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Age-related frailty and its association with biological markers of ageing.

Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, Kirkwood TB - BMC Med (2015)

Bottom Line: Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype.Higher values of each FI were associated with higher mortality risk.The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Dalhousie University, Halifax, NS, B3H 2E1, Canada. arnold.mitnitski@dal.ca.

ABSTRACT

Background: The relationship between age-related frailty and the underlying processes that drive changes in health is currently unclear. Considered individually, most blood biomarkers show only weak relationships with frailty and ageing. Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype.

Methods: We analyzed baseline data and up to 7-year mortality in the Newcastle 85+ Study (n = 845; mean age 85.5). The FI-B combined 40 biomarkers of cellular ageing, inflammation, haematology, and immunosenescence. The Kaplan-Meier estimator was used to stratify participants into FI-B risk strata. Stability of the risk estimates for the FI-B was assessed using iterative, random subsampling of the 40 FI-B items. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves.

Results: The mean FI-B was 0.35 (SD, 0.08), higher than the mean FI-CD (0.22; SD, 0.12); no participant had an FI-B score <0.12. Higher values of each FI were associated with higher mortality risk. In a sex-adjusted model, each one percent increase in the FI-B increased the hazard ratio by 5.4 % (HR, 1.05; CI, 1.04-1.06). The FI-B was more powerful for mortality prediction than any individual biomarker and was robust to biomarker substitution. The ROC analysis showed moderate discriminative ability for 7-year mortality (AUC for FI-CD = 0.71 and AUC for FI-B = 0.66). No individual biomarker's AUC exceeded 0.61. The AUC for combined FI-CD/FI-B was 0.75.

Conclusions: Many biological processes are implicated in ageing. The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death. In the future, blood biomarkers may indicate the nature of the underlying causal deficits leading to age-related frailty, thereby helping to expose targets for early preventative interventions.

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a Kaplan-Meier survival curves of the FI-B for the four risk strata defined by the following cut points: blue <0.25 (low risk, n = 31), red 0.25–0.38 (low-intermediate risk, n = 217), green 0.38–0.49 (intermediate-high risk, n = 154), pink ≥0.50 (highest risk, n = 32). b Kaplan-Meier survival curves of the FI-B calculated from 30 biomarkers randomly chosen from the total 40 and stratifying them in four groups with the same cut points as in A. The sampling was repeated 300 times
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Fig2: a Kaplan-Meier survival curves of the FI-B for the four risk strata defined by the following cut points: blue <0.25 (low risk, n = 31), red 0.25–0.38 (low-intermediate risk, n = 217), green 0.38–0.49 (intermediate-high risk, n = 154), pink ≥0.50 (highest risk, n = 32). b Kaplan-Meier survival curves of the FI-B calculated from 30 biomarkers randomly chosen from the total 40 and stratifying them in four groups with the same cut points as in A. The sampling was repeated 300 times

Mentions: The FI-B was strongly associated with 7-year mortality. In the Cox proportional hazards model adjusted for sex (female sex is protective; HR, 0.73; CI, 0.60–0.88), each one percent increase in FI-B was associated with 5.4 % increase in the hazards ratio (HR, 1.05; CI, 1.04–1.07). Likewise, the Kaplan-Meier survival curves showed the effect of increasing frailty across the four FI-B strata (Fig. 2a). This pattern was robust: random sub-samplings of 30 biomarkers out of the 40 available also showed good separation between the four strata, with only little overlap between neighbouring groups (Fig. 2b). With decreasing numbers of biomarkers included, the overlap between the groups greatly increased (Additional file 1: Figure S2). Notably, amongst those who clinically were not frail (FI-CD scores in the lowest quartile) having an FI-B higher than median (0.33) was associated with much higher mortality (Fig. 3).Fig. 2


Age-related frailty and its association with biological markers of ageing.

Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, Kirkwood TB - BMC Med (2015)

a Kaplan-Meier survival curves of the FI-B for the four risk strata defined by the following cut points: blue <0.25 (low risk, n = 31), red 0.25–0.38 (low-intermediate risk, n = 217), green 0.38–0.49 (intermediate-high risk, n = 154), pink ≥0.50 (highest risk, n = 32). b Kaplan-Meier survival curves of the FI-B calculated from 30 biomarkers randomly chosen from the total 40 and stratifying them in four groups with the same cut points as in A. The sampling was repeated 300 times
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4499935&req=5

Fig2: a Kaplan-Meier survival curves of the FI-B for the four risk strata defined by the following cut points: blue <0.25 (low risk, n = 31), red 0.25–0.38 (low-intermediate risk, n = 217), green 0.38–0.49 (intermediate-high risk, n = 154), pink ≥0.50 (highest risk, n = 32). b Kaplan-Meier survival curves of the FI-B calculated from 30 biomarkers randomly chosen from the total 40 and stratifying them in four groups with the same cut points as in A. The sampling was repeated 300 times
Mentions: The FI-B was strongly associated with 7-year mortality. In the Cox proportional hazards model adjusted for sex (female sex is protective; HR, 0.73; CI, 0.60–0.88), each one percent increase in FI-B was associated with 5.4 % increase in the hazards ratio (HR, 1.05; CI, 1.04–1.07). Likewise, the Kaplan-Meier survival curves showed the effect of increasing frailty across the four FI-B strata (Fig. 2a). This pattern was robust: random sub-samplings of 30 biomarkers out of the 40 available also showed good separation between the four strata, with only little overlap between neighbouring groups (Fig. 2b). With decreasing numbers of biomarkers included, the overlap between the groups greatly increased (Additional file 1: Figure S2). Notably, amongst those who clinically were not frail (FI-CD scores in the lowest quartile) having an FI-B higher than median (0.33) was associated with much higher mortality (Fig. 3).Fig. 2

Bottom Line: Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype.Higher values of each FI were associated with higher mortality risk.The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Dalhousie University, Halifax, NS, B3H 2E1, Canada. arnold.mitnitski@dal.ca.

ABSTRACT

Background: The relationship between age-related frailty and the underlying processes that drive changes in health is currently unclear. Considered individually, most blood biomarkers show only weak relationships with frailty and ageing. Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype.

Methods: We analyzed baseline data and up to 7-year mortality in the Newcastle 85+ Study (n = 845; mean age 85.5). The FI-B combined 40 biomarkers of cellular ageing, inflammation, haematology, and immunosenescence. The Kaplan-Meier estimator was used to stratify participants into FI-B risk strata. Stability of the risk estimates for the FI-B was assessed using iterative, random subsampling of the 40 FI-B items. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves.

Results: The mean FI-B was 0.35 (SD, 0.08), higher than the mean FI-CD (0.22; SD, 0.12); no participant had an FI-B score <0.12. Higher values of each FI were associated with higher mortality risk. In a sex-adjusted model, each one percent increase in the FI-B increased the hazard ratio by 5.4 % (HR, 1.05; CI, 1.04-1.06). The FI-B was more powerful for mortality prediction than any individual biomarker and was robust to biomarker substitution. The ROC analysis showed moderate discriminative ability for 7-year mortality (AUC for FI-CD = 0.71 and AUC for FI-B = 0.66). No individual biomarker's AUC exceeded 0.61. The AUC for combined FI-CD/FI-B was 0.75.

Conclusions: Many biological processes are implicated in ageing. The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death. In the future, blood biomarkers may indicate the nature of the underlying causal deficits leading to age-related frailty, thereby helping to expose targets for early preventative interventions.

Show MeSH
Related in: MedlinePlus