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Article

Dynamics of Cognitive Impairment in Older Adults Linked to Suicide-Related Single-Nucleotide Polymorphisms: A 3-Year Follow-Up Study

by
Yana Zorkina
1,2,*,
Alexander Berdalin
1,
Irina Morozova
1,
Alisa Andryushchenko
1,3,
Konstantin Pavlov
1,2,
Olga Pavlova
2,
Olga Abramova
1,2,
Valeriya Ushakova
1,2,
Angelina Zeltzer
1,
Marat Kurmishev
1,
Victor Savilov
1,
Olga Karpenko
1,
Georgy Kostyuk
1,3,4,5 and
Anna Morozova
1,2
1
Mental-Health Clinic No. 1 Named After N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
2
Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
3
Faculty of Psychology, M. V. Lomonosov Moscow State University, 125009 Moscow, Russia
4
Department of Psychiatry, Federal State Budgetary Educational Institution of Higher Education “Moscow State University of Food Production”, Volokolamskoye Highway 11, 125080 Moscow, Russia
5
Department of Psychiatry and Psychosomatics, I. M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
*
Author to whom correspondence should be addressed.
Psychiatry Int. 2025, 6(2), 64; https://doi.org/10.3390/psychiatryint6020064
Submission received: 17 February 2025 / Revised: 17 March 2025 / Accepted: 15 May 2025 / Published: 3 June 2025
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Abstract

Background: Cognitive decline during aging is a factor that inevitably affects everyone. In some older adults, cognitive function declines more rapidly to mild cognitive impairment (MCI) and eventually dementia. Our work aimed to determine the associations between suicide-related single-nucleotide genetic polymorphisms (SNPs) and cognitive function dynamics in people over 65 years old over a three-year follow-up. Suicide-related SNPs have already shown an association with dementia in our previous study. Methods: The present study included 66 participants over 65 without subjective cognitive decline. Cognitive impairment was assessed at two follow-up points (at the start of the study in 2020–2021 and 3 years later) using the Montreal Cognitive Assessment (MoCA). Patients were also genotyped for 16 SNPs. Results: We found associations between rs10898553 and rs165774 and MoCA 3-year dynamics, with a certain genetic variant related to more significant progression. For rs7982251, associations with scale scores were found, but no effect on its dynamics. Conclusions: The research focused on analyzing genetic factors of cognitive decline in healthy older adults without subjective cognitive decline. Identifying these markers can help predict the development of pathology at early stages and start timely treatment.

1. Introduction

The spectrum of cognitive decline in older adults ranges from normal cognitive decline associated with aging to subjective cognitive impairment (cognitive complaint on a normal cognitive screening test) to mild cognitive impairment (MCI) and dementia [1].
Cognitive impairment during aging is not considered a disease but a normal developmental process. It is characterized more by functional impairment than by the neuronal degeneration observed in dementia [2].
MCI represents an intermediate stage between healthy aging and dementia, where cognitive impairment is more severe than in typical aging. Approximately 10–20% of adults over the age of 65 are diagnosed with MCI [3]. Prevalence varies due to differences in the diagnostic criteria of MCI used in studies. There is currently no standardized method to assess the severity of cognitive impairment. Variations in the prevalence of MCI may be attributed to the use of different scales and techniques to assess cognitive impairment. Additionally, authors distinguish between subgroups of amnestic and non-amnestic types, focusing on a specific subtype of MCI [1]. Individuals diagnosed with MCI do not experience limitations in daily activities, and mild impairments in memory or executive function are almost undetectable.
The treatment of late-stage dementia is challenging, and clinical trials fail in more than 99% of cases [4]. Therefore, MCI is considered by clinicians as a period where preventive treatment can potentially delay the progression to severe outcomes and the development of dementia.
Variation in cognitive function levels among individuals is largely influenced by genetic factors, which account for up to 60% of cognitive impairment in older adults [5]. A study on 798 twins without dementia followed for 13 years reported significant inheritability in the rate of change in cognitive function, ranging from 50 to 80% [6].
A lot of research is focused on genetic factors associated with dementia. These are “case-control” GWAS studies for different types of dementia conducted on different populations [7,8,9,10]. The problem with GWAS research is that some rare genetic variants are poorly studied, and that samples of non-European origin are still quite small.
The study of genetic variants associated with dementia is important for understanding the prognosis of the disease. For this reason, cohort prospective studies of polygenic risk assessment are being carried out in individuals initially without dementia [11,12].
Since cognitive performance always declines with age but may not always lead to the development of dementia, it is important to investigate the influence of genetic factors on this parameter also. These genetic loci are not always the same as those found for dementia [13,14].
In our previous study [15], we identified some associations between single-nucleotide polymorphisms (SNPs) that were previously associated with suicide risk. As indicated earlier in our work, we hypothesized that these genes might be associated with various psychiatric disorders such as anxiety and depression, which are closely related to cognitive impairment. Additionally, there is an increased risk of suicide following a diagnosis of dementia [16].
Cognitive abilities often decline with aging, and clinicians may not always be able to diagnose MCI in time [17]. Thus, even in initially healthy individuals without complaints of cognitive functioning, it is important to understand the dynamics of cognitive decline and the contributing factors. Crucially, cognitive decline is a universal factor affecting all older adults.
Our work aimed to determine the associations between SNPs associated with suicide and the dynamics of cognitive functions in people over 65 years of age over a three-year follow-up period.

