226152}}{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}{{pmid|12634284}}{{pmid|17921421}}{{pmid|18840798}}{{pmid|18597867}}{{pmid|28110151}} | ||
| | | Urea{{pmid|23213031}}{{pmid|26150497}}{{pmid|27216811}}{{pmid|28110151}}{{pmid|30999227}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|31693736}} | ||
| | | Urea{{pmid|27191382}}{{pmid|29340580}}{{pmid|30644411}} | ||
|- | |- | ||
| | | Creatinine{{pmid|23213031}}{{pmid|17889950}}{{pmid|23642770}}{{pmid|28110151}}{{pmid|30899733}} | ||
| | | Creatinine{{pmid|28110151}}{{pmid|30899733}} | ||
| | | Creatinine{{pmid|23213031}}{{pmid|26150497}}{{pmid|27216811}}{{pmid|28958059}}{{pmid|30999227}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|31693736}}{{pmid|34038024}} | ||
| | | Creatinine{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | | eGFR{{pmid|31179487}} | ||
| | | eGFR{{pmid|31179487}} | ||
| | | eGFR{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | |||
| | |||
| | | Uric acid{{pmid|30999227}}{{pmid|31693736}} | ||
| | | Uric acid{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | | Cystatin C{{pmid|29188884}}{{pmid|24659482}}{{pmid|24522464}}{{pmid|19940465}} | ||
| | |||
| | | Cystatin C{{pmid|34453631}} | ||
|- | |- | ||
| | | Creatinine clearance{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Urine specific gravity{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Urine pH{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| colspan="1" rowspan="12" |Digestive system | | colspan="1" rowspan="12" |Digestive system | ||
| | | ALT{{pmid|23642770}} | ||
| | | ALT{{pmid|8803500}} | ||
| | |||
| | | ALT{{pmid|34453631}} | ||
|- | |- | ||
| | | AST{{pmid|3226152}}{{pmid|23642770}}{{pmid|28110151}} | ||
| | | AST{{pmid|3226152}}{{pmid|2737197}}{{pmid|2282902}}{{pmid|28110151}} | ||
| | | AST{{pmid|28110151}} | ||
| | | AST{{pmid|34453631}} | ||
|- | |- | ||
| | | ALP{{pmid|23213031}}{{pmid|950448}}{{pmid|23642770}} | ||
| | | ALP{{pmid|23213031}} | ||
| | | ALP{{pmid|23213031}}{{pmid|26150497}}{{pmid|27216811}}{{pmid|28958059}}{{pmid|30999227}}{{pmid|31693736}}{{pmid|34038024}} | ||
| | | ALP{{pmid|27191382}} | ||
|- | |- | ||
| | | Total protein{{pmid|23642770}} | ||
| | |||
| | | Total protein{{pmid|27216811}} | ||
| | | Total protein{{pmid|29340580}} | ||
|- | |- | ||
| | | Albumin{{pmid|23213031}}{{pmid|3226152}}{{pmid|23642770}} | ||
| | | Albumin{{pmid|23213031}}{{pmid|3226152}}{{pmid|12634284}}{{pmid|17921421}}{{pmid|18840798}}{{pmid|18597867}} | ||
| | | Albumin{{pmid|23213031}}{{pmid|26150497}}{{pmid|27216811}}{{pmid|28958059}}{{pmid|30999227}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|31693736}}{{pmid|34038024}} | ||
| | | Albumin{{pmid|27191382}}{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | | A/G{{pmid|3226152}}{{pmid|23642770}} | ||
| | | A/G{{pmid|3226152}}{{pmid|12634284}}{{pmid|12672981}} | ||
| | |||
| | |||
|- | |- | ||
| | | Total bilirubin{{pmid|23642770}} | ||
| | |||
| | |||
| | | Total bilirubin{{pmid|29340580}} | ||
|- | |- | ||
| | | Direct bilirubin{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Amylase{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Lactate dehydrogenase{{pmid|7162237}}{{pmid|23642770}} | ||
| | | Lactate dehydrogenase{{pmid|2282902}}{{pmid|8803500}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Alpha 2 globulin{{pmid|27191382}} | ||
|- | |- | ||
| Gamma glutamyl | |||
transpeptidase | transpeptidase{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| colspan="1" rowspan="21" |Hematologic System | | colspan="1" rowspan="21" |Hematologic System | ||
| | | Red blood cell{{pmid|30899733}} | ||
| | | Red blood cell{{pmid|12634284}}{{pmid|18840798}}{{pmid|30899733}} | ||
| | | Red blood cell{{pmid|30899733}}{{pmid|32946548}} | ||
| | | Red blood cell{{pmid|27191382}}{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| Red blood cell volume | |||
distribution width | distribution width{{pmid|30999227}} | ||
| Red blood cell volume | |||
distribution width | distribution width{{pmid|27191382}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | | Hematocrit{{pmid|12634284}}{{pmid|17921421}} | ||
| | |||
| | | Hematocrit{{pmid|27191382}}{{pmid|29340580}} | ||
|- | |- | ||
| | |||
| | |||
| Mean corpuscular | |||
volume | volume{{pmid|30999227}}{{pmid|31566204}}{{pmid|31693736}} | ||
| Mean corpuscular | |||
volume | volume{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| Mean corpuscular | |||
hemoglobin | hemoglobin{{pmid|12672981}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Mean corpuscular hemoglobin concentration{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | | Hemoglobin{{pmid|3226152}}{{pmid|31179487}} | ||
| | | Hemoglobin{{pmid|3226152}}{{pmid|2737197}}{{pmid|2282902}}{{pmid|8803500}}{{pmid|12634284}}{{pmid|31179487}} | ||
| | | Hemoglobin{{pmid|27216811}}{{pmid|31179487}} | ||
| | | Hemoglobin{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | | White blood cell{{pmid|30999227}}{{pmid|31566204}}{{pmid|31693736}} | ||
| | | White blood cell{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Granulocytes{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Neutrophils{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Basophils{{pmid|34453631}}, Eosinophils{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | | Lymphocytes{{pmid|30999227}}{{pmid|31566204}}{{pmid|31693736}} | ||
| | | Lymphocytes{{pmid|27191382}}{{pmid|34453631}} | ||
|- | |- | ||
| | | Monocytes{{pmid|31179487}} | ||
| | | Monocytes{{pmid|31179487}} | ||
| | | Monocytes{{pmid|31179487}} | ||
| | | Monocytes{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | | Platelet{{pmid|32946548}} | ||
| | | Platelet{{pmid|29340580}}{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Mean platelet volume{{pmid|34453631}} | ||
