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      Biomarker signatures of aging

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          Summary

          Because people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study ( LLFS), age range 30–110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age‐related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.

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          Quantification of biological aging in young adults.

          Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.
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            Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?

            Biological age (BA) is useful for examining differences in aging rates. Nevertheless, little consensus exists regarding optimal methods for calculating BA. The aim of this study is to compare the predictive ability of five BA algorithms. The sample included 9,389 persons, aged 30-75 years, from National Health and Nutrition Examination Survey III. During the 18-year follow-up, 1,843 deaths were counted. Each BA algorithm was compared with chronological age on the basis of predictive sensitivity and strength of association with mortality. Results found that the Klemera and Doubal method was the most reliable predictor of mortality and performed significantly better than chronological age. Furthermore, when included with chronological age in a model, Klemera and Doubal method had more robust predictive ability and caused chronological age to no longer be significantly associated with mortality. Given the potential of BA to highlight heterogeneity, the Klemera and Doubal method algorithm may be useful for studying a number of questions regarding the biology of aging.
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              Metabolic Signatures of Extreme Longevity in Northern Italian Centenarians Reveal a Complex Remodeling of Lipids, Amino Acids, and Gut Microbiota Metabolism

              The aging phenotype in humans has been thoroughly studied but a detailed metabolic profiling capable of shading light on the underpinning biological processes of longevity is still missing. Here using a combined metabonomics approach compromising holistic 1H-NMR profiling and targeted MS approaches, we report for the first time the metabolic phenotype of longevity in a well characterized human aging cohort compromising mostly female centenarians, elderly, and young individuals. With increasing age, targeted MS profiling of blood serum displayed a marked decrease in tryptophan concentration, while an unique alteration of specific glycerophospholipids and sphingolipids are seen in the longevity phenotype. We hypothesized that the overall lipidome changes specific to longevity putatively reflect centenarians' unique capacity to adapt/respond to the accumulating oxidative and chronic inflammatory conditions characteristic of their extreme aging phenotype. Our data in centenarians support promotion of cellular detoxification mechanisms through specific modulation of the arachidonic acid metabolic cascade as we underpinned increased concentration of 8,9-EpETrE, suggesting enhanced cytochrome P450 (CYP) enzyme activity. Such effective mechanism might result in the activation of an anti-oxidative response, as displayed by decreased circulating levels of 9-HODE and 9-oxoODE, markers of lipid peroxidation and oxidative products of linoleic acid. Lastly, we also revealed that the longevity process deeply affects the structure and composition of the human gut microbiota as shown by the increased extrection of phenylacetylglutamine (PAG) and p-cresol sulfate (PCS) in urine of centenarians. Together, our novel approach in this representative Italian longevity cohort support the hypothesis that a complex remodeling of lipid, amino acid metabolism, and of gut microbiota functionality are key regulatory processes marking exceptional longevity in humans.
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                Author and article information

                Contributors
                sebas@bu.edu
                Journal
                Aging Cell
                Aging Cell
                10.1111/(ISSN)1474-9726
                ACEL
                Aging Cell
                John Wiley and Sons Inc. (Hoboken )
                1474-9718
                1474-9726
                06 January 2017
                April 2017
                : 16
                : 2 ( doiID: 10.1111/acel.2017.16.issue-2 )
                : 329-338
                Affiliations
                [ 1 ] Department of BiostatisticsBoston University School of Public Health 801 Massachusetts Avenue Boston MA 02118USA
                [ 2 ] Department of Laboratory Medicine and PathologyUniversity of Minnesota Medical School MMC 609 Mayo 420 Delaware Minneapolis MN 55455USA
                [ 3 ] Department of Epidemiology Sergievsky CenterColumbia University Mailman School of Public Health 630 West 168th Street New York NY 10032USA
                [ 4 ] Department of EpidemiologyUniversity of Pittsburgh A527 Crabtree Hall 130 DeSoto Street Pittsburgh PA 15261USA
                [ 5 ] Department of Medicine Harvard Medical SchoolBrigham and Women's Hospital 221 Longwood Avenue Boston MA 02115USA
                [ 6 ] Department of Medicine Geriatrics SectionBoston University School of Medicine and Boston Medical Center Robinson 2400 88 E Newton St Boston MA 02118USA
                Author notes
                [*] [* ] Correspondence

                Paola Sebastiani, Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, USA. Tel.: +1(617)6385877; fax: +1(617)6386484; e‐mail: sebas@ 123456bu.edu

                Article
                ACEL12557
                10.1111/acel.12557
                5334528
                28058805
                0abffcf7-47aa-42c3-bc4b-2fde390c3aca
                © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 November 2016
                Page count
                Figures: 3, Tables: 4, Pages: 10, Words: 8767
                Funding
                Funded by: National Institute on Aging
                Award ID: U01‐AG023712
                Award ID: U01‐AG23744
                Award ID: U01‐AG023746
                Award ID: U01‐AG023749
                Award ID: U01‐AG023755
                Award ID: U19 AG023122
                Funded by: NIAID
                Funded by: NHLBI
                Award ID: R21HL128871
                Funded by: NHLBI/NIH
                Award ID: N 01HC25195
                Funded by: Boston University School of Medicine
                Funded by: Marty and Paulette Samowitz Foundation
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                acel12557
                April 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.0.9 mode:remove_FC converted:23.03.2017

                Cell biology
                biological aging,biomarkers,healthy aging,morbidity and mortality
                Cell biology
                biological aging, biomarkers, healthy aging, morbidity and mortality

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