Analysis of the relationship between mild cognitive impairment and serum klotho protein and insulin-like growth factor-1 in the elderly

Abstract

BACKGROUND:

Mild cognitive impairment (MCI) is a mild memory or cognitive impairment.

OBJECTIVE:

To explore the relationship between serum klotho (K1) protein and insulin-like growth factor-1 and mild cognitive impairment in the elderly in order to provide accurate and appropriate indicators for clinical diagnosis and treatment of MCI.

METHODS:

This randomized stratified study adopted a multistage cluster sampling method. 161 elderly patients with mild cognitive impairment were included as the MCI group, and 161 healthy people matched with the MCI group in gender, age and education were selected as the control group.

RESULTS:

The levels of serum K1 protein and insulin-like growth factor-1 in the MCI group were lower than those in the control group ( $P<$ 0.05). Both IGF-1 and K1 had predictive value for MCI ( $P<$ 0.05). The area under the curve (AUC) of IGF-1 for predicting MCI was 0.859 (95% CI: 0.790 $\sim$ 0.929), and the AUC of K1 for predicting MCI was 0.793 (95% CI: 0.694 $\sim$ 0.892). The value of joint prediction of the two indicators was the highest, with an AUC of 0.939 (95% CI: 0.896–0.993).

CONCLUSION:

High serum K1 and insulin-like growth factor-1 are the protective factors of cognitive impairment in MCI patients. Both IGF-1 and serum K1 proteins have predictive value for MCI, and the combination of the two indicators has the highest predictive value.

Keywords

Mild cognitive impairment klotho protein serum insulin-like growth factor-1

1. Introduction

Mild cognitive impairment (MCI) is a clinical state between normal aging and early senile dementia, and is more likely to develop into Alzheimer’s disease than normal people [1, 2, 3]. Alzheimer’s disease not only seriously affects the quality of life of the elderly, but also brings a heavy burden to the family and society. MCI is the best stage of preventive intervention of Alzheimer’s disease progression [3], however, due to the mild cognitive impairment of MCI, it is difficult to attract the attention of patients and their families. Although resting-state fMRI can detect amyloidosis plaques in the brain, because amyloidosis in the brain of patients with MCI is not obvious, imaging methods are still unable to be applied to the diagnosis of MCI [4, 5]. The same problem also appears in the cerebrospinal fluid screening diagnostic methods [6]. Therefore, serum biomarkers are of great significance for early diagnosis and screening of MCI.

Klotho (K1) gene is mainly expressed in the kidney and brain choroid [7, 8], and its secreted protein is a hormone related to aging [9]. Loss of K1 gene expression can lead to similar manifestations of human aging, including shortening of life span, cognitive impairment, hearing loss and other symptoms [10, 11, 12]. Studies have shown that the upregulation of K1 levels in brain and serum significantly improves the A $\beta$ protein load, neuron and synapse loss and cognitive impairment [13]. K1 treatment significantly enhanced microglia mediated A $\beta$ protein, which provides a new idea for the treatment of Alzheimer’s disease [14], so it is speculated that this gene may have a regulatory role in the occurrence and development of aging-related diseases, such as MCI. Studies also have shown that IGF-1 can improve cognitive function by removing A $\beta$ protein in CSF and inhibiting the excessive phosphorylation of Tau protein, promoting the proliferation and differentiation of nerve cells, and improving insulin resistance [15, 16, 17]. The absence of insulin-like growth factor 1 receptor (IGF-1R) can affect brain size and development, and lead to behavioral changes [18]. IGF-1 activates a similar signal cascade through insulin receptor and its own receptor IGF-1R [19]. Other studies have shown that IGF-1R resistance exists in the brain of patients with Alzheimer’s disease [20, 21].

At present, there are few studies on the relationship between IGF-1 and K1 protein and MCI. Moreover, the effects of IGF-1 and K1 protein were studied separately, and the relationship between both of them and cognitive impairment is rarely studied. Therefore, this study explores the relationship between MCI and IGF-1 and K1 protein, so as to provide further basis for studying the pathogenesis of MCI and its prevention and treatment in the elderly.