2. Materials and Methods

2.1. Participants

Healthy volunteers were recruited between 2020 and 2021 among volunteers attending periodic medical examinations at Polyclinic No. 121 (Moscow). Informed consent was obtained from all participants. The study was conducted following the recommendations of the Declaration of Helsinki. Human experiments were conducted per the ethical standards of Protocol No. 5 of 20 September 2020 Ethics Committee of the L.I. Sverzhevsky Clinical Research Institute of the Moscow Department of Health. A total of 146 healthy volunteers participated in the study at the first observation point [15].
Inclusion criteria were subjects over 65 years of age without subjective cognitive impairment. Exclusion criteria were mental illness, a positive family history of mental illness among first-line relatives, substance abuse, and severe comorbid somatic or neurological disorders. We excluded somatic pathology according to medical records. We excluded patients with severe disorders of cardiovascular system, respiratory system, hematopoietic system, and nervous system, accompanied by significant organ dysfunction and oncologic diseases under therapy. Each person had a general blood test and basic biochemical analysis. We excluded patients with abnormalities in the general blood analysis, and if there were significant abnormalities in the biochemical analysis which were not explained by concomitant pathology.
At the first follow-up point in 2020–2021, patients were genotyped and interviewed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Three years after inclusion in the study, patients were invited by phone to come for a follow-up examination. Out of 146 subjects, 3 people could not attend for health reasons, 38 people declined without explanation, and 39 people did not respond to the call. In total, 80 individuals dropped out of the study. At the second follow-up point, patients were again interviewed using the MMSE and MoCA scales.

2.2. Cognitive Status Assessment

MMSE and MoCA were used to assess cognitive functions. The MMSE is a short 30-item questionnaire widely applied for the assessment and screening of cognitive impairments, including dementia. It is also used to evaluate the dynamics of cognitive functions during ongoing therapy [18]. Subjects who scored 28–30 on the MMSE scale were considered healthy.
The MoCA assesses various cognitive functions, including attention and concentration, executive functions, memory, speech, spatial optics, conceptual thinking, counting, and orientation. It takes approximately 10 min to administer the MoCA. The maximum score is 30, and the typical score is 26 or higher [19]. The Russian version of the MoCA test has been validated [20].
The HADS consists of two subscales: anxiety (HADS-A, specificity 94.0%, sensitivity 73.8%) and depression (HADS-D, sensitivity 72.9%, specificity 92.5%). Each subscale includes seven items with four answer options that reflect the severity of symptoms. Answers range from 0 (absence) to 3 (maximum severity). Anxiety was determined by a total score of 10 or more on the HADS-A. Depression was indicated by a score of at least 9 points on the HADS-D [21].

