Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort.

OBJECTIVE: To examine relationships between knee osteoarthritis (KOA) and obesity, diabetes mellitus (DM), and cardiovascular disease (CVD). METHODS: Associations of time-dependent obesity, DM, and CVD with KOA transition states over approximately 18 years were examined among 4093 participants from a community-based cohort. Transition states were 1) no knee symptoms and no radiographic KOA (rKOA; Kellgren-Lawrence grade ≥2 in at least one knee), 2) asymptomatic rKOA, 3) knee symptoms only, 4) symptomatic rKOA (sxKOA; rKOA and symptoms in same knee). Markov multistate models estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for associations between comorbid conditions and transitions across states, adjusting for baseline age, sex, race, education, enrollment cohort, birth year, and time-dependent knee injury history. RESULTS: At baseline, 40% of participants had obesity, 13% had DM, and 22% had CVD (mean age = 61 years; 34% Black; 37% male). Compared with those without obesity, those with obesity had a higher hazard of worsening from no rKOA/no symptoms to asymptomatic rKOA (aHR = 1.7; 95% CI = 1.3-2.2) and from knee symptoms to sxKOA (aHR = 1.7; 95% CI = 1.3-2.3), as well as a lower hazard of symptom resolution from sxKOA to asymptomatic rKOA (aHR = 0.5 [95% = CI 0.4-0.7]). Compared with those without CVD, those with CVD had a higher hazard of worsening from no rKOA/symptoms to knee symptoms (aHR = 1.5; 95% CI = 1.1-2.1). DM was not associated with transitions of rKOA. CONCLUSION: Prevention of obesity and CVD may limit the development or worsening of rKOA and symptoms.

Fall Risk and Utilization of Balance Training for Adults With Symptomatic Knee Osteoarthritis: Secondary Analysis From a Randomized Clinical Trial.

BACKGROUND AND PURPOSE: Knee osteoarthritis (KOA) is a common disease that hinders activity participation in older adults. Associated symptoms and physiological changes can increase risk of falling in individuals with KOA. Balance training can decrease fall risks in older adults. Limited evidence exists regarding utilization of balance training in physical therapy (PT) for this population. This secondary data analysis investigated the proportion of participants at high risk for falling in the PhysicAl THerapy vs. INternet-based Exercise Training for Patients with Osteoarthritis (PATH-IN) study and the frequency with which balance training was utilized as an intervention in PT. METHODS: PATH-IN study participants (N = 344) performed the Four-Stage Balance Test and the Timed Up and Go (TUG) test during baseline assessment. Participants were randomly allocated to PT, an Internet-based exercise program, or a control group. Participants were classified as being at high risk for falling if they did not progress to the single-leg stance (SLS) during the Four-Stage Balance Test, were unable to maintain SLS for 5 seconds, or took longer than 13.5 seconds to complete the TUG test. The proportion of participants at high risk for falling was calculated for all participants and separately for those allocated to PT. In addition, PT notes were coded for balance training and the frequency of balance training utilization was calculated. RESULTS AND DISCUSSION: Upon enrollment, 35.5% (N = 122) of all participants and 36.2% (N = 50) of those allocated to PT were at high risk for falling. Of participants allocated to PT with documentation available for coding (N = 118), 35.5% (N = 42) were at high risk for falling. Balance training was provided to 62.7% (N = 74) during at least one PT session. Of those classified as being at high risk for falling, 33.3% (N = 14) did not receive balance training. CONCLUSIONS: The finding of high fall risks in more than one-third of all participants with KOA is consistent with previous reports of a higher risk of falling in this population. Many PT participants did receive some balance training; however, one-third of participants at high risk for falling did not. Balance training for individuals with KOA at high risk for falling may be underutilized.

How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection procedure works in an independent dataset. In this paper, we computed FLoSS using the lifespan data of 234,155 individuals from a large comprehensive genealogically-based resource, the Utah Population Database (UPDB), born between 1779 and 1910 with mortality follow-up through 2012-2013. Computations of FLoSS in a specific year (1980) confirmed the survival advantage of the "exceptional" sibships (defined by LLFS FLoSS threshold, FLoSS ≥ 7). We found that the subsample of the UPDB participants born after 1900 who were from the "exceptional" sibships had survival curves similar to that of the US participants from the LLFS probands' generation. Comparisons between the offspring of parents with "exceptional" and "ordinary" survival showed the survival advantage of the "exceptional" offspring. Investigators seeking to explain the extent genetics and environment contribute to exceptional survival will benefit from the use of exceptionally long-lived individuals and their relatives. Appropriate ranking of families by survival exceptionality and their availability for the purposes of providing genetic and phenotypic data is critical for selecting participants into such studies. This study validated the FLoSS as selection criteria in family longevity studies using UPDB.

