Methods of Diagnosis and Treatment of Alzheimer's Disease

Abstract

A method for diagnosing and treating Alzheimer's disease is provided herein, comprising: (a) providing a biological sample obtained from a subject; (b) measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins as described herein from the obtained sample; (c) comparing the concentration levels of Alzheimer's-related metabolites and / or proteins from the obtained sample to the corresponding reference concentration levels of Alzheimer's-related metabolites and / or proteins from an Alzheimer's-negative sample; (d) identifying the subject as having Alzheimer's disease if the concentration levels of Alzheimer's-related metabolites and / or proteins from the obtained sample differ from the concentration levels of reference Alzheimer's-related metabolites and / or proteins from an Alzheimer's-negative sample; and (e) treating or having the subject treated.

Description

Technical Field

[0001] The present disclosure generally relates to the field of evaluating proteins and / or metabolites in subjects with Alzheimer's disease.

Background Art

[0002] (Background) Alzheimer's disease is a progressive neurodegenerative disease characterized by loss of memory and cognitive ability. This disease is caused by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. These plaques and neurofibrillary tangles interfere with communication between brain cells, cause inflammation, and lead to neuron death and atrophy of brain tissue. Furthermore, this disease is associated with a decrease in neurotransmitters, particularly acetylcholine, which are essential for normal brain function. As a result, Alzheimer's disease gradually impairs memory, thinking, language, judgment, and / or motor skills.

[0003] Amyloid plaques are formed by the abnormal accumulation of a protein called β-amyloid in the spaces between nerve cells in the brain. These plaques cause inflammation and lead to neuron death and further plaque formation. The accumulation of amyloid plaques also causes the formation of tangles, which are abnormal aggregates of another protein called tau.

[0004] Neurofibrillary tangles prevent the transport of nutrients and other essential substances within brain cells, causing neurons to die and resulting in the cognitive decline seen in Alzheimer's disease. The combination of amyloid plaques and neurofibrillary tangles causes extensive damage to brain tissue, leading to the progressive decline in cognitive function characteristic of Alzheimer's disease.

[0005] An accurate diagnosis of Alzheimer's disease is desirable. A correct diagnosis is a crucial first step towards receiving appropriate treatment, care, family education, and future planning. Early symptoms of Alzheimer's disease include mild memory impairment, such as forgetting new information and difficulty completing familiar tasks. The early stages of Alzheimer's disease can be difficult to diagnose, particularly because measuring cognitive impairment is challenging. Furthermore, it can be difficult to rule out other conditions that cause similar symptoms, such as thyroid disorders and vitamin B12 deficiency. Currently, doctors assess the degree of cognitive impairment by performing mental state tests and evaluating the scores on those tests. Researchers are studying ways to diagnose Alzheimer's disease more accurately, including the evaluation of biomarkers such as tau, but progress in this area is slow.

[0006] Therefore, there is a need for improved methods that can provide a more reliable diagnosis of Alzheimer's disease, its risk of developing, and its treatment. Such methods could further provide improved ways to monitor disease progression.

[0007] (overview) This disclosure provides methods for diagnosing Alzheimer's disease, assessing the risk of developing Alzheimer's disease, and treating or having the disease treated. Furthermore, or alternatively, such methods may further provide improved methods for monitoring the progression of Alzheimer's disease.

[0008] According to this disclosure, novel metabolite and / or proteome profiles of Alzheimer's disease can be used for disease diagnosis, assessment of risk of developing the disease, management of symptoms, monitoring of disease progression, and / or improvement of the disease.

[0009] According to this disclosure, unique combinations of metabolites and / or proteomic signatures are identified that have high predictive value for Alzheimer's disease and significantly improve disease identification and treatment compared to conventional methods. In a non-exclusive example, subjects at risk of or having Alzheimer's disease are identified via metabolome / proteome analysis performed on biological samples using at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or all Alzheimer's-related proteins or fragments selected from trigonelline, lysophosphatidylcholine (lysoPC) (including, but not limited to, C18:2 fatty acid metabolites and / or C6:1 fatty acid metabolites), hydroxyphenylacetic acid (HPA), and homocysteine.

[0010] In further or alternative embodiments, novel metabolite profiles and / or proteome profiles are used to monitor disease progression or to provide improvement in disease status.

[0011] In one embodiment, the novel metabolites and / or proteome profiles of Alzheimer's disease are selected from hydroxyphenylacetic acid (HPA); trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C26:0, or C17:0 fatty acid metabolites; lysophosphatidylcholine (lysoPC); C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; and LysoPC. C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, at least five, at least six, or at least seven Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII The formula includes at least one, at least two, at least three, at least four, at least five, or at least six Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor.

[0012] According to one aspect of the present disclosure, a method is provided for diagnosing and treating Alzheimer's disease in a subject, the method comprising: (a) providing a biological sample obtained from the subject; and (b) selecting from the obtained sample hydroxyphenylacetic acid (HPA); trigonelline, lysophosphatidylcholine (lysoPC) selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII (c) Measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; callistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (a) comparing the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample to the corresponding reference concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample;(d) If the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ from the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample, the subject is identified as having Alzheimer's disease; (e) Optionally, the identified subject is treated or has treated with an Alzheimer's treatment plan.

[0013] In one embodiment of any aspect of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC); C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; and / or at least homocysteine, and the Alzheimer's-related protein is selected from coagulation factor XIIIA chain; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; C-reactive protein; and / or tenascin C.

[0014] In one embodiment of any aspect of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid; or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain; complement factor D; or C-reactive protein.

[0015] According to one aspect of the present disclosure, a method is provided for diagnosing and selectively treating Alzheimer's disease in a subject, the method comprising: (a) providing a biological sample obtained from the subject; and (b) lysophosphatidylcholine (lysoPC) selected from hydroxyphenylacetic acid (HPA); trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII The concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor were measured from the obtained sample or measured with a spectroscopic unit;(c) comparing or having compared the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins measured by a spectroscopic unit with the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample; (d) identifying a subject as having Alzheimer's disease if the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ from the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample; (e) optionally treating or having the identified subject treated with an Alzheimer's treatment plan.

[0016] In one embodiment of the foregoing aspects of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC); C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; and / or at least homocysteine, and the protein is selected from coagulation factor XIIIA chain; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.

[0017] In one embodiment of the foregoing aspects of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) or trigonelline, and the Alzheimer's-related protein is selected from coagulation factor XIIIA chain or complement factor D.

[0018] In one embodiment of any aspect of the present disclosure, an optional Alzheimer's treatment plan includes adjusting the blood concentration of one or more Alzheimer's-related metabolites and / or Alzheimer's-related proteins in a subject who has been diagnosed with Alzheimer's disease, has been diagnosed with a predisposition to develop Alzheimer's disease, or a combination thereof.

[0019] In one embodiment of any aspect of the present disclosure, the blood concentration of one or more Alzheimer's-related metabolites and / or Alzheimer's-related proteins in the subject is adjusted until an improvement in the symptoms of Alzheimer's disease is observed in the subject.

[0020] In one embodiment of any aspect of the present disclosure, adjusting the blood concentration of one or more Alzheimer's-related metabolites includes adjusting the composition of the target gut microbiota.

[0021] In one embodiment of any aspect of the present disclosure, identification is performed when it is determined that the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ by about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, or about 70% or more compared to the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample.

[0022] In one embodiment of any aspect of the present disclosure, identification is performed when it is determined that the concentration levels of homocysteine, C18:2 fatty acid metabolites and / or C6:1 fatty acid metabolites from the obtained sample are lower than the concentration levels of the corresponding reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the Alzheimer's-negative sample.

[0023] In one embodiment of any aspect of the present disclosure, identification is performed when it is determined that the concentration level of coagulation factor XIIIA chain and / or hydroxyphenylacetic acid (HPA) from the obtained sample is increased compared to the concentration level of the corresponding reference Alzheimer's-related metabolite and / or Alzheimer's-related protein from the Alzheimer's-negative sample.

[0024] In one embodiment of any aspect of this disclosure, the sample obtained is blood or urine, preferably serum, plasma, or urine.

[0025] In one embodiment of any aspect of the present disclosure, Alzheimer's-related metabolites and / or Alzheimer's-related proteins are measured by a spectroscopic technique selected from the group consisting of liquid chromatography, gas chromatography, liquid chromatography-mass spectroscopy, gas chromatography-mass spectroscopy, high-performance liquid chromatography-mass spectroscopy, capillary electrophoresis-mass spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, Raman spectroscopy, and infrared spectroscopy.

[0026] In one embodiment of any aspect of the present disclosure, the comparison of the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample with the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample includes using multivariate statistical analysis.

[0027] In one embodiment of any aspect of the present disclosure, the multivariate statistical analysis is selected from principal component analysis (PCA) or partial least squares latent structure discriminant analysis (PLS-DA).

[0028] According to another aspect of the present disclosure, a method is provided for monitoring the progression of Alzheimer's disease in a subject and for selectively treating Alzheimer's disease, the method comprising (a) providing a first biological sample obtained from the subject at a first time point; and (b) selecting from the obtained first sample hydroxyphenylacetic acid (HPA); trigonelline, lysophosphatidylcholine (lysoPC) selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII Evaluate a first Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile by measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; callistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor;(c) comparing the first Alzheimer-related metabolite and / or Alzheimer-related proteome profile with a reference Alzheimer-related metabolite profile and / or Alzheimer-related proteome profile from an Alzheimer-negative sample; (d) determining that there is a first difference between the first Alzheimer-related metabolite and / or Alzheimer-related proteome profile and the reference Alzheimer-related metabolite and / or Alzheimer-related proteome profile from an Alzheimer-negative sample, and that the first difference indicates Alzheimer; (e) providing a second biological sample obtained from the subject at a second time point after the first time point; (f) evaluating a second Alzheimer-related metabolite and / or Alzheimer-related proteome profile by measuring the concentration levels of Alzheimer-related metabolites and / or Alzheimer-related proteins from the obtained second sample; (g) comparing the second Alzheimer-related metabolite and / or Alzheimer-related proteome profile with a reference Alzheimer-related metabolite and / or proteome profile from an Alzheimer-negative sample; (h) determining that there is a second difference between the first Alzheimer-related metabolite and / or proteome profile and the reference Alzheimer-related metabolite and / or proteome profile from an Alzheimer-negative sample, and that the second difference indicates Alzheimer; (i) determining the progression of Alzheimer based on at least a portion of the first and second differences; (j) optionally, treating the identified subject with an Alzheimer treatment plan.;

[0029] According to another aspect of the present disclosure, there is provided a kit for use in any embodiment or aspect of the present disclosure, the kit comprising reagents for measuring the concentration levels of Alzheimer-related metabolites and / or Alzheimer-related proteins, and optionally, instructions for use.

