Factor analysis of chronic disease and mortality in NBER, NHIS, NSHAP, TILDA, MIDUS, CLHLS and Kaiser Permanente Study of the Oldest Old.
Figure 1A presents the summary data for the 606228 cancer patient decedents (neoplasia as the primary diagnosis) and 1604462 non-cancer/non-dementia controls. The data were collected for the years 1999, 2004, 2011 and 2017 in the National Bureau of Economic Research (NBER) depository [NBER]. The results show a consistent trend of greater educational attainment in the cancer decedents vs. the same in the control, exceeding the margin of random error (CI95). The individuals with the shorter lifespans (plotted on the abscissa) demonstrate greater absolute and relative differences in educational performance, with the cancer patients outperforming. The extent of the outperforming is attenuated for the longer lifespans.
Figure 1B presents the correlation of the life-long educational attainment with the number of cancer foci in the individuals. To ensure that the age differences between these subsets do not introduce a biasing trend, the ages of the groups were equalized at 78 (± 0.5) years each. Under these conditions, a monotonous increase in lifelong educational performance becomes a correlate of cancer spread (foci number in different tissues).
Figure 1C explores reproducibility of the relationship between education and cancer in additional datasets [Wisconsin Longitudinal Study, TILDA, NHIS], composed of living individuals and measuring cancer prevalence (not mortality). In all of them (W, T and N), the increase in educational achievement parallels decreased all-cause mortality rate, increased cancer prevalence rate and decreased multimorbidity (cardiovascular, stroke, diabetes, renal and pulmonary). The odds ratio of cancer to serios non-cancer comorbidity varies ~ 2-fold between the most and least educated population strata across these 3 datasets. The individuals with higher education demonstrate lower rate of physical disability and polypharmacy, higher self-assessed health, visual acuity, and cognitive performance and are taller than the less educated counterparts. They also demonstrate lower smoking rate (but higher cancer rate despite lower smoking rate). Our results confirm that educational performance is an important health and longevity predictor [Gotfredson et al., Deary et al] (Figure 1A-C).
The cancer-education relationship extracted in NBER reproduces in similar formats in National Social Life, Health, and Aging Project (NSHAP) 2005-2011 [NSHAP] and in National Health Interview Survey (NHIS) 1994-2000 [NHIS]; with the results presented in Figure 2A-C.
Figure 2A presents the data of NHIS survey showing the consistently increased educational performance for the cancer patients vs. the non-dementia control [Sun et al.; Hayes-Larson et al.]. Figure 2B presents the data for the NSHAP survey showing a significantly higher cognitive score in neoplasia and a slower rate of cognitive decline vs. control, especially in more advanced ages. Figure 2C presents the positive correlation of the education level in the NSHAP individuals with the variable number of neoplasia foci.
A review of additional datasets identified a further list of behavioral factors correlating with the cancer prevalence (Supplemental Table 1). The analysis includes Midlife in the United States (MIDUS) series [MIDUS], the National Health Interview Survey (NHIS) series [NHIS] and the Social Life, Health and Aging Project (NSHAP) [NSHAP].
Supplemental Table 1 presents cancer and control cohorts carefully balanced by age, gender, race, and non-cancer comorbidity in the NBER, MIDUS, NHIS and NSHAP sources. The presence of neoplasia and its correlates differentiates the cohorts, explaining the higher mortality rates, lower lifespans, and lower self-assessed physical health in the cancer cohort. Despite lower vitality in the neoplasia cohorts, the cohort members display statistically significant higher educational achievement (A1PB1, SF53, EDUC), a trend to greater personal independence (A1SF1Z, SF3448, SF491), a stronger sense of duty (A1SK7K, SF507), a trend to maintain relationships (SF48, MARITLST), higher household income (A1PC8, A1SJ13, SF58, SF61, SF3448, SF3548, [Larsen et al.]), altruistic behaviors (A1SK6F, SF533), higher cognitive ability (MOCA score).
Physical features are also contributing to the distinction between the cohorts, such as height (A1SA25, SF93), waist-to-hip ratio (A1SWSTHI), BMI (A1SBMI), (WAIST x HIP)/HEIGHT^2, systolic blood pressure (SYSTOLIC_1, 2), changes in sexuality (SEXCHGES). A consistent biological distinction is the smaller size of household in the present and future neoplasia patients (A1PB35, A1SE18B, SF2051) and the gender of the first child (A1PB36A1). The emotional abuse in childhood (A1SE17E), smoking and greater alcohol use in the cancer patients suggest both higher stress level in the neoplasia cohort [Kruk et al.] and direct carcinogenic effect of alcohol and tobacco [Klein et al.]. Higher arthritis incidence in cancer as well as higher supplement use in neoplasia suggests more advanced aging in the cancer cohort [Armenian et al.; Zhu et al.], consistent with overall greater comorbidity count (non-cancer + cancer) and shorter lifespan in the case cohort.
