JP2019015546A - Method, device, program, system, and terminal device for evaluating cachexia of patient with pancreas cancer - Google Patents

Abstract

To provide a method for evaluation that can provide highly reliable information that can help you know the future state of the cachexia of an evaluation subject with pancreas cancer.SOLUTION: According to an embodiment, the future state of the cachexia of an evaluation subject with pancreas cancer is evaluated, using at least one concentration value of 19 types of amino acids (Tyr, Ser, Asn, for example) and 56 types of metabolites (18-Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, for example) in the blood of the subject.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an evaluation method, an evaluation apparatus, an evaluation program, an evaluation system, and a terminal apparatus for evaluating the future state of cachexia in patients with pancreatic cancer using the concentrations of amino acids and metabolites in blood. .

  Treatment that maintains the patient's physical condition and quality of life (Quality Of Life (QOL)) continuously from the early stage of cancer diagnosis is important (Non-patent Document 1), but anorexia, weight loss, general weakness In addition, a cachexia state that causes fatigue and the like is a serious adverse effect (Non-patent Document 2). Cachexia is a multifactorial syndrome characterized by a continuous loss of skeletal muscle mass that cannot be safely recovered by conventional nutritional support, causing progressive dysfunction and its pathology Is characterized by a balance of negative protein and energy caused by various combinations of reduced food intake and metabolic abnormalities ”(Non-Patent Document 1).

  The incidence of cachexia varies with the type of cancer. Cancers with a high incidence are lung cancer, pancreatic cancer, gastric cancer and esophageal cancer, while breast cancer has a low incidence (Non-patent Document 3). Cachexia is the main cause of death in patients with pancreatic cancer (Non-Patent Document 4), and no effective treatment has been established. The currently anticipated therapeutic strategy is to intervene at the time of early cachexia, that is, at a stage where the degree of metabolic abnormalities is mild. Anticipating the future progression of cachexia and taking appropriate nutritional / motor interventions or drug interventions will delay the progression of cachexia, improve therapeutic effects, and improve physical and mental quality of life. it can. Early prediction and diagnosis of cachexia and cachexia disease state have high clinical significance (Non-patent Document 5).

  European Palliative Care Research Collaborative (EPCRC) has proposed a cachexia diagnostic criterion (Non-Patent Document 6). However, it has been reported that early diagnosis of cachexia is difficult by EPCRC cachexia diagnostic criteria (Non-patent Document 7). As a classification of cachexia using blood biomarkers, prognostic CRP classification and modified glasgow prognostic score have been reported, but there is no report useful for early detection of cachexia (Non-patent Document 8). And Non-Patent Document 9).

  Cachexia is characterized by multifaceted exacerbations. EPCRC proposed the SIPP tool (Non-Patent Document 10) as an index for evaluating multifaceted deterioration of cachexia. The SIPP tool evaluates blood levels of C-reactive protein (CRP), an acute inflammatory protein as an indicator of appetite, body composition, physical activity, and malignant tumor activity. To evaluate the cachexia condition. Previous reports suggest that weight loss can be combined with anorexia, acute inflammatory protein C-reactive protein (CRP), and changes in body composition to enable early diagnosis of cachexia There is also a report (non-patent document 7).

  Before the pancreatic cancer patient falls into cachexia, the profile of blood metabolites including amino acids changes. As pancreatic cancer develops and the disease progresses, the plasma concentrations of valine and alanine decrease (Non-Patent Document 11), and a characteristic blood amino acid profile is exhibited even in a cachexia state (Non-Patent Document 12). In patients with advanced pancreatic cancer before falling into the cachexia state, body fat has already decreased (Non-patent Document 13), but pancreatic cancer cachexia model mice exhibit lipolysis and skeletal muscle catabolism even in situations where fat atrophy is dominant. It was enhanced (Non-Patent Document 14).

  Patent documents 1, 2, and 3 relating to a method for associating an amino acid concentration with a biological state are disclosed as prior patents. Patent Document 4 relating to a method for evaluating the state of pancreatic cancer using amino acid concentration is disclosed.

International Publication No. 2004/052191 International Publication No. 2006/098192 International Publication No. 2009/054351 International Publication No. 2014/084290

Tisdale MJ: Pathogenesis of cancer cachexia. J Support Oncol 1: 159-168,2003 Dewys WD, Begg C, Lavin PT, Band PR, Bennett JM, Bertino JR, Cohen MH, Douglass HO Jr, Engstrom PF, Ezdinli EZ, Horton J, Johnson GJ, Moertel CG, Oken MM, Perlia C, Rosenbaum C, Silverstein MN, Skeel RT, Sponzo RW, Tormey DC.Prognostic effect of weight loss prior to chemotherapy in cancer patients.Easter Cooperative Oncology Group.Am J Med.1980 Oct; 69 (4): 491-7. Laviano, A. & Meguid, M. M .: Nutritional issues in cancer management. Nutrition 12 (5): 358-371, 1996. Mao C, Domenico DR, Kim K, et al. Observations on the developmental patterns and the consequences of pancreatic exocrine adenocarcinoma. Findings of 154 autopsies. Arch Surg 1995; 130: 125-134. Early recognition of malnutrition and cachexia in the cancer patient: a position paper of a European School of Oncology Task Force.M. Aapro, J. Arends, F. Bozzetti, K. Fearon, SM Grunberg, J. Herrstedt, J. Hopkinson, N. Jacquelin-Ravel, A. Jatoi, S. Kaasa, F. Strasser Annals of Oncology 25: 1492-1499, 2014 Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, Jatoi A, Loprinzi C, MacDonald N, Mantovani G, Davis M, Muscaritoli M, Ottery F, Radbruch L, Ravasco P, Walsh D, Wilcock A , Kaasa S, Baracos VE. (2011) Definition and classification of cancer cachexia: an international consensus Lancet Oncol 12: 489-495. Blum D, Stene GB, Solheim TS, Fayers P, Hjermstad MJ, Baracos VE, Fearon K, Strasser F, Kaasa S; Euro-Impact .. Validation of the Consensus-Definition for Cancer Cachexia and evaluation of a classification model--a study based on data from an international multicentre project (EPCRC-CSA). Ann Oncol. 2014 Aug; 25 (8): 1635-42 Mitsunaga S, Ikeda M, Shimizu S, Ohno I, Takahashi H, Okuyama H, Ueno H, Morizane C, Kondo S, Sakamoto Y, Okusaka T, Ochiai A. (2016) C-Reactive Protein Level Is an Indicator of the Aggressiveness of Advanced Pancreatic Cancer. Pancreas 45: 110-116. Bye A, Wesseltoft-Rao N, Iversen PO, Skjegstad G, Holven KB, Ulven S, Hjermstad MJ. Alterations in inflammatory biomarkers and energy intake in cancer cachexia: a prospective study in patients with inoperable pancreatic cancer. Med Oncol. 2016 Jun; 33 (6): 54 Radbruch L, Elsner F, Trottenberg P, Strasser F, Fearon K. Aachen, Department of Palliative Medicinen / European Palliative Care Research Collaborative; 2010 Fukutake N, Ueno M, Hiraoka N, Shimada K, Shiraishi K, Saruki N et al. A novel multivariate index for pancreatic cancer detection based on the plasma free amino acid profile.PLoS One 2015; 10: e0132223. Ubhi Baljit K, Cheng Kian Kai, Dong Jiyang, Janowitz Tobias, Jodrell Duncan, Tal-Singer Ruth, MacNee William, Lomas David A, Riley John H, Griffin Julian L, Connor Susan C Targeted metabolomics identifies perturbations in amino acid metabolism that sub -classify patients with COPD. Mol. BioSyst., 2012, 8, 3125-3133 TatematsuN. Mitsunaga S. Ikeda M. Characterization of advanced pancreatic cancer patients showing a decrease of the skeletal muscle mass while receiving first-line chemotherapy. Ann Oncol (2016) 27 (suppl_7) Imoto A. Mitsunaga S. Neural invasion induces cachexia via astrocytic activation of neural route in pancreatic cancer. Int. J. Cancer: 131, 2795-2807 (2012)

  However, a useful diagnostic method for predicting cachexia in pancreatic cancer patients at an early stage and grasping the extent of cachexia after treatment has not been developed and has not been put to practical use. There was a problem.

