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| 001 | 178822 | ||
| 005 | 20240229143557.0 | ||
| 024 | 7 | _ | |a 10.1093/ageing/afab271 |2 doi |
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| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Srour, Bernard |0 P:(DE-He78)0644671d309776d45e0fc705d1156cac |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Serum markers of biological ageing provide long-term prediction of life expectancy-a longitudinal analysis in middle-aged and older German adults. |
| 260 | _ | _ | |a Oxford |c 2022 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1644839234_25508 |2 PUB:(DE-HGF) |
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| 500 | _ | _ | |a #EA:C020#LA:C020# |
| 520 | _ | _ | |a lifestyle behaviours and chronic co-morbidities are leading risk factors for premature mortality and collectively predict wide variability in individual life expectancy (LE). We investigated whether a pre-selected panel of five serum markers of biological ageing could improve predicting the long-term mortality risk and LE in middle-aged and older women and men.we conducted a case-cohort study (n = 5,789 among which there were 2,571 deaths) within the European Prospective Investigation into Cancer-Heidelberg cohort, a population cohort of middle-aged and older individuals, followed over a median duration of 18 years. Gompertz models were used to compute multi-adjusted associations of growth differentiation factor-15, N-terminal pro-brain natriuretic peptide, glycated haemoglobin A1c, C-reactive protein and cystatin-C with mortality risk. Areas under estimated Gompertz survival curves were used to estimate the LE of individuals using a model with lifestyle-related risk factors only (smoking history, body mass index, waist circumference, alcohol, physical inactivity, diabetes and hypertension), or with lifestyle factors plus the ageing-related markers.a model including only lifestyle-related factors predicted a LE difference of 16.8 [95% confidence interval: 15.9; 19.1] years in men and 9.87 [9.20; 13.1] years in women aged ≥60 years by comparing individuals in the highest versus the lowest quintiles of estimated mortality risk. Including the ageing-related biomarkers in the model increased these differences up to 22.7 [22.3; 26.9] years in men and 14.00 [12.9; 18.2] years in women.serum markers of ageing are potentially strong predictors for long-term mortality risk in a general population sample of older and middle-aged individuals and may help to identify individuals at higher risk of premature death, who could benefit from interventions to prevent further ageing-related health declines. |
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| 650 | _ | 7 | |a ageing biomarkers |2 Other |
| 650 | _ | 7 | |a biological ageing |2 Other |
| 650 | _ | 7 | |a life expectancy |2 Other |
| 650 | _ | 7 | |a lifestyle factors |2 Other |
| 650 | _ | 7 | |a prevention older people |2 Other |
| 700 | 1 | _ | |a Hynes, Lucas Cory |0 P:(DE-He78)f55fe2dee9fdef0b4db17187de23a9bf |b 1 |u dkfz |
| 700 | 1 | _ | |a Johnson, Theron |0 P:(DE-He78)79ab945544e5bc017a2317b6146ed3aa |b 2 |u dkfz |
| 700 | 1 | _ | |a Kühn, Tilman |0 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe |b 3 |
| 700 | 1 | _ | |a Katzke, Verena A |0 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4 |b 4 |u dkfz |
| 700 | 1 | _ | |a Kaaks, Rudolf |0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |b 5 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1093/ageing/afab271 |g Vol. 51, no. 2, p. afab271 |0 PERI:(DE-600)2065766-3 |n 2 |p afab271 |t Age & ageing |v 51 |y 2022 |x 0002-0729 |
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