Molecular epidemiological studies into frailty and aging biomarkers

The overarching objective of my research is to learn to understand why some people become frail with age while others do not. I also do research into aging biomarkers, such as the epigenetic clocks and circulating cell-free DNA.

Juulia Jylhävä

Project leader
C8 Department of Medical Epidemiology and Biostatistics
Figure 1. Longitudinal frailty trajectory in the SATSA cohort using up to 15 repeated measurements for the frailty index.

In our research, we make use of various epidemiological and omics approaches and use samples from large data sets, such as the Swedish Twin and Danish Twin Registries (STR and DTR) and UK Biobank. Our specific objectives in the frailty research are to investigate the developmental trajectories of frailty, identify longitudinally the drivers for interindividual differences and analyze whether the associations between frailty and adverse outcomes are age-dependent. In the aging biomarker work, we focus on analyzing how the different markers relate to each other, and whether they reflect different aspects of aging and risk of mortality.

Causes and consequences of frailty

Frailty is a geriatric syndrome characterized by increased vulnerability to stressors and leading to increased risk of disability, morbidity and mortality. Despite being recognized as a major public health concern, the underlying causes of frailty remain poorly understood. Although several risk factors, such as sedentary life style, socioeconomic adversity and malnutrition have been identified for frailty, it is currently uncertain if and how the progression of frailty could be delayed. Hence, better understanding on the causes and consequences of frailty is urgently needed to be able to tackle frailty with the right measures at the right time.

In our ongoing work using longitudinal data in the Swedish Adoption/Twin Study of Aging (SATSA), we have shown that the average rate of increase in frailty, measured using the Rockwood frailty index (FI), accelerates more than two-fold after the age 65 (Figure 1). There is also considerable interindividual variation in the frailty trajectories (Figure 1). In our recent work using the Screening Across the Lifespan Twin study (SALT) we have shown that although frailty has been traditionally considered as a syndrome of the oldest-old, higher levels of FI in fact confer a relatively greater mortality risk at midlife than in old age (Li et al., BMC Med. 2019 May 15;17(1):94). Our ongoing work also includes looking into the genetic and epigenetic determinants of frailty.

Biomarkers of aging

Not everyone ages at the same rate. Individuals with the same calendar age might differ greatly in their biological age and exhibit different risks for morbidity, disability and mortality. There is a myriad of candidates presented for aging biomarkers, some of which only reflect some part of the aging process or aging-related disease, while others, such as the epigenetic clocks, can also estimate the biological age of the individual (reviewed by us in Jylhävä et al. EBioMedicine. 2017 Jul;21:29-36). Different types of aging biomarkers and biological age predictors are nevertheless needed, as no single marker alone can capture the wide variation in biological aging. The ultimate goal in our aging biomarker research is to identify which markers could be used in monitoring the aging process and predicting adverse outcomes.


Functional Aging Index Complements Frailty in Prediction of Entry into Care and Mortality.
Finkel D, Sternäng O, Jylhävä J, Bai G, Pedersen NL
J. Gerontol. A Biol. Sci. Med. Sci. 2019 Jun;():

Can markers of biological age predict dependency in old age?
Jylhävä J, Jiang M, Foebel AD, Pedersen NL, Hägg S
Biogerontology 2019 06;20(3):321-329

The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study.
Li X, Ploner A, Karlsson IK, Liu X, Magnusson PKE, Pedersen NL, et al
BMC Med 2019 May;17(1):94

A Frailty Index for UK Biobank Participants.
Williams DM, Jylhävä J, Pedersen NL, Hägg S
J. Gerontol. A Biol. Sci. Med. Sci. 2019 Mar;74(4):582-587

Fcμ receptor as a Costimulatory Molecule for T Cells.
Meryk A, Pangrazzi L, Hagen M, Hatzmann F, Jenewein B, Jakic B, et al
Cell Rep 2019 Mar;26(10):2681-2691.e5

