Cognitive decline is correlated with frailty in community-dwelling older adults

doi:10.21203/rs.3.rs-5229144/v1

Background

Aging impacts both mental and physical health. The relationship between cognitive decline and frailty in aged individuals remains unclear. This study aimed to elucidate the correlation between cognitive and physical decline in elders, identify contributing factors, and explore potential blood biomarkers associated with these conditions.

Methods

Three groups at average aged of 65 (288 subjects), aged of 71 (196 subjects), and aged of 79 (80 subjects) were selected from the cohort of the Healthy Aging Longitudinal Study in Taiwan (HALST) database. Variables included global cognitive function, assessed by the Mini-Mental State Examination (MMSE), Fried frailty phenotype, and plasma tumor necrosis factor receptor-1 (TNFR1) were included. Statistical analyses utilized multiple regression analysis of variance, accounting for potential confounding variables.

Results

We found a gradual MMSE decline from average age of 65 to age of 79, which correlated with an increase in total frailty rating scores, especially in the components of exhaustion, slow walking speed, and weak grip strength (p ≦ 0.008). In addition, higher frailty scores were strongly associated with elevated plasma TNFR1 levels, particularly in components such as low physical activity, and weak grip strength (p ≦ 0.008).

Conclusion

Cognitive decline correlates with increase in frailty during aging process. Plasma TNFR1 emerges as a valuable indicator for identifying frailty in individuals.

Cognitive decline

MMSE

frailty

bioindicator

TNFR1

Due to the rapid aging of Taiwan's population, the National Development Council of Taiwan has estimated that by 2025, the elderly population will exceed 20%, making it a 'super-aged society'. The prevalence of sarcopenia, a major component of frailty, in Taiwan ranged from 6.7% to 10% in communities and 50% in clinical settings[1, 2]. Compared to the prevalence of sarcopenia, the prevalence of mild cognitive impairment or dementia is lower, estimated to be less than 2% in both the Taiwanese population and globally. Therefore, sarcopenia may develop earlier than cognitive impairment. According to the 2017 National Health Interview Survey, one in six elderly individuals aged 65 and above had fallen events. To achieve healthy aging, it is important to maintain muscle strength and endurance to reduce the risk of frailty.

Cognitive impairment is a gradual deterioration of cognitive functions, including memory, attention, executive function, and impaired judgment and reasoning [3]. The deterioration process is influenced by a wide range of contributing factors [4]. Although cognitive impairment and frailty are sometimes reported together [5, 6], fewer studies have deciphered the interrelations between the cognitive function and frailty during the aging process. Frailty, a common clinical syndrome in older adults, is characterized by symptoms such as unintentional weight loss, weak grip strength, exhaustion, slow walking speed, and low physical activity, and carries an increased risk for disability and mortality [7, 8]. Frailty prevalence in Taiwan was 14.6% and increased with age [9]. Because age-related frailty and cognitive decline often occur simultaneously, the term 'cognitive frailty' is used to describe the concurrent presence of physical frailty and cognitive impairment, although some experts question whether it should be classified as a distinct disease [10]. Frailty and cognitive impairment share common risk factors, including aging, chronic diseases, unhealthy life-styles, and vascular risks. Whether both conditions can mutually exacerbate each other's severity and lead to further declines in quality of life warrants further study.

Additionally, the biological factors related to cognitive impairment and frailty are not yet fully understood. Identifying biomarkers for the different age stages of these degenerative processes remains an unmet clinical need. Observational studies have identified two biochemistry markers associated with cognitive and physical aging: insulin-like growth factor-1 (IGF-1), and tumor necrosis factor receptor 1 (TNFR1). IGF-1 affects neuronal excitability, metabolism, and cell survival [11]. TNFR1, which can circulate in a soluble form (sTNFR1) to modulate inflammation, is elevated in dementia [12]. However, their roles in age-related cognitive decline and frailty remain unclear.

This study hypothesizes that age-related cognitive and physical decline can occur concurrently with a strong correlation. To explore this relationship and potential biomarkers, we analyzed data from three prospective cohort study of community-dwelling older adults from average of 65, 71, and 79 years old, focusing on changes in cognitive and physical functions. Additionally, we aimed to identify biomarkers for the early stages of these degenerative processes, as early identification of at-risk individuals is crucial for prevention.

