Driven by a personal interest in human longevity, I’ve explored the extensive discourse surrounding this topic. It’s a subject that captivates both the general public and the scientific community, whether the focus is on genetic predispositions, environmental influences, or lifestyle choices impacting how long we live. The commercial sector also shows considerable interest in the potential of extending human lifespan. Notably, influential figures like Bryan Johnson, through his “Project Blueprint,” advocate the idea that aging, the physiological decline linked to longevity, can be slowed or even halted with the right interventions, as evidenced by his significant annual investment in anti-aging efforts. Johnson spends about $2M/year to reverse aging following an anti-aging attempt called “Project Blueprint”).
Given the vast amount of literature on human aging, I wanted to share some of my own consolidated insights, which I hope may also be informative to others.
Longevity Is not Lifespan
Although “lifespan” and “longevity” are frequently used to mean the same thing, there’s a slight distinction between them. Lifespan refers to the total length of a life, while longevity specifically implies a relatively long lifespan, often extending beyond the average life expectancy. Furthermore, longevity includes the inherent potential of an individual to achieve a long life, shaped by elements such as their genetic makeup, lifestyle choices, and surrounding environment.
Life Expectancy Is on an Upward Trajectory
Life expectancy is indeed a key metric when we talk about longevity. It tells us the average number of years a person at a specific age can expect to live. However, it’s important to remember that life expectancy doesn’t give us the complete picture of an individual’s lifespan. It also doesn’t tell us about the quality of those years, particularly in relation to good health, which significantly influences how long we live. Despite these limitations, life expectancy is a valuable starting point. It serves as a useful indicator of the overall health of a population and allows us to make comparisons between different groups and regions.
How Life Expectancy Has Changed Over the Years
The change in human life expectancy over the past 150 years has been remarkable. Before 1870, the global average life expectancy was approximately 29.7 years (around 36.2 years in Europe, 35.1 years in the Americas, and 34.7 years in Oceania, as shown in Figure 1). In contrast, the current worldwide average is 73.3 years, reaching as high as 79.1 years in Europe and Oceania (Figure 1). While the difference between life expectancy in 1870 and 2023 is substantial, it’s important to note that the data available before 1940 was less comprehensive and did not include countries in Africa or the Middle East. Given that these regions are primarily composed of low- and middle-income countries (LMICs), the data presented in Figure 1 likely provides a reasonably accurate overview.
The chart also highlights a couple of notable declines. The first is the significant dip in the Asia line between 1959 and 1961, which corresponds to the devastating Great Chinese Famine and resulted in the deaths of an estimated 15 to 45 million people. The second is the dip observed across all regions in 2021, largely attributable to the impact of the COVID-19 pandemic.
Figure 1: Life expectancy across different global regions displayed in linear chart setting (Source: Life Expectancy)
Many Factors Influence Life Expectancy
There are numerous interconnected factors that affect life expectancy. These include genetic predispositions like family history and specific gene variants, as well as the accessibility and quality of healthcare, encompassing preventive measures and the management of chronic conditions. Lifestyle choices such as diet and nutrition, exercise habits, and smoking and alcohol consumption also play a significant role. Furthermore, socioeconomic factors, including income, education levels, and living environment, have a considerable impact. Lastly, gender, ethnicity, and geographic location can influence life expectancy due to variations in healthcare access, socioeconomic conditions, and environmental factors.
As Figure 2 illustrates, place of residence has a substantial influence on life expectancy. In many LMICs, life expectancy remains below 65 years.
Figure 2: Life expectancy map of 2023.
The Blue Zones and Life Expectancy
Blue Zones are geographical areas, as illustrated in Figure 3, where inhabitants typically enjoy remarkably long lifespans, frequently exceeding 100 years, thanks to a confluence of lifestyle choices. Dan Buettner coined the term, pinpointing five such regions: Okinawa (Japan), Ikaria (Greece), Loma Linda (USA), Sardinia (Italy), and Nicoya (Costa Rica). These locations stand out due to their low incidence of chronic illnesses and a significant number of centenarians.
Buettner and Skemp’s 2016 findings highlighted additional elements contributing to longevity, such as a predominantly plant-based diet, regular physical activity, robust social networks, a clear sense of purpose, and effective stress management. Notably, C. elegans research by Passarino and team (2016) suggests that genetics accounts for only 20–50% of how long we live.
