The last 17 features not yet discussed belonged to the categories of personal well-being, accomplishment, and close relationships

Another set of beliefs we tested concerned the question of when middle age starts and ends. People who were predicted to be older by PsychoAge responded to specified higher boundaries of this life period. For example, people whose PsychoAge matched their SubjAge estimated male middle age period to start at 39-47 years on average. In the meantime, people from the same SubjAge groups but with PsychoAge 20 years larger suggested that men’s middle age started at 45-50 years. Despite the large effect on PsychoAge, the concept of middle age did not affect SubjAge. People from the same PsychoAge group were extremely consistent in their definitions of male and female middle age, even when coming from different SubjAge cohorts.

Variables describing marital status and sex life were a rare occurrence of concordant effect on both PsychoAge and SubjAge. More fulfilling sex life or being married decreased the predictions of both these aging clocks.

The level of life satisfaction and personal accomplishment correlated with lower SubjAge, and in some cases – with higher PsychoAge. Among the variables that did not affect PsychoAge were effort put into work, expectations of work, and contributing to the well-being of other people in 10 years.

Psychological aging and mortality

SubjAge is a more significant all-cause mortality risk factor than PsychoAge. Hazard ratios were obtained with Cox regression. Delta is the difference between actual age (chronological for PsychoAge or subjective for Subjective) and their DNN-derived estimations. Each row represents a hazard ratio and the 95% confidence interval associated with a specific feature. Note: “***” for P-value of 0.001; “**” for P-value of 0.01; “*” for P-value of 0.05.

We then compared the 50 features used by both PsychoAge and Subjage plus chronological age within one Cox regression ( Figure 7 and Supplementary Figure 2). The 10 most powerful risk factors based on HR magnitude belonged to the categories of:

Ten most important features associated with mortality risk in a Cox regression adjusted for 49 psychosocial variables, sex, and chronological age

– Personality (“Conscientiousness”, “Agency”); – Psychological beliefs (“Live for today”, “Positive reappraisal”, “Lower aspirations”); – Personal well-being (“Satisfied with life at present”); – Demographics (“Chronological age”).

Among these risk factors only “Live for today”, “Health compared to others your age”, “Chronological age”, “Rate current health”, sexy Ubon ratchathani american women “Lower aspiration” and “Shortness of breath” were deemed significant.

Surprisingly, the optimistic outlook associated with higher “Live for today values” was the largest contributor to the mortality risk (HR=1.35), while lower aspirations were a significant mortality protector variable (HR=0.84). These results, however, are in line with the previous findings in the mixed-effects analysis, in which both these features were shown to increase SubjAge, while only insignificantly affecting PsychoAge.

“Health compared to others your age” also increased SubjAge (coef = 1.91) but also decreased PsychoAge predictions (coef = -2.14). Despite this compensatory effect of reduced PsychoAge, “Health compared to others your age” (1=”Much better”, 6=”Much worse”) was recognized as a significant risk factor (HR=1.16). This may be attributed to the previously established higher mortality associated with increased SubjAge predictions ( Figure 6 ). “Rate current health” behaved similarly, although due to its reversed scale (0=”Worst”, 10=”Best”) its HR was below one – 0.86.

Significantly low HR for “Shortness of breath” may also be misleading, since it is a binary variable, in which “1” signifies no problems with breathing, while “2” – shortness of breath when walking uphill. In the mixed-effects analysis having short breath was associated with, on average, 2.91 years larger SubjAge predictions and only 0.88 years smaller PsychoAge predictions. This furthers the point that the most significant all-cause mortality risk factors are associated with disproportionately larger SubjAge predictions.