Transparency and Openness

The data were selected based on sample size estimation from the Healthy Aging Longitudinal Study in Taiwan (HALST), conducted by the National Health Research Institutes (ClinicalTrials.gov: NCT02677831), which focuses on healthy aging [13]. This study was approved by the institute's ethics committee (protocol number: EC0970608 and EC1020805). HALST recruited community-dwellers aged range 55 and above from seven living areas near the selected hospitals. In the first-wave (baseline; 2009 - 2013) of recruitment, 94% subjects completed interviews and hospital-based examinations [13]. Participants were deemed healthy if they did not have highly contagious diseases, severe illnesses (e.g., active cancer), physician-diagnosed dementia, bedridden status, severe mental disorders or cognitive impairment (Mini-Mental State Examination, MMSE score <16), mental retardation, or severe hearing loss, and were not hospitalized or institutionalized at recruitment.

Participants, assessment of frailty, matching criteria

A total of 288 subjects, with an average age of 65 (Group 1), a total of 196 subjects with an average age of 71 (Group 2), and a total of 80 subjects with an average age of 79 (Group 3), were selected from HALST. This study first selected subjects from the HALST database depending on the frailty status. Frailty status was categorized as either frail or robust based on the presence (≥1 score) or absence of frailty phenotype components. Frail cases were those classified as frail in both Group 1 and Group 2 and average aged 65-71. In contrast, Group 3 was selected according to slow walking speed, and weak grip strength. Age, sex, metabolic syndromes, education level, and the residency area were recorded among the three groups. There were 115 frailty in Group 1, 79 frailty in Group 2, and 32 frailty in Group 3 (Fig. 1).

Global cognitive function was assessed using the MMSE score. Frailty status was evaluated with the Fried frailty phenotype, including unintentional weight loss (> 4.5kg in the past year), weak grip strength (lowest 20% of sex- and height-adjusted values), self-reported exhaustion (CES-D scale), slow walking speed (slowest 20% of sex- and BMI-adjusted values), and low physical activity (lowest 20% of sex-specific physical activity, < 105.2 kcal/week for men and < 46.1 kcal/week for women) [8]. Each component contributed one point. Scores of 1-3 or more indicated frailty, and 0 indicated robustness.

Blood pressure measurement

Participants emptied their bladders before measurements. They provided information on recent smoking of the last cigarette timing, exercise in the past 2 weeks, hypertension medication use. After rest for five minutes, three consecutive blood pressure (BP) measurements were taken, with one-minute intervals. With the first reading discarded, the average of the second and third readings represented the BP levels, including systolic (SBP) and diastolic BP (DBP). Pulse pressure (PP), calculated as the difference between SBP and DBP, and mean arterial pressure (MAP), calculated as 1/3(SBP) + 2/3(DBP), were also determined [14].

Biochemistry measurement

Blood samples were taken after 8 hours of fasting. All blood samples were centrifuged for plasma or serum collection, and stored in a -80℃ refrigerator for biochemical factors analyses [13].

The serum levels of low-density and high-density lipoprotein cholesterol (LDL-C and HDL-C) and triglycerides were determined on an ADVIA XPT chemical analyzer by Enzymatic GPO method (Triglycerides) and Elimination/ catalase principle (HDL-C and LDL-C). The Siemens ADVIA XPT system was used to measure glucose, high sensitivity C-reactive protein (hsCRP) and creatinine. Serum hsCRP were assayed by immunoturbidimetry method and creatinine levels were assayed by Jaffe reaction with rate-blanked method. Hemoglobin levels were determined by the Sysmex XN9000 system utilizing the Laser Flow Cytometry method. HbA1c levels were detected using the Bio-Rad D-100, a high-throughput high-performance liquid chromatography system.

Plasma levels of IGF-1 and TNFR1 were measured using enzyme-linked immunosorbent assays with the Human ELISA kit from R&D Systems, Inc. The inter- and intra-assay coefficients of variation (CVs) of IGF-1 were 2.75%–12.84% and 6.38%, respectively. The inter- and intra-assay CVs of TNFR1 were 2.12%–14.25% and 8.42%, respectively.

