Use of mathematical models of survivorship in the study of biomarkers of aging: the role of heterogeneity

Piantanelli, L.; Rossolini, G.; Basso, Andrea; Piantanelli, Anna; Malavolta, Marco; Alimazighi, Zaïa

doi:10.1016/s0047-6374(01)00271-8

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…Fractal analysis can measure variations of complexity in biosystems that evolve with time by following different trajectories. The specific individual genetic-environment interactions determine the senescent phenotype that evolves as 'normal' aging, pathological aging, or successful aging [33,40]. Dealing with mutations of mtDNA as gaps in the nucleotide sequence, fractal lacunarity represents the most suitable tool to differentiate between PD and aging.…”

Section: Discussionmentioning

confidence: 99%

“…The method was developed taking into account the complexity of living beings and fractal properties of many anatomic and physiologic structures, among which is mtDNA [34][35][36][37][38][39]. In particular, the concept that aging can be considered as a "secondary product" of the temporal evolution of a dynamic nonlinear system [40][41][42], governed by the laws of deterministic chaos, can explain the variability observed in the senescent phenotype [33]. In addition, the concept that a complex system with a chaotic behaviour often generates fractal structures [43] highlights the usefulness of fractal analysis as a suitable tool to measure biocomplexity and its changes with aging at both functional and structural levels [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease

Alimazighi

Maponi

Zannotti

et al. 2020

IJMS

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Increasing evidence implicates mitochondrial dysfunction in the etiology of Parkinson’s disease (PD). Mitochondrial DNA (mtDNA) mutations are considered a possible cause and this mechanism might be shared with the aging process and with other age-related neurodegenerative disorders such as Alzheimer’s disease (AD). We have recently proposed a computerized method for mutated mtDNA characterization able to discriminate between AD and aging. The present study deals with mtDNA mutation-based profiling of PD. Peripheral blood mtDNA sequences from late-onset PD patients and age-matched controls were analyzed and compared to the revised Cambridge Reference Sequence (rCRS). The chaos game representation (CGR) method, modified to visualize heteroplasmic mutations, was used to display fractal properties of mtDNA sequences and fractal lacunarity analysis was applied to quantitatively characterize PD based on mtDNA mutations. Parameter β, from the hyperbola model function of our lacunarity method, was statistically different between PD and control groups when comparing mtDNA sequence frames corresponding to GenBank np 5713-9713. Our original method, based on CGR and lacunarity analysis, represents a useful tool to analyze mtDNA mutations. Lacunarity parameter β is able to characterize individual mutation profile of mitochondrial genome and could represent a promising index to discriminate between PD and aging.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease

Alimazighi

Maponi

Zannotti

et al. 2020

IJMS

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“…Furthermore, nonmonotonic age patterns of biomarkers (e.g., body mass index, which may rise and fall over the life course) introduce additional challenges for using biomarkers to measure biological aging (Yashin et al 2013). Other important biomarkers of aging are unknown or cannot be measured (Piantanelli et al 2001). Together, measured and unmeasured biomarkers characterize the biological mechanisms involved in the regulation of aging.…”

Section: Discussionmentioning

confidence: 99%

Aging in the Context of Cohort Evolution and Mortality Selection

Zheng

2014

Demography

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This study examines historical patterns of aging through the perspectives of cohort evolution and mortality selection, where the former emphasizes the correlation across cohorts in the age dependence of mortality rates, and the latter emphasizes cohort change in the acceleration of mortality over the life course. In the analysis of historical cohort mortality data, I find support for both perspectives. The rate of demographic aging, or the rate at which mortality accelerates past age 70, is not fixed across cohorts; rather, it is affected by the extent of mortality selection at young and late ages. This causes later cohorts to have higher rates of demographic aging than earlier cohorts. The rate of biological aging, approximating the rate of the senescence process, significantly declined between the mid- and late-nineteenth century birth cohorts and stabilized afterward. Unlike the rate of demographic aging, the rate of biological aging is not affected by mortality selection earlier in the life course but rather by cross-cohort changes in young-age mortality, which cause lower rates of biological aging in old age among later cohorts. These findings enrich theories of cohort evolution and have implications for the study of limits on the human lifespan and evolution of aging.

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“…The current trends in the survival curves show interesting results: the scale effect (characteristic life) linearly increases over time, indicating ‘living longer’, and the shape effect (stretched exponent) gradually shifts towards rectangularisation, indicating ‘growing older’. Most ageing studies have focused on death (mortality) rate patterns171819202122, but important implications of survival curves may be overlooked without scale and shape analyses of these curves. Our methodology could be useful for performing better tracking of ageing statistics, and it is also possible that this methodology can help identify the causes of current trends in human ageing.…”

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confidence: 99%

Trends in scale and shape of survival curves

Weon

2012

Sci Rep

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The ageing of the population is an issue in wealthy countries worldwide because of increasing costs for health care and welfare. Survival curves taken from demographic life tables may help shed light on the hypotheses that humans are living longer and that human populations are growing older. We describe a methodology that enables us to obtain separate measurements of scale and shape variances in survival curves. Specifically, ‘living longer’ is associated with the scale variance of survival curves, whereas ‘growing older’ is associated with the shape variance. We show how the scale and shape of survival curves have changed over time during recent decades, based on period and cohort female life tables for selected wealthy countries. Our methodology will be useful for performing better tracking of ageing statistics and it is possible that this methodology can help identify the causes of current trends in human ageing.

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Use of mathematical models of survivorship in the study of biomarkers of aging: the role of heterogeneity

Cited by 19 publications

References 38 publications

Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease

Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease

Aging in the Context of Cohort Evolution and Mortality Selection

Trends in scale and shape of survival curves

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