Nail metabolism marker for diagnosing Alzheimer disease, marker combination and application thereof
By analyzing the metabolic characteristics in nail samples, lauric acid and its combination are identified as markers of diagnosis and progress monitoring of Alzheimer's disease, the problems of traumatic and cost in the existing technology are solved, non-invasive and convenient diagnosis and monitoring are achieved, and the development of personalized treatment is promoted.
Patent Information
- Application Number
- CN202510027602.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-08
- Publication Date
- 2025-04-18
AI Technical Summary
The prior art has traumatic and cost-effective problems in the early diagnosis and monitoring of Alzheimer's disease, especially in the elderly population, where a non-invasive, cost-effective alternative approach is needed.
By analyzing metabolic characteristics in nail samples, lauric acid and its combinations, including cholesterol-3,5-diene and methyl 13-methyltetradecanoate, were identified as markers for diagnostic and progress monitoring of Alzheimer's disease, and kits for these markers were developed.
Achieve non-invasive and convenient diagnosis and progress monitoring of Alzheimer's disease has been achieved, reducing the burden on patients and providing new avenues for large-scale screening and personalized treatment.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the field of biomedical technology, and in particular to a nail metabolism marker, a marker combination and an application thereof for diagnosing Alzheimer's disease. Background Art
[0002] Alzheimer's disease (AD) is the leading cause of dementia worldwide, resulting in a significant social and economic burden as the population ages. Over the past few decades, efforts have been devoted to identifying reliable biomarkers of cognitive impairment and AD for early detection and intervention. Traditional biomarker studies have focused on cerebrospinal fluid (CSF) and blood samples. However, in the elderly population, obtaining CSF is invasive, requiring lumbar puncture, and repeated blood draws can be challenging. These limitations highlight the need for alternative minimally invasive strategies that can be used for large-scale screening and longitudinal monitoring.
[0003] In recent years, keratinized biological matrices, particularly nails, have emerged as promising, non-invasive matrices for metabolomics studies. Unlike blood and urine, which reflect relatively short-term metabolic fluctuations, nails continue to grow over months and can accumulate metabolites, thereby capturing a long-term biochemical “footprint.” This feature is particularly relevant to chronic, progressive diseases such as Alzheimer’s disease. While neuroimaging approaches and blood-based markers have shown considerable potential, their expense and patient burden may limit routine use in early clinical assessments. In contrast, nail sampling is painless, cost-effective, and exhibits good patient compliance, making it well suited for repeated testing in fragile or elderly populations.
[0004] Metabolomics is a high-throughput method for analyzing small molecule profiles, which can provide insights into disease-associated biochemical dysregulations. Applying metabolomics to nails can provide a holistic view of long-term metabolic alterations associated with AD pathogenesis, including oxidative stress, imbalanced energy metabolism, and other key pathways that may be involved in the disease continuum. Although nail metabolomics has been used in other diseases such as diabetes, gout, and stress, from mild cognitive impairment (MCI) to advanced dementia stages, its potential to detect and characterize subtle metabolic signatures of cognitive decline in AD patients remains largely unexplored. Summary of the invention
[0005] The object of the present invention is to provide a nail metabolism marker, a marker combination and application thereof for diagnosing Alzheimer's disease in view of the above problems.
[0006] In order to achieve its purpose, the present invention adopts the following technical solution:
[0007] A first aspect of the present invention provides a nail metabolism marker for diagnosis or progression monitoring of Alzheimer's disease, wherein the nail metabolism marker is lauric acid.
[0008] A second aspect of the present invention provides a nail metabolite marker combination for diagnosis or progression monitoring of Alzheimer's disease, wherein the nail metabolite marker combination comprises lauric acid, cholesta-3,5-diene and 13-methyltetradecanoic acid methyl ester.
[0009] The third aspect of the present invention provides the use of the above-mentioned nail metabolite marker or the above-mentioned nail metabolite marker combination in the preparation of a kit for diagnosing or monitoring the progression of Alzheimer's disease.
[0010] In the above-mentioned application technology scheme, the nail metabolite marker or the combination of nail metabolite markers is used to diagnose and differentiate Alzheimer's disease patients, mild cognitive impairment patients and cognitively normal people, or to monitor the progression of Alzheimer's disease patients.
[0011] In the above-mentioned application technology scheme, compared with people with normal cognition, lauric acid, cholesta-3,5-diene, and 13-methyltetradecanoic acid methyl ester were all monotonically downregulated in patients with Alzheimer's disease.