Genetics of Human Longevity From Incomplete Data: New Findings From the Long Life Family Study.

The special design of the Long Life Family Study provides a unique opportunity to investigate the genetics of human longevity by analyzing data on exceptional lifespans in families. In this article, we performed two series of genome wide association studies of human longevity which differed with respect to whether missing lifespan data were predicted or not predicted. We showed that the use of predicted lifespan is most beneficial when the follow-up period is relatively short. In addition to detection of strong associations of SNPs in APOE, TOMM40, NECTIN2, and APOC1 genes with longevity, we also detected a strong new association with longevity of rs1927465, located between the CYP26A1 and MYOF genes on chromosome 10. The association was confirmed using data from the Health and Retirement Study. We discuss the biological relevance of the detected SNPs to human longevity.

Fear of Movement and Associated Factors Among Adults With Symptomatic Knee Osteoarthritis.

OBJECTIVE: To examine the frequency of and factors associated with fear of movement (FOM) among patients with symptomatic knee osteoarthritis (KOA), using the new Brief Fear of Movement (BFOM) measure. METHODS: Participants (n = 350) enrolled in a clinical trial completed the BFOM scale prior to randomization. The relationships of BFOM with the following characteristics were examined: age, sex, race, education, pain and activities of daily living (ADL) subscales of the Knee Injury and Osteoarthritis Outcome Score (KOOS), knee symptom duration, depressive symptoms (8-item Patient Health Questionnaire [PHQ-8]), history of falls and knee injury, family history of knee problems, self-efficacy for exercise (SEE), and unilateral balance test. A proportional odds logistic regression model examined multivariable associations of participant characteristics with a 3-level BFOM variable (agreement with 0, 1-2, or ≥3 items). RESULTS: The majority of participants (77%) agreed with at least 1 item on the BFOM scale, and 36% endorsed 3+ items, suggesting a high degree of FOM. In the multivariable model, the following remained significant after backward selection: age (odds ratio [OR] 0.79 per 10-point increase, 95% confidence interval [95% CI] 0.66-0.95), KOOS ADL (OR 0.86 per 10-point increase, 95% CI 0.76-0.97), PHQ-8 (OR 1.15, 95% CI 1.08-1.22), and SEE (OR 0.87 per 10-point increase, 95% CI 0.78-0.96). CONCLUSION: FOM was common among patients with symptomatic KOA, and this could negatively impact physical activity. Psychological variables were significantly associated with FOM, suggesting behavioral and psychological interventions may decrease FOM and improve outcomes among individuals with symptomatic KOA.

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.

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity.

Increasing proportions of elderly individuals in developed countries combined with substantial increases in related medical expenditures make the improvement of the health of the elderly a high priority today. If the process of aging by individuals is a major cause of age related health declines then postponing aging could be an efficient strategy for improving the health of the elderly. Implementing this strategy requires a better understanding of genetic and non-genetic connections among aging, health, and longevity. We review progress and problems in research areas whose development may contribute to analyses of such connections. These include genetic studies of human aging and longevity, the heterogeneity of populations with respect to their susceptibility to disease and death, forces that shape age patterns of human mortality, secular trends in mortality decline, and integrative mortality modeling using longitudinal data. The dynamic involvement of genetic factors in (i) morbidity/mortality risks, (ii) responses to stresses of life, (iii) multi-morbidities of many elderly individuals, (iv) trade-offs for diseases, (v) genetic heterogeneity, and (vi) other relevant aging-related health declines, underscores the need for a comprehensive, integrated approach to analyze the genetic connections for all of the above aspects of aging-related changes. The dynamic relationships among aging, health, and longevity traits would be better understood if one linked several research fields within one conceptual framework that allowed for efficient analyses of available longitudinal data using the wealth of available knowledge about aging, health, and longevity already accumulated in the research field.

