Maintaining physiological state for exceptional survival: What is the normal level of blood glucose and does it change with age?

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study

Studying relationships between longitudinal changes in omics variables and risks of events requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study focusing on understudied associations of longitudinal changes in lysophosphatidylcholines (LPC) with mortality and aging-related outcomes (23 LPC species, 5,790 measurements of each in 4,011 participants, 1,431 of whom died during follow-up). JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest effect size observed for LPC 15:0/0:0 (hazard ratio: 0.715, 95% CI (0.649, 0.788)). SPM applications to LPC 15:0/0:0 revealed how the association found in JM reflects underlying aging-related processes: decline in robustness to deviations from optimal LPC levels, better ability of males' organisms to return to equilibrium LPC levels (which are higher in females), and increasing gaps between the optimum and equilibrium levels leading to increased mortality risks with age. Our results support LPC as a biomarker of aging and related decline in robustness/resilience, and call for further exploration of factors underlying age-dynamics of LPC in relation to mortality and diseases.

Fasting blood glucose as a predictor of mortality: Lost in translation

Aging leads to profound changes in glucose homeostasis, weight, and adiposity, which are considered good predictors of health and survival in humans. Direct evidence that these age-associated metabolic alterations are recapitulated in animal models is lacking, impeding progress to develop and test interventions that delay the onset of metabolic dysfunction and promote healthy aging and longevity. We compared longitudinal trajectories, rates of change, and mortality risks of fasting blood glucose, body weight, and fat mass in mice, nonhuman primates, and humans throughout their lifespans and found similar trajectories of body weight and fat in the three species. In contrast, fasting blood glucose decreased late in life in mice but increased over the lifespan of nonhuman primates and humans. Higher glucose was associated with lower mortality in mice but higher mortality in nonhuman primates and humans, providing a cautionary tale for translating age-associated metabolic changes from mice to humans.

Eight sessions of endurance training decrease fasting glucose and improve glucose tolerance in middle-aged overweight males

Exercise improves metabolic regulation and reduces the risk of developing type 2 diabetes and other metabolic diseases. The recommendations for exercise are rather general and the health benefits of controlled training studies are important to make better recommendations. In the present study, we report that eight endurance training sessions over 3 weeks reduced fasting glucose, and improved glucose tolerance and plasma lipids in sedentary middle-aged males (44-64 years) with overweight or obesity (BMI: 27-38). The decrease in fasting glucose was substantial (from 5.3 ± 0.3 to 4.8 ± 0.2 mM; p < .001). The training sessions consisted of 60-min indoor-cycling at ∼83% of peak heart rate divided in four blocks of 15 min cycling, with 2-min rest between blocks. Maximal oxygen uptake did not increase (38.8 ± 1.8 vs. 39.0 ± 1.6 ml kg^-1 min^-1). In conclusion, 3-weekly sessions of moderate-/high-intensity endurance training can be recommended for untrained males with overweight or obesity to improve glucose homeostasis.

Oscillations of C-peptide in the euglycemic clamp and their effect on the pharmacodynamic assessment of insulin preparations

C-peptide should be continuously suppressed. However, increased postdosing C-peptide is not an uncommon phenomenon in euglycemic clamp studies involving healthy participants. This study aimed to determine the extent to which the postdosing C-peptide increases from the baseline that could affect the accuracy of glucodynamics in euglycemic clamp studies involving healthy subjects. First, 10 healthy males underwent a 10-h euglycemic clamp without exogenous insulin administration to obtain a reference interval (RI) for the ratio of C-peptide after 0 min (CP_t ) to baseline C-peptide (CP₀ ). Then, the data from a pharmacokinetic and pharmacodynamic study of insulin aspart (IAsp) were analyzed, and 70 eligible clamps were grouped by CP_t /CP₀ : group A ([CP_t /CP₀ ]_max   > upper limit of RI), group B (1<[CP_t /CP₀ ]_max  ≤ upper limit of RI), and group C ([CP_t /CP₀ ]_max  ≤ 1). The differences in basal and clamped blood glucose, CP_t /CP₀ , and the pharmacokinetics and pharmacodynamics of IAsp were compared, and the relationship between elevated CP_t and the accuracy of pharmacodynamics was analyzed. The RI of CP_t /CP₀ was 22.7%-152.1%; 1.5 × baseline might be a ceiling for the increase in CP_t under stable conditions. The maximum glucose infusion rate (GIR) in group A tended to be higher than that in group B or C (P_{for trend}  = 0.033). The AUC_GIR,0-10h in groups A, B, and C was 1983 ± 650,1682 ± 454, and 1479 ± 440 mg/kg (P = 0.047), respectively, under comparable IAsp exposure. No intergroup difference was detected in clamped glucose, IAsp dose, or body mass index. In conclusion, postdosing C-peptide over 1.5× baseline indicates insufficient inhibition of endogenous insulin secretion, which could compromise the pharmacodynamics of insulin preparations.