[0012] A fourth aspect of the present invention provides a kit for diagnosing or monitoring the progression of Alzheimer's disease, comprising the standard product of the above-mentioned nail metabolite marker or the standard product of the above-mentioned nail metabolite marker combination.
[0013] The kit also includes an extraction reagent for extracting the nail metabolism marker or marker combination.
[0014] Preferably, the extraction reagent comprises methyl chloroformate, sodium hydroxide, methanol and sodium bicarbonate.
[0015] The kit also includes reagents for gas chromatography-mass spectrometry analysis.
[0016] The beneficial effects of the present invention are:
[0017] The present invention identifies metabolic features in nail samples collected from elderly people with normal cognitive function, MCI patients, and mild, moderate, and severe AD patients, and finds a new nail metabolic marker or marker combination for diagnosing Alzheimer's disease. The nail metabolic marker provided by the present invention is used to diagnose or monitor the progression of AD. The detection of the marker is convenient and non-invasive, which reduces the burden on patients. The present invention not only deepens the understanding of the underlying disease mechanism, provides a potential scalable method for large-scale screening and monitoring of cognitive impairment, but also provides a new approach for preventive health care and personalized treatment strategies; it is expected to achieve large-scale screening, promote personalized disease monitoring, and help solve the problem of the increasing global prevalence of AD and MCI by improving early detection and intervention. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 Differentially expressed metabolites (DEMs) between CDR=0, CDR=0.5, CDR=1, CDR=2 and CDR=3 are shown.
[0019] Figure 2 Figure 2 is a heat map of differential metabolites in the five CDR categories.
[0020] Figure 3 Eight different clusters were obtained from cluster analysis of 30 DEMs.
[0021] Figure 4 Shown are: (A) ordinal logistic regression analysis results OR, odds ratio; CI, confidence interval; CDR=0.5 (B) Comparison of lauric acid levels in nails of participants with CDR=1 (C), CDR=2 (D) and CDR=1 (E).
[0022] Figure 5 Figure 2 is the Venn diagram of differential metabolites.
[0023] Figure 6 KEGG enrichment results of DEMs.
[0024] Figure 7 This is the correlation analysis between lauric acid and differential clinical indicators.
[0025] Figure 8 It is a scatter plot of lauric acid and AVLT_IR (AVLT-IR: Auditory Verbal Learning Test-Immediate Recall).
[0026] Fig. 9 It is a scatter plot of lauric acid and ADAS-Cog (ADAS-Cog: Alzheimer's Disease Assessment Scale-Cognitive Subscale).
[0027] Fig.10 Sort the results by variable importance.
[0028] Fig.11 Box plots of the abundance of lauric acid, cholesta-3,5-diene, and 13-methyltetradecanoic acid methyl ester. DETAILED DESCRIPTION
[0029] The present invention will be further described below in conjunction with embodiments, but the present invention is not limited thereto.
[0030] The experimental methods in the following examples are conventional methods unless otherwise specified.
[0031] Example 1
[0032] 1 Method
[0033] 1.1 Study design and patient recruitment
[0034] Participants aged 60 to 90 years were recruited from the memory clinic of the Department of Geriatrics, the First Affiliated Hospital of Chongqing Medical University. The subjects were clinically diagnosed as normal cognition (NC), MCI due to AD, and AD. Demographic information such as age, gender, and education level were also collected. Participants were further classified by the Clinical Dementia Rating (CDR). Normal cognition (CDR = 0), mild cognitive impairment stage (CDR = 0.5), and dementia stage (mild: CDR = 1; moderate: CDR = 2; severe: CDR = 3). This study was approved by the Ethics Committee of Chongqing Medical University (No. 20212901). All procedures were in accordance with the Declaration of Helsinki. Written informed consent was obtained from each participant.
[0035] 1.2 Inclusion criteria:
[0036] 1) Normal (NC): age ≥ 60 years, MMSE score ≥ 28 points, and no diagnosis of cognitive impairment confirmed by clinical evaluation and cognitive assessment.
[0037] 2) Mild cognitive impairment (MCI): Patients diagnosed with mild cognitive impairment according to the Petersen criteria, including subjective memory decline, objective memory impairment, and mild impairment of complex instrumental daily abilities, but maintaining independent daily activities (CDR = 0.5).
[0038] 3) Alzheimer's disease (AD): patients diagnosed with AD according to the criteria established by the National Institute on Aging and the Alzheimer's Association (NIA-AA). AD patients were further divided into mild (CDR=1), moderate (CDR=2) and severe (CDR=3) according to the Clinical Dementia Rating (CDR) scale.