[0030] According to another aspect of the present disclosure, a kit for diagnosing Alzheimer's disease, comprising: (a) from a biological sample obtained, lysophosphatidylcholine (lysoPC) selected from hydroxyphenylacetic acid (HPA); trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor, and / or von Willebra (b) a detector configured to detect the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from end factors; (c) a composition comprising a control level corresponding to an Alzheimer's-negative control group and corresponding Alzheimer's-related metabolites and / or Alzheimer's-related proteins; and a multivariate analysis system configured to analyze the difference between the concentration levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins and the control level.(d) optionally, including an instruction manual for the Alzheimer's diagnostic method; the method includes measuring the levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained biological sample using a detector, and comparing the obtained levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins with the control levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins obtained from Alzheimer's-negative subjects, and a kit is provided.;

[0031] In one embodiment of the foregoing aspect of the present disclosure, the Alzheimer's-related metabolites are selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the Alzheimer's-related proteins are selected from at least coagulation factor XIII A chain; complement factor D; IgG Fc-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.;

[0032] In one embodiment of the foregoing aspect of the present disclosure, the Alzheimer's-related metabolites are selected from at least hydroxyphenylacetic acid (HPA); or trigonelline, and the Alzheimer's-related proteins are selected from coagulation factor XIII A chain, C-reactive protein, tenascin C, serum amyloid A-1, or complement factor D.;

[0033] In one embodiment of the foregoing aspect of the present disclosure, the detector includes a multi-metabolite detector and / or a multi-proteome detector configured to measure the levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins.;

[0034] According to another aspect of the present disclosure, a computer-implemented method is provided for processing a biological sample of a subject, diagnosing Alzheimer's disease, and optionally treating Alzheimer's disease, the computer-implemented method comprising: (a) receiving a biological sample obtained from a subject; (b) processing the sample in a spectroscopic unit directly or wirelessly connected to a processing device, the processing device having memory for storing measurement data from the spectroscopic unit; and (c) the spectroscopic unit processing lysophosphatidylcholine (lysoPC), C6:1 fatty acid metabolites selected from hydroxyphenylacetic acid (HPA); trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0; C26:0 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein;(d) measuring the levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from insulin-like growth factor and / or von Willebrand factor, and storing the measurement data in a processor; or (d) comparing the stored measurement data with a value in memory representing an Alzheimer's-negative sample using multivariate statistical analysis; and (e) storing in a processing device the results corresponding to at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample, wherein the measurement data representing the levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins are Alzheimer's-negative. If the results differ from the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from a sex sample, the results include identifying the subject as having Alzheimer's disease; (f) displaying an Alzheimer's treatment plan on an electronic display connected directly or wirelessly to a processor for subjects identified as having Alzheimer's disease or being predisposed to developing Alzheimer's disease, the displayed treatment plan including electronic text on a graphical user interface; and (iv) selectively adjusting the blood concentration of one or more Alzheimer's-related metabolites and / or Alzheimer's-related proteins in subjects diagnosed as having Alzheimer's disease or being predisposed to developing Alzheimer's disease until an improvement in the subject's cognitive abilities is observed.

[0035] According to the foregoing aspects of the present disclosure, the Alzheimer's-related metabolite is selected from hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; and / or at least homocysteine, and the protein is selected from at least coagulation factor XIIIA chain; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; and / or haptoglobin.

[0036] According to the aforementioned aspects of this disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain or complement factor D.

[0037] A further aspect of this disclosure relates to a method for diagnosing and selectively treating Alzheimer's disease, comprising: (a) obtaining a signature of metabolites and proteins from a biological sample of interest, the signature being lysophosphatidylcholine (lysoPC) selected from hydroxyphenylacetic acid (HPA); trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or Alzheimer's-related metabolites selected from serotonin; and / or C-reactive proteins; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII (b) to be obtained by measuring at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (b) classifying the patient into an Alzheimer's disease risk class or a healthy class using a statistical analysis with at least 85% predictive value, which includes comparing the obtained signature with a control signature, wherein the classification includes a statistical analysis performed by computer;A method is provided comprising: (c) identifying that a subject has Alzheimer's disease or is at risk of developing it, if the subject is classified into an Alzheimer's disease risk class; and (d) optionally treating or having the subject identified in step (c) treated with an agent effective in treating, improving or reducing the symptoms of Alzheimer's disease, wherein the treatment optionally modulates the level of one or more metabolites and / or proteins identified to be present in the signature at different levels from the control.

[0038] According to embodiments of the foregoing aspects of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; and / or at least homocysteine, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.

[0039] According to embodiments of the foregoing aspects of the present disclosure, the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain, complement factor D or C-reactive protein.

[0040] According to embodiments of any of the foregoing aspects of the present disclosure, the method further comprises modulating or causing to modulate levels of metabolites and / or proteins, and includes nucleic acid therapy. In one embodiment, nucleic acid therapy comprises reducing levels of proteins expressed or metabolites produced by siRNA or antisense therapy.

[0041] According to embodiments of any of the aforementioned aspects of this disclosure, nucleic acid therapy includes increasing the levels of proteins expressed or metabolites produced by mRNA therapy.

[0042] According to embodiments of any of the foregoing aspects of the present disclosure, the measured metabolite and / or proteome profile is based on a profile identified by a prior computer-based statistical analysis model that has at least 90% predictive value and includes at least one of mean comparison, PCA, PLS-DA, or recurrent SVM data analysis.

[0043] According to embodiments of any of the aforementioned aspects of the present disclosure, metabolite and / or proteome profiles have been previously identified as having predictive values ​​by classifying diseased and non-diseased samples into two groups by a computer implementation method based on predicted metabolites and / or proteins used in a computer model, and then evaluating whether the groups are separated.

[0044] According to embodiments of another aspect of the present disclosure, a method for identifying a subject at risk of developing Alzheimer's disease or identifying a subject that has Alzheimer's disease, comprising: (a) obtaining one or more biological samples from the subject; (b) performing metabolome and proteome analysis of one or more biological samples; (c) selecting at least one, at least two, at least three, or all metabolites from trigonelline, lysophosphatidylcholine (e.g., C18:2 fatty acid metabolites and / or C6:1 fatty acid metabolites) and / or hydroxyphenylacetic acid (HPA), as well as coagulation factor XIIIA chain, complement factor D, C-reactive protein, IgGFc-binding protein, ceruloplasmin, haptoglobin, tenascin C and / or Alternatively, a method is provided comprising measuring at least one, at least two, at least three, or all proteins or fragments thereof selected from serum amyloid A-1, wherein the measured metabolite(s) and / or protein(s) have a predictive score of at least 0.5 as determined by a VIP plot; (d) identifying the subject as having Alzheimer's disease or being at risk of developing it if the levels of the metabolite(s) and / or protein(s) differ from those of a control standard; and (e) optionally providing the results of a metabolome and proteome analysis or a treatment plan based on the analysis via an online platform if the subject is identified as having Alzheimer's disease or being at risk of developing it.

[0045] According to any of the aforementioned aspects or embodiments, the protein or fragment and / or metabolite is at least one, two or all of homocysteine, C18:2 fatty acid metabolites and C6:1 fatty acid metabolites, and the measured levels of at least one, two or all of the corresponding homocysteine, C18:2 fatty acid metabolites and C6:1 fatty acid metabolites are reduced compared to the Alzheimer's-negative sample.

[0046] According to any of the aforementioned embodiments or models, the protein, its fragments and / or metabolites include at least homocysteine ​​and C18:2 fatty acid metabolites.

[0047] According to any of the aforementioned embodiments or models, the protein, its fragments and / or metabolites comprise at least homocysteine ​​and C6:1 fatty acid metabolites.

[0048] According to any of the aforementioned embodiments or models, the protein, its fragments and / or metabolites include at least C18:2 fatty acid metabolites and C6:1 fatty acid metabolites. [Brief explanation of the drawing]

[0049] The specification concludes with claims that specifically point out and clearly assert the disclosures, but the disclosures are expected to be better understood from the following description of the drawings.

[0050] [Figure 1A] This shows the age range of the cohort.

[0051] [Figure 1B] This shows the number of men and women in the cohort.

[0052] [Figure 1C] This shows the age distribution of the cohort by sex.

[0053] [Figure 2A] This shows statistical metabolic biomarker data for lysophosphatidylcholine (lysoPC) C18:2. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0054] [Figure 2B] The graph shows statistical metabolic biomarker data for lysoPC C18:0. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0055] [Figure 2C] The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0056] [Figure 2D] The graph shows statistical metabolic biomarker data for lysoPC C16:0. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0057] [Figure 2E] The graph shows statistical metabolic biomarker data for lysoPC C14:0. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0058] [Figure 2F] The graph shows statistical metabolic biomarker data for lysophosphatidylcholine (lysoPC) C16:1. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0059] [Figure 2G] The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0060] [Figure 2H] The graph shows statistical metabolic biomarker data for lysophosphatidylcholine (lysoPC) C20:4. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0061] [Figure 2I] This shows statistical metabolic biomarker data for lysophosphatidylcholine (lysoPC) C17:0. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0062] [Figure 2J]The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0063] [Figure 3] This is a scatter plot of the principal component analysis (PCA) of the metabolites tested in Example 1.

[0064] [Figure 4] This is a 2D PCA score plot of the metabolites tested in Example 1.

[0065] [Figure 5] This is a scatter plot of the partial least squares discriminant analysis (PLS-DA) results for the metabolites tested in Example 1.

[0066] [Figure 6] This is a 2D PLS-DA score plot of the metabolites tested in Example 1.

[0067] [Figure 7] This is a bar graph showing the Q2, R2, and precision of the metabolites tested in Example 1.

[0068] [Figure 8] This graph shows the recurrent support vector machine (R-SVM) data for the metabolites tested in Example 1. The data is plotted as error rate versus number of variables (levels).

[0069] [Figure 9] The receiver operating curve (ROC) and 95% confidence interval of the SVM model are shown (Example 1).

[0070] [Figure 10] The predicted class probabilities of the metabolites tested in Example 1 are shown.

[0071] [Figure 11]The top 15 metabolites from Example 1, ranked based on their average importance in the Support Vector Machine (SVM) model, are shown below.

[0072] [Figure 12] This is a 2D PCA score plot of the metabolites tested in Example 2.

[0073] [Figure 13] This is a 2D PLS-DA score plot of the metabolites tested in Example 2.

[0074] [Figure 14] This is a bar graph showing the Q2, R2, and precision of the metabolites tested in Example 2.

[0075] [Figure 15] This is a frequency-versus-permutation statistical graph of the metabolites tested in Example 2.

[0076] [Figure 16] The top 15 metabolites ranked based on the VIP score of Example 2 are shown.

[0077] [Figure 17] The ROC and 95% confidence interval of the SVM model are shown (Example 2).

[0078] [Figure 18A] The statistical proteomic biomarker data for the C-reactive protein in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0079] [Figure 18B] The statistical proteomic biomarker data for α-1-antichymotrypsin in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0080] [Figure 18C]The statistical proteomic biomarker data for serotransferrin in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0081] [Figure 18D] The statistical proteomic biomarker data for leucine-rich α-2-glycoprotein 1 in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0082] [Figure 18E] The statistical proteomic biomarker data for serum albumin in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0083] [Figure 18F] The statistical proteomic biomarker data for coagulation factor XIIIA chain in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0084] [Figure 18G] The statistical proteomic biomarker data for biotinidase in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0085] [Figure 18H] The statistical proteomic biomarker data for gelzolin in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0086] [Figure 18I] The statistical proteomic biomarker data for apolipoprotein A-IV in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0087] [Figure 18J]The statistical proteomic biomarker data for α-2-HS glycoprotein in Example 3 is shown. The graph on the left shows the original concentration, and the graph on the right shows the normalized concentration data.

[0088] [Figure 19] This is a scatter plot of the principal component analysis (PCA) of the proteins tested in Example 3.

[0089] [Figure 20] This is a 2D PCA score plot of the proteins tested in Example 3.

[0090] [Figure 21] This is a scatter plot of the partial least squares discriminant analysis (PLS-DA) results for the proteins tested in Example 3.

[0091] [Figure 22] This is a 2D PLS-DA score plot of the proteins tested in Example 3.

[0092] [Figure 23] This is a bar graph showing the Q2, R2, and precision of the proteins tested in Example 3.

[0093] [Figure 24] This graph shows the recurrent support vector machine (R-SVM) data for the protein tested in Example 3. The data is plotted as error rate versus number of variables (levels).

[0094] [Figure 25] The receiver operating curve (ROC) and 95% confidence interval of the SVM model are shown (Example 3).

[0095] [Figure 26] The predicted class probabilities for the proteins tested in Example 3 are shown.

[0096] [Figure 27]The top 15 proteins from Example 3, ranked based on their average importance in the Support Vector Machine (SVM) model, are shown.

[0097] [Figure 28] The metabolites tested in Example 4 are shown in a 2D PCA score plot.