By now, the Blue Zones concept has become a subject of considerable debate, facing criticism regarding its scientific foundation and potential data inaccuracies (Ignacio Amigo, 2024). While the emphasis on lifestyle factors for longevity is generally appreciated, some argue that the Blue Zones movement may overstate the supporting evidence and depend on data that may not be entirely accurate or reliable. I won’t delve into the specifics of this debate, as they are thoroughly discussed in the Science article by Amigo (2024). As a result of this debate, there are currently two officially recognized Blue Zones, with four regions commonly agreed upon: Sardinia, Okinawa, Nicoya, and Ikaria (as shown in Figure 3).
A recent study by Poulain and colleagues in 2021 provided a comparison of lifestyle, health status, and specific genetic markers among populations in the Longevity Blue Zones (LBZs) of Ikaria and Sardinia, relative to reference populations in Italy and Greece. The data for this study were sourced from the GEHA (Genetics of Healthy Aging) database (Skytthe et al., 2011). The findings indicated a notably higher proportion of individuals in the LBZs who had either never married or were married and still living with their spouse. Furthermore, a greater percentage of nonagenarian men and women in these zones reported a high level of optimism and/or excellent self-rated health. Among the variables with lower frequency were the proportion of the widowed, the percentage of subjects who had suffered a stroke and the frequency of Apoε4 (having at least one copy of Apoε4 significantly increases the risk of developing Alzheimer’s disease) and Apoε2 (usually associated with slower cognitive decline during aging and actually may contribute to increased lifespan) and the TT genotype of FOXO3A gene (plays a significant role in the aging process and is strongly linked to longevity) (Poulain et al., 2021).
These comparisons suggest that behavioral and health indicators might have a more significant influence than genetic factors within LBZ populations. However, the authors emphasize the need for further research to explore potential epigenetic traits that could play a crucial role through the interplay of genetics and the surrounding human and physical environments.
Figure 3: The “Blue Zone” regions. Today there are two “official” lists of blue zones, one curated by Dan Buettner and the other by Michel Poulain. The lists agree on four locations (blue) but there are some discrepancies (red).
Human Longevity, Life Expectancy, and Mortality Data
Here are some of the open questions I have regarding human longevity, as I believe data holds the key to understanding this complex topic:
- What are the established predictors of exceptional longevity?
- How significant is parental longevity as a predictor of an individual’s survival?
- What is the impact of nutrition or exercise on lifespan?
- In what ways does epigenetics specifically affect cellular aging and overall lifespan?
- How do DNA methylation (and other biomarkers), i.e., biological aging clocks, influence longevity?
- To what extent can telomere length serve as an accurate biomarker for aging and a reliable predictor of it?
- What is the potential of various drugs (e.g., metformin (diabetes medication), rapamycin (used in transplant medicine), rilmenidine (blood pressure medication), and related compounds, GLP-1 agonists, anti-inflammatories (e.g., statins), resveratrol, spermidine, and various senolytics) in slowing down or delaying the effects of aging?
- How might microbiome interventions, including dietary changes, probiotic supplementation, and even fecal microbiota transplantation, contribute to promoting healthy ageing?
- Which companies are prominent in the human longevity field, what are their claims, and what is the evidence supporting these claims?
Each of these questions necessitates a thorough examination of the available data. Fortunately, a substantial amount of relevant data is publicly accessible. Furthermore, numerous scientific publications offer detailed evaluations and findings on specific aspects of human longevity, which should provide valuable insights. I intend to explore some of these specific areas in future posts, with the hope of finding answers to these questions.
Human Longevity Data We All Can Access
I also wanted to provide some information regarding the availability of human longevity data. This includes a wide range of data such as life expectancy estimates, mortality statistics, and research exploring the genetic and environmental factors that affect lifespan.
Key sources for this type of data include:
- Centers for Disease Control and Prevention (CDC)
- World Health Organization (WHO)
- Human Mortality Database (HMD)
- United States Mortality DataBase (USMDB)
For those interested in exploring specific areas in more detail, Table 1 lists additional data sources such as life expectancy estimates, mortality statistics, and research exploring the genetic and environmental factors that affect lifespan.
