Method for predicting prognosis of patient with skeletal muscle atrophy or effectiveness of nutrition therapy for patient with skeletal muscle atrophy

Abstract

The present invention addresses the problem of providing a prediction method and the like capable of providing information useful for determining the prognosis of a patient with skeletal muscle atrophy or the effectiveness of nutrition therapy for the patient with skeletal muscle atrophy. The present embodiment involves predicting the prognosis of a patient with skeletal muscle atrophy or the effectiveness of nutrition therapy for the patient with skeletal muscle atrophy by using an analysis value of the phosphorylation state of a branched-chain α-keto acid dehydrogenase (BCKDH) in the liver tissue of the patient with skeletal muscle atrophy, or a concentration value of carnitines in the blood, urine, sweat, liver tissue, or muscle tissue of the patient with skeletal muscle atrophy.

Description

Method for predicting the prognosis of patients with skeletal muscle loss or the effectiveness of nutritional therapy for patients with skeletal muscle loss

[0001] The present invention relates to a method, a prediction device, a prediction program, a recording medium, a prediction system, and a terminal device for predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for a patient with skeletal muscle loss.

[0002] Skeletal muscle loss is caused by aging, decreased activity, diseases such as organ failure, inflammatory diseases, endocrine diseases, and malignant tumors, or malnutrition (Non-Patent Document 1). A decrease in skeletal muscle mass beyond physiological levels due to aging is called sarcopenia, and it results in muscle mass and strength loss, physical functional disorders such as falls and gait disturbances, a decrease in quality of life, and an increased risk of death (Non-Patent Documents 2 and 3). Sarcopenia is broadly divided into primary sarcopenia caused by aging and secondary sarcopenia caused by inactivity, disease, or malnutrition. However, in elderly people aged 65 and over, sarcopenia is a complex phenomenon that is often caused not only by aging itself, but also by age-related decreases in activity and other diseases (Non-Patent Document 1).

[0003] Possible causes of skeletal muscle loss include a decrease in muscle satellite cells or motor neurons, decreased secretion of growth hormone, testosterone, and ghrelin, increased secretion of inflammatory cytokines, decreased mitochondrial function, abnormal myokine production, or weight loss due to anorexia, all of which commonly result in an imbalance between muscle protein synthesis and degradation (Non-Patent Document 4). Therapeutic interventions for skeletal muscle loss or sarcopenia involve providing nutrients or oxygen to skeletal muscle through nutritional or exercise interventions. However, while dietary diversity and protein intake have been shown to be effective in preventing age-related loss of skeletal muscle mass or strength, no definitive conclusions have been reached regarding the effectiveness of nutritional interventions in preventing the onset of pathological sarcopenia associated with diabetes, chronic renal failure, heart failure, or malignant tumors, although this has been shown to be possible (Non-Patent Document 3).

[0004] Cachexia, characterized by skeletal muscle loss, is a complex metabolic syndrome associated with underlying diseases. Cachexia is observed not only in malignant tumors but also in many other underlying diseases, including chronic heart failure, chronic renal failure, chronic obstructive pulmonary disease, rheumatoid arthritis, acquired immunodeficiency syndrome (AIDS), and inflammatory bowel disease. Cachexia is a pathological condition distinct from starvation, age-related muscle loss, depression, malabsorption, or hyperthyroidism, and has been defined as an increased risk factor for these conditions (Non-Patent Document 5). The incidence of cachexia varies depending on factors such as the severity of the disease (Non-Patent Document 6), with reported rates of 28-57% in cancer, 16-42% in chronic heart failure, and 30-60% in chronic renal failure (Non-Patent Document 7). Cachexia and sarcopenia (especially secondary sarcopenia) share many common factors in their pathogenesis. In particular, metabolic abnormalities driven by various inflammatory cytokines contribute to muscle atrophy, decreased appetite, and decreased body fat mass (Non-Patent Document 8). Many patients with advanced cachexia are resistant to nutritional therapy due to anabolic resistance, a condition in which protein synthesis in muscle tissue is not normal. Therefore, it is important to start nutritional care before the nutritional status deteriorates, thereby minimizing avoidable factors that worsen the nutritional status, such as inadequate nutritional intake (starvation) or lack of exercise, maintaining the nutritional status, and preventing the progression of cachexia (Non-Patent Documents 9 and 10).

[0005] In addition to typical symptoms such as weight loss or loss of appetite, cancer cachexia can also lead to reduced efficacy of chemotherapy, side effects, treatment interruption, and even survival rates. Weight loss in cancer patients worsens prognosis depending on the degree of weight loss, necessitating aggressive treatment (Non-Patent Document 11). The timing of intervention for cancer cachexia varies depending on the type and stage of cancer, and there is no clear evidence regarding the timing of its completion. Excessive intervention in patients with refractory cachexia may increase the burden on the patient. In particular, given the limited effectiveness of nutritional therapy (see Non-Patent Document 12) and the fact that weight loss can occur as a result of nutritional and exercise therapy (see Non-Patent Document 13), careful consideration is required when selecting nutritional therapy.

[0006] Blood albumin, CRP, cachexia index, and nutritional index have been reported as cancer prognosis prediction indexes (see Non-Patent Documents 14, 15, and 16). Patent Document 1 also discloses a method for evaluating the prognosis of cancer patients based on tissue amino acid profiles.

