Urinary Amino Acid-Conjugated Acrolein and Taurine as New Biomarkers for Detection of Dementia

Abstract

Background:

Dementia, including Alzheimer’s disease (AD), is one of the serious diseases at advanced age, and its early detection is important for maintaining quality of life (QOL).

Objective:

In this study, we sought novel biomarkers for dementia in urine.

Methods:

Samples of urine were collected from 57 control subjects without dementia, 62 mild cognitive impairment (MCI) patients, and 42 AD patients. Mini-Mental State Examination (MMSE) was evaluated when subjects were examined by medical doctors. Urinary amino acid (lysine)-conjugated acrolein (AC-Acro) was measured using N^ɛ-(3-formyl-3, 4-dehydropiperidine) lysine (FDP-Lys) ELISA kit, and taurine content was measured using a taurine assay kit. Values were normalized by creatinine content which was measured with the colorimetric assay kit.

Results:

We found that urinary amino acid (lysine)-conjugated acrolein (AC-Acro) and taurine negatively correlated with MMSE score and are significantly lower in dementia patients compared to the normal subjects. When AC-Acro and taurine were evaluated together with age using an artificial neural network model, median relative risk values for subjects with AD, subjects with mild cognitive impairment, and control subjects were 0.96, 0.53, and 0.06, respectively.

Conclusion:

Since urine is relatively easy to collect, our findings provide a novel biomarker for dementia without invasiveness.

Keywords

Alzheimer’s disease amino acid-conjugated acrolein (AC-Acro)Mini-Mental State Examination taurine urinary biomarkers

INTRODUCTION

The most common dementia, Alzheimer’s disease (AD) is a neurodegenerative illness, accompanied with an increasingly severe breakdown of cognitive function and behavior in the elderly [1, 2]. For evaluation of the severity of AD and mild cognitive impairment (MCI) in patients, positron emission tomography (PET) using fluorodeoxyglucose (FDP) or amyloid has been used as imaging markers [3, 4]. However, this is not used frequently, because PET instrumentation is costly and time consuming. Therefore, simpler diagnostic tools, including easily measurable biomarkers for evaluation of AD, are sought. For detection of dementia, several biomarkers were identified, and their efficacy tested [5]. They include amyloid-β (Aβ) and tau protein levels in cerebrospinal fluid (CSF) and plasma. The collection of CSF and blood samples is burdensome to patients. Thus, more effective and easy-to-perform biomarkers are sought using urine.

Cell damage has been thought to be mainly caused by reactive oxygen species (ROS) including superoxide anion radical (O₂^–•), hydrogen peroxide (H₂O₂), and hydroxyl radical (●OH) [6]. However, when the toxicity of an unsaturated aldehyde acrolein (CH₂ = CH-CHO) and ROS was compared, it was found that acrolein has stronger toxicity than H₂O₂ [7], and has slightly stronger toxicity than ●OH [8]. In addition, acrolein is thought to be produced from unsaturated fatty acids by lipid peroxidation [9], but it was found that acrolein was more efficiently produced from spermine, one of the polyamines, consisting of putrescine [NH₂(CH₂)₄NH₂], spermidine [NH₂(CH₂)₃NH(CH₂)₄NH₂], and spermine [NH₂(CH₂)₃NH(CH₂)₄NH(CH₂)₃NH₂], which are abundantly present (mM order) and essential for cell growth and viability mainly through stimulation of specific kinds of protein synthesis in both eukaryotes and prokaryotes [10, 11]. Furthermore, we have reported that the measurements of interleukin-6 (IL-6), C-reactive protein (CRP), and protein-conjugated acrolein (PC-Acro) in plasma together with age could identify a silent brain infarction; i.e., a small brain infarction, with 84% sensitivity and specificity [12 –14]. Acrolein is mainly produced by spermine oxidase in vivo and is profoundly involved in tissue damage during brain stroke [8, 15]. It was also found that both PC-Acro and amyloid-β_40/42 in plasma became the biomarkers for dementia [16]. However, mild cognitive impairment (MCI) and AD could not be distinguished by these two biomarkers [16].

