Scientists continue to discover conclusive evidence that correlates death age to happenings within our bodies. So if we can read those signals, can we do something about it?

Life insurance companies, for example, want to get really good at predicting when and how we’ll die so that they can adequately insure us while still maintaining a scalable business. It’s also helping people “manage risks” – namely by helping individuals identify factors that play significant roles in our estimated lifespan. Northwestern Mutual, for example, even created this handy “Lifespan Calculator” that aims to predict how many years you’ll live by answering key questions that contribute to your overall healthy aging and longevity. These factors are some of the most well-established factors that contribute to our health as we age. (Check it out.)²

The factors in most traditional longevity calculators like smoking history, healthy eating habits, exercise, hereditary diseases/family history of disease, and more, are very fundamental and important ways to influence your own healthy aging. But what is happening on a cellular level that scientists can use to predict when and how you will die? And what can we do to influence our cells’ longevity for a longer, healthier life?

Recently, scientists have leveraged artificial intelligence and machine learning to go even deeper into our understanding and ability to predict life expectancy. In one study, mouse frailty – or age and life expectancy of the mouse subjects – was investigated on a cellular level to identify genes and other interventions that can slow or reverse aging.¹ Frailty Indices are “composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not [yet] known.”

As the biological process of aging progresses, organ failure (and therefore death) can be predicted by machine learning. The development of a clock that can be used as a strong predictor of chronological age and remaining lifespan is a huge breakthrough in aging science. Two clock-like mechanisms – or calculators – when used concurrently, could, in theory, be used to accelerate “the identification of longevity genes and aging interventions.” Such studies have already uncovered basic cellular evidence that we could harness to regain some control – which is why we’re so passionate about NAD+ and NMN. It’s something we can measure, and control.

We’re probably a long, long way away from predicting the exact nature and date of death for most people – and would you really want to know that date and time, anyway? But, what we can do is look at traditional commonly-used risk factors and other biomarkers to make an impact on our own predictions of our mortality.

Common risk factors for mortality:

• Diseases
• Lifestyle
• Social habits
• Psychological factors
• Age
• Sex
• Traditional cardiovascular risk factors
  • Systolic blood pressure
  • Cholesterol levels
  • Smoking
  • Diabetes
  • Cardiovascular disease
  • Cancer
  • Alcohol consumption
  • BMI
  • Creatine Levels

Biomarkers of mortality (and brain age estimators):

•  
• DNA methylation
• Telomere length
• Specific proteins / peptides / plasma proteome
• NAD+
• So much more that we have yet to uncover

Bodies are such mysteries, still, even with all that we’ve learned in the past few decades about what contributes to healthy aging, what negatively impacts our longevity, and how we can ultimately slow down and reverse the signs of aging. The more we learn about the science of aging, the more we can harness the endless possibilities of anti-aging and convince our cells it’s not yet our time.

Learn how one of our favorite molecules – and the most abundant molecule in our bodies besides water – plays a role in aging and longevity. Plus, now you can do something about your own NAD+ levels. Abundant NAD+ keeps you feeling younger by supporting multiple aspects of healthy aging, cellular metabolism, body composition, and normal circadian rhythm function. Explore NAD+ now!

Sources:

1. https://www.nature.com/articles/s41467-020-18446-0
2. https://media.nmfn.com/tnetwork/lifespan/#0
3. https://www.nature.com/articles/s42003-021-02289-6