[0007] Japanese Patent Application Publication No. 2018-100963

[0008] Cruz-Jentoft AJ, et al. Age Ageing. 2010;39(4):412-423. Journal of the Japanese Society of Internal Medicine, Vol. 107, No. 9. Sarcopenia Clinical Guidelines 2017 Edition. Jpn J Rehabil Med 2007; 44: 144-170. Evans WJ, et al.: Cachexia: a new definition. Clinical Nutrition 2008; 27: 793-799. Morley JE, et al.: Cachexia: pathophysiology and clinical relevance. Am J Clin Nutr 2006; 83: 735-743. Farkas J, et al.: Cachexia as a major public health problem: frequent, costly, and deadly. J Cachex Sarcopenia Muscle 2013; 4: 173-178. Medical Progress vol. 274(6). 555-561, 2020Arends J, et al: ESPEN expert group recommendations for action against cancer-related malnutrition. Clin Nutr 2017; 36: 1187-1196.Deutz NEP, et al: Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group. Clin Nutr 2014; 33: 929-936.Cancer Cachexia Handbook March 2019Bourdel-Marchasson I, et al. PLoS One. 20147th Japanese Society of Supportive Care in Cancer 2022 PS1-10Forrest LM, et al. Br J Cancer. 2003; 89: 1028-30Support Care Cancer.2015; 23: 1699-1708 Surgery, Metabolism, and Nutrition Vol. 51 No. 4 Imoto A, Mitsunaga S, et al: Neural invasion induces cachexia via astrocytic activation of the neural route in pancreatic cancer. Int J Cancer. 2012; 131: 2795-80.

[0009] If it were possible to predict the prognosis of patients with skeletal muscle loss (such as those with advanced cancer) or the effectiveness of nutritional therapy for these patients, it would be possible to select patients with skeletal muscle loss who would benefit from nutritional therapy before intervention, or to propose alternative treatments (such as treatment with metabolic inhibitors) to patients with skeletal muscle loss for whom nutritional therapy is not effective. This would be beneficial for both patients and from a medical economic perspective, but no established method has been established for making such predictions.

[0010] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a prediction method, prediction device, prediction program, recording medium, prediction system, and terminal device that can provide information that can be useful in determining the prognosis of patients with skeletal muscle loss or the effectiveness of nutritional therapy for patients with skeletal muscle loss.

[0011] In order to solve the above-mentioned problems and achieve the object, the prediction method of the present invention is characterized by including a prediction step of predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH (branched-chain α-keto acid dehydrogenase) in the liver tissue of the patient or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

[0012] In the prediction method according to the present invention, the patient with skeletal muscle loss may be a patient with advanced cancer.

[0013] In the prediction method according to the present invention, the prediction step may be executed by a control unit of an information processing device including a control unit.

[0014] Furthermore, the prediction device according to the present invention is a prediction device equipped with a control unit, and the control unit is characterized by having a prediction means for predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH in the liver tissue of the patient with skeletal muscle loss or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

[0015] The prediction device according to the present invention may be communicably connected via a network to a terminal device that provides the analysis value or the concentration value, and in the prediction device according to the present invention, the control unit may include a data receiving means that receives the analysis value or the concentration value transmitted from the terminal device, and a result transmitting means that transmits the prediction result obtained by the prediction means to the terminal device.

[0016] Furthermore, the prediction program according to the present invention is a prediction program to be executed by an information processing device having a control unit, and is characterized by including a prediction step to be executed by the control unit, which predicts the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss using an analytical value of the phosphorylation state of BCKDH in the liver tissue of the patient with skeletal muscle loss or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

[0017] Furthermore, a recording medium according to the present invention is a computer-readable recording medium having the prediction program recorded thereon. Specifically, the recording medium according to the present invention is a non-transitory computer-readable recording medium, characterized in that it contains programmed instructions for causing an information processing device to execute the prediction method.

[0018] Furthermore, the prediction system according to the present invention is a prediction system configured by connecting a prediction device having a control unit and a terminal device having a control unit so that they can communicate with each other via a network, wherein the control unit of the terminal device comprises a data transmission means for transmitting to the prediction device an analytical value of the phosphorylation state of BCKDH in the liver tissue of a patient with skeletal muscle loss or a concentration value of carnitines in the blood, urine, sweat, liver tissue, or muscle tissue of the patient with skeletal muscle loss, and a result receiving means for receiving a prediction result regarding the prognosis of the patient with skeletal muscle loss or a prediction result regarding the effectiveness of nutritional therapy for the patient with skeletal muscle loss, which is transmitted from the prediction device; and the control unit of the prediction device comprises a data receiving means for receiving the analytical value or the concentration value transmitted from the terminal device, a prediction means for predicting the prognosis of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss using the analytical value or the concentration value received by the data receiving means, and a result transmitting means for transmitting the prediction result obtained by the prediction means to the terminal device.

[0019] Furthermore, the terminal device according to the present invention is a terminal device equipped with a control unit, and the control unit is equipped with a result acquisition means for acquiring a prediction result regarding the prognosis of a patient with skeletal muscle loss or a prediction result regarding the effectiveness of nutritional therapy for the patient with skeletal muscle loss, wherein the prediction result is a result of predicting the prognosis of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss using an analytical value of the phosphorylation state of BCKDH in the liver tissue of the patient with skeletal muscle loss or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

[0020] The present invention has the effect of providing information that can be used as a reference for determining the prognosis of patients with skeletal muscle loss or the effectiveness of nutritional therapy for patients with skeletal muscle loss.