2.3. DNA Extraction and Genotyping

Genomic DNA was extracted from peripheral blood collected in EDTA-containing tubes using a DNA extraction kit (Syntol, LLC., Moscow, Russia) according to the manufacturer’s instructions. Genotyping was performed using the commercial TaqMan® assay according to the manufacturer’s instructions on the QuantStudio™ 5 Real-Time PCR System (Thermo Fischer, Watlam, MA, USA). The genes and SNPs chosen for analysis have been described previously [7]. We did not perform genotyping anew for the present study but used the results of previous work [7]. The genes and polymorphisms chosen for analysis are as follows: rs9475195 (HCRTR2), rs7982251 (FLT1), rs2834789 (RUNX1), rs358592 (KCNIP4), rs4918918 (SORBS1), rs3781878 (NCAM1), rs10903034 (IFNLR1), rs165774 (COMT), rs16841143 (PTH2R), rs11833579 (NINJ2), rs10898553 (PRSS23), rs7296262 (TMEM132C), rs3806263 (COQ8A (ADCK3)), rs2462021 (JCAD). Test for Hardy–Weinberg equilibrium was performed in our previous study [15].

2.4. Statistical Analysis

Statistical analyses were performed using IBM SPSS version 26.0 (IBM, New York, NY, USA). Two-sided versions of statistical hypotheses were used in all cases. The null hypothesis was rejected at a significance level of p ≤ 0.05. A general linear model with repeated measures was used to assess the effect of polymorphisms on the dynamics of cognitive function. The change in total MoCA score from 1 observation point to 3 years later was used as a within-subject factor; the status of a given polymorphism was used as a between-subject factor. The dependent variable was the MoCA total score. Both MMSE and MoCA total scores are shown as medians and quartiles. Differences between the percentages of people with MMSE and MoCA scores were evaluated using the Chi-square test, and the Wilcoxon test was used to compare mean scores. Any associations between the genetic markers and HADS were analyzed using the SNPStats service (www.snpstats.net (accessed on 7 February 2025)). Data from codominant, dominant, and recessive models were used. Scale scores of dropouts and remainders were compared using the Kruskal–Wallis criterion.