  The present invention has been made in view of the above, and an evaluation method and evaluation capable of providing highly reliable information that can be used as a reference in knowing the future state of cachexia in an evaluation subject having pancreatic cancer. An object is to provide a device, an evaluation program, an evaluation system, and a terminal device.

  The present inventors have found that in pancreatic cancer patients, various metabolic abnormalities and changes in nutritional status are expressed from the early stage of cancer, and there is a possibility that dynamic metabolic changes have occurred in metabolites including amino acids. We considered the development of technology for early prediction and diagnosis of cachexia in patients with pancreatic cancer using blood metabolites. Then, the present inventors have identified amino acids and metabolites that can evaluate and predict cachexia status in patients with pancreatic cancer, and have cachexia status using amino acid variables and metabolite variables. The present invention has been completed by finding a correlation formula (index formula) that can evaluate and predict the above.

  In order to solve the above-described problems and achieve the object, the evaluation method according to the present invention comprises 19 kinds of amino acids (Tyr, Ser, Asn, Gln, Pro) shown below in the blood to be evaluated having pancreatic cancer. , Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg) and 56 types of metabolites (18-Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidicoet Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitylyl-glycero-3-phosphoethanolamine, 5-Oxoproline, holesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, Etiocholan-3α-ol-17-one sulfate, Glycerophosphocholine, Hexanoic acid, Indole-3-carboxaldehyde, Isobutyric acid, Butyric acid, Octanoic acid, Oleoyl ethanolamide, Sarcosine, Sphingosine, Uric acid , Α-Tocopherol, Isocholic acid, 2-Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid d, Citric acid, Hypoxanthine, Ithethic acid, N6-Methyllysine, Diethanolamine, 3-Hydroxyline acid, Acyl carinine (18: 1), Hydrocyline (18: 1), Hydroxyline, 18 , Decanoic acid, Arachidonic acid, Pyruvic acid, cis-8, 11, 14-Eicosatrienic acid, Oleic acid, Palmitoylethanolide, Palmiticic acid 1, Euricic-1, Eicosadienic acid, cis-11-Eicosenoic acid, Palmitic acid, 2-Hydroxyvaleric acid, Steadic acid, 1-Methylhistidine, 3-Hydroxyid, 3-Hydrocytic acid The evaluation object includes an evaluation step of evaluating a future state of cachexia.

Here, although various amino acids are mainly represented by abbreviations in the present specification, their formal names are as follows.
(Abbreviation) (official name)
Ala Alanine
Arg Arginine
Asn Asparagine
Cit Circleline
Gln Glutamine
Gly Glycine
His Histide
Ile Isolucine
Leu Leucine
Lys Lysine
Met Methionine
Orn Origine
Phe Phenylalanine
Pro Proline
Ser Serine
Thr Threoneine
Trp Tryptophan
Tyr Tyrosine
Val Valine

  In the evaluation method according to the present invention, in the evaluation method, in the evaluation step, an expression including a variable into which at least one of the 19 types of amino acids and the 56 types of metabolites is substituted. Is used to evaluate the future state of cachexia for the evaluation object by calculating the value of the equation.

  The evaluation apparatus according to the present invention is an evaluation apparatus including a control unit, wherein the control unit is configured to store the 19 types of amino acids and the 56 types of metabolites in blood to be evaluated having pancreatic cancer. An evaluation means for evaluating the future state of cachexia is provided for the evaluation object using at least one of the concentration values.

  The evaluation method according to the present invention is an evaluation method executed in an information processing apparatus including a control unit, and is executed in the control unit, the 19 types in the blood to be evaluated having pancreatic cancer An evaluation step of evaluating a future state of cachexia for the evaluation object using a concentration value of at least one of the amino acids and the 56 types of metabolites.

  An evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus including a control unit, and is executed in the blood of an evaluation target having pancreatic cancer for execution in the control unit. The method includes an evaluation step of evaluating a future state of cachexia for the evaluation object using a concentration value of at least one of 19 types of amino acids and the 56 types of metabolites.

  A recording medium according to the present invention is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.

  The evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device including a control unit and a terminal device including the control unit via a network so as to be able to communicate with each other. The control unit of the device transmits concentration data relating to the concentration value of at least one of the 19 types of amino acids and the 56 types of metabolites in the blood to be evaluated having pancreatic cancer to the evaluation device. A transmission unit; and a result reception unit that receives an evaluation result regarding a future state of cachexia in the evaluation target, transmitted from the evaluation device, and the control unit of the evaluation device receives from the terminal device Concentration data receiving means for receiving the transmitted concentration data; and the 19 types of amino acids and the amino acids contained in the concentration data received by the concentration data receiving means. Using the concentration value of at least one of the 56 types of metabolites, for the evaluation object, evaluation means for evaluating the future state of cachexia, and the evaluation result obtained by the evaluation means And a result transmitting means for transmitting to the terminal device.

  Further, the terminal device according to the present invention is a terminal device including a control unit, and the control unit acquires a result acquisition unit that acquires an evaluation result relating to a future state of cachexia in an evaluation target having pancreatic cancer. The evaluation result is obtained by using a concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites in the blood of the evaluation object for the future of cachexia for the evaluation object. It is a result of evaluating the state.

  Further, the terminal device according to the present invention is configured such that, in the terminal device, the evaluation unit is configured to be communicably connected to an evaluation device that evaluates a future state of cachexia with respect to the evaluation target, and the control unit Further comprises concentration data transmitting means for transmitting concentration data relating to the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites in the blood to be evaluated to the evaluation device, and the result The acquisition means receives the evaluation result transmitted from the evaluation device.

  The evaluation apparatus according to the present invention is an evaluation apparatus including a control unit that is communicably connected to a terminal device via a network, and the control unit transmits pancreas transmitted from the terminal device. Concentration data receiving means for receiving concentration data relating to concentration values of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites in the blood to be evaluated, and the concentration data receiving means received by the concentration data receiving means Evaluation means for evaluating the future state of cachexia for the evaluation object using the concentration value of at least one of the 19 types of amino acids and the 56 types of metabolites included in the concentration data And a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

  According to the present invention, there is an effect that it is possible to provide highly reliable information that can serve as a reference in knowing the future state of cachexia in an evaluation subject having pancreatic cancer.

FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. FIG. 3 is a diagram illustrating an example of the overall configuration of the present system. FIG. 4 is a diagram showing another example of the overall configuration of the present system. FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of this system. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b. FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e. FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d. FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system. FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system. FIG. 14 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 15 is a diagram showing amino acid variables used in the equations with the area under the ROC curve up to the top 100 and the area under the ROC curve for the equation. FIG. 16 is a diagram showing the distribution of the area under the ROC curve for the equations using the amino acid variables shown in FIGS. 14 and 15. FIG. 17 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 18 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 19 is a diagram showing the distribution of the area under the ROC curve for the equations using the amino acid variables shown in FIG. 17 and FIG. FIG. 20 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 21 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 22 is a diagram showing the distribution of the area under the ROC curve for the equations using the amino acid variables shown in FIG. 20 and FIG. FIG. 23 is a diagram showing amino acid variables included in the expressions with the top 100 areas under the ROC curve and the area under the ROC curve for the expression. FIG. 24 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 25 is a diagram showing the distribution of the area under the ROC curve for the equations using the amino acid variables shown in FIG. 23 and FIG. FIG. 26 is a diagram showing metabolites having statistical significance in comparison between groups. FIG. 27 is a diagram showing metabolites having statistically significant discrimination ability in the 2-group discrimination. FIG. 28 is a diagram showing metabolites having statistical significance in comparison between groups. FIG. 29 is a diagram showing metabolites having statistically significant discrimination ability in the 2-group discrimination. FIG. 30 is a diagram showing metabolites having statistical significance in comparison between groups. FIG. 31 is a diagram showing metabolites having statistically significant discrimination ability in the two-group discrimination. FIG. 32 is a diagram showing metabolites having statistical significance in comparison between groups. FIG. 33 is a diagram showing metabolites having statistically significant discrimination ability in the 2-group discrimination. FIG. 34 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 35 is a diagram showing amino acid variables included in the equations with the top 100 areas under the ROC curve and the area under the ROC curve for the equations. FIG. 36 is a diagram showing the distribution of the area under the ROC curve for the equations using the amino acid variables shown in FIG. 34 and FIG. FIG. 37 is a diagram showing metabolites having statistical significance in comparison between groups. FIG. 38 is a diagram showing metabolites having statistically significant discrimination ability in the two-group discrimination.