[0021] FIG. 1 is a diagram illustrating the basic principle of the first embodiment. FIG. 2 is a diagram illustrating 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 block diagram illustrating an example of the configuration of the prediction device 100 of the present system. FIG. 5 is a diagram illustrating an example of information stored in the data file 106a. FIG. 6 is a diagram illustrating an example of information stored in the prediction result file 106b. FIG. 7 is a block diagram illustrating the configuration of the prediction unit 102b. FIG. 8 is a block diagram illustrating an example of the configuration of the client device 200 of the present system. FIG. 9 is a diagram illustrating overall survival time curves after the start of treatment for a patient group whose pBCKDH phosphorylation level is equal to or greater than the cutoff value and a patient group whose pBCKDH phosphorylation level is below the cutoff value. FIG. 10 is a diagram illustrating progression-free survival time curves after the start of treatment for a patient group whose pBCKDH phosphorylation level is equal to or greater than the cutoff value and a patient group whose pBCKDH phosphorylation level is below the cutoff value. Figure 11 is a scatter plot of the Pearson correlation coefficient between pBCKDH phosphorylation levels and each metabolite concentration before the start of treatment, and the Pearson correlation coefficient between pBCKDH phosphorylation levels and each metabolite concentration one month after the start of treatment. Figure 12 is a graph showing overall survival time curves after the start of treatment for a patient group with malonylcarnitine levels equal to or greater than the cutoff value and a patient group with malonylcarnitine levels below the cutoff value. Figure 13 is a radar chart showing the distribution of blood amino acid concentrations in a mouse model of pancreatic cancer neural invasion and a mouse model of pancreatic cancer subcutaneous tumor. Figure 14 is a bar graph showing the P-BCKDH / BCKDH ratio in a mouse model of pancreatic cancer neural invasion and a mouse model of pancreatic cancer subcutaneous tumor. Figure 15 is a bar graph showing leucine metabolic flux in a mouse model of severe cachexia pancreatic cancer neural invasion, a mouse model of moderate cachexia pancreatic cancer neural invasion, and a mouse model of subcutaneous tumor. Figure 16 is a scatter plot of the P-BCKDH / BCKDH ratio and leucine metabolic flux in a mouse model of neural invasion of pancreatic cancer and a mouse model of subcutaneous pancreatic cancer. Figure 17 is a graph showing overall survival curves by acylcarnitine blood concentration. Figure 18 is a graph showing the distribution of hepatic pBCKDH expression in plasma 1 to 6. Figure 19 is a graph showing acylcarnitine blood concentrations by carbon number. Figure 20 is a graph showing acylcarnitine tissue concentrations by carbon number in a mouse model of neural invasion of pancreatic cancer and a mouse model of subcutaneous pancreatic cancer.

[0022] Hereinafter, an embodiment (first embodiment) of a prediction method according to the present invention and an embodiment (second embodiment) of a prediction device, a prediction method, a prediction program, a recording medium, a prediction system, and a terminal device according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[0023] [First embodiment] [1-1. Outline of the first embodiment] Here, an outline of the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the basic principle of the first embodiment.

[0024] First, an analytical value of the phosphorylation state of BCKDH in the liver tissue of a patient with skeletal muscle loss (e.g., the pBCKDH phosphorylation level described in Example 1 below or the pBCKDH phosphorylation level described in Example 6 below) or a concentration value of carnitines in the blood (e.g., plasma or serum), urine, sweat, liver tissue, or muscle tissue of a patient with skeletal muscle loss (e.g., the carnitine concentration value described in Example 2 below (specifically, the malonylcarnitine level described in Example 3 below, the acylcarnitine concentration described in Example 5 below, the acylcarnitine concentration by carbon number described in Example 6 below, or the acylcarnitine concentration by carbon number described in Example 7 below)) is obtained (step S11).

[0025] Here, a "patient with skeletal muscle loss" refers to a patient who has been determined to be in a state of reduced skeletal muscle mass according to predetermined criteria using (1) the circumference of the upper arm or lower limb, (2) skeletal muscle mass measured by methods such as DXA (Dual-energy X-ray Absorbtiometry), BIA (Bioelectrical Impedance Analysis), CT or MRI image analysis, or (3) an index of reduced muscle strength or physical function measured by grip strength, walking speed, or a stand-up test. The type of disease suffered by the patient with skeletal muscle loss is not important.

[0026] The analytical value of the phosphorylation state of BCKDH in liver tissue from a patient with skeletal muscle loss may be obtained, for example, by the following method. Normal liver tissue from a patient with skeletal muscle loss (e.g., normal liver tissue surrounding a liver metastasis if the patient has advanced pancreatic cancer) is collected by ultrasound-guided needle biopsy, and the collected tissue is homogenized in a buffer containing a protein denaturant and a surfactant and frozen and stored at -80°C as quickly as possible. When measuring protein expression, proteins in the frozen and thawed solution are immediately recovered by resin adsorption and eluted with a buffer containing a surfactant to obtain a protein solution. Proteins in the protein solution are separated according to molecular weight by capillary electrophoresis and then immobilized on the inner wall of the capillary. An immunoreaction is then carried out with a primary antibody and a secondary antibody that recognize BCKDH and phosphorylated BCKDH, and the immobilized proteins are detected and measured by a chemiluminescence reaction.