3. Results

In our work, we conducted an observational study using a sample from our previous study, which included individuals without subjective cognitive decline who came for regular check-ups at a polyclinic. There were 146 people at the first follow-up point. After three years, these people were re-invited for cognitive assessment. The dropout rate was 55%. The patients’ sociodemographic data and total scores on cognitive scales at the first and second points of examination are given in Table 1.
No significant differences in the total cognitive scale score were found between the first and second follow-up points (p = 0.959 for MoCA and p = 0.186 for MMSE).
Among those who participated in our study, 73% had MoCA scores below 25, which corresponds to mild cognitive impairment. After 3 years, this percentage decreased to 56%.
At the first follow-up, 9 individuals (6.5%) scored 24 or below on the MMSE scale, which corresponds based on scale scores to mild dementia, while at the second follow-up, this number increased to 11 individuals (16.7%). The number of people who could be diagnosed with mild dementia increased significantly (ꭓ2 = 5.783; p = 0.017). Cognitive decline of more than three points on the MoCA scale was observed in 10 individuals (15%) over 3 years of follow-up.
We analyzed the associations between MoCA dynamics and 16 SNPs. Three SNPs showed statistically significant associations with MoCA dynamics, and one genetic variant showed differences at the trend level (Figure 1).
For rs10898553, dynamics and individual genetic variation had no significant effect (p = 0.255 and 0.407, respectively), but MoCA dynamics differed significantly between the different gene variants (p = 0.007). Heterozygotes showed negative MoCA dynamics (Figure 1A).
For rs165774, the temporal dynamics were insignificant (p = 0.403), but the average effect of the gene was significant (p = 0.004, with the G/G variant having lower MoCA values). There was a significant trend in the interaction of factors (p = 0.083): A/A carriers had positive MoCA dynamics, heterozygotes showed no dynamics, and G/G carriers had negative dynamics (Figure 1B).
For rs7982251, temporal dynamics and factor interaction were insignificant (p = 0.764 and 0.815, respectively), but the individual effect of the gene was significant (p = 0.019); i.e., there were no pronounced dynamics or dependence on the gene, but the MoCA level was significantly different between the gene forms, with C/C carriers having significantly higher MoCA scores (Figure 1C).
For rs2462021, there were no significant effects for dynamics, individual polymorphism, or interaction, but the interaction showed a pronounced trend (p = 0.065). T/T carriers tended to have increased MoCA scores compared to other gene variants (Figure 1D).
The main effects described remained consistent when age correction was introduced as a covariate. For example, for rs165774, the age-adjusted interaction effect was significant (p = 0.024); the shape of the relationship remained the same (Figure 2).
Thus, four genetic variations showed associations with MoCA scores. Specifically, associations with MoCA dynamics were shown for rs108985553, rs165774, and rs2462021. Patients with different genetic variants of rs7982251 had statistically different MoCA levels throughout the follow-up period.
We analyzed depression and anxiety symptoms using the HADS. These symptoms were assessed only at the first follow-up point. A total of 38 (26%) patients had symptoms of anxiety (HADS-A ≥ 10) and 47 (32%) of depression (HADS-D ≥ 9).
We analyzed the association between the presence of anxiety and depression and genetic variation. We found only one association for the HADS-D. For rs3806263, significant associations were shown for heterozygotes compared to homozygotes in the codominant model (OR = 0.28, 95% CI = 0.11–0.67, p = 0.0095) and for A-allele carriers compared to individuals with the G/G genotype in the dominant model (OR = 0.31, 95% CI = 0.14–0.69, p = 0.0044). The data for the other genetic polymorphisms are not presented and are available upon request.
Further, we compared scores on the MoCA, MMSE, HADS-A, and HADS-D scales at the first follow-up point in patients who dropped out and those who came three years after inclusion. The data are shown in Table 2.