[0098] [Figure 29] This is a 2D PLS-DA score plot of the metabolites tested in Example 4.

[0099] [Figure 30] This is a bar graph showing the Q2, R2, and precision of the metabolites tested in Example 4.

[0100] [Figure 31] This is a frequency-versus-permutation statistical graph of the metabolites tested in Example 4.

[0101] [Figure 32] The top 15 metabolites ranked based on the VIP score of Example 4 are shown.

[0102] [Figure 33] The ROC and 95% confidence interval of the SVM model are shown (Example 4).

[0103] The drawings illustrate exemplary embodiments. It should be clearly understood that the description and drawings are intended solely to illustrate specific embodiments and aid understanding. They should not be construed as limiting the invention in any way. [Modes for carrying out the invention]

[0104] (Detailed explanation) A detailed description of one or more embodiments of the present invention is provided below, along with accompanying diagrams illustrating the principles of the present invention. While the present invention is described in relation to such embodiments, it is not limited to the specific embodiments described herein. The scope of the present invention is limited only by the claims and their equivalents. In order to fully understand the present invention, a number of specific details are described below. These details are provided for the purpose of illustrating non-limiting examples, and the present invention can be carried out in accordance with the claims without some or all of these specific details. For the sake of clarity, certain technical documents known in the art related to the present invention are not described in detail so as not to unnecessarily obscure the present invention by such descriptions. definition

[0105] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art relating to the present invention. Except as used herein, and unless otherwise stated or required by context, the following terms shall have the definitions set forth below.

[0106] Articles such as "a" and "an" used within a claim are understood to mean one or more of the things described or described in the claim.

[0107] The terms “Alzheimer’s” and “Alzheimer’s disease” are used interchangeably to generally describe Alzheimer’s as identified by a qualified individual.

[0108] The term "Alzheimer's negative" generally refers to a biological sample from an individual who does not have Alzheimer's disease or has not developed Alzheimer's disease.

[0109] The term “Alzheimer’s treatment plan” generally refers to interventions undertaken to manage a person with Alzheimer’s disease. The objectives of the plan include, but are not limited to, one or more of the following: symptom relief or prevention, delaying or halting the progression or worsening of Alzheimer’s, and remission of Alzheimer’s. In some embodiments, “Alzheimer’s treatment plan” refers to therapeutic measures (e.g., changes in Alzheimer’s-related metabolite levels and / or protein levels), dietary adjustments, nutritional supplements, and / or cognitive training.

[0110] The terms "comprises," "comprising," "include," "includes," "including," "contain," "contains," and "containing" have a non-restrictive meaning, meaning that other steps and sections can be added that do not affect the end result. The terms above include the terms "consist of" and "essentially become from."

[0111] The term "improvement of cognitive function" generally refers to preventing, to any degree, or reducing the severity or frequency of one or more behavioral disturbances, symptoms, and / or abnormalities exhibited by an individual with Alzheimer's disease or a pathological condition involving one or more symptoms of Alzheimer's disease. Non-limiting examples of cognitive symptoms include memory impairment and other cognitive abilities, as well as judgment abilities. Improvement may be observed by the individual receiving treatment or by others (i.e., healthcare professionals). Assessment of whether improvement exists may include administering tests to assess memory impairment and other thinking abilities, determine functional abilities, and identify behavioral changes.

[0112] The term "metabolite" generally refers to any molecule involved in metabolism. Metabolites can be products, substrates, or intermediates in metabolic processes. Metabolites include, but are not limited to, amino acids, peptides, acylcarnitines, monosaccharides, lipids and phospholipids, lysophospholipids, sphingolipids, glycerophospholipids, glucose, prostaglandins, hydroxyeicosatetraenoic acid, hydroxyoctadecadienoic acid, steroids, bile acids, and glycolipids and phospholipids.

[0113] The terms “Alzheimer’s-related metabolites” or “metabolite profile” generally refer to metabolites associated with Alzheimer’s disease, including one or more metabolites, or combinations thereof, as described herein.

[0114] The terms “preferred,” “preferred,” and variations thereof generally refer to embodiments of the Disclosure that provide a particular benefit under specific circumstances. However, other embodiments may also be preferred under the same or other circumstances. Furthermore, the enumeration of one or more preferred embodiments does not imply that other embodiments are unhelpful, nor is it intended to exclude other embodiments from the scope of the Disclosure.

[0115] The terms "prevent" and "prevent" are used interchangeably and generally refer to any activity that leads to a reduction in the risk of developing the Alzheimer's disease in question.

[0116] The terms “Alzheimer’s-related proteins” or “proteome profile” generally refer to a profile of proteins, protein fragments, and / or peptides associated with Alzheimer’s disease, including two, three, four, or five or more proteins, or combinations thereof, as described herein. As will be understood by those skilled in the art, protein quantification may include quantification of its fragments or peptides.

[0117] The terms “subject” or “patient” are used without limitation and generally refer to vertebrates such as mammals. The term “mammal” is defined as an individual belonging to the class Mammalia and includes, without limitation, humans, livestock and farm animals, as well as zoo animals, sports animals or pet animals such as sheep, dogs, horses, cats, or cattle. In some embodiments, the subject is human.

[0118] The term “treating” or “treatment” generally refers to interventions carried out in response to the symptoms of Alzheimer’s disease or related conditions presenting to the individual. The goals of treatment include, but are not limited to, one or more of the following: mitigation or prevention of Alzheimer’s, delaying or halting the progression or worsening of Alzheimer’s, and remission of Alzheimer’s. In certain embodiments, “treatment” refers to therapies, dietary therapies, supplement therapies, and / or behavioral therapies.

[0119] In all embodiments of this disclosure, all percentages, concentrations, part counts, and ratios are based on the total weight of the composition of this disclosure unless otherwise specified. All such weights for the listed components are based on effective levels and therefore do not include solvents or by-products that may be present in commercial products unless otherwise stated.

[0120] Unless otherwise specified, all ratios are weight ratios. Unless otherwise specified, all temperatures are in degrees Celsius (°C). All dimensions and values ​​disclosed herein (e.g., quantities, percentages, parts, proportions) should not be understood as being strictly limited to the exact numerical values ​​stated. Instead, unless otherwise specified, each dimension or value shall mean both the stated value and a functionally equivalent range around that value. For example, a dimension disclosed as "40mm" shall mean "approximately 40mm". Methods of Diagnosis and Treatment of Alzheimer's Disease

[0121] In one embodiment, the disclosure relates to a method for the (early) diagnosis and treatment of Alzheimer's disease and any related symptoms in a subject. The disclosure presupposes, at least in part, the identification of novel metabolites and / or novel proteins that provide etiological information related to Alzheimer's, and provides an opportunity for an objective metabolite-based and / or protein-based diagnosis of Alzheimer's in a subject that may lead to more effective treatments.

[0122] Given the complexity of the interaction between genetics and the environment, the metabolic and / or proteomic profiling described herein can provide molecular-based testing useful for personalized treatment planning. Metabolic and / or proteomic-based analysis has the advantage of identifying biomarker profiles derived from the interaction of an individual's genetic characteristics and / or current lifestyle behaviors (e.g., smoking, alcohol consumption, sleep behavior, physical activity), gut microbiota, diet, and environmental factors that contribute to the unique metabolite and / or protein profiles of Alzheimer's disease patients. Combining this with early diagnosis and Alzheimer's treatment planning offers the further advantage of improved treatment outcomes. This specification describes methods that provide the identification of novel metabolite and / or proteomic profiles in subjects with Alzheimer's disease, useful for the diagnosis and treatment of such subjects. Therefore, this disclosure represents an advance in the art.

[0123] According to this disclosure, a novel metabolite profile of Alzheimer's disease is identified in subjects with Alzheimer's disease. The Alzheimer's metabolite profile includes hydroxyphenylacetic acid; lysophosphatidylcholine (lysoPC) selected from trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; and LysoPC. The formula comprises at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine (C4-OH)(C3-DC(C4-OH)); and serotonin.

[0124] In another embodiment, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein, α-1 antichymotrypsin, serotransferrin, leucine-rich α-2-glycoprotein 1, serum albumin, coagulation factor XIIIA chain, biotinidase, gelzolin, apolipoprotein A-IV, or α-2-HS glycoprotein.

[0125] In another embodiment, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, haptoglobin, adipocyte membrane, intercellular adhesion protein, hemopexin, cadherin 5, coagulation factor X, α-1-antichymotrypsin, complement C1s subcomponent, L-selectin, fibronectin, tenascin C, transthyretin, calistatin, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, and / or von Willebrand factor.

[0126] In another embodiment, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, or haptoglobin.

[0127] In another embodiment, the metabolite profile of Alzheimer's disease includes at least one of the following: coagulation factor XIIIA chain, trigonelline, lysophosphatidylcholine (lysoPC) C18:2 fatty acid metabolites, and hydroxyphenylacetic acid (HPA).

[0128] Hydroxyphenylacetic acid (HPA) is a metabolic byproduct that inhibits the activity of key enzymes involved in neurotransmitter metabolism, thereby lowering neurotransmitter levels.

[0129] Trigonelline is a type of alkaloid with anti-inflammatory and antioxidant properties. The inventors' model identified low levels of trigonelline in samples from Alzheimer's disease patients.

[0130] C18:2 and C6:1 are types of fatty acid metabolites, and C18:2 concentrations in the blood of individuals with Alzheimer's disease tend to be higher than in healthy individuals. Individuals with Alzheimer's disease may also have altered levels of C6:1 in their blood compared to healthy individuals. The C18:2 fatty acid metabolite may contribute to the formation of beta-amyloid, a protein that forms the plaques characteristic of Alzheimer's disease. Furthermore, C18:2 may promote inflammation in the brain, contributing to neuronal death and the cognitive decline seen in Alzheimer's disease. The inventors' model shows low levels of C18:2 and C6:1 in samples of individuals with Alzheimer's disease, which is a surprising observation.

[0131] Homocysteine ​​is an amino acid that may contribute to the development of Alzheimer's disease. Elevated levels of homocysteine ​​can be observed in individuals with Alzheimer's disease, and high homocysteine ​​levels are associated with an increased risk of developing the disease. Homocysteine ​​has been shown to disrupt normal brain function by inhibiting the activity of enzymes involved in neurotransmitter metabolism, leading to decreased neurotransmitter levels and cognitive decline. The inventors' model shows low levels of homocysteine ​​in samples of Alzheimer's disease, which is a surprising observation. Protein or proteome profile

[0132] This disclosure identifies a new proteomic profile of Alzheimer's disease in subjects with Alzheimer's. The proteomic profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion proteins; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; callistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor.

[0133] According to one example, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, haptoglobin, adipocyte membrane, intercellular adhesion protein, hemopexin, cadherin 5, coagulation factor X, α-1-antichymotrypsin, complement C1s subcomponent, L-selectin, fibronectin, tenascin C, transthyretin, calistatin, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, and / or von Willebrand factor.

[0134] According to further examples, the proteomic profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein, α-1 antichymotrypsin, serotransferrin, leucine-rich α-2-glycoprotein 1, serum albumin, coagulation factor XIIIA chain, biotinidase, gelzolin, apolipoproteins A-IV; or α-2-HS glycoprotein.

[0135] According to further examples, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, C-reactive protein, tenascin C, serum amyloid A-1, or haptoglobin.

[0136] The proteomic profile of Alzheimer's disease may include at least two Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, or C-reactive proteins.

[0137] Beyond theory, coagulation factor XIII chain (FXIII-A) is a protein involved in the blood clotting process. Individuals with Alzheimer's disease may have altered levels of FXIII-A in their blood compared to healthy individuals. Furthermore, FXIII-A may contribute to oxidative stress and inflammation in the brain, exacerbating damage to brain cells and potentially contributing to the progression of Alzheimer's disease.