[0039] 1.3 Exclusion criteria:
[0040] 1) Patients with major comorbidities that may affect metabolism (such as diabetes, cancer, or cardiovascular disease).
[0041] 2) Patients who are receiving treatments known to affect metabolism (e.g. chemotherapy, glucocorticoid therapy).
[0042] 3) Patients with skin or nail diseases (such as onychomycosis) that may affect the quality of nail samples.
[0043] 1.4 Nail sample collection
[0044] Participants were asked to wash their hands thoroughly and clean their nails with 70% isopropyl alcohol before sample collection to minimize the influence of external contaminants. Nails (approximately 10 mg) were collected from each hand using sterile stainless steel nail clippers. The collected nail samples were immediately placed in sterile polyethylene sample tubes, labeled with the participant's ID, and stored at -80°C to maintain the integrity of the metabolites.
[0045] Avoid using nail polish, hand cream, or other nail products for at least two weeks before sample collection to avoid chemical interference. Sterilize nail clippers with 70% isopropyl alcohol before collection to ensure no cross-contamination between samples.
[0046] 1.5 Sample preparation for metabolomics analysis
[0047] ① Transfer each nail sample (5.0±0.5 mg) to a 1.5 mL centrifuge tube. Soak the sample in acetone and distilled water (10 min each) in sequence, and then air-dry in a fume hood.
[0048] ②Add 400 μL of 1 M sodium hydroxide to each sample and incubate at 95°C for 60 min to hydrolyze the nail sample.
[0049] ③ After cooling to room temperature, add 200 μL of methanol and 4 μL of multiple internal reference mixture (components: D4-alanine, D2-tyrosine, D5-phenylalanine, 10 mM; Sigma, USA) to each sample and centrifuge at 12,000×g for 10 min.
[0050] ④ Transfer 300 μL of supernatant from each sample into a glass derivatization tube.
[0051] ⑤ Add 34 μL pyridine and 15 μL methyl chloroformate (MCF) for derivatization and vortex for 30 seconds.
[0052] ⑥Add 15 μL MCF again and vortex for 30 seconds.
[0053] ⑦ Add 400 μL of chloroform and vortex for 10 seconds.
[0054] ⑧Add 400 μL 50 mM sodium bicarbonate (NaHCO3) and vortex for 10 seconds.
[0055] ⑨ Centrifuge at 2000 rpm for 10 min at 4°C. Discard the upper aqueous phase and the middle protein layer, and transfer 170 μL of the lower layer to an LC vial for subsequent GC-MS analysis.
[0056] ⑩ Prepare a pooled quality control (QC) sample by combining aliquots of all individual samples and following the same preparation steps as the experimental samples to assess analytical reproducibility during the analysis and data processing stages.
[0057] The derivatized metabolites were analyzed using an Agilent Intuvo9000 gas chromatography system with a 5977B series mass selective detector (MSD). The gas chromatography column was BD-1701 (30m×250μm id×0.25μm, Agilent). The derivatized samples were injected into the splitless inlet at 290°C with a helium flow rate of 1mL / min. The GC-oven program was maintained at 45°C for 2min in the first stage, and the temperature was increased from 45°C to 180°C at a rate of 9°C / min and maintained for 5min in the second stage. The temperature was increased to 220°C at a rate of 40°C / min and maintained for 5min in the third stage. The temperature was increased to 240°C at a rate of 40°C / min and maintained for 11.5min in the fourth stage. Finally, the temperature was increased to 280°C at a rate of 80°C / min. The temperatures of the protection chip, auxiliary device, quadrupole mass spectrometer, and mass spectrometer source were 280°C, 250°C, 230°C, and 150°C, respectively. The mass range was 30 μm to 550 μm. The scanning speed was set to 1.563 μ / s, and the solvent delay time was 5.5 min.
[0058] 1.6 Machine Learning Model Training
[0059] Machine learning methods were used to further investigate the potential predictive value. The 29 DEMs were divided into a training set (70%) and a test set (30%). The target variable included three groups: control group (CDR = 0), mild group (CDR = 0.5), and severe group (CDR = 1). The automated machine learning process was completed using the "h2o" R package, and predictions were made on the test set using "h2o". The area under the curve (AUC) value was calculated using the "multi_roc" function.
