Age and Sex Distributions of Age-Related Biomarker Values in Healthy Older Adults from the Long Life Family Study

Paola Sebastiani, Bharat Thyagarajan, Fangui Sun, Lawrence S. Honig, Nicole Schupf, Stephanie Cosentino, Mary F. Feitosa, Mary Wojczynski, Anne B. Newman, Monty Montano, Thomas T. Perls

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Objectives: To determine reference values for laboratory tests in individuals aged 85 and older. Design: Cross-sectional cohort study. Setting: International. Participants: Long Life Family Study (LLFS) participants (N~5,000, age: range 25–110, median 67, 45% male). Measurements: Serum biomarkers were selected based on association with aging-related diseases and included complete blood count, lipids (triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol), 25-hydroxyvitamin D2 and D3, vitamin D epi-isomer, diabetes mellitus–related biomarkers (adiponectin, insulin, insulin-like growth factor 1, glucose, glycosylated hemoglobin, soluble receptor for advanced glycation endproduct), kidney disease–related biomarkers (albumin, creatinine, cystatin), endocrine biomarkers (dehydroepiandrosterone, sex-hormone binding globulin, testosterone), markers of inflammation (interleukin 6, high-sensitivity C-reactive protein, N-terminal pro b-type natriuretic peptide), ferritin, and transferrin. Results: Of 38 measured biomarkers, 34 were significantly correlated with age. Summary statistics were generated for all biomarkers according to sex and 5-year age increments from 50 and up after excluding participants with diseases and treatments that were associated with biomarkers. A biomarker data set was also generated that will be useful for other investigators seeking to compare biomarker levels between studies. Conclusion: Levels of several biomarkers change with older age in healthy individuals. The descriptive statistics identified herein will be useful in future studies and, if replicated in additional studies, might also become useful in clinical practice. The availability of the reference data set will facilitate appropriate calibration of biomarkers measured in different laboratories.

Original languageEnglish (US)
Pages (from-to)e189-e194
JournalJournal of the American Geriatrics Society
Issue number11
StatePublished - Nov 1 2016

Bibliographical note

Funding Information:
This work was supported by National Institute on Aging (NIA) cooperative agreements U01-AG023712, U01-AG23744, U01-AG023746, U01-AG023749, and U01-AG023755. Conflicts of Interest: Nicole Schupfs has received grants from the National Institutes of Health (NIH) and the Alzheimer's Association. Lawrence S. Honig receives some NIH funding for research; this project is one such project that receives NIH funding. Bharat Thyagarajan has received several grants and contracts from the NIH to support research projects. Monty Montano is a founder of and consultant for and has stock in MyoSyntax. Author Contributions: Sebastiani: study design, data analyses, generation of results, writing the manuscript. Cosentino: data analysis, revision of manuscript. Sun: data analysis. Schupfs: acquisition of data, preparation of manuscript. Honig: acquisition of subjects and data, analysis and interpretation of data, preparation of manuscript. Thyagarajan: generation of biomarker data, interpretation of data, writing of manuscript. Perls: acquisition of subjects and data, preparation of manuscript. Newman: study concept and design, acquisition of subjects and data, critical revisions of manuscript. Sponsor's Role: The NIA played no role in any of the above aspects of this paper.


  • Long Life Family Study
  • healthy aging
  • reference values
  • serum biomarkers

Fingerprint Dive into the research topics of 'Age and Sex Distributions of Age-Related Biomarker Values in Healthy Older Adults from the Long Life Family Study'. Together they form a unique fingerprint.

Cite this