"Physiological Dysregulation" as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease

Recently suggested novel implementation of the statistical distance measure (DM) for evaluating "physiological dysregulation" (PD) in aging individuals (based on measuring deviations of multiple biomarkers from baseline or normal physiological states) allows reducing high-dimensional biomarker space into a single PD estimate. Here we constructed DM using biomarker profiles from FRAMCOHORT (Framingham Heart Study) and CHS (Cardiovascular Health Study) Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center, and estimated effect of PD on total survival, onset of unhealthy life (proxy for "robustness") and survival following the onset of unhealthy life (proxy for "resilience"). We investigated relationships between PD and declines in stress resistance and adaptive capacity not directly observed in data. PD was more strongly associated with the onset of unhealthy life than with survival after disease suggesting that declines in robustness and resilience with age may have overlapping as well as distinct mechanisms. We conclude that multiple deviations of physiological markers from their normal states (reflected in higher PD) may contribute to increased vulnerability to many diseases and precede their clinical manifestation. This supports potential use of PD in health care as a preclinical indicator of transition from healthy to unhealthy state.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

Heterogeneity of Human Aging and Its Assessment

Understanding the heterogeneity in health of older adults is a compelling question in the biology of aging. We analyzed the performance of five measures of health heterogeneity, judging them by their ability to predict mortality. Using clinical and biomarker data on 1,013 participants of the Canadian Study of Health and Aging who were followed for up to 6 years, we calculated two indices of biological age using the Klemera and Doubal method, which controversially includes using chronological age as a "biomarker," and three frailty indices (FIs) that do not include chronological age: a standard clinical FI, an FI from standard laboratory blood tests and blood pressure, and their combination (FI-combined). Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under the receiver-operating characteristic curves. All five measures showed moderate performance that was improved by combining measures to evaluate larger numbers of items. The greatest addition in explanatory power came from the FI-combined that showed the best mortality prediction in an age-adjusted model. More extensive comparisons across different databases are required, but these results do not support including chronological age as a biomarker.

Association between fasting glucose and all-cause mortality according to sex and age: a prospective cohort study

The association of fasting glucose with the risk of death according to sex and age remains unclear, and insufficient information is available on sex- and age-specific glucose concentrations within ethnic groups. This study analyzed a sample of 12,455,361 Korean adults who participated in health examinations during 2001-2004, and were followed up until 2013. Men had 3.0 mg/dL (0.167 mmol/L) higher mean glucose concentrations than women (94.7 vs. 91.7 mg/dL), although women over 73 years had higher levels. For glucose levels of 100-199 mg/dL, each 18 mg/dL (1 mmol/L) increase in fasting glucose increased mortality by 13% (HR = 1.13, [95% CI 1.12 to 1.13], p < 0.001). In individuals with fasting glucose levels of 100-125 mg/dL, each 18 mg/dL increase in fasting glucose was associated with a 30% increase in the risk for mortality (1.30, [1.18 to 1.43]) in those aged 18-34 years, a 32% increase (1.32, [1.26 to 1.39]) in those aged 35-44 years, and a 10% increase (1.10, [1.02 to 1.19]) in those aged 75-99 years. The fasting glucose levels associated with the lowest mortality were 80-94 mg/dL regardless of sex and age. Prediabetes (100-125 mg/dL) was associated with higher mortality. The associations of hyperglycemia with mortality were stronger at younger ages.

stpm: an R package for stochastic process model

Background

The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas; however, there are no publicly available software tools that implement this methodology.