|- | |- | ||
| | |||
| | |||
| | |||
| | | Platelet distribution width{{pmid|34453631}} | ||
|- | |- | ||
| Erythrocyte | |||
| Erythrocyte | |||
| | |||
| | |||
|- | |- | ||
| | | sedimentation rat{{pmid|950448}}{{pmid|17889950}} | ||
| | | sedimentation rat{{pmid|18597867}} | ||
| | |||
| | |||
|- | |- | ||
| | |||
| | | D-dimer{{pmid|24659482}} | ||
Fibrinogen | Fibrinogen{{pmid|19940465}} | ||
| | |||
| | | D-dimer{{pmid|34453631}} | ||
|- | |- | ||
| | | Ferritin{{pmid|28110151}} | ||
| | | Ferritin{{pmid|28110151}} | ||
| | | Ferritin{{pmid|28110151}}{{pmid|32946548}} | ||
| | | Ferritin{{pmid|30644411}} | ||
|- | |- | ||
| | |||
| | |||
| | | Fransferrin{{pmid|32946548}} | ||
| | |||
|- | |- | ||
| colspan="1" rowspan="6" |Sensory system | | colspan="1" rowspan="6" |Sensory system | ||
| | | Visual accommodation{{pmid|6667707}}{{pmid|20005245}} | ||
| | |||
| | | Visual accommodation{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Visual reaction time{{pmid|20005245}} | ||
| | |||
| | | Visual reaction time{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Visual acuity{{pmid|5841151}}{{pmid|3226152}} | ||
| | | Visual acuity{{pmid|3226152}} | ||
| | |||
| | |||
|- | |- | ||
| | | Hearing{{pmid|5841151}}{{pmid|7162237}}{{pmid|6667707}}{{pmid|20005245}} | ||
| | | Hearing{{pmid|18597867}} | ||
| | | Hearing{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | | Vibrotactile{{pmid|5841151}}{{pmid|6610563}}{{pmid|20005245}} | ||
| | |||
| | | Vibrotactile{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| Retinal photos | |||
|- | |- | ||
| colspan="1" rowspan="4" |Inflammatory index | | colspan="1" rowspan="4" |Inflammatory index | ||
| | | CRP{{pmid|23213031}} | ||
| | | CRP{{pmid|23213031}} | ||
| | | CRP{{pmid|23213031}}{{pmid|26150497}}{{pmid|28958059}}{{pmid|28977464}}{{pmid|30999227}}{{pmid|31566204}}{{pmid|32946548}}{{pmid|31693736}}{{pmid|34038024}} | ||
| | | CRP{{pmid|34453631}} | ||
|- | |- | ||
| | | Cytomegalovirus optical density{{pmid|23213031}} | ||
| Cytomegalovirus | |||
optical density | optical density{{pmid|23213031}} | ||
| Cytomegalovirus | |||
optical density | optical density{{pmid|23213031}}{{pmid|26150497}}{{pmid|31566204}} | ||
| | |||
|- | |- | ||
| colspan="1" rowspan="2" | | | colspan="1" rowspan="2" | | ||
| colspan="1" rowspan="2" | | | colspan="1" rowspan="2" | | ||
| | | Interleukin-6{{pmid|28977464}} | ||
| colspan="1" rowspan="2" | | | colspan="1" rowspan="2" | | ||
|- | |- | ||
| | | P-selectin{{pmid|28977464}} | ||
|- | |- | ||
| colspan="1" rowspan="5" |Motion index | | colspan="1" rowspan="5" |Motion index | ||
| | | Grip strength{{pmid|5841151}}{{pmid|6610563}}{{pmid|31179487}}{{pmid|20005245}} | ||
| | | Grip strength{{pmid|22433233}}{{pmid|31179487}} | ||
| | | Grip strength{{pmid|31179487}}{{pmid|20005245}} | ||
| | |||
|- | |- | ||
| | |||
| | | Vertical jump{{pmid|22433233}} | ||
| | |||
| | |||
|- | |- | ||
| | | Timed up and go test{{pmid|31179487}} | ||
| | | Timed up and go test{{pmid|31179487}} | ||
| | | Timed up and go test{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | | Chair rise time{{pmid|31179487}} | ||
| | | Chair rise time{{pmid|31179487}} | ||
| | | Chair rise time{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | |||
| | |||
| | |||
| | | 1-week physical activity{{pmid|29581467}}{{pmid|31388024}} | ||
|- | |- | ||
| colspan="1" rowspan="9" |Body morphology index | | colspan="1" rowspan="9" |Body morphology index | ||
| | | WC{{pmid|23642770}}{{pmid|28110151}} | ||
| | | WC{{pmid|18597867}}{{pmid|22433233}}{{pmid|28110151}}{{pmid|28203066}} | ||
| | | WC{{pmid|28110151}} | ||
| | |||
|- | |- | ||
| | | Waist-to-hip ratio{{pmid|17889950}}{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Waist-to-height ratio{{pmid|30899733}} | ||
| | | Waist-to-height ratio{{pmid|30899733}} | ||
| | | Waist-to-height ratio{{pmid|30899733}} | ||
| | |||
|- | |- | ||
| | | Body mass index{{pmid|17889950}}{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Weight{{pmid|6667707}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | | Height{{pmid|31179487}} | ||
| | | Height{{pmid|31179487}} | ||
| | | Height{{pmid|31179487}} | ||
| | |||
|- | |- | ||
| | | Body fat{{pmid|17889950}}{{pmid|23642770}} | ||
| | | Body fat{{pmid|18597867}} | ||
| | |||
| | |||
|- | |- | ||
| | | Lean body mass{{pmid|17889950}}{{pmid|23642770}} | ||
| | |||
| | |||
| | |||
|- | |- | ||
| | |||
| | | Soft lean mass{{pmid|22433233}} | ||
| | |||
| | |||
|- | |- | ||
| Genetic index | |||
| | |||
| Terminal telomere restriction | |||
fragment | fragment{{pmid|24659482}} | ||
| | |||
| | |||
|- | |- | ||
| Genetic index | |||
| | |||
| Terminal telomere restriction | |||
fragment | fragment{{pmid|24659482}} | ||
| | |||
| | |||
|} | |} | ||
SBP, systolic blood pressure; DBP, diastolic blood pressure; NT-proBNP, N-terminal pro brain natriuretic peptide; MVEA, mitral annulus peak E anterior wall; MVEL, mitral valve annulus lateral wall of peak velocity of early filling; MVES, mitral valve annulus ventricular septum of the peak velocity of early filling; FEV1, forced expiratory volume in 1.0 s; FVC, forced vital capacity; MMSE, mini-mental state examination; eGFR, estimated glomerular filtration rat; HBA1C, glycosylated hemoglobin; HDL, high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid stimulating hormone; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; ALP, alkaline ''phosphatase; A/G, ratio of albumin to globulin; CRP, c-reactive protein; WC, waist circumference''. | SBP, systolic blood pressure; DBP, diastolic blood pressure; NT-proBNP, N-terminal pro brain natriuretic peptide; MVEA, mitral annulus peak E anterior wall; MVEL, mitral valve annulus lateral wall of peak velocity of early filling; MVES, mitral valve annulus ventricular septum of the peak velocity of early filling; FEV1, forced expiratory volume in 1.0 s; FVC, forced vital capacity; MMSE, mini-mental state examination; eGFR, estimated glomerular filtration rat; HBA1C, glycosylated hemoglobin; HDL, high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid stimulating hormone; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; ALP, alkaline ''phosphatase; A/G, ratio of albumin to globulin; CRP, c-reactive protein; WC, waist circumference''. |