The patterns of Supplemental Table 1 for NBER present negative correlation between the dementia and cancer frequencies (r = - 0.39, p = 0.044). In all studied categories the cancer mortality is significantly decreased as a function of behavioral or neurological impairments. For example, the hazard ratio of cancer mortality among the individuals with the congenital nervous system malformations is HR = 0.3, p = 2.3x10-34. The similar hazard ratio for the injury-prone population is HR = 0.4, p = 0, while for the never married cohort the HR = 0.9, p = 2.25x10-80. For the cohorts suffering injury, trauma, accident and assault the dementia marker is elevated with the HR = 2.0, p = 0. The effect of smoking is significant and meaningful biologically. As expected, smoking correlates with the increase of cancer mortality causes per a patient from 0.32 in control to 0.99 in the smoking cohort (HR = 3.1, p = 0). The effects of marital status are also noted, with singleness increased in the non-cancer control and married status enriched in cancer cohorts. The effects of educational achievement were considered earlier.
Figure 3A-B presents an additional validation of Wisconsin Longitudinal Study (WLS) results in the Kaiser Permanente Study of the Oldest Old [Kaiser] and Figure 3C-D presents the Chinese Longitudinal Healthy Longevity Survey (CLHLS) [CLHLS].
Factor analysis of chronic disease and mortality in the National Alzheimer’s Coordination Center dataset.
This section presents the analysis of the data in the version 3 of the National Alzheimer’s Coordination Center dataset [URL, NACC]. The dataset is accumulated since 2004 and by the freeze date 03/2020 included > 150000 visits and > 42500 patients longitudinally monitored for the presence of comorbidities, including cognitive decline, cancer, diabetes, cardiovascular disease and more (Table 1). Versions 1 and 2 were excluded as not monitoring the entire list of comorbidities, together with the 25% of the profiles in the Version 3 that were incomplete. The remaining subset included 31495 doctor visits or telephone interviews. The dataset reports dozens of psychometric parameters and is relatively large, with the cancer cohort > 5000 visits and the control of > 25000. Thus, the expected statistical resolution is also high in NACC. The presence of dementia in the resulting trimmed dataset with the average age of 82 years was 24%, approaching the expected prevalence for the age [Bickel et al]. The results are presented in Table 1.
Table 1
t-stat profiles of the pre-morbidity factors (row 1) across comorbidity states (column 1). The left-most column 1 presents the list of outcomes, modelled by the factors preceding the onset of later-life morbidity. The upper row represents the traced factors: #1 – age; #2 – education; #3 – right-hand is dominant; #4 – height; #5 – BMI; #6 – weight; #7 – female gender; #8 – never married; #9 – divorced or never married; #10 – married; #11 – smoking measured as packs per day x years of smoking; race: #12- Hispanic; #13 – Caucasian; #14 – Black; #15 – Asian. The numbers are the t-stat values of the multiple regression models fitting the outcomes to the parameters, where the outcome is represented as (+1), while the absence is represented as (0).
