Handgrip Strength Could Be an Early Predictor of Cognitive Impairment in the Chinese Population

doi:10.21203/rs.3.rs-400381/v1

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Research Article

Handgrip Strength Could Be an Early Predictor of Cognitive Impairment in the Chinese Population

https://doi.org/10.21203/rs.3.rs-400381/v1

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Journal Publication

published 11 Aug, 2021

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Background

This study aimed to explore the level and changes of handgrip strength in pre-clinical Alzheimer’s disease (AD) and AD participates, and to evaluate the association between handgrip strength and cognitive function.

Methods

1431 participants from the memory clinic of Shanghai JiaoTong University Affiliated Sixth People’s Hospital and community were enrolled into the final analysis, included 596 AD, 288 mild cognitive impairment (MCI), and 547 normal individuals (NC). All participants received a comprehensive neuropsychological assessment. Mini-mental state examination (MMSE), montreal cognitive assessment-Basic (MoCA-BC), and the Chinese version of the Addenbrooke’s cognitive examination (ACE-III-CV) were used as cognitive tests. The receiver operating characteristic curve (ROC) was plotted to assess the power of the handgrip strength as a screening measure to discriminate AD and MCI.

Results

The results showed that participants with lower handgrip strength had lower MMSE, MoCA-BC, and ACE-III-CV scores (P <0.05). Handgrip strength in the mild cognitive impairment (MCI) group was significantly lower than that of normal individuals (NC), and the AD group had a further decline (both P<0.01). Multivariate logistic regression was performed with the handgrip strength quartiles, the results showed ORs of AD for increasing levels of handgrip strength were 1.00, 0.58 (0.46–0.78), 0.51 (0.36–0.73), and 0.50 (0.35–0.68), showing a decreasing trend (P_{for trend}< 0.001). The receiver operating characteristic curve demonstrated that the handgrip strength cut-off points for identification of AD were16.8 kg and 20.7 kg among the female participates above and under 70 yrs, 24.4 kg and 33.3 kg for the male participates above and under 70 yrs, respectively.

Conclusions

Stronger handgrip strength was associated with better performances on cognitive function, handgrip strength could be an early predictor of cognitive impairment in the Chinese population. The current study provides a foundation for non-cognitive features as early predictors of cognitive impairment, researches on the association between frailty and cognition will be further developed in the future.

Geriatrics & Gerontology

Mild cognitive impairment (MCI)

Alzheimer's disease (AD)

Handgrip strength

Alzheimer’s disease (AD) is the most common type of dementia with typical clinical symptom appearing probably decades after progressive cognitive function loss with the initial phase of subtle changes¹. Identifying predictive clinical signs of cognitive decline and dementia is imperative for the implementation of an adapted treatment². Thus, pre-clinical AD research is of great significance to study the dynamics of AD pathogenesis, and provide supporting evidence for early prevention strategies^3,4. Once cognitive impairment progresses to dementia, it can hardly be reversed. Mild cognitive impairment (MCI) is considered as a transitional phase between normal cognitive aging and AD^5,6. The latest high-quality research suggested that MCI was associated with the faster amyloid accumulation and selective vulnerability of entorhinal cortical thinning^7,8.

Previously, studies have shown that in addition to the cognitive impairment, AD can also be accompanied by the performance of other systems of the organism, such as fatigue, slowed gait, and decreased muscle strength. These non-cognitive features may become early predictors of cognitive impairment. Grip strength is one of the main indicators of body muscle strength and physical state, and it is also a relatively simple evaluation method⁹. Several studies reported that poor handgrip strength is associated with a greater risk of cognitive impairment^9–13. Furthermore, there are studies suggested that higher handgrip strength at baseline was a protective factor in preventing the development of AD¹⁴. Considering that AD is preceded by a “silent” clinical period that can last longer than a decade. Identifying such “soft” physical signs associated with the progressive decline of cognitive function was significant for early intervention^9,14. However, few studies explored the relationship between handgrip strength and cognition in pre-clinical AD participates, especially unclear among Chinese population. This study aimed to evaluate the association between handgrip strength and cognitive function, provide an analysis of AD screening efficiency for handgrip strength in a large population sample.