[0027] In addition, carnitine concentrations in the blood, urine, sweat, liver tissue, or muscle tissue of patients with skeletal muscle loss may be obtained, for example, by a method using a tandem mass spectrometer, a method using an enzymatic cycling method, or the following method. To prepare the sample, 50 μL of human plasma collected from a patient with skeletal muscle loss was added to 200 μL of methanol solution prepared to a concentration of 2 μM of internal standard, and the mixture was stirred. 150 μL of Milli-Q water was added to the stirred solution, which was then further stirred and transferred to an ultrafiltration tube (Ultrafree MC PLHCC, HMT, centrifugal filter unit 5 kDa). The tube was centrifuged (9,100 × g, 4°C, 120 minutes) for ultrafiltration. The filtrate was dried and redissolved in Milli-Q water for measurement. Measurements were performed in cation mode and anion mode under the following conditions (see Japanese Patent No. 6106864). Based on the peak intensity and shape, measurements were performed using a capillary electrophoresis-Fourier transform mass spectrometer (CE-FTMS). Peaks with a signal-to-noise (S / N) ratio of 3 or greater were automatically extracted from the peaks detected by CE-FTMS using the automatic integration software "MasterHands ver. 2.19.0.2" (developed by Keio University), and the mass-to-charge ratio (m / z), peak area, and migration time (MT) were obtained. The peak area values ​​were used to calculate relative values ​​based on differences in the phosphorylation state of BCKDH.[Cation mode] - Instrument: CE: Agilent CE system MS: Q Exactive Plus Capillary: Fused silica capillary id 50μm×80cm - Measurement conditions: Run buffer: Cation Buffer Solution (p / n: H3301-1001) Rinse buffer: Cation Buffer Solution (p / n: H3301-1001) Sample injection: Pressure injection 50 mbar, 10 sec CE voltage: Positive, 30 kV MS ionization: ESI Positive MS capillary voltage: 4,000 V MS scan range: m / z 60-900 Sheath liquid: HMT Sheath Liquid (p / n: I3301-1040) [Anesthetic mode] - Instrument: CE: Agilent CE system MS: Q Exactive Plus Capillary: Fused silica capillary id 50μm×80cm - Measurement conditions: Run buffer: Anion Buffer Solution (p / n: H3302-1023) Rinse buffer: Anion Buffer Solution (p / n: H3302-1023) Sample injection: Pressure injection 50 mbar, 25 sec CE voltage: Positive, 30 kV MS ionization: ESI Negative MS capillary voltage: 3,500 V MS scan range: m / z 70-1,050 Sheath liquid: HMT Sheath Liquid (p / n: I3301-1040).

[0028] Next, the values ​​(analysis values ​​or concentration values) acquired in step S11 are used to predict (evaluate) the prognosis (e.g., life prognosis or progression-free survival) of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss (step S12). Here, if the patient has advanced cancer, "predicting the prognosis of the patient with skeletal muscle loss" means, for example, predicting survival time after the start of systemic chemotherapy for the primary and metastatic lesions (e.g., number of days of survival counted from the start of chemotherapy), predicting survival time after the start of chemoradiotherapy, predicting progression-free survival, or predicting survival rate (e.g., 1-year survival rate, 3-year survival rate, 5-year survival rate, or 10-year survival rate). Furthermore, when a patient with skeletal muscle loss has chronic heart failure, "predicting the prognosis of a patient with skeletal muscle loss" means, for example, predicting the period until readmission due to worsening heart failure, predicting survival time (e.g., the number of days a patient will survive from the date of diagnosis), or predicting survival rate (e.g., 1-year survival rate, 3-year survival rate, 5-year survival rate, or 10-year survival rate). "Predicting the effectiveness of nutritional therapy" means predicting the maintenance or increase of body weight or skeletal muscle after the start of nutritional therapy, predicting healthy life expectancy (e.g., the number of days an individual can maintain an independent lifestyle from the start of nutritional therapy), etc.

[0029] As described above, in the first embodiment, in step S11, analytical values ​​or concentration values ​​are acquired, and in step S12, the acquired values ​​(analytical values ​​or concentration values) acquired in step S11 are used to predict the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for that patient (in other words, information for predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for that patient is acquired). This makes it possible to provide information that can be useful in knowing the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for that patient.