Revision as of 01:13, 31 January 2024
Biological Age (BA) is a concept used to assess an individual's aging status, offering a more nuanced understanding than Chronological Age (CA). Chronological age refers simply to the amount of time that has elapsed since a person's birth, while biological age provides a measure of aging based on various physiological, biochemical, and molecular factors. This distinction is crucial because individuals of the same chronological age can exhibit significantly different aging processes and health statuses.
Key Aspects of Biological Age
Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk.
- Biomarkers: Biological age is typically determined by analyzing a range of biomarkers. These can include genetic markers, epigenetic alterations, cellular senescence, telomere length, metabolic markers, and more. The specific biomarkers chosen depend on the method of estimation and the focus of the study.
- Health and Functionality: Biological age reflects the functional state of an individual's organs and systems. A lower biological age compared to chronological age might indicate better health and lower risk for age-related diseases, whereas a higher biological age suggests accelerated aging and potentially increased health risks.
- Variability: Unlike chronological age, which is uniform and progresses at a constant rate (one year per year), biological age can vary significantly between individuals. Factors such as lifestyle, genetics, environment, and disease can influence the rate at which a person's biological systems age. For example, biological age estimated from blood markers ranged between 20-years younger and 20-years older than individuals' chronological age.[1]
Importance in Research and Medicine
- Research Tool: In scientific research, biological age is valuable for understanding the aging process, identifying aging biomarkers, and evaluating the effectiveness of anti-aging interventions.
- Clinical Applications: In a clinical setting, biological age can be used to assess an individual's overall health status, predict the risk of age-related diseases, and personalize healthcare and treatment plans.
Biological Age Estimation Methods
Biological age estimation has emerged as a significant tool in gerontology, aiming to provide a more accurate measure of aging than chronological age.
The accurate estimation of biological age has significant implications for clinical practice, including predicting disease onset and prognosis, improving the quality of life for the elderly, and promoting successful aging. Each method offers unique insights, and a comprehensive understanding of these methods can lead to better clinical decision-making and more effective interventions for aging-related conditions.
Various methods have been developed to estimate biological age, each with its unique approach and criteria:[2]
- Multiple Linear Regression (MLR) is a statistical technique that estimates biological age by relating several independent variables (biomarkers) to a dependent variable (chronological age). In this method, chronological age is used as a criterion for selecting biomarkers and is treated as an independent index.
- Principal Component Analysis (PCA) is another statistical technique used in biological age estimation. PCA reduces the dimensionality of the data by transforming multiple biomarkers into a set of linearly uncorrelated variables, known as principal components.
- Hochschild’s Method differs from MLR and PCA by making chronological age an independent variable. It aims to estimate biological age by adjusting chronological age based on specific biomarkers.
- Klemera and Doubal’s Method (KDM) shares a similar concept with Hochschild’s method but uses a more complex statistical approach. It treats chronological age as an independent variable and incorporates multiple biomarkers to estimate biological age.
- Deep Learning-based approaches represent an emerging and powerful method for biological age estimation. Leveraging complex neural network architectures, these methods can analyze large datasets of biomarkers, integrating diverse types of biological data. Deep learning models can uncover intricate patterns in the data that may be missed by traditional statistical techniques, offering potentially more accurate and personalized biological age estimations. However, they require large, well-curated datasets to be effective.
Role of Chronological Age
The role of chronological age in the estimation of biological age varies significantly between different methods. In MLR and PCA, chronological age is used as a criterion for selecting biomarkers. This means that the biomarkers are chosen based on how well they correlate with chronological age. In this context, chronological age is not an independent variable in the statistical model but rather a reference point against which the predictive power of biomarkers is measured.
In contrast, Hochschild’s method and KDM treat chronological age as an independent variable. This means chronological age is directly incorporated into the model as one of the variables that predict biological age.
