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Abstract
Comparing biomarker profiles measured at similar ages, but earlier in life, among exceptionally long-lived individuals and their shorter-lived peers can improve our understanding of aging processes. This study aimed to (i) describe and compare biomarker profiles at similar ages between 64 and 99 among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between specific biomarker values and the chance of reaching age 100, and (iii) examine to what extent centenarians have homogenous biomarker profiles earlier in life. Participants in the population-based AMORIS cohort with information on blood-based biomarkers measured during 1985–1996 were followed in Swedish register data for up to 35 years. We examined biomarkers of metabolism, inflammation, liver, renal, anemia, and nutritional status using descriptive statistics, logistic regression, and cluster analysis. In total, 1224 participants (84.6% females) lived to their 100th birthday. Higher levels of total cholesterol and iron and lower levels of glucose, creatinine, uric acid, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, lactate dehydrogenase, and total iron-binding capacity were associated with reaching 100 years. Centenarians overall displayed rather homogenous biomarker profiles. Already from age 65 and onwards, centenarians displayed more favorable biomarker values in commonly available biomarkers than individuals dying before age 100. The differences in biomarker values between centenarians and non-centenarians more than one decade prior death suggest that genetic and/or possibly modifiable lifestyle factors reflected in these biomarker levels may play an important role for exceptional longevity.
Introduction
The global number of centenarians—individuals who survive at least to their 100th birthday—has roughly doubled every decade since 1950 and is projected to quintuple between 2022 and 2050 [1, 2]. Exceptional longevity is the result of a complex interplay of several determinants, which is yet poorly understood and includes both genetic predisposition and lifestyle factors [3]. Studying centenarians and exploring differences between them and their shorter-lived peers provides an opportunity to improve our understanding of how aging processes unfold and exceptionally long survival is promoted.
Despite the claim that chance plays an important role in the achievement of exceptional longevity, it has repeatedly been shown that already earlier in life, centenarians are a selected group with fewer disabilities, comorbidities, hospitalizations, and better cognitive function compared to non-centenarians [4,5,6]. While the cited studies focus on specific health outcomes, blood-based biomarkers can provide additional information about health status already before other observable outcomes occur. A Japanese cohort study found that low inflammation defined by cytomegalovirus titer, interleukin-6, tumor necrosis factor-alpha, and C-reactive protein (CRP) was an important predictor for exceptional survival [7]. Improved survival in old age has also been linked to lower creatinine, higher albumin, and several circulating biomarkers (N-terminal pro-B-type natriuretic peptide, interleukin-6, cystatin C, and cholinesterase) [8]. Cross-sectional studies have found centenarians to have lower total cholesterol [9] and insulin tolerance [10] than younger elderly. However, since biomarkers change with age, it is difficult to draw conclusions from cross-sectional studies that compare samples drawn at different ages.
Knowledge of how centenarians’ biomarker profiles differ from those of non-centenarians at comparable ages already earlier in life is scarce. The lack of suitable, large prospective data with long follow-up is one likely reason for this. The Japanese cohort mentioned above included individuals aged 85+ only, and more than half of them were already centenarians at baseline enrollment. Since health selection likely starts even earlier than age 85, it is important to examine potential differences between long-lived individuals and those with average life spans already several years before—or during the process of—health deterioration.
Moreover, several studies have reported that centenarians are not such a homogeneous population as sometimes perceived. An Italian study based on 602 centenarians identified three subgroups with distinct health profiles [11]. It was found that 20% of the centenarians were in good health, 33% had intermediate health status, and 47% were in poor health. A Danish study also detected three distinct subgroups defined by health status: robust, intermediate, and frail centenarians [12]. About half of the Danish centenarians were in the “robust” group. A German study using health insurance data from 1121 centenarians found four distinct comorbidity profiles, and only a small proportion of centenarians had a low morbidity burden [13]. These findings raise the question of whether such heterogeneity in centenarians’ health profiles is already visible earlier in life and, for example, reflected in their biomarker profiles. Uncovering potential heterogeneity in such profiles more than one decade ago may help us understand characteristics of health trajectories associated with exceptional longevity.
The AMORIS (Apolipoprotein MOrtality RISk) cohort offers a unique opportunity to compare biomarkers measured at similar ages but earlier in life between centenarians and their shorter-lived peers. The cohort contains a variety of biomarkers assessed approximately 30 years ago and was linked to several administrative health registers with data until 2020. Using these data, we aim to (i) describe biomarker profiles earlier in life among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between a set of biomarkers and the chance of reaching age 100 with up to 35 years of follow-up, and (iii) investigate differences in biomarker profiles within the centenarian population.
