Employing an “aging paradox” to uncover effective measures for advancing productive longevity

Clinical material

Integrative Health Technologies, Inc., a Clinical Research Organization in San Antonio, Texas, has amassed a 40-year Longitudinal Database of Medical Biomarkers derived from dual energy X-ray total body scans dual energy X-ray absorptiometry (DEXAs) and fasting 45-blood chemistry panels. Although not a true representative of a national sample, the data were obtained from study participants living in all 50 states in the US. All study participants executed a written statement granting permission to use their redacted data in the future clinical trials and research publications. For the most part, the volunteers were relatively healthy, run-of-the-mill individuals. Therefore, the most measurements in this single-center and observational cohort study are expected to fall in generally accepted “normal” ranges. Thus, linear correlations become a key means to assess trends in developments related to health matters, that is, slope-based rather than threshold analyses are employed for the most part here.^[23,24]

To begin with, baseline data in the present report were accumulated from both male and female subjects (age 16– 91 years). The material was collected from tests conducted from February 2014 to July 2019. Data were limited to purportedly “normal subjects” who had volunteered after they were provided an informed consent form and were asked to review the form with their health-care provider to ensure that they had no medical conditions that would preclude their participation. It is especially important to note that material was used only from the subjects possessing a circulating FBG level in the non-diabetic range (125 mg/dl or less). In addition, individual data employed for analyses were limited to values that fell within three standard deviations of the group mean. Despite all, this resulted in very little elimination of material. After removing information from volunteers with FBG levels greater than 125 mg/dl, data from 718 subjects were still left to evaluate. “Resting” blood pressures and heart rates were obtained after study participants had remained prone for ~15 min while completing their DEXA testing. All study participants executed an informed consent form in compliance with the Helsinki Declaration and were allowed to participate by independent Institutional Solutions Review Board (IRB), (http://ohrp.cit.nih.gov/search/IrbDtl.aspx). To reemphasize, material was obtained only from volunteers who granted written permission to use the redacted data in the future analyses. Quest Diagnostics conducted all blood testing after study participants had fasted for at least 10 h.

Time intervals considered

Based on the previous experiences and prior first approximations, we chose to arbitrarily divide data derived from the human life cycle into 3-time periods to examine two of them separately. The 1^st time consists of the developmental stage where individuals are growing from childhood and maturing into adulthood.^[26] This time interval devoted largely to growth and development was designated to last through age 24 years. Information describing this maturation period is not included in the present investigation.

The 2^nd time begins with the initiation of a well-recognized and characteristic decline in the renal glomerular filtration rate, because we believe this mechanism to be related in some manner to the general aging phenomenon.^[26] Accordingly, this life cycle aging period in our data interpretation begins at 25 years of age and lasts until 70 years. During this longest period of the life cycle, many risk factors pertaining to MS and the aging process are progressing. The functioning of a variety of risk factors such as blood pressure, a multitude of dyslipidemias, and glucose-insulin perturbations is taking place and providing a continuum of metabolic risks linked to the aging process despite not usually reaching levels consistent with any disease diagnoses, especially diabetes mellitus. As a first approximation, observations will be made in this time interval that we have labeled in the past “Continuum of risks” for comparative purposes.^[26] The ^3rd time sequence (AP) was set as a first approximation to begin after 71 years and ends here at 91 years of age. It contains unexpected paradoxes that often defy reason when compared to the preceding period.^[23,24,26]

Statistical analysis

Multivariate regression analyses were analyzed using KaleidaGraph graphing and datum analysis, Version 4.1.3 Synergy Software, Reading, PA. P < 0.05 (two-tail) determined using a table of correlation coefficients (Pearson r values) was statistically significant, whereas P > 0.05 < 0.10 was designated as a trend. Statistics between two columns were carried out using Student’s t-test.

Confusion over aging definition

The term chronological aging presents little difficulty in understanding: It is what it is. The same cannot be said for biological aging. The present ways to define this entity appear unsatisfactory to many and certainly should not appease the most exacting of scientists.^[2-5] Nevertheless, something is needed here as a point of departure. Aging develops from sequential or progressive physiological changes in an organism that leads to senescence, that is, increased risk of debility through poor adaptation to metabolic stress, greater potential to develop all sorts of diseases, and often death.^[4,5] This can be linked to a reduction in reparative and regenerative potential in tissues and organs as well. Often the definition is accompanied by a cautionary warning: It is important to distinguish between the purely physicochemical processes of aging and the incidences of disease and injury that can interrupt mortality. Concerning the last statement, ironically, age represents the primary risk factor for chronic diseases -- including, cardiovascular, malignant, and neurodegenerative conditions.^[4]

Popular background associations with biological aging

At any rate, perhaps, the greatest deterrent to a concrete definition of the entity is that no single theory to explain all the phenomena linked to biological aging has ever been recognized. Some potential involvements are listed below and dealt with in an overly simplified fashion to favor brevity yet maintain some modicum of background knowledge that includes genetics, cross-linking reactions in vivo, autoimmune reactions, oxidative stress- induced damage, glycation reactions, and endocrine abnormalities. These are intrinsically involved at varying degrees in the so-called aging process.^[2-5]

Role of the glucose-insulin system in aging

In the context of the endocrine theory, emphasis will be placed on the glucose-insulin system and its nutritional implications with the realization that it is only one aging factor among a group of others, but a very important one that influences lifespan.^[3,8-13] IR is a principal driving force behind T2DM, a metabolic disorder that has been linked to premature aging time and again; and second, IR is ameliorated to a very significant degree by caloric restriction, a practice, perhaps, the leading one that has long been recognized to extend longevity.^[7,28-34]

