Clinical biomarkers and associations with healthspan and lifespan: Evidence from observational and genetic data

Li, Xia; Ploner, Alexander; Wang, Yunzhang; Zhan, Yiqiang; Pedersen, Nancy L.; Magnusson, Patrik K. E.; Jylhävä, Juulia; Hägg, Sara

doi:10.1016/j.ebiom.2021.103318

Cited by 19 publications

(14 citation statements)

References 40 publications

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“…As disease pathogenesis is often associated with underlying aging characteristics, common clinical serum biomarkers have the potential to assess longevity and healthy aging [ 2 , 3 ]. A test of 12,098 individuals aged 47–94 years indicated that glycemic, lipid, and inflammatory factors were significantly correlated with risks of chronic diseases and death, and biomarkers were predictive of health state and lifespan [ 4 ]. Blood biochemical indices and inflammatory markers were closely associated with disease production and progression.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging

Ren

et al. 2022

BioMed Research International

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Background. The pursuit of health and longevity is the eternal theme of humanity. Guangxi has a remarkable phenomenon of longevity in long-lived regions and ranks with the highest number of longevity villages in China, thus providing a natural advantage for health longevity research. Methods. In this study, we selected 117 natives of a longevity area in Guangxi, covering a large age range (38-118 years old) as subjects to measure peripheral leukocyte telomere length (LTL). Nineteen physical examination indicators and two inflammatory factor levels were measured. Results. Pearson’s analysis revealed a significant negative correlation between age and LTL ( r = − 0.3694 , p = 0.003 ), as well as alanine aminotransferase, albumin, total bilirubin, direct bilirubin, γ-glutamyltransferase, triglycerides, Interleukin-10, and tumor necrosis factor type-α. Systolic blood pressure and blood urea nitrogen were positively correlated with age. In addition, LTL decreased in people aged 38–89 years, and an upward trend was observed in people aged older than 90 years. Conclusions. Longevity individuals have characteristics, such as longer LTL, good hepatic function, and lower triglycerides and inflammation levels.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging

Ren

et al. 2022

BioMed Research International

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“…These include HbA1c, fasting blood glucose, C-reactive protein, triglycerides, high-density lipoprotein cholesterol, ApoA1, and total cholesterol (X. Li et al, 2021). Ultimately, the utility of BA being reduced without a concomitant functional improvement and/or a decreased risk of mortality is questionable.…”

Section: Outstanding Questions and Limitations In The Fieldmentioning

confidence: 99%

“…In addition to estimating BA, it would be helpful to measure classical clinical biomarkers that are known to associate with lifespan and healthspan. These include HbA1c, fasting blood glucose, C‐reactive protein, triglycerides, high‐density lipoprotein cholesterol, ApoA1, and total cholesterol (X. Li et al, 2021). Ultimately, the utility of BA being reduced without a concomitant functional improvement and/or a decreased risk of mortality is questionable.…”

Section: Introductionmentioning

confidence: 99%

Human age reversal: Fact or fiction?

et al. 2022

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Although chronological age correlates with various age‐related diseases and conditions, it does not adequately reflect an individual's functional capacity, well‐being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age‐related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant‐based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non‐interventional studies have connected high‐quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha‐ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.

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“…In this issue of EBioMedicine, Li and colleagues [4] studied the predictive value of commonly used clinical biomarkers of ageing using healthspan and lifespan as outcomes. To this end, they used a large and relatively healthy Swedish cohort for which extensive long-term longitudinal follow-up data on disease diagnosis and mortality was available.…”

mentioning

confidence: 99%

“…Some of the identified omics-based biomarkers have already shown to be better predictors of lifespan than the well-established clinical biomarkers of ageing, especially at higher ages [8À10]. Hence, it will be interesting to apply the methodological framework described in this proof-of-principle study by Li and colleagues [4] to determine the predictive value of these omics-based biomarkers on healthspan (and lifespan). The best (independent) omics-based biomarkers of ageing can subsequently be developed further so they can be incorporated in ongoing clinical studies to amend or replace the commonly used clinical biomarkers with the goal to identify and treat vulnerable individuals before the occurrence of (multi)morbidity.…”

mentioning

confidence: 99%

Targeting multimorbidity: Using healthspan and lifespan to identify biomarkers of ageing that pinpoint shared disease mechanisms

Deelen

2021

EBioMedicine

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Advancing age is the major risk factor for chronic diseases, such as cardiovascular disease, type 2 diabetes, cancer and Alzheimer's disease. The rising number of older individuals is thus resulting in an increased burden on our healthcare system. Instead of tackling each of these age-related diseases one by one, focused approaches aimed at the identification of shared disease mechanisms, that can ideally be targeted using lifestyle and/or pharmacological interventions, could be more productive. But, how do we identify the vulnerable people in the population that are at high risk for developing (multi) morbidity? This is a question that the research community tries to answer by looking for so-called biomarkers of ageing, i.e. markers that are predictive of age-related morbidity and mortality. When they are successfully identified, such markers can be used as surrogate endpoints in clinical trials or intervention studies that are aimed at improving general health.According to the criteria of the American Federation for Aging Research (AFAR) a perfect biomarker of ageing should; (a) outperform chronological age in predicting age-related disease and mortality, (b) be simple (i.e. accurate and reproducible) and inexpensive to test without harming the test subject, and (c) work in both humans and model organisms to make findings directly translatable [1]. Several potential biomarkers of ageing have been proposed [2], including clinical markers involved in physiological (e.g. fasting blood glucose, glycated haemoglobin, and blood lipids) and immune function (e.g. C-reactive protein), but, thus far, none has met all the proposed AFAR criteria.Many of the proposed biomarkers have shown to be associated with the risk of specific age-related diseases, e.g. an imbalance in fasting glucose or blood lipids has been associated with the onset and progression of type 2 diabetes or cardiovascular disease, respectively. However, it is still unclear if these biomarkers are able to predict

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Clinical biomarkers and associations with healthspan and lifespan: Evidence from observational and genetic data

Cited by 19 publications

References 40 publications

[Retracted] Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging

[Retracted] Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging

Human age reversal: Fact or fiction?

Targeting multimorbidity: Using healthspan and lifespan to identify biomarkers of ageing that pinpoint shared disease mechanisms

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