Participants

A total of 1431 participants were recruited from the memory clinic of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital and community in Shanghai. This study was approved by the Institutional Ethic Reviewing Board of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. Each subject had a uniform structured evaluation performed by a neurologist, which included a medical history inquiry and neurological examination. Inclusion criteria were: normal vision and hearing to complete cognitive tests; no history of alcoholism, drug abuse, and head trauma. The exclusion criteria of this cohort included aged ≤40 or above 90 years, low education level (≤5 years); presence of neurological or psychiatric antecedents; diseases involving the central nervous system; severe diseases such as cancer, kidney disease, chronic liver disease. Relevant laboratory tests were carried out to exclude metabolic disorders, nutritional deficiencies and infectious diseases which may be adversely affecting cognitive function, such as abnormalities in folic acid, vitamin B12, thyroid function, and rapid plasma regain or treponema pallidum particle agglutination. Cranial magnetic resonance imaging scanning was performed routinely to exclude any potential causes of cognitive decline, such as cerebral infarction, subdural hematomas, hydrocephalus, intracranial tumors and infections.

Neuropsychology

All participants received a comprehensive neuropsychological assessment, which were carried out by trained raters who were blind to diagnosis. Six neuropsychological tests in three cognitive domains were examined, AVLT 30-minute delayed free recall auditory verbal learning test (AVLT-N5) and AVLT recognition (AVLT-N7) for memory domain¹⁵; animal fluency test (AFT, total score) and 30-item Boston naming test (BNT, total score) for language domain^16,17; shape trails test (STT), parts A and B (time to completion) for executive domain¹⁸. Mini-mental state examination (MMSE)^19-21, Montreal cognitive assessment-basic (MoCA-BC) ^22,23, and the third version of Addenbrooke’s Cognitive examination (Chinese version, ACE-III-CV)²⁴were also tested as global cognition. Activities of daily living (ADL) and functional activities questionnaire (FAQ) were used to assess functional capacity based on the reports of informants^25,26. Each neuropsychological test has been standardized using published normative data, and widely used in China with good reliability and validity.

Measures

AD diagnosed based on the recommendations from the National Institute on Aging-Alzheimer’s Association (NIA-AA) workgroups²⁷. MCI based on Bondi’s criteria as above^5,6,28 , diagnosis of MCI was given if the participant met one of the following criteria: 1. impaired scores (defined as >1 standard deviation (SD) below the age-corrected normative mean) on two of the six neuropsychological indexes in the same cognitive domain (AVLT 30-minute delayed free recall and AVLT recognition for memory, AFT and BNT for language, STT-A and STT-B for executive function); 2. impaired scores (defined as >1 SD below the age-corrected normative mean) in each of the three cognitive domains. Individuals who did not meet all of these criteria and have no cognitive impairment were identified as normal individuals (NC).

Collection of clinical information

Height and body weight were measured, and body mass index (BMI) was calculated as weight/height² (kg/m²). Handgrip strength (kg) was estimated through the dynamometer (WCS-100, Nantong, China). Participants were asked to squeeze the dynamometer for a practice trial using submaximal effort to determine their understandings on the procedure and the grip size adjustments. They were randomly assigned to start the test with their dominant or non-dominant hand. To complete the test, participants were asked to use one hand to squeeze the dynamometer as hard as possible, and repeat using the other hand for a total of three alternating hands. Similar to previous studies using this measure, we extracted the maximum value achieved using either hand as the summary measure^29,30.