  Embodiments of an evaluation method according to the present invention (first embodiment) and embodiments of an evaluation apparatus, an evaluation method, an evaluation program, a recording medium, an evaluation system, and a terminal device according to the present invention (second embodiment) ) Will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[First Embodiment]
[1-1. Overview of First Embodiment]
Here, an overview of the first embodiment will be described with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.

  First, at least one of the 19 amino acids and the 56 metabolites in blood (eg, plasma, serum, etc.) collected from an evaluation subject (eg, an individual such as an animal or a human) having pancreatic cancer. Density data relating to one density value is acquired (step S11). Here, the evaluation object having pancreatic cancer is, for example, an evaluation object for which a definitive diagnosis of pancreatic cancer has been made.

In step S11, for example, density data measured by a company or the like that performs density value measurement may be acquired. In addition, concentration data may be obtained by measuring concentration values from blood collected from an evaluation object by, for example, the following measurement method (A), (B), (C), or (D). Here, the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
(A) The collected blood sample is collected in a tube treated with EDTA-2Na, and plasma is separated from the blood by centrifuging the tube. All plasma samples are stored frozen at −80 ° C. until the concentration value is measured. At the time of concentration value measurement, sulfosalicylic acid is added and protein removal treatment is performed by adjusting the concentration to 3%, and then the concentration value is analyzed with an amino acid analyzer based on the principle of high performance liquid chromatography (HPLC) using a ninhydrin reaction in a post column. To do.
(B) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are stored frozen at −80 ° C. until the concentration value is measured. When measuring the concentration value, sulfosalicylic acid is added to remove the protein, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
(C) The collected blood sample is subjected to blood cell separation using a membrane, MEMS technology, or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at −80 ° C. until the concentration value is measured. At the time of measuring the concentration value, the concentration value is analyzed by quantifying a substance that increases or decreases by substrate recognition or a spectroscopic value using a molecule that reacts with or binds to a target blood substance such as an enzyme or an aptamer.
(D) Plasma is separated from blood by centrifuging the collected blood sample. Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at −80 ° C. until the concentration value is measured. At the time of measuring the concentration value, the concentration value is analyzed by quantification using various mass spectrometry techniques (mass spectrometry).

  Next, the future state of cachexia for the evaluation target using the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites contained in the concentration data obtained in step S11. Is evaluated (step S12). Note that before executing step S12, data such as missing values and outliers may be removed from the density data acquired in step S11. Here, evaluating the future state of cachexia is, in other words, predicting the state of cachexia. The future includes, for example, when a certain period of time has elapsed since the time of blood collection.

  As described above, according to the first embodiment, concentration data to be evaluated is acquired in step S11, and in step S12, the 19 types of amino acids and the 56 amino acids included in the concentration data to be evaluated acquired in step S11. The concentration value of at least one of the types of metabolites is used to evaluate the future state of cachexia for the evaluation object (in short, information or evaluation for evaluating the future state of cachexia in the evaluation object) Obtain reliable information that can be helpful in knowing the future state of cachexia in the subject). This provides information to evaluate the future state of cachexia in assessment subjects with pancreatic cancer or reliability that can be used as a reference in knowing the future state of cachexia in assessment subjects with pancreatic cancer. High information can be provided. In addition, the method according to the present embodiment greatly contributes to prevention or improvement of cachexia through drug-based or non-drug-based intervention, improvement of patient QOL, improvement of pancreatic cancer treatment results, and the like.

Further, it may be determined that the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites reflects a future state of cachexia in the evaluation target, and further, the concentration value May be determined by, for example, the following method, and the converted value may reflect the future state of cachexia in the evaluation target. In other words, the density value or the converted value itself may be treated as an evaluation result regarding the future state of cachexia in the evaluation target.
A possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 For example, an arbitrary value is added / subtracted / divided / divided from / to the density value, or the density value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion). , Angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, or power conversion), or by combining these calculations for the density value to convert the density value May be. For example, a value of an exponential function having a concentration value as an index and a Napier number as a base (specifically, a natural logarithm ln (p / (1-p)) when a probability p exceeding a set cachexia score is defined) (P / (1-p) value in the case where is equal to the density value), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, (The value of the probability p) may be further calculated.
Further, the density value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Further, for each amino acid and each metabolite, the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10.
These conversions may be performed by gender or age.

  Moreover, you may evaluate the future state of cachexia about evaluation object using the value after converting a density | concentration value with the conversion method mentioned above, for example.

  In addition, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is obtained from at least the 19 kinds of amino acids and the 56 kinds of metabolites. When one concentration value or the concentration value is converted, it is generated using the converted value, and the generated position information is determined to reflect the future state of cachexia in the evaluation target. Also good. Note that the predetermined ruler is for evaluating the future state of cachexia, for example, a ruler with a scale, such as a range of possible concentration values or converted values, or , A part corresponding to the upper limit value and the lower limit value in “a part of the range”. The predetermined mark corresponds to the density value or the converted value, and is, for example, a circle mark or a star mark.

  The concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites is a predetermined value (mean value ± 1SD, 2SD, 3SD, N quantile, N percentile, or clinical significance is recognized. The future state of cachexia may be evaluated with respect to the evaluation target when the value is lower than or lower than a predetermined value or higher than the predetermined value or higher than the predetermined value. At that time, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid and then making the deviation value so that the average is 50 and the standard deviation is 10) It may be used. For example, when the concentration deviation value is less than the average value −2SD (when the concentration deviation value <30) or when the concentration deviation value is higher than the average value + 2SD (when the concentration deviation value> 70), the cachexia for the evaluation target You may evaluate the future state of

  Further, a variable into which at least one concentration value of the 19 kinds of amino acids and the 56 kinds of metabolites and at least one concentration value of the 19 kinds of amino acids and the 56 kinds of metabolites are substituted. The future state of cachexia may be evaluated for the evaluation object by calculating the value of the expression using an expression including

In addition, the calculated formula value may be determined to reflect the future state of cachexia in the evaluation target, and further, the formula value is converted by, for example, the method described below, and after conversion May be determined to reflect the future state of cachexia in the evaluation subject. In other words, the value of the expression or the converted value itself may be treated as an evaluation result regarding the future state of cachexia in the evaluation target.
A possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10. For example, an arbitrary value is added / subtracted / divided / divided from / to an expression value, or an expression value is converted into a predetermined conversion method (for example, exponential conversion). , Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation or power transformation), or by combining these calculations on the value of the expression, The value of the expression may be converted. For example, the value of the exponential function with the value of the expression as the exponent and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p) when the probability p exceeding the set cachexia score is defined) ) Is equal to the value of the formula, the value of p / (1-p)) may be further calculated, and the value of the calculated exponential function divided by the sum of 1 and the value ( Specifically, the value of probability p) may be further calculated.
Further, the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the value of the equation may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Further, the values of the equations may be converted into deviation values so that the average is 50 and the standard deviation is 10.
These conversions may be performed by gender or age.
Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.

  In addition, if the position information about the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is converted into the value of the expression or the value of the expression It may be generated using the later value, and the generated position information may be determined to reflect the future state of cachexia in the evaluation target. Note that the predetermined ruler is for evaluating the future state of cachexia, for example, a ruler with a scale, and a range of possible values of the formula or the value after conversion, Or, at least a scale corresponding to the upper limit value and the lower limit value in “a part of the range” is shown. The predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.

  Moreover, you may evaluate qualitatively the grade of the risk (possibility) that evaluation object will develop cachexia in the future. Specifically, “at least one concentration value of the 19 amino acids and the 56 metabolites and one or more preset threshold values” or “the 19 amino acids and the 56 At least one concentration value of the types of metabolites, an equation including a variable into which the concentration values of at least one of the 19 types of amino acids and the 56 types of metabolites are substituted, and a preset one Alternatively, the evaluation target may be classified into any one of a plurality of categories defined in consideration of at least the degree of future risk of cachexia. In addition, there are several categories: a category for assigning subjects with a high risk of developing cachexia in the future, a category for assigning subjects with a low risk of developing cachexia in the future, and a category of cachexia A classification for belonging to a subject with a moderate risk of future onset may be included. In addition, the plurality of categories may include a category for belonging to a subject having a high risk of developing cachexia and a category for assigning a subject having a low risk of developing cachexia in the future. . Alternatively, the density value or the expression value may be converted by a predetermined method, and the evaluation target may be classified into one of a plurality of categories using the converted value.