As for a urinary biomarker, we have found that 3-hydroxypropyl mercapturic acid (3-HPMA), which is a metabolic product of acrolein-glutathione conjugate, is decreased in the urine of stroke patients [17, 18]. It was suggested that acrolein produced in the infarct tissue consumes glutathione, and the urinary 3-HPMA decreased in patients with stroke. According to this result, we hypothesized that urinary acrolein metabolites can be a biomarker for detection of dementia, as the disease is neurodegenerative and is associated with cell damage induced by acrolein production. In this study, we sought novel biomarkers for dementia in urine which are able to distinguish MCI and AD, and found that the decrease in amino acid (Lys)-conjugated acrolein (AC-Acro) and taurine becomes a sensitive biomarker to distinguish MCI and AD.

MATERIALS AND METHODS

Collection of urine and plasma samples

Samples of urine and plasma were collected from 57 control subjects without dementia (25 males, 32 females; aged 71.0±8.5 years), 62 MCI patients (23 males, 39 females; aged 82.0±5.0 years), and 42 AD patients (16 males, 26 females; aged 81.0±4.5 years). Diagnosis of dementia including AD and MCI patients was performed as previously described [19], according to the criteria for AD [20] and for MCI [21].

Mini-Mental State Examination (MMSE) [22] was evaluated when subjects were examined by medical doctors. The severity of tissue damage in control subjects and MCI and AD patients was evaluated as Z-score using VSRAD (Voxel-Based Science Regional Analysis System for Alzheimer’s Disease) as described previously [16]. Informed consent was obtained from each participant or the relative. The research protocol was approved as described previously [16]. Experiments were carried out in accordance with the Declaration of Helsinki principles. Samples of urine and plasma were preserved at –80°C until use.

Measurement of urinary amino acid-conjugated acrolein and taurine contents

Urinary amino acid (lysine)-conjugated acrolein (AC-Acro) was measured using N^ɛ-(3-formyl-3, 4-dehydropiperidine) lysine (FDP-Lys) ELISA kit (Takara Bio Inc.) [9]. Urinary taurine content was measured using a taurine assay kit (Cell Biolabs, Inc.). Values were normalized by creatinine content which was measured with the colorimetric assay kit (Cayman Chemical).

Measurement of plasma Aβ₄₀ and Aβ₄₂

Aβ₄₀ and Aβ₄₂ in plasma were measured using Human β Amyloid (1–40) ELISA Kit II and Human β amyloid (1–42) ELISA Kit, High-Sensitive (FUJIFILM Wako Chemical Co.), respectively [16].

Statistics

Statistical analysis was performed using GraphPad Prism® Software (GraphPad Software). Values are shown as median±interquartile deviation. Groups were compared using Kruskal-Wallis test. Relative risk value (RRV) was evaluated with artificial neural networks using NeuralWorks Predict (SET Software Co., Ltd.) according to the manufacturer’s manual [23]. Age and three biochemical markers (AC-Acro, taurine and creatinine) of 57 control subjects and 62 subjects with MCI or 42 with AD were used as prediction output values 0 and 1, respectively, and the rules to build RRV were obtained. Then, RRV (0-1) for control subjects, and subjects with MCI and AD was calculated according to the rules. Sensitivity and specificity for subjects with MCI or AD versus control subjects were evaluated using a receiver operating characteristic (ROC) curve [24]. Candidates of cutoff value were set up as the closest point on ROC curve from the P point, i.e., sensitivity = 1 and 1-specificity = 0. Correlation between cognitive assessment and biomarkers in control, MCI and AD subjects were compared using Kruskal-Wallis or chi-square test. For examination of the statistical correlation between MMSE or plasma Aβ_40/42 ratio and urinary biochemical markers, Spearman’s rank correlation coefficient was used.

RESULTS

Correlation between MMSE, Z-score, and age, and the decrease in urinary AC-Acro and taurine

Since urine is relatively easy to collect, we looked for sensitive biomarkers for dementia in urine. We have previously reported that decrease in AC-Acro/Cre is more sensitive than that in 3-HPMA/Cre to evaluate MCI and AD subjects [16, 25]. In addition, it was found that taurine, another scavenger of acrolein (manuscript in preparation), in urine also decreased together with AC-Acro. Thus, it was evaluated how MMSE, age, AC-Acro/Cre, and taurine/Cre are changed between control subjects, and subjects with MCI and AD. In Table 1, cognitive assessment and urine biomarkers of control subjects and subjects with MCI and AD are shown.