Comparison
Comparisons of various biological age estimation methods
Method | Proposed | Core concept | Advantage | Disadvantage |
---|---|---|---|---|
MLR | 1965, Hollingsworth[3] | Aging biomarkers are determined by the correlation with chronological age using MLR model |
|
|
PCA | 1988, Nakamura[7] | PCA uses fewer uncorrelated variables to explain the main variance |
| |
Hochschild’s method | 1989, Hochschild[9] | Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy[9] |
|
|
KDM | 2006, Klemera and Doubal[8] | KDM is based on minimizing the distance between m regression lines and m biomarker points in an m-dimensional space of all biomarkers[8] |
| |
Deep learning | 2015[13] | Deep learning is a subfield of machine learning, where good features can be learned automatically using a general-purpose learning procedure[13]. Deep neural networks (DNNs)[14][15][16][17], convolutional neural networks (CNNs)[18][19], and recurrent neural networks (RNNs)[20] have been employed to build BA models in recent years. |
|
Biomarkers for Biological Age Estimation
Organ system | PCA | MLR | Hochschild’s | KDM |
---|---|---|---|---|
Cardiovascular system | Pulse pressure[21][22][23] | |||
Systolic blood pressure[7][24][25][26][27][28][29][12] | Systolic blood pressure[11][30][3][6][12] | Systolic blood pressure[12] | ||
Heart rate[7][24] | ||||
Intima-media thickness[21][31][22] | ||||
Maximum internal diameter of carotid artery[31][22] | ||||
End diastolic velocity[21] | ||||
Mitral valve annulus ventricular septum of the peak velocity of early filling[31] | ||||
Mitral valve annulus lateral wall of peak velocity of early filling[21] | ||||
Mitral annulus peak E anterior wall[22] | ||||
Ratio of peak velocity of early filling to atrial filling[21] | ||||
Respiratory system | VO2 max[32][26] | |||
Forced expiratory volume in 1 second[32][25][26][27][28] | Forced expiratory volume in 1 second[11][30][12] | Forced expiratory volume in 1 second[33] | Forced expiratory volume in 1 second[12] | |
Forced vital capacity[7][24][29] | Forced vital capacity[10] | Forced vital capacity[33] | ||
Maximal mid expiratory flow rate 75/25[31] | Vital capacity[3] | |||
Nervous system | Trail making test[31][23] | |||
Digital symbol test[10][22] | Digital symbol test[10] | |||
Memory test linking names with faces[10] | Memory test linking names with faces[10] | |||
Memory test: which picture is at what place[10] | Memory test: which picture is at what place[10] | |||
Speed test: pointing icons from 1 to 15 sequentially, mixed in random positions[10] | ||||
Visual reaction time[10][32] | Visual reaction time[33] | |||
Sequence of lamps[33] | ||||
Alternate button tapping time with/without decision[33] | ||||
Movement time with/without decision[33] | ||||
Renal system | Blood urea nitrogen[7][24][25][26][34][28][29] | Blood urea nitrogen[11][30][12] | Blood urea nitrogen[12] | |
Serum creatinine[12] | Serum creatinine[12] | |||
Cystatin C[21][31][22][23] | ||||
Liver | Serum albumin[25][26][34][28] | Serum albumin[6] | ||
Glutamic oxaloacetic transaminase[7][24] | Glutamic oxaloacetic transaminase[12] | Glutamic oxaloacetic transaminase[12] | ||
Glutamic pyruvic transaminase[29] | ||||
Ratio of albumin to globulin[27] | ||||
Lactate dehydrogenase[7][24][29] | ||||
Serum globulin[6] | ||||
Alkaline phosphatase[6] | ||||
Hematologic system | Erythrocyte sedimentation rate[26] | Erythrocyte sedimentation rate[6] | ||
Mean corpuscular hemoglobin[27] | ||||
Red blood cell count[28] | ||||
Hematocrit[25] | ||||
Hemoglobin concentration[7][24] | ||||
Fibrinogen[21] | ||||
Ferratin[12] | Ferratin[12] | |||
Metabolism | Glycosylated hemoglobin[26] | |||
Glucose[27][29] | Glucose[6] | |||
Low-density cholesterol[26] | ||||
Atherogenic index[7][24] | ||||
Triglyceride[24] | Triglycerides[30] | |||
Total cholesterol[29] | Total cholesterol[11][30][3][12] | Total cholesterol[12] | ||
Muscle and fat | Grip strength[10][32] | Grip strength[10][3] | ||
Soft lean mass[32] | ||||
Waist circumference[32][26] | Waist circumference[12] | Waist circumference[12] | ||
Percent body fat[26] | ||||
Sensory system | Hearing threshold[26] | |||
Highest audible pitch[10] | Highest audible pitch[10] | Highest audible pitch[33] | ||
Light extinction test[3] | ||||
Visual acuity[3] | ||||
Auditory function[3] | ||||
Vibrotactile sensitivity[3] | Vibrotactile sensitivity[33] | |||
Auditory reaction time[10] | Auditory reaction time[33] | |||
Focal range test using a Landolt ring[10] | Visual accommodation[33] | |||
Genetic index | Telomere restriction fragment[22] |
Biomarkers 2
The common aging biomarkers of four methods in different systems.