Statistical analysis

Characteristics of data at Group 1, Group 2, and Group 3 were reported by continuous and categorical variables. Group comparisons for continuous variables were performed using the Kruskal-Wallis test, followed by pairwise comparisons using the Mann-Whitney U test, where appropriate. For categorical variables, either the Chi-square test or Fisher’s exact test was applied. Following these group comparisons, we further examined the differences in robustness and frailty within each group, utilizing the same statistical methods described above (Mann-Whitney U test for continuous variables and Chi-square or Fisher’s exact test for categorical variables). Multiple regression models were employed to examine the associations between independent variables, specifically metabolic and biochemical indicators, and the dependent variables, including the MMSE and TSF. Variables that demonstrated a p-value of less than 0.05 in the simple regression analyses were considered potential confounders and were subsequently included in the multiple regression models. After the multiple regression, we further used the five components of TSF as predictors for MMSE and TNFR1. Given that the MMSE, TSF, and TNFR1 were not normally distributed, a rank transformation was applied in the regression analysis to account for this non-normality [15]. All analyses were conducted using SAS software, Version 9.4 (SAS Institute, Inc., Cary, NC), and the figure was created with BioRender.com.

Characteristics of subjects aged 65 to 79 years

Table 1 displays general demographics and comparisons among average age 65 (Group 1), 71 (Group 2), and 79 (Group 3) years old of 288, 196, and 80 of subjects. Overall MMSE scores significantly decreased from age 71 to 79 years (27.08 ± 2.39 to 24.13 ± 4.39, p < 0.0001), but did not change from age 65 to 71. Female subjects exhibited a more significant decrease in MMSE scores from age 65 to 79 compared to male subjects (women: 26.84 ± 2.76 to 22.65 ± 4.25, p < 0.0001; men: 27.39 ± 2.59 to 25.94 ± 3.89, p = 0.09). The total score of frailty (TSF) did not differ among the three groups. The percentage of frailty components, specifically slow walking speed and weak grip strength, showed a significant increase in the age 79 group (p ≤ 0.0003). In the confounding metabolic indicators listed in Table 2, individuals showed significant increases in SBP and PP (p ≤ 0.0003), while there were decreases in height, weight, DBP, triglyceride levels, LDL-C levels, and the proportion of current smokers among the three groups (p ≤ 0.005). In biochemical indicators, individuals showed significant decreases in IGF-1 levels (p = 0.0003) and increases in TNFR1 levels (p < 0.0001).

Frailty individuals had significantly lower MMSE scores compared to robust individuals in Group 3, with an average age of 79 (p = 0.004) (see Supplementary Table S1, Table S2, and Table S3). Women experienced a more significant reduction in MMSE scores compared to men. In Group 1, women had scores of 27.35 ± 2.37 (robust) versus 26.04 ± 3.14 (frailty), with a p-value of 0.009. In Group 3, scores were 23.93 ± 4.11 (robust) versus 20.27 ± 3.51 (frailty), with a p-value of 0.008. Only frailty patients had total frailty scores. TNFR1 levels were significantly higher in frailty individuals compared to robust individuals across all three groups (Group 1: 992.6 ± 317.07 vs. 1234.08 ± 627.06 pg/ml, p = 0.0005; Group 2: 1092.91 ± 366.18 vs. 1169.67 ± 341.30 pg/ml, p = 0.038; Group 3: 1316.68 ± 578.56 vs. 1728.84 ± 829.52 pg/ml, p = 0.034). IGF-1 levels significantly decreased in frailty individuals compared to robust individuals only in Group 3, with an average age of 79 (62.27 ± 20.21 vs. 51.36 ± 17.4 ng/ml, p = 0.014).

Cognitive Decline was correlated with total score of frailty

Cognitive decline, as assessed by MMSE score, showed a strong positive correlation with education (β = 0.22, p < 0.0001) and with age between 71 and 79 years (β = 0.05, p = 0.006). However, it exhibited a strong negative correlation with the total score of frailty (TSF) in multiple regression analyses (β = -0.05, p = 0.0002) (see Table 3). Each confounding factor in the simple regression analysis involving MMSE was detailed as a reference in Supplementary Table S4.