Results

We developed an R package stpm for the SPM-methodology. The package estimates several versions of SPM currently available in the literature including discrete- and continuous-time multidimensional models and a one-dimensional model with time-dependent parameters. Also, the package provides tools for simulation and projection of individual trajectories and hazard functions.

Conclusion

In this paper, we present the first software implementation of the SPM-methodology by providing an R package stpm, which was verified through extensive simulation and validation studies. Future work includes further improvements of the model. Clinical and academic researchers will benefit from using the presented model and software. The R package stpm is available as open source software from the following links: https://cran.r-project.org/package=stpm(stable version) or https://github.com/izhbannikov/spm(developer version).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1538-7) contains supplementary material, which is available to authorized users.

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

BACKGROUND AND OBJECTIVE

To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues.

DATA AND METHODS

We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways.

RESULTS

We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants.

CONCLUSIONS

. The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.

Dynamics of biomarkers in relation to aging and mortality

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing one to analyze their dynamics in relation to mortality, morbidity, or other health-related outcomes. Rich and diverse data collected in such studies provide opportunities to investigate how various socio-economic, demographic, behavioral and other variables can interact with biological and genetic factors to produce differential rates of aging in individuals. In this paper, we review some recent publications investigating dynamics of biomarkers in relation to mortality, which use single biomarkers as well as cumulative measures combining information from multiple biomarkers. We also discuss the analytical approach, the stochastic process models, which conceptualizes several aging-related mechanisms in the structure of the model and allows evaluating "hidden" characteristics of aging-related changes indirectly from available longitudinal data on biomarkers and follow-up on mortality or onset of diseases taking into account other relevant factors (both genetic and non-genetic). We also discuss an extension of the approach, which considers ranges of "optimal values" of biomarkers rather than a single optimal value as in the original model. We discuss practical applications of the approach to single biomarkers and cumulative measures highlighting that the potential of applications to cumulative measures is still largely underused.

Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.

Insulin-like growth factor 1, glycation and bone fragility: implications for fracture resistance of bone

Despite our extensive knowledge of insulin-like growth factor 1 (IGF1) action on the growing skeleton, its role in skeletal homeostasis during aging and age-related development of certain diseases is still unclear. Advanced glycation end products (AGEs) derived from glucose are implicated in osteoporosis and a number of diabetic complications. We hypothesized that because in humans and rodents IGF1 stimulates uptake of glucose (a glycation substrate) from the bloodstream in a dose-dependent manner, the decline of IGF1 could be associated with the accumulation of glycation products and the decreasing resistance of bone to fracture. To test the aforementioned hypotheses, we used human tibial posterior cortex bone samples to perform biochemical (measurement of IGF1, fluorescent AGEs and pentosidine (PEN) contents) and mechanical tests (crack initiation and propagation using compact tension specimens). Our results for the first time show a significant, age-independent association between the levels of IGF1 and AGEs. Furthermore, AGEs (fAGEs, PEN) predict propensity of bone to fracture (initiation and propagation) independently of age in human cortical bone. Based on these results we propose a model of IGF1-based regulation of bone fracture. Because IGF1 level increases postnatally up to the juvenile developmental phase and decreases thereafter with aging, we propose that IGF1 may play a protective role in young skeleton and its age-related decline leads to bone fragility and an increased fracture risk. Our results may also have important implications for current understanding of osteoporosis- and diabetes-related bone fragility as well as in the development of new diagnostic tools to screen for fragile bones.

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.

Impact of health counselling on cardiovascular disease risk in middle aged men: influence of socioeconomic status

BACKGROUND

The inverse association between socioeconomic status and cardiovascular disease is well documented. We examined whether the impact of health counselling on cardiovascular risk factors in middle-aged men differed according to socioeconomic status.

METHODS

We used data from a community based study assessing the risk for cardiovascular disease among middle-aged men in Helsinki, Finland. Traditional cardiovascular disease risk factors were measured and cardiovascular disease risk was assessed by a modified risk tool used in the North Karelia project (CVD Risk Score). Those men with increased risk for cardiovascular disease at their baseline visit in 2006 received lifestyle counselling. After two years these high-risk men were invited to a follow-up visit. The same measurements and risk assessments were repeated.