Genetics of aging, health, and survival: dynamic regulation of human longevity related traits.

BACKGROUND: The roles of genetic factors in human longevity would be better understood if one can use more efficient methods in genetic analyses and investigate pleiotropic effects of genetic variants on aging and health related traits. DATA AND METHODS: We used EMMAX software with modified correction for population stratification to perform genome wide association studies (GWAS) of female lifespan from the original FHS cohort. The male data from the original FHS cohort and male and female data combined from the offspring FHS cohort were used to confirm findings. We evaluated pleiotropic effects of selected genetic variants as well as gene-smoking interactions on health and aging related traits. Then we reviewed current knowledge on functional properties of genes related to detected variants. RESULTS: The eight SNPs with genome-wide significant variants were negatively associated with lifespan in both males and females. After additional QC, two of these variants were selected for further analyses of their associations with major diseases (cancer and CHD) and physiological aging changes. Gene-smoking interactions contributed to these effects. Genes closest to detected variants appear to be involved in similar biological processes and health disorders, as those found in other studies of aging and longevity e.g., in cancer and neurodegeneration. CONCLUSIONS: The impact of genes on longevity may involve trade-off-like effects on different health traits. Genes that influence lifespan represent various molecular functions but may be involved in similar biological processes and health disorders, which could contribute to genetic heterogeneity of longevity and the lack of replication in genetic association studies.

Genetic Structures of Population Cohorts Change with Increasing Age: Implications for Genetic Analyses of Human aging and Life Span.

BACKGROUND: Correcting for the potential effects of population stratification is an important issue in genome wide association studies (GWAS) of complex traits. Principal component analysis (PCA) of the genetic structure of the population under study with subsequent incorporation of the first several principal components (PCs) in the GWAS regression model is often used for this purpose. PROBLEM: For longevity related traits such a correction may negatively affect the accuracy of genetic analyses. This is because PCs may capture genetic structure induced by mortality selection processes in genetically heterogeneous populations. DATA AND METHODS: We used the Framingham Heart Study data on life span and on individual genetic background to construct two sets of PCs. One was constructed to separate population stratification due to differences in ancestry from that induced by mortality selection. The other was constructed using genetic data on individuals of different ages without attempting to separate the ancestry effects from the mortality selection effects. The GWASs of human life span were performed using the first 20 PCs from each of the selected sets to control for possible population stratification. RESULTS: The results indicated that the GWAS that used the PC set separating population stratification induced by mortality selection from differences in ancestry produced stronger genetic signals than the GWAS that used PCs without such separation. CONCLUSION: The quality of genetic estimates in GWAS can be improved when changes in genetic structure caused by mortality selection are taken into account in controlling for possible effects of population stratification.

How lifespan associated genes modulate aging changes: lessons from analysis of longitudinal data.

BACKGROUND AND OBJECTIVE: The influence of genes on human lifespan is mediated by biological processes that characterize body's functioning. The age trajectories of these processes contain important information about mechanisms linking aging, health, and lifespan. The objective of this paper is to investigate regularities of aging changes in different groups of individuals, including individuals with different genetic background, as well as their connections with health and lifespan. DATA AND METHOD: To reach this objective we used longitudinal data on four physiological variables, information about health and lifespan collected in the Framingham Heart Study (FHS), data on longevity alleles detected in earlier study, as well as methods of statistical modeling. RESULTS: We found that phenotypes of exceptional longevity and health are linked to distinct types of changes in physiological indices during aging. We also found that components of aging changes differ in groups of individuals with different genetic background. CONCLUSIONS: These results suggest that factors responsible for exceptional longevity and health are not necessary the same, and that postponing aging changes is associated with extreme longevity. The genetic factors which increase lifespan are associated with physiological changes typical of healthy and long-living individuals, smaller mortality risks from cancer and CVD and better estimates of adaptive capacity in statistical modeling. This indicates that extreme longevity and health related traits are likely to be less heterogeneous phenotypes than lifespan, and studying these phenotypes separately from lifespan may provide additional information about mechanisms of human aging and its relation to chronic diseases and lifespan.

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.

Evaluation of genotype-specific survival using joint analysis of genetic and non-genetic subsamples of longitudinal data.