4. Discussion

Our previous study identified associations with dementia for rs10903034 in the IFNLR1 gene [15]. Previous work has focused on assessing associations with a diagnosis of dementia when cognitive impairment is severe and can be easily diagnosed clinically. We used suicide-related SNPs because the biochemical mechanisms of these disorders may overlap. Additionally, suicide occurs more frequently in people of older ages [15]. The risk of suicidal behavior increases in patients with frontotemporal dementia [22] and in patients with Alzheimer’s disease [23]. The increase in suicide rates in later life is linked to many factors: physical health problems, loss of independence, social isolation and loneliness, psychological and emotional factors, and lower quality of life. Some authors suggest that cognitive deficits can probably increase the risk of suicide due to irrational decision making. Suicide in late life is closely related to cognitive impairment [24], and suicidal behavior in people over 55 years of age may even be considered a prodromal symptom of dementia [25]. However, genetic factors have also been associated with suicidal behavior in older adults. In a Swedish population over 70 years of age, the authors showed associations of polygenic risk for neuroticism, general cognitive performance, and depression with suicidal ideation in older adults without a diagnosis of major depressive disorder [26]. In a population-based study on older adults in Korea, associations between a functional polymorphism in the promoter region of the serotonin transporter gene (HTTLPR), stressful life events, social support deficits, and suicidal ideation were found [27]. Moreover, the well-known genetic factor allele ε4 of Apolipoprotein (APOE4) is a risk factor for suicide in older adults [24] and patients with Alzheimer’s disease [23]. Thus, in the last few years since our article was published, more evidence has accumulated of an association between increased suicide risk and neurodegeneration and cognitive impairment, indicating that genetic factors may be crucial in this context.
Therefore, following a previous study, we aimed to follow up healthy controls for 3 years to determine whether suicide-related SNPs would be associated not with a diagnosis of dementia but with cognitive decline in older adults who did not report subjective cognitive decline at the time of inclusion in the study.
It should be noted that during the 3-year follow-up, there was a group of people who experienced severe cognitive decline based on MoCA scores (15%). Despite this, we found no differences when comparing MoCA scores at the first and second follow-up points, which may indicate an increased variability between groups; some individuals’ cognitive functioning remained unchanged or even improved, while others showed a severe decline.
In our study, we assessed associations with the dynamics of cognitive impairment using the MoCA scale. It is a widely used instrument at this time, and its use extends to patients with multiple disorders [28]. Even a slight decrease in MoCA scores may reflect subtle changes in cognitive domains. These changes, although not always clinically significant in isolation, may serve as early indicators of cognitive processes that may progress to more severe forms of impairment [29]. Some papers report that, due to the paucity of studies, the MoCA cannot yet fully replace neuropsychological tests, especially in cases of diagnostic difficulties in the context of diagnostic uncertainties, but it provides additional information that generally correlates with the results of other assessments [30]. In previous work, we used the MMSE scale because we were comparing healthy older adults with a group of patients suffering from dementia. There are several reasons for choosing the MoCA scale. First, the MoCA is better at detecting cognitive heterogeneity, and the prevalence of MCI is higher when the MoCA is used compared to the MMSE [31]. Second, the MoCA is more sensitive than the MMSE at detecting age-related physiological decline in cognitive function in typical aging [32]. Additionally, comparisons between the MoCA and MMSE have shown that the MoCA is more sensitive at differentiating individuals with MCI from those with unimpaired cognitive function [33]. According to Jongsiriyanyong et al. [1], the MMSE is not recommended as a screening tool for MCI. Notably, the MoCA requires additional standardization, and it cannot be used to unequivocally predict the development of impairment [34,35]. The MoCA takes longer than the MMSE, which can be inconvenient for rapid screening, and the test is also subject to a learning effect. It is also important to note that the results of both scales depend on level of education and cultural context. In this study, we chose the MoCA scale in order to assess the degrees of cognitive impairment.
Patient testing revealed that more than 70% of individuals had cognitive decline at the first visit, corresponding to MCI based on scale scores, and 56% at the second visit. It should be noted that healthy individuals were included in our study without subjective cognitive decline, but it was detected during screening. However, diagnosing MCI also requires collecting anamnesis and more detailed consultation with a medical specialist.