[0138] Without being limited to theory, serum amyloid A1 protein (SAA1) is a protein primarily produced in the liver and involved in the body's response to inflammation. The inventors' model shows that the level of SAA in the blood of individuals with Alzheimer's disease may differ from that of healthy individuals.

[0139] Beyond theoretical limitations, alpha-1 antichymotrypsin (A1AT) is a protein involved in regulating inflammation and removing proteases, which are enzymes that break down proteins. Levels of A1AT in the blood and brain of Alzheimer's disease patients can vary.

[0140] Adipocyte membrane-associated proteins (APMAPs) are identified here as Alzheimer's-associated proteins and therefore may play a role in the development of Alzheimer's disease.

[0141] The inventors have discovered remarkable metabolite and / or proteome profiles in subjects with Alzheimer's disease compared to non-Alzheimer's individuals and / or Alzheimer's-negative individuals. In particular, in some embodiments, levels of Alzheimer's-related metabolites and / or Alzheimer's proteins may be altered in the circulation of subjects with Alzheimer's disease compared to non-Alzheimer's individuals. In certain embodiments, levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins are altered in the blood (e.g., serum, plasma), body fluids (e.g., cerebrospinal fluid, pleural fluid, amniotic fluid, semen, or saliva), urine, and / or feces of subjects with Alzheimer's. While not bound by theory, it is thought that Alzheimer's-related metabolites and / or Alzheimer's-related proteins play a causal role in the expression of Alzheimer's-related behaviors in subjects with Alzheimer's. Alternatively, the alteration in levels of Alzheimer's-related metabolites and / or Alzheimer's-related proteins may be caused by Alzheimer's disease.

[0142] In one embodiment, the present disclosure provides a method for diagnosing and treating Alzheimer's disease in a subject. The method comprises step (a) providing a biological sample obtained from a subject, preferably a human. According to the method disclosed herein, any type of biological sample taken from anywhere in the subject's body can be examined, including but not limited to blood (including, but not limited to, serum or plasma), cerebrospinal fluid ("CSF"), pleural fluid, urine, feces, sweat, tears, exhaled condensate, saliva, vitreous fluid, tissue samples, amniotic fluid, chorionic villi samples, brain tissue, etc., and biopsies of any solid tissue such as tumors, adjacent normal tissue, smooth and skeletal muscle, adipose tissue, liver, skin, hair, brain, kidneys, pancreas, lungs, etc. Preferably, the biological sample obtained from a living subject is urine. Alzheimer's-related metabolites and / or proteins can be extracted from their biological source using any number of extraction / cleanup procedures commonly used in quantitative analytical chemistry.

[0143] The method further involves selecting from the obtained sample hydroxyphenylacetic acid; trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites lysophosphatidylcholine (lysoPC); C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII The procedure includes (b) measuring the concentration levels of Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor.

[0144] In certain embodiments, the method includes measuring at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 Alzheimer's-related metabolites and / or proteins from the obtained sample.

[0145] In certain embodiments, the measurement of concentration levels of Alzheimer's-related metabolites and / or proteins may be performed via mass spectrometry, including but not limited to gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (e.g., LC-MS, LC-MS-MS, LC-MRM, LC-SIM, and LC-SRM). Preferably, Alzheimer's-related metabolites are measured by spectroscopic techniques, which are selected from the group consisting of liquid chromatography, gas chromatography, liquid chromatography-mass spectrometry, high-performance liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, Raman spectroscopy, and infrared spectroscopy. Measurements may also be performed under other methodologies, such as colorimetric analysis, enzymatic methods, immunological methodologies, and gene expression analysis, such as real-time PCR, RT-PCT, Northern spectroscopy, and in situ hybridization.

[0146] In some embodiments, a mass spectrometry process for determining whether Alzheimer's-related proteins are elevated includes enzymatically or chemically digesting proteins or peptide fragments of a sample obtained from a subject into peptide fragments. The peptide fragments are optionally separated and / or ionized and captured by mass spectrometry. Digestion may include proteolytic digestion, which involves treating a preparation containing Alzheimer's-related proteins with an acid, a base, or an enzyme such as trypsin or other proteolytic enzymes. One embodiment includes shotgun proteomics quantification, which aims to hydrolyze or cleave whole proteins in complex mixtures such as serum, urine, and cell lysates into peptides, followed by multidimensional HPLC-MS to generate a global profile of the protein mixture as genomic "shotgun" sequencing.

[0147] Accordingly, according to one aspect of the present disclosure, a method is provided for determining whether or not there is an increase in Alzheimer's-related proteins or their peptide fragments in a sample obtained from a subject, where Alzheimer's-related proteins or their peptide fragments include C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII. A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor, selected from at least one of these, the method comprising: enzymatically or chemically digesting the protein or its peptide fragments in a sample obtained from the subject to form peptide fragments; and optionally liquid chloroform This includes: introducing a solution containing peptide fragments into a mass spectrometer after performing one or more processes, including matrixing or other processing, to quantify the peptide fragments; determining the concentration of the peptide fragments relative to a baseline such as a standard (e.g., a peptide standard); evaluating whether the fragments are elevated compared to the baseline or standards; identifying, if one or more peptides are elevated compared to the baseline or standards, that the subject has Alzheimer's disease or is predisposed to developing Alzheimer's disease; and optionally treating or having the subject treated with Alzheimer's disease treatments, including drugs approved for use in the treatment of Alzheimer's disease in the relevant jurisdiction.

[0148] According to one example, the peptide fragment comprises at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, haptoglobin, adipocyte membrane, intercellular adhesion protein, hemopexin, cadherin 5, coagulation factor X, α-1-antichymotrypsin, complement C1s subcomponent, L-selectin, fibronectin, tenascin C, transthyretin, calistatin, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, and / or von Willebrand factor.

[0149] According to further examples, the peptide fragment comprises at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein, tenascin C, serum amyloid A-1, α-1 antichymotrypsin, serotransferrin, leucine-rich α-2-glycoprotein 1, serum albumin, coagulation factor XIIIA chain, biotinidase, gelzolin, apolipoprotein A-IV; or α-2-HS glycoprotein.

[0150] According to further examples, the peptide fragment for Alzheimer's disease comprises at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, C-reactive protein, tenascin C, serum amyloid A-1, or haptoglobin.

[0151] In further embodiments, the peptide fragment comprises one or two Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, or C-reactive proteins.

[0152] In certain embodiments, any of the methods described herein may further include measuring the concentration levels of one or more additional Alzheimer's-related metabolites and / or proteins, including but not limited to any of those described herein. The novel approach of this disclosure identifies biomarkers that have high predictive values ​​for a subset of diagnostic classes (i.e., Alzheimer's in this case). In one embodiment, the advantage of including additional Alzheimer's-related metabolites and / or proteins is that it creates an opportunity to reveal additional metabolite subtypes and / or improves the overall sensitivity of known diagnostic methods. Non-limiting examples of additional Alzheimer's-related metabolites and / or proteins are β-amyloid-42, tau, and phosphorylated tau. If the concentration levels of one or more additional Alzheimer's-related metabolites and / or proteins, as well as other biomarkers, obtained from a biological sample differ from the concentration levels of a reference Alzheimer's-negative sample, the subject is identified as having Alzheimer's disease. Non-limiting examples of additional Alzheimer's disease biomarkers are described in U.S. Patent Nos. 10,914,749, 9,285,374, and U.S. Patent Application Publication No. 2021 / 10109115, the relevant contents of which are incorporated herein by reference.

[0153] The method described herein further includes step (c) comparing the concentration levels of Alzheimer's-related metabolites and / or proteins from the obtained sample with the concentration levels of reference Alzheimer's-related metabolites and / or proteins from an Alzheimer's-negative sample. Those skilled in the art will understand that for comparison, a reference can be established as a value representing the levels of Alzheimer's-related metabolites and / or proteins in a non-Alzheimer's population that does not have Alzheimer's disease. Various criteria may be used to determine whether to include and / or exclude specific subjects from the reference population, including the age of the subjects (e.g., the reference subjects may be in the same age group as the subjects requiring treatment) and the sex of the subjects (e.g., the reference subjects may be in the same sex as the subjects requiring treatment). In certain embodiments, the reference is from an Alzheimer's-negative sample obtained from a non-Alzheimer's adult about 65 years of age or older.

[0154] The methods described herein further include step (d) identifying a subject as having Alzheimer's disease if the concentration levels of Alzheimer's-related metabolites and / or proteins from the obtained sample differ from reference concentration levels.

[0155] In certain embodiments, the identifying step (d) is performed when it is determined that the concentration level of at least one Alzheimer's-related metabolite and / or protein from the obtained sample differs by about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, or about 70% or more, compared to the concentration level of at least one reference Alzheimer's-related metabolite and / or protein from an Alzheimer's-negative sample. In certain embodiments, the identifying step (d) is performed when it is determined that the concentration levels of at least two, at least three, at least four, or at least five Alzheimer's-related metabolites and / or proteins from the obtained sample differ by about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, or about 70% or more, compared to the concentration level of a reference Alzheimer's-related metabolite and / or protein from an Alzheimer's-negative sample.

[0156] In certain embodiments, the identifying step (d) is performed when it is determined that the concentration levels of homocysteine, C18:2 and / or C6:1 are lower than the concentration levels of a reference from an Alzheimer's-negative sample.

[0157] In some embodiments, the concentration levels of homocysteine, C18:2 and / or C6:1 from the obtained samples are approximately 20% or more, approximately 30% or more, approximately 40% or more, approximately 50% or more, approximately 60% or more, or approximately 70% or more lower than the concentration levels of homocysteine, C18:2 and / or C6:1 from Alzheimer's-negative samples.

[0158] In certain embodiments, the identifying step (d) is performed when it is determined that the concentration levels of one or more of hydroxyphenylacetic acid, trigonelline, coagulation factor XIIIA chain, or complement factor D from the obtained sample are increased compared to the concentration levels of hydroxyphenylacetic acid, trigonelline, coagulation factor XIIIA chain, or complement factor D from the Alzheimer's-negative sample. In some embodiments, the concentration levels of hydroxyphenylacetic acid, trigonelline, coagulation factor XIIIA chain, or complement factor D from the obtained sample are increased by about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, or about 70% or more compared to the concentration levels of hydroxyphenylacetic acid, trigonelline, coagulation factor XIIIA chain, or complement factor D from the Alzheimer's-negative sample.

[0159] The methods described herein further include step (e) treating a subject identified as having Alzheimer's disease with an Alzheimer's treatment plan.

[0160] In certain embodiments, the comparison of the concentration level of at least one Alzheimer's-related metabolite and / or protein from a sample obtained using any of the methods described herein with the concentration level of a reference Alzheimer's-related metabolite and / or protein from an Alzheimer's-negative sample includes the use of multivariate statistical analysis. Preferably, the multivariate statistical analysis is selected from principal component analysis ("PCA") or partial least squares latent structure discriminant analysis ("PLS-DA"). In certain embodiments, a computer is used for the statistical analysis. Data for the statistical analysis can be extracted from the chromatogram (i.e., the spectrum of the mass signal) using software for statistical methods known in the art.

[0161] In some embodiments, the disclosure relates to methods for monitoring the progression of Alzheimer's disease in a subject and for treating Alzheimer's disease. In some embodiments, the method includes quantifying Alzheimer's-related metabolites and / or proteins at one or more time points after the initiation of treatment in order to monitor the progression of Alzheimer's disease in the subject (e.g., the rate of decline or improvement in the progression of Alzheimer's disease).