Further examine the correlation between cognitive function rated by MMSE scores with five frailty components among different age groups, the component of exhaustion (β = -0.15, p = 0.004, partial r² = 0.022), slow walking speed (β = -0.15, p = 0.002, partial r² = 0.04), and weak grip strength (β = -0.1, p = 0.008, partial r² = 0.012) showed significant negative correlation with MMSE scores using multiple regression analyses (Supplementary Table S5). Reduced cognitive function may be associated with more severe frailty components, such as exhaustion, slow walking speed, and weak grip strength. MMSE was not correlated with unintentional weight loss or low physical activity.

Frailty was correlated with TNFR1

To understand the factors contributing to frailty in the aging process, changes in the total score of frailty (TSF) from an average age of 65 to 79 years were correlated with HALST variables (Table 4). In the TSF multiple regression analyses, MMSE was negatively correlated with TSF (β = -0.02, p < 0.0001, partial r² = 0.047). Conversely, plasma TNFR1 (β = 0.00009, p < 0.0001, partial r²= 0.028) and current smoking (β = 0.1, p = 0.008, partial r²= 0.016) were positively correlated with TSF (see Table 3). These findings indicate a bidirectional relationship between frailty and cognitive function. TNFR1 may serve as an indicator of the severity of frailty. Each confounding factor in the simple regression analysis with TSF was detailed as a reference in Supplementary Table S6.

Further analysis of plasma TNFR1 showed that it was positively correlated with frailty components: low physical activity (β = 0.07, p = 0.008, partial r²= 0.018) and weak grip strength (β = 0.12, p = 0.001, partial r²= 0.018) (Supplementary Table S7). Elevated plasma TNFR1 may serve as an indicator of frailty components such as low physical activity and weak grip strength.

This study found that cognitive decline is correlated with frailty in older adults. Although there was no decline in MMSE scores between ages 65 and 71, a dramatic decline of approximately 2.95 points was observed from ages 71 to 79. The total frailty score remained consistent from ages 65 to 79. However, there was a strong correlation between cognitive decline, as measured by MMSE, and total frailty scores. This strong correlation maybe due to the contribution of frailty vs. robust within each age group (Supplementary Table S1-3). In addition to the roles of robustness and frailty in the correlation between cognitive decline and the total frailty score, men showed less cognitive decline than women. Women in Group 3 experienced a significant reduction in cognitive function by age 79. This observation aligns with global data, which indicate that women have a higher proportion of dementia cases compared to men [16].

The correlation between cognitive function and frailty has been reported in literature through the observation of muscle mass and strength in relation to cognitive function [17]. This study supports the correlation between cognitive decline and frailty and provides more detailed information regarding each frailty components related to cognition. Specifically, the presence of exhaustion, slow walking speed, and weak grip strength was associated with more cognitive decline compared to their absence (Supplementary Table S5).

In this study, individuals with cognitive decline at age 79 exhibited reductions in height, weight, and diastolic blood pressure (DBP), while vascular resistance increased, as indicated by an elevation in pulse pressure (PP), compared to ages 65 and 71 (Table 2). To effectively prevent cognitive decline, early multi-domain interventions targeting physical fitness have been emphasized [18]. Prior studies have highlighted the importance of metabolism and physical fitness [18, 19]. Likewise, controlling hypertension and managing vascular risk factors have been identified as essential in mitigating late-life brain amyloid burden [19].

For biomarkers, this study demonstrates that plasma TNFR1 level was elevated in the frailty group compared to the robust group. Elevated plasma TNFR1 levels supported the observed frailty, indicating potential neuronal injury and inflammation in older adults with frailty. Frailty may also link to a systemic inflammatory state, and TNFR1 plays a crucial role in cellular signaling across various pathological processes. Activation of TNFR1 through TNF-α triggers a series of intracellular signaling events that leading to diverse cellular responses [12]. Previous studies have shown a strong association between frailty and inflammation in older individuals [20]. Our study supported that elevated plasma TNFR1 is an indicator of frailty, especially in slow physical activity and weak grip strength (Supplementary Table S6).

IGF-1 is a hormone released by the liver in response to growth hormone from the pituitary gland. Its dysfunction has been extensively studied in relation to aging [11, 21-23]. This study found a significant reduction in plasma IGF-1 levels at age 79. Although cognitive decline was also observed at this age, IGF-1 was not associated with MMSE scores after multiple regression analysis. These results suggest that IGF-1 may not be a strong predictor of cognitive decline in older adults. While our study did not find a direct link between plasma IGF-1 and frailty scores (Supplementary Table S7), a marginal correlation between IGF-1 and the total frailty score was observed in the age 79 group (p=0.014 in univariate si mple regression analysis). A review has suggested that decreased IGF-1 levels are associated with reduced muscle mass and function[24]. These findings indicate that the correlation between IGF-1 and frailty may occur in individuals older than 79 years.