Table 1

Correlation between dementia evaluations and urine biomarkers in control, MCI, and AD subjects

	Control		MCI		AD
Gender (% Female)	56.1	n = 57	62.9^ns	n = 62	61.9^ns	n = 42
Age (y)	71.0±8.5	n = 57	82.0±5.0^****	n = 62	81.0±4.5^****	n = 42
MMSE (points)	29.0±2.0	n = 57	21.0±2.6^****	n = 62	15.5±2.1^****	n = 42
Z-score	0.79±0.37	n = 33	2.25±0.81^****	n = 48	3.07±0.85^****	n = 37
urine AC-Acro/Cre (nmol/mg Cre)	236.4±138.4	n = 57	167.2±63.5^*	n = 62	147.6±48.9^***	n = 42
urine Taurine/Cre (nmol/mg Cre)	476.7±310.6	n = 57	339.3±259.5^ns	n = 62	235.0±127.1^**	n = 42
plasma Aβ_40/42 (pmol/L)	9.2±5.4	n = 53	17.4±12.0^****	n = 62	21.9±9.3^**	n = 42

Values are shown as median±interquartile deviation. Groups were compared using Kruskal-Wallis test or chi-square test. ^****p < 0.0001; ^***p < 0.001; ^**p < 0.01; ^*p < 0.05; ns, not significant.

As shown in Fig. 1A and 1B, median MMSE values decreased from 29.0±2.0 (control subjects) to 21.0±2.6 (MCI subjects) and 15.5±2.1 (AD subjects), and median ages increased from 71.0±8.5 years old (control subjects) to 82.0±5.0 years old (MCI subjects) and 81.0±4.5 years old (AD subjects). Significant differences were not seen in age between MCI and AD subjects (Fig. 1A, B), and also MMSE and age in male and female (data not shown).

Fig. 1

Comparison of MMSE, age, and urinary biomarkers among control, MCI, and AD subjects. Distribution of MMSE, age, AC-Acro, and taurine among control, MCI, and AD subjects is shown. A horizontal line within the box indicates median, the bottom and the top of the box indicates the 25th and 75th percentiles, and the whiskers (vertical lines) indicate the 5th and 95th percentiles. ^****p < 0.0001; ^***p < 0.001; ^**p < 0.01; ^*p < 0.05; ns, not significant.

Next, AC-Acro/Cre and taurine/Cre in urine were measured. As shown in Fig. 1C and 1D, the level of AC-Acro/Cre in control subjects, and MCI and AD subjects was 236±138.4, 167.2±63.5, and 147.6±48.9 nmol/mg Cre, respectively, and that of taurine was 476.7±310.6, 339.3±257.9, and 235.0±127.1 nmol/mg Cre, respectively. However, the values of these biomarkers were similar between female and male in control, MCI, and AD subjects (data not shown). Hereafter, AC-Acro/Cre and taurine/Cre are mentioned as AC-Acro and taurine, and MCI and AD subjects as MCI and AD, respectively. It is notable that the level of AC-Acro did not alter significantly between MCI and AD, and that of taurine did not alter significantly between control and MCI. Accordingly, measurements of these two markers can distinguish three groups, i.e., control, MCI, and AD.

We next examined whether MCI and AD could be distinguished by AC-Acro and taurine using a ROC curve, a general technique for assessing the accuracy of disease diagnosis and prediction [24]. Because age was one of the important factors for evaluation of MCI and AD (Fig. 1), all values were analyzed including age as a factor in artificial neural networks. The ROC curve for detection of dementia through measurements of AC-Acro plus taurine together with age is shown in Fig. 2A. Area under curve (AUC) for AD was 0.9607, and that for MCI was 0.8031, and the median relative risk value (RRV) was 0.06, 0.53, and 0.96 for control, MCI, and AD, respectively (Fig. 2B). Sensitivity and specificity were 92.9% and 91.2% for AD, and 72.6% and 73.7% for MCI, respectively. The results indicate that severity of MCI and AD are evaluated properly through measurement of AC-Acro and taurine in urine together with age.

Fig. 2

ROC curves and relative risk values evaluated by age/AC-Acro/taurine for AD versus control subjects (A, B), and for MCI versus control subjects (C, D). A, C) ROC curve analysis was performed as described in the Materials and Methods. AUC, area under curve. B, D) Relative risk values (RRVs) were calculated from artificial neural networks. Details are described in the legend of Fig. 1. ^****p < 0.0001; ^***p < 0.001; ^**p < 0.01; ns, not significant.