In our previous study, rs10903034 was associated with dementia. The absence of associations of rs10903034 with the dynamics of the MoCA scores can be explained by the fact that not all subjects were subsequently diagnosed with dementia. However, all older adults will experience some cognitive decline to a greater or lesser degree. Therefore, the aim of this study did not correspond with our previous study.
In the present study, rs10898553, rs165774, rs2462021, and rs7982251 were associated with three-year MoCA dynamics.
rs10898553 is located in the serine protease 23 gene. Serine proteases are signaling molecules involved in various processes in the organism, including the regulation of insulin-like growth factor (IGF) transport, uptake by insulin-like growth factor binding proteins (IGFBP), and protein metabolism. IGF and IGFBP have been associated with cognitive decline in individuals experiencing healthy aging [36]. Quinlan et al. [37] showed that IGF-1 was associated with the risk of vascular dementia but not dementia caused by Alzheimer’s disease. rs10898553 has been linked to suicide attempts among veterans of the US military [38].
rs165774 is located in the Catechol-O-methyl transferase gene, which encodes an enzyme involved in the degradation of catecholamines. The dopamine system plays a role in cognitive impairment development, and high dopamine levels prevent cognitive decline in aging [39]. Healthy aging is characterized by changes in the brain’s dopamine system that are associated with impaired cognition [40]. Individual differences in dopamine concentration affect executive functions, including cognitive flexibility, even in healthy young adults [40]. rs165774 has been associated with suicide attempts in bipolar disorder [41].
rs2462021 is located in the JCAD (Junctional Cadherin 5-Associated) or KIAA1462 gene. This gene is associated with coronary heart disease. Intercellular contacts play a crucial role in the organization and function of endothelial cells. The accumulation of JCAD in intercellular contacts in cultured endothelial cells is impaired by suppression of VE-cadherin expression mediated by RNA interference [42]. It was shown that this gene might be a candidate gene for Alzheimer’s disease with late onset in APOE carriers [43].
rs7982251 is located in the VEGFR1 (vascular endothelial growth factor receptor 1) gene. This gene is involved in endothelial cell proliferation, survival, and angiogenesis in adulthood. VEGFR1 is also associated with microglia migration and astrocyte activation [44]. The role of VEGF and its receptor in the development of dementia has been demonstrated in a transgenic mouse model of Alzheimer’s disease [45]. Treatment with bevacizumab, an antibody that specifically binds to VEGF and prevents it from interacting with its receptors (Flt-1 and KDR) on the surface of endothelial cells, reversed the altered receptor expression, making it comparable to levels in wild-type mice. Zhang et al. [45] demonstrated a protective effect of bevacizumab on cerebrovascular function and memory in 5 × FAD mice, a transgenic animal model of Alzheimer’s disease. Ryu et al. [46] showed that injection of an anti-VEGFR-1 monoclonal antibody into the CA1 layer of the hippocampus reduced microglia activation and increased the number of hippocampal neurons in a model of Alzheimer’s disease induced by Aβ administration. rs7982251 has also been associated with suicide attempts in bipolar disorder [41].
In our study, we found associations between rs10898553 and rs165774 with cognitive scale dynamics, where patients with a particular genetic variant showed stronger dynamics. For rs7982251, we found associations with scale scores but no effect on dynamics.
Most studies focus on preclinical dementia or healthy older adults with no follow-up. Therefore, there are still no clear answers regarding the degree to which pathology specific to Alzheimer’s disease may contribute to cognitive decline in older adults who do not later develop Alzheimer’s disease [47]. However, the cognitive decline observed in the general population without dementia is partially attributable to Alzheimer’s-disease-related pathologies more than six years before the eventual clinical onset of dementia [47]. Alzheimer’s disease and nonpathological cognitive decline during aging have phenotypic similarities, which may be affected by an overlapping set of genetic variants. Approximately 24% of the common genetic variants in Alzheimer’s disease and nonpathological cognitive decline overlap [48]. While APOE is associated with both Alzheimer’s disease and cognitive impairment during aging, it is unknown to what extent other common genetic variants with smaller effect sizes overlap. Additionally, the polygenic risk of Alzheimer’s disease was not associated with nonpathological cognitive decline [48].