Limitations

This study comprehensively clarifies the correlation between cognitive decline and frailty across different age groups, addressing a previously unclear relationship. This study also highlights the strong correlation between frailty and elevated plasma TNFR1 levels. However, there are several study limitations. First, this study encompassed non-randomized sampling of robust and frail individuals. Besides, the total frailty score of group 3 may be interfered by selective criteria included slow walking speed and weak grip strength. However, there were no significant difference of the total frailty score in 3 groups. The contribution of the total frailty score to cognitive function may be attributed to the differences in total frailty scores between robust individuals and those with frailty in each age group. Additionally, although cognitive function assessed by the MMSE may exhibit a ceiling effect, this study reveals a significant decline in MMSE scores from the average age of 71 to the age 79 group. Comprehensive neuropsychological tests can provide a more detailed analysis of impairment across various cognitive domains.

The study demonstrated a correlation between declines in cognition and physical function that may begin around age 65 and continue to age 79. Cognitive decline was associated with deteriorating changes in frailty, particularly in components such as exhaustion, slow walking speed, and weak grip strength in older adults. Plasma TNFR1 levels may be useful for identifying individuals at risk of frailty compared to robust individuals. The frailty components correlated with plasma TNFR1 included low physical activity and weak grip strength.

HALST	Healthy Aging Longitudinal Study in Taiwan
MMSE	Mini-Mental State Examination
TNFR1	Tumor necrosis factor receptor-1
IGF-1	Insulin-like growth factor 1
sTNFR1	Soluble form TNFR1
BP	Blood pressure
SBP	Systolic BP
DBP	Diastolic BP
PP	Pulse pressure
MAP	Mean arterial pressure
LDL-C	Low-density lipoprotein cholesterol
HDL-C	High-density lipoprotein cholesterol
hsCRP	High sensitivity C-reactive protein
CVs	Coefficients of variation
TSF	Total score of frailty

Acknowledgement

The authors thank Dr. Kung-Yee Liang for discussions on research design and for comments. Dr. Hsing-Yi Chang, Ray-Chin Wu, and Hui-Ling Chen revised the HALST database recruitment protocol, clinical assessments and methods. We also thank to the HALST staff for their recruitment contribution. The authors thank Dr. Kung-Yee Liang for discussions on research design and for comments. Dr. Hsing-Yi Chang, Ray-Chin Wu, and Hui-Ling Chen revised the Healthy Aging Longitudinal Study in Taiwan (HALST) database recruitment protocol, clinical assessments and methods. We also thank to the HALST staff for their recruitment contribution. The HALST data and study materials can be made available to other researchers upon reasonable request by contacting Dr. Chih-Cheng Hsu. The analytic methods have been documented in the main text.

Funding

This work was supported by grants from National Health Research Institutes in Taiwan (NP-112-SP-01 and NP-112-PP-03) (Y-L Liu), National Science and Technology Council (NSTC 112-2321-B-400-001-) (W-J Chen), and National Science and Technology Council (NSTC 111-2314-B-182A-134-MY2) (Y-C Chen). The funding organization did not have any involvement in the content and writing of the manuscript.

Declaration of competing interest

None

Ethical approval

not needed

Author contribution:

Conceptualization and study design: YCC, YYW, CCH, CAH, WJC, and YLL. Literature search management: YCC, YYW and YLL. Writing the first draft of the manuscript: YCC, YYW, CJL, CCH, and YLL. Conducting statistical analysis and interpreting the data: RHC and THL. All authors contributed to and have approved the final manuscript.

