577586 research-article2015

JAHXXX10.1177/0898264315577586Journal of Aging and HealthResnick et al.

Article

The Impact of Genetics on Physical Resilience and Successful Aging

Journal of Aging and Health 1­–21 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264315577586 jah.sagepub.com

Barbara Resnick, PhD, CRNP, FAAN, FAANP1, N. Jennifer Klinedinst, PhD, MPH, RN1, Laura Yerges-Armstrong, PhD2, Eun Yong Choi, PhD3, and Susan G. Dorsey, PhD RN FAAN1

Abstract Objective: To better understand the impact of genetics on resilience and successful aging, we tested a model of successful aging. Method: This was a descriptive study with a single interview and blood draw done with residents in a continuing care retirement community. Five genes associated with resilience were included in the model. The hypothesis was tested using structural equation modeling. Results: A total of 116 participants completed the survey. Two SNPs from SLC6A4 (rs25533 and rs1042173) and age were the only variables associated with physical resilience and explained 9% of the variance. Cognitive status, age, and depression were directly associated with successful aging; variance in rs25532 or rs1042173, resilience, and pain were indirectly associated with successful aging through depression. Discussion: Continued research to replicate these findings is needed so as to be able to recognize older adults at risk of low physical resilience and implement appropriate interventions.

1University

of Maryland School of Nursing, Baltimore, USA of Maryland School of Medicine, Baltimore, USA 3University of Maryland Greenebaum Cancer Center, Baltimore, USA 2University

Corresponding Author: Barbara Resnick, University of Maryland School of Nursing, 655 West Lombard Street, Baltimore, MD 21201, USA. Email: [email protected]

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Keywords geriatrics, resilience, genetics The concept of successful aging emerged in the late 1980s and early 1990s as a departure from the loss-focused geriatric and gerontological research that preceded the concept. In their groundbreaking article “Human Aging: Usual and Successful,” Rowe and Kahn (1998) argued that the cognitive and physiological losses documented in the literature as age-related changes were mischaracterizations of the natural aging process. They believed that aging, or what occurred during the aging process, was in large part due to lifestyle, habits, diet, and an array of psychosocial factors extrinsic to the aging process. Guided in part by this work, successful aging has been conceptualized as having (a) no major disease-related symptoms, (b) no impairments in activities of daily living, (c) limited difficulty with physical functioning, (d) no significant cognitive impairment, and (e) remaining “actively engaged” (Strawbridge, Wallhagen, & Cohen, 2002). Genetic research tends to conceptualize successful aging as achieving “extreme old age,” generally considered to be 100 years of age and older (Pinti et al., 2004; Sebastiani et al., 2009). Unfortunately, these conceptualizations are limited and biased and do not consider the perspective of older adults. With increased numbers of older adults living well into their 90s and 100s, it is critically important to gain a better understanding of successful aging and how to help older individuals, and those along the aging continuum, achieve success through the aging process.

Alternative Conceptualization of Successful Aging Since the early conceptualizations of successful aging, there has been a transition from a focus on the absence of disease or disability to one in which successful aging is determined based on the perspective of the older adult (Flood, 2006; McCarthy, 2009). Others have combined objective and subjective information to evaluate successful aging (Flood, Nies, & Seo, 2010; Lewis, 2011; McLaughlin, Connell, Heeringa, Li, & Roberts, 2010; Pruchno, Wilson-Genderson, & Cartwright, 2010; Troutman, Nies, & Mavellia, 2011; Troutman, Nies, Small, & Bates, 2011). Objective indicators included such things as engagement in meaningful activities and maintenance of functional ability. Subjective indicators were focused on the individual’s perception of his or her successful aging (i.e., how well they were aging and how they would rate their life). When considering both objective and subjective aspects of successful aging, it is those who engage in physical activity and volunteer

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activities that consider themselves to have aged successfully. It is likely that both objective and subjective aspects of successful aging are important, as is an appreciation of the heterogeneous nature of the aging process.

Factors That Influence Successful Aging The effects of demographics on successful aging are variable. Although not always consistent, there is some evidence that women were more likely to age successfully than men (Depp & Jeste, 2006; Flood et al., 2010; KozarWestman, Troutman-Jordan, & Nies, 2013). Neither race nor marital status was consistently related to successful aging (Pruchno et al., 2010). As one might expect, older age is generally associated with lower evaluations of successful aging (McLaughlin et al., 2010). Education and socioeconomic status have also been associated with successful aging. Those with more education and higher socioeconomic status tend to report higher successful aging (Depp & Jeste, 2006; Flood et al., 2010; McLaughlin et al., 2010). Pain, particularly pain that influences daily life, and depressive symptoms were negatively associated with successful aging (Jeste et al., 2013; Pruchno et al., 2010; Stevens-Ratchford & Lookingbill, 2004).

