Immune metabolic health pipeline

Goal

To improve vitality and resilience by boosting immune-metabolic health in ageing and age-related disease. Biological ageing processes affecting immune and metabolic health are actionable and when left unattended contribute to physical, mental decline and (multi)morbidity.

Ongoing Research Lines

Mechanistic research: adverse and protective determinants

• Determinants of adverse and protective immune metabolic pathways in ageing and age-related disease are investigated in observational studies by genomic approaches in NEO (obesity), Leiden Longevity Study, RAAK/GARP (OA patients) in collaboration with cohort consortia BBMRI/NCC, NCDC (dementia cohorts) and (UKB) UKBiobank. The socio-genomic perspective of resilience and longevity is studied in demographic data bases (UTAH, HSNand CBS data).
• Causal inference of potential risk factors for age-related disease from a lifecourse perspective is studied using Mendelian Randomization in large online available datasets and in collaborative epigenome based projects.
• Functional genomics studies of novel gene variants discovered for disease protection (longevity) and osteoarthritis are performed at LUMC and in collaboration with the Max Planck Institute for Biology of Ageing Cologne and the Colombia University.
• Studies into cellular senescence in osteoarthritis and immunosenescence in ageing.

Biomarkers of immune metabolic health in ageing: defining those at risk

• Data science in biomarker studies (into the metabolome , proteome, epigenome, novel clinical asays , oxidative stress and cellular senescence) to explore improvements in prediction and prognosis of age-related diseases and to estimate vulnerability of the 55+ population in population and clinic. These efforts joins the data-science of multiple partners and disciplines including Molecular Epidemiology (LLS, RAAK), Geriatrics and Gerontology (TENT, COOP, PROPSER), Elderly home care (HIP fractures), Clinical Epidemiology (NEO), epidemiologists at Doetinchem/Rotterdam studies, cohort consortia (BBMRI/NCC), the Cologne Cluster of Excellence (CECAD), the VOILA consortium (PPP on ageing), MetaboDelta (Medical Delta projects with TUDelft, UL and EUMC).
• Generation of biomarker assays using novel technology and methodology, based on immunity, inflammation and metabolism with Netherlands Metabolomics Center, Nightingale Health.

Effective interventions supporting vitality and resilience

• Lifestyle interventions to improve immune metabolic health in both population health and clinical patients (prehabilitation) using developed biomarkers are focused on vulnerable 55+ target groups in AGO, GOTO, OPTIJD, and the VOILA-intervention study (with Wageningen University and Maastricht UMC, Friesland Campina) and include development of novel instruments for refining target populations and for sensitive monitoring of responses. Focus is on improving muscle, sleep health, immune-metabolic health, gut health. Novel instruments include accelerometry (with LIACS and the CHARGE consortium Accelerometer working group) in combination with classical health and novel omics profiles (data-cience with TU Delft).
• By taking the best biomarkers from cohort and intervention studies as proof of principle, we initiate pilot studies and a strategy to implement the use of healthchecks for estimating vulnerability in elderly populations coupled to health improvement and lifestyle programmes for Population Health with LAVA, the Health Campus and Hogeschool The Hague, Leefstijlcentrum Haaglanden, Zorg & Zekerheid and Zorg.nl.

Future

Targets for the future: broaden biomarker research into clinical populations; define target groups and individualize biomarkers, implement evidence based effective interventions for classified target groups in the 55+ population and discover therapeutic targets to decelerate ageing in those at risk.

Key references of which the majority open access.

Strategy

• Ageing, age-related diseases and oxidative stress: What to do next? (Luo J, Mills K, le Cessie S, Noordam R, & van Heemst D, Ageing Res Rev, 2020)
• DNA methylation aging clocks: challenges and recommendations. (Bell CG et al., Genome Biol, 2019)
• Facing up to the global challenges of ageing. (Partridge L, Deelen J, & Slagboom PE, Nature, 2018)

Mechanistic research

• Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors. (Ao L et al., Geroscience, 2024)
• Increasing number of long-lived ancestors marks a decade of healthspan extension and healthier metabolomics profiles. (van den Berg N, Rodríguez-Girondo M, van Dijk IK, Slagboom PE, & Beekman M, Nat Commun, 2023)
• Lipid-induced transcriptomic changes in blood link to lipid metabolism and allergic response. (Dekkers KF et al., Nat Commun, 2023)
• Stratification of Type 2 Diabetes by Age of Diagnosis in the UK Biobank Reveals Subgroup-Specific Genetic Associations and Causal Risk Profiles. (Noordam R et al., Diabetes, 2021)
• Longevity Relatives Count score identifies heritable longevity carriers and suggests case improvement in genetic studies. (van den Berg N et al., Aging Cell, 2020)
• A meta-analysis of genome-wide association studies identifies multiple longevity genes. (Deelen J et al., Nat Commun, 2019)

Biomarker research

• Identified senescence endotypes in aged cartilage are reflected in the blood metabolome (Boone I et al., GeroScience, 2023)
• Evaluation of the Value of Waist Circumference and Metabolomics in the Estimation of Visceral Adipose Tissue. (Boone SC et al., Am J Epidemiol, 2022)
• Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression. (Jonkman TH et al., Genome Biol, 2022)
• MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health’s 1H-NMR metabolomics data. (Bizzarri D, Reinders MJT, Beekman M, Slagboom PE, & van den Akker EB, Bioinformatics, 2022)
• The Relation Between Adult Weight Gain, Adipocyte Volume, and the Metabolic Profile at Middle Age. (Verkouter I et al., J Clin Endocrinol Metab, 2021)
• Diet-Derived Circulating Antioxidants and Risk of Coronary Heart Disease: A Mendelian Randomization Study. (Luo J, le Cessie S, van Heemst D, & Noordam R, J Am Coll Cardiol, 2021)
• Metabolic Age Based on the BBMRI-NL 1H-NMR Metabolomics Repository as Biomarker of Age-related Disease. (van den Akker EB et al., Circ Genom Precis Med, 2020)
• Metabolomics Profiling of Visceral Adipose Tissue: Results From MESA and the NEO Study. (Neeland IJ et al., J Am Heart Assoc, 2019)
• A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. (Deelen J et al., Nat Commun, 2019)

Interventions

• Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial (Bogaards FA et al., Nat Commun, 2024)
• PLIS: A metabolomic response monitor to a lifestyle intervention study in older adults. (Bogaards FA et al., FASEB J, 2022)
• Metabolic effects of a 13-weeks lifestyle intervention in older adults: The Growing Old Together Study. (van de Rest O et al., Aging (Albany NY), 2016)
• Dose-response effects of a Web-based physical activity program on body composition and metabolic health in inactive older adults: additional analyses of a randomized controlled trial. (Vroege DP et al., J Med Internet Res, 2014)
• Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial. (Wijsman CA et al., J Med Internet Res, 2013)