[0162] Therefore, the method is to (a) provide a first biological sample obtained from the subject at a first time point; and (b) from the obtained first sample, select lysophosphatidylcholine (lysoPC); C6:1 fatty acid metabolites selected from hydroxyphenylacetic acid; trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII (c) assessing a first Alzheimer's-related metabolite profile by measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; callistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (a) comparing the first Alzheimer's-related metabolite and / or proteome profile with a reference Alzheimer's-related metabolite profile from an Alzheimer's-negative sample;(d) Determining that there is a first difference between the first Alzheimer's-related metabolite and / or proteome profile and a reference Alzheimer's-related metabolite and / or proteome profile from an Alzheimer's-negative sample, and that the first difference indicates Alzheimer's; (e) Providing a second biological sample obtained from the subject at a second time point after the first time point; (f) Evaluating the second Alzheimer's-related metabolite and / or proteome profile by measuring the concentration levels of Alzheimer's-related metabolites and / or proteins from the obtained second sample; (g) comparing a second Alzheimer's-related metabolite and / or proteome profile with a reference Alzheimer's-related metabolite and / or proteome profile from an Alzheimer's-negative sample; (h) determining that there is a second difference between the first Alzheimer's-related metabolite and / or proteome profile and the reference Alzheimer's-related metabolite and / or proteome profile from an Alzheimer's-negative sample, and that the second difference indicates Alzheimer's; (i) determining the progression of Alzheimer's disease based on at least some of the first and second differences; and (j) treating the identified subject with an Alzheimer's treatment plan.

[0163] In certain embodiments of the above method, the period between the first time and the second time is at least one month, at least two months, at least three months, at least six months, at least nine months, or at least twelve months, preferably at least three months. In some embodiments, the subject is treated before the first two biological samples are obtained. In other embodiments, the treatment is administered to the subject within the interval(s) between the collection of the biological samples. In certain embodiments, the first biological sample, the second biological sample, or both are blood or urine, preferably serum, plasma, or urine.

[0164] According to the embodiment of the method described above, the metabolite profile of Alzheimer's disease is selected from hydroxyphenylacetic acid; trigonelline; lysophosphatidylcholine (lysoPC); C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; LysoPC The formula comprises at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine (C4-OH)(C3-DC(C4-OH)); and / or serotonin.

[0165] In another embodiment, the Alzheimer's disease metabolite profile includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from C-reactive protein, tenascin C, serum amyloid A-1, α-1 antichymotrypsin, serotransferrin, leucine-rich α-2-glycoprotein 1, serum albumin, coagulation factor XIIIA chain, biotinidase, gelzolin, apolipoprotein A-IV, or α-2-HS glycoprotein.

[0166] In another embodiment, the Alzheimer's disease metabolite profile includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, haptoglobin, adipocyte membrane, intercellular adhesion protein, hemopexin, cadherin 5, coagulation factor X, α-1-antichymotrypsin, complement C1s subcomponent, L-selectin, fibronectin, tenascin C, transthyretin, calistatin, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, and / or von Willebrand factor.

[0167] In another embodiment, the Alzheimer's disease metabolite profile includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid;A-1 protein, ceruloplasmin, or haptoglobin.

[0168] In another embodiment, the metabolite profile of Alzheimer's disease includes at least one of coagulation factor XIIIA chain and hydroxyphenylacetic acid.

[0169] According to embodiments of the above method, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, haptoglobin, adipocyte membrane, intercellular adhesion protein, hemopexin, cadherin 5, coagulation factor X, α-1-antichymotrypsin, complement C1s subcomponent, L-selectin, fibronectin, tenascin C, transthyretin, calistatin, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, and / or von Willebrand factor.

[0170] According to embodiments of the above method, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein, tenascin C, serum amyloid A-1, α-1 antichymotrypsin, serotransferrin, leucine-rich α-2-glycoprotein 1, serum albumin, coagulation factor XIIIA chain, biotinidase, gelzolin, apolipoprotein A-IV; or α-2-HS glycoprotein.

[0171] According to embodiments of the above method, the proteome profile of Alzheimer's disease includes at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, IgGFc-binding protein, serum amyloid A-1 protein, ceruloplasmin, C-reactive protein, tenascin C, serum amyloid A-1, or haptoglobin.

[0172] According to the method described above, the proteomic profile of Alzheimer's disease may include at least two Alzheimer's-related proteins selected from coagulation factor XIIIA chain, complement factor D, or C-reactive proteins.

[0173] In certain embodiments of the above method, the concentration levels of homocysteine, C18:2 and / or C6:1 are reduced compared to the reference concentration levels from an Alzheimer's-negative sample.

[0174] In some examples, the concentration levels of homocysteine, C18:2 and / or C6:1 from the obtained samples were approximately 20% or more, approximately 30% or more, approximately 40% or more, approximately 50% or more, approximately 60% or more, or approximately 70% or more lower compared to the concentration levels of homocysteine, C18:2 and / or C6:1 from Alzheimer's-negative samples. Alzheimer's Treatment Plan

[0175] An Alzheimer's treatment plan is selected from a group consisting of dietary adjustments, nutritional supplements, or a combination thereof, for subjects diagnosed with Alzheimer's disease or diagnosed with a predisposition to develop Alzheimer's disease. Preferably, the Alzheimer's treatment plan has the effect of adjusting the concentration level of one or more Alzheimer's-related metabolites and / or proteins in subjects diagnosed with Alzheimer's disease or diagnosed with a predisposition to develop Alzheimer's disease to the corresponding level of reference Alzheimer's-related metabolites and / or proteins from an Alzheimer's-negative sample.

[0176] Various methods can be used to adjust the concentration levels of Alzheimer's-related metabolites and / or proteins in the subject, for example, blood concentrations (e.g., serum concentrations). Preferably, the concentration levels of one or more Alzheimer's-related metabolites in the subject are adjusted until an improvement in the subject's behavioral function is observed.

[0177] In certain embodiments, antibodies that specifically bind to Alzheimer's-related metabolites, intermediates for the in vivo synthesis of Alzheimer's-related metabolites, or substrates for the in vivo synthesis of Alzheimer's-related metabolites may be administered to the target. For example, antibodies that specifically bind to one or more metabolites and / or proteins on a metabolite and / or proteome profile can be used to reduce their levels in the target.

[0178] In certain embodiments, the concentration levels of one or more Alzheimer's-related metabolites, such as blood concentrations (e.g., serum concentrations), are adjusted by modifying the composition of the target gut microbiota.

[0179] In further embodiments, nucleic acid therapy can be used to reduce the concentration of proteins (including peptides) by, for example, reducing protein expression using siRNA or antisense oligonucleotides. Similarly, nucleic acid therapy can be used with mRNA therapy to express proteins that are present at low levels in a reference without Alzheimer's disease. Therapeutic nucleic acids can be encapsulated in a suitable delivery vehicle. Nucleic acid therapy can also be used, for example, with mRNA, antisense or siRNA therapy to increase the levels of metabolites described herein by modulating the activity of proteins involved in metabolism.

[0180] In another embodiment, a person diagnosed or identified as having a predisposition to develop Alzheimer's disease is treated or made to be treated with an approved Alzheimer's drug, such as a medication. The medication may be approved by any applicable regulatory authority. Non-exclusive examples of Alzheimer's medications include galantamine, rivastigmine, and donepezil, which are cholinesterase inhibitors prescribed for mild to moderate Alzheimer's symptoms. These medications may help alleviate or control some cognitive and behavioral symptoms. kit

[0181] The metabolites and / or proteome profiles described herein may be used in tests, assays, methods, and kits for the diagnosis, prediction, adjustment, or monitoring of Alzheimer's disease, including ongoing evaluation, monitoring, and / or susceptibility assessment. This disclosure includes a kit for diagnosing Alzheimer's disease by measuring and identifying at least one Alzheimer's-related metabolite and / or Alzheimer's-related protein. Preferably, the kit may include an appropriate Alzheimer's treatment plan to be initiated when Alzheimer's disease is diagnosed. Thus, the kit may include (a) lysophosphatidylcholine (lysoPC) selected from hydroxyphenylacetic acid; trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; and LysoPC. C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein;(c) a detector configured to detect the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins (e.g., peptides) selected from insulin-like growth factor and / or von Willebrand factor control levels corresponding to a control group of Alzheimer's-negative subjects; (d) a multivariate analysis system configured to analyze the difference between the concentration levels of Alzheimer's-related metabolites and / or proteins and the control levels; and (d) optionally, a description of an Alzheimer's diagnostic method; the method comprising using the detector to measure the levels of Alzheimer's-related metabolites and / or proteins from a obtained biological sample and comparing the obtained levels of Alzheimer's-related metabolites and / or proteins to the control levels of Alzheimer's-related metabolites and / or proteins obtained from Alzheimer's-negative subjects. Preferably, the Alzheimer's diagnostic method includes a multi-metabolite or protein (e.g., peptide) detector configured to measure the levels of Alzheimer's-related metabolites and / or proteins.

[0182] In some embodiments, the kit may be for measuring Alzheimer's-related metabolites and / or proteins by physical separation techniques (as described herein above). In some embodiments, the kit may be for measuring Alzheimer's-related metabolites and / or proteins by methods other than physical separation, such as, but not limited to, colorimetric, enzymatic, or immunological methodologies. The kit may also include one or more suitable negative and / or positive controls. The kits of this disclosure may include other reagents, such as buffers and solutions, necessary to perform the tests. Computer implementation method

[0183] In further embodiments, the measured metabolite and / or proteome profiles have at least 80%, 85%, 90%, 92%, 94%, or 96% predicted values ​​and are based on profiles identified by a previously performed computer-based statistical analysis model, including at least one of mean comparison, PCA, PLS-DA, or recurrent SVM data analysis.

[0184] In further embodiments, samples are classified into two groups by a computer implementation method based on predicted metabolites and / or proteins used in a computer model, thereby identifying metabolite and / or proteome profiles that have predicted values, and the data from the two groups are well separated on a score plot or the like (see, for example, the following examples and Figures 4, 6, and 16 illustrating the separation of control and model data).

[0185] This disclosure also relates to a computer implementation for processing control biological samples, diagnosing Alzheimer's disease, and treating (or having treated) controls diagnosed with Alzheimer's disease. The computer implementation further enables monitoring the progression of Alzheimer's disease over multiple points in time to support more effective treatment planning.

[0186] The computer implementation method involves receiving a biological sample from the subject; processing the sample with a spectroscopic unit directly or wirelessly linked to a processing device, or utilizing any suitable communication technology, wherein the processing device has memory for storing measurement data from the spectroscopic unit; and the spectroscopic unit processes lysophosphatidylcholine (lysoPC) selected from hydroxyphenylacetic acid; trigonelline; C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; and LysoPC. C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII The method comprises measuring the levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; callistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; and storing the measurement data in a processor. The processing device includes one or more data storage devices which may be configured or adapted to store data related to the method.For example, a data storage device may be configured or adapted to store measurement data from a spectroscopic unit. The data storage device may also include computer program code stored therein. The program code in this embodiment may include program code for performing at least the steps of the method when it is executed.

[0187] The computer implementation method further involves using multivariate statistical analysis to compare the stored measurement data with the memory value representing the Alzheimer's-negative sample; and selecting from the obtained sample hydroxyphenylacetic acid; trigonelline; lysophosphatidylcholine (lysoPC); C6:1 fatty acid metabolites selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII The process involves storing in a processing device the corresponding results of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor, wherein if the measured data representing the levels of Alzheimer's-related metabolites and / or proteins differs from the concentration values ​​of reference Alzheimer's-related metabolites and / or proteins from an Alzheimer's-negative sample, the result identifies the subject as having Alzheimer's disease;The system includes displaying an Alzheimer's treatment plan on an electronic display directly or wirelessly connected to a processor for subjects identified as having Alzheimer's disease or being predisposed to developing Alzheimer's disease. The displayed treatment plan may include electronic text (optionally with graphical icons) on a graphical user interface describing one or more of the following for subjects diagnosed with or being predisposed to Alzheimer's disease: dietary adjustments, nutritional supplements, behavioral training, or a combination thereof; or it may include adjusting the blood concentration of one or more Alzheimer's-related metabolites and / or proteins in subjects diagnosed with or being predisposed to Alzheimer's disease until improvement in the subject's cognitive and / or behavioral function is observed; preferably, the adjustment of the blood concentration of one or more Alzheimer's-related metabolites may include adjusting the composition of the subject's gut microbiota.

