Aging biomarkers and the measurement of health and risk

Green, Sara; Hillersdal, Line

doi:10.1007/s40656-021-00367-w

Cited by 25 publications

(37 citation statements)

References 67 publications

(74 reference statements)

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“…In this retrospective study, the sample included consisted of the following: Fifty undernourished children (defined as having a body mass index of BMI ≤ 2, standard deviation score, SDS, according to World Health Organization [ 8 ]) with severe NI (Level 5m according to Gross Motor Function Classification System [ 6 ]; cerebral palsy was apparent in 38% of subjects; 36% suffered from epileptic encephlopathy; a neurological disability in dysmorphic syndrome was clear in 26%). All NI children were bedridden and lived in sheltered communities.…”

Section: Methodsmentioning

confidence: 99%

“…This is important when referring to a specific subset of individuals with high cumulative biological risk, such as children with neurological impairments (NI) and children with obesity [ 3 , 4 ]. In these risk groups, cellular senescence may occur in response to various forms of cellular stress, and it is characterized by a pro-inflammatory secretory phenotype [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Metabolically Unhealthy Phenotype: A Key Factor in Determining “Pediatric” Frailty

Calcaterra

Cena

Ruggieri³

et al. 2021

Pediatric Reports

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Frailty (FI) and metabolic syndrome (MS) are each associated with adverse health outcomes. A relationship between FI and MS has previously been described in adults. We considered the prevalence of a metabolically unhealthy phenotype (MUP) in malnourished children with neurological impairment and in subjects with obesity in comparison to a group of elderly individuals at risk of FI, and we did so in order to define the potential similarities that may underline the risk of FI in specific children. We considered 50 undernourished (defined as having a body mass index of BMI ≤ 2, standard deviation score, SDS, according to World Health Organization) disabled children; 50 children with obesity (BMI ≥ 2 SDS); 50 children who were a normal weight (−1 SDS ≤ BMI ≤ +1 SDS); 21 patients who were >75 years old. MUP was defined as the presence of at least one of the following risk factors: hypertension, hyperglycemia or diabetes, hypercholesterolemia, and hypertriglyceridemia. In children with a disability and obesity, a higher prevalence (p < 0.001) and risk (disability OR 54.88, obesity OR 13.37) of MUP was noted compared to children of a normal weight. Compared to elderly patients, the prevalence of MUP did not differ in disabled children. On the contrary, MUP was lower in children with obesity (p < 0.001) and in pediatric subjects of a normal weight (p < 0.01). MS might play a key role in “pediatric” frailty. The extremities of the aging process and malnutrition are likely key factors in the development of FI. A multidisciplinary approach to FI may represent an important milestone for pediatric care.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metabolically Unhealthy Phenotype: A Key Factor in Determining “Pediatric” Frailty

Calcaterra

Cena

Ruggieri³

et al. 2021

Pediatric Reports

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“…A possible alternative approach to age classification is to use a more direct measure, or biomarker, of biological age that does not rely on chronological age (Green and Hillersdal, 2021). Magda Hamczyk and colleagues suggest using such a measure for the medical purpose of preventing cardiovascular disease (Hamczyk et al, 2020).…”

Section: Revising Our Classificatory Practicesmentioning

confidence: 99%

What’s my age again? Age categories as interactive kinds

Maung

2021

HPLS

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This paper addresses a philosophical problem concerning the ontological status of age classification. For various purposes, people are commonly classified into categories such as “young adulthood”, “middle adulthood”, and “older adulthood”, which are defined chronologically. These age categories prima facie seem to qualify as natural kinds under a homeostatic property cluster account of natural kindhood, insofar as they capture certain biological, psychological, and social properties of people that tend to cluster together due to causal processes. However, this is challenged by the observation that age categories are historically unstable. The properties that age categories are supposed to capture are affected by healthcare and cultural developments, such that people are staying biologically, psychologically, and socially young for longer. Furthermore, the act of classifying people into age categories can bring about changes in their behaviors, which in turn alter the biological, psychological, and social properties that the categories are supposed to capture. Accordingly, I propose that age categories are best understood as interactive kinds that are influenced in dynamic ways by looping effects. I consider some implications of these looping effects for our classificatory practices concerning age, including how different disciplines may need to review the ways they define and use age categories in their inductive inferences.

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“…In their article "Ageing Biomarkers and the Measurement of Health and Risk," Sara Green and Line Hillersdal skilfully blend philosophical analysis and ethnographic fieldwork to explore both challenges and opportunities of measuring and managing ageing through bodily signs that are not straightforwardly linked to symptomatic disease (Green & Hillersdal, 2021). More specifically, this paper documents how the aim to measure and quantify ageing, more precisely, as risk is confronted with both experimental and conceptual challenges.…”

Section: The Collectionmentioning

confidence: 99%