| Type of Data | Type of Data / Source | Details |
|---|---|---|
| Life Expectancy Data | o GHE: Life expectancy and healthy life expectancy / World Health Organization o Mortality in the US, 2022/ CDC o Actuarial Life Table / Social Security Administration o FastStats – Life Expectancy / CDC o Life Expectancy / Our World Data o World Life Expectancy Map | Life expectancy data – expectancy estimates at birth, as well as life expectancy for various age groups, racial/ethnic groups, and genders. |
| Mortality | o Mortality Data / CDC o Deaths and Mortality / CDC (National Center for Health Statistics) o United States Mortality DataBase / USMDB o U.S. Mortality Data, 1969-2023 / NCI o National Vital Statistics System – Mortality (NVSS-M) / OASH o National Longitudinal Mortality Study (NLMS) / United States Census Bureau o Publicly Available Sources Data for Health & Social Determinants of Health / University of Pittsburgh o WHO Mortality Database / WHO o Home of the U.S. Government’s Open Data – Mortality Data / Data.gov o Human Mortality Database (HMD) / Mortality.org | Data on mortality rates, including age-specific and cause-specific mortality, is collected by the CDC and other organizations to understand trends and factors influencing lifespan. HMD: A comprehensive database of mortality statistics for various countries, including the US, providing detailed data for researchers and policymakers. |
| Genetic Research | o LongevityMap: Human Longevity Genetic Variants / Human Aging Genomic Resources (HAGR) o GenAge / (Database of Ageing-Related Genes / Database Commons o HALL (Human Ageing and Longevity Landscape) / NGDC | Studies on genetic variants associated with longevity are being conducted, including the LongevityMap project, which compiles genetic associations with lifespan. |
| Anti-Aging Drugs | o DrugAge: The Database for Ageing-Related Drugs / HAGR | A database that contains an extensive compilation of drugs, compounds, and supplements (including natural products and nutraceuticals) with anti-aging properties that extend longevity in model organisms. The focus is on drugs/compounds potentially impacting on aging, and therefore drugs/compounds extending lifespan in disease-prone animals (e.g., cancer models) are excluded. |
| Animal Aging and Longevity | o AnAge: The Animal Ageing and Longevity Database / HAGR | A curated database of aging and life history in animals, including extensive longevity records. AnAge was primarily developed for comparative biology studies, in particular studies of longevity and ageing, but can also be useful for ecological and conservation studies and as a reference for zoos and field biologist |
Table 1: Some available databases that have longevity, aging, anti-aging drugs, and environmental factors data.
Some Mortality Data Highlights
There are some notable data points from the CDC regarding mortality in the US:
- Life expectancy saw an increase to 77.5 years in 2022, up from 2021.
- Interestingly, age-specific death rates rose for the 1–4 and 5–14 age groups between 2021 and 2022, while they decreased for all groups aged 15 and older.
- The infant mortality rate experienced a 3.1% increase compared to the previous year.
- There was a significant decrease of 9.2% in the age-adjusted death rate per 100,000 US standard population in 2022 compared to 2021. This decrease was observed across all racial and ethnic groups, as detailed in Figure 4.
- The top 10 leading causes of death in 2022 remained the same as in 2021, although their order shifted slightly. Heart disease and cancer continued to be the leading causes (see Figure 4).
Figure 4: Age-adjusted death rate, by race, Hispanic origin, and sex in the U.S., 2021 and 2022. (Source: Mortality in the United States, 2022)
Top Leading Causes of Death
The primary causes of mortality consistently include heart disease, stroke, chronic lower respiratory diseases, Alzheimer’s disease, diabetes (specifically Type 2 for this summary), kidney disease, and chronic liver diseases and cirrhosis, as illustrated in Figure 5. Upon closer examination, many of these conditions are classified as chronic diseases or arise over time due to factors such as inadequate self-care, encompassing poor dietary habits, smoking, excessive alcohol consumption, and insufficient physical activity.
Chronic diseases have a substantial impact on human longevity by diminishing quality of life, increasing mortality rates, and placing a considerable strain on healthcare systems. Although some chronic diseases can be managed effectively, they typically necessitate continuous treatment and may result in debilitating complications, thereby reducing overall life expectancy.
Figure 5: Age-adjusted death rate for the 10 leading causes of death in the U.S., 2021 and 2022. (Source: Mortality in the United States, 2022)
Life Expectancy and Chronic Ailment around Retirement Age
A study by DuGoff et al. (2014) highlights the significant impact of multiple chronic conditions on the life expectancy of older Americans. The analysis of 1.4 million Medicare enrollees indicates that as the number of chronic conditions increases after retirement, life expectancy tends to decrease. This correlation may explain the observed slowdown in the overall increase in life expectancy among the elderly.
The findings suggest that preventing the development of additional chronic conditions in older adults could be a crucial factor in continuing to improve life expectancy. For instance, the study found that a 75-year-old woman with no chronic conditions could expect to live an average of 17.3 more years, extending her life beyond 92. However, for a 75-year-old woman with five chronic conditions, the average additional lifespan is reduced to 12 years (reaching age 87), and for those with 10 or more conditions, it’s only five years (reaching age 80). See Figure 6 for a detailed overview.
The study also noted existing disparities, with women tending to live longer than men, and white individuals longer than Black individuals.