[0188] In one embodiment, the graphical user interface includes a dashboard with graphical icons that illustrate one or more treatment plans. [Examples]

[0189] The following embodiments describe several exemplary ways of carrying out the specific methods described herein. It should be understood that the embodiments are for illustrative purposes only and do not limit the scope of the systems and methods described herein.

[0190] Supervised machine learning is a type of artificial intelligence that can be used to build predictive models for disease prediction. The process uses large datasets of labeled patient information, including demographic information, medical and family history, and clinical outcomes, to train machine learning algorithms that identify patterns and relationships associated with the risk of developing a particular disease. Once the model is trained, it can be used to make predictions based on the demographic and clinical information of new patients.

[0191] The inventors utilized this process to generate a machine learning model that predicts Alzheimer's disease based on a biobank sample of Alzheimer's patients and healthy controls. Using both demographic, clinical, and molecular (metabolomics, proteomics) data, they predicted the likelihood of patients developing Alzheimer's disease. The inventors' model identifies metabolite and protein characteristics, which can be used to classify new patients into either an Alzheimer's disease risk class or a healthy control class with 96% accuracy. The model also identifies several proteins associated with the disease mechanism, providing valuable insights as potential targets for therapeutic interventions to prevent or delay disease onset.

[0192] The identification of novel metabolite / proteome panels was carried out in two separate studies comparing proteins and metabolites in Alzheimer's patients and a control group. The novel metabolite profiles from Study 1 and Study 2 are described in Examples 1 and 2, respectively. The novel proteome profiles from Study 1 and Study 2 are shown in Examples 3 and 4, respectively. Below the detection limit (BLQ) and above the detection limit (ALQ).

[0193] To maximize the number of samples and biomarkers included in the analysis (i.e., to reduce data loss), concentration values ​​below the limit of detection (BLQ) but above the limit of quantification (ALQ) were processed before analysis.

[0194] Measurements marked as BLQ were assigned random concentration values ​​ranging from 0 to the detection / quantification limit (LLOD / LLOQ). Measurements marked as ALQ were assigned a concentration value of 1.5* upper limit of quantification (ULOQ).

[0195] In proteomics, raw data for the same protein may result in different ALQ entries. To account for these inconsistencies, we used a "universal" upper limit of quantification (ULOQ) from the batch to calculate the concentration values ​​of biomarkers marked as ALQ. Missing values

[0196] After processing the BLQ and ALQ values, the metabolomics data contained no missing values, so there was no need to further exclude samples or biomarkers from the analysis. Scaling and transformation

[0197] Sample normalization was not performed. The data were converted to log(cardin 10) to account for the rightward-sloping nature of biological data. The data were scaled to be mean-centered and divided by the standard deviation of each biomarker for classification modeling. demographics

[0198] In total, the analyses for Examples 1 and 4 included 63 samples. Age distribution and gender Example 1: Metabolomics Study 1

[0199] In the first metabolomics study, 150 metabolites were included in the analysis, of which 72 biomarkers showed significant differences between the case and control groups using a two-sample t-test for mean comparison. False detection rates were accounted for and adjusted for.

[0200] The ages of this cohort ranged from 60 to 95 years, with a mean age of 76.1 years and a median age of 76.0 years, showing a fairly even distribution. The youngest participant was 60 years old, and the oldest participant was 95 years old (Figure 1A). The cohort consisted of an equal number of men (n=34) and women (n=29) (Figure 1B). Although the age range of the women in the cohort tended to be younger, the median mean age was slightly higher than that of the men (Figure 1C).

[0201] The 10 metabolites with the lowest p-values ​​are shown in Table 1 below, and the results of the statistical analysis are shown graphically in Figures 2A to J. [Table 1]

[0202] The study identified various lysophosphatidylcholines (LPCs) as key metabolites associated with Alzheimer's disease. LPCs are a type of phospholipid involved in the development of Alzheimer's disease. While the exact mechanisms by which LPCs contribute to the development of Alzheimer's disease are still under investigation, the inventors' findings suggest that LPCs play a crucial role in the disease process and may be promising targets for the development of new therapies. Principal component analysis (PCA)

[0203] A PCA score plot is a graphical representation of the results of principal component analysis (PCA) on a dataset. PCA is a statistical technique used to reduce the dimensionality of a dataset by identifying and removing redundant and correlated variables and projecting the data onto a small number of orthogonal (uncorrelated) dimensions called principal components.

[0204] A PCA score plot is a scatter plot showing the projection of data onto the first two principal components. Each point on the plot represents a single data point from the original dataset, and the position of the point reflects the value of the data point on the two principal components. The x-axis of the plot represents the first principal component, and the y-axis represents the second principal component. A PCA scatter plot is shown in Figure 3.

[0205] PCA score plots allow you to visualize the structure and relationships within your data. For example, if data points are grouped into distinct groups on the plot, it may indicate the presence of underlying subgroups or clusters within the data. PCA score plots can also be used to identify outliers or unusual data points, or to assess the amount of variation explained by the first two principal components.

[0206] Based on the PCA 2D score plot, there appears to be a separation between the biomarkers of the AD case group and the control group, suggesting that a new biosignature of this disease can be identified (Figure 4). Partial Least Squares Discriminant Analysis (PLS-DA)

[0207] Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) are both dimensionality reduction techniques used in data analysis and machine learning. However, there are several important differences between the two methods.

[0208] PCA is an unsupervised dimensionality reduction technique that identifies and removes redundant or correlated variables in a dataset, projecting the data to fewer orthogonal dimensions. PLS-DA is a supervised learning technique that combines partial least squares regression (PLS) and linear discriminant analysis (LDA) to classify data into different classes.

[0209] The main difference between PCA and PLS-DA is that PCA is a linear method based on the variance of the data, while PLS-DA is a nonlinear method based on the relationship between the predictor and response variables. PCA is used to find the principal components that explain the variation in the data, while PLS-DA is used to find the latent variables that best distinguish between classes.

[0210] Similar to PCA analysis, PLS-DA appears to exhibit some separation of metabolites. Generally, this suggests that the PLS-DA model has a good ability to classify samples into two groups based on the predicted metabolites used (Figures 5 and 6). Q2 and R2 Analysis

[0211] Q2, R2, and accuracy are three metrics used to evaluate the performance of a model or prediction. These metrics can be used to assess a PLS-DA model's ability to classify data into different classes or categories.

[0212] Q2 is a measure of the model's predictive ability. It is calculated by dividing the sum of the squares of the differences between the observed and predicted values ​​of the response variable by the sum of squares. A high Q2 value indicates that the PLS-DA model is good at predicting the class membership of new data points.

[0213] R² is a measure of the goodness of fit of a PLS-DA model. It is calculated by dividing the sum of the squares of the differences between the observed and predicted values ​​of the response variable by the sum of squares. A high R² value indicates that the PLS-DA model explains the majority of the variation in the response variable.

[0214] Accuracy is a metric that measures how accurately a PLS-DA model predicts the class membership of data points. It is calculated by dividing the number of correct predictions made by the model by the total number of predictions made. High accuracy indicates that the PLS-DA model is making a large number of correct predictions.

[0215] Generally, PLS-DA models with high Q2, R2, and accuracy values ​​are considered superior because they have a good ability to predict class membership of new data points and can explain a large portion of the variation in the response variable. However, the appropriate values ​​for these metrics vary depending on the specific context and purpose of the analysis. The results are shown in Figure 7. Recursive SVM (R-SVM)

[0216] R-SVM (Recursive SVM) is a feature selection algorithm that uses a Support Vector Machine (SVM) to classify data. First, all features in the data are selected and used to train an SVM model. Next, the least important features are recursively removed based on their relative contribution to the classification, using the cross-validation error rate. This process creates a series of SVM models, each using a different subset of features. The features used by the best model are identified as the most useful. This approach is particularly useful when there are many features, some of which are irrelevant or redundant. By eliminating irrelevant features, R-SVM can improve the accuracy and computational efficiency of the model.

[0217] The results of the recurrent SVM classification of metabolites are shown in Figure 8. Linear Support Vector Machine (SVM)

[0218] A linear SVM is used as the classification model. A Support Vector Machine (SVM) is a type of supervised machine learning algorithm that can be used for classification. Linear SVM, in particular, is a variant of SVM that uses linear boundaries to separate different classes in data.

[0219] Linear SVMs find an optimal boundary, called a hyperplane, that separates different classes within the data. Once the optimal hyperplane is found, it can be used to determine which side of the boundary a new data point lies on, thereby classifying the new data point.

[0220] Linear SVMs have several advantages over other classification algorithms: they are efficient, effective in high-dimensional spaces, memory efficient, and have a low tendency towards overfitting.

[0221] The average predictive accuracy of this model, which uses metabolomics data, is 89.6%.

[0222] Figure 9 shows the receiver operating curve (ROC) and 95% confidence interval of the SVM model. The area under the curve (AUC) is 96.3%. ROC is a graphical representation of the performance of a binary classification model. It is plotted on a 2D graph with the true positive rate on the y-axis and the false positive rate on the x-axis. Models with a high true positive rate and a low false positive rate perform better.

[0223] AUC and prediction accuracy are two different metrics that measure the performance of binary classification models.

[0224] Predictive accuracy (in this case, an average of 89.6%) is a metric that measures how accurately a model can correctly classify new data points into the correct class. It is calculated as the percentage of correct predictions made by the model. While predictive accuracy is a simple and intuitive measure of performance, it can be susceptible to changes in the class distribution of the data. Figure 10 shows the predicted class probabilities.

[0225] On the other hand, AUC (96.3% in this case) is an indicator that measures the separability of classes in the data. It is calculated as the area under the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate (sensitivity) and false positive rate (1-specificity) at various threshold settings. AUC is a single value between 0 and 1 that represents the overall performance of the model, regardless of the decision threshold. AUC is unaffected by changes in the class distribution and does not depend on a specific decision threshold.

[0226] Prediction accuracy measures how correctly the prediction was made, while AUC measures how accurately the model can separate two classes.

[0227] In summary, in the context of distinguishing between Alzheimer's disease patients (positive class) and healthy individuals (negative class), the model can correctly classify new data points into the correct class with an average probability of 89.6%. Furthermore, an AUC of 96.3% indicates that the model can correctly classify individuals with Alzheimer's disease as positive and healthy individuals as negative in 96.3% of cases. This means that the model can correctly separate the two classes in most cases, and therefore can be considered to have good performance.

[0228] Figure 11 shows the top 15 metabolites ranked based on their average importance in the SVM model.

[0229] The metabolites are summarized in Table 2 below. [Table 2]

[0230] The top five metabolites are explained below.

[0231] In the inventors' model, hydroxyphenylacetic acid (HPA) was identified as having the highest average importance among the metabolites tested in Study 1 (Figure 11). HPA concentrations were lower in Alzheimer's disease patients compared to the control sample. HPA may interfere with normal brain function by inhibiting the activity of certain enzymes involved in neurotransmitter metabolism, thereby lowering neurotransmitter levels. This may contribute to the cognitive decline seen in Alzheimer's disease. Furthermore, HPA may contribute to oxidative stress and inflammation in the brain, further exacerbating damage to brain cells and potentially contributing to the progression of Alzheimer's disease.

[0232] The inventors' model also identified trigonelline as having a consistently high importance among the metabolites tested (Figure 11). While not limited to theory, trigonelline has anti-inflammatory and antioxidant properties and may help protect brain cells from damage and reduce oxidative stress in the brain. The inventors observed that their model identified low levels of trigonelline in samples from Alzheimer's disease patients.