2. Subjects and methods

2.1 Study subjects

Pair design at ratio of 1:1 was performed in this study. The sample size was estimated using PASS12 software. The experimental and control groups would require at least 161 samples. Therefore, this study collected 161 MCI cases aged 60 years and over from Shijiazhuang People’s Hospital between July 2022 and January 2023 as the case group by convenience sampling. Healthy individuals with gender, age, blood pressure, and nationality similar to the case group according to pair design at ratio of 1:1 were selected as the control group. Study subjects or family members signed the informed consent form for the study. The study was approved by the ethics committee of Shijiazhuang People’s Hospital.

2.2 Inclusion criteria

Case inclusion criteria: MCI diagnostic criteria was based on the MCI Diagnosis Criteria of Diagnostic and Statistical Manual of Mental Disorders Revision IV (DSM-IV) of the American Psychiatric Society: (1) Subjective symptoms are memory loss, which is confirmed by others; (2) The patient had mild cognitive impairment, and the score of the Montreal Cognitive Assessment Scale (MoCA Scale) was 19–26 points (using the Beijing version. The full score of the scale was 30 points. If the length of education is 32 years, 1 point will be added to the test results to correct the impact of education level); (3) Objective examination showed evidence of mild cognitive impairment, such as MMSE scoring: $<$ 25–27 for over middle school education, $<$ 21–24 for primary school, $<$ 18–21 for illiteracy; (4) Reduction in daily life and social function, ADL $<$ 26 points; (5) HIS score $\leqslant$ 4 points, excluding cognitive decline caused by depression and specific causes; (6) Course of disease $>$ 3 months; (7) The patient did not meet the diagnostic criteria for dementia. According to the above diagnostic criteria, 161 patients with MCI were finally included.

Inclusion criteria for the control group: Individuals did not meet the MCI diagnostic criteria according to the DSM-IV and were matched to the case group in terms of nationality, gender, age, and blood pressure. According to the ratio of 1:1, 161 individuals were finally selected.

2.3 Exclusion criteria

(1) people with a history of mental illness or congenital mental retardation and depression; (2) Patients with neurological diseases such as stroke, Parkinson’s disease, and brain tumors that can cause brain dysfunction; (3) Patients with a history of head trauma and special drug use, and patients with severe infectious diseases and toxic encephalopathy; (4) Patients with confirmed heart, lung, liver and kidney diseases, hypertension, diabetes and other diseases.

2.4 Study methods

2.4.1 Data collection

General data of the subjects were collected, including height, weight, age, blood pressure, gender, marital status, educational level, previous medical history and other data. A total of 5 ml of fasting blood was collected through the cubital vein, placed in a biochemical tube, and then separated by a centrifuge. Serum was placed in the EP tube with uniform number, and then stored in the $-$ 70 ${{}^{\circ}}$ C scientific research refrigerator for testing.

2.4.2 Detection of serological indicators

Level of fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) were measured by an automatic biochemical analyzer. Homocysteine (Hcy) levels were determined by the enzymatic cycle method. Serum insulin-like growth factor-1 (IGF-1) and K1 protein were determined by enzyme-linked immunosorbent assay (ELISA). Each sample was measured three times in parallel, and the average value was taken. The kits were purchased from Wuhan Huamei Biological Engineering Co., Ltd., and the testing process was conducted in strict accordance with the instructions.

2.5 Statistical analysis

Statistical analysis was performed using the SPSS22.0 software. Measurement data are expressed by $x\pm s$ . Independent sample $t$ -test was used for comparison between the two groups, and ANOVA among multiple groups. The count data are expressed as the number of cases and percentage, and $\chi^{2}$ test and rank sum test were used for comparison. Correlations between continuous variables were analyzed by the Pearson-test. Multiple Logistic regression was applied to analyze independent risk factors of senile cognitive impairment. The receiver operating characteristic curve (ROC) of the two groups was compared for AUC analysis. The diagnostic prediction effect of the two biomarkers was estimated by providing the AUC value, accuracy, sensitivity, specificity and 95% CI. $P<$ 0.05 was statistically different.