FACTORS/OUTCOMES
|
#1
|
#2
|
#3
|
#4
|
#5
|
#6
|
#7
|
#8
|
#9
|
#10
|
#11
|
#12
|
#13
|
#14
|
#15
|
All-cause mortality
|
3.7
|
-8.2
|
1.0
|
-0.7
|
0.2
|
-2.3
|
-14.6
|
-1.3
|
4.8
|
0.4
|
0.0
|
-7.4
|
2.5
|
0.7
|
-0.5
|
Angina
|
2.5
|
-3.3
|
0.0
|
-0.6
|
1.6
|
-0.4
|
-7.4
|
-0.6
|
0.1
|
-1.3
|
1.9
|
-1.0
|
-4.6
|
-3.0
|
-4.6
|
Antipsychotics
|
5.9
|
-6.9
|
-2.8
|
-4.6
|
-4.1
|
4.5
|
-4.8
|
-0.7
|
-0.1
|
1.2
|
-0.5
|
1.6
|
1.1
|
0.1
|
0.5
|
Anxiety
|
4.4
|
-12.0
|
-2.3
|
-0.5
|
-0.8
|
-0.6
|
-2.3
|
0.8
|
-3.7
|
5.4
|
4.2
|
5.4
|
0.0
|
-2.3
|
-1.0
|
Arthritis
|
7.0
|
-1.2
|
2.0
|
-5.9
|
1.3
|
3.1
|
10.2
|
-3.2
|
3.8
|
-5.5
|
1.6
|
0.6
|
-2.7
|
-0.1
|
-5.4
|
Atrium fibrillation
|
-0.7
|
-1.9
|
1.5
|
1.5
|
-0.5
|
1.5
|
-7.2
|
-0.6
|
4.7
|
-2.7
|
-0.8
|
-7.0
|
2.3
|
0.4
|
0.8
|
B12 deficiency
|
0.4
|
-5.9
|
-1.7
|
1.0
|
0.9
|
-0.3
|
-0.8
|
-2.9
|
-0.8
|
-2.2
|
0.9
|
-0.5
|
1.1
|
0.9
|
0.2
|
Cancer
|
1.4
|
6.0
|
1.2
|
-0.5
|
-2.2
|
1.5
|
-4.0
|
-1.1
|
3.4
|
-1.3
|
1.2
|
-5.0
|
1.7
|
2.0
|
1.1
|
Cognitive decline
|
12.5
|
-27.8
|
-4.5
|
-1.6
|
-0.6
|
-3.2
|
-22.9
|
1.0
|
3.4
|
14.4
|
4.1
|
-1.8
|
4.0
|
1.8
|
2.0
|
Congestive heart failure
|
0.7
|
-3.3
|
0.7
|
-0.3
|
1.5
|
0.2
|
-5.4
|
-0.3
|
6.8
|
-1.4
|
-1.0
|
-1.6
|
-0.3
|
1.2
|
-0.6
|
Depression
|
4.3
|
-10.8
|
-3.6
|
-0.5
|
-0.7
|
0.8
|
-2.1
|
1.1
|
-2.8
|
-0.1
|
6.9
|
5.2
|
-1.4
|
-3.4
|
-2.0
|
Diabetes
|
4.9
|
-12.0
|
0.4
|
-4.0
|
2.8
|
4.7
|
-7.9
|
-1.4
|
2.8
|
-2.3
|
1.4
|
15.0
|
-6.4
|
-0.5
|
-0.6
|
Hypercholesterolemia
|
8.8
|
-3.1
|
1.6
|
-6.1
|
4.4
|
0.4
|
-17.9
|
-1.9
|
2.8
|
1.0
|
4.5
|
2.2
|
-0.7
|
0.0
|
0.7
|
Hypertension
|
12.5
|
-8.9
|
-0.4
|
-10.5
|
2.3
|
5.7
|
-17.3
|
-2.0
|
7.7
|
-2.7
|
2.6
|
1.8
|
-2.7
|
3.9
|
-0.5
|
Motor disorders
|
3.0
|
-14.4
|
-1.2
|
0.5
|
0.4
|
-1.2
|
-8.3
|
-1.0
|
-1.0
|
4.3
|
-0.2
|
-1.8
|
0.4
|
-1.0
|
-0.7
|
Myocardial infarction
|
4.8
|
-2.5
|
-0.9
|
-3.0
|
-1.7
|
2.7
|
-14.4
|
-2.3
|
1.7
|
-1.4
|
2.6
|
-1.0
|
-1.1
|
-1.0
|
-0.7
|
Pacemaker
|
1.2
|
-4.9
|
1.0
|
0.6
|
1.8
|
-1.8
|
-10.8
|
-0.1
|
3.6
|
-0.3
|
-2.6
|
-4.7
|
0.5
|
0.7
|
-0.1
|
Sleep apnea
|
6.3
|
4.0
|
-3.5
|
-8.2
|
-0.4
|
11.1
|
-16.3
|
-0.7
|
-3.0
|
-0.8
|
1.5
|
-1.5
|
-1.0
|
-1.8
|
-0.7
|
Thyroid
|
0.4
|
3.5
|
1.3
|
-2.6
|
0.6
|
0.7
|
22.9
|
-0.8
|
3.6
|
2.4
|
-0.3
|
-3.2
|
3.2
|
-1.1
|
1.4
|
Table 1 presents the t-stats of the multiple regression models computed for all-cause mortality and 20 comorbidities available in the NACC data. Vertical columns represent the t-stat profiles for the given factor across the disease models. The #2 (respondent’s education) is the second most consistent factor negatively correlating with the presence of most of conditions, including mortality and cognitive decline. [Gottfredson LS, Deary]. However, the trend reverses for neoplasia (see also Figures 1, 2). The most consistent factors are gender (#7) and the chronological age (#1). For the elderly population of NACC, the t-stat effect of chronological age is not dominant and ranks the third after gender and education in its correlation with the health conditions. Across the 21 conditions of Table 1, divorce and singleness correlated positively with comorbidity, height correlated negatively, while weight and smoking correlated positively. The effects of height, weight, un-married status, and smoking were each less significant than the effect of education. The results of this and the prior sections are consistent with cancer propensity accompanied by higher education as compared to other comorbidities at the same age and mortality rate.