[0233] C18:2 and C6:1 are fatty acid metabolites associated with the development of Alzheimer's disease. Studies have shown that levels of C18:2 in the blood of individuals with Alzheimer's disease tend to be higher than in healthy individuals. Several studies have suggested that levels of C6:1 in the blood of individuals with Alzheimer's disease tend to be altered compared to healthy individuals. The C18:2 fatty acid metabolite has been shown to contribute to the formation of beta-amyloid, a protein that forms plaques characteristic of Alzheimer's disease. Furthermore, C18:2 has been shown to promote inflammation in the brain, potentially contributing to neuronal death and the cognitive decline seen in Alzheimer's disease. The inventors' model has identified low levels of C18:2 and C6:1 in samples of individuals with Alzheimer's disease, which is a surprising observation.

[0234] Homocysteine ​​is an amino acid that is thought to be involved in the development of Alzheimer's disease. Elevated levels of homocysteine ​​are observed in individuals with Alzheimer's disease, and high homocysteine ​​levels are associated with an increased risk of developing the disease. Homocysteine ​​has been shown to disrupt normal brain function by inhibiting the activity of enzymes involved in neurotransmitter metabolism, leading to decreased neurotransmitter levels and cognitive decline. The inventors' model identified low levels of homocysteine ​​in samples of Alzheimer's disease patients, which is a surprising observation. Example 2: Metabolomics Study 2

[0235] A second metabolomics study was conducted to identify the metabolites most strongly associated with Alzheimer's disease. This study included the analysis of 148 metabolites. A total of 64 samples were analyzed, 32 from Alzheimer's patients and 32 from a control group. No cases were excluded, and logarithmic transformation and Pareto scaling were used for normalization.

[0236] As shown in Figures 12-15, there was a clear separation between the Alzheimer's disease samples and the control samples for the measured metabolites.

[0237] Figure 16 shows the top 15 metabolites ranked from Study 2 based on the mean importance (VIP score) of the SVM model. In both Study 1 and Study 2, trigonelline, C18:2-lysophosphatidylcholine, hydroxyphenylacetic acid (HPA), and 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA) were found to have high mean importance in the VIP plot (see Figures 11 and 16). In this study, trigonelline was lower and HPA was higher compared to the non-AD control cohort. Lysophosphatidylcholine a C18:0, lysophosphatidylcholine a C18:1, lysophosphatidylcholine a C16:1, lysophosphatidylcholine a C16:0, lysophosphatidylcholine a C20:4, cotinine, lysophosphatidylcholine a C14:0, lysophosphatidylcholine a C17:0, glucose, malonylcarnitine (C3-DC) + 3-hydroxybutyrylcarnitine (C4-OH) (C3-DC(C4-OH)), and serotonin were additional proteins included in the top 15 metabolites identified by the VIP score of the inventors' model.

[0238] The metabolites identified as having high VIP scores in Study 2 are summarized in Table 3 below. [Table 3] Example 3: Proteomics Study 1 average comparison

[0239] Of the 142 proteins included in the analysis, 87 biomarkers showed significant differences between the case and control groups using a two-sample t-test for mean comparison. False detection rates were accounted for and adjusted for.

[0240] Table 4 summarizes the 10 proteins with the lowest p-values. [Table 4]

[0241] Leucine-rich α-2-glycoprotein 1 (LRG1) is produced in various tissues throughout the body, including the brain, liver, lung, spleen, and blood vessels. Leucine-rich α-2-glycoprotein 1 (LRG1) is a protein associated with various diseases. The inventors observed that there is a significantly high level of LRG1 in the blood of AD samples. This is probably produced in response to the AD disease mechanism.

[0242] Serotransferrin, also called transferrin, is a protein involved in the transport and regulation of iron in the body. Principal component analysis (PCA)

[0243] Figure 19 shows pairwise score plots of the top three PCs. PC1 explains 84.8% of the variation in the data. PC2 and PC3 account for 5% and 1.6% respectively.

[0244] Similar to metabolomics, there appears to be a separation between the biomarkers of the AD case group and the control group, suggesting that a new biosignature for this disease can be identified (Figure 20). Partial least squares discriminant analysis (PLS-DA)

[0245] Figure 21 shows pairwise score plots of the top three components. Component 1 explains 82.8% of the variance in the data, Component 2 explains 7%, and Component 3 explains 1.4%.

[0246] Similar to PCA analysis, proteins can be separated using PLS-DA (Figure 22). Generally, this indicates that the PLS-DA model has an excellent ability to classify samples into two groups based on the predicted proteins used.

[0247] The performance of each component was evaluated using accuracy, R2, and Q2. The results are in Figure 23. Recursive SVM classification is shown in Figure 24. Linear SVM

[0248] Linear SVM was used as a method for classifying proteomics data. The average prediction accuracy of this model using proteomics data is 88.4%.

[0249] Figure 25 shows the Receiver Operating Characteristic (ROC) curve of the SVM model along with the 95% confidence interval. The Area Under the Curve (AUC) is 94.0%.

[0250] When distinguishing between Alzheimer's disease (positive class) and healthy individuals (negative class), the model can correctly classify new data points into the correct class with an average probability of 88.4%. Furthermore, an AUC of 94.0% indicates that the model can correctly classify individuals with Alzheimer's disease as positive and healthy individuals as negative in 94.0% of cases. This means that in most cases the model can correctly separate the two classes and is thus considered to have good performance.

[0251] Figure 26 shows the predicted classification of the dataset using linear SVM modeling. In this iteration, 2 out of 63 samples were false positives and 2 were false negatives.

[0252] Figure 27 shows the top 15 proteins ranked based on average importance in the SVM model.

[0253] The proteins identified as having high predictive power by the SVM model are summarized in Table 5 below.

Table 5

[0254] Coagulation factor XIII (FXIII) is a protein involved in the process of blood coagulation.

[0255] Serum amyloid A1 protein (SAA1) is mainly produced in the liver and is a protein involved in the body's response to inflammation.

[0256] Alpha-1 antichymotrypsin (A1AT) is a protein involved in regulating inflammation and removing proteases, which are enzymes that break down proteins.

[0257] The role of adipocyte membrane-associated proteins (APMAPs) in Alzheimer's disease is not well understood and has not been extensively studied. Currently, there is limited evidence suggesting that APMAPs may play any role in the development of Alzheimer's disease. Example 4: Proteomics Research 2

[0258] The second proteome study was conducted on the same patient sample as in metabolomics study 2 (Example 2). In the second study, 140 proteins were included in the analysis. A total of 64 samples were analyzed, 32 from Alzheimer's patients and 32 from the control group. Two cases were excluded, and logarithmic transformation and Pareto scaling were used for normalization.

[0259] As shown in Figures 28-31, there was a clear separation between the Alzheimer's disease samples and the control samples.

[0260] Figure 32 shows the top 15 proteins ranked based on their average importance in the SVM model. The proteins are summarized in Table 6 below.

[0261] In both Study 1 and Study 2, serum amyloid A-1 and α-1-antichymotrypsin were found to have high mean importance in the VIP plot (see Figures 27 and 32). Serum amyloid A-1 protein and α-1-antichymotrypsin levels were higher compared to the non-AD control cohort. C-reactive protein (CRP), tenascin C, transthyretin, calistatin, apolipoprotein A-IV, Xaa-Pro dipeptide, lipopolysaccharide-binding protein, insulin-like growth factor, leucine-rich α-2-glycoprotein, apolipoprotein, von Willebrand, IgGFc-binding, and serotransferrin were also among the top 15 proteins. [Table 6]

[0262] Figure 33 shows the ROC curve of the SVM model with a 95% confidence interval. The ROC curve demonstrates the high diagnostic capability of the inventors' model.

[0263] All documents referenced herein, including any cross-references or related patents or applications and any patent applications or patents to which this application claims priority or interest, are incorporated herein by reference in their entirety unless expressly excluded or limited. No document reference constitutes prior art with respect to any disclosure disclosed or claimed herein, nor does it teach, suggest or disclose any such disclosure, either alone or in any combination with any other reference(s). Furthermore, in the event of any conflict between the meaning or definition of a term in this document and the meaning or definition of the same term in any document incorporated by reference, the meaning or definition assigned to that term in this document shall prevail.

[0264] The headings used throughout this specification should not be construed as limiting the invention.

[0265] While specific embodiments of this disclosure are illustrated and described, it will be apparent to those skilled in the art that various other changes and modifications are possible without departing from the scope of this disclosure. Accordingly, all such changes and modifications within the scope of this disclosure are intended to be covered by the appended claims.

Claims

1. A method for diagnosing and treating Alzheimer's disease in a subject, wherein the method is (a) To provide biological samples obtained from the subject; (b) From the obtained samples, hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC) selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; lysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII Measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (c) Comparing the concentration levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample with the concentration levels of the corresponding reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample; (d) If the concentration levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ from the concentration levels of the reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the Alzheimer's-negative sample, the subject is identified as having Alzheimer's disease; (e) A method comprising optionally treating or having the identified subject treated with an Alzheimer's treatment plan.

2. The method according to claim 1, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC); C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the Alzheimer's-related protein is selected from coagulation factor XIII A chain; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; C-reactive protein; and / or tenascin C.

3. The method according to claim 2, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid; or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain; complement factor D; or C-reactive protein.

4. A method for diagnosing and selectively treating Alzheimer's disease in a subject, wherein the method is (a) To provide biological samples obtained from the subject; (b) Hydroxyphenylacetic acid (HPA); lysophosphatidylcholine (lysoPC) selected from trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII Measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor from the obtained sample or by measuring with a spectroscopic unit; (c) Comparing or having compared the concentration levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins measured by the spectroscopic unit with the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample; (d) If the concentration levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ from the concentration levels of the reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the Alzheimer's-negative sample, the subject is identified as having Alzheimer's disease; (e) A method comprising optionally treating or having the identified subject treated with an Alzheimer's treatment plan.

5. The method according to claim 4, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC); C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the protein is selected from coagulation factor XIII A chain; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.

6. The method according to claim 5, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) or trigonelline, and the Alzheimer's-related protein is selected from coagulation factor XIIIA chain or complement factor D.

7. The method according to claim 4, 5, or 6, wherein the optional Alzheimer's treatment plan comprises adjusting the blood concentration of one or more of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins in the subject who has been diagnosed with Alzheimer's disease, has been diagnosed with a predisposition to develop Alzheimer's disease, or a combination thereof.

8. The method according to claim 7, wherein the adjustment of the blood concentration of one or more of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins in the subject is carried out until an improvement in the symptoms of Alzheimer's disease is observed in the subject.

9. The method according to claim 8, wherein adjusting the blood concentration of one or more of the Alzheimer's-related metabolites includes adjusting the composition of the target intestinal microbiota.

10. The method according to any one of claims 1 to 9, wherein the identifying step (d) is performed when it is determined that the concentration levels of at least one, at least two, at least three, at least four, or at least five of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample differ by about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, or about 70% or more compared to the concentration levels of the reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the Alzheimer's-negative sample.

11. The method according to any one of claims 1 to 10, wherein the identifying step (d) is performed when it is determined that the concentration levels of the homocysteine, C18:2 fatty acid metabolite and / or C6:1 fatty acid metabolite from the obtained sample are lower than the concentration levels of the corresponding reference Alzheimer's-related metabolite and / or Alzheimer's-related protein from the Alzheimer's-negative sample.

12. The method according to claim 1 or 2, wherein the identifying step (d) is performed when it is determined that the concentration level of coagulation factor XIIIA chain and / or hydroxyphenylacetic acid (HPA) from the obtained sample is increased compared to the concentration level of the corresponding reference Alzheimer's-related metabolite and / or Alzheimer's-related protein from the Alzheimer's-negative sample.

13. The method according to any one of claims 1 to 12, wherein the obtained sample is blood or urine, preferably serum, plasma, or urine.