3. Results

3.1 Comparison of the baseline data between the two groups

As shown in Table 1, the mean age of the MCI group was (68.48 $\pm$ 7.15) years, mean systolic pressure (123.25 $\pm$ 9.57) mmHg and mean diastolic pressure (73.45 $\pm$ 7.53) mmHg, including 81 males and 80 females. The mean age of the control group was (67.27 $\pm$ 6.87) years, the mean systolic pressure (124.42 $\pm$ 9.99) mmHg, and the mean diastolic pressure (72.40 $\pm$ 8.31) mmHg, including 80 males and 81 females. There were no differences in gender, age, body weight, height, systolic BP, diastolic BP, and abdominal circumference between the two groups ( $P>$ 0.05). The baseline equilibrium is consistent with good comparability.

Table 1
Comparison of baseline data between MCI group and the control group

Gender (male/female)	81/80		80/81		$\chi^{2}=$ 0.050	0.824
	The MCI group		The control group		Statistics	$P$ value
Age (year)	68.48	$\pm$ 7.15	67.27	$\pm$ 6.87	$t=$ 1.557	0.121
Weight (kg)	61.82	$\pm$ 11.96	62.94	$\pm$ 12.61	$t=$ 0.818	0.414
Height (cm)	159.32	$\pm$ 8.89	159.81	$\pm$ 11.94	$t=$ 1.122	0.305
SBP (mmHg)	123.25	$\pm$ 9.57	124.42	$\pm$ 9.99	$t=$ 1.076	0.283
DBP (mmHg)	73.45	$\pm$ 7.53	72.40	$\pm$ 8.31	$t=$ 1.187	0.236
Abdominal circumference (cm)	86.5	$\pm$ 10.17	87.43	$\pm$ 10.55	$t=$ 0.803	0.423

Note: SBP: systolic blood pressure; DBP: diastolic blood pressure.

3.2 Comparison of the serological data between the two groups

The MCI group had higher Hcy level and lower IGF-1 and K1 protein level than the control group ( $P<$ 0.05). There was no significant difference in FBG, TC, TG, LDL-C, and HDL-C level between the two groups ( $P>$ 0.05) (Table 2).

Table 2
Comparison of baseline data between the MCI group and the control group

	The MCI group		The control group		Statistics	$P$ value
FBG (mmol/L)	6.08	$\pm$ 2.58	5.93	$\pm$ 3.54	$t=$ 0.438	0.662
TC (mmol/L)	4.03	$\pm$ 1.61	4.07	$\pm$ 1.02	$t=$ 1.062	0.289
TG (mmol/L)	1.28	$\pm$ 0.24	1.33	$\pm$ 0.31	$t=$ 0.896	0.372
Hcy ( $\mu$ mol/L)	15.17	$\pm$ 2.38	12.52	$\pm$ 2.55	$t=$ 2.656	0.009
IGF-1 (ng/mL)	95.74	$\pm$ 17.88	112.03	$\pm$ 15.39	$t=$ 4.874	0.000
Klotho protein (ng/mL)	85.9	$\pm$ 13.7	121.6	$\pm$ 24.8	$t=$ 4.254	0.000
HDL-C (mmol/L)	1.83	$\pm$ 1.14	1.62	$\pm$ 0.96	$t=$ 1.718	0.087
LDL-C (mmol/L)	2.93	$\pm$ 0.83	2.79	$\pm$ 0.66	$t=$ 1.691	0.092

Note: FBG: fasting blood glucose; TC: total cholesterol; TG: triglycerides; Hcy: homocysteine; IGF-1: insulin-like growth factor-1; HDL: high density lipoprotein; LDL: low density lipoprotein.

3.3 Multivariate logistic regression analysis for cognitive impairment

The results of multivariate logistic regression analysis showed that high IGF-1 and K1 protein were protective factors for cognitive impairment in MCI patients ( $P<$ 0.05), while Hcy was not associated with cognitive impairment ( $P>$ 0.05) (Table 3).