Behavioral Factors Associated With and Defining Successful Aging As noted above, there are a number of behaviors that older adults choose to do that are associated with and reflective of successful aging. These behaviors include engaging in meaningful activities (Pruchno et al., 2010; Resnick, 2010), eating a heart healthy diet and maintaining ideal body weight (Levveille, Guralnik, Ferrucci, & Langlois, 1999), exercising regularly (Britton, Shipley, Singh-Manoux, & Marmot, 2008), not smoking (Depp & Jeste, 2006), and drinking moderate amounts of alcohol (Cawthon et al., 2008). Building off these findings, we conceptualized successful aging as involvement in volunteer activities (assumedly meaningful activity), health promoting activities (regular exercise, heart healthy diets, moderate alcohol intake, not smoking), and overall physical activity.

The Effect of Resilience and Genetics on Successful Aging The actual health (e.g., number and impact of comorbidities) of the individual may have little to do with successful aging (Jahn & Cukrowicz, 2012;

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McLaughlin, Jette, & Connell, 2012). Rather, it may be that the individual’s conceptualization of his or her health status and acceptance of age and disease associated changes has a stronger linkage to successful aging. For successful aging to occur, the changes experienced as part of aging must be accepted, addressed, and adjusted to. Thus, resilience is central to the ability to age successfully (Resnick, Galik, Dorsey, Scheve, & Gutkin, 2011; Resnick & Inguito, 2011). Resilience refers to the capacity to spring back from a physical, emotional, financial, or social challenge. Being resilient indicates that the individual has the human ability to adapt in the face of tragedy, trauma, adversity, hardship, and ongoing significant life stressors (Newman, 2005). Resilient individuals tend to manifest adaptive behavior, especially with regard to social functioning, morale, and somatic health (Wagnild, 2003; Wagnild & Young, 1993) and are less likely to succumb to illness (O’Connell & Mayo, 1998). Resilience is a dynamic process that is influenced by life events and challenges (Hardy, Concato, & Gill, 2004; Hegney et al., 2007) and is an important contributor to successful aging throughout the life span (Hsu & Jones, 2012). While much of the focus on resilience has considered psychological resilience, it is increasingly recognized that there may be different types of resilience including economic resilience (Sanders, Lim, & Sohn, 2008), emotional resilience (Chow, Hamagani, & Nesselroade, 2007), and physical resilience (Resnick & Inguito, 2011). Given the importance of physical activity as one of the aspects of successful aging, we focused on physical resilience as the measure of resilience in this study. Physiological resilience has been associated with the individual’s flexibility in his or her neurochemical stress response systems and the neural circuitry involved in stress responses. It is possible, therefore, that genetic make-up indirectly influences resilience through impact on multiple neurochemical pathways such as via the Hypothalmic- Pituitary Adrenal Axis (HPA), serotenergic, dopaminergic, or neurotrophin signaling pathways (Charmey, 2004; Cicchetti & Blender, 2006). Of these possible pathways, serotonin has been the most extensively studied neurotransmitter associated with depression and resilience (Halser, 2010). For example, meta-analyses indicate that variations of the serotonin gene, SLC6A4, are associated with several neuropsychiatric disorders including bipolar affective disorders (Cho et al., 2005); autism (Kistner-Griffin et al., 2011); depression, anxiety, and related traits (Wray et al., 2009); and obsessive-compulsive disorders (Bloch et al., 2008). In addition, the serotonin gene, SLC6A4 is the only gene noted to be associated with resilience among older adults (Feder, Nestler, & Charney, 2009; O’Hara et al., 2012; Rana et al., 2014). There are a number of additional genes that were believed to modulate adaptive responses to fear and prefrontal cortex reactivity and thus may be associated with resilience

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but results are inconclusive. These include genes such as: BDNF, CRHR1, FKBP5, and COMT (Feder et al., 2009). Gaining a better understanding of the impact of genetics on both physical resilience and successful aging will help identify individuals who may be at risk of having low physical resilience and being less able to age successfully. Identification of those low in physical resilience will allow for a targeted approach to implementation of behavioral interventions needed to strengthen physical resilience and thereby increase the likelihood for successful aging. To evaluate the factors that influence successful aging among older adults, a model was developed and tested. Successful aging was conceptualized as involvement in volunteer activities (assumedly meaningful activity), health promoting activities (e.g., participation in exercise, moderate alcohol intake, smoking cessation), and overall physical activity. It was hypothesized that controlling for age, cognition, comorbidities, pain, and depressive symptoms, polymorphisms in five genes would be directly and indirectly associated with physical resilience and successful aging (Figure 1). The five candidate genes chosen, SLC6A4, BDNF, FKBP5, CRHR1, and COMT, were those noted in the literature to be associated with resilience (see Table 1).