In addition, the form used for the evaluation is not particularly limited, but for example, the following form may be used.
・ Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method ・ Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression ・ In addition to generalized linear model Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in

  In addition, the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 that is an international application by the present applicant or International Publication No. 2006/098192 that is an international application by the present applicant. You may create by the method. In addition, if it is a formula obtained by these methods, the formula is suitably used for evaluating the future state of cachexia regardless of the unit of the amino acid concentration value in the concentration data as input data. be able to.

  Here, in the multiple regression equation, multiple logistic regression equation, and canonical discriminant function, a coefficient and a constant term are added to each variable. The coefficient and the constant term are preferably real numbers, and more preferably. May be any value belonging to the range of 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably obtained for performing the various classifications from the data. Any value may be used as long as it falls within the range of 95% confidence interval of the obtained coefficient and constant term. Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto. When logistic regression, linear discriminant, multiple regression, etc. are used for evaluation, linear transformation (addition of constants, constant multiplication) and monotonic increase (decrease) transformation (eg logit transformation) change evaluation performance. However, since it is equivalent to before conversion, the one after these conversions may be used.

  Further, the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. The fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers. In some fractional expressions and those in which the numerator and denominator variables are interchanged, the sign of the correlation with the target variable is generally reversed, but their correlation is maintained. Since the evaluation performance can be regarded as equivalent, the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.

And when evaluating the future state of cachexia, in addition to the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites, values related to other biological information (for example, listed below) Value etc.) may be further used. In addition, in the expression used for the evaluation, in addition to the variable to which at least one concentration value of the 19 kinds of amino acids and the 56 kinds of metabolites is substituted, values related to other biological information (for example, One or more variables to which a given value) is substituted may also be included.
1. 1. Concentration values of blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. Albumin, total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST), GPT (ALT), GGTP (γ-GTP), glucose (blood glucose level), CRP (C-reactive protein), red blood cell, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell, platelet count, etc. Value 3. 3. Value obtained from image information such as ultrasonic echo, X-ray, CT, MRI, endoscopic image, etc. Age, height, weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, meal information, drinking information, exercise information, stress information, sleep information, family history information, disease history information (diabetes, etc.) ), Values related to biological indicators such as cancer stage, antitumor effect, etc. Protein expression level

[Second Embodiment]
[2-1. Outline of Second Embodiment]
Here, an overview of the second embodiment will be described with reference to FIG. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. In the description of the second embodiment, the description overlapping the first embodiment described above may be omitted. In particular, the case where the value of the formula or the value after the conversion is used as an example when evaluating the future state of cachexia is described here. For example, the 19 kinds of amino acids and the 56 kinds Alternatively, a concentration value of at least one of the metabolites or a converted value (for example, a concentration deviation value) may be used.

  The control unit acquires concentration data acquired in advance regarding the concentration value of at least one of the 19 types of amino acids and the 56 types of metabolites in the blood of an evaluation target (eg, an individual such as an animal or a human) having pancreatic cancer. At least one concentration value of the 19 amino acids and the 56 metabolites, and at least one concentration value of the 19 amino acids and the 56 metabolites, The future state of cachexia is evaluated for the evaluation object by calculating the value of the expression using the expression stored in advance in the storage unit including the variable to be substituted (step S21). Thereby, it is possible to provide highly reliable information that can serve as a reference in knowing the future state of cachexia in an evaluation subject having pancreatic cancer.

  Note that the formula used in step S21 may be created based on formula creation processing (steps 1 to 4) described below. Here, an overview of the formula creation process will be described. Note that the processing described here is merely an example, and the method of creating an expression is not limited to this.

  First, the control unit can select a candidate formula (based on a predetermined formula creation method from index state information stored in advance in the storage unit (data having missing values, outliers, etc. may be removed in advance)). For example, y = a1x1 + a2x2 +... + Anxn, y: index data, xi: density data, ai: constant, i = 1, 2,..., N) are created (step 1). The index state information includes concentration data and index data related to an index representing cachexia state obtained after a certain period of time has passed since the blood on which the concentration data was collected (for example, described in the examples). Cachexia score etc.).

  In step 1, a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination are made from index state information. A plurality of candidate expressions may be created using a combination of multivariate analysis such as trees). Specifically, a number of different algorithms are used for index state information, which is multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of pancreatic cancer groups. Multiple groups of candidate formulas may be created concurrently. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas. Alternatively, the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum expression for evaluation.

  Here, the candidate formula created using principal component analysis is a linear formula including each variable that maximizes the variance of all density data. Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there. The candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups. Moreover, the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data. The candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model. The candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability. In the k-means method, k neighborhoods of each density data are searched, and the largest number of groups to which neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as. Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.

  Returning to the description of the formula creation process, the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2). Candidate expressions are verified for each candidate expression created in step 1. In Step 2, the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate formula of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve). Thereby, a candidate formula having high predictability or robustness in consideration of the index state information and the evaluation conditions can be created.

  Here, the discrimination rate is an evaluation object according to the present embodiment, and an evaluation object whose true state is negative (for example, an evaluation object that does not exceed a certain cachexia threshold after a certain period of time from the time of blood collection) ) Is evaluated as negative correctly, and the evaluation target that evaluates the true state as positive (for example, the evaluation target that exceeds a certain cachexia threshold after a certain period of time from the time of blood collection) is positive It is. Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment. Further, the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment. The Akaike Information Criterion is a standard that indicates how much the observation data matches the statistical model in the case of regression analysis, etc., and is “−2 × (maximum log likelihood of statistical model) + 2 × (statistics). The model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best. ROC_AUC is defined as the area under the curve of the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1-specificity, sensitivity) on two-dimensional coordinates, and ROC_AUC The value of 1 is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability. The predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas. Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.

  Returning to the description of the formula creation process, the control unit selects a combination of density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method. (Step 3). In step 3, the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately. Then, Step 1 is executed again using the index state information including the density data selected in Step 3. In step 3, the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2. The best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.

  Returning to the description of the formula creation process, the control unit repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4). The selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.

  As described above, in the formula creation process, processing related to creation of candidate formulas, verification of candidate formulas and selection of variables of candidate formulas is organized (systemized) in a series of flows based on index state information. By doing so, it is possible to create a formula that is optimal for evaluating the future state of cachexia. In other words, the formula generation process uses the concentration of at least one of the 19 amino acids and the 56 metabolites for multivariate statistical analysis and variable selection to select an optimal and robust set of variables. Combine formulas and cross-validation to extract formulas with high evaluation performance.

[2-2. Configuration of Second Embodiment]
Here, the configuration of an evaluation system according to the second embodiment (hereinafter may be referred to as the present system) will be described with reference to FIGS. 3 to 14. This system is merely an example, and the present invention is not limited to this. In particular, the case where the value of the formula or the value after the conversion is used as an example when evaluating the future state of cachexia is described here. For example, the 19 kinds of amino acids and the 56 kinds Alternatively, a concentration value of at least one of the metabolites or a converted value (for example, a concentration deviation value) may be used.

  First, the overall configuration of this system will be described with reference to FIG. 3 and FIG. FIG. 3 is a diagram showing an example of the overall configuration of the present system. FIG. 4 is a diagram showing another example of the overall configuration of the present system. As shown in FIG. 3, the present system includes an evaluation apparatus 100 that evaluates the future state of cachexia for an individual to be evaluated, and the 19 amino acids and 56 metabolites in blood. A client apparatus 200 (corresponding to a terminal apparatus of the present invention) that provides individual density data relating to at least one density value is configured to be communicably connected via a network 300. In addition to the evaluation apparatus 100 and the client apparatus 200, this system stores index state information used when creating an expression in the evaluation apparatus 100, an expression used for evaluation, and the like as shown in FIG. The database device 400 may be configured to be communicably connected via the network 300.

  Next, the configuration of the evaluation apparatus 100 of the present system will be described with reference to FIGS. FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

  The evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path. Here, the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus). For example, a configuration (hardware and monitor) that calculates (measures) a concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites in blood and outputs the calculated value (printing, monitor display, etc.) In a small analyzer equipped with software, an evaluation unit 102d described later may be further provided, and a result obtained by the evaluation unit 102d may be output using the above configuration.

