Personalized Financial Planning Using Applied Genetics

Abstract

Forthcoming advances in geroscience will influence the health span of current and future generations and generate both challenges and opportunities for those approaching or reaching retirement ages. The resulting changes in the life course will influence those reaching stages in life that are commonly associated with retirement. How people plan for that later phase of life is critical—especially given that current approaches to planning are either nonexistent or outdated. In this review, we show how advances in applied genetics can yield valuable information for individuals that are facing the challenges and opportunities that will accompany anticipated advances in geroscience and their unique influence on the life span and health span of current and future generations.

Two of the most important demographic and medical events that have occurred in human history occurred during the last 120 years—the rapid aging of our species (Uhlenberg 2009; Bloom et al. 2015) and life extension brought forth by advances in public health and medicine. Anticipated advances in geroscience have the potential to extend these advances further—with a profound influence on the health span of current and future generations. Survival to older ages is now the norm rather than the exception in most parts of the world—leading to unprecedented opportunities and challenges. By way of illustration, based on death rates prevailing in the United States in 1900, only 39.2% of the babies born in that year were expected to reach the age of 65, 5.5% would reach ages 85+, and 0.25% would survive to ages 100+ (Bell and Miller 2005). Today, survival rates notably improved to 83.9% to ages 65+, 44.0% to 85+, and 3.2% to 100+ (mortality.org). Among those that reached age 65 in 1900, only 13.9% survived to age 85. By 2019, survival to age 85 conditional on having reached age 65, increased to 52.5%.

As a result of these historic events, humanity is now able to experience a much longer and later phase of life with a predictability never before experienced by previous generations. Peter Laslett, in 1991, recognized the importance of this unprecedented survival by labeling it a “fresh map of life” or the “third age” (Laslett 1991). Streeter et al. (2020) recently indicated that a new map of life is needed to accommodate the new reality of regular survival into what used to be when retirement and death occurred, and Ken Dychtwald (Dychtwald and Morrison 2021) emphasized that the world is now experiencing a new version of disengagement/reengagement to and from the workforce that is no longer characterized by a single discrete “retirement” event.

While longer and healthier lives are a welcome respite from a history filled with the tragedy of death that has been common at younger and middle ages, this third age is not without challenges (Brown 2015). Extended survival brings the dual challenge of rising chronic age-related diseases associated with operating our living machines beyond their biological warranty period (Olshansky 2021) and the problem of how to plan, pay for, and properly use those extra years (Sharpe 2021). All of the challenges and opportunities linked to extended survival will be exacerbated by successful efforts to modulate aging through the geroscience interventions described in this article.

In the United States, participation in retirement plans declined in the first two decades of this century during a time when the baby boom cohort was approaching their third age (www.epi.org/publication/the-state-of-american-retirement-savings/). More than one-third of the U.S. population has no retirement savings at all, >60% of those earning <$30 K per yr have not saved anything for retirement, and 38% of the population plans to work past age 65—in part, because they have to fund extra years in retirement (tippinsights.com/36-of-americans-dont-have-any-retirement-savings). In fact, only 44% of workers in the United States participate in a work-related retirement plan, primarily because many employers do not offer retirement plans, and even when they do, the amounts most people save are not even close to ensuring adequate post-retirement living standards (about 70%–80% of income received at age 65) (Ghilarducci et al. 2015).

The COVID pandemic has further challenged retirement decision-making as millions of Americans retired earlier than expected since the pandemic began (www.aarp.org/work/careers/pandemic-workers-early-retirement), or they began taking early distributions from their retirement accounts because they needed the money (www.accountingtoday.com/news/millions-of-taxpayers-took-early-distributions-from-retirement-accounts-due-to-covid), further jeopardizing their ability to finance the third phase of life. A recent poll by the Commonwealth Fund (www.commonwealthfund.org/publications/surveys/2021/sep/impact-covid-19-older-adults) documented that 19% of the population aged 65 and older in the United States either used up most or all of their savings or had a significant loss in income because of COVID, and among Blacks and Hispanics these percentages were 32% and 39%, respectively.

While better retirement planning requires some knowledge of estimated survival, the life span “calculators” available in the marketplace today are overly simplistic actuarial models that often grossly overestimate or underestimate duration of life because of their reliance on additive algorithms (www.newretirement.com/retirement/longevity-trends-and-life-expectancy-calculators). Additive survival estimation models operate under the premise that risk factors that influence survival operate independent of each other, allowing for the simple addition or subtraction of the survival effects of various mortality-related risk factors relative to a baseline survival estimate for the entire population. One important consequence of using an additive model in a life span calculator is that the life-shortening or life-extending effects of different risk factors are counted more than once. The magnitude of the double or triple counting is influenced by the degree to which the covariates used to estimate survival are correlated with each other. The result is a life span estimate that exaggerates survival prospects by extending the tails of the survival distribution in both directions. This yields an overestimate or underestimate of the true effects of risk factors on life span—an issue made even worse when considering potential life-extending effects of advances in geroscience.