14. The method according to any one of claims 1 to 13, wherein the Alzheimer's-related metabolites and / or Alzheimer's-related proteins are measured by a spectroscopic technique, the spectroscopic technique being selected from the group consisting of liquid chromatography, gas chromatography, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, high-performance liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, nuclear magnetic resonance spectroscopy (NMR), Raman spectroscopy, and infrared spectroscopy.

15. The method according to any one of claims 1 to 14, wherein the comparison of the concentration levels of the Alzheimer's-related metabolite and / or Alzheimer's-related protein from the obtained sample with the concentration levels of the reference Alzheimer's-related metabolite and / or Alzheimer's-related protein from the Alzheimer's-negative sample is performed using multivariate statistical analysis.

16. The method according to claim 15, wherein the multivariate statistical analysis is selected from principal component analysis (PCA) or partial least squares latent structure discriminant analysis (PLS-DA).

17. A method for monitoring the progression of Alzheimer's disease in a subject and selectively treating the Alzheimer's disease, wherein the method is: (a) to provide a first biological sample obtained from the subject at a first time point; (b) From the first sample obtained, hydroxyphenylacetic acid (HPA); lysophosphatidylcholine (lysoPC) selected from trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; lysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII Evaluate a first Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile by measuring the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (c) Comparing the first Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile with a reference Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile from an Alzheimer's-negative sample; (d) determining that there is a first difference between the first Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile and the reference Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile from the Alzheimer's-negative sample, and that the first difference indicates Alzheimer's; (e) to provide a second biological sample obtained from the subject at a second time point after the first time point; (f) Evaluating the second Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile by measuring the concentration levels of the Alzheimer's-related metabolite and / or Alzheimer's-related protein from the obtained second sample; (g) Comparing the second Alzheimer's-related metabolite and / or Alzheimer's-related proteome profile with the reference Alzheimer's-related metabolite and / or proteome profile from the Alzheimer's-negative sample; (h) There is a second difference between the first Alzheimer's-related metabolite and / or proteome profile and the reference Alzheimer's-related metabolite and / or proteome profile from the Alzheimer's-negative sample, and it is determined that the second difference indicates Alzheimer's; (i) determining the progression of Alzheimer's disease based on at least some of the differences between the first and second; (j) A method comprising, optionally, treating the identified subject with an Alzheimer's treatment plan.

18. A kit for use in the method according to any one of claims 1 to 17, comprising a reagent for measuring the concentration level of the Alzheimer's-related metabolite and / or the Alzheimer's-related protein, and optionally an instruction manual.

19. A kit for diagnosing Alzheimer's disease, (a) From the obtained biological sample, select hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC) selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolite; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or A detector configured to detect the concentration levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor, (b) A composition comprising the corresponding Alzheimer's-related metabolite and / or Alzheimer's-related protein at a control level corresponding to a control group of Alzheimer's-negative subjects; (c) A multivariate analysis system configured to analyze the difference between the concentration levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins and the control levels; (d) optionally comprising instructions for a method for diagnosing Alzheimer's disease; the method comprising: measuring the level of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained biological sample using the detector; and comparing the obtained level of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins to a control level of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins obtained from an Alzheimer's-negative subject;

20. The kit according to claim 19, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the Alzheimer's-related protein is selected from at least coagulation factor XIII A chain; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.

21. The kit according to claim 20, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) and / or trigonelline, and the Alzheimer's-related protein is selected from coagulation factor XIII A chain, C-reactive protein, tenascin C, serum amyloid A-1, or complement factor D.

22. The kit according to claim 19, 20, or 21, wherein the detector comprises a multi-metabolite detector and / or a multi-proteome detector configured to measure the levels of the Alzheimer's-related metabolites and / or the Alzheimer's-related proteins.

23. A computer implementation method for processing a target biological sample, diagnosing Alzheimer's disease, and selectively treating Alzheimer's disease, wherein the computer implementation method is: (a) to receive biological samples obtained from the subject; (b) Processing the sample with a spectroscopic unit connected directly or wirelessly to a processing device, wherein the processing device has a memory for storing measurement data from the spectroscopic unit; (c) The spectroscopic unit selects lysophosphatidylcholine (lysoPC) from hydroxyphenylacetic acid (HPA); trigonelline, C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0; C26:0 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; LysoPC C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites selected from serotonin; and / or C-reactive protein; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII (d) measuring the levels of at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor, and storing the measurement data in the processor; or (d) comparing the stored measurement data with the values ​​in the memory representing Alzheimer's-negative samples using multivariate statistical analysis; (e) storing in the processing device results corresponding to at least one, at least two, at least three, at least four, or at least five Alzheimer's-related metabolites and / or Alzheimer's-related proteins from the obtained sample, wherein if the measurement data representing the levels of the Alzheimer's-related metabolites and / or Alzheimer's-related proteins differs from the concentration levels of reference Alzheimer's-related metabolites and / or Alzheimer's-related proteins from an Alzheimer's-negative sample, the results identify the subject as having Alzheimer's disease; (f) For the subject identified as having Alzheimer's disease or being predisposed to developing Alzheimer's disease, displaying an Alzheimer's treatment plan on an electronic display connected directly or wirelessly to the processor, wherein the displayed treatment plan includes electronic text on a graphical user interface; (iv) A computer implementation method comprising: (iv) selectively adjusting the blood concentration of one or more Alzheimer's-related metabolites and / or Alzheimer's-related proteins in a subject diagnosed with Alzheimer's disease or predisposed to developing Alzheimer's disease until an improvement in the subject's cognitive ability is observed.

24. The method according to claim 23, wherein the Alzheimer's-related metabolite is selected from hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the protein is selected from at least coagulation factor XIII A chain; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C and / or haptoglobin.

25. The method according to claim 24, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIIIA chain or complement factor D.

26. A method for diagnosing and selectively treating Alzheimer's disease, (a) Obtaining metabolite and protein signatures from a biological sample of interest, wherein the signatures include: hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC) selected from C18:2, C18:0, C18:1, C16:0, C14:0, C16:1, C20:4, C17:0, or C26:0 fatty acid metabolites; C6:1 fatty acid metabolites; homocysteine; putrescine; C14:1-OH; histidine; and LysoPC. C26:0; alanine; pyruvate; 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA); trimethylamine N-oxide (TMAO); benzoic acid; citrulline; cotinine; glucose; C3-DC)+3-hydroxybutyrylcarnitine(C4-OH)(C3-DC(C4-OH)); and / or Alzheimer's-related metabolites selected from serotonin; and / or C-reactive proteins; α-1-antichymotrypsin; serotransferrin; leucine-rich α-2-glycoprotein 1; serum albumin; coagulation factor XIII This is obtained by measuring at least one, at least two, at least three, at least four, or at least five Alzheimer's-related proteins selected from A chain; biotinidase; gelzolin; apolipoprotein A-IV; α-2-HS-glycoprotein; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; adipocyte membrane; intercellular adhesion protein; hemopexin; cadherin-5; complement C1s subcomponent; L-selectin; fibronectin; tenascin-C; transthyretin; calistatin; Xaa-Pro dipeptide; lipopolysaccharide-binding protein; insulin-like growth factor; and / or von Willebrand factor; (b) Classifying a patient into an Alzheimer's disease risk class or a healthy class using a statistical analysis having at least 85% accuracy, which includes comparing the obtained signature with a control signature, wherein the classification includes a statistical analysis performed by computer; (c) If the subject is classified into the Alzheimer's disease risk class, identify that the patient has Alzheimer's disease or is at risk of developing it; (d) A method comprising, optionally, treating or causing the subject identified in step (c) with an agent effective in treating, improving or reducing the symptoms of Alzheimer's disease, wherein the treatment optionally involves adjusting the levels of one or more of the metabolites and / or proteins identified in the signature to be present at different levels than the control.

27. The method according to claim 26, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine; C18:2 fatty acid metabolite; C6:1 fatty acid metabolite; and / or at least homocysteine, and the Alzheimer's-related protein is selected from at least coagulation factor XIII A chain; complement factor D; IgG c-binding protein; serum amyloid A-1 protein; ceruloplasmin; C-reactive protein; tenascin C; serum amyloid A-1 and / or haptoglobin.

28. The method according to claim 27, wherein the Alzheimer's-related metabolite is selected from at least hydroxyphenylacetic acid (HPA) and / or trigonelline, and the Alzheimer's-related protein is selected from at least coagulation factor XIII A chain, complement factor D or C-reactive protein.

29. The method according to any one of claims 1 to 26, further comprising adjusting or causing to adjust the levels of the metabolites and / or proteins, and comprising nucleic acid therapy.

30. The method according to claim 29, wherein the nucleic acid therapy comprises reducing the level of a protein expressed or metabolite produced by siRNA or antisense therapy.

31. The method according to claim 29, wherein the nucleic acid therapy comprises increasing the level of a protein expressed or metabolite produced by mRNA therapy.

32. The method according to any one of claims 1 to 31, wherein the measured metabolite and / or proteome profile has at least 90% predictive value and is based on a profile identified by a previously performed computer statistical analysis model including at least one of mean comparison, PCA, PLS-DA, or recursive SVM data analysis.

33. The method of claim 32, wherein the metabolite and / or proteome profile has been previously identified to have the predicted value by classifying diseased and non-diseased samples into two groups by a computer implementation method based on the predicted metabolite and / or protein used in a computer model, and evaluating whether the groups are separated.

34. A method for identifying a subject who is at risk of developing Alzheimer's disease or who has Alzheimer's disease, (a) Obtaining one or more biological samples from the subject; (b) Perform metabolome and proteome analysis of one or more of the biological samples; (c) Measuring at least one, at least two, at least three, or all metabolites selected from trigonelline, lysophosphatidylcholine (e.g., C18:2 fatty acid metabolites and / or C6:1 fatty acid metabolites) and / or hydroxyphenylacetic acid (HPA), and at least one, at least two, at least three, or all proteins or fragments selected from coagulation factor XIIIA chain, complement factor D, C-reactive protein, IgGfc-binding protein, ceruloplasmin, haptoglobin, tenascin C and / or serum amyloid A-1, wherein optionally, the measured metabolite(s) and / or protein(s) have a predictive score of at least 0.5 as determined by the VIP plot; (d) If the levels of the metabolite(s) and / or protein(s) differ from the control criteria, the subject is identified as having Alzheimer's disease or being at risk of developing it; (e) If the subject is identified as having Alzheimer's disease or being at risk of developing it, the method includes optionally providing the results of the metabolome and proteome analysis or a treatment plan based on the analysis via an online platform.

35. The method or kit according to any one of claims 1 to 34, wherein the protein or fragment and / or metabolite is at least one, two or all of homocysteine, C18:2 fatty acid metabolites and C6:1 fatty acid metabolites, and the measured levels of at least one, two or all of the corresponding homocysteine, C18:2 fatty acid metabolites and C6:1 fatty acid metabolites are reduced compared to an Alzheimer's-negative sample.

36. The method according to claim 35, wherein the protein, its fragments and / or metabolites are at least homocysteine ​​and C18:2 fatty acid metabolites.

37. The method according to claim 35, wherein the protein, its fragments and / or metabolites are at least homocysteine ​​and C6:1 fatty acid metabolites.

38. The method according to claim 35, wherein the protein, its fragments and / or metabolites are at least C18:2 fatty acid metabolites and C6:1 fatty acid metabolites.

39. The method according to any one of claims 35 to 38, further comprising measuring a metabolite selected from at least one of hydroxyphenylacetic acid (HPA); trigonelline; lysophosphatidylcholine (lysoPC); C18:2 fatty acid metabolites; C6:1 fatty acid metabolites; and homocysteine.

40. The method according to any one of claims 35 to 39, further comprising measuring a protein selected from at least one of the following: coagulation factor XIII A chain; complement factor D; IgGFc-binding protein; serum amyloid A-1 protein; ceruloplasmin; haptoglobin; C-reactive protein; and tenascin C.

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