Study Design This was a descriptive study based on data from a one-time face-to-face interview and blood draw with older adults living in a continuing care retirement community (CCRC). Residents were eligible to participate if they lived in the CCRC, were 65 years of age or older, and scored a two out of three recall on the three-item recall question within the Mini-Cog (Borson, Scanlan, Chen, & Ganguli, 2003). The interviews followed informed consent and were all scheduled at a time that was convenient for the resident and done privately in his or her apartment or in the outpatient health care office setting. The study was approved by the University of Maryland, Baltimore, Institutional Review Board.

Sample There were 244 residents living in the facility during the year in which the interviews occurred. A non-random sample of 149 (61%) residents consented to participate in the study. Thirty-one residents (13%) refused to participate and the remaining 64 (26%) were not reachable during the study period (e.g., out of town, never available to schedule a meeting). Among those consented, 2 were not eligible to participate due to cognitive issues leaving 147 participants who completed the interview. Of the residents who participated in the

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Figure 1.  Successful aging model with rs25532.

interview, 121 (82% of participants) also consented to the genetics portion of the study. The remaining individuals either refused blood or we were not able to schedule the blood draw at a time that was convenient for the participant. The DNA from 5 individuals was not concentrated enough for analysis, leaving a final sample size of 116. The majority of the participants were female (75%) and White (98%), and analyses reported here were limited to those who were White (N = 114). This was done deliberately to control for racial and ethnic differences in genotype. The average age of the participants was 87.0 (SD = 6) years with a range from 71 to 103 years of age.

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Resnick et al. Table 1.  Genes and Associated SNPs Considered. Gene

SNP

Chr

Position

Function

FKBP5

rs3800373 rs755658 rs9296158 rs1360780 rs1334894 rs9470080 rs4713916 rs2203877 rs7124442 rs6265 rs2049045 rs7103411 rs1042173 rs25532 rs7209436 rs4792887 rs110402 rs242924 rs242941 rs242940 rs242939 rs1876828 rs737865 rs4680 rs165599

6 6 6 6 6 6 6 11 11 11 11 11 17 17 17 17 17 17 17 17 17 17 22 22 22

35542476 35549670 35567082 35607571 35615130 35646435 35669983 27670910 27677041 27679916 27694241 27700125 28525011 28564170 43870142 43877020 43880047 43885367 43892520 43892600 43895579 43911525 19930121 19951271 19956781

3’-UTR Intronic Intronic Intronic Intronic Intronic Intronic Downstream of gene 3’-UTR Exonic—missense Intronic Intronic 3’-UTR Upstream of gene Intronic Intronic Intronic Intronic Intronic Intronic Intronic Intronic Intronic Exonic—missense 3’-UTR

BDNF

SLC6A4 CRHR1

COMT

Note. SNP= Single Nucleotide Polymorphism; Chr = Chromosome; 3’-UTR = 3 Prime Untranslated Region.

Measures The survey included 28 items focused on volunteering within and outside the CCRC setting, and if they answered affirmatively to any of these activities, they were considered to be someone who volunteered. Healthy behaviors included participation in age-appropriate cancer screenings for colon, breast, prostate, and skin cancers (United States Preventive Services Task Force, 2014), regular intake of alcohol, not smoking, and engaging in regular exercise activities (30 min of moderate level physical activity at least three times per week). Total time spent in all types of physical