RESULTS

Based on the CVD Risk Score there were significant differences between the groups at baseline (p = 0.001) and at follow-up (p<0.001) with the highest scores in the lowest educational group. There were no significant differences in traditional cardiovascular risk factors according to educational attainment between groups either at baseline or at follow-up. Baseline lifestyle characteristics differed between the groups regarding use of soft fat (p = 0.019). All groups responded positively to lifestyle counselling.

CONCLUSIONS

The present study showed that lifestyle counselling is feasible in high-risk middle-aged men and lifestyle intervention works in all educational groups. Interestingly the traditional risk factors did not show improvement, but the risk score improved. From a practical point of view our findings stress the importance of using risk score calculators in health counselling instead of looking at individual cardiovascular disease risk factors.

Hemoglobin A1c, fasting plasma glucose, and 2-hour plasma glucose distributions in U.S. population subgroups: NHANES 2005-2010

PURPOSE

Although mean concentrations of hemoglobin A1c (A1C), fasting plasma glucose, and 2-hour plasma glucose differ by demographics, it is unclear what other characteristics of the distributions may differ, such as the amount of asymmetry of the distribution (skewness) and shift left or right compared with another distribution (shift).

METHODS

Using kernel density estimation, we created smoothed plots of the distributions of fasting plasma glucose (N = 7250), 2-hour plasma glucose (N = 5851), and A1C (N = 16,209) by age, race-ethnicity, and sex in the 2005-2010 National Health and Nutrition Examination Survey, a nationally representative sample of U.S. adults including people with and without diabetes. We tested differences in distributions using cumulative logistic regression.

RESULTS

The distributions were generally unimodal and right-skewed. All distributions were shifted higher and more right-skewed for older age groups (P < .001 for each marker). Compared with non-Hispanic whites, the distribution of fasting plasma glucose was shifted higher for Mexican-Americans (P = .01), whereas the distribution of A1C was shifted higher for non-Hispanic blacks (P < .001). The distribution of fasting plasma glucose was shifted higher for men (P < .001) and the distribution of 2-hour plasma glucose was shifted higher for women (P = .01).

CONCLUSIONS

We provide a graphic reference for comparing these distributions and diabetes cut-points by demographic factors.

Biochemical markers of aging for longitudinal studies in humans

Much progress has been made in the past decades in unraveling the mechanisms that are responsible for aging. The discovery that particular gene mutations in experimental species such as yeast, flies, and nematodes are associated with longevity has led to many important insights into pathways that regulate aging processes. However, extrapolating laboratory findings in experimental species to knowledge that is valid for the complexity of human physiology remains a major challenge. Apart from the restricted experimental possibilities, studying aging in humans is further complicated by the development of various age-related diseases. The availability of a set of biomarkers that really reflect underlying aging processes would be of much value in disentangling age-associated pathology from specific aging mechanisms. In this review, we survey the literature to identify promising biochemical markers of aging, with a particular focus on using them in longitudinal studies of aging in humans that entail repeated measurements on easily obtainable material, such as blood samples. Our search strategy was a 2-pronged approach, one focused on general mechanisms of aging and one including studies on clinical biomarkers of age-related diseases.

The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span

A better understanding of processes and mechanisms linking human aging with changes in health status and survival requires methods capable of analyzing new data that take into account knowledge about these processes accumulated in the field. In this paper, we describe an approach to analyses of longitudinal data based on the use of stochastic process models of human aging, health, and longevity which allows for incorporating state of the art advances in aging research into the model structure. In particular, the model incorporates the notions of resistance to stresses, adaptive capacity, and "optimal" (normal) physiological states. To capture the effects of exposure to persistent external disturbances, the notions of allostatic adaptation and allostatic load are introduced. These notions facilitate the description and explanation of deviations of individuals' physiological indices from their normal states, which increase the chances of disease development and death. The model provides a convenient conceptual framework for comprehensive systemic analyses of aging-related changes in humans using longitudinal data and linking these changes with genotyping profiles, morbidity, and mortality risks. The model is used for developing new statistical methods for analyzing longitudinal data on aging, health, and longevity.