Meng NH, Li CI, Liu CS, Lin CH, Lin WY, Chang CK, et al. (2015) Comparison of height- and weight-adjusted sarcopenia in a Taiwanese metropolitan older population. Geriatr Gerontol Int 15(1):45-53.
Chen YP, Wong PK, Tsai MJ, Chang WC, Hsieh TS, Leu TH, et al. (2020) The high prevalence of sarcopenia and its associated outcomes following hip surgery in Taiwanese geriatric patients with a hip fracture. J Formos Med Assoc 119(12):1807-1816.
Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. (2020) Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396(10248):413-446.
Gajewski PD, Getzmann S, Brode P, Burke M, Cadenas C, Capellino S, et al. (2022) Impact of Biological and Lifestyle Factors on Cognitive Aging and Work Ability in the Dortmund Vital Study: Protocol of an Interdisciplinary, Cross-sectional, and Longitudinal Study. JMIR Res Protoc 11(3):e32352.
Bae S, Shimada H, Lee S, Makino K, Chiba I, Katayama O, et al. (2023) Subjective Cognitive Decline and Frailty Trajectories and Influencing Factors in Japanese Community-Dwelling Older Adults: A Longitudinal Study. J Clin Med 12(18).
Shaaban CE, Rosano C, Zhu X, Rutherford BR, Witonsky KR, Rosso AL, et al. (2023) Discordant Biological and Chronological Age: Implications for Cognitive Decline and Frailty. J Gerontol A Biol Sci Med Sci 78(11):2152-2161.
Xue QL (2011) The frailty syndrome: definition and natural history. Clin Geriatr Med 27(1):1-15.
Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56(3):M146-M156.
To TL, Doan TN, Ho WC and Liao WC (2022) Prevalence of Frailty among Community-Dwelling Older Adults in Asian Countries: A Systematic Review and Meta-Analysis. Healthcare (Basel) 10(5):895.
Kelaiditi E, Cesari M, Canevelli M, van Kan GA, Ousset PJ, Gillette-Guyonnet S, et al. (2013) Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging 17(9):726-34.
Bassil F, Fernagut PO, Bezard E and Meissner WG (2014) Insulin, IGF-1 and GLP-1 signaling in neurodegenerative disorders: targets for disease modification? Prog Neurobiol 118:1-18.
Salai KHT, Wu LY, Chong JR, Chai YL, Gyanwali B, Robert C, et al. (2023) Elevated Soluble TNF-Receptor 1 in the Serum of Predementia Subjects with Cerebral Small Vessel Disease. Biomolecules 13(3):525.
Hsu CC, Chang HY, Wu IC, Chen CC, Tsai HJ, Chiu YF, et al. (2017) Cohort Profile: The Healthy Aging Longitudinal Study in Taiwan (HALST). Int J Epidemiol 46(4):1106-1106j.
Sesso HD, Stampfer MJ, Rosner B, Hennekens CH, Gaziano JM, Manson JE, et al. (2000) Systolic and diastolic blood pressure, pulse pressure, and mean arterial pressure as predictors of cardiovascular disease risk in Men. Hypertension 36(5):801-7.
Joel AT, Ojo OD, Timothy OA, Ibukun OA, Jonathan OA and Ayedun AC (2020) Comparison of Rank Transformation Test Statistics with Its Nonparametric Counterpart Using Real-Life Data.
Lloyd-Hazlegreaves P, Hayes L and Pearce MS (2023) Associations between physical inactivity and dementia prevalence: ecological study using global data. Public Health 225:299-304.
Hatanaka S, Sasai H, Shida T, Osuka Y, Kojima N, Ohta T, et al. (2024) Association between dynapenia and cognitive decline in community-dwelling older Japanese adults: The IRIDE Cohort Study. Geriatr Gerontol Int 24 Suppl 1:123-129.
Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. (2015) A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 385(9984):2255-63.
Gottesman RF, Schneider AL, Zhou Y, Coresh J, Green E, Gupta N, et al. (2017) Association Between Midlife Vascular Risk Factors and Estimated Brain Amyloid Deposition. JAMA 317(14):1443-1450.
Van Epps P, Oswald D, Higgins PA, Hornick TR, Aung H, Banks RE, et al. (2016) Frailty has a stronger association with inflammation than age in older veterans. Immun Ageing 13:27.
Tenuta M, Carlomagno F, Cangiano B, Kanakis G, Pozza C, Sbardella E, et al. (2021) Somatotropic-Testicular Axis: A crosstalk between GH/IGF-I and gonadal hormones during development, transition, and adult age. Andrology 9(1):168-184.
Westwood AJ, Beiser A, Decarli C, Harris TB, Chen TC, He XM, et al. (2014) Insulin-like growth factor-1 and risk of Alzheimer dementia and brain atrophy. Neurology 82(18):1613-9.
Galle SA, Geraedts IK, Deijen JB, Milders MV and Drent ML (2020) The Interrelationship between Insulin-Like Growth Factor 1, Apolipoprotein E epsilon4, Lifestyle Factors, and the Aging Body and Brain. J Prev Alzheimers Dis 7(4):265-273.
Clemmons DR (2009) Role of IGF-I in skeletal muscle mass maintenance. Trends Endocrinol Metab 20(7):349-56.