Table 3
Multivariate logistic regression analysis for cognitive impairment

Factors	Regression coefficient	Standard error	Wald value	OR	95% CI	$P$ value
Hcy	0.171	0.137	1.552	1.186	0.907 $\sim$ 1.552	0.213
IGF-1	$-$ 0.475	0.226	4.394	0.622	0.399 $\sim$ 0.970	0.036
K1 protein	$-$ 0.114	0.033	11.745	0.892	0.835 $\sim$ 0.952	0.001

3.4 Predictive value of IGF-1 and K1 protein for MCI

Both IGF-1 and K1 proteins have predictive value for MCI ( $P<$ 0.05). The area under the curve (AUC) of IGF-1 for predicting MCI was 0.859 (95% CI: 0.790 $\sim$ 0.929), with an optimal cutoff value of 103.83 ng/mL. The AUC of K1 protein predicting MCI was 0.793 (95% CI: 0.694 to 0.892), with an optimal cutoff value of 105.77 ng/mL. The joint prediction of the two indicators had the highest value, with an AUC of 0.939 (95% CI: 0.896 $\sim$ 0.993), as shown in Table 4.

Table 4
Value of IGF-1 and klotho proteins alone and in combination for predicting MCI

Indicator	Accuracy	Sensitivity	Specificity	AUC	95% CI	Optimal cutoff value
IGF-1	0.872	0.843	0.852	0.859	0.790 $\sim$ 0.929	103.83 ng/mL
K1 protein	0.855	0.811	0.813	0.793	0.694 $\sim$ 0.892	105.77 ng/mL
Joint prediction	0.922	0,.911	0.954	0.939	0.896 $\sim$ 0.993	Combined of the two

4. Discussion

This study found that the levels of serum K1 protein and insulin-like growth factor-1 in the MCI group were lower than those in the control group. Both IGF-1 and serum K1 proteins had predictive value for MCI, and the value of joint prediction of the two indicators was the highest.

Neurotrophic factors play an important role in the growth, survival and migration of neurons in the CNS. Recent studies on neurotrophic factors for neurodegenerative diseases have been increasing [22]. As one of them, IGF-1 has been confirmed to be one of the protective factors for neurodegenerative diseases [23]. It is a polypeptide hormone composed of amino acids. Maintaining the proper level of IGF-1 in vivo can inhibit nerve cell apoptosis and promote nerve cell survival [24]. Decreased $\beta$ amyloid clearance occurs in patients with cognitive impairment, leading to neuronal degeneration and death [25]. IGF-1 has the effect of protecting nerve cells, which can increase the clearance of $\beta$ amyloid in brain tissue and avoid its accumulation in nerve cells leading to cognitive impairment. The short-term increase of serum IGF-1 indicates that the nervous system repair mechanism is activated [26, 27]. This study also found statistically significant differences in serum IGF-1 level between the normal subjects and MCI patients. Further association analysis showed that serum IGF-1 was positively correlated with normal cognitive function. At the same time, multivariate logistic regression analysis showed that high IGF-1 and K1 protein levels were significantly correlated with cognitive function in MCI patients. When brain tissue is damaged or cognitive function declines, the secretion and release of IGF-1 is reduced, resulting in a slow accumulation of $\beta$ amyloid and further cognitive impairment [28]. Some studies have also confirmed the role of neurotrophic factor-IGF-1 in the progression of Alzheimer’s disease neurodegeneration induced by $\beta$ amyloid [29]. The data on MCI cases showed that cerebral insulin resistance preceded dementia. Amnestic and non-amnestic MCI patients showed basic abnormalities reflecting impaired insulin signaling, especially the decrease of IR/IGF-1R signal [30]. The ROC curve in our study also showed high predictive value of IGF-1 for MCI, with an AUC of 0.859 (95% CI: 0.790 $\sim$ 0.929).