Cognitive status as a survival marker in aging and chronic disease.
Figure 4A-C presents the analysis of NACC organized in longitudinal format, with the timelines available for 42500 patients as of 3/2020. All versions and 151500 visits were included in the analysis, with the average 3-4 visits per a patient.
Figure 4A shows the effect of cognitive status and academic performance on the survival and cancer prevalence. In agreement with all prior results, cancer prevalence is decreased in the patients with the educational status < GED, while the highest prevalence is in the patients with the advanced degrees. The trend persists in the cognitively impaired and intact patients. Of note, the age when academic achievement is established temporally precedes the age of cancer development, suggesting a putative causal relationship between the parameters of nervous system and tumorigenesis. The annual probability of mortality H(T) decreases with educational performance for the cognitively normal individuals but does not form a trend for the cognitively impaired cohort. The 3-fold ratio of mortalities is observed between the cognitively impaired and intact strata, but the dynamic ratio decreases to 2-fold for the <GED cohorts.
Figure 4B shows the protective effect of retained cognitive normalcy on the survival in chronic disease. At the baseline ages 70.5-72, there is a dramatic difference between the H(T) values in the cognitively impaired and normal patients. The dynamic range is 6-fold for the patients without cardiovascular disease, 3.5-fold for a single condition and 2-fold for two comorbidities. Analogously, similar dynamic ranges are observed for cancer, with the minimal range for the metastatic or multi-focal disease. The same trend exists for polypharmacy. This decrease of the dynamic range between the cognitively intact and impaired cohorts in the more challenging conditions is observed across all computations.
Figure 4C shows the analysis for the specific values of cognitive score and 4 ages, spanning the range between 66.5 and 93.5 years. In each bin of cognitive score, the values for H(T) follow the ages in the ascending order from left to right. The dynamic range is ~ 15 between the age 66.5 and 93.5 years at the top maximal cognitive score 30. The dynamic range becomes ~ 6 at the score 29, ~ 4.5 at 27-28, ~ 3.5 at 24-26, ~ 2.2 at 20-23 and only ~ 1.8 for the scores < 20. The absolute values of H(T) sharply depend on the value of cognitive score. The left-most bar of the plot is the H(T) for the 66.5 years old patients with the score 30 at the baseline and it is the lowest. The left-most bar for the score 29 indicates ~ 2.5 higher annual probability of mortality for the same age as compared to the score 30. The annual mortality rate doubles again for the bin 27-28 and doubles yet again for the bin 24-26 for the same age. At the age 93.5 the annual mortality rate still responds to the factor of cognitive score, but with lesser sensitivity as compared to the younger ages. The annual probability of mortality reaches ~ 0.26 for the 93.6 years old patients at the cognitive score 30 but increases to 0.58 for the score range < 20 at the same baseline age. By contrast, the H(T) is 0.016 for 66.5 years age at the cognitive score 30 and 0.32 at the cognitive score < 20, indicating a ~ 20-fold dynamic range. The annual mortality rate becomes age-independent with the cognitive loss (Figure 4C) or less age-dependent with accumulation of other senescence markers such as serious comorbidity (Figure 4B). Cognitive score exceeds both cardiovascular diseases and cancer as predictor of mortality (Figure 4B) and shows the effect commeasurable with the effect of ~ 25 years of age (Figure 4C). The effects of academic performance in early life persist over lifetime and reflect on both the cancer prevalence and cognitive decline in later life (Figure 4A, Figure 5). The results of the section support a conclusion that the condition of central nervous system is rate-limiting in the progression to mortality, but other factors compete with the role of CNS at the extremely old ages or other sources of injury.