As such, life span calculators based on additive models are most appropriately used for encouraging people to adopt healthier lifestyles by modifying behavioral risk factors. They cannot and should not be used to generate realistic personalized estimates of survival for retirement planning or life insurance underwriting because they are not designed for that purpose. While it is possible to address the problem of additivity by weighting risk factors relative to each other, the creators of commercial life span calculators have no incentive to pursue accuracy and reliability over expediency because the latter is not their goal.

One novel technology that has yet to make its way into the world of life span and health span assessment is to use personal genetic information to generate hyperpersonalized assessments of survival and health as a way to help people plan better for retirement (e.g., financial planning) (Sanese et al. 2019). Empirical evidence supports the use of genetic biomarkers (Brooks-Wilson 2013) to better predict late-age mortality and assess survival and health at the level of individuals from the study of methylation age (Horvath 2013) and telomere length (Cawthon et al. 2003; Bendix et al. 2014). Additional evidence suggests that the FOX03 (Willcox et al. 2008) and APOE (Sebastiani et al. 2019) genetic polymorphisms exhibit consistent associations with longevity and health in diverse human populations, making them potentially valuable as an additional hyperpersonal longevity assessment tool. As such, applied genetics (AG) holds the potential to become a revolutionary new tool that can help generations across the age structure and their financial planners develop a much more personalized and well justified plan for living life in the third age.

Presented here is a case study of a married couple with an illustration of how assessments of survival and health dynamics using the FOX03 and APOE genetic polymorphisms, present or absent within these individuals, could be used to help make more informed decisions for retirement planning relative to the use of generic forecasting models, generic assumptions about survival and health, or, as is often the case in financial planning today, no assumptions at all about future health and survival.

Consider the following hypothetical example of a couple that is approaching what is often thought of as a traditional retirement age in their mid-60s. Presented here is a financial scenario that is commonly observed among higher net worth couples, although AG could be used to augment retirement decisions for people of all socioeconomic backgrounds (Table 1).

Table 1.

Case study of couple approaching traditional retirement age

	Financial status and demographics
	Malea	Femalea	Total
Age	63	60
Assets			$1,000,000b
Income	$90,000	$60,000	$150,000c
Target income in retirement			75% of current salary
Retirement savings/year	5%	5%	$7500
Home value			$800,000d
Withdrawal rate			4%
Genetic profile
FOX03	Noe	Yesf
APOE	e4,e4g	e2,e2h
Life span, survival, and late-onset AD risk
Generic life spani	83.0	85.3
Generic survival to age 90i	25.3%	35.9%

^aAnticipated retirement age of 68.

^bAccumulated retirement savings.

^cFixed annual income.

^dMortgage paid off.

^ePeople who do not carry the FOX03 genetic polymorphism have an average probability of reaching ages 90+.

^fCarriers of the FOX03 genetic polymorphism have a 50% greater chance of surviving to ages 90+ relative to average.

^gCarriers of this version of APOE e4,e4 [C/C] have a 12-fold elevated risk of developing late-onset Alzheimer's disease (AD) and a 61-fold elevated risk of developing early-onset AD.

^hCarriers of this rare version of APOE have a much lower risk of developing any form of AD, and carriers also have amplified chances of living a long and healthy life—especially when paired with positive carrier status for FOX03.

ⁱBased on a generic life table for the total resident population of the United States by gender (www.mortality.org/Country/Country?cntr=USA).

WHEN TO RETIRE

Generic recommendations (GRs) on retirement age fall into two categories: (1) the client picks a retirement age based on their personal needs, desires, and job circumstances without any knowledge about or forecasts of personal life span or health span; or (2) the ages of 68–69 are often recommended by an advisor because it offers a higher return relative to full retirement age, but it also allows for an earlier monthly cash flow. The generic assessment of survival shown here also indicates that the female will outlive the male by 2.3 years—an issue that is highly relevant to a decision on when to retire.

AG Recommendation

Data from AG, however, suggest that the female has a higher probability of surviving to age 90 relative to the male because she carries the longevity-enhancing polymorphism of APOE, increasing the chances that the gender gap in survival is likely to exceed 2.3 years, perhaps significantly. The recommendation coming from an advisor with information from AG might include delayed retirement for both spouses to accommodate the possibility that the female could survive more than 2.3 years beyond her partner, and that the female could run out of money given her higher-than-average probability of late-age survival. This approach increases the chances of the female having enough funds to cover her expected additional survival time above and beyond her spouse at an annual level of income consistent with her current standard of living.