  The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.

  The input / output interface unit 108 is connected to the input device 112 and the output device 114. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114). As the input device 112, a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.

  The storage unit 106 is a storage unit, and for example, a memory device such as a RAM (Random Access Memory) and a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression related information database 106d, and an evaluation result file 106e.

  The concentration data file 106a stores concentration data relating to the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites in the blood. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. As shown in FIG. 6, the information stored in the density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data. Here, in FIG. 6, the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. Moreover, you may combine the value regarding the biometric information mentioned above with density | concentration data.

  Returning to FIG. 5, the index state information file 106b stores the index state information used when creating the formula. FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b. As shown in FIG. 7, the information stored in the index state information file 106b includes an individual number, index data relating to an index indicating cachexia status (for example, cachexia score described in the embodiment), concentration, and the like. Data is associated with each other. Here, in FIG. 7, the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.

  Returning to FIG. 5, the designated index state information file 106c stores the index state information designated by the designation unit 102b described later. FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.

  Returning to FIG. 5, the formula-related information database 106d is configured by a formula file 106d1 that stores formulas created by a formula creation unit 102c described later. The expression file 106d1 stores expressions used for evaluation. FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes the rank, the expression (in FIG. 9, Fp (Phe,...), Fp (Gly, Leu, Phe), Fk (Gly, Leu, Phe,...)), A threshold value corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.

  Returning to FIG. 5, the evaluation result file 106e stores the evaluation result obtained by the evaluation unit 102d described later. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106d. Information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the future state of cachexia ( For example, the value of the formula calculated by the calculation unit 102d1 described later, the value obtained by converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification unit 102d4 described later. Classification results, etc.) are associated with each other.

  Returning to FIG. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 removes data with missing values, removes data with many outliers, and has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.

  The receiving unit 102 a receives information (specifically, density data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The designating unit 102b designates index data and density data that are targets for creating an expression.

  The formula creating unit 102c creates a formula based on the index status information received by the receiving unit 102a and the index status information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. The formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) that stores the formula in advance.

  The evaluation unit 102d is configured to obtain formulas obtained in advance (for example, formulas created by the formula creation unit 102c or formulas received by the reception unit 102a) and concentration data of individuals having pancreatic cancer received by the reception unit 102a. The future state of cachexia is evaluated for an individual by calculating the value of the equation using the concentration value of at least one of the 19 kinds of amino acids and the 56 kinds of metabolites included in the above. In addition, the evaluation unit 102d uses the concentration value of at least one of the 19 types of amino acids and the 56 types of metabolites or a converted value of the concentration value (for example, concentration deviation value) to determine a cachex for the individual. The future state of quality may be assessed.

  Here, the configuration of the evaluation unit 102d will be described with reference to FIG. FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion related to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

  The calculation unit 102d1 substitutes at least one concentration value of the 19 types of amino acids and the 56 types of metabolites and at least one concentration value of the 19 types of amino acids and the 56 types of metabolites. The value of the expression is calculated using an expression including at least the variable to be processed. Note that the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

  The conversion unit 102d2 converts the value of the expression calculated by the calculation unit 102d1 using, for example, the conversion method described above. Note that the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e. Further, the conversion unit 102d2 may convert at least one concentration value of the 19 kinds of amino acids and the 56 kinds of metabolites included in the concentration data by, for example, the conversion method described above.

  The generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visible on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion in (which may be a density value or a value after conversion of the density value). The evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

  The classification unit 102d4 uses the value of the equation calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value), Classify into one of a plurality of categories defined with at least the degree of future risk of onset.

  The result output unit 102e outputs the processing results (including the evaluation results obtained by the evaluation unit 102d) in each processing unit of the control unit 102 to the output device 114.

  The transmission unit 102 f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400.

  Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

  The client device 200 includes a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are connected via an arbitrary communication path. Are connected to communicate. The client device 200 is an information processing device (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System)) connected to peripheral devices such as a printer, a monitor, and an image scanner as necessary. It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as a PDA (Personal Digital Assistant), or the like.

  The input device 250 is a keyboard, a mouse, a microphone, or the like. A monitor 261, which will be described later, also realizes a pointing device function in cooperation with the mouse. The output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like. The input / output IF 270 is connected to the input device 250 and the output device 260.

  The communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other. In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line. Thereby, the client apparatus 200 can access the evaluation apparatus 100 according to a predetermined communication protocol.

  The control unit 210 includes a reception unit 211 and a transmission unit 212. The receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280. The transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.

  The control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit. The ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU in cooperation with the OS and performing various processes. The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. Further, the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary. . In addition, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.

  Here, the control unit 210 includes an evaluation unit 210a (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided. And when the evaluation part 210a is provided in the control part 210, the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2) ( A density value), or position information corresponding to an expression value or a value after conversion (a density value or a value after conversion of the density value) may be generated by the generation unit 210a3, or a classification unit 210a4 The individual may be classified into any one of a plurality of categories using the value of the formula or the value after conversion (which may be the density value or the value after conversion of the density value).

  Next, the network 300 of this system will be described with reference to FIGS. The network 300 has a function of connecting the evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other. For example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like. It is. The network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a dedicated line network (including both analog and digital), CATV ( Community Antenna TeleVision (PDM), mobile circuit switching network or mobile packet switching network (International Mobile Telecommunication (IMT) 2000 system, Global System for Mobile Communications (PDC) PPD (PDC)) System), a wireless paging network, a local wireless network such as Bluetooth (registered trademark), a PH An S network, a satellite communication network (including CS (Communication Satellite), BS (Broadcasting Satellite), ISDB (Integrated Services Digital Broadcasting), etc.) may be used.

  Next, the configuration of the database apparatus 400 of this system will be described with reference to FIG. FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.

  The database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like. As shown in FIG. 13, the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414. The output interface unit 408 is configured to be communicable via an arbitrary communication path.

  The storage unit 406 is a storage unit, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 406 stores various programs used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 414. In addition to the keyboard, mouse, and microphone, the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.

  The control unit 402 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, required data, and the like, and executes various types of information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b. The transmission unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100. The receiving unit 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100.

In this description, the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result. In the case where the client device 200 includes the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of the expression. For example, conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 200.
For example, when the client device 200 receives the value of the expression from the evaluation device 100, the evaluation unit 210a converts the value of the expression in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3. Or the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion.
Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value.
When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.

[2-3. Other Embodiments]
The evaluation device, the evaluation method, the evaluation program, the evaluation system, and the terminal device according to the present invention are not limited to the second embodiment described above, but various different embodiments within the scope of the technical idea described in the claims. May be implemented.

  In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method.

  In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including registration data for each processing, parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings Can be changed arbitrarily.

  Further, regarding the evaluation apparatus 100, each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.

  For example, all or some of the processing functions provided in the evaluation apparatus 100, particularly the processing functions performed by the control unit 102, may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic. The program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, in the storage unit 106 such as a ROM or an HDD (Hard Disk Drive), a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded. This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.

  The computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and may be downloaded in whole or in part as necessary.

  In addition, the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, or may be configured as a program product. Here, the “recording medium” includes a memory card, a USB (Universal Serial Bus) memory, an SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), and an EEPROM (Electric). Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disc), and DVD (Digital Versatile Disc) Any “portable physical media It is intended to include.

  The “program” is a data processing method described in an arbitrary language or description method, and may be in any form such as source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.

  Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.

  Further, the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus to which an arbitrary peripheral device is connected. The evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.

  Furthermore, the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.

In the present example, the number of burdens of disease deterioration of each SIPP tool constituent factor for evaluating multifaceted cachexia disease state is expressed as a score, and used as an index of the cachexia disease state complex degree. Specifically, the cachexia score defined by the corresponding number of the following four items (1) to (4) (possible scores are five types of 0, 1, 2, 3, 4), Used as an indicator of cachexia.
(1) Weight loss for one month is 2% or more.
(2) Anorexia score in MDASI-J (Japan version of the MD Anderson Symptom Inventory) is 4 or more.
(3) Karnofsy Performance Scale is 90 or more.
(4) The value of CRP is 0.5 mg / dL or more.