The convergence of type 1 and type 2 diabetes in childhood: the accelerator hypothesis

It seems likely that type 1 and type 2 diabetes lie at different points of the same spectrum, separated by the misunderstanding that one belongs to childhood and the other to adulthood. The spectrum is that of tempo--the rate at which beta cell function is lost over time. A combination of beta cell up-regulation (insulin demand, largely determined by obesity) and the genetically-determined immune response to it ('autoimmunity') determines tempo, ranging from slow to fast with every variant in between. There is good evidence that people who go on to develop type 1 (fast) diabetes are, like those who develop type 2 (slow diabetes), insulin resistant, and overwhelming evidence that body mass plays a key role. The prevention of type 1 diabetes may lie in weight restriction from an early age.

Development and application of biological age prediction models with physical fitness and physiological components in Korean adults

BACKGROUND

Several biological age (BA) prediction models have been suggested with a variety of biomarkers. Valid models should be able to measure BA in a relatively short time period and predict subsequent physiological capability. Physiological and physical fitness variables have been shown to be distinctive markers for predicting BA and morbidity. The practical and noninvasive nature of such variables makes them useful as clinical assessment tools in estimating BA for in-depth diagnosis and corresponding intervention.

OBJECTIVE

To identify, develop and evaluate biomarkers and BA prediction models and validate their clinical usefulness for the practical diagnosis of functional aging.

METHODS

Fourteen variables were measured in 3,112 male and 1,233 female participants aged 30 and older between the years 2004 and 2007. Through a series of parsimonious stepwise elimination processes, two sets of 8 gender-specific variables were selected as candidate biomarkers for 1,604 men and 760 women. Principal component analysis, linear regression analysis and adjustment methods were further applied to obtain two sets of true BA (TBA) prediction models. The TBA models were examined for validity by comparing TBA to the corresponding chronological age (CA) with clinical risk factors.

RESULTS

TBA prediction models with r(2) values of 0.638 and 0.672 were developed, each unique to men and women, respectively. The overall mean TBA and CA of the participants were 53.9 and 51.8 years, respectively, with a marginal difference of -2.1 and -1.3 years. The regression slopes or rates of TBA as a function of CA were 1.00 and 1.28 for men and women with r values of 0.799 and 0.820 (p < 0.001), respectively. In comparing TBA to CA rates between healthy and clinical risk groups, both sarcopenic and obese groups showed significant increases in TBA.

CONCLUSIONS

The selected biomarkers encompass various complex physiopathological factors related to intrinsic and extrinsic physiological and functional aging. The BA prediction models based on the selected biomarkers could be practical in assessing BA for Korean adults.

Effect of the APOE Polymorphism and Age Trajectories of Physiological Variables on Mortality: Application of Genetic Stochastic Process Model of Aging

We evaluated effects of the APOE polymorphism (carriers versus noncarriers of the e4 allele) and age trajectories of total cholesterol (CH) and diastolic blood pressure (DBP) on mortality risk in the Framingham Heart Study (original cohort). We found that long-lived carriers and noncarriers have different average age trajectories and long-lived individuals have consistently higher levels and less steep declines at old ages compared to short-lived individuals. We applied the stochastic process model of aging aimed at joint analyses of genetic and nongenetic subsamples of longitudinal data and estimated different aging-related characteristics for carriers and noncarriers which otherwise cannot be evaluated from data. We found that such characteristics differ in carriers and noncarriers: (1) carriers have better adaptive capacity than noncarriers in case of CH, whereas for DBP the opposite situation is observed; (2) mean allostatic trajectories are higher in carriers and they differ from “optimal” trajectories minimizing mortality risk; (3) noncarriers have lower baseline mortality rates at younger ages but they increase faster than those for carriers resulting in intersection at the oldest ages. Such observations strongly indicate the presence of a genetic component in respective aging-related mechanisms. Such differences may contribute to patterns of allele- and sex-specific mortality rates.