IR and some accompanying risk factors intensify during the early portion of life cycle (continuum of risks)

In most of the relatively healthy and non-diabetics examined here during the period between ages 25 and 70 years, both circulating FBG and FM levels show steady statistically significant increases that still fall generally into ranges considered by many “typical, innocuous readings” – chemistry values that do not connote a diagnosis of T2DM [Table 1, Figure 1a]. However, we hypothesize that while seemingly slight on the face of it, the correlation upward for IR (FBG) and its unfavorable accompaniments, such as increasing blood concentrations of ALT [Table 1] and triglycerides [Table 1, Figure 4a], can portend detrimental outcomes over time. Comparable depictions have been shown in the previous communications denoting continuum of risks happenings.^[23,24,26]

The AP period heralds the potential to recognize whether and what common risk factors may prolong lifespan

That improvement in values of some parameters dealing with health unexpectedly takes place in the “older elderly” is now being recognized in a variety of published communications.^[20,21,24] In the first two tables and some of the ensuing figures shown [Figures 1a, b, 4a], comparing the correlative slopes from the AP periods with those from the continuum of risks periods often demonstrate marked changes between them – usually occurring sometime after age 70 years. These sources from older volunteers show that the upward age-related trends for FBG, FM, ALT, and triglycerides found in the younger stage are reversed demonstrating improvement health-wise in these risk factors later [Table 3]. The further reduction in body weight and FFM during AP may imply the presence also of some form of caloric restriction in the total scenario dealing with the AP.

Examining circulating lipid levels during aging provides some intriguing observations. LDL-cholesterol, like total cholesterol and non-HDL-cholesterol, shows essentially no real change between the continuum and paradox periods, that is, really slight and insignificant improvement when considering survivor bias [Figure 3]. In contrast, triglycerides display a marked decrease [Figure 4a] in the latter period suggesting an overall improvement in fitness in both cases. Many sources in the past have questioned the utility of treating total, LDL-, and non-HDL-cholesterol, especially in the elderly.^[35-39] Our data support that conclusion when using the AP to arrive at that decision point. The lack of change in systolic blood pressure [Figure 2] has been noted earlier for both IR and caloric restriction and is not unexpected, although difficult to explain in some respects at present.^{[11,29,40,41]}

Aging Paradox Relates to Survivor Bias

The late temporal changes found in the AP period for FBG, FM, ALT, and triglycerides may require further investigation to fully decipher. Still, it would be hard to believe that all these onerous health risks that amplify over the early life cycle could mend with aging. In addition, the fact that the linear correlative relationships occurring in the two designated life cycle periods (the continuum of risks and AP) between FBG as the independent variable and both fat mass [Figure 1c] and triglycerides [Figure 4b] as the dependent variables essentially do not deviate in any way fortifies, to some degree, the overall assumption that substantial metabolic changes are not taking place between the CR and AP periods.

Hence, we favor the “survivor bias” mechanism among the human volunteers as a primary explanation for the findings depicted in the tables and figures. This theory is exemplified elegantly in a previous laboratory study working with caloric restriction and the glucose-insulin system in rats.^[33] Two separate groups of rats were maintained – a caloric restricted group alongside an ad libitum food consuming one. Throughout this temporal study, the circulating FBG levels in the ad libitum fed rats were decidedly greater at all the timed measurements – basically indicating that the free-eating group possessed a higher level of IR. Even so, the combined FBG data from both groups were averaged over the lifecycle to give a reading falling halfway between each group. Eventually, the less healthy, ad libitum-eating rats died earlier on than the caloric restricted ones, no doubt because of the nutritional status. This removed their higher FBG levels from the averaging procedure near the study termination and thus lowered the final mean FBG level of the survivors. An AP is evident – derived to a great extent through a lower average FBG attained exclusively from the longer living, healthier, and caloric restricted rat population toward the end of their life cycle.^[23,33] Clearly, a significant metabolic changes within a given living organism near the end of the lifespan are not the explanation behind the AP described here.^[23,33]

Indeed, Rosedale et al. point out that human centenarians have lower blood glucose, insulin, and serum triglycerides than those who do not live to be over 100 years old.^[11] Similarly, the present investigation reveals that most elderly volunteers available to contribute data are the ones possessing the most ideal glucose-insulin metabolism over their earlier life cycle. Changes in FBG and other contributors to the AP [Table 3] as well as the ratio triglyceride/HDL-cholesterol (another surrogate for IR) support the postulate that improvement in IR was responsible for at least some of the favorable survivorship among the research subjects in the previous clinical studies.^[42-45]

Often, the average human in the past has demonstrated limited interest in undertaking and maintaining promising health strategies that commit to extensive use of time – certainly when it comes to a lifespan. To change direction, we believe that at least two prerequisites are vital to overcome this unfortunate circumstance. First, the presence of straight-forward and favorable evidence indicating a strong likelihood of success from the involved strategies must be clear; and second, any therapeutic approaches should be obviously safe and, for the most part, easy to follow.^[11,34] A good example supporting the last statement would be the attempts to introduce caloric restrictive measures as continuing lifestyle practices.^[31,32,34] Even though the weight of evidence shows marked success at lengthening lifespan in animal models through this practice, convincing successful examples dealing with human experiences are not readily available. In the face of this, it becomes hard to institute and maintain a program of general fasting, especially when constantly confronted by the ubiquitous presence of flavor-enticing modern Western dietary choices. Dietary choices in other parts of the world would need to be factored in by researchers in this field to fully establish the cause and effects associated with the AP.