  Blood samples were obtained from 150 pancreatic cancer patients prior to the start of the first treatment. From the obtained blood sample, Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn by the amino acid analysis method (A) described above. , Lys, Arg 19 blood amino acids were measured. And before starting the first treatment and about one month after the start of the treatment (hereinafter sometimes referred to simply as “one month later”), blood is collected again from the 150 pancreatic cancer patients, Each patient's cachexia score was calculated. In addition, the value of CRP used in the calculation of cachexia score is measured based on blood collected again. There were 42 patients with pancreatic cancer who had a cachexia score of 0 after 1 month, and 108 patients with a cachexia score of 1, 2, 3, 4 after 1 month .

Multiple logistic regression was performed on all combinations from one amino acid and two amino acids to six amino acids extracted from the 19 kinds of amino acids, and the discriminant represented by the following Equation 1 was obtained. Created. Then, the discrimination performance regarding the two-group discrimination between the group in which the cachexia score after 1 month became 0 and the group in which any one of 1, 2, 3, and 4 became 1 for the pancreatic cancer patients was expressed as an ROC curve. Evaluation was based on the lower area.
logitp (x) = β ₀ + β ₁ × concentration of amino acid A + β ₂ × concentration of amino acid B +.
(Formula 1)
* In Equation 1, p (x) represents the probability of exceeding the set cachexia score after one month.

  FIG. 14 and FIG. 15 are diagrams showing amino acids used in the discriminant in which significant discriminability was obtained when the significance level was 5% on both sides. In addition, from the discriminant using the combination of two types of amino acids to the discriminant using the combination of six types of amino acids, the formulas with the area under the ROC curve up to the top 100 are shown. FIG. 16 is a diagram showing the distribution of the area under the ROC curve of the discriminant using the combination of one kind of amino acid and plural kinds of amino acids shown in FIG. 14 and FIG. The maximum area under the ROC curve obtained with a combination of 6 amino acids was 0.743 (95% confidence interval is 0.660-0.825).

  From the above results, the univariate discriminant using one kind of amino acid shown in FIGS. 14 and 15 and the multivariate discriminant using the combination of plural kinds of amino acids shown in FIG. 14 and FIG. It proved to be useful as an index for discriminating the state of cachexia later.

  The blood sample and cachexia score obtained in Example 1 were used. 79 pancreatic cancer patients with a cachexia score of 0 or 1 after 1 month, 71 patients with a cachexia score of 2, 3 or 4 after 1 month there were. Similar to Example 1, multiple logistic regression was performed on all combinations of one amino acid and two to six amino acids extracted from the 19 amino acids. Created discriminant to be used. And the discrimination performance regarding the two-group discrimination of the group in which the cachexia score after 1 month became any of 0 and 1 and the group that became any of 2, 3 and 4 for pancreatic cancer patients, The area under the ROC curve was evaluated.

  FIG. 17 and FIG. 18 are diagrams showing amino acids used in the discriminant in which significant discriminability was obtained when the significance level was 5% on both sides. In addition, with respect to the discriminant using the combination of three kinds of amino acids to the discriminant using the combination of six kinds of amino acids, the formulas with the area under the ROC curve up to the top 100 are shown. FIG. 19 is a diagram showing the distribution of the area under the ROC curve of the discriminant using the combination of one kind of amino acid and plural kinds of amino acids shown in FIG. 17 and FIG. The maximum area under the ROC curve obtained with a combination of 6 amino acids was 0.784 (95% confidence interval 0.712-0.857).

  From the above results, the univariate discriminant using one kind of amino acid shown in FIG. 17 and FIG. 18 and the multivariate discriminant using the combination of plural kinds of amino acids shown in FIG. 17 and FIG. It proved to be useful as an index for discriminating the state of cachexia later.

  The blood sample and cachexia score obtained in Example 1 were used. 117 patients with pancreatic cancer with a cachexia score of 0, 1 or 2 after 1 month, 33 patients with a cachexia score of 3 or 4 after 1 month there were. Similar to Example 1, multiple logistic regression was performed on all combinations of one amino acid and two to six amino acids extracted from the 19 amino acids. Created discriminant to be used. And the discrimination performance regarding the two-group discrimination of the group in which the cachexia score after 1 month became any of 0, 1, 2 and the group that became any of 3, 4 for pancreatic cancer patients, The area under the ROC curve was evaluated.

  FIG. 20 and FIG. 21 are diagrams showing amino acids used in the discriminant in which significant discriminability was obtained when the significance level was 5% on both sides. In addition, with respect to the discriminant using the combination of three kinds of amino acids to the discriminant using the combination of six kinds of amino acids, the formulas with the area under the ROC curve up to the top 100 are shown. FIG. 22 is a diagram showing the distribution of the area under the ROC curve of the discriminant using the combination of one kind of amino acid and plural kinds of amino acids shown in FIG. 20 and FIG. The maximum area under the ROC curve obtained with a combination of 6 amino acids was 0.860 (95% confidence interval 0.790-0.930).

  From the above results, the univariate discriminant using one kind of amino acid shown in FIG. 20 and FIG. 21 and the multivariate discriminant using the combination of plural kinds of amino acids shown in FIG. 20 and FIG. It proved to be useful as an index for discriminating the state of cachexia later.

  The blood sample and cachexia score obtained in Example 1 were used. There were 139 patients with pancreatic cancer who had a cachexia score of 0, 1, 2 or 3 after 1 month and 11 patients with a cachexia score of 4 after 1 month . Similar to Example 1, multiple logistic regression was performed on all combinations of one amino acid and two to six amino acids extracted from the 19 amino acids. Created discriminant to be used. The discrimination performance regarding the two-group discrimination between the group in which the cachexia score after one month became 0, 1, 2 or 3 and the group in which the cachex score became 4 for pancreatic cancer patients was expressed as an ROC curve. Evaluation was based on the lower area.

  FIG. 23 and FIG. 24 are diagrams showing amino acids used in the discriminant in which significant discriminability was obtained when the significance level was 5% on both sides. In addition, from the discriminant using the combination of two types of amino acids to the discriminant using the combination of six types of amino acids, the formulas with the area under the ROC curve up to the top 100 are shown. FIG. 25 is a diagram showing the distribution of the area under the ROC curve of the discriminant using the combination of plural types of amino acids shown in FIG. 23 and FIG. The maximum area under the ROC curve obtained with a combination of 6 amino acids was 0.743 (95% confidence interval is 0.660-0.825).

  From the above results, it was found that the multivariate discriminant using the combination of plural kinds of amino acids shown in FIG. 23 and FIG. 24 is useful as an index for discriminating the state of cachexia after one month.

  Blood samples were obtained from 55 pancreatic cancer patients before the start of the first treatment to obtain blood samples. The obtained blood sample is comprehensively metabolized by the aforementioned CE-TOFMS (capillary type-electrophoresis time-of-flight mass spectrometer) method and LC-TOFMS (liquid chromatography-time-of-flight mass spectrometer) method. Physical analysis was performed. And before the start of the first treatment and about one month after the start of treatment (hereinafter sometimes referred to simply as “one month later”), blood is collected again from the 55 pancreatic cancer patients, The cachexia score shown in Example 1 was calculated for each patient. In addition, the value of CRP used in the calculation of cachexia score is measured based on blood collected again. Of these 55 patients, a group consisting of 13 pancreatic cancer patients whose cachexia score shown in Example 1 was 0 after one month, and among those 55 patients, the cachexia score was 1 after 1 month. , 2, 3, 4 A group consisting of 42 patients with pancreatic cancer was compared between metabolite concentrations.

  FIG. 26 is a diagram showing metabolites that showed p <0.1 when the statistical significance between groups was evaluated by performing an unpaired t-test. Here, the purpose of this example is to provide judgment criteria and an appropriate screening method for predicting early changes in cachexia in patients with pancreatic cancer, and to provide early intervention for patients who should undergo intervention. Therefore, the emphasis is on preventing oversight of patients with significance of intervention rather than oversight of patients without significance of intervention. Therefore, it is not necessary to strictly control the probability that a patient who does not have the significance of intervention erroneously determines that there is an intervention effect, that is, the first type of error probability. Therefore, in the comparison of metabolite concentrations between groups, p <0.1 was adopted as a criterion for statistical significance.