Small sample size of genetic data is often a limiting factor for desirable accuracy of estimated genetic effects on age-specific risks and survival. Longitudinal non-genetic data containing information on survival or disease onsets of study participants for whom the genetic data were not collected may provide an additional "reserve" for increasing the accuracy of respective estimates. We present a novel method for joint analyses of "genetic" (covering individuals for whom both genetic information and mortality/morbidity data are available) and "non-genetic" (covering individuals for whom only mortality/morbidity data were collected) subsamples of longitudinal data. Our simulation studies show substantial increase in the accuracy of estimates in such joint analyses compared to analyses based on genetic subsample alone. Application of this method to analysis of the effect of common apolipoprotein E (APOE) polymorphism on survival using combined genetic and non-genetic subsamples of the Framingham Heart Study original cohort data showed that female, but not male, carriers of the APOE e4 allele have significantly worse survival than non-carriers, whereas empirical analyses did not produce any significant results for either sex.

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.

Exceptional survivors have lower age trajectories of blood glucose: lessons from longitudinal data.

Exceptional survival results from complicated interplay between genetic and environmental factors. The effects of these factors on survival are mediated by the biological and physiological variables, which affect mortality risk. In this paper, we evaluated the role of blood glucose (BG) in exceptional survival using the Framingham heart study data for the main (FHS) and offspring (FHSO) cohorts. We found that: (1) the average cross-sectional age patterns of BG change over time; (2) the values of BG level among the longest lived individuals in this study differ for different sub-cohorts; (3) the longitudinal age patterns of BG differ from those of cross-sectional ones. We investigated mechanisms forming average age trajectories of BG in the FHS cohort. We found that the two curves: one, characterizing the average effects of allostatic adaptation, and another, minimizing mortality risk for any given age, play the central role in this process. We found that the average BG age trajectories for exceptional survivors are closer to the curve minimizing mortality risk than those of individuals having shorter life spans. We concluded that individuals whose age trajectories of BG are located around the curve minimizing chances of premature death at each given age have highest chances of reaching exceptional longevity.

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

The levels of blood glucose (BG) in humans tend to increase with age deviating from the norm specified for the young adults. Such elevation is often considered as a factor contributing to an increase in risks of disease and death. The proper use of intervention strategies coping with or preventing consequences of BG elevation requires understanding the roles of external forces and intrinsic senescence in this process. To address these issues, we performed analyses of longitudinal data on BG collected in the Framingham Heart Study using methods of descriptive statistics and statistical modeling. The approach allows us to separate effects of persistent external disturbances from "normal" aging-related changes due to the senescence process. We found that the BG level corresponding to the lowest mortality risk tends to increase with age. The changes in the shape of the mortality risk with age indicate the aging-related decline in resistance to stresses affecting the BG level. The results show that analyzing longitudinal data using advanced methods may substantially increase our knowledge on factors and mechanisms responsible for aging-related changes in humans.

Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data.

Many longitudinal studies of aging collect genetic information only for a sub-sample of participants of the study. These data also do not include recent findings, new ideas and methodological concepts developed by distinct groups of researchers. The formal statistical analyses of genetic data ignore this additional information and therefore cannot utilize the entire research potential of the data. In this paper, we present a stochastic model for studying such longitudinal data in joint analyses of genetic and non-genetic sub-samples. The model incorporates several major concepts of aging known to date and usually studied independently. These include age-specific physiological norms, allostasis and allostatic load, stochasticity, and decline in stress resistance and adaptive capacity with age. The approach allows for studying all these concepts in their mutual connection, even if respective mechanisms are not directly measured in data (which is typical for longitudinal data available to date). The model takes into account dependence of longitudinal indices and hazard rates on genetic markers and permits evaluation of all these characteristics for carriers of different alleles (genotypes) to address questions concerning genetic influence on aging-related characteristics. The method is based on extracting genetic information from the entire sample of longitudinal data consisting of genetic and non-genetic sub-samples. Thus it results in a substantial increase in the accuracy of statistical estimates of genetic parameters compared to methods that use only information from a genetic sub-sample. Such an increase is achieved without collecting additional genetic data. Simulation studies illustrate the increase in the accuracy in different scenarios for datasets structurally similar to the Framingham Heart Study. Possible applications of the model and its further generalizations are discussed.