Next, these biomarkers of control and MCI were used as prediction output values, and a ROC curve was prepared (Fig. 2C). In this case, AUC for MCI increased slightly from 0.8031 to 0.8548, but that for AD decreased from 0.9607 to 0.8346, and the median RRV was 0.25, 0.73, and 0.67 for control, MCI, and AD, respectively (Fig. 2D). Sensitivity and specificity were 81.0% and 79.0% for AD, and 82.3% and 75.4% for MCI, respectively. The results suggest that it is better to use the corresponding disease’s value to calculate RRV, i.e., severity of MCI is more precisely evaluated when biomarkers of control and MCI were used as prediction output values.

When age was removed from the evaluation, AUC for AD was 0.8972, and that for MCI was 0.6627, and the median relative risk value for control, MCI and AD was 0.12, 0.46, and 0.73, respectively (Fig. 3A, B). When these biochemical markers of control and MCI were used as prediction output values, AUC for AD was 0.7517, and that for MCI was 0.7179, and the median risk value for control, MCI and AD was to be 0.43, 0.63, and 0.61, respectively (Fig. 3C, D). The results indicate that it is better to add age as one of the factors to evaluate the severity of cognitive impairment.

Fig. 3

ROC curves and relative risk values evaluated by AC-Acro/taurine for AD versus control subjects (A, B), and for MCI versus control subjects (C, D). ROC curves and relative risk values (RRVs) of AC-Acro/taurine for AD versus control subjects (A, B), and MCI versus control subjects (C, D). ROC curve analysis and calculation of RRVs were performed as described in the legend of Fig. 2. ^****p < 0.0001; ^***p < 0.001; ^**p < 0.01; ns, not significant.

When the evaluation was performed with one marker, sensitivity decreased greatly. In the case of AC-Acro, AUC for AD was 0.7289, and that for MCI was 0.6324, and in case of taurine, AUC for AD was 0.6637, and that for MCI was 0.5116 (Supplementary Figure 1). These results indicate that two factors, AC-Acro and taurine, are absolutely necessary for the evaluation of the severity of dementia.

Evaluation of the relative risk value of control, MCI, and AD by Aβ_40/42 ratio in plasma

In plasma, we have previously shown that the ratio of Aβ_40/42 increases in MCI and AD [16], which confirmed the previous reports from other groups [26 –28]. Increment of Aβ_40/42 ratio in cognitive impairment was confirmed using newly collected plasma (Fig. 4). AUC for MCI and AD was 0.7249 and 0.7066, respectively, and the median values of the ratio of Aβ_40/42 for control, MCI, and AD were 9.2, 17.4, and 21.9, respectively. These results confirm that it is difficult to differentiate MCI from AD through measurement of Aβ_40/42 ratio in plasma, and the difference of RRV is smaller compared with the RRV obtained from AC-Acro and taurine in urine.

Fig. 4

ROC curve evaluated by Aβ_40/42 ratio and its level in control, MCI, and AD subjects. A) ROC curves for MCI and AD subjects. ROC curve analysis was performed as described in the legend of Fig. 2 B) Plasma Aβ_40/42 ratio in control, MCI, and AD subjects. ^****p < 0.0001; ^**p < 0.01; ns, not significant.

Correlation between urinary biomarkers and MMSE or Aβ_40/42 ratio

As shown in Table 2, both AC-Acro and taurine were correlated with MMSE, and only AC-Acro was correlated with Aβ_40/42 ratio, judging from the correlation coefficient (rs) and p value. These results also support an idea that combination of urinary biochemical markers (AC-Acro and taurine) is a very effective biomarker for cognitive impairment.

Table 2

Correlation between MMSE or plasma Aβ_40/42 and urinary biomarkers

		Parameter
		urine AC-Acro/Cre	urine Taurine/Cre	AD_AC-Acro/Cre	AD_Age, AC-Acro/Cre	MMSE	plasma Aβ_40/42
				and Taurine/Cre	and Taurine/Cre
MMSE	N	161	161	161	161	–	157
	rs	0.3192	0.1628	–0.4353	–0.6249	–	–0.4108
	p	<0.0001^***	0.039^*	<0.0001^***	<0.0001^***	–	<0.0001^***
plasma Aβ_40/42	N	157	157	157	157	157	–
	rs	–0.4268	–0.1026	0.2632	0.3929	–0.4108	–
	p	<0.0001^***	0.2012	0.0009^***	<0.0001^***	<0.0001^***	–

Data of 161 or 157 subjects were evaluated by Spearman’s rank correlation analysis (rs and p). ^***p < 0.001; ^*p < 0.05.