Table 1. General demographics of individuals aged 65 to 79 years
	Group 1		Group 2		Group 3		p-value
Variable	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	Overall	1 vs 2	1 vs 3	2 vs 3
Age	288	65.52 ± 2.49	196	71.17 ± 2.27	80	79.28 ± 5.56	< 0.0001 ^k	< 0.0001 ^m	< 0.0001 ^m	< 0.0001 ^m
Sex (women)	140	(48.61%)	114	(58.16%)	44	(55.00%)	0.11 ^c
Education (Illiterate or elementary school)	103	(36.27%)	95	(48.47%)	56	(71.79%)	< 0.0001 ^c	0.008 ^c	< 0.0001 ^c	0.0005 ^c
MMSE	288	27.13 ± 2.68	193	27.08 ± 2.39	78	24.13 ± 4.39	< 0.0001 ^k	0.39 ^m	< 0.0001 ^m	< 0.0001 ^m
MMSE: men	148	27.39 ± 2.59	82	27.09 ± 2.42	35	25.94 ± 3.89	0.09 ^k
MMSE: women	140	26.84 ± 2.76	111	27.08 ± 2.38	48	22.65 ± 4.25	< 0.0001 ^k	0.75 ^m	< 0.0001 ^m	< 0.0001 ^m
Total score of frailty ^a	288	0.56 ± 0.83	196	0.50 ± 0.67	80	0.84 ± 1.14	0.39 ^k
Unintentional weight loss	11	(3.82%)	6	(3.06%)	1	(1.25%)	0.51 ^c
Exhaustion	20	(6.94%)	4	(2.04%)	5	(6.25%)	0.0501 ^c
Low physical activity	83	(28.82%)	45	(22.96%)	13	(16.25%)	0.051 ^c
Slow walking speed	15	(5.21%)	10	(5.10%)	24	(30.00%)	<0.0001 ^c	0.96 ^c	< 0.0001 ^c	< 0.0001 ^c
Weak grip strength	33	(11.46%)	33	(16.84%)	24	(30.00%)	0.0003 ^c	0.09 ^c	< 0.0001 ^c	0.014 ^c
SD, Standard deviation. ^k Kruskal Wallis test. ^m Mann-Whitney U test. ^c Chi-square test. p < 0.05 is highlighted in bold.
MMSE: Mini-Mental State Examination; there were 3 missing values in Group 2 and 6 missing values in Group 3. ^a If any of the five components contained a missing value, the total score cannot be calculated.

Table 2. Metabolic and biochemical indicators in individuals aged 65 to 79 years

Group 1

Group 2

Group 3

p-value

Variable

n

Mean ± SD

n

Mean ± SD

n

Mean ± SD

Overall

1 vs 2

1 vs 3

2 vs 3

Metabolic indicators

BMI (kg/m²)

288

25.16 ± 3.48

196

24.72 ± 3.34

80

24.2 ± 3.2

0.06 ^k

Height

288

159.8 ± 8.01

196

158.19 ± 7.59

80

156.91 ± 9.75

0.005 ^k

0.040 ^m

0.004 ^m

0.09 ^m

Weight

288

64.43 ± 11.24

196

62.02 ± 10.4

80

59.75 ± 10.59

0.002 ^k

0.023 ^m

0.0009 ^m

0.09 ^m

Waistline

283

87.29 ± 10.3

196

87.54 ± 9.67

80

89.22 ± 9.82

0.16 ^k

SBP

284

125.36 ± 15.43

196

128.43 ± 16.46

80

133.89 ± 17.9

0.0003 ^k

0.043 ^m

< 0.0001 ^m

0.017 ^m

DBP

287

70.8 ± 9.21

196

68.72 ± 9.73

80

66.6 ± 9.6

0.002 ^k

0.034 ^m

0.0008 ^m

0.10 ^m

PP

283

54.36 ± 10.56

196

59.71 ± 11.11

80

67.29 ± 15.26

< 0.0001 ^k

< 0.0001 ^m

MAP

283

88.99 ± 10.47

196

88.62 ± 11.23

80

89.03 ± 10.79

0.90 ^k

Glucose (AC) (mg/dL)