System | MLR | PCA | KDM | Deep learning |
---|---|---|---|---|
Cardiovascular system | SBP[11][3][5][6][35][36][37][12][38] | SBP[11][5][7][24][29][39][27][25][28][26][32][12][38] | SBP[11][40][12][41][42][43][38][44][45][46] | |
DBP[36][37][47] | DBP[47] | DBP[48][47] | ||
Pulse pressure[37] | Pulse pressure[23][31][21] | |||
Mean arterial pressure[49] | ||||
Pulse[5] | Pulse[5][7] | |||
Pulse wave velocity[35] | ||||
Heart rate[24] | ||||
Intima-media thickness[22][21] | ||||
Minimum intima-media | ||||
End diastolic velocity[21] | ||||
mitral valve E/A peak[23][21] | ||||
MVEL[21], MVES[31], MVEA[22] | ||||
Atherosclerosis index[7][24] | ||||
NT-proBNP[17] | ||||
Cardiac troponin I[17] | ||||
Creatine phosphokinase[37] | ||||
Homocysteine[37] | ||||
Respiratory
system |
FVC[5][36][37][10] | FVC[5][7][24][29][39] | FVC[10] | |
FEV1[11][6][50][36][37][12][38][47] | FEV1[11][39][27][25][28][26][32][12][38][47] | FEV1[11][40][12][41][42][38][47] | ||
Vital capacity[3][35] | ||||
Maximal midexpiratory
flow rate 75/25[31] |
||||
VO2 max[26][32] | ||||
Chest radiography[19] | ||||
Nervous system | MMSE[47] | MMSE[47] | MMSE[47] | |
Digital symbol test[35] | Digital symbol test[22] | |||
Numeric memory[10] | Numeric memory[10] | |||
Associated memory[10] | Associated memory[10] | |||
Topological memory[10] | Topological memory[10] | |||
Short-time memory[51] | ||||
Concentration[10] | Concentration[10] | |||
Intellectuality -mental defect[35] | ||||
Trail making test[23][31] | ||||
Endocrine metabolic system | Glucose[36][37] | Glucose[29][27][49] | Glucose[42] | Glucose[14][15][16][17] |
HBA1C[11][37] | HBA1C[11][26] | HBA1C[11][40][41][43][44][45][52][46] | HBA1C[16] | |
C-peptide[17] | ||||
Insulin[17] | ||||
Triglyceride[6][36][37] | Triglyceride[24][49] | Triglyceride[45] | Triglyceride[15][17] | |
TC[11][3][5][6][50][36][37][12] | TC[11][5][29][12] | TC[11][40][12][41][42][44][45][46] | TC[14][15] | |
HDL[36], LDL[37] | HDL[49], LDL[26] | HDL[15][17],LDL[15][17] | ||
Apolipoprotein A1 and B[17] | ||||
TSH[36] | TSH[48] | |||
Testosterone[36] | Testosterone[17] | |||
Vitamin D[38] | Vitamin D[38] | Vitamin D[38] | Vitamin D[17] | |
Calcium[48] | Calcium[15] | |||
Potassium[15] | ||||
Sodium[15] | ||||
Inorganic phosphorus[48] | ||||
Urinary system | Urea[11][5][6][37][12] | Urea[11][5][7][24][29][39][25][28][26][12] | Urea[11][40][48][12][43][44][45][52] | Urea[14][15][16] |
Creatinine[11][36][37][12][38] | Creatinine[12][38] | Creatinine[11][40][48][41][43][44][45][52][46] | Creatinine[15][17] | |
eGFR[47] | eGFR[47] | eGFR[47] | ||
Uric acid[43][52] | Uric acid[17] | |||
Cystatin C[23][22][31][21] | Cystatin C[17] | |||
Creatinine clearance[37] | ||||
Urine specific gravity[37] | ||||
Urine pH[37] | ||||
Digestive system | ALT[37] | ALT[29] | ALT[17] | |
AST[5][37][12] | AST[5][7][24][12] | AST[12] | AST[17] | |
ALP[11][6][37] | ALP[11] | ALP[11][40][48][41][43][52][46] | ALP[14] | |
Total protein[37] | Total protein[48] | Total protein[15] | ||
Albumin[11][5][37] | Albumin[11][5][39][25][28][26] | Albumin[11][40][48][41][43][44][45][52][46] | Albumin[14][15][17] | |
A/G[5][37] | A/G[5][39][27] | |||
Total bilirubin[37] | Total bilirubin[15] | |||
Direct bilirubin[37] | ||||
Amylase[37] | ||||
Lactate dehydrogenase[50][37] | Lactate dehydrogenase[24][29] | |||
Alpha 2 globulin[14] | ||||
Gamma glutamyl
transpeptidase[37] |
||||
Hematologic System | Red blood cell[38] | Red blood cell[39][28][38] | Red blood cell[38][45] | Red blood cell[14][15][17] |
Red blood cell volume
distribution width[43] |
Red blood cell volume | |||
Hematocrit[39][25] | Hematocrit[14][15] | |||
Mean corpuscular | Mean corpuscular | |||
Mean corpuscular
hemoglobin[27] |
||||
Mean corpuscular hemoglobin concentration[15][17] | ||||
Hemoglobin[5][47] | Hemoglobin[5][7][24][29][39][47] | Hemoglobin[48][47] | Hemoglobin[15][17] | |
White blood cell[43][44][52] | White blood cell[17] | |||
Granulocytes[17] | ||||
Neutrophils[17] | ||||
Basophils[17], Eosinophils[17] | ||||
Lymphocytes[43][44][52] | Lymphocytes[14][17] | |||
Monocytes[47] | Monocytes[47] | Monocytes[47] | Monocytes[17] | |
Platelet[45] | Platelet[15][17] | |||
Mean platelet volume[17] | ||||
Platelet distribution width[17] | ||||
Erythrocyte | Erythrocyte | |||
sedimentation rat[6][36] | sedimentation rat[26] | |||
D-dimer[22]
Fibrinogen[21] |
D-dimer[17] | |||
Ferritin[12] | Ferritin[12] | Ferritin[12][45] | Ferritin[16] | |
Fransferrin[45] | ||||
Sensory system | Visual accommodation[35][10] | Visual accommodation[10] | ||
Visual reaction time[10] | Visual reaction time[10] | |||
Visual acuity[3][5] | Visual acuity[5] | |||
Hearing[3][50][35][10] | Hearing[26] | Hearing[10] | ||
Vibrotactile[3][51][10] | Vibrotactile[10] | |||
Retinal photos | ||||
Inflammatory index | CRP[11] | CRP[11] | CRP[11][40][41][42][43][44][45][52][46] | CRP[17] |
Cytomegalovirus optical density[11] | Cytomegalovirus
optical density[11] |
Cytomegalovirus | ||
Interleukin-6[42] | ||||
P-selectin[42] | ||||
Motion index | Grip strength[3][51][47][10] | Grip strength[32][47] | Grip strength[47][10] | |
Vertical jump[32] | ||||
Timed up and go test[47] | Timed up and go test[47] | Timed up and go test[47] | ||
Chair rise time[47] | Chair rise time[47] | Chair rise time[47] | ||
1-week physical activity[18][20] | ||||
Body morphology index | WC[37][12] | WC[26][32][12][49] | WC[12] | |
Waist-to-hip ratio[36][37] | ||||
Waist-to-height ratio[38] | Waist-to-height ratio[38] | Waist-to-height ratio[38] | ||
Body mass index[36][37] | ||||
Weight[35] | ||||
Height[47] | Height[47] | Height[47] | ||
Body fat[36][37] | Body fat[26] | |||
Lean body mass[36][37] | ||||
Soft lean mass[32] | ||||
Genetic index | Terminal telomere restriction
fragment[22] |
|||
Genetic index | Terminal telomere restriction
fragment[22] |
SBP, systolic blood pressure; DBP, diastolic blood pressure; NT-proBNP, N-terminal pro brain natriuretic peptide; MVEA, mitral annulus peak E anterior wall; MVEL, mitral valve annulus lateral wall of peak velocity of early filling; MVES, mitral valve annulus ventricular septum of the peak velocity of early filling; FEV1, forced expiratory volume in 1.0 s; FVC, forced vital capacity; MMSE, mini-mental state examination; eGFR, estimated glomerular filtration rat; HBA1C, glycosylated hemoglobin; HDL, high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid stimulating hormone; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; ALP, alkaline phosphatase; A/G, ratio of albumin to globulin; CRP, c-reactive protein; WC, waist circumference.