[0060] 1.7 Statistical analysis
[0061] Continuous variables that conformed to normal distribution were expressed as mean ± standard deviation, and those that did not conform to normal distribution were expressed as median (interquartile range). To evaluate the difference in metabolite expression between the two groups, the Wilcoxon test was used for comparison between the two groups, and the Log value (fold change) was calculated. For comparisons involving more than two groups, analysis of variance or Kruskal-Wallis test was used. Identification of differentially expressed metabolites (DEMs) was based on p < 0.01 or |Log (fold change)| > 1. Cluster analysis was performed using the Mfuzz R package to group DEMs according to their expression patterns, which helped to identify key metabolites associated with disease progression. An ordered logistic regression model was established using the “polr” function in the MASS software package to explore the relationship between DEMs and disease severity (CDR score), controlling for potential confounding factors such as age, sex, and years of education. The correlation coefficients between DEMs and clinical indicators were calculated using the “cor.test” function. For pathway enrichment analysis, multiple comparisons were performed (CDR=0 vs CDR≥0.5, CDR=0 vs CDR=0.5 vs CDR≥1, and CDR=0 vs CDR=0.5 vs CDR=1 vs CDR=2 vs CDR=3). DEMs were mapped to KEGG pathways using the MBRole 3 tool, and further enrichment analysis was performed in MetaboAnalyst 6.0.
[0062] 2 Results
[0063] 2.1 Characteristics of selected participants
[0064] There were 199 participants in total, with 33 CDR = 0, 64 CDR = 0.5, 41 CDR = 1, 35 CDR = 2, and 26 CDR = 3. There was no significant difference in gender distribution between the groups (P>0.05). However, there were significant differences in age, years of education, and cognitive function indicators between the two groups (all P<0.05). Cognitive function scores showed an expected gradient, with obvious impairment in the CDR = 3 group, followed by CDR = 2, CDR = 1, CDR = 0.5, and CDR = 0 (all P<0.001).
[0065] 2.2 Differences in metabolites between groups
[0066] There are 30 DEMs in 5 CDR categories, see Figure 1 , Figure 2 .
[0067] Cluster analysis of the 30 identified DEMs yielded 8 different clusters, with the fourth cluster (dominated by dodecanoic acid) showing a significant monotonic downregulation in all CDR-defined groups. This means that lauric acid has significant continuous changes related to cognition, with the expression of lauric acid in nails showing a significant downward trend from the cognitively normal group (CDR = 0) to the late AD group (CDR = 3). Figure 3 .
[0068] To account for possible confounding factors such as age, sex, and education, an ordered logistic regression model was used; lauric acid was still independently associated with disease severity (OR < 1) ( Figure 4 A). In addition, in the category of CDR>0, we compared the lauric acid levels between individuals who underwent 18F-AV45 PET examination and those who did not by Wilcoxon test, and no significant difference was observed in the box plot (p>0.05) ( Figure 4 BE). The overall trend showed that lauric acid levels tended to decrease with disease progression. These findings suggest a potential role for lauric acid levels in monitoring disease progression.
[0069] 2.3 KEGG pathway enrichment and its correlation with clinical indicators
[0070] The metabolic impact of AD progression was further explored by comparing the groups (CDR=0 vs CDR≥0.5; CDR=0 vs CDR=0.5 vs CDR≥1; CDR=0 vs CDR=0.5 vs CDR=1 vs CDR=2 vs CDR=3). Figure 5 ).
[0071] Figure 6 Significantly altered metabolic pathways in the KEGG database are shown. Key enriched pathways include fatty acid biosynthesis, unsaturated fatty acid biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis. Notably, lauric acid appears in a prominent position in the fatty acid biosynthesis pathway, indicating that fatty acid metabolism disorders play a key role in the pathophysiology of AD.
[0072] Correlation analysis showed that ( Figure 7 ), lauric acid was positively correlated with AVLT-IR (r = 0.29) ( Figure 8 ) was negatively correlated with ADAS-Cog (r=-0.32) ( Fig. 9 ). These relationships suggest that lower lauric acid levels correspond to poorer memory and more pronounced cognitive impairment.
[0073] 2.4 Machine Learning Model Performance
[0074] To evaluate the diagnostic potential of these metabolites, we trained multiple machine learning models and evaluated their performance in distinguishing different CDR groups. Among these models, deep learning achieved the highest classification accuracy (67.24%) ( Fig.10 ). It is noteworthy that lauric acid, 13-methyltetradecanoic acid methyl ester and cholesta-3,5-diene made significant contributions to the model performance ( Fig.11 ), highlighting their promise as candidate biomarkers for diagnosis and progression monitoring of Alzheimer's disease.