DISCUSSION

In this study, we analyzed the contents of AC-Acro and taurine in urinary samples from normal and cognitive impairment subjects. Urinary AC-Acro and taurine levels correlated with MMSE and were significantly reduced in the subjects with cognitive impairment compared to control subjects (Fig. 2). The results indicate that the reduction of AC-Acro and taurine levels is correlated with neuronal damage in cognitive impairment subjects. Although urinary AC-Acro and taurine may be affected by diet and other diseases such as silent brain infarction [29], periodic monitoring of these values will help to recognize the risk of cognitive impairment which is usually lacking self-awareness, and improve lifestyles of the elderly.

The accuracy of evaluating AD with the combination of AC-Acro, taurine, and age using neural networks was 92.9% sensitivity and 91.2% specificity, respectively (Fig. 2A), and that to evaluate MCI was 82.3% sensitivity and 79.0% specificity, respectively (Fig. 2C). Data analysis was also performed by a multi-variable regression analysis. Essentially the same results were obtained as those with artificial neural networks (data not shown). Although the brain damage in dementia patients was normally less than that in stroke patients [16], the accuracy of evaluation of the severity of cognitive impairment through measurements of AC-Acro and taurine is very high. It is noted that several subjects in the normal group have high relative risk value for cognitive impairment. A follow-up study is needed to clarify whether they will develop dementia in the future. It is recommended to evaluate their RRV through measurements of these biomarkers every year.

Urine is relatively easy to collect and the burden to subjects is light compared to the plasma samples. Our findings provide a novel biomarker for dementia with low invasiveness. This biomarker is likely to be useful for the maintenance of QOL of the elderly.

It has been reported that 3-HPMA in urine also decreased in patients with MCI and AD [25, 30]. However, 3-HPMA content was normally less than 5% of AC-Acro in urine. In addition, preparation of a sensitive antibody against 3-HPMA is relatively difficult [18]. Thus, AC-Acro, but not 3-HPMA, was selected as a biomarker to obtain reproducible results.

Our data shown in Figs. 1 and 2 were obtained employing median age 71.0 years old control, 82.0 years old MCI, and 81.0 years old AD subjects as shown in Table 1. Thus, there is a significant difference of age between control subjects and MCI and AD subjects. Therefore, our data were also analyzed using at similar age group: i.e., the median 77.0 years old control, 82.0 years old MCI, and 81.0 years old AD subjects. As shown in Supplementary Figures 2 and 3, and Supplementary Table 1, results obtained were similar to those shown in Figs. 1 and 2, and Table 1, although the number of control subjects decreased from 57 to 33. These results confirm that the decrease in AC-Acro and taurine in urine is mainly due to the change of brain functions in MCI and AD subjects.

Footnotes

ACKNOWLEDGMENTS

We would like to appreciate Dr. A. J. Michael for his help in preparing this manuscript.

FUNDING

This work was supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan [] to KK.

CONFLICT OF INTEREST

The authors declare there is no conflict of interest.

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The supplementary material is available in the electronic version of this article: .

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Urinary Amino Acid-Conjugated Acrolein and Taurine as New Biomarkers for Detection of Dementia

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

INTRODUCTION

MATERIALS AND METHODS

Collection of urine and plasma samples

Measurement of urinary amino acid-conjugated acrolein and taurine contents

Measurement of plasma Aβ40 and Aβ42

Statistics

RESULTS

Correlation between MMSE, Z-score, and age, and the decrease in urinary AC-Acro and taurine

Evaluation of the relative risk value of control, MCI, and AD by Aβ40/42 ratio in plasma

Correlation between urinary biomarkers and MMSE or Aβ40/42 ratio

DISCUSSION

Footnotes

ACKNOWLEDGMENTS

FUNDING

CONFLICT OF INTEREST

DATA AVAILABILITY

References

Measurement of plasma Aβ₄₀ and Aβ₄₂

Evaluation of the relative risk value of control, MCI, and AD by Aβ_40/42 ratio in plasma

Correlation between urinary biomarkers and MMSE or Aβ_40/42 ratio