Studies of genetic polymorphisms in the context of cognitive performance and dementia in other studies show significant associations. For example, Andrews et al. (2016) found a link between 28 single-nucleotide polymorphisms (SNPs) and the cognitive performance of individuals without dementia, which highlights the potential of genetic markers for predicting age-related cognitive decline [49]. Most other studies have focused on genes associated with Alzheimer’s disease, but only a small fraction of SNPs have shown a significant association with cognitive decline [13]. A study by Lin et al. (2017) analyzed 588 SNPs in 27 genes previously associated with Alzheimer’s disease risk and revealed a significant association between the CASS4-rs911159 SNP and cognitive decline, as well as possible interactions between SNPs in SLC24A4, MEF2C, FERMT2, EPHA1, and CASS4 [14]. Additional studies have confirmed the importance of individual SNPs in EPHA1 and MEF2C for predisposition to cognitive decline [50]. One of the large-scale studies is the work of Rietman et al. (2022), conducted over a period of 20 years. The study examined the effect of 433 SNPs on cognitive decline, but only rs429358-C encoding apolipoprotein E showed a significant association [51]. The totality of the studies reviewed highlights the connection between genetic factors and cognitive decline, and the need for further research to understand their role.
Therefore, we believe that it is important to investigate not only associations with a diagnosis of Alzheimer’s disease or another type of dementia but also associations with cognitive decline in people without subjective cognitive decline. This may be a more significant practical task since cognitive decline will eventually be encountered by anyone in old age, and the clinical profile at an early stage does not allow for determining the exact diagnosis of dementia.
Specific genetic markers may be influenced by different factors, differentially affecting cognitive decline in people without suicidal tendencies. Further studies are needed to confirm whether our findings can be generalized.
Given the above, it is currently not possible to answer whether there are common genetic variants for dementia and cognitive decline in typical aging. Case-control studies also cannot answer this question, as it is unknown to what severity the cognitive deficits will develop in people included in the control group, as well as what type of dementia they may develop.
Lifestyle includes many factors, such as physical activity level, diet, social activity, and stress levels, which together can alter the rate of cognitive decline [52]. Physical and mental health history, including depression and anxiety disorders, may also play a role [53]. Older people often have anxiety and depressive symptoms. In our study, such patients constituted 30 percent of the cohort. Vascular factors, for example, have a direct impact on cognitive function, as impaired cerebral blood flow can accelerate neurodegenerative processes [54]. In addition, socioeconomic factors [55] and other unrecorded factors may play a role and may be important in interpreting the dynamics of cognitive change over three years.
Conducting studies to analyze the factors of cognitive decline in healthy older adults without subjective cognitive decline will help predict the development of pathology at early stages and start treatment in time. Genetic factors do not change over time, and studying the associations of genetic factors with the dynamics of cognitive impairment may be a useful tool.
The first limitation of our study was the small sample size and the inability to determine why patients did not attend the examination or answer the call. Patients may have declined to participate in the study due to serious cognitive impairment or health problems. We compared scores on scales at the first follow-up point between the dropouts and those who remained in the study. We found that the dropouts had lower scores on the cognitive scales. Cognitive decline may have caused them to not be able to come in 3 years later. In addition, the dropouts were found to have lower anxiety. We suggest that concern about their health and slightly more severe anxiety symptoms (but on average not reaching the level of pathology) was one of the factors that made the patients come for a follow-up study. Thus, we can make some assumptions about the reasons for patient dropout. However, in prospective studies, it is difficult to avoid patient attrition and the formation of a biased sample, where after 3 years of follow-up, initially more cognitively intact patients showing higher concern and care for their health come for a follow-up.
A second limitation was that healthy individuals without subjective cognitive decline showed MCI and mild dementia on screening tests. However, these individuals did not complain of cognitive deficits. We do not suggest that people with a high score on cognitive scales corresponding to a typical cognitive level should have been selected for this study, as we evaluated the dynamics of cognitive decline and wanted to show a general cross-section of people over 65. We believe that further research is necessary to evaluate the relationship between cognitive decline and the markers rs10898553, rs165774, rs7982251, and rs2460221 as well as further extensive study of the relation between suicide predisposition and cognitive dynamics.

