Evidence map›Paper›PMID 27773987›Full record

ArticleNorth American actuarial journal : NAAJ2016

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.

Anatoliy I Yashin, Konstantin G Arbeev, Deqing Wu, Liubov Arbeeva, Alexander Kulminski, Irina Kulminskaya, Igor Akushevich, Svetlana V Ukraintseva

Abstract read
In one paragraph

Article in North American actuarial journal : NAAJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Anatoliy I YashinProfessor, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102E, Durham, NC 27705, USA. Tel.: (+1) 919-668-2713.
Konstantin G ArbeevSr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102F, Durham, NC 27705, USA. Tel.: (+1) 919-668-2707.
Deqing WuSr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A104, Durham, NC 27705, USA. Tel.: (+1) 919-684-6126.
Liubov ArbeevaStatistician, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102G, Durham, NC 27705, USA. Tel.: (+1) 919-613-0715.
Alexander KulminskiSr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A106, Durham, NC 27705, USA. Tel.: (+1) 919-684-4962.
Irina KulminskayaResearch Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102D, Durham, NC 27705, USA. Tel.: (+1) 919-681-8232.
Igor AkushevichSr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A107, Durham, NC 27705, USA. Tel.: (+1) 919-668-2715.
Svetlana V UkraintsevaSr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A105, Durham, NC 27705, USA. Tel.: (+1) 919-668-2712.
Duke University · US

Funding

STALLARD Admin Supp: Predicting Future Care Needs and Costs for Individual Medicare Enrollees with Incident Suspected Alzheimer's DiseaseP30AG034424 · NIA · DUKE UNIVERSITY · PI SCOTT M. LYNCH · 2009 to 2026
$12.2M
Relationships among Genetic Regulators of Aging Health and LifespanP01AG043352 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2014 to 2018
$8.7M
Genetics of changes in population pyramids: Implications for health forecasting with a focus on Alzheimer's Disease and related dementiasRF1AG046860 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2019 to 2019
$3.1M
YASHIN Admin Supp: Time Trends in Alzheimer's Disease: Addressing the Changing Role of Risk Factors and Instrument ValidityR01AG046860 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2014 to 2018
$3.1M
EVALUATING NEW GENETIC EFFECTS ON HEALTH AND AGING FROM LONGITUDINAL DATAR01AG030612 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2007 to 2011
$2.9M
The future of major geriatric disorders in the US elderlyR01AG032319 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2008 to 2012
$1.1M
SLEEP AND ENTRAINMENT OF SCN FUNCTIONR01HL064278 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI STROHL, KINGMAN PERKINS · 1999 to 2002
$871k
New Methods of Studying Aging, Health and Longevity: Combining Longitudinal DataR01AG030198 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2009 to 2010
$779k
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
NHLBI NIH HHS HL64278NHLBI NIH HHS N01 HC025195NHLBI NIH HHS N02 HL064278NIA NIH HHS P01 AG043352NIA NIH HHS P30 AG034424NIA NIH HHS R01 AG030198NIA NIH HHS R01 AG030612NIA NIH HHS R01 AG032319NIA NIH HHS R01 AG046860NIA NIH HHS RF1 AG046860
6 · The paper itself

Abstract

BACKGROUND AND

objectiveTo clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues. DATA AND

methodsWe performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways.

resultsWe showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants.

conclusions. The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.

Indexed as

genetic model of mortalitygenetics of aginggenetics of longevityphysiological variablesstress resistance

Identifiers

PMID27773987
PMCPMC5070546
OpenAlexW2470559861

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.