Academic performance and cognitive status are inversely proportional to the rate of comorbidity accumulation.
Figures 5A and B present the results of analyzing the kinetics of changes in the level of aging markers as a function of academic performance or cognitive status. Such markers were cognitive score, the presence of cardiovascular disease and non-supplement (prescription) polypharmacy. One data point was measured at the beginning of follow up and the second point at the end, and the difference was related to the length of observation, producing the derivatives correlating with the biological aging rate.
Exploration of Figure 5A presents the aging-associated trends in the frequency of cardiovascular disease (CVD) as a function of cognitive score, with lower frequency of CVD associated with higher cognitive scores. Interestingly, the time-derivative of cardiovascular morbidity as well as time-derivative of prescription polypharmacy also follows the identical trend. Figure 5B presents the same for the dataset stratified by the educational achievement. Dramatic differences between “no GED” and “advanced degree” categories are observed for the dementia fractions reached by the baseline age (0.6 vs. 0.3) and the fraction of the “resisters”, defined as individuals retaining clinician-assessed cognitive norm through the entire follow up (0.19 vs 0.58). Analogously to Figure 5A, the rate of cognitive decline depends on the original value of cognitive score, accelerating for the “no GED” and “GED” categories. The statistically significant trends in the same direction are observed for H(T) and polypharmacy (CI95 are provided), although not so pronounced as the fractions of baseline dementia and percentage of “resisters” to dementia conversion. Each of these metrics is a surrogate of biological aging process and the data of Figure 5 support the conclusion that the increased stability and performance of central nerve system correlates with delayed aging rates.
The results produced in NACC were independently verified in NSHAP WAVE 2 (N = 3200), which is recruited randomly nationwide, while NACC is recruited based on the complains and volunteering (see Figure 6 below). The baseline ages in NSHAP were equalized in the 68±1 narrow range (N = 2000), to minimize the age-related biasing that can arise in the process of stratification. Figure 6A presents the trends analogous to all previously observed in NACC and other sources. Specifically, H(T) is strongly and inversely correlated to the MOCA values, producing > 4-fold dynamic range between the MOCA “1-14” and “26-30” subranges. The dynamic range in NSHAP is lower than that in NACC due to a smaller dataset size and higher noise-to-signal ratio. The MOCA score in late life in NSHAP strongly correlates the academic achievement in early life (the education code average is 1.5 in the MOCA “1-14” bin and 3.2 in the MOCA “26-30” bin). The fraction of cancer varies from 0.16 in the MOCA “1-14” bin to 0.28 in the MOCA “26-30” bin. Finally, the multimorbidity is ~ 1 condition/person in the “1-14” MOCA bin and 0.69 in the “26-30” bin.
Figure 6B compares the H(T) values at the different severity of health condition and values of MOCA score. An approximately 3-fold decrease in mortality is observed at 0-2 conditions, but the effect is less pronounced in the “>2” comorbidity range. Analogously to the observation for NACC, the impact of the cognitive score on H(T) exceeds the impact of one serious health condition.
Retention of cognitive norm according to a clinical evaluation while experiencing decreased performance in cognitive score correlates with anomalously low mortality rates.
NACC provides multiple measures of cognitive state, in addition to MMSE and MOCA. The cognitive scores typically align with the objective diagnoses or functional norm, but not absolutely (Figure 6C). An assessment by a trained clinician is decisive and such scores are NACCNORM (clinical norm during all visits) and NACCUSDS (1 – norm, 2 – pre-MCI, 3 – MCI, 4-dementia). The discrepancies between the clinician-produced diagnoses and the diagnoses suggested by the cognitive scores are biologically important (below).
Figure 6C demonstrates that a small percent of dementia as defined by a clinician coexists at the baseline with the highest cognitive score 30, while a comparably tiny fraction of 1.3% of cognitive norm exists in the “<20” cognitive score stratum at the baseline. Still greater percentages of stable cognitive norm exist in the borderline score strata “20-23” and “24-26”.
We find especially interesting the anomalies that result from the superposition of the clinician-generated assessment of normalcy (NACCUSDS = 1 at the baseline and at the end of follow-up) and low cognitive score (MMSE/MOCA < 26) at the end of follow up. Such decrease of cognitive score places the patient into a risk category, but the clinician-assessed normalcy despite the increased risk suggests resistance to dementia conversion. These low-score resisters (termed +/- phenotype) differ from high score resisters (+/+ phenotype) or dementia converters (-/- phenotype). The data are presented in Figure 7.