Methods
Data sources and study population
The population-based AMORIS cohort consists of all individuals who underwent clinical laboratory testing at the Central Automation Laboratories, either as part of routine general health checkups or as outpatients referred for laboratory testing, between 1985 and 1996 in Stockholm County, which applies to more than 800,000 individuals. The cohort has been described in detail elsewhere [14, 15]. All laboratory analyses were performed using fully automated procedures on fresh blood samples, employing a consistent and well-documented methodology [14, 15]. Several Swedish registers have been linked to the AMORIS cohort through the unique Swedish personal identification number enabling longitudinal follow-up of the participants until the end of 2020. In this study, the National Patient Register was used to retrieve information on disease diagnoses, the Cause of Death Registry to identify the date of death, and the Total Population Registry to ensure individuals were alive and residing in Sweden. Charlson Comorbidity Index (CCI) was calculated based on hospitalizations recorded in the National Patient Register 10 years prior to the date of the first blood sample [16]. Detailed diagnose codes (ICD 8 and 9) and weighting of specific diagnoses were based on a previous study with publicly available script [16].
Birth cohorts born between 1893 to 1920 were included, enabling follow-up of all participants until age 100. Individuals were 64 to 99 years old at the time of their blood measurement. Individuals who emigrated during the follow-up were excluded (n=247). The final study population consisted of 44,636 participants followed from their first blood measurement until their date of death. Of these, 1224 individuals (2.7%) reached their 100th birthday, comprising the centenarian population. This proportion is very similar to the chance of reaching 100 in the general population of Stockholm in the same time period.
The study was approved by the Stockholm regional ethical review board (reference number 2018/2401-31). The ethical board waived the need for informed consent due to the size of the cohort and the fact that many of the participants had already died.
Biomarker measurement
Twelve blood-based biomarkers related to inflammation and metabolic, liver, and kidney function as well as potential malnutrition and anemia were included, all of which have been associated with aging or mortality in previous studies (supplemental table 1) [8, 17,18,19]. The biomarker related to inflammation was uric acid; total cholesterol (TC) and glucose to metabolic status/function; alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), albumin, gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), and lactate dehydrogenase (LD) to liver function; creatinine to kidney functioning; iron and total iron-binding capacity (TIBC) to anemia; and albumin to nutrition. The first measurement of each biomarker was used. For individuals with missing values on some biomarkers (see supplemental table 2 for more information on missingness), we decided to impute these values since complete case analysis (excluding participants with missing values) can lead to selection bias. Missing values were imputed using multiple imputation. Detailed methods of multiple imputation are explained in the supplemental materials. A comparison of imputed and complete case data is shown in supplemental table 2 and 3. Analyses were additionally run for complete-case data and are shown in the supplemental materials as sensitivity analyses.
Statistical analysis
In the first step, we investigated the distributions of biomarker values between centenarians and non-centenarians by estimating the 10th, 25th, 50th, 75th, and 90th quantiles of the respective distribution. Note that results additionally stratifying non-centenarians by age at death are included in the supplemental materials, as well as results from quantile regressions indicating if the quantiles are statistically different.
In the second step, we investigated the associations between each biomarker and the likelihood of becoming a centenarian. Logistic regression models were fitted separately for each biomarker. In these models, biomarkers were categorized into five groups (very low, low-medium, medium, high-medium, and very high) based on the quintiles of their respective distributions across all individuals. The mid category (Q3) was chosen as the reference. Models were adjusted for age at biomarker measurement in 5-year age groups, sex, and CCI. Effect modification by age or sex was investigated using likelihood ratio tests analyzing the joint null hypothesis of no multiplicative interaction using three age groups 64–75, 75–84, and 84–99 as well as sex [20]. No effect modification by age or sex was found for the associations between any of the biomarkers and the odds of reaching age 100 (all p-values of the likelihood ratio test were >0.05). In a sensitive analysis, we additionally adjusted the logistic regression models for specific morbidities.
In the third step and in order to see if centenarians displayed homogenous biomarker profiles, we conducted cluster analysis using K-median clustering using the Miclust R package (see supplemental materials for further details) [21]. Potential differences in biomarker values between the centenarian clusters and non-centenarians were explored by comparing the respective quantiles of each biomarker distribution among clusters. Note that results from quantile regressions are included in the supplemental materials. Age-stratified analyses (79 years old or less and 80 years old or more) were also conducted as a sensitivity analysis. These results are available in the supplemental materials.
All statistical analyses were conducted using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria).