Statistical analysis

SPSS, version 19.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. Based on a normality test, all variables for which the data were normally distributed are presented as mean ± standard deviation values, and skewed data are presented as median values with interquartile ranges, categorical variables are presented as percentages (%). Pair analyses were carried out using the paired Student’s t test and the Wilcoxon signed rank sum test. Intergroup comparisons of skewed data were conducted using the Kruskal-Wallis test. Spearman correlation analysis, logistic regression analysis, and multiple stepwise regression analysis were conducted to identify the associations between handgrip strength and cognitive function. A two-tailed P value of less than 0.05 was considered to be statistically significant. The receiver operating characteristic curve (ROC) was plotted to assess the power of the handgrip strength as a screening measure to discriminate AD and MCI.

Baseline characteristics

Overall, 1431 participants were enrolled into the final analysis, with 48.8% men and 51.2% women, the mean age was 69.8 ± 9.9 yrs. Of the 1,431 participants, there were 596 (41.6%) AD, 288 (20.2%) MCI, and 547 (38.2%) NC. We further divided participants into four subgroups according to the quartile of handgrip strength (Q1: handgrip strength ≤ 18.5, Q2: 18.5 < handgrip strength ≤ 23.4, Q3: 23.4 < handgrip strength ≤ 29.8, Q4: handgrip strength > 29.8). Table 1 compares the demographic characteristics of study participants with different handgrip strength on quintiles. The results showed that participants with lower handgrip strength had higher age (P

Table 1

Demographic and clinical characteristics of study participants.
Variable	Q1 (handgrip strength ≤ 18.5)	Q2 (18.5 < handgrip strength ≤ 23.4)	Q3 (23.4 < handgrip strength ≤ 29.8)	Q4 (handgrip strength > 29.8)	F(P Value)
n	358	361	357	355
Sex (female/male)	207/151	206/155	173/184	147/208	17.9(<0.001)
Age (years)	74.9 ± 8.5	69.8 ± 9.8	68.4 ± 9.8	66.2 ± 9.1	55.7(<0.001)
Education (years)	9.4 ± 4.0	11.2 ± 4.5	11.3 ± 4.4	11.4 ± 4.4	2.98(0.217)
Height(cm)	156.7 ± 14.1	160.6 ± 11.1	164.7 ± 9.1	170.7 ± 10.4	20.3(<0.001)
BMI (kg/m²)	21.4 ± 3.3	22.7 ± 3.0	23.8 ± 3.6	24.5 ± 3.1	4.46(0.034)
AD (n,%)	250 (69.8%)	142 (29.3%)	106 (29.9%)	98 (29.9%)	14.7(<0.001)
MCI (n,%)	85 (23.5%)	78 (21.8%)	68 (19.2%)	57 (15.9%)	26.3(<0.001)
MMSE	18.3 ± 7.6	22.8 ± 7.0	24.4 ± 5.7	26.5 ± 6.0	68.5(<0.001)
MoCA-BC	13.9 ± 7.6	18.9 ± 7.7	20.8 ± 6.6	21.4 ± 7.6	73.7(<0.001)
ACE-III-CV	50.0 ± 21.5	62.4 ± 21.8	68.5 ± 18.4	71.8 ± 19.0	85.8(<0.001)
ADL	29.3 ± 11.8	24.4 ± 9.3	21.9 ± 11.8	20.0 ± 6.6	53.6(<0.001)
FAQ	9.3 ± 9.0	5.0 ± 7.9	2.9 ± 5.6	2.3 ± 5.9	57.0(<0.001)
Data are presented as mean ± standard deviation or median (interquartile range). ADL, activities of daily living; AD, alzheimer’s disease; ACE-III, the third version of Addenbrooke’s Cognitive Examination; BMI, body mass index; FAQ, functional activities questionnaire; MCI, mild cognitive impairment; MMSE, Mini-mental State Examination; MoCA-BC, Montreal Cognitive Assessment-Basic.
< 0.001), higher ADL and FAQ scores (all P_{for trend} <0.001), but a lower BMI (P_{for trend} <0.05), lower MMSE, MoCA-BC, and ACE-III-CV scores (all P_{for trend} <0.001). No significant significance was found in terms of education years among subgroups (P = 0.207). There were fewer AD and MCI subjects in people with higher grip strength (both P < 0.001). Women had significantly lower handgrip strength (22.4 vs. 31.0 kg, P < 0.001) compared to men.