Figure 7A shows dramatically different levels of annual mortality probability as a function of the phenotypes and follow up. The highest annual probability is observed in dementia, followed by the high score resisters. Abstraction of cardiovascular morbidity decreases the annual mortality rates in the high score resisters, but the previous trend remains. The bottom graph in Figure 7A refers to the low score resisters and demonstrates the lowest mortality rate. Remarkably, in the subset 7 years after the baseline (the age of 85 years), the H(T) is ~ 0.015, that is the value corresponding to the general population annual mortality rate at 66 years. The annual mortality rate increases to 0.025 by 90 years, still maintaining the ~ 20 years gap with the average population. Figure 7B presents the fraction of cardiovascular disease at baseline. Cardiovascular disease is maximal in the shortest follow up interval and in the dementia converter phenotype. Cardiovascular disease is lower in the high-score resisters and much lower in the low-score resisters, suggesting the explanation of the lower mortality and resistance to dementia conversion. However, as Figure 7A demonstrates, the complete exclusion of cardiovascular morbidity in the high-score resisters produces a population with the annual mortality still exceeding that of low-score resisters. Figure 7C demonstrates that the low-score resisters are biologically distinct from both dementia converters and high-score resisters. The latter produce closely matching attrition profiles (the numbers of the patients leaving the study at a particular year of follow up) with the tangent ~ - 0.26. By contrast, the low-score resisters demonstrate slow attrition profile with the tangent ~ -0.15. This tangent is proportional to the exponential Gompertz constant beta [Ricklefs et al.], suggesting that the low-score resisters age at the rate of ~ 0.6 of the general population, during at least a segment of overall lifespan. The relatively reduced fraction of cardiovascular disease is likely not a cause but a consequence of the slow-aging phenotype, which is also characterized by high tolerance to the reduction of cognitive score without developing clinical dementia. The exclusion of cardiovascular morbidity shown in Figure 7A is expected to change the attrition profile in Figure 7C if cardiovascular health was the primary factor in these patterns. However, the profile after exclusion remains unchanged and still differs from that of the low-score resisters.
Life-expectancy after the baseline age is proportional to the baseline cognitive score.
NACC recruits the patients and relatives since 2004 up to the present time, with the median year of recruitment being 2011. The patients are observed until the accumulated disability makes further collaboration unfeasible or until the patients die. The follow-up intervals are variable and reflect the baseline state of health and potential longevity of an individual. The individuals with longer follow up intervals are healthier and achieve longer lifespans. Figure 7 indicates that the mortality rate disproportionally decreases in the longer follow up cohorts, but this observation can be an artifact. The study used in this report was released in the March of 2020 and in longer follow ups (ending closely to the release date) the decedents do not have time to accumulate. Thus, the direct mortality measurement is not appropriate, and instead we applied the proxy markers such as cognitive state, polypharmacy, and cardiovascular disease, being all linked as benchmarks of aging [Franceschi C et al.].
Figure 8A shows that the follow up length correlates to the baseline value of cognitive score. The shorter follow ups (1-2 years) associate with lower baseline cognitive scores 24-25, while the longer follow ups (9-10 years) correspond to almost maximal cognitive scores of 29.5. The final cognitive score before the attrition is almost constant in all cohorts and is about ~20, considered a borderline value between mild cognitive impairment and dementia. The baseline polypharmacy is lower in the longer-observed individuals, but the final polypharmacy is higher in the older individuals at longer follow ups. Figure 8B shows the time-derivatives of cardiovascular disease frequency, polypharmacy, cognitive score aligned with the annual mortality H(T). All derivatives form a similar profile, being 3-fold higher for the shorter follow-ups. The H(T) profile is confirmed by that of the surrogates. The result of Figure 8 show that once the baseline level of senescence is high, further senescence proceeds with acceleration. There is the final level of neurological and non-neurological damage that leads to attrition from the study that precedes mortality by 0-3 years. The decline of cognitive score to ~ 20, the presence of cardiovascular disease in ~ 60-90% of the cohort and the polypharmacy in the range 4.6-5.7 on average define this threshold. The extent of non-neurological damage is higher in longer-living patients, suggesting the reason why H(T) stops to respond to cognitive status in the oldest old, as shown previously. Not only the protective effect of relatively healthy nervous system weakens with age, but the senescence pressure increases exponentially, as suggested in Figure 8B.