Results
Of 44,636 participants, 5851 (13.1%) died before their 80th birthday, 21,234 (47.6%) between their 80th and 90th birthdays, 16,327 (36.6%) between their 90th and 100th birthdays, and 1224 (2.7%) became centenarians. The mean age (SD) at first biomarker measurement was 79.6 (7.5) years for centenarians and 76.7 (6.2) years for non-centenarians. Half of the participants were followed for more than 10 years after biomarker assessment and 13% were followed for more than 20 years. The mean follow-up time was 11.0 (SD 7.4) years. Table 1 shows baseline characteristics for centenarians and non-centenarians. The proportion of females was higher in centenarians (84.6%) than in non-centenarians (61.2%). Despite being on average older at first blood measurement, the prevalence of morbidities was lower among individuals becoming centenarians than among non-centenarians. The proportion of participants with a CCI ≥ 2 was 3.7% in centenarians and 13.8% in non-centenarians. Congestive heart failure was the most frequent morbidity with a prevalence of 2.6% in centenarians compared to 8.7% in non-centenarians.
Introduction
The global number of centenarians—individuals who survive at least to their 100th birthday—has roughly doubled every decade since 1950 and is projected to quintuple between 2022 and 2050 [1, 2]. Exceptional longevity is the result of a complex interplay of several determinants, which is yet poorly understood and includes both genetic predisposition and lifestyle factors [3]. Studying centenarians and exploring differences between them and their shorter-lived peers provides an opportunity to improve our understanding of how aging processes unfold and exceptionally long survival is promoted.
Despite the claim that chance plays an important role in the achievement of exceptional longevity, it has repeatedly been shown that already earlier in life, centenarians are a selected group with fewer disabilities, comorbidities, hospitalizations, and better cognitive function compared to non-centenarians [4,5,6]. While the cited studies focus on specific health outcomes, blood-based biomarkers can provide additional information about health status already before other observable outcomes occur. A Japanese cohort study found that low inflammation defined by cytomegalovirus titer, interleukin-6, tumor necrosis factor-alpha, and C-reactive protein (CRP) was an important predictor for exceptional survival [7]. Improved survival in old age has also been linked to lower creatinine, higher albumin, and several circulating biomarkers (N-terminal pro-B-type natriuretic peptide, interleukin-6, cystatin C, and cholinesterase) [8]. Cross-sectional studies have found centenarians to have lower total cholesterol [9] and insulin tolerance [10] than younger elderly. However, since biomarkers change with age, it is difficult to draw conclusions from cross-sectional studies that compare samples drawn at different ages.
Knowledge of how centenarians’ biomarker profiles differ from those of non-centenarians at comparable ages already earlier in life is scarce. The lack of suitable, large prospective data with long follow-up is one likely reason for this. The Japanese cohort mentioned above included individuals aged 85+ only, and more than half of them were already centenarians at baseline enrollment. Since health selection likely starts even earlier than age 85, it is important to examine potential differences between long-lived individuals and those with average life spans already several years before—or during the process of—health deterioration.
Moreover, several studies have reported that centenarians are not such a homogeneous population as sometimes perceived. An Italian study based on 602 centenarians identified three subgroups with distinct health profiles [11]. It was found that 20% of the centenarians were in good health, 33% had intermediate health status, and 47% were in poor health. A Danish study also detected three distinct subgroups defined by health status: robust, intermediate, and frail centenarians [12]. About half of the Danish centenarians were in the “robust” group. A German study using health insurance data from 1121 centenarians found four distinct comorbidity profiles, and only a small proportion of centenarians had a low morbidity burden [13]. These findings raise the question of whether such heterogeneity in centenarians’ health profiles is already visible earlier in life and, for example, reflected in their biomarker profiles. Uncovering potential heterogeneity in such profiles more than one decade ago may help us understand characteristics of health trajectories associated with exceptional longevity.
The AMORIS (Apolipoprotein MOrtality RISk) cohort offers a unique opportunity to compare biomarkers measured at similar ages but earlier in life between centenarians and their shorter-lived peers. The cohort contains a variety of biomarkers assessed approximately 30 years ago and was linked to several administrative health registers with data until 2020. Using these data, we aim to (i) describe biomarker profiles earlier in life among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between a set of biomarkers and the chance of reaching age 100 with up to 35 years of follow-up, and (iii) investigate differences in biomarker profiles within the centenarian population.
Methods
Data sources and study population
The population-based AMORIS cohort consists of all individuals who underwent clinical laboratory testing at the Central Automation Laboratories, either as part of routine general health checkups or as outpatients referred for laboratory testing, between 1985 and 1996 in Stockholm County, which applies to more than 800,000 individuals. The cohort has been described in detail elsewhere [14, 15]. All laboratory analyses were performed using fully automated procedures on fresh blood samples, employing a consistent and well-documented methodology [14, 15]. Several Swedish registers have been linked to the AMORIS cohort through the unique Swedish personal identification number enabling longitudinal follow-up of the participants until the end of 2020. In this study, the National Patient Register was used to retrieve information on disease diagnoses, the Cause of Death Registry to identify the date of death, and the Total Population Registry to ensure individuals were alive and residing in Sweden. Charlson Comorbidity Index (CCI) was calculated based on hospitalizations recorded in the National Patient Register 10 years prior to the date of the first blood sample [16]. Detailed diagnose codes (ICD 8 and 9) and weighting of specific diagnoses were based on a previous study with publicly available script [16].