[0075] 3 Analysis
[0076] In this study, we used nail metabolomics to explore potential biomarkers for different clinical stages of cognitive impairment. This innovative approach provides a comprehensive, long-term perspective on metabolic changes, complementing existing traditional blood- and tissue-based detection methods. Our results not only highlight the feasibility of using nail samples in metabolomics studies, but also identify lauric acid as a new biomarker for AD staging and progression monitoring. The key contribution of this work is the comprehensive metabolic profiling of AD progression. By analyzing nail samples from participants with CDR scores of 0 to 3, we captured different metabolites associated with disease severity. Notably, after adjusting for age, sex, and education, the nail metabolite lauric acid showed a decreasing trend with increasing CDR scores, positively correlated with AVLT-IR scores, and negatively correlated with ADAS-Cog. Our correlation analysis showed that nail lauric acid levels can reflect the development of cognitive impairment, and low lauric acid supply may exacerbate cognitive deficits and emphasize its prospect as a protective biomarker for MCI and early AD.
[0077] The stability of lauric acid in reflecting cognitive status regardless of whether individuals underwent 18F-AV45 PET imaging underscores the potential for lauric acid as a reliable, novel marker for the early diagnosis of AD, implying the clinical utility and reliability of lauric acid measurement in settings where PET imaging is not routinely available or where resources are limited. Importantly, the use of nail samples as a biological matrix offers distinct advantages over traditional samples such as blood or cerebrospinal fluid. Nails continue to grow for weeks to months, thereby recording cumulative metabolic information while minimizing the impact of acute dietary or environmental changes. This aspect is particularly relevant to chronic, progressive diseases such as AD, where long-term metabolic alterations may provide key insights into disease mechanisms. Noninvasive and highly acceptable nail collection also improves patient compliance and simplifies large-scale screening or longitudinal follow-up.
[0078] From a clinical application perspective, the potential of lauric acid as a biomarker for Alzheimer's disease is further highlighted by our machine learning model, which showed robust performance in distinguishing individuals with normal cognitive function from those with varying degrees of impairment. Lauric acid can be combined with other metabolites such as cholesta-3,5-diene and 13-methyltetradecanoic acid methyl ester as a biomarker for Alzheimer's disease, which may pave the way for improved early diagnosis or risk stratification.
[0079] This study introduces nail metabolomics as a promising, noninvasive strategy for biomarker discovery and disease staging in cognitive impairment. The finding that lauric acid is a reliable, independent candidate biomarker underscores the central role of fatty acid metabolism in AD progression. By offering high patient acceptability, longitudinal stability, and correlation with existing clinical indices, nail metabolomics could open new avenues for early diagnosis, risk assessment, and personalized therapeutic interventions in AD.
Claims
1. A nail metabolic marker for diagnosis or progression monitoring of Alzheimer's disease, characterized in that: The nail metabolism marker is lauric acid.
2. A nail metabolic marker combination for diagnosis or progression monitoring of Alzheimer's disease, characterized in that: The nail metabolism marker combination includes lauric acid, cholesta-3,5-diene and 13-methyltetradecanoic acid methyl ester.
3. Use of the nail metabolite marker according to claim 1 or the nail metabolite marker combination according to claim 2 in the preparation of a kit for diagnosing or monitoring the progression of Alzheimer's disease.
4. The use according to claim 3, characterized in that: The nail metabolite marker or the nail metabolite marker combination is used to diagnose and differentiate Alzheimer's disease patients, mild cognitive impairment patients and cognitively normal people, or to monitor the progression of Alzheimer's disease patients.
5. The use according to claim 3, characterized in that: Compared with cognitively normal subjects, lauric acid, cholesta-3,5-diene, and 13-methyltetradecanoic acid methyl ester were all monotonically downregulated in patients with Alzheimer's disease.
6. A kit for diagnosing or monitoring the progression of Alzheimer's disease, characterized in that: A standard product comprising the nail metabolite marker according to claim 1 or a standard product of the nail metabolite marker combination according to claim 2.
7. The kit according to claim 6, characterized in that: Also included is an extraction reagent for extracting the nail metabolism marker or marker combination.
8. The kit according to claim 7, characterized in that: The extraction reagent includes methyl chloroformate, sodium hydroxide, methanol and sodium bicarbonate.
9. The kit according to claim 6, characterized in that use: Also included are reagents for gas chromatography-mass spectrometry analysis.