[0030] The acquired value may be determined to reflect the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such patients, or to be an indicator of the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such patients. Furthermore, the acquired value may be converted, for example, using the methods listed below, and the converted value may be determined to reflect the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such patients, or to be an indicator of the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such patients. In other words, the acquired value or the converted value itself may be treated as a prediction result for the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such patients. To ensure that the range of possible values ​​of the acquired value falls within a predetermined range (e.g., 0.0 to 1.0, 0.0 to 10.0, 0.0 to 100.0, or -10.0 to 10.0), the acquired value may be transformed by, for example, adding, subtracting, multiplying, or dividing an arbitrary value with respect to the acquired value, or by converting the acquired value using a predetermined transformation method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by performing a combination of these calculations on the acquired value. For example, the value of an exponential function with the acquired value as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the concentration value when p is defined as the probability that the prognosis is poor or that nutritional therapy is ineffective) may be further calculated, or the value of the calculated exponential function divided by the sum of 1 and the value (specifically, the value of the probability p) may be further calculated. The acquired values ​​may also be converted so that the converted values ​​under specific conditions become specific values. For example, the acquired values ​​may be converted so that the converted value becomes 5.0 when the specificity is 60% and 8.0 when the specificity is 90%, or so that the converted value becomes 5.0 when the specificity is 80% and 8.0 when the specificity is 95%. Furthermore, the distribution of the acquired values ​​may be converted into a normal distribution, and the concentration values ​​may be converted into standard deviation values ​​based on the normal distribution so that the mean is 50 and the standard deviation is 10.The above-described various conversions may be performed by gender or age. The acquired value in this specification may be the acquired value itself or a value obtained by converting the acquired value.

[0031] Furthermore, the obtained values ​​may be converted using the conversion method described above, and then used to predict the prognosis of patients with skeletal muscle loss or the effectiveness of nutritional therapy for patients with skeletal muscle loss.

[0032] Alternatively, 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 may be generated using the acquired value or, if the acquired value is converted, the converted value. The generated position information may be determined to reflect the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient, or to be an indicator of the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient. The predetermined ruler is used to predict the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient, and may be, for example, a ruler with a scale that shows at least the upper and lower limits of the "range within which the acquired value or its converted value can be taken, or a portion of that range." The predetermined mark may be, for example, a circle or a star, corresponding to the acquired value or its converted value.

[0033] Furthermore, when the acquired value is lower than or equal to a predetermined value (such as the mean value ± 1 SD, 2 SD, 3 SD, N quantile, N percentile, or a cutoff value recognized for clinical significance), or is equal to or higher than the predetermined value, it may be predicted that the prognosis of the patient with skeletal muscle loss is good or that nutritional therapy for the patient with skeletal muscle loss is highly effective. In this case, instead of the acquired value itself, a standard deviation (a value obtained by normalizing the distribution of the acquired values ​​by gender and then standardizing the acquired values ​​based on the normal distribution so that the mean is 50 and the standard deviation is 10) may be used. For example, when the standard deviation is less than the mean value - 2 SD (when the standard deviation is < 30) or when the standard deviation is higher than the mean value + 2 SD (when the standard deviation is > 70), it may be predicted that the prognosis of the patient with skeletal muscle loss is good or that nutritional therapy for the patient with skeletal muscle loss is highly effective.

[0034] The prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for such a patient may also be qualitatively predicted. Specifically, the "acquired value and one or more preset thresholds" may be used to classify the patient into one of a plurality of categories defined by at least considering the prognosis or the degree of effectiveness. The plurality of categories may include a category for subjects with a good prognosis or high efficacy, a category for subjects with a poor prognosis or low efficacy, and a category for subjects with a moderate prognosis or efficacy. The plurality of categories may also include a category for subjects with a good prognosis or high efficacy, and a category for subjects with a poor prognosis or low efficacy. The acquired value may also be converted using a predetermined method, and the converted value may be used to classify the patient into one of a plurality of categories.

[0035] Furthermore, when predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for a patient with skeletal muscle loss, the following values ​​related to other biological information may be used in addition to the acquired values: 1. Concentration values ​​of amino acids or other blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. 2. Blood test values ​​such as albumin, total protein, triglycerides (neutral fats), 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 cells, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell count, and platelet count. 3. Values ​​obtained from image information such as ultrasound echo, X-ray, CT, MRI, and endoscopic images 4. Values ​​related to biomarkers such as age, height, weight, BMI, abdominal circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleep information, family medical history, and disease history (diabetes, etc.) 5. Test information obtained from pathological tissue tests such as cancer mass, infiltration and growth pattern, lymphatic invasion, venous invasion, intrapancreatic nerve invasion, and progression within the main pancreatic duct

[0036] [Second embodiment] [2-1. Overview of second embodiment] Here, an overview of the second embodiment will be described with reference to Fig. 2. Fig. 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in the description of this second embodiment, descriptions that overlap with the first embodiment described above may be omitted.

[0037] The control unit uses the acquired values ​​obtained from the patient with skeletal muscle loss to predict the prognosis of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss (step S21), thereby providing information that can be useful in determining the prognosis of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss.

[0038] [2-2. Configuration of Second Embodiment] Here, the configuration of a prediction system according to the second embodiment (hereinafter, sometimes referred to as the present system) will be described with reference to Figures 3 to 8. Note that the present system is merely an example, and the present invention is not limited to this.

[0039] First, the overall configuration of the present system will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the overall configuration of the present system. As shown in Fig. 3, the present system is configured by connecting a prediction device 100 that predicts the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss and a client device 200 (corresponding to a terminal device in the present invention) that provides data of values ​​obtained from the patient with skeletal muscle loss so that they can communicate with each other via a network 300. Note that in this system, the client device 200 that provides the data used for prediction and the client device 200 that receives the prediction results may be separate devices.

[0040] Next, the configuration of the prediction device 100 of this system will be described with reference to Figures 4 to 7. Figure 4 is a block diagram showing an example of the configuration of the prediction device 100 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0041] The prediction device 100 is composed of a control unit 102 such as a CPU (Central Processing Unit) that controls the prediction device in an overall manner, a communication interface unit 104 that communicatively connects the prediction device to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line, a memory unit 106 that stores various databases, tables, files, etc., and an input / output interface unit 108 that connects to an input device 112 and an output device 114, and these units are communicatively connected via any communication path. Here, the prediction device 100 may be configured in the same housing as a device used to obtain analysis values ​​or concentration values.

[0042] The communication interface unit 104 mediates communication between the prediction device 100 and the network 300 (or a communication device such as a router). In other words, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.

[0043] The input / output interface unit 108 is connected to an input device 112 and an output device 114. Here, the output device 114 may be a monitor (including a home television), a speaker, or a printer (hereinafter, the output device 114 may be referred to as the monitor 114). The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function.

[0044] The storage unit 106 may be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc. The storage unit 106 stores a computer program that works in cooperation with an OS (Operating System) to issue commands to a CPU to perform various processes. As shown in the figure, the storage unit 106 stores a data file 106a and a prediction result file 106b.

[0045] The data file 106a stores data on analytical values ​​or concentration values. FIG. 5 is a diagram showing an example of information stored in the data file 106a. As shown in FIG. 5, the information stored in the data file 106a is configured by correlating an identification number for uniquely identifying a patient with data on analytical values ​​or concentration values. Here, in FIG. 5, the data on analytical values ​​or concentration values ​​is treated as a numerical value, i.e., a continuous scale, but the data on analytical values ​​or concentration values ​​may be a nominal scale or an ordinal scale. Note that when the data on analytical values ​​or concentration values ​​is a nominal scale or an ordinal scale, any numerical value assigned to each state may be used for prediction. Furthermore, the data on analytical values ​​or concentration values ​​may be combined with values ​​related to other biological information (see above).

[0046] Returning to Fig. 4, the prediction result file 106b stores the prediction results obtained by the prediction unit 102b (described later). Fig. 6 is a diagram showing an example of information stored in the prediction result file 106b. The information stored in the prediction result file 106b is configured by interrelating an identification number for uniquely identifying the patient, analysis value or concentration value data, and prediction results regarding the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for a patient with skeletal muscle loss (e.g., values ​​obtained by converting acquired values ​​by a conversion unit 102b1 (described later), location information generated by a generation unit 102b2 (described later), or classification results obtained by a classification unit 102b3 (described later)).

[0047] 4, the control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 102 is roughly divided into an acquisition unit 102a, a prediction unit 102b, a result output unit 102c, and a transmission unit 102d.

[0048] The acquiring unit 102a acquires information (e.g., data on acquired values, etc.). The acquiring unit 102a may acquire information by receiving information (e.g., data on acquired values, etc.) transmitted from the client device 200 via the network 300, etc. The acquiring unit 102a may receive data on acquired values ​​transmitted from a client device 200 different from the client device 200 to which the prediction result is transmitted. Furthermore, for example, if the prediction device 100 includes a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquiring unit 102a may acquire information by reading information (e.g., data on acquired values, etc.) recorded on the recording medium via the mechanism.

[0049] The prediction unit 102b predicts the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for a patient with skeletal muscle loss using the data of the acquired values ​​acquired by the acquisition unit 102a. Note that the prediction unit 102b may perform prediction using the acquired values ​​or their converted values ​​(e.g., standard deviations).

[0050] The configuration of the prediction unit 102b will now be described with reference to Fig. 7. Fig. 7 is a block diagram showing the configuration of the prediction unit 102b, conceptually illustrating only the parts of the configuration that are relevant to the present invention. The prediction unit 102b further includes a conversion unit 102b1, a generation unit 102b2, and a classification unit 102b3.

[0051] The conversion unit 102b1 converts the acquired value using, for example, the conversion method described above. Note that the prediction unit 102b may store the value converted by the conversion unit 102b1 as a prediction result in a predetermined storage area of ​​the prediction result file 106b.

[0052] The generation unit 102b2 generates position information regarding the position of a predetermined mark on a predetermined ruler that is visibly shown on a display device such as a monitor or a physical medium such as paper, using the acquired value or the value converted by the conversion unit 102b1. Note that the prediction unit 102b may store the position information generated by the generation unit 102b2 as a prediction result in a predetermined storage area of ​​the prediction result file 106b.

[0053] The classification unit 102b3 uses the acquired values ​​or the values ​​converted by the conversion unit 102b1 to classify the target patient with skeletal muscle loss into one of multiple categories defined by taking into consideration at least the prognosis of the patient with skeletal muscle loss or the degree of effectiveness of nutritional therapy for the patient with skeletal muscle loss.

[0054] The result output unit 102c outputs the processing results of each processing unit in the control unit 102 (including the prediction result obtained by the prediction unit 102b) to the output device 114.

[0055] The transmitting unit 102d transmits the prediction result to the client device 200 that is the source of the acquired value data. Note that the transmitting unit 102d may transmit the prediction result to a client device 200 different from the client device 200 that is the source of the acquired value data used for the prediction.

[0056] Next, the configuration of the client device 200 of this system will be described with reference to Fig. 8. Fig. 8 is a block diagram showing an example of the configuration of the client device 200 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0057] The client device 200 is composed of a control unit 210, a ROM 220, a 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, and these components are communicably connected via any communication path. The client device 200 may be based on an information processing device (e.g., an information processing terminal such as a personal computer, a workstation, a home game device, an Internet TV, a PHS (Personal Handyphone System) terminal, a mobile terminal, a mobile communication terminal, or a PDA (Personal Digital Assistant)) to which peripheral devices such as a printer, a monitor, and an image scanner are connected as needed.

[0058] The input device 250 includes a keyboard, a mouse, a microphone, etc. A monitor 261, which will be described later, also functions as a pointing device in cooperation with the mouse. The output device 260 is an output means for outputting 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, etc. The input / output IF 270 is connected to the input device 250 and the output device 260.

[0059] The communication IF 280 communicatively connects the client device 200 to the network 300 (or a communication device such as a router). In other words, the client device 200 is connected to the network 300 via a communication device (such as a modem, a terminal adapter (TA), a router, etc.) and a telephone line or via a dedicated line. This allows the client device 200 to access the prediction device 100 in accordance with a predetermined communication protocol.

[0060] The control unit 210 includes a receiving unit 211 and a transmitting unit 212. The receiving unit 211 receives various information such as prediction results transmitted from the prediction device 100 via the communication IF 280. The transmitting unit 212 transmits various information such as data of acquired values ​​to the prediction device 100 via the communication IF 280. Note that the control unit 210 may also include a prediction unit 210a (including a conversion unit 210a1, a generation unit 210a2, and a classification unit 210a3) having functions similar to those of the prediction unit 102b provided in the control unit 102 of the prediction device 100.

[0061] The control unit 210 may implement all or any part of the processing performed by the control unit using a CPU and a program that is interpreted and executed by the CPU. Computer programs that work in cooperation with the OS to issue instructions to the CPU and perform various processes are recorded in the ROM 220 or the HD 230. The computer programs are executed by being loaded into the RAM 240 and cooperate with the CPU to form the control unit 210. The computer programs may also be recorded on an application program server connected to the client device 200 via a network, and the client device 200 may download all or any part of the computer programs as needed. All or any part of the processing performed by the control unit 210 may also be implemented using hardware such as wired logic.

[0062] Next, the network 300 of this system will be described with reference to Fig. 3. The network 300 has a function of connecting the prediction device 100, the client device 200, and the database device 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (including both wired and wireless networks). The network 300 may be a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a leased line network (including both analog and digital), a CATV (Community Antenna TeleVision) network, a mobile circuit switching network or a mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) (Global System for Mobile Communications) system, or PDC (Personal Digital The communication network may be a wireless communication network (including a cellular / PDC-P system, etc.), a radio paging network, a local wireless network such as Bluetooth (registered trademark), a PHS network, a satellite communication network (including a CS (Communication Satellite), a BS (Broadcasting Satellite), or an ISDB (Integrated Services Digital Broadcasting), etc.), etc.

[0063] In this description, an example has been given in which the prediction device 100 receives data on acquired values, predicts prognosis or efficacy, and transmits the prediction results, and the client device 200 receives the prediction results. However, if the client device 200 is equipped with the prediction unit 210a, the prediction device 100 and the client device 200 may share the tasks of, for example, converting the acquired values, generating location information, and classifying the patient into categories, as appropriate. For example, when the client device 200 receives converted values ​​of the acquired values ​​from the prediction device 100, the prediction unit 210a may generate location information corresponding to the converted values ​​using the generation unit 210a2, or classify the patient into one of a plurality of categories using the converted values ​​using the classification unit 210a3. Furthermore, when the client device 200 receives converted values ​​of the acquired values ​​and location information from the prediction value device 100, the prediction unit 210a may classify the patient into one of a plurality of categories using the converted values ​​using the classification unit 210a3.

[0064] [2-3. Other Embodiments] The prediction device, prediction method, prediction program, recording medium, prediction system, and terminal device according to the present invention may be implemented in various different embodiments other than the second embodiment described above within the scope of the technical idea set forth in the claims.

[0065] Furthermore, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0066] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed as desired unless otherwise specified.

[0067] Furthermore, with regard to each device constituting the prediction system, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0068] For example, all or any part of the processing functions of the prediction device 100, particularly the processing functions performed by the control unit 102, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing an information processing device to execute the prediction method of the present invention, and is mechanically read by the prediction device 100 as needed. That is, a computer program for providing instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106, such as a ROM or HDD (hard disk drive). This computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0069] In addition, this computer program may be stored in an application program server connected to the prediction device 100 via any network, and all or part of it may be downloaded as needed.

[0070] Furthermore, the prediction program according to the present invention may be stored in a non-transitory computer-readable recording medium, and may also be configured as a program product. Here, this "recording medium" includes memory cards, USB (Universal Serial Bus) memories, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROMs, EPROMs (Erasable Programmable Read Only Memory), EEPROMs (Electrically Erasable and Programmable Read Only Memory) (registered trademark), CD-ROMs (Compact Disc Read Only Memory), MOs (Magneto-Optical disks), DVDs (Digital Versatile Disks), and more. "Portable physical media" includes any "portable physical medium" such as a Blu-ray Disc, a DVD, a DVD player, a Blu-ray Disc, etc.

[0071] Furthermore, a "program" is a data processing method written in any language or description method, and does not matter whether it is in the form of source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0072] The various databases stored in the memory unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0073] The prediction device 100 may be configured as a known information processing device such as a personal computer or a workstation, or as an information processing device connected to any peripheral device. The prediction device 100 may also be realized by installing software (including a program or data) that causes the information processing device to implement the prediction method of the present invention.

[0074] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the above-mentioned embodiments can be implemented selectively.

[0075] A study was conducted to predict the prognosis of patients with advanced pancreatic cancer using hepatic pBCKDH phosphorylation level stratification technology. Forty-seven patients with advanced pancreatic cancer with liver metastases who had not yet received initial treatment were selected from the 55 patients described in Example 2 below. Normal liver tissue samples were collected from the area surrounding the metastatic lesions during liver biopsy. Clinical information was also collected from all patients, including background information, symptoms such as anorexia, and body composition such as skeletal muscle mass.

[0076] Soluble protein fractions were extracted from the collected normal liver tissue samples, and the BCKDH phosphorylation status was assessed by Western blotting. Specifically, Western blotting was performed using a "Jess Simple Western automated nano-immunoassay system" (Protein Simple) with primary and secondary antibodies that recognize BCKDH and phosphorylated BCKDH. The proteins were separated according to molecular weight by capillary electrophoresis and immobilized on the inner wall of the capillary, and detected and measured by a chemiluminescent reaction. The resulting images were analyzed using "Compass software" (Protein Simple) to quantify the signal intensity of total BCKDH and phosphorylated BCKDH. The signal intensity of phosphorylated BCKDH was then divided by the signal intensity of total BCKDH to obtain a corrected quantitative value (pBCKDH phosphorylation level).

[0077] Analysis was performed using the medical information for all patients whose pBCKDH phosphorylation levels were measured. Specifically, the median pBCKDH phosphorylation level of all patients was used as the cutoff value, and all patients were classified into two groups: those whose pBCKDH phosphorylation levels were equal to or greater than the cutoff value (High group) and those whose pBCKDH phosphorylation levels were less than the cutoff value (Low group).

[0078] The overall survival curves after the start of treatment for the High and Low groups are shown in Figure 9. Overall survival was defined as the period from the start of initial systemic chemotherapy to the date of death. The High group had a significantly shorter life expectancy than the Low group, demonstrating that the pBCKDH phosphorylation level can be used to predict the treatment prognosis of advanced pancreatic cancer.

[0079] The progression-free survival curves after the start of treatment for the High and Low groups are shown in Figure 10. Progression-free survival was defined as the shorter of the periods from the start of initial systemic chemotherapy to the date of tumor progression or death. The High group had significantly shorter life prognosis and progression-free survival than the Low group, demonstrating that the pBCKDH phosphorylation level can be used to predict the treatment prognosis of advanced pancreatic cancer.

[0080] The study involved 55 patients with advanced pancreatic cancer and liver metastases who had not yet undergone initial treatment. Blood samples were taken before and approximately one month after the start of initial treatment, and normal liver tissue samples were taken from around the metastatic lesions during liver metastasis biopsy. Clinical information was also collected from all patients, including background information, symptoms such as anorexia, and body composition such as skeletal muscle mass.

[0081] Blood samples collected from 20 of the 55 subjects were subjected to comprehensive metabolite analysis using CE-TOFMS and LC-TOFMS. Normal liver tissue samples collected from the 20 subjects were also used to assess BCKDH phosphorylation levels using the method described in Example 1.

[0082] Correlation analysis between BCKDH phosphorylation levels and the concentrations of each metabolite was performed, and carnitines were detected as a blood metabolite that showed a high correlation with hepatic BCKDH phosphorylation levels ( Figure 11 ). Figure 11 shows a scatter plot in which the X-axis represents the Pearson correlation coefficient between BCKDH phosphorylation levels (expression levels) obtained from normal liver tissue samples and blood metabolite concentrations before initial treatment, and the Y-axis represents the Pearson correlation coefficient between the BCKDH phosphorylation levels and blood metabolite concentrations one month after the start of initial treatment.

[0083] Blood samples were collected from 48 patients with advanced pancreatic cancer with liver metastasis who had not yet received initial treatment, selected from the 55 patients described in Example 2. Patient background information, symptoms such as anorexia, and body composition such as skeletal muscle mass were also collected as medical information from all patients.

[0084] Malonylcarnitine levels were assessed for the collected blood samples using the method described in Example 2.

[0085] All patients whose malonylcarnitine levels were measured were analyzed using medical information. Specifically, the median malonylcarnitine level of all patients was used as the cutoff value, and all patients were classified into two groups: those whose malonylcarnitine levels were equal to or greater than the cutoff value (high group) and those whose malonylcarnitine levels were less than the cutoff value (low group).

[0086] Figure 12 shows the overall survival time curves after the start of treatment for the high and low level groups. Overall survival was defined as the period from the start of initial systemic chemotherapy to the date of death. The high level group had a significantly shorter life expectancy than the low level group, demonstrating that the treatment prognosis of advanced pancreatic cancer can be predicted using blood malonylcarnitine levels.

[0087] A