Birth cohorts born between 1893 to 1920 were included, enabling follow-up of all participants until age 100. Individuals were 64 to 99 years old at the time of their blood measurement. Individuals who emigrated during the follow-up were excluded (n=247). The final study population consisted of 44,636 participants followed from their first blood measurement until their date of death. Of these, 1224 individuals (2.7%) reached their 100th birthday, comprising the centenarian population. This proportion is very similar to the chance of reaching 100 in the general population of Stockholm in the same time period.
The study was approved by the Stockholm regional ethical review board (reference number 2018/2401-31). The ethical board waived the need for informed consent due to the size of the cohort and the fact that many of the participants had already died.
Biomarker measurement
Twelve blood-based biomarkers related to inflammation and metabolic, liver, and kidney function as well as potential malnutrition and anemia were included, all of which have been associated with aging or mortality in previous studies (supplemental table 1) [8, 17,18,19]. The biomarker related to inflammation was uric acid; total cholesterol (TC) and glucose to metabolic status/function; alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), albumin, gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), and lactate dehydrogenase (LD) to liver function; creatinine to kidney functioning; iron and total iron-binding capacity (TIBC) to anemia; and albumin to nutrition. The first measurement of each biomarker was used. For individuals with missing values on some biomarkers (see supplemental table 2 for more information on missingness), we decided to impute these values since complete case analysis (excluding participants with missing values) can lead to selection bias. Missing values were imputed using multiple imputation. Detailed methods of multiple imputation are explained in the supplemental materials. A comparison of imputed and complete case data is shown in supplemental table 2 and 3. Analyses were additionally run for complete-case data and are shown in the supplemental materials as sensitivity analyses.
Statistical analysis
In the first step, we investigated the distributions of biomarker values between centenarians and non-centenarians by estimating the 10th, 25th, 50th, 75th, and 90th quantiles of the respective distribution. Note that results additionally stratifying non-centenarians by age at death are included in the supplemental materials, as well as results from quantile regressions indicating if the quantiles are statistically different.
In the second step, we investigated the associations between each biomarker and the likelihood of becoming a centenarian. Logistic regression models were fitted separately for each biomarker. In these models, biomarkers were categorized into five groups (very low, low-medium, medium, high-medium, and very high) based on the quintiles of their respective distributions across all individuals. The mid category (Q3) was chosen as the reference. Models were adjusted for age at biomarker measurement in 5-year age groups, sex, and CCI. Effect modification by age or sex was investigated using likelihood ratio tests analyzing the joint null hypothesis of no multiplicative interaction using three age groups 64–75, 75–84, and 84–99 as well as sex [20]. No effect modification by age or sex was found for the associations between any of the biomarkers and the odds of reaching age 100 (all p-values of the likelihood ratio test were >0.05). In a sensitive analysis, we additionally adjusted the logistic regression models for specific morbidities.
In the third step and in order to see if centenarians displayed homogenous biomarker profiles, we conducted cluster analysis using K-median clustering using the Miclust R package (see supplemental materials for further details) [21]. Potential differences in biomarker values between the centenarian clusters and non-centenarians were explored by comparing the respective quantiles of each biomarker distribution among clusters. Note that results from quantile regressions are included in the supplemental materials. Age-stratified analyses (79 years old or less and 80 years old or more) were also conducted as a sensitivity analysis. These results are available in the supplemental materials.
All statistical analyses were conducted using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria).
Results
Of 44,636 participants, 5851 (13.1%) died before their 80th birthday, 21,234 (47.6%) between their 80th and 90th birthdays, 16,327 (36.6%) between their 90th and 100th birthdays, and 1224 (2.7%) became centenarians. The mean age (SD) at first biomarker measurement was 79.6 (7.5) years for centenarians and 76.7 (6.2) years for non-centenarians. Half of the participants were followed for more than 10 years after biomarker assessment and 13% were followed for more than 20 years. The mean follow-up time was 11.0 (SD 7.4) years. Table 1 shows baseline characteristics for centenarians and non-centenarians. The proportion of females was higher in centenarians (84.6%) than in non-centenarians (61.2%). Despite being on average older at first blood measurement, the prevalence of morbidities was lower among individuals becoming centenarians than among non-centenarians. The proportion of participants with a CCI ≥ 2 was 3.7% in centenarians and 13.8% in non-centenarians. Congestive heart failure was the most frequent morbidity with a prevalence of 2.6% in centenarians compared to 8.7% in non-centenarians.
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Blood biomarker profiles and exceptional longevity [re: Mod vege]
(старо куче)