WHEN TO DRAW SOCIAL SECURITY

GR provides little guidance on when to draw social security—this is often a decision based on current financial status and anticipated cash flow. In this case, the male is capable of drawing social security at his current age of 63, but the couple is young enough to have time to change their minds if they see that their financial circumstances allow for delays in drawing social security. In other words, in the absence of science-based estimates of life span, most clients have been inclined to begin taking social security well before the maximum age of 70 (crr.bc.edu/wp-content/uploads/2015/05/IB_15-8.pdf).

AG Recommendation

Due to anticipated extended survival for the female from evidence collected through AG, both individuals would be advised to delay drawing social security until the latest possible age, assuming job and cash flow circumstances allows this to occur comfortably. The reason is that the male is carrying a genetic polymorphism that increases the probability of early-onset and late-onset AD, which would have added costs, and the female has to prepare to fund a remaining period of life that extends for possibly many years beyond her spouse.

LONG-TERM CARE

GR from advisors is not currently based on scientific assessments of health span, so this type of insurance might currently be recommended by advisors based on something other than evidence for or against need.

AG Recommendation

Based on the presence of the APOE e4,e4 genetic polymorphism in the male, it would be appropriate to recommend that only the male take out a long-term care insurance policy. The presence of the APOE e2,e2 and FOX03 genetic polymorphisms in the female means there is justification for avoiding the expense of long-term care in her case. Carrying these genetic polymorphisms is not a guarantee of health, longevity, or a need for long-term care, but at least the couple has the ability to make a better-informed decision based on AG.

PORTFOLIO RISK

Many people moving into the retirement phase of life are moving from an accumulation phase to a distribution phase. Aggressively growing assets is no longer the priority; maintaining and protecting accumulated assets becomes critical. Most retirees, and especially females, become more conservative with the level of risk they are willing to take in retirement because of the uncertainty about how much money will be needed (VanDerhei and Bajtelsmit 1995). A generic approach to risk aversion results in lower income replacement in instances where the true prospects for survival are different from those derived through averaging assumptions.

In this hypothetical case, because of the higher-than-average probability that the female will live to ages 90+ based on her assessment using AG, an even more risk-averse set of investment decisions may be justified. Knowledge about the presence or absence of FOX03 and APOE genetic polymorphisms enables this couple to base their chosen level of risk aversion in their portfolio management to a highly personal and scientifically tangible metric, and it yields specific guidance on the magnitude of the risk aversion to consider (e.g., whether to invest in a long-term care policy or guaranteeing income using an annuity).

PRODUCT MIX

Which financial products a client chooses when creating a financial plan can be greatly influenced by the risks they are willing to take and the goals they are trying to accomplish. Someone with a long life span and low risk tolerance may choose to have a smaller amount of their investments allocated to the stock market and may have a more bond-heavy portfolio, or may choose other alternatives, such as annuities or life insurance. Someone with a small nest egg and long anticipated life span may be comfortable taking more risk—they might decide to have a good portion of their assets in stocks to help grow the pool of assets, so they last as long as possible. Having access to personalized AG makes it possible for clients to make asset allocation and product mix decisions based on personalized assessments as opposed to those based on generic or averaging assumptions.

GENERIC ADVISOR RECOMMENDATION

Financial advisors, without access to AG or other methods of estimating life span and health span, often make investment recommendations for their clients under the simplifying assumption that each spouse will live to somewhere between the ages of 90 and 100 (www.investmentnews.com/how-do-advisors-estimate-a-clients-longevity-80482). While there is a relatively small chance this length of life would actually occur in the real world for most people (25% for average males and 36% for average females in the United States today at the ages of the people presented here), advisors might think of this assumption as a “safe” recommendation because it might guarantee their clients will not run out of money while they are alive. The problem is that the primary goal of financial planning is to enhance the quality of life of their clients while they are alive, not plan exclusively to ensure the largest inheritance possible. An advisor recommendation of planning to live to 95 can dampen (often dramatically) the monthly cash flow of the retirees for their remaining years of life for the majority of the population that will not live this long.

Most advisors also use the assumption of a 4% withdrawal rate to provide the income needed for clients throughout retirement (www.schwab.com/resource-center/insights/content/beyond-4-rule-how-much-can-you-safely-spend-retirement), meaning the client can withdraw 4% from their investment account each year. This is assumed to be a “safe” withdrawal rate and will allow the assets to provide the income needed and potentially continue to grow. However, in the hypothetical case study provided here, the typical advisor may not consider long-term care or life insurance as investment vehicles because the simplifying assumption of survival to age 95 precludes the need for such investments. However, an advisor that has access to the results of AG may assume, reasonably, that the husband in this case will require a minimum of 3 years of assistance in a nursing home and likely much more (Genworth cost of care survey 2020: median cost data tables, www.genworth.com/aging-and-you/finances/cost-of-care.html).