268

112.27 ± 30.57

196

110.33 ± 30.46

80

107.51 ± 20.97

0.92 ^k

Triglyceride (mg/dL)

285

139.82 ± 82.40

196

127.07 ± 76.89

80

113.83 ± 61.98

0.004 ^k

0.031 ^m

0.003 ^m

0.14 ^m

HDL-C (mg/dL)

288

51.43 ± 14.12

196

53.36 ± 15.15

80

52.21 ± 16.13

0.34 ^k

LDL-C (mg/dL)

283

116.23 ± 34.21

196

114.30 ± 33.18

80

103.13 ± 28.86

0.007 ^k

0.35 ^m

0.002 ^m

0.014 ^m

Currently smoking (yes)

44

(15.28%)

10

(5.10%)

4

(5.00%)

0.0004 ^c

0.0005 ^c

0.016 ^c

1.00 ^f

Biochemical Indicators

TNFR1 (pg/mL)

285

1088.34 ± 479.17

195

1123.62 ±357.54

79

1478.41 ± 712.3

< 0.0001 ^k

0.012 ^m

< 0.0001 ^m

0.0002 ^m

IGF-1 (ng/mL)

288

67.72 ± 20.56

196

64.48 ± 22.53

80

57.91 ± 19.77

0.0003 ^k

0.052 ^m

< 0.0001 ^m

0.020 ^m

hsCRP (mg/dL)

287

0.21 ± 0.45

196

0.26 ± 0.55

80

0.18 ± 0.32

0.14 ^k

SD, Standard deviation. ^k Kruskal Wallis test. ^m Mann-Whitney U test. ^c Chi-square test. ^f Fisher's exact test. p < 0.05 is highlighted in bold.

SBP: Systolic blood pressure; DBP: Diastolic blood pressure; PP (pulse pressure): SBP-DBP; MAP, 1/3(SBP)+2/3(DBP);
HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; hsCRP: High-sensitivity C-reactive protein.

Table 3. Multiple regression analyses of the MMSE
Variable	β	S.E.	t	p-value	Partial r²	VIF
Group (ref.: Group3(Age=79)
Group1(Age=65)	0.09	0.04	2.53	0.012	0.018	2.82
Group2(Age=71)	0.05	0.02	2.75	0.006	0.039	2.54
Sex (ref.: women)	0.05	0.02	2.34	0.020	0.016	1.10
Education (ref.: Illiterate or elementary school)	0.22	0.02	9.82	< 0.0001	0.153	1.10
Total score of frailty	-0.05	0.01	-3.77	0.0002	0.025	1.10
Since the MMSE was non-normally distributed, the rank transformation was conducted. β: regression coefficient; S.E.: standard error of regression coefficient;
t: the test statistics on slope of the regression line; VIF: variance inflation factor.
Variables selected from Supplementary Table 1 (p < 0.05), and set the PP, LDL-C, and TNFR1 as confounding variables. p < 0.05 is highlighted in bold.

Table 4. Multiple regression analysis of the total score of frailty
Variable	β	S.E.	t	p-value	Partial r²	VIF
MMSE	-0.02	0.004	-4.76	<0.0001	0.047	1.28
TNFR1 (pg/mL)	0.00009	0.00002	4.03	<0.0001	0.028	1.11
Currently smoking (ref.: yes)	0.10	0.04	2.66	0.008	0.016	1.02
Since the total score of frailty was non-normally distributed, the rank transformation was conducted. β: regression coefficient; S.E.: standard error of regression coefficient;
t: the test statistics on slope of the regression line; VIF: variance inflation factor.
Variables selected from Supplementary Table 2 (p < 0.05), and set education, Glucose (AC), Triglyceride, and LDL-C as confounding variables. p < 0.05 is highlighted in bold.

No competing interests reported.

6SupplementaryTableHALSTn564wave2THv1.docx

Cognitive decline is correlated with frailty in community-dwelling older adults

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1