Further Reading
- 2017, Common methods of biological age estimation [2]
- 2023, Progress in biological age research [53]
Todo
- 2017, Biological Age Predictors [54]
- 2022, Biological Age Predictors: The Status Quo and Future Trends [55]
- 2021, Predictors of Biological Age: The Implications for Wellness and Aging Research [56]
- 2023, Biological age estimation using circulating blood biomarkers [1]
- 2021, Deep learning for biological age estimation [57]
See Also
- Epigenetic Clocks
- Wikipedia - Biomarkers of aging
References
- ↑ Jump up to: 1.0 1.1 Bortz J et al.: Biological age estimation using circulating blood biomarkers. Commun Biol 2023. (PMID 37884697) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 2.0 2.1 Jia L et al.: Common methods of biological age estimation. Clin Interv Aging 2017. (PMID 28546743) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 3.12 3.13 3.14 3.15 Hollingsworth JW et al.: Correlations between tests of aging in Hiroshima subjects--an attempt to define "physiologic age". Yale J Biol Med 1965. (PMID 5841151) [PubMed] [Full text]
- ↑ Dubina TL et al.: Biological age and its estimation. II. Assessment of biological age of albino rats by multiple regression analysis. Exp Gerontol 1983. (PMID 6873212) [PubMed] [DOI] Abstract
- ↑ Jump up to: 5.00 5.01 5.02 5.03 5.04 5.05 5.06 5.07 5.08 5.09 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20 5.21 Nakamura E et al.: Assessment of biological age by principal component analysis. Mech Ageing Dev 1988. (PMID 3226152) [PubMed] [DOI] Abstract
- ↑ Jump up to: 6.00 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.10 6.11 6.12 6.13 Webster IW & Logie AR: A relationship between functional age and health status in female subjects. J Gerontol 1976. (PMID 950448) [PubMed] [DOI] Abstract
- ↑ Jump up to: 7.00 7.01 7.02 7.03 7.04 7.05 7.06 7.07 7.08 7.09 7.10 7.11 7.12 7.13 7.14 7.15 Nakamura E et al.: Biological age versus physical fitness age. Eur J Appl Physiol Occup Physiol 1989. (PMID 2737197) [PubMed] [DOI] Abstract
- ↑ Jump up to: 8.0 8.1 8.2 8.3 Klemera P & Doubal S: A new approach to the concept and computation of biological age. Mech Ageing Dev 2006. (PMID 16318865) [PubMed] [DOI] Abstract
- ↑ Jump up to: 9.0 9.1 Hochschild R: Improving the precision of biological age determinations. Part 1: A new approach to calculating biological age. Exp Gerontol 1989. (PMID 2684676) [PubMed] [DOI] Abstract
- ↑ Jump up to: 10.00 10.01 10.02 10.03 10.04 10.05 10.06 10.07 10.08 10.09 10.10 10.11 10.12 10.13 10.14 10.15 10.16 10.17 10.18 10.19 10.20 10.21 10.22 10.23 10.24 10.25 10.26 10.27 10.28 10.29 10.30 10.31 10.32 10.33 10.34 10.35 10.36 10.37 10.38 Cho IH et al.: An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI). Mech Ageing Dev 2010. (PMID 20005245) [PubMed] [DOI] Abstract
- ↑ Jump up to: 11.00 11.01 11.02 11.03 11.04 11.05 11.06 11.07 11.08 11.09 11.10 11.11 11.12 11.13 11.14 11.15 11.16 11.17 11.18 11.19 11.20 11.21 11.22 11.23 11.24 11.25 11.26 11.27 11.28 11.29 11.30 11.31 11.32 11.33 11.34 11.35 Levine ME: Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?. J Gerontol A Biol Sci Med Sci 2013. (PMID 23213031) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 12.00 12.01 12.02 12.03 12.04 12.05 12.06 12.07 12.08 12.09 12.10 12.11 12.12 12.13 12.14 12.15 12.16 12.17 12.18 12.19 12.20 12.21 12.22 12.23 12.24 12.25 12.26 12.27 12.28 12.29 12.30 12.31 12.32 12.33 12.34 12.35 12.36 12.37 12.38 12.39 12.40 Jee H & Park J: Selection of an optimal set of biomarkers and comparative analyses of biological age estimation models in Korean females. Arch Gerontol Geriatr 2017. (PMID 28110151) [PubMed] [DOI] Abstract
- ↑ Jump up to: 13.0 13.1 13.2 13.3 LeCun Y et al.: Deep learning. Nature 2015. (PMID 26017442) [PubMed] [DOI] Abstract
- ↑ Jump up to: 14.00 14.01 14.02 14.03 14.04 14.05 14.06 14.07 14.08 14.09 14.10 14.11 Putin E et al.: Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY) 2016. (PMID 27191382) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 15.00 15.01 15.02 15.03 15.04 15.05 15.06 15.07 15.08 15.09 15.10 15.11 15.12 15.13 15.14 15.15 15.16 15.17 15.18 15.19 Mamoshina P et al.: Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. J Gerontol A Biol Sci Med Sci 2018. (PMID 29340580) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 16.0 16.1 16.2 16.3 16.4 Mamoshina P et al.: Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Sci Rep 2019. (PMID 30644411) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 17.00 17.01 17.02 17.03 17.04 17.05 17.06 17.07 17.08 17.09 17.10 17.11 17.12 17.13 17.14 17.15 17.16 17.17 17.18 17.19 17.20 17.21 17.22 17.23 17.24 17.25 17.26 17.27 17.28 17.29 17.30 17.31 17.32 17.33 17.34 Gialluisi A et al.: Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. Eur J Epidemiol 2022. (PMID 34453631) [PubMed] [DOI] Abstract
- ↑ Jump up to: 18.0 18.1 Pyrkov TV et al.: Extracting biological age from biomedical data via deep learning: too much of a good thing?. Sci Rep 2018. (PMID 29581467) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 19.0 19.1 Raghu VK et al.: Deep Learning to Estimate Biological Age From Chest Radiographs. JACC Cardiovasc Imaging 2021. (PMID 33744131) [PubMed] [DOI] Abstract
- ↑ Jump up to: 20.0 20.1 Rahman SA & Adjeroh DA: Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity. Sci Rep 2019. (PMID 31388024) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 21.00 21.01 21.02 21.03 21.04 21.05 21.06 21.07 21.08 21.09 21.10 21.11 21.12 21.13 Bai X et al.: Evaluation of biological aging process - a population-based study of healthy people in China. Gerontology 2010. (PMID 19940465) [PubMed] [DOI] Abstract
- ↑ Jump up to: 22.00 22.01 22.02 22.03 22.04 22.05 22.06 22.07 22.08 22.09 22.10 22.11 22.12 22.13 Zhang WG et al.: Select aging biomarkers based on telomere length and chronological age to build a biological age equation. Age (Dordr) 2014. (PMID 24659482) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 23.0 23.1 23.2 23.3 23.4 23.5 23.6 23.7 Zhang W et al.: Model Construction for Biological Age Based on a Cross-Sectional Study of a Healthy Chinese Han population. J Nutr Health Aging 2017. (PMID 29188884) [PubMed] [DOI] Abstract
- ↑ Jump up to: 24.00 24.01 24.02 24.03 24.04 24.05 24.06 24.07 24.08 24.09 24.10 24.11 24.12 24.13 24.14 24.15 24.16 24.17 Nakamura E et al.: Biological age versus physical fitness age in women. Eur J Appl Physiol Occup Physiol 1990. (PMID 2282902) [PubMed] [DOI] Abstract
- ↑ Jump up to: 25.0 25.1 25.2 25.3 25.4 25.5 25.6 25.7 25.8 25.9 Nakamura E & Miyao K: A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol A Biol Sci Med Sci 2007. (PMID 17921421) [PubMed] [DOI] Abstract
- ↑ Jump up to: 26.00 26.01 26.02 26.03 26.04 26.05 26.06 26.07 26.08 26.09 26.10 26.11 26.12 26.13 26.14 26.15 26.16 26.17 26.18 26.19 26.20 26.21 Park J et al.: Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men. Arch Gerontol Geriatr 2009. (PMID 18597867) [PubMed] [DOI] Abstract
- ↑ Jump up to: 27.0 27.1 27.2 27.3 27.4 27.5 27.6 27.7 27.8 27.9 Ueno LM et al.: Biomarkers of aging in women and the rate of longitudinal changes. J Physiol Anthropol Appl Human Sci 2003. (PMID 12672981) [PubMed] [DOI] Abstract
- ↑ Jump up to: 28.0 28.1 28.2 28.3 28.4 28.5 28.6 28.7 28.8 28.9 Nakamura E & Miyao K: Sex differences in human biological aging. J Gerontol A Biol Sci Med Sci 2008. (PMID 18840798) [PubMed] [DOI] Abstract
- ↑ Jump up to: 29.00 29.01 29.02 29.03 29.04 29.05 29.06 29.07 29.08 29.09 29.10 29.11 29.12 29.13 29.14 Nakamura E et al.: Effects of habitual physical exercise on physiological age in men aged 20-85 years as estimated using principal component analysis. Eur J Appl Physiol Occup Physiol 1996. (PMID 8803500) [PubMed] [DOI] Abstract
- ↑ Jump up to: 30.0 30.1 30.2 30.3 30.4 Borkan GA & Norris AH: Assessment of biological age using a profile of physical parameters. J Gerontol 1980. (PMID 6967883) [PubMed] [DOI] Abstract
- ↑ Jump up to: 31.00 31.01 31.02 31.03 31.04 31.05 31.06 31.07 31.08 31.09 31.10 31.11 Zhang WG et al.: Construction of an integral formula of biological age for a healthy Chinese population using principle component analysis. J Nutr Health Aging 2014. (PMID 24522464) [PubMed] [DOI] Abstract
- ↑ Jump up to: 32.00 32.01 32.02 32.03 32.04 32.05 32.06 32.07 32.08 32.09 32.10 32.11 32.12 Jee H et al.: Development and application of biological age prediction models with physical fitness and physiological components in Korean adults. Gerontology 2012. (PMID 22433233) [PubMed] [DOI] Abstract
- ↑ Jump up to: 33.0 33.1 33.2 33.3 33.4 33.5 33.6 33.7 33.8 33.9 Hochschild R: Improving the precision of biological age determinations. Part 2: Automatic human tests, age norms and variability. Exp Gerontol 1989. (PMID 2583248) [PubMed] [DOI] Abstract
- ↑ Jump up to: 34.0 34.1 Zhang WG et al.: Peripheral Blood Leukocyte Telomere Length Is Associated with Age but Not Renal Function: A Cross-Sectional Follow-Up Study. J Nutr Health Aging 2018. (PMID 29380856) [PubMed] [DOI] Abstract
- ↑ Jump up to: 35.0 35.1 35.2 35.3 35.4 35.5 35.6 35.7 Voitenko VP & Tokar AV: The assessment of biological age and sex differences of human aging. Exp Aging Res 1983. (PMID 6667707) [PubMed] [DOI] Abstract
- ↑ Jump up to: 36.00 36.01 36.02 36.03 36.04 36.05 36.06 36.07 36.08 36.09 36.10 36.11 36.12 36.13 36.14 36.15 Bae CY et al.: Development of models for predicting biological age (BA) with physical, biochemical, and hormonal parameters. Arch Gerontol Geriatr 2008. (PMID 17889950) [PubMed] [DOI] Abstract
- ↑ Jump up to: 37.00 37.01 37.02 37.03 37.04 37.05 37.06 37.07 37.08 37.09 37.10 37.11 37.12 37.13 37.14 37.15 37.16 37.17 37.18 37.19 37.20 37.21 37.22 37.23 37.24 37.25 37.26 37.27 37.28 37.29 37.30 37.31 37.32 Bae CY et al.: Models for estimating the biological age of five organs using clinical biomarkers that are commonly measured in clinical practice settings. Maturitas 2013. (PMID 23642770) [PubMed] [DOI] Abstract
- ↑ Jump up to: 38.00 38.01 38.02 38.03 38.04 38.05 38.06 38.07 38.08 38.09 38.10 38.11 38.12 38.13 38.14 38.15 38.16 Jee H: Selection of a set of biomarkers and comparisons of biological age estimation models for Korean men. J Exerc Rehabil 2019. (PMID 30899733) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 39.0 39.1 39.2 39.3 39.4 39.5 39.6 39.7 39.8 Nakamura E & Miyao K: Further evaluation of the basic nature of the human biological aging process based on a factor analysis of age-related physiological variables. J Gerontol A Biol Sci Med Sci 2003. (PMID 12634284) [PubMed] [DOI] Abstract
- ↑ Jump up to: 40.0 40.1 40.2 40.3 40.4 40.5 40.6 40.7 40.8 40.9 Belsky DW et al.: Quantification of biological aging in young adults. Proc Natl Acad Sci U S A 2015. (PMID 26150497) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 41.0 41.1 41.2 41.3 41.4 41.5 41.6 41.7 Brown PJ et al.: Biological Age, Not Chronological Age, Is Associated with Late-Life Depression. J Gerontol A Biol Sci Med Sci 2018. (PMID 28958059) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 42.0 42.1 42.2 42.3 42.4 42.5 42.6 Murabito JM et al.: Measures of Biologic Age in a Community Sample Predict Mortality and Age-Related Disease: The Framingham Offspring Study. J Gerontol A Biol Sci Med Sci 2018. (PMID 28977464) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 43.00 43.01 43.02 43.03 43.04 43.05 43.06 43.07 43.08 43.09 43.10 43.11 Hastings WJ et al.: Comparability of biological aging measures in the National Health and Nutrition Examination Study, 1999-2002. Psychoneuroendocrinology 2019. (PMID 30999227) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 44.00 44.01 44.02 44.03 44.04 44.05 44.06 44.07 44.08 44.09 44.10 Gaydosh L et al.: Testing Proposed Quantifications of Biological Aging in Taiwanese Older Adults. J Gerontol A Biol Sci Med Sci 2020. (PMID 31566204) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 45.00 45.01 45.02 45.03 45.04 45.05 45.06 45.07 45.08 45.09 45.10 45.11 Liu Z: Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies. J Gerontol A Biol Sci Med Sci 2021. (PMID 32946548) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 46.0 46.1 46.2 46.3 46.4 46.5 46.6 Kuo CL et al.: Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell 2021. (PMID 34038024) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 47.00 47.01 47.02 47.03 47.04 47.05 47.06 47.07 47.08 47.09 47.10 47.11 47.12 47.13 47.14 47.15 47.16 47.17 47.18 47.19 47.20 47.21 47.22 47.23 47.24 47.25 47.26 47.27 47.28 47.29 Zhong X et al.: Estimating Biological Age in the Singapore Longitudinal Aging Study. J Gerontol A Biol Sci Med Sci 2020. (PMID 31179487) [PubMed] [DOI] Abstract
- ↑ Jump up to: 48.0 48.1 48.2 48.3 48.4 48.5 48.6 48.7 48.8 48.9 Mitnitski A et al.: Heterogeneity of Human Aging and Its Assessment. J Gerontol A Biol Sci Med Sci 2017. (PMID 27216811) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 49.0 49.1 49.2 49.3 49.4 Kang YG et al.: Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome. Clin Interv Aging 2017. (PMID 28203066) [PubMed] [DOI] [Full text] Abstract
- ↑ Jump up to: 50.0 50.1 50.2 50.3 Takeda H et al.: Evaluation of biological age and physical age by multiple regression analysis. Med Inform (Lond) 1982. (PMID 7162237) [PubMed] [DOI]
- ↑ Jump up to: 51.0 51.1 51.2 Dubina TL et al.: Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age in cross-sectional and longitudinal studies. Exp Gerontol 1984. (PMID 6610563) [PubMed] [DOI] Abstract
- ↑ Jump up to: 52.0 52.1 52.2 52.3 52.4 52.5 52.6 52.7 52.8 52.9 Parker DC et al.: Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults. J Gerontol A Biol Sci Med Sci 2020. (PMID 31693736) [PubMed] [DOI] [Full text] Abstract
- ↑ Li Z et al.: Progress in biological age research. Front Public Health 2023. (PMID 37124811) [PubMed] [DOI] [Full text] Abstract
- ↑ Jylhävä J et al.: Biological Age Predictors. EBioMedicine 2017. (PMID 28396265) [PubMed] [DOI] [Full text] Abstract
- ↑ Erema VV et al.: Biological Age Predictors: The Status Quo and Future Trends. Int J Mol Sci 2022. (PMID 36499430) [PubMed] [DOI] [Full text] Abstract
- ↑ Lohman T et al.: Predictors of Biological Age: The Implications for Wellness and Aging Research. Gerontol Geriatr Med 2021. (PMID 34595331) [PubMed] [DOI] [Full text] Abstract
- ↑ Ashiqur Rahman S et al.: Deep learning for biological age estimation. Brief Bioinform 2021. (PMID 32363395) [PubMed] [DOI] [Full text] Abstract