  In addition, for the two-group discrimination using the metabolites measured exhaustively, the group having a cachexia score of 0 after 1 month and the group having any one of 1, 2, 3, and 4 are shown in FIG. Was evaluated by the area under the ROC curve. FIG. 27 is a diagram showing metabolites for which significant discrimination ability was obtained when the significance level was 5% on both sides.

  From the above results, it was found that the metabolites shown in FIG. 26 and FIG. 27 are useful as an index for discriminating the state of cachexia after 1 month of pancreatic cancer patients.

  A group consisting of 27 pancreatic cancer patients whose cachexia score shown in Example 1 became 0 or 1 after one month among 55 pancreatic cancer patients targeted in Example 5, Among the 55 patients, a group consisting of 28 patients with pancreatic cancer whose cachexia score was 2, 3, or 4 after one month was compared between metabolites.

  FIG. 28 is a diagram showing metabolites that showed p <0.1 when the statistical significance between groups was evaluated by performing an unpaired t-test. For the same reason as described in Example 5, p <0.1 was adopted as the criterion for statistical significance.

  In addition, for the two-group discrimination using the metabolites measured exhaustively, the group having a cachexia score of 0, 1 after 1 month and the group having any of 2, 3, 4 The discrimination performance was evaluated by the area under the ROC curve. FIG. 29 is a diagram showing metabolites for which significant discrimination ability was obtained when the significance level was 5% on both sides.

  From the above results, it has been found that the metabolites shown in FIGS. 28 and 29 are useful as an index for discriminating the state of cachexia after 1 month of pancreatic cancer patients.

  A group consisting of 46 pancreatic cancer patients whose cachexia score shown in Example 1 became 0, 1, or 2 after one month among 55 patients with pancreatic cancer targeted in Example 5 Then, among the 55 patients, a group consisting of 9 pancreatic cancer patients whose cachexia score became one of 3 and 4 after one month was compared between groups of metabolite concentrations.

  FIG. 30 is a diagram showing metabolites that showed p <0.1 when an unpaired t-test was performed to evaluate statistical significance between groups. For the same reason as described in Example 5, p <0.1 was adopted as the criterion for statistical significance.

  In addition, using the metabolites measured exhaustively, the two-group discrimination between the group having a cachexia score of 0, 1, 2 and the group having any of 3, 4 after one month Performance was evaluated by the area under the ROC curve. FIG. 31 is a diagram showing metabolites for which significant discrimination ability was obtained when the significance level was 5% on both sides.

  From the above results, it was found that the metabolites shown in FIG. 30 and FIG. 31 are useful as an index for discriminating the state of cachexia after 1 month of pancreatic cancer patients.

  Of 55 patients with pancreatic cancer targeted in Example 5, it consists of 53 patients with pancreatic cancer whose cachexia score shown in Example 1 was 0, 1, 2, 3 after one month. And a group composed of 2 patients with pancreatic cancer who had a cachexia score of 4 after one month among these 55 patients, were compared between groups for metabolite concentrations.

  FIG. 32 is a diagram showing metabolites that showed p <0.1 when the statistical significance between groups was evaluated by performing an unpaired t-test. For the same reason as described in Example 5, p <0.1 was adopted as the criterion for statistical significance.

  In addition, for the two-group discrimination using the metabolites measured exhaustively, the group having a cachexia score of 0, 1, 2 or 3 and the group having 4 after one month, the discrimination performance is The area under the ROC curve was evaluated. FIG. 33 is a diagram showing metabolites for which significant discriminability was obtained when the significance level was 5% on both sides.

  From the above results, it was found that the metabolites shown in FIG. 32 and FIG. 33 are useful as an index for discriminating the state of cachexia after 1 month of pancreatic cancer patients.

The case where one or more of the following three items (1) to (3) corresponds is defined as Cancer Cache on EPCRC (CAC), and whether or not the item corresponds to CAC is used as an index for cachexia diagnosis.
(1) The rate of weight loss is 5% or more compared to half a year ago.
(2) BMI is less than 20 and the rate of weight loss is more than 2% compared to half a year ago.
(3) Sarcopenia (Janssen Criteria (see "Janssen I, et al. (2004) Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol 159 (4): 413-21.") And the rate of weight loss is more than 2% compared to half a year ago.

  Blood samples were obtained from 150 pancreatic cancer patients prior to the start of the first treatment. From the obtained blood sample, Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn by the amino acid analysis method (A) described above. , Lys, Arg 19 blood amino acids were measured. Whether or not the 150 pancreatic cancer patients fall under CAC before the start of the first treatment and about one month after the start of treatment (hereinafter sometimes referred to simply as “one month later”). This pancreatic cancer patient was classified into two groups. There were 101 pancreatic cancer patients who met CAC after one month, and 59 pancreatic cancer patients who did not meet CAC after one month.

Multiple logistic regression was performed on all combinations from one amino acid and two amino acids to six amino acids extracted from the 19 kinds of amino acids, and the discriminant represented by the following Equation 2 was obtained. Created. And the discrimination performance regarding the 2-group discrimination | determination of the group which did not become CAC after 1 month and the group which became CAC for the pancreatic cancer patient was evaluated by the area under the ROC curve.
logitp (x) = β ₀ + β ₁ × concentration of amino acid A + β ₂ × concentration of amino acid B +.
(Formula 2)
* In Equation 2, p (x) represents the probability of CAC after one month.

  FIG. 34 and FIG. 35 are diagrams showing amino acids used in the discriminant in which significant discriminability was obtained when the significance level was 5% on both sides. In addition, with respect to the discriminant using the combination of three kinds of amino acids to the discriminant using the combination of six kinds of amino acids, the formulas with the area under the ROC curve up to the top 100 are shown. FIG. 36 is a diagram showing the distribution of the area under the ROC curve of the discriminant using the combination of one kind of amino acid and plural kinds of amino acids shown in FIG. 34 and FIG. The maximum area under the ROC curve obtained with a combination of 6 amino acids was 0.705 (95% confidence interval 0.619-0.791).

  From the above results, the univariate discriminant using one kind of amino acid shown in FIG. 34 and FIG. 35 and the multivariate discriminant using the combination of plural kinds of amino acids shown in FIG. 34 and FIG. It proved to be useful as an index for discriminating the state of cachexia later.

  Of the 55 pancreatic cancer patients targeted in Example 5, a group consisting of 20 pancreatic cancer patients not corresponding to the CAC shown in Example 9 after one month, and among these 55 CAC one month later A group consisting of 35 patients with pancreatic cancer corresponding to the above was compared between metabolite concentrations.

  FIG. 37 is a diagram showing metabolites that showed p <0.1 when statistical significance between groups was evaluated by performing an unpaired t-test. For the same reason as described in Example 5, p <0.1 was adopted as the criterion for statistical significance.

  Moreover, the discrimination performance was evaluated by the area under the ROC curve for two-group discrimination between a group that did not become CRC after one month and a group that became CRC using metabolites measured exhaustively. FIG. 38 is a diagram showing metabolites for which significant discrimination ability was obtained when the significance level was 5% on both sides.

  From the above results, it was found that the metabolites shown in FIGS. 37 and 38 are useful as an index for discriminating the state of cachexia after 1 month of pancreatic cancer patients.

  As described above, the present invention can be widely implemented in many industrial fields, in particular, pharmaceuticals, foods, and medical fields, and in particular, biotechnology for predicting the future of cachexia in pancreatic cancer patients. Very useful in the field of informatics.