Figure 6: Life expectancy for American women and men starting at age 55. (Source: Social Security Administration Life Expectancy Calculator)
It’s important to remember that when discussing health in retirement, the specific types of diseases someone may have are just as crucial as the number of conditions. For instance, diabetes frequently occurs alongside other issues like heart disease. Comprehensive medical care must consider a patient’s overall health and the potential for interactions between different medications. Naturally, when more organ systems are involved, treatment planning becomes more complex.
Interestingly, research on the impact of retirement on health presents varied findings. While some studies, like the one by Moon et al. in 2012, suggest a decline in health post-retirement, others, such as Westlund et al.‘s 2010 study, indicate improvements. In their 2010 prospective study, Westlund and colleagues aimed to determine if retirement leads to changes in the risk of developing chronic diseases, depressive symptoms, and fatigue through longitudinal modeling of repeat data spanning seven years before and after retirement. Their analysis showed that retirement did not alter the risk of major chronic diseases. However, it was linked to a significant decrease in mental and physical fatigue and depressive symptoms, especially among individuals with pre-existing chronic conditions.
Retirement Age of 65
Interestingly, research from the Harvard School of Public Health (Moon et al., 2013) involving 5,422 individuals revealed a 40% higher likelihood of heart attack or stroke among retirees compared to those still working. This elevated risk was most apparent in the first year post-retirement before stabilizing. While this could be correlated with pre-existing chronic conditions, it also suggests the importance of purpose and engagement, which often shifts significantly with retirement. This aligns with findings from blue zone studies, indicating that societies where older individuals retain meaningful roles tend to exhibit greater longevity and better health in old age.
Furthermore, the Mental Health Foundation reports that one in five current retirees experiences depression. This risk is amplified for those living alone due to loss or divorce. Physical health issues can also exacerbate mental health vulnerabilities. Recent studies suggest that retirement may increase the likelihood of clinical depression by approximately 40%, in addition to a 60% rise in diagnosed physical illnesses among retirees.
Now I’m contemplating the commonly accepted retirement age of 65. It strikes me as somewhat arbitrary, especially when viewed through today’s lens, as the initial rationale has evolved considerably. This concept originated in Germany with Chancellor Otto von Bismarck’s implementation in 1889, later set at 65 in 1916. Its widespread adoption in the United States followed the Industrial Revolution, driven by observations of aging factory workers whose declining productivity and increased absences contrasted with the perceived benefits of younger workers.
Reflecting on the significant increase in global average life expectancy from 34.1 years in 1916 to 73.3 years today, it raises an interesting question: Could adjusting the retirement age potentially mitigate some of the factors that negatively affect health in later life? While the economic and potential health ramifications of such a change would undoubtedly be substantial and complex, it’s certainly a thought-provoking consideration.
To Conclude
This post offers a brief overview of human longevity and life expectancy. It touches on how these are measured, summarizes key data, and points to where this information can be found. Please note that this discussion is intended to be introductory, and we plan to explore these topics in greater detail in future blog posts.
References
- Buettner and Skemp, Blue Zones: Lessons From the World’s Longest Lived (2016) Am J Lifestyle Med, doi: 10.1177/1559827616637066
- DuGoff et al., Multiple Chronic Conditions and Life Expectancy, A Life Table Analysis (2014), doi: 10.1097/MLR.0000000000000166
- Ignacio Amago, Shades of Blue (2024) Science, doi: 10.1126/science.adu7169
- Johnson, Byron Blueprint Project (2021) Medium
- Moon et al., Transition to retirement and risk of cardiovascular disease: prospective analysis of the US health and retirement study (2012) doi: 10.1016/j.socscimed.2012.04.004
- Passarino et al., Human longevity: Genetics or Lifestyle? It takes two to tango (2016), doi: 10.1186/s12979-016-0066-z
- Poulain et al., Specific features of the oldest old from the Longevity Blue Zones in Ikaria and Sardinia (2021), doi: https://doi.org/10.1016/j.mad.2021.111543
- Skytthe et al., Design, recruitment, logistics, and data management of the GEHA (Genetics of Healthy Ageing (2011) doi: 10.1016/j.exger.2011.08.005.
- Westlund et al., Effect of retirement on major chronic conditions and fatigue: French GAZEL occupational cohort study (2010) doi: 10.1136/bmj.c6149
- Zijdeman et al., James C. Riley. “Data Page: Life expectancy at birth,” part of the following publication: Saloni Dattani, Lucas Rodes-Guirao, Hannah Ritchie, Esteban, Ortiz-Ospina, and Max Roser (2023) – “Life Expectancy”. Data adapted from Human Mortality Database, United Nations, Retrieved from https://ourworldindata.org/grapher/life-expectancy [online resource]
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