Globally, people are living longer, and the number of older adults is increasing. Aging causes significant changes in the human body and results in numerous chronic conditions, which make the older population progressively more susceptible to diseases and poor outcomes (1, 2). This has been unequivocally demonstrated by the COVID-19 pandemic. Counterintuitively, aging also causes some of the risk factors to gain – protective properties. Identifying predictors of poor outcomes in older adults would contribute to timely disease prevention and provide a better understanding of the physiology of aging. Sadly, older adults are too often excluded from relevant studies, the findings of which could directly affect their wellbeing. Here, we focused on long-living adults who represent a unique aging phenotype. A number of studies have addressed mortality in long-living adults has been addressed in (3–5). The Danish 1905-Cohort Survey investigated factors associated with mortality in over 2000 participants aged 85 years and above. The authors found that the 15-month mortality was associated with the degree of disability; a low level of mental and physical activity; and, in women, low self-esteem (6). In the PLAD study of mortality in a Chinese cohort of the oldest-old, age and aging-associated diseases were predictors of mortality, whereas high MMSE scores and high level of physical activity contributed to survival; moreover, the survival rate was higher in women (7). The Mugello Study found that cognitive disorders, ADL, polypragmasy, and renal dysfunction were predictors of the 12-month mortality in nonagenarians (8).

During the COVID-19 pandemic, several studies analyzed the COVID-19-related mortality in people older than 90 years of age. The mortality rate was higher in the 90-year-old hospitalized patients with functional dependencies (1). Analysis of mortality rates in people aged 60 years and above showed that dementia was associated with an increased COVID-19-related mortality rate (9).

These used data from cohorts from different countries and projects and have constantly revealed common patterns, which significantly contribute to mortality. They were mostly focused on non-modifiable markers reflective of current health status. However, prevention of undesirable outcomes largely relies on predictive and modifiable markers.

In the present study, we analyzed data from 1,641 long-living Russian adults aged 90 years and above. We assessed their health status and examined associations between geriatric syndromes (GSs), clinical and biochemical parameters and one-year mortality. Presently, this study is the largest of this design type in Russia. Coincidentally, it was carried out during the COVID-19 pandemic; therefore, we investigated the association between the studied factors and COVID-19-related mortality.

Out of 1,641 participants, 603 (36.7%) were home-based; 538 (32.8%) resided in elderly care facilities; 500 (30.5%) were inpatients. Vital status (dead or alive) was available for 1,641 participants (out of 2020 initially enrolled); 75% of 1,641 them were women, 32.1% of whom lived alone. The excluded participants (n = 379) did not exhibit any significant differences from other long-living individuals in the given parameters. i.e., gender, age, inclusion criteria, and clinical profiles.

The age median was 92 years (Q1-Q3 91–94 years). By late 2021, 552 (33.62%) participants died. Figure 1 shows the participant inclusion algorithm and key examination results.

By 2021, 33.6% (n = 552) of the participants (n = 1,641) had died. Table 2 shows characteristics of the participants.

Most participants were affected by at least one GS at the time of enrollment; 90% of them suffered from frailty. The design of this study did not require random sampling; therefore, we cannot provide a specific estimate of the prevalence of GSs in the older adults in Moscow. Nonetheless, we can safely say that there was a positive correlation between age and the number of GSs in this region (11).

Figure 1 shows that the main causes of death in the older adults were cerebrovascular accidents (28.7%) and cardiovascular diseases (19.6%). Another major cause of mortality was COVID-19. This number, however, could be even higher due to COVID-19 complications, but which might have been classified as resulting from cardiovascular diseases.

With a sharp decline in the rate of non-senescent mortality, the overwhelming majority of deaths are now caused by aging. This trend could be partially attributed to the quality of life, access to health care and other socioeconomic factors. Today, 15.8% of Russians, or one out of seven, is an older than 65 years of age (18), compared with 15.5% in early 2020. Population aging entails economic, budgetary, and health care implications. If this trend continues, the number of people of working age might decrease drastically. Therefore, for timely screening and disease prevention as part of ambulatory care, it is crucial to identify the causes of mortality, health risks, and protective factors in older adults and long-living individuals. Presently, the study of aging has emerged as a new and promising trend. However, long-living individuals—the most abundant source of biological data—are under-examined. The present study focused on this growing population group in Russia.