DESCRIPTION OF SYMBOLS 100 Evaluation apparatus 102 Control part 102a Receiving part 102b Specification part 102c Formula creation part 102d Evaluation part 102d1 Calculation part 102d2 Conversion part 102d3 Generation part 102d4 Classification part 102e Result output part 102f Transmission part 104 Communication interface part 106 Storage part 106a Concentration data file 106b Index state information file 106c Designated index state information file 106d Expression related information database 106d1 Expression file 106e Evaluation result file 108 Input / output interface unit 112 Input device 114 Output device 200 Client device (terminal device (information communication terminal device))
300 network 400 database device

Claims (8)

Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg, 18 in the blood to be evaluated having pancreatic cancer -Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, Etio holan-3α-ol-17-one sulfate, Glycerophhocholine, Hexanoic acid, Indole-3-carboidal hydride, Isobutylic acid, Butyric acid, Octanoic acid, Oleoic acid, Oleoyl acid Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Itheticic acid, N6-Methyllysine, Diethanola mine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carcinine (16: 1), ADMA, Acyl carcinacic, 20%, Creatineine acetic acid, Decanic acid id 11,14-Eicosatrienic acid, Oleic acid, Palmitoylanolamide, Palmitoleic acid, Elucic acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palmitide The future state of cachexia is evaluated with respect to the evaluation target using at least one concentration value of id, Steadic acid, 1-Methylhistidine, 3-Methylhistidine, 2-Hydrochloridic acid, cis-Acoustic acid, Ethanolamine. Including an evaluation step,
Evaluation method characterized by In the evaluation step, by further using a formula including a variable into which the concentration value is substituted, the future value of cachexia is evaluated for the evaluation target by calculating the value of the formula,
The evaluation method according to claim 1, wherein: An evaluation device including a control unit,
The controller is
Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg, 18 in the blood to be evaluated having pancreatic cancer -Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, Etio holan-3α-ol-17-one sulfate, Glycerophhocholine, Hexanoic acid, Indole-3-carboidal hydride, Isobutylic acid, Butyric acid, Octanoic acid, Oleoic acid, Oleoyl acid Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Itheticic acid, N6-Methyllysine, Diethanola mine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carcinine (16: 1), ADMA, Acyl carcinacic, 20%, Creatineine acetic acid, Decanic acid id 11,14-Eicosatrienic acid, Oleic acid, Palmitoylanolamide, Palmitoleic acid, Elucic acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palmitide The future state of cachexia is evaluated with respect to the evaluation target using at least one concentration value of id, Steadic acid, 1-Methylhistidine, 3-Methylhistidine, 2-Hydrochloridic acid, cis-Acoustic acid, Ethanolamine. Provided with evaluation means,
An evaluation apparatus characterized by. An evaluation method executed in an information processing apparatus including a control unit,
Executed in the control unit,
Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg, 18 in the blood to be evaluated having pancreatic cancer -Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, Etio holan-3α-ol-17-one sulfate, Glycerophhocholine, Hexanoic acid, Indole-3-carboidal hydride, Isobutylic acid, Butyric acid, Octanoic acid, Oleoic acid, Oleoyl acid Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Itheticic acid, N6-Methyllysine, Diethanola mine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carcinine (16: 1), ADMA, Acyl carcinacic, 20%, Creatineine acetic acid, Decanic acid id 11,14-Eicosatrienic acid, Oleic acid, Palmitoylanolamide, Palmitoleic acid, Elucic acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palmitide The future state of cachexia is evaluated with respect to the evaluation target using at least one concentration value of id, Steadic acid, 1-Methylhistidine, 3-Methylhistidine, 2-Hydrochloridic acid, cis-Acoustic acid, Ethanolamine. Including an evaluation step,
Evaluation method characterized by An evaluation program for execution in an information processing apparatus provided with a control unit,
For executing in the control unit,
Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg, 18 in the blood to be evaluated having pancreatic cancer -Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, Etio holan-3α-ol-17-one sulfate, Glycerophhocholine, Hexanoic acid, Indole-3-carboidal hydride, Isobutylic acid, Butyric acid, Octanoic acid, Oleoic acid, Oleoyl acid Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Itheticic acid, N6-Methyllysine, Diethanola mine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carcinine (16: 1), ADMA, Acyl carcinacic, 20%, Creatineine acetic acid, Decanic acid id 11,14-Eicosatrienic acid, Oleic acid, Palmitoylanolamide, Palmitoleic acid, Elucic acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palmitide The future state of cachexia is evaluated with respect to the evaluation target using at least one concentration value of id, Steadic acid, 1-Methylhistidine, 3-Methylhistidine, 2-Hydrochloridic acid, cis-Acoustic acid, Ethanolamine. Including an evaluation step,
An evaluation program characterized by An evaluation system configured by connecting an evaluation device including a control unit and a terminal device including a control unit via a network so that they can communicate with each other,
The control unit of the terminal device is
Concentration data transmitting means for transmitting concentration data relating to the concentration values of amino and / or metabolites in the blood to be evaluated having pancreatic cancer to the evaluation device;
A result receiving means for receiving an evaluation result relating to a future state of cachexia in the evaluation target transmitted from the evaluation device;
With
The control unit of the evaluation apparatus includes:
Density data receiving means for receiving the density data transmitted from the terminal device;
Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn included in the density data received by the density data receiving means. , Lys, Arg, 18-Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosteron 3-sulfate, Etiocholan-3α-ol-17-one sulfate, Glycerophosphocholine, Hexanoic acid, Indole-3-carboxaldehyde, Isobutyric acid, Butyric acid, Octanoic acid, Oleoyl ethanolamide, Sarcosine, Sphingosine, Uric acid, α-Tocopherol, Isocitric acid, 2-Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Isethic acid, N6-Methylly sine, Diethanolamine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carnitine (16: 1), ADMA, Acyl carcinine (20: 1), Creatine acid, 20 8,11,14-Eicosatrienic acid, Oleic acid, Palmitoylanthanolide, Palmitoleic acid, Euric acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palidomic acid Using at least one of the concentration values of the evaluation object, the concentration value of the evaluation object, and the quality of the evaluation object using at least one of the concentration values of the evaluation object, An evaluation means to evaluate;
A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
Having
An evaluation system characterized by A terminal device comprising a control unit,
The controller is
A result acquisition means for acquiring an evaluation result relating to a future state of cachexia in an evaluation subject having pancreatic cancer;
The evaluation results are Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg, in the blood to be evaluated. 18-Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosterone 3-sulfate, E iocholan-3α-ol-17-one sulfate, Glycerophosphocholine, Hexanoic acid, Indole-3-carboxaldehyde, Isobutyric acid, Butyric acid, Octanoic acid, Oleoyl ethanolamide, Sarcosine, Sphingosine, Uric acid, α-Tocopherol, Isocitric acid, 2- Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Itheticic acid, N6-Methyllysine, Diethane olamine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carnineine (16: 1), ADMA, Acyl cariline, Decanic acid, 20%, Creatine acetic acid, Decidic acid. 11,14-Eicosatrienic acid, Oleic acid, Palmitoylanolamide, Palmitolic acid, Elucic acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palmitide Using the concentration value of at least one of c acid, Steadic acid, 1-Methylhistidine, 3-Methylhistidine, 2-Hydrochloridic acid, cis-Acidic acid, and Ethanolamine, a future state of cachexia is evaluated for the evaluation object. The result of
A terminal device characterized by the above. An evaluation device including a control unit that is communicably connected to a terminal device via a network,
The controller is
Concentration data receiving means for receiving concentration data related to the concentration value of amino acids and / or metabolites in the blood of an evaluation target having pancreatic cancer transmitted from the terminal device;
Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn included in the density data received by the density data receiving means. , Lys, Arg, 18-Hydroxycorticosterone-2-Cortisol, Glyceric acid, Guanidoacetic acid, Palmitoylcarnitine, Urea, Gluconic acid, Myristic acid, 1,2-Dipalmitoyl-glycero-3-phosphoethanolamine, 5-Oxoproline, Cholesterol sulfate, Cystine, Dehydroisoandrosteron 3-sulfate, Etiocholan-3α-ol-17-one sulfate, Glycerophosphocholine, Hexanoic acid, Indole-3-carboxaldehyde, Isobutyric acid, Butyric acid, Octanoic acid, Oleoyl ethanolamide, Sarcosine, Sphingosine, Uric acid, α-Tocopherol, Isocitric acid, 2-Hydroxybutyric acid, 2-Hydroxyisobutyric acid, cis-Aconic acid, Citric acid, Hypoxanthine, Isethic acid, N6-Methylly sine, Diethanolamine, 3-Hydroxybutyric acid, Acyl carnitine (18: 1), Hydroxyproline, Acyl carnitine (16: 1), ADMA, Acyl carcinine (20: 1), Creatine acid, 20 8,11,14-Eicosatrienic acid, Oleic acid, Palmitoylanthanolide, Palmitoleic acid, Euric acid, cis-11, 14-Eicosadienic acid, cis-11-Eicosenoic acid, Palidomic acid Using at least one of the concentration values of the evaluation object, the concentration value of the evaluation object, and the quality of the evaluation object using at least one of the concentration values of the evaluation object, An evaluation means to evaluate;
A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
Having
An evaluation apparatus characterized by.

Images