New stochastic carcinogenesis model with covariates: an approach involving intracellular barrier mechanisms

In this paper we present a new multiple-pathway stochastic model of carcinogenesis with potential of predicting individual incidence risks on the basis of biomedical measurements. The model incorporates the concept of intracellular barrier mechanisms in which cell malignization occurs due to an inefficient operation of barrier cell mechanisms, such as antioxidant defense, repair systems, and apoptosis. Mathematical formalism combines methodological innovations of mechanistic carcinogenesis models and stochastic process models widely used in studying biodemography of aging and longevity. An advantage of the modeling approach is in the natural combining of two types of measures expressed in terms of model parameters: age-specific hazard rate and means of barrier states. Results of simulation studies allow us to conclude that the model parameters can be estimated in joint analyses of epidemiological data and newly collected data on individual biomolecular measurements of barrier states. Respective experimental designs for such measurements are suggested and discussed. An analytical solution is obtained for the simplest design when only age-specific incidence rates are observed. Detailed comparison with TSCE model reveals advantages of the approach such as the possibility to describe decline in risk at advanced ages, possibilities to describe heterogeneous system of intermediate cells, and perspectives for individual prognoses of cancer risks. Application of the results to fit the SEER data on cancer risks demonstrates a strong predictive power of the model. Further generalizations of the model, opportunities to measure barrier systems, biomedical and mathematical aspects of the new model are discussed.

Age trajectories of physiological indices in relation to healthy life course

We analysed relationship between the risk of onset of "unhealthy life" (defined as the onset of cancer, cardiovascular diseases, or diabetes) and longitudinal changes in body mass index, diastolic blood pressure, hematocrit, pulse pressure, pulse rate, and serum cholesterol in the Framingham Heart Study (Original Cohort) using the stochastic process model of human mortality and aging. The analyses demonstrate how decline in resistance to stresses and adaptive capacity accompanying human aging can be evaluated from longitudinal data. We showed how these components of the aging process, as well as deviation of the trajectories of physiological indices from those minimising the risk at respective ages, can lead to an increase in the risk of onset of unhealthy life with age. The results indicate the presence of substantial gender difference in aging related decline in stress resistance and adaptive capacity, which can contribute to differences in the shape of the sex-specific patterns of incidence rates of aging related diseases.

Dynamic determinants of longevity and exceptional health

It is well known from epidemiology that values of indices describing physiological state in a given age may influence human morbidity and mortality risks. Studies of connection between aging and life span suggest a possibility that dynamic properties of age trajectories of the physiological indices could also be important contributors to morbidity and mortality risks. In this paper we use data on longitudinal changes in body mass index, diastolic blood pressure, pulse pressure, pulse rate, blood glucose, hematocrit, and serum cholesterol in the Framingham Heart Study participants, to investigate this possibility in depth. We found that some of the variables describing individual dynamics of the age-associated changes in physiological indices influence human longevity and exceptional health more substantially than the variables describing physiological state. These newly identified variables are promising targets for prevention aiming to postpone onsets of common elderly diseases and increase longevity.

"Predicting" parental longevity from offspring endophenotypes: data from the Long Life Family Study (LLFS)

While there is evidence that longevity runs in families, the study of long-lived families is complicated by the fact that longevity-related information is available only for the oldest old, many of whom may be deceased and unavailable for testing, and information on other living family members, primarily descendents, is censored. This situation requires a creative approach for analyzing determinants of longevity in families. There are likely biomarkers that predict an individual's longevity, suggesting the possibility that those biomarkers which are heritable may constitute valuable endophenotypes for exceptional survival. These endophenotypes could be studied in families to identify human longevity genes and elucidate possible mechanisms of their influence on longevity. In this paper, we analyze data collected in the Long Life Family Study (LLFS) investigating whether indicators of physiological state, cognitive functioning and health/well-being among offspring predict longevity in parents. Good predictors can be used as endophenotypes for exceptional survival. Our analyses revealed significant associations between cumulative indices describing physiological state, as well as a number of offspring phenotypes, and parental lifespan, supporting both their familial basis and relevance to longevity. We conclude that the study of endophenotypes within families is a valid approach to the genetics of human longevity.