Reactive oxygen species produced by body metabolism are an important cause of cell damage, which we called oxidative stress. It can cause damage to many biological macromolecules such as DNA, lipids and proteins, reducing the cell function and eventually leading to the aging characteristics of the body [31]. The K1 gene is recently found closely related to human aging, which is believed to play an important role in resisting the damage caused by reactive oxygen species to the body [32]. Thus, it is closely associated with manifestations of aging such as osteopenia/osteoporosis, atherosclerosis and calcium abnormalities Hu and others [33] found that K1 protein, as an expression product of the K1 gene, can interact with a variety of vasoactive substances in vivo, protecting the endothelium, expanding blood vessels, and promoting angiogenesis and anti-atherosclerosis. The concentration of K1 protein has some predictive significance for the occurrence and development of MCI [34]. In addition, animal experiments showed that K1 protein can promote adipocyte differentiation, reduce lipid deposition, and play an anti-atherogenic effect, and may also promote the development of MCI when its concentration drops [35]. The results of this study are also consistent with the above studies. The results of logistic multivariate regression analysis showed that compared with patients in the control group, patients in the MCI group had decreased K1 protein, and K1 protein was a protective factor for MCI. When brain tissue is damaged or cognitive function declines, the secretion of K1 protein is reduced. The ROC curve also showed its high predictive value for MCI, with an AUC of 0.793 (95% CI: 0.694–0.892). Moreover, the joint prediction of IGF-1 and K1 protein had the highest value, with an AUC of 0.939 (0.896 to 0.993).

This study also has some shortcomings. First, this study is a cross-sectional study, which can only show the correlation of serum K1 and IGF-1 level and MCI, and cannot provide direct evidence that K1 is a protective factor of MCI. Prospective studies are needed to confirm whether the low serum K1 protein and IGF-1 level in the elderly MCI can lead to the increased incidence of later cognitive dysfunction. Secondly, the present study did not deeply study the relationship between K1 and IGF-1 level and the severity of cognitive impairment. Further studies can be conducted to stratify MCI by severity and further explore their association. Finally, in order to facilitate the study, this study adopted convenience sampling. The sample of this method is less representative than probability sampling, and it is needed to systematically explore the relationship between K1 and IGF-1 level and cognitive impairment by probability sampling.

5. Conclusion

K1 protein and serum IGF-1 are closely related to the occurrence of MCI. The evaluation results of clinical diagnostic value also showed that both had some value for predicting MCI, and the joint prediction of IGF-1 and K1 protein had the highest value. Therefore, these two indicators can be used to assess the risk of early cognitive impairment, which can help to further improve the diagnostic accuracy.

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of Shijiazhuang People’s Hospital. Written informed consent was obtained from all participants.

Availability of data and materials

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Competing interests

None of the authors has any personal, financial, commercial, or academic conflicts of interest to report.

Funding

This study did not receive funding in any form.

Author contributions

WH conceived the study; CL, GL, GH and ZH participated in the study design, data analysis and statistics; CL helped draft the manuscript. All authors read and approved the final manuscript.

Footnotes

Acknowledgments

None to report.

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Analysis of the relationship between mild cognitive impairment and serum klotho protein and insulin-like growth factor-1 in the elderly

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Subjects and methods

2.1 Study subjects

2.2 Inclusion criteria

2.3 Exclusion criteria

2.4 Study methods

2.4.1 Data collection

2.4.2 Detection of serological indicators

2.5 Statistical analysis

3. Results

3.1 Comparison of the baseline data between the two groups

Table 1 Comparison of baseline data between MCI group and the control group

Table 2 Comparison of baseline data between the MCI group and the control group

Table 3 Multivariate logistic regression analysis for cognitive impairment

Table 4 Value of IGF-1 and klotho proteins alone and in combination for predicting MCI

5. Conclusion

Ethics statement

Availability of data and materials

Competing interests

Funding

Author contributions

Footnotes

Acknowledgments

References

Table 1
Comparison of baseline data between MCI group and the control group

Table 2
Comparison of baseline data between the MCI group and the control group

Table 3
Multivariate logistic regression analysis for cognitive impairment

Table 4
Value of IGF-1 and klotho proteins alone and in combination for predicting MCI