OTHER AG-SPECIFIC RECOMMENDATIONS IN THIS CASE STUDY

The result of these new recommendations based on AG is that the wife will likely have all the income she needs throughout her life span to age 92 and still have a sizable amount of money left over. This allows her an opportunity to spend more throughout retirement, if she chooses, or to provide an inheritance to her family and/or give to charity at her death. When this happens, a “positive wealth span” will have been achieved (www.amazon.com/Pursuing-Wealthspan-Revolutionizing-Management-Methuselah/dp/B08DF2FLCJ). This basically means that through sound financial planning based on the use of AG, couples have a greater chance of accomplishing the goal of securing adequate cash flow during their remaining years of life.

AG leads to better information, which leads to more informed planning and a higher probability of achieving a better health and financial outcome.

DISCUSSION

Forthcoming advances in geroscience have the potential to revolutionize health and survival in the coming decades. While radical life extension is not likely, enough changes are forthcoming to warrant the development and use of assessment tools that take into account hyperpersonalized longevity and health assessments of individuals that will enable them to better plan for the changes in life history that geroscience will bring forth.

Two of the most critical pieces of information required for sound retirement planning include an estimate of how long someone is likely to live, and how many of those remaining years of life are likely to be healthy or accompanied by some level of frailty or disability that require additional financial resources. At present, it is rare when either of these personal attributes are measured or used by financial planners as part of retirement planning. If estimated life span is used at all by financial planners, the task often falls on the clients to approximate their own duration of life, or the planner uses averaging assumptions based on generic life tables. Few advisors or retirees have the expertise required to make a judgment on their own anticipated life span, and the use of averaging assumptions guarantees that the vast majority of people being advised are basing their retirement decisions on information known in advance to be false or exaggerated in one direction or another.

It is demonstrated here that AG involving knowledge just about the FOX03 and APOE e2,e3,e4 genetic polymorphisms can be a valuable tool for augmenting science-based estimates of life span and health span derived from personal demographic and health information collected when an advisor meets with their client.

LIMITATIONS

AG in survival estimation is not a guarantee that an individual will live a long or short life, or conversely, that the presence of genetic polymorphisms associated with an expanded or contracted health span will occur as predicted. The proper interpretation of AG in survival analysis for individuals is that the stated statistical probabilities will be observed for the stated proportion of a population that shares a genetic trait. When an individual is informed they carry the FOX03 allele, this should be interpreted to mean they belong to a subgroup of the population that has, for example, a higher chance of surviving to age 90 than noncarriers. Other factors also influence survival such as behavioral risk factors and stochastic events. Regardless, the elevated probability of extended survival associated with carrying the FOX03 allele yields information that the individual did not previously have.

During a wealth management relationship, the final decision on the best course of action for retirement planning will rest with the client, but with the additional information from AG, both the advisor and client have access to powerful predictive information they previously did not have access to. A similar interpretation and decision-making process takes place when AG is used to help women carrying the BRCA mutation (associated with an elevated risk of breast cancer) on how to plan and proceed with their medical care either before or after a cancer diagnosis (Trainer et al. 2010).

CONCLUSIONS

The third phase of life is a new phenomenon in human history, only experienced with regularity by the last few generations. As welcome as the gift of added life may be—manufactured intentionally through advances in public health and medical technology—this new phase of life challenges the financial integrity of social safety nets such as Social Security and Medicare (Sheshinski and Caliendo 2021) and fundamentally transforms modern notions of work and retirement. Advances in geroscience will amplify these challenges and opportunities in the coming decades.

While life past age 65 can be some of the most rewarding and enjoyable years of life as long as health is maintained (Steptoe and Wardle 2012), financial preparedness for survival into the third age has not been practiced routinely in the United States where close to one-third of non-retirees have no retirement savings or pension at all (www.sec.gov/files/retirement-readiness-white-paper.pdf). A similar lack of preparedness exists elsewhere in both developed and developing nations. Furthermore, advanced age itself is often accompanied by an elevated risk of frailty that is associated with the normal aging of our cells and tissues rather than the common risk factors we have grown accustomed to hearing about (e.g., smoking and obesity) (Sprott 2010).

AG offers unique insights into prospective survival and health dynamics that has practical uses for financial planning—especially given the fact that such technology has never before been used to help individuals plan for their retirement. The hypothetical scenarios presented here suggest that AG using carrier status of just the FOX03 and APOE e2, e3, and e4 genetic polymorphisms would likely yield valuable information that advisors and their clients could use to help guide retirement planning. Hyperpersonalized genetic information collected from individuals would likely help individuals and couples experience a “positive wealth span” or at the least a “neutral wealth span,” while at the same time lessening the chances of a “negative wealth span” occurring (e.g., running out of money). Using personal genetic information as a supplemental guide to a hyperpersonalized assessment of life span and health span would likely avoid misguided financial advice that could otherwise occur in the absence of a personalized life span/health span assessment.