The geriatric assessment and analysis showed that mortality in the long-living cohort was associated with cognitive dysfunction of any cause, frailty, malnutrition, depression, functional disability, and comorbidity; COVID-19-related mortality was associated with depression, frontal lobe dysfunction, and frailty. Many authors have reported associations between mortality and various cognitive dysfunctions (including dementia) in the youngest-and middle-old (19, 20); however, reports on the associations in the oldest-old, or long-living adults, are scarce. In the German longitudinal six-year-long study, the authors used the Cognitive Telephone Screening Instrument (COGTEL) to assess cognitive functioning in people over 70 years of age. The results showed that the mortality rate in the subjects with low COGTEL scores were 60% higher than in those with higher COGTEL scores, especially in men (20).

Interestingly, some studies consider cognitive functioning dynamics to be another predictor of mortality, along with cognitive impairment (21). A Chinese study of older adults (mean = 82 years) showed a 75% higher mortality rate in the subjects who had demonstrated a more rapid decline in cognitive functioning measured by the MMSE. However, this association was more marked in the youngest old (under 80) and those who had initially scored higher (22).

Cognitive decline and its association with a high risk of mortality could sometimes result from cerebrovascular diseases (19). Reduced cerebral perfusion inevitably leads to chronic cerebrovascular ischemia that often affects cognitive functions, such as memory. Hence, cognitive disorders have proven to be a sensitive marker of clinical outcome in the oldest-old. Wang et al. (23) found associations between COVID-19-related mortality and dementia: the results showed that the patients with AD, not only vascular dementia, were at a significantly higher risk of death. Depression often accompanies dementia. In our study, depression in the participants who had recovered from COVID-19 was associated with a higher risk of mortality; pre- COVID-19 depression was a risk factor for COVID-19-related mortality. Our results are consistent with the results of a study conducted in a smaller cohort of younger older adults (24). These results substantiate the need for early prevention and screening for cognitive dysfunctions and monitoring cognitive functioning and mood in older adults.

We found that malnutrition was another significant, yet modifiable, risk factor for mortality, along with the laboratory markers of inadequate nutrition. The prognostic model demonstrated that its contribution was comparable to that of the age of the participants, unlike the above-described syndromes. We feel obligated to once again stress the high prevalence of this syndrome in older adults, even though malnutrition has been mentioned in many other studies as a prevalent condition associated with poor outcome (25–28). Long-living individuals, despite their exceptional characteristics (and probably, due to these characteristics), are often one of the most disadvantaged groups lacking access to some of the basic things, such as information and social and economic support. Risk of malnutrition and the effect of diagnosed malnutrition on poor outcome were clearly shown by routine examination/tests results. BMI, insulin and leptin levels were negatively correlated with the risk of mortality. Previous studies have shown that a slightly higher BMI could be a protective factor (29, 30), in older adults, probably, because aging is often accompanied by emaciation. Statistically significant were the correlations between mortality and such biochemical indicators as protein and iron turnovers (total protein, albumin, hemoglobin levels and mean cell hemoglobin concentration, and the size of red blood cells). Unfortunately, these results demonstrate inadequate testing of older individuals for these basic parameters, despite the long-standing discussions on the increased need for protein-rich food in older people (31, 32). Hence, proper screening for malnutrition and the risk of malnutrition with a follow-up nutritional therapy should be put in place. It is safe to assume that there is no need to abide by a strict range of glucose metabolism and BMI, since they could be protective at slightly increased levels. However, further research is needed to establish the target levels of these markers in the advanced age.

We also found significant associations between mortality and ADL, IADL, and frailty. These syndromes have been associated with COVID-19-related mortality in a number of studies (33–35). Many GSs could not be treated in people over 90; however, the data on their association with mortality can aid in identifying those at risk of COVID-19-related death, performing detailed diagnosis and developing a more effective treatment strategy.

We found that the level of 25(ОН)D was a vital factor in the long-living individuals. This association has been described in younger older adults. In February 2022, DeJaeger et al. (36) published their study of 1915 men aged 49 to 74 with a follow-up of about 12 years. Their results showed that 25(ОН)D deficiency doubled the risk of mortality. In our study, most participants were very deficient in 25(ОН)D; however, in those who died from COVID-19 and other causes, a critical deficiency in 25(ОН)D had been a risk factor for mortality. This indicates another potential therapeutic target and serves as the evidence of inadequate geriatric care. The therapeutic benefit of 25(ОН)D was tested in many studies during the pandemic. Oristrell et al. (37) reported that patients supplemented with 25(ОН)D until achieving 25OHD levels ≥30 ng/ml were at a lower risk of lower risk of SARS-CoV2 infection and severe COVID-19. Our results confirm that 25(ОН)D supplementation can be beneficial even in people over 90.

The long-living individuals who died within a year since the beginning of the study had been affected by many aging-associated diseases, had elevated levels of cystatin C, GGT, N-terminal proBNP, and total cholesterol. Those who died from COVID-19 had higher while blood cell and neutrophile counts, while those who died from other causes—higher apha-1-globulin and hsCRP levels. For the most part, all these parameters were within the normal range; however, their increase in the cohort of more vulnerable individuals indicated raised inflammatory markers. Therefore, comorbidity and inflammaging are directly associated with not only all-cause mortality but also with COVID-19 -related mortality, including in long-living individuals (38, 39). Increased levels of free T3 free in our study contributed to a higher survival rate. There is evidence to suggest that hyperthyroidism in an advanced age is more dangerous than hypothyroidism (40). Its levels in most participants were within the normal range which could be the reason we did not observe this association in our study.

Low IGF-1 was associated with a higher risk of mortality. The contribution of IGF-1 to longevity is still unclear. On the one hand, long-living individuals demonstrated low IGF-1, which could be genetic (41). On the other hand, other studies also demonstrated the association between low IGF-1 and mortality (42). This is yet another evidence of the delicate “equilibrium” observed in long-living individuals. Promising strategies to expand life expectancy in the oldest-old are shown in the Figure 5.

The present study has its limitations. First, all participants were from Moscow and the Moscow region; therefore, the results could not be extrapolated to the entire country. Second, COVID-19 status could not be established for a number of participants, which might have rendered the COVID-19 group (of those who contracted the disease or died from it) incomplete.

Despite the above-mentioned limitations, our study clearly demonstrated insufficiencies in geriatric care even in the regions with high-quality healthcare. Improved geriatric care could aid in expanding the active period of life, particularly in the oldest-old. Moreover, our results showed that biological, not chronological, age takes the lead in determining health, even in an advanced age. Therefore, prevention of aging as a complex phenomenon can facilitate a solution of socioeconomic and health care problems.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by the Local Ethics Committee of the Russian Gerontological Research and Clinical Center (Protocol No 30, December 24, 2019). The patients/participants provided their written informed consent to participate in this study.

DK, VE, MG, AAA, and IS: conceptualization ideas. AR, ES, and MI: data curation. ES, MI, and AT: formal analysis and software. AAA, IS, IK, AR, and DK: investigation. DK, VE, MG, and VY: methodology. VY, VM, OT, SK, and SY: project administration. AAA, IS, IK, and AIA: resources. DK, OT, VY, SK, and SY: supervision. DK, VE, MG, and AY: validation. ES, MI, MT, and VE: visualization. DK, VE, MG, ES, AY, MI, LM, and AR: writing—original draft. DK, VE, MG, ES, MI, MT, AR, VY, and LM: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

This study was funded by the Centre for Strategic Planning and Management of Biomedical Health Risks.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.