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activity was based on completion of the Yale Physical Activity Survey (YPAS; Dipietro, Caspersen, Ostfeld, & Nadel, 1993). The YPAS includes questions about housework, yard work, caregiving (e.g., animals, pets, childcare, or care of another adult), recreational activities (e.g., needlework), and moderate-level physical activity (e.g., biking, dancing, brisk walking, jogging). Prior use of the YPAS provided evidence of test–retest reliability (r = .63, p < .001) and construct validity (Dipietro et. al., 1993; Pescatello, DiPietro, Fargo, Ostfeld, & Nadel, 1994). Depression was evaluated using the three-item Useful Depression Screening Tool (UDST; Klindinst & Resnick, in press). The UDST was developed by combining the two items from the Patient Health Questionnaire (PHQ-2; Kroenke, Spitzer, & Williams, 2003) with an additional item reflecting a subjective sense of usefulness (Gruenewald, Karlamangla, Greendale, Singer, & Seeman, 2007). The first item of the PHQ-2 focused on how often in the last 2 weeks participants experienced “little interest or pleasure in doing things” and the second item explored how often in the last 2 weeks the participant felt down, depressed, or hopeless. Responses were based on a 4-point Likert-type scale and included 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. The third item, focused on usefulness, asked participants to respond to the following: “How often do you feel useful to your family and friends?” with response items including never, rarely, sometimes, or frequently. Total possible scores for the threeitem measure range from 0 to 9, with higher scores indicating more depression. Prior research has supported the reliability and validity of this brief measure (Klindinst & Resnick, in press). A score of 4 or greater was considered to be a positive screening for depression. Pain was evaluated using a single-item measure asking participants to rate their pain, at the time of testing, on a scale of 0 to 10. Prior use of this assessment has been shown to be reliable and valid when used with older adults (Herr & Mobily, 1991). Physical resilience was measured using the Physical Resilience Scale (Resnick et al., 2011) with two additional items added to differentiate those particularly high in physical resilience. The original Physical Resilience Scale included 15 items that focused on aspects of resilience. Participants were asked to identify the most difficult physical challenge they encountered associated with aging (e.g., vision changes, arthritis, hip fracture, pneumonia, stroke, etc.) and agree or disagree with each item. Items included such things as, “I was determined to recover,” “I adjusted to the new changes,” “I believed I could recover,” and “I accepted the new challenges.” The two new items focused on the challenge being so difficult that the individual did not engage in usual activities, and the challenge being so difficult that he or she did not even try to recover. The items were summed with a point given for each

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response indicative of being resilient. Scores ranged from 0 to 17 with higher scores reflecting greater resilience.

DNA Extraction and Genotyping DNA was extracted from 3.5 to 7 ml of whole blood drawn into a BD vacutainer® ACD blood collection tube (BD, Ref# 364606). The sample was transported on ice to the Translational Core Lab (University of Maryland School of Medicine, Baltimore MD), and DNA was either isolated immediately or blood was frozen at −20°C within 1 hr of blood draw. If the blood was frozen, then DNA was isolated within 3 weeks. DNA was isolated using either the QIAamp® DNA blood maxi kit (Qiagen, Cat#51194; Valencia, AC) or the BioRobot®EZ1, QIAGEN Instruments AG with the EZ1® DNA blood 350ul kit (Qiagen, Cat# 951054; Valencia, CA) according to manufacturer’s recommendations. After purification, DNA concentration was measured using a NanoDrop 8000 (Thermo Fisher Scientific, Waltham, MA). DNA was stored at −80°C until genotyping was done.

Genotyping We identified 25 SNP variants in 5 candidate genes (Table 1) that have been previously associated with psychological or physical resilience (Lipsky, Hu, & Goldman, 2009; Southwick, Litz, Charney, & Friedman, 2011; Wendland et al., 2008) for genotyping in our sample. The 64 SNP OpenArray chip was created using the online design software from Life Technologies. Eighteen of the 64 assays were custom assays, designed by the software; the others were off-the-shelf Taqman assays. Assays were run on the QuantStudio 12K Flex with the OpenArray block, following published protocols (Applied Biosystems QuantStudio, 2012). Data were analyzed using the provided Genotyper software.

Data Analysis Descriptive analyses were done to describe the sample and the activities reported (e.g., volunteering, physical activity). A conceptual measurement model of successful aging was tested as well as a full model of factors influencing successful aging using structural equation modeling (SEM) and the AMOS statistical program. The sample covariance matrix was used as input and a maximum likelihood solution sought. The chi-square statistic, the normed fit index (NFI), and Steigers root mean square error of approximation (RMSEA) were used to estimate model fit. The larger the probability

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Table 2.  Description Information of Participants. Variable Total number of preventive behaviors Physical resilience Depression Total number of comorbidities Age Pain Total activity (minutes per week)

Possible range

M

SD

0-5 2-16 0-7 0-9 71-103 0-10 0-650

2.51 11.6 1.80 3.45 86.58 3.58 179.28

1.41 2.93 1.72 2.06 6.28 3.33 145.75

associated with the chi-square, the better the fit of the model to the data (Bollen, 1989; Loehlin, 1998). Because the chi-square statistic is sample size dependent, the chi-square divided by degrees of freedom (df) was used to control for sample size effects (Bollen, 1989; Loehlin, 1998). A ratio of ≤3 is considered to be a good fit (Bollen, 1989; Loehlin, 1998). The NFI tests the hypothesized model against a baseline model and should be 1.0 if there is perfect model fit. The NFI is “normed” so that the values cannot be below 0 or above 1. The RMSEA is a population-based index and consequently is insensitive to sample size. An RMSEA of

The Impact of Genetics on Physical Resilience and Successful Aging.

To better understand the impact of genetics on resilience and successful aging, we tested a model of successful aging...
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