5. Conclusions

This study concentrated on examining the genetic factors related to cognitive decline in healthy older adults who did not exhibit subjective cognitive decline. The genetic variants rs108985553, rs165774, and rs2462021 were linked to cognitive dynamics as measured by the total MoCA score. Identifying these markers can be a useful tool for predicting cognitive decline and enabling early-stage treatment.

Author Contributions

Clinical data collection: I.M., O.K., M.K. and V.S.; statistical analysis: A.B.; methodology—genotyping: K.P. and O.P.; writing—original draft preparation: Y.Z.; writing—review and editing: O.A., A.M., V.U. and A.Z.; project administration: A.A., G.K. and A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Research Clinical Institute named after L.I. Sverzhevsky Moscow Healthcare Department (Protocol No. 5, dated 20 September 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data are available upon request due to restrictions on privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviation

The following abbreviations are used in this manuscript:
APOEapolipoprotein E
IGFinsulin-like growth factor
JCADJunctional Cadherin 5-Associated
MCImild cognitive impairment
MMSEMini-Mental State Examination
MoCAMontreal Cognitive Assessment
SNPssingle-nucleotide polymorphisms
VEGFvascular endothelial growth factor

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Psychiatryint 06 00064 g001
Figure 1. The estimated marginal averages of the MoCA total score at the 1st and 2nd observation points. (A)—rs10898553; (B)—rs165774; (C)—rs7982251; (D)—rs2460221.
Figure 1. The estimated marginal averages of the MoCA total score at the 1st and 2nd observation points. (A)—rs10898553; (B)—rs165774; (C)—rs7982251; (D)—rs2460221.
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Figure 2. Estimated marginal averages for rs165774 at 1st and 2nd observation points with age as covariate for MoCA dynamics.
Figure 2. Estimated marginal averages for rs165774 at 1st and 2nd observation points with age as covariate for MoCA dynamics.
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Table 1. General characteristics of patients at inclusion in study and after 3 years.
Table 1. General characteristics of patients at inclusion in study and after 3 years.
Study Inclusion3-Year Follow-Up
Number of participants14666
Age68 ± 6.371 ± 7.7
Women (%)136 (93%)62 (94%)
Higher education (%)95 (65%)45 (68%)
MMSE total score27 (26; 27)28 (27; 29)
MoCA total score24 (21; 26)25 (22; 28)
Table 2. Scale scores at the first follow-up point in dropouts and 3-year completers.
Table 2. Scale scores at the first follow-up point in dropouts and 3-year completers.
Moca MMSE Anxiety Depression
M (Q1; Q3)Remained25 (22; 26)27 (26; 29)8 (6; 10)7 (5; 9)
Drop-out23 (20; 25)26 (26; 27)6 (4; 9)6 (4; 9)
MinimumRemained112201
Drop-out4900
MaximumRemained30301914
Drop-out29301718
Kruskal–Wallis χ28.2410.139.793.08
p0.0040.0010.0020.0079
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Zorkina, Y.; Berdalin, A.; Morozova, I.; Andryushchenko, A.; Pavlov, K.; Pavlova, O.; Abramova, O.; Ushakova, V.; Zeltzer, A.; Kurmishev, M.; et al. Dynamics of Cognitive Impairment in Older Adults Linked to Suicide-Related Single-Nucleotide Polymorphisms: A 3-Year Follow-Up Study. Psychiatry Int. 2025, 6, 64. https://doi.org/10.3390/psychiatryint6020064

AMA Style

Zorkina Y, Berdalin A, Morozova I, Andryushchenko A, Pavlov K, Pavlova O, Abramova O, Ushakova V, Zeltzer A, Kurmishev M, et al. Dynamics of Cognitive Impairment in Older Adults Linked to Suicide-Related Single-Nucleotide Polymorphisms: A 3-Year Follow-Up Study. Psychiatry International. 2025; 6(2):64. https://doi.org/10.3390/psychiatryint6020064

Chicago/Turabian Style

Zorkina, Yana, Alexander Berdalin, Irina Morozova, Alisa Andryushchenko, Konstantin Pavlov, Olga Pavlova, Olga Abramova, Valeriya Ushakova, Angelina Zeltzer, Marat Kurmishev, and et al. 2025. "Dynamics of Cognitive Impairment in Older Adults Linked to Suicide-Related Single-Nucleotide Polymorphisms: A 3-Year Follow-Up Study" Psychiatry International 6, no. 2: 64. https://doi.org/10.3390/psychiatryint6020064

APA Style

Zorkina, Y., Berdalin, A., Morozova, I., Andryushchenko, A., Pavlov, K., Pavlova, O., Abramova, O., Ushakova, V., Zeltzer, A., Kurmishev, M., Savilov, V., Karpenko, O., Kostyuk, G., & Morozova, A. (2025). Dynamics of Cognitive Impairment in Older Adults Linked to Suicide-Related Single-Nucleotide Polymorphisms: A 3-Year Follow-Up Study. Psychiatry International, 6(2), 64. https://doi.org/10.3390/psychiatryint6020064

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