Comparison of handgrip strength levels among NC, MCI, and AD

The handgrip strength between among cognitive function were compared in both male and female group. As shown in Fig. 1, the handgrip strength of NC, MCI, and AD group in male were 36.0 (30.7–41.0) kg, 31.0 (28.2–37.5) Kg, 27.7 (20.7–33.3) Kg, respectively. The handgrip strength of NC, MCI, and AD group in female were 23.8 (21.5–26.0) kg, 23.3 (20.2–35.8.5) kg, 17.2 (13.4–21.0) kg, respectively. The results suggested that handgrip strength showed decreases trend with the decline of cognitive function in both male and female groups (both P_{for trend}<0.001). The level of handgrip strength in the MCI group was significantly lower than that of NC, and the AD group had a further decline (both P < 0.001) .

Associations between handgrip strength and cognitive function

After dividing the population into sex subgroups of different sexes, spearman correlation analysis revealed that handgrip strength was negatively associated with age, FAQ, and ADL scores (all P < 0.001), and positively associated with BMI, MMSE, MoCA-BC, and ACE-III-CV scores (Table 2, P < 0.001). Multivariate logistic regression was performed with the handgrip strength quartiles, and the independent

Table 2

Spearman correlation of relationship between of handgrip strength and age, BMI, education years, neuropsychology test scores.
	Male		Famale
	r	P	r	P
Age	−0.566	< 0.001	−0.503	< 0.001
BMI	0.492	< 0.001	0.512	< 0.001
Education	0.128	0.416	0.188	0.302
MMSE	0.440	< 0.001	0.558	< 0.001
MoCA-BC	0.430	< 0.001	0.443	< 0.001
ACE-III-CV	0.418	< 0.001	0.436	< 0.001
FAQ	−0.481	< 0.001	−0.436	< 0.001
ADL	−0.502	< 0.001	−0.437	< 0.001

ADL, activities of daily living; ACE-III, the third version of Addenbrooke’s Cognitive Examination

BMI, body mass index; FAQ, functional activities questionnaire; MMSE, Mini-mental State Examination. MoCA-BC, Montreal Cognitive Assessment-Basic.

variables were different stages of cognition. After adjusted for age and BMI, the results showed that handgrip strength tended to decrease with the decline of cognition in subjects with AD and MCI (Table 3, both P_{for trend} < 0.001). Multivariable-adjusted ORs of AD and MCI for increasing levels of handgrip strength were 1.00, 0.58 (0.46–0.78), 0.51 (0.36–0.73), and 0.50 (0.36–0.68); and 1.00, 0.36 (0.26–0.49), 0.29 (0.21–0.40), and 0.16 (0.10–0.21), respectively, showing a decreasing trend (both P_{for trend} < 0.001). Fitting curve of handgrip strength and MMSE scores was showed as Figure.2, further multivariable-adjusted linear regression showed each increase kg of handgrip strength was associated with 0.348 (95% CI: 0.214 to 0.357) and 0.450 (95% CI: 0.422 to 0.528) higher scores of MMSE in male and female, respectively (both P < 0.001).

Handgrip strength screening for the AD and MCI

Taking into account the influence of age and sex on grip strength, we have performed ROC curve analysis separately in different sex and age subgroups. Figure 3a-3d presents the ROC curves for the handgrip strength detecting AD based on recommendations from NIA-AA. The analysis demonstrated that the handgrip strength cut-off point for the identification of AD was 16.8 kg for the female ≥ 70 yrs, with a sensitivity, specificity, and area under the curve (AUC) of 68.5% (95% confidence interval [CI]: 63.3–74.2%), 77.3% (95%CI: 68.1–83.8%), and 0.732 (95%CI: 0.686–0.775), respectively. The cut-off point of AD for was 20.7 kg the female < 70 yrs, with a sensitivity, specificity, and AUC of 66.8% (CI: 62.2–73.8%), 71.2% (95%CI: 64.3–78.5%), and 0.700 (95%CI: 0.617–0.734), respectively. For the male above and

Table 3

Odds ratios of participates with AD and MCI by quartiles of handgrip strength
	All participates		Male		Female
	AD	MCI	AD	MCI	AD	MCI
Q4	1.00	1.00	1.00	1.00	1.00	1.00
Q3	0.58 (0.46–0.78)	0.36 (0.26–0.49)	0.54 (0.39–0.74)	0.31 (0.22–0.39)	0.63 (0.46–0.85)	0.40 (0.29–0.53)
Q2	0.51 (0.36–0.73)	0.29 (0.21–0.40)	0.48 (0.34–0.63)	0.26 (0.20–0.36)	0.54 (0.39–0.75)	0.33 (0.24–0.45)
Q1	0.50 (0.35–0.68)	0.16 (0.10–0.21)	0.45 (0.32–0.67)	0.14 (0.11–0.21)	0.53 (0.39–0.70)	0.17 (0.12–0.24)
P _{for trend}	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
AD, alzheimer’s disease; MCI, mild cognitive impairment.

under 70, cut-off points were 24.4 kg and 33.3 kg, with sensitivity, specificity, and AUC of 69.1% (CI: 65.6–74.2%), 72.5% (95%CI: 67.1–79.4%), 0.634 (95%CI: 0.602–0.673); and 61.1% (CI: 53.4–77.8%), 72.5% (95%CI: 65.0–78.9%), 0.695 (95%CI: 0.632–0.754), respectively. Similarly, for the identification of MCI, cut-off points were 17.5 kg and 21.9 kg for the female participates above 70 yrs and under 70 yrs (Fig. 3.e-f), 25.8 kg and 36.2 kg for the male participates above 70 yrs and under 70 yrs (Fig. 3.g-h), respectively.

This study was the first to analyze and compare the levels of handgrip strength in Chinese pre-clinical AD and AD participates, and explore the association between handgrip strength and cognitive function. The results showed that the level of grip strength in the MCI group was significantly lower than that of NC, and the AD group had a further decline. We found that stronger handgrip strength was associated with better performance on cognitive, each increase kg of handgrip strength was associated with 0.348 (95% CI: 0.214 to 0.357) and 0.450 (95% CI: 0.422 to 0.528) higher scores of MMSE in male and female, respectively. Finally, we proposed that the sex-specific and age-specific cut-off points for the initial identification of AD and MCI using grip strength, which provides evidence for early detection of AD in the Chinese population.

Handgrip strength is a non-invasive measure of physical health that has been widely used in research and clinical settings. It can represent total body muscle strength, can also be an overall indicator of the integrity of the central nervous system³¹. Weak grip strength may indicate sarcopenia or central nervous system damage, which accelerates functional limitation. Prior studies have established an association between handgrip strength and cognition in aging cohort studies ^32,33, impaired cognitive function and functional limitation present a mutually aggravating process. A 2-year cohort study of older Japanese adults found a significant correlation between lower MMSE scores and greater odds of functional decline³⁴. A single-blind randomized controlled trial with 5 years of follow-up showed that cognitive training could slow functional decline in self-reported instrumental activities of daily life³⁵. Therefore, handgrip strength has been suggested as a useful tool in geriatric practice in monitoring cognitive function decline ³¹. Our findings showed that lower handgrip strength suggested a high risk of cognitive impairment, especially reported the handgrip strength levels in preclinical AD population. Each increase in kg of handgrip strength was associated 0.348 (95% CI: 0.214 to 0.357) and 0.450 (95% CI: 0.422 to 0.528) higher scores of MMSE scores in male and female groups with a U-shape association curve, which are similar to the previous studies³⁶. In addition, we proposed the cut-off points for the initial identification of AD and MCI using grip strength among different age and sex subgroups, the results indicated that it is reasonable to employ grip strength for AD simple screening.

Mechanistically, our findings are in accordance with the most notable hypotheses known as the ‘common cause hypothesis’, which demonstrates that cognition and muscle strength may share the same brain regions and networks³⁷. This form of bounded rationality provides a reasonably straightforward way to implement the concept that simple motor tests or physical functions could be studied as biomarkers for identifying patients at a higher risk of cognitive impairment and dementia. However, there is still no direct imaging evidence to prove the rationality of this theory. Although it could be speculated from some studies that brain areas between motor coordination and cognitive function have an overlap³⁸, we would need a significantly intuitive research design to prove and refine this theory.

There are some limitations in this study. First, this was a single-center study, and data from multiple centers are needed to further confirm the findings. Second, longitudinal study need to be conducted to investigate the association between handgrip strength and cognitive function in these participants at follow-up study. Further studies need to be performed with the larger volume of samples to investigate the difference among different cognitive domain impairments.

This study showed that the level of grip strength in the MCI group was significantly lower than that of NC, and the AD group had a further decline. We confirmed that stronger handgrip strength was associated with better performances on cognitive function, and proposed using grip strength can well identify early AD patients in the Chinese population. The current study provides a foundation for non-cognitive features as early predictors of cognitive impairment, researches on the association between frailty and cognition will be further developed in the future.

ADL, activities of daily living;

AD, alzheimer’s disease;

ACE-III, the third version of Addenbrooke’s cognitive examination;

BMI, body mass index;

FAQ, functional activities questionnaire;

MCI, mild cognitive impairment;

MMSE, mini-mental state examination;

MoCA-BC, montreal cognitive assessment-basic；

ROC, receiver operating characteristic curve;

NC, normal individuals.

The funding source

This study was funded by the National Key R&D Program of China (2016Y FC1306305).

Authors’ contributions

The authors declare that they have no conflicts of interest. Q.G. designed the study. H.S. and X.S. collected the data. H.S. analyzed the data and wrote the draft. F.L. provided technical support. F.L. and Q.G. revised the paper and contributed to the discussion. All of the authors read and approved the final manuscript.

Acknowledgments

We would like to thank all of the involved clinicians, nurses, and technicians for dedicating their time and skills to the completion of this study. We would like to thank all participants for their dedication to data collection and laboratory measurements.

Compliance with ethical standards

Competing interests

Upon manuscript submission, all the authors declare that they have no competing interests.

Ethical approval

This study was approved by the Ethics Committee of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital and was in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Availability of Data and Materials

The datasets generated and/or analyzed during the current study are not publicly available due the policy but are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer's disease. Lancet. 2016;388(10043):505-517.
Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, DeKosky ST, Gauthier S, Selkoe D, Bateman R,et alAdvancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. The Lancet Neurology. 2014 ;13(6):614–29.
Reiman EM. Alzheimer’s disease and other dementias: advances in 2013. The Lancet Neurology. 2014;13(1):3–5.
Petersen RC. Alzheimer’s disease: progress in prediction. The Lancet Neurology. 2010;9(1):4–5.
Huang L, Chen K, Liu Z, Guo Q. A Conceptual Framework for Research on Cognitive Impairment with no Dementia in Memory Clinic. Curr Alzheimer Res. 2020;17(6):517-525.
Thomas KR, Edmonds EC, Eppig J, Salmon DP, Bondi MW; Alzheimer’s Disease Neuroimaging Initiative. Using Neuropsychological Process Scores to Identify Subtle Cognitive Decline and Predict Progression to Mild Cognitive Impairment. J Alzheimers Dis. 2018;64(1):195-204.
Edmonds EC, Delano-Wood L, Galasko DR, Salmon DP, Bondi MW; Alzheimer’s Disease Neuroimaging Initiative. Subtle Cognitive Decline and Biomarker Staging in Preclinical Alzheimer's Disease. J Alzheimers Dis. 2015;47(1):231-242.
Eckerström M, Göthlin M, Rolstad S, Hessen E, Eckerström C, Nordlund A, Johansson B, Svensson J, Jonsson M, Sacuiu S, Wallin A. Longitudinal evaluation of criteria for subjective cognitive decline and preclinical Alzheimer's disease in a memory clinic sample. Alzheimers Dement (Amst). 2017 May 16;8:96-107.
Liu Y, Cao X, Gu N, Yang B, Wang J, Li C. A Prospective Study on the Association Between Grip Strength and Cognitive Function Among Middle-Aged and Elderly Chinese Participants. Front Aging Neurosci. 2019;11:250.
Camargo EC, Weinstein G, Beiser AS, Tan ZS, DeCarli C, Kelly-Hayes M, Kase C, Murabito JM, Seshadri S. Association of Physical Function with Clinical and Subclinical Brain Disease: The Framingham Offspring Study. J Alzheimers Dis. 2016;53(4):1597-1608.
Praetorius Björk M, Johansson B, Hassing LB.I forgot when I lost my grip-strong associations between cognition and grip strength in level of performance and change across time in relation to impending death. Neurobiol Aging. 2016;38:68-72.
Veronese N, Stubbs B, Trevisan C, Bolzetta F, De Rui M, Solmi M, Sartori L, Musacchio E, Zambon S, Perissinotto E, et al. What physical performance measures predict incident cognitive decline among intact older adults? A 4.4year follow up study. Exp Gerontol. 2016;81:110-118.
Hooghiemstra AM, Ramakers IHGB, Sistermans N, Pijnenburg YAL, Aalten P, Hamel REG, Melis RJF, Verhey FRJ, Olde Rikkert MGM, Scheltens P, et al. Gait Speed and Grip Strength Reflect Cognitive Impairment and Are Modestly Related to Incident Cognitive Decline in Memory Clinic Patients With Subjective Cognitive Decline and Mild Cognitive Impairment: Findings From the 4C Study. J Gerontol A Biol. Sci Med Sci. 2017;72(6):846-854.
Rijk JM, Roos PR, Deckx L, van den Akker M, Buntinx F. Prognostic value of handgrip strength in people aged 60 years and older: A systematic review and meta-analysis. Geriatr Gerontol. Int 2016;16(1):5-20.
Zhao Q, Guo Q, Liang X, Chen M, Zhou Y, Ding D, Hong Z. Auditory Verbal Learning Test is Superior to Rey-Osterrieth Complex Figure Memory for Predicting Mild Cognitive Impairment to Alzheimer’s Disease. Current Alzheimer Research. 2015;12(6): 520-526.
Guo Q, Hong Z, Shi W, Sun Y, LV C.Boston naming test using by Chinese elderly, patient with mild cognitive impairment and Alzheimer’s dementia. Chinese Mental Health Journal. 2006;20(2): 81-85.
Zhao Q, Guo Q, Hong Z. Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment. Neurosci Bull. 2013;29(1):75-82.
Zhao Q, Guo Q, Li F, Zhou Y, Wang B, Hong Z. The Shape Trail Test: Application of a New Variant of the Trail Making Test. PLoS ONE. 2013;8(2): e57333.
Katzman R, Zhang MY, Ouang-Ya-Qu, Wang ZY, Liu WT, Yu E, Wong SC, Salmon DP, Grant I. A Chinese version of the mini-mental state examination; Impact of illiteracy in a Shanghai dementia survey. Journal of Clinical Epidemiology. 1988;41(10):971-978.
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412-4.
Worboys M. The Hamilton Rating Scale for Depression: The making of a "gold standard" and the unmaking of a chronic illness, 1960-1980. Chronic Illn. 2013;9(3):202-19.
Huang L, Chen KL, Lin BY, Tang L, Zhao QH, Lv YR, Guo QH. Chinese version of Montreal cognitive assessment Basic for discrimination among different severities of Alzheimer’s disease. Neuropsychiatric Disease and Treatment. 2018;14: 2133–2140.
Chen KL, Xu Y, Chu AQ, Ding D, Liang XN, Nasreddine ZS, Dong Q, Hong Z, Zhao QH, Guo QH. Validation of the Chinese Version of Montreal Cognitive Assessment Basic for Screening Mild Cognitive Impairment. J Am Geriatr Soc. 2016;64(12):e285-e290.
Pan FF, Wang Y, Huang L, Huang Y, Guo QH. Validation of the Chinese Version of Addenbrooke’s Cognitive Examination III for detecting mild cognitive impairment. Aging Ment Health. 2021;3:1-8.
Chen P, Yu ES, Zhang M, Liu WT, Hill R, Katzman R. ADL dependence and medical conditions in Chinese older persons: A population-based survey in Shanghai, China. J Am Geriatr Soc. 1995,43:378-383.
Pfeffer RI, Kurosaki TT, Harrah CH Jr, Chance JM, Filos S. Measurement of functional activities in older adults in the community. J Gerontol. 1982;37(3):323-329.
Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-562.
Bondi MW, Edmonds EC, Jak AJ, Clark LR, Delano-Wood L, McDonald CR, Nation DA, Libon DJ, Au R, Galasko Det al. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimers Dis. 2014;42(1):275-289.
Metter EJ, Conwit R, Tobin J, Fozard JL. Age-associated loss of power and strength in the upper extremities in women and men. J Gerontol A Biol Sci Med Sci. 1997;52(5):B267-B276.
Gallagher D, Heymsfield SB. Muscle distribution: variations with body weight, gender, and age. Appl Radiat Isot. 1998;49(5-6):733-734.
Fritz NE, McCarthy CJ, Adamo DE. Handgrip strength as a means of monitoring progression of cognitive decline-A scoping review. Ageing Res Rev. 2017;35:112-123.
Clouston SA, Brewster P, Kuh D, Richards M, Cooper R, Hardy R, Rubin MS, Hofer SM. The dynamic relationship between physical function and cognition in longitudinal aging cohorts. Epidemiol Rev. 2013;35(1):33-50.
Veronese N, Stubbs B, Trevisan C, Bolzetta F, De Rui M, Solmi M, Sartori L, Musacchio E, Zambon S, Perissinotto E, et al. What physical performance measures predict incident cognitive decline among intact older adults? A 4.4year follow up study. Exp Gerontol. 2016;81:110-118.
Ishizaki T, Yoshida H, Suzuki T, Watanabe S, Niino N, Ihara K, Kim H, Fujiwara Y, Shinkai S, Imanaka Y. Effects of cognitive function on functional decline among community-dwelling non-disabled older Japanese. Arch Gerontol Geriatr. 2006;42:47–58.
Willis SL, Tennstedt SL, Marsiske M, Ball K, Elias J, Koepke KM, Morris JN, Rebok GW, Unverzagt FW, Stoddard AM, et al. ACTIVE Study Group. Longterm effects of cognitive training on everyday functional outcomes in older adults. JAMA. 2006;296:2805–2814.
Yang L, Koyanagi A, Smith L, Hu L, Colditz GA, Toriola AT, López Sánchez GF, Vancampfort D, Hamer M, Stubbs B, et al. Hand grip strength and cognitive function among elderly cancer survivors. PLoS One. 2018, 13(6):e0197909.
Christensen H, Mackinnon AJ, Korten A, Jorm AF. The "common cause hypothesis" of cognitive aging: evidence for not only a common factor but also specific associations of age with vision and grip strength in a cross-sectional analysis. Psychol Aging 2001;16(4):588-599.
Rosano C, Snitz BE. Predicting Dementia from Decline in Gait Speed: Are We There Yet?. J Am Geriatr Soc 2018;66(9):1659-1660.

No competing interests reported.

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Handgrip Strength Could Be an Early Predictor of Cognitive Impairment in the Chinese Population

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Abstract

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Background

Methods

Results

Baseline characteristics

Comparison of handgrip strength levels among NC, MCI, and AD

Associations between handgrip strength and cognitive function

Handgrip strength screening for the AD and MCI

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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Journal Publication

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