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Quantitative measures of healthy aging and biological age.

Kim S, Jazwinski SM - Healthy Aging Res (2015)

Bottom Line: To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging.Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians.Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

View Article: PubMed Central - PubMed

Affiliation: Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA.

ABSTRACT

Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the 'frailty index', which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

No MeSH data available.


Related in: MedlinePlus

Age-dependent variation of FI34 and RMR. The “resid.FI34” on the y axis represents residuals (the differences between the observed FI34 scores and the predicted FI34 scores) from a linear regression of FI34 on age with adjustments for sex, fat mass and fat-free mass. Likewise, “resid.RMR” on the x axis represents residuals (the differences between the observed RMR scores and the predicted RMR scores) from a linear regression of RMR on age with adjustments for sex, fat mass and fat-free mass. A, 28 subjects aged 22–34 (“young”); B, 42 subjects aged 60–74 (“middle”); C, 67 nonagenarians. FI34 (y axis) becomes more variable (spread) in older age groups (p=5.8·10−7 for “young” vs. “middle”; p=0.019 for “middle” vs. nonagenarian; p=7.2·10−11 for “young” vs. nonagenarian, according to an F test to compare the variances). On the other hand, RMR (x axis) does not exhibit much change over the three age groups (p ≫ 0.05). Note that the red dotted line in each plot represents the correlation between resid.FI34 and resid.RMR. This “residual” correlation is significant only in the oldest-old group as indicated.
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Figure 5: Age-dependent variation of FI34 and RMR. The “resid.FI34” on the y axis represents residuals (the differences between the observed FI34 scores and the predicted FI34 scores) from a linear regression of FI34 on age with adjustments for sex, fat mass and fat-free mass. Likewise, “resid.RMR” on the x axis represents residuals (the differences between the observed RMR scores and the predicted RMR scores) from a linear regression of RMR on age with adjustments for sex, fat mass and fat-free mass. A, 28 subjects aged 22–34 (“young”); B, 42 subjects aged 60–74 (“middle”); C, 67 nonagenarians. FI34 (y axis) becomes more variable (spread) in older age groups (p=5.8·10−7 for “young” vs. “middle”; p=0.019 for “middle” vs. nonagenarian; p=7.2·10−11 for “young” vs. nonagenarian, according to an F test to compare the variances). On the other hand, RMR (x axis) does not exhibit much change over the three age groups (p ≫ 0.05). Note that the red dotted line in each plot represents the correlation between resid.FI34 and resid.RMR. This “residual” correlation is significant only in the oldest-old group as indicated.

Mentions: It is noteworthy that FI34 becomes more variable in older age groups, as shown previously [35], and the individual variability of FI34 is positively correlated with the individual variability of RMR (Figure 5). One interpretation of these results is that those among the oldest-old whose frailty markedly surpasses that of their peers have corresponding increases in RMR. This is consistent with our conclusion that elevated levels of RMR are linked to declining health in the oldest-old [46]. On the other hand, increased variability with age was not as obvious for RMR (Figure 5). According to Johannsen et al. [55], mean values of RMR variability declined in older age groups when compared to the 20–34 year-old group. An increase in mean RMR variability was observed from the middle-age group (60–74) to the oldest-old group (≥90), but it did not reach statistical significance. It should be noted that these were all cross-sectional findings, and longitudinal assessment may be more informative.


Quantitative measures of healthy aging and biological age.

Kim S, Jazwinski SM - Healthy Aging Res (2015)

Age-dependent variation of FI34 and RMR. The “resid.FI34” on the y axis represents residuals (the differences between the observed FI34 scores and the predicted FI34 scores) from a linear regression of FI34 on age with adjustments for sex, fat mass and fat-free mass. Likewise, “resid.RMR” on the x axis represents residuals (the differences between the observed RMR scores and the predicted RMR scores) from a linear regression of RMR on age with adjustments for sex, fat mass and fat-free mass. A, 28 subjects aged 22–34 (“young”); B, 42 subjects aged 60–74 (“middle”); C, 67 nonagenarians. FI34 (y axis) becomes more variable (spread) in older age groups (p=5.8·10−7 for “young” vs. “middle”; p=0.019 for “middle” vs. nonagenarian; p=7.2·10−11 for “young” vs. nonagenarian, according to an F test to compare the variances). On the other hand, RMR (x axis) does not exhibit much change over the three age groups (p ≫ 0.05). Note that the red dotted line in each plot represents the correlation between resid.FI34 and resid.RMR. This “residual” correlation is significant only in the oldest-old group as indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440677&req=5

Figure 5: Age-dependent variation of FI34 and RMR. The “resid.FI34” on the y axis represents residuals (the differences between the observed FI34 scores and the predicted FI34 scores) from a linear regression of FI34 on age with adjustments for sex, fat mass and fat-free mass. Likewise, “resid.RMR” on the x axis represents residuals (the differences between the observed RMR scores and the predicted RMR scores) from a linear regression of RMR on age with adjustments for sex, fat mass and fat-free mass. A, 28 subjects aged 22–34 (“young”); B, 42 subjects aged 60–74 (“middle”); C, 67 nonagenarians. FI34 (y axis) becomes more variable (spread) in older age groups (p=5.8·10−7 for “young” vs. “middle”; p=0.019 for “middle” vs. nonagenarian; p=7.2·10−11 for “young” vs. nonagenarian, according to an F test to compare the variances). On the other hand, RMR (x axis) does not exhibit much change over the three age groups (p ≫ 0.05). Note that the red dotted line in each plot represents the correlation between resid.FI34 and resid.RMR. This “residual” correlation is significant only in the oldest-old group as indicated.
Mentions: It is noteworthy that FI34 becomes more variable in older age groups, as shown previously [35], and the individual variability of FI34 is positively correlated with the individual variability of RMR (Figure 5). One interpretation of these results is that those among the oldest-old whose frailty markedly surpasses that of their peers have corresponding increases in RMR. This is consistent with our conclusion that elevated levels of RMR are linked to declining health in the oldest-old [46]. On the other hand, increased variability with age was not as obvious for RMR (Figure 5). According to Johannsen et al. [55], mean values of RMR variability declined in older age groups when compared to the 20–34 year-old group. An increase in mean RMR variability was observed from the middle-age group (60–74) to the oldest-old group (≥90), but it did not reach statistical significance. It should be noted that these were all cross-sectional findings, and longitudinal assessment may be more informative.

Bottom Line: To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging.Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians.Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

View Article: PubMed Central - PubMed

Affiliation: Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA.

ABSTRACT

Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the 'frailty index', which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

No MeSH data available.


Related in: MedlinePlus