Researchers developed a simplified metabolic health score using routine blood markers to identify people at greater risk of developing cardiometabolic disease.
European researchers have developed a personalized metabolic profiling tool that analyzes 10 circulating substances in the blood to assess overall health in middle age, predict mortality risk, and evaluate trajectories of functional decline.
Presented during FAPESP Week Netherlands by Joris Deelen of Leiden University Medical Center (LUMC), the clinical indicator is designed to identify patients at elevated risk of chronic illness before severe symptoms occur. According to researchers, a one-point increase on the updated scale corresponds to an average delay of 14 years in the onset of an initial cardiometabolic disease, including type 2 diabetes, stroke, and myocardial infarction.
The tool aims to address broad disparities in aging and chronic morbidity. Studies indicate that approximately 60% of adults older than 65 years currently manage two or more chronic conditions simultaneously.
“The length of time spent in good health after age 65 varies dramatically across Europe: in Romania, it’s about four years, while in the Netherlands, it’s 10 years,” said Deelen, a researcher at LUMC. “That disparity stems primarily from socioeconomic status, educational level, lifestyle, and genetic factors.”
Refining the Biomarker Panel for Routine Clinical Testing
Because biological aging cannot be directly quantified through a single measurement, investigators from LUMC and the Max Planck Institute for Biology of Aging in Cologne, Germany, examined blood samples from 44,000 volunteers across 12 European cohort studies. The team initially profiled more than 200 circulating analytes, including lipid fractions, glucose measures, and systemic inflammatory markers. Over the longitudinal follow-up period, 5,500 patient deaths were recorded, allowing scientists to correlate specific biochemical signatures with all-cause mortality risk.
To adapt the research panel for routine clinical laboratory operations, the team partnered with researchers in Brazil, including Gabriela Natalle Lopes, a student at the Albert Einstein Israelite Faculty, to update and streamline the algorithm across three phases.
First, investigators adjusted the mathematical model using Dutch population data to generate individualized patient results. Second, they narrowed the panel from hundreds of parameters down to 10 blood markers already routinely evaluated in clinical practice. Finally, the scoring scale was inverted so that higher numerical values reflect superior metabolic health rather than increased mortality risk.
“In the original scoring, a high number was bad; we decided to change it so that a higher score indicates better health,” said Deelen.
Beyond predicting cardiometabolic diagnoses, the composite score correlates with physical and neurological indicators of aging, including cognitive performance, lung capacity, and handgrip strength.
Tracking Targeted Lifestyle Interventions
Investigators evaluated whether the metabolic profile could serve as an actionable monitoring tool by enrolling participants in structured strength training and behavioral intervention programs.
The data revealed that targeted lifestyle modifications generated measurable biological benefits specifically in participants who presented with baseline scores below average. In this higher-risk cohort, interventions significantly reduced circulating triglycerides and fasting insulin levels while improving body composition.
“When we analyzed the change in the group’s score, we didn’t see a significant change; however, when we divided the individuals by their initial score, we saw that the lifestyle intervention was specifically effective for those with a score below zero – that is, the people who were less healthy at the beginning,” said Deelen. “That indicates that the score is an excellent indicator for identifying who really needs intervention and for monitoring the health gains provided by exercise.”
Cohort analyses demonstrated that the metabolic score is determined 40% by genetic inheritance and 60% by environmental and lifestyle factors. Cigarette smoking represented the most detrimental behavioral contributor, whereas habitual physical activity markedly improved scores. Early developmental factors also altered long-term baseline status; childhood malnutrition was linked to persistently elevated metabolic scores in adulthood.
Biochemical Crosstalk and Diagnostic AI Applications
Complementary research by Tiago da Silva Alexandre, a professor at the Federal University of São Carlos (UFSCar) in Brazil, outlines the biological mechanisms connecting metabolic health, musculoskeletal decline, and neurodegenerative disease.
According to Alexandre, musculoskeletal aging and dementia are mediated by continuous biochemical signaling between muscle, bone, adipose tissue, and the brain via exercise-induced myokines. With advancing age, lipid infiltration into skeletal muscle fibers disrupts carbohydrate and lipid metabolism, elevating the risk of metabolic syndrome, functional immobility, and cognitive impairment.
Alexandre also identified a detrimental pathway involving abdominal adiposity and micronutrient sequestration. Excess visceral fat sequesters circulating vitamin D, reducing serum concentrations necessary for muscle protein synthesis and neuronal tissue protection.
“There’s a clear biological cycle: people with more abdominal fat are at greater risk of vitamin D deficiency. Since that hormone is essential for protein synthesis and muscle strength, a deficiency weakens the muscles and reduces muscle mass. At the same time, because it plays a direct role in protecting brain tissue, vitamin D deficiency significantly increases the risk of cognitive decline in older adults,” said Alexandre.
To implement multi-analyte and clinical data into centralized care pathways, UFSCar established the Science Center for the Development of Intelligent Methods in the SUS for the Treatment of People with Dementia (CCD-MIDEM). Supported by FAPESP, the initiative combines artificial intelligence algorithms, electronic health records, imaging metrics, and biomarker profiles to improve early differential diagnosis between Alzheimer disease and vascular dementia across Brazil’s public health network.
Photo caption: Researcher Joris Deelen
Photo credit: Elton Alisson/Agência FAPESP