Longitudinal changes in the genetic and environmental influences on the epigenetic clocks across old age: Evidence from two twin cohorts.
Jylhävä J, Hjelmborg J, Soerensen M, Munoz E, Tan Q, Kuja-Halkola R, et al
EBioMedicine 2019 Feb;40():710-716

Plasma cell-free DNA and qSOFA score predict 7-day mortality in 481 emergency department bacteraemia patients.
Rannikko J, Seiskari T, Huttunen R, Tarkiainen I, Jylhävä J, Hurme M, et al
J. Intern. Med. 2018 Oct;284(4):418-426

DNA Methylation and All-Cause Mortality in Middle-Aged and Elderly Danish Twins.
Svane AM, Soerensen M, Lund J, Tan Q, Jylhävä J, Wang Y, et al
Genes (Basel) 2018 Feb;9(2):

Frailty index as a predictor of all-cause and cause-specific mortality in a Swedish population-based cohort.
Jiang M, Foebel AD, Kuja-Halkola R, Karlsson I, Pedersen NL, Hägg S, et al
Aging (Albany NY) 2017 12;9(12):2629-2646

Human endogenous retrovirus HERV-K(HML-2) env expression is not associated with markers of immunosenescence.
Marttila S, Nevalainen T, Jylhävä J, Kananen L, Jylhä M, Hervonen A, et al
Exp. Gerontol. 2017 10;97():60-63

Body Mass Index and Waist Circumference as Predictors of Disability in Nonagenarians: The Vitality 90+ Study.
Lisko I, Tiainen K, Raitanen J, Jylhävä J, Hurme M, Hervonen A, et al
J. Gerontol. A Biol. Sci. Med. Sci. 2017 Oct;72(11):1569-1574

Biological Age Predictors.
Jylhävä J, Pedersen NL, Hägg S
EBioMedicine 2017 Jul;21():29-36

Obesity accelerates epigenetic aging in middle-aged but not in elderly individuals.
Nevalainen T, Kananen L, Marttila S, Jylhävä J, Mononen N, Kähönen M, et al
Clin Epigenetics 2017 ;9():20

FGF21 is a biomarker for mitochondrial translation and mtDNA maintenance disorders.
Lehtonen JM, Forsström S, Bottani E, Viscomi C, Baris OR, Isoniemi H, et al
Neurology 2016 Nov;87(22):2290-2299

Increased Paternal Age at Conception Is Associated with Transcriptomic Changes Involved in Mitochondrial Function in Elderly Individuals.
Nevalainen T, Kananen L, Marttila S, Jylhävä J, Jylhä M, Hervonen A, et al
PLoS ONE 2016 ;11(11):e0167028

The trajectory of the blood DNA methylome ageing rate is largely set before adulthood: evidence from two longitudinal studies.
Kananen L, Marttila S, Nevalainen T, Kummola L, Junttila I, Mononen N, et al
Age (Dordr) 2016 Jun;38(3):65

Methylomic predictors demonstrate the role of NF-κB in old-age mortality and are unrelated to the aging-associated epigenetic drift.
Jylhävä J, Kananen L, Raitanen J, Marttila S, Nevalainen T, Hervonen A, et al
Oncotarget 2016 Apr;7(15):19228-41

Cardiometabolic and Inflammatory Biomarkers as Mediators Between Educational Attainment and Functioning at the Age of 90 Years.
Enroth L, Raitanen J, Hervonen A, Lehtimäki T, Jylhävä J, Hurme M, et al
J. Gerontol. A Biol. Sci. Med. Sci. 2016 Mar;71(3):412-9

Aging-associated DNA methylation changes in middle-aged individuals: the Young Finns study.
Kananen L, Marttila S, Nevalainen T, Jylhävä J, Mononen N, Kähönen M, et al
BMC Genomics 2016 Feb;17():103

The concentration of cell-free DNA in video-EEG patients is dependent on the epilepsy syndrome and duration of epilepsy.
Alapirtti T, Jylhävä J, Raitanen J, Mäkinen R, Peltola J, Hurme MA, et al
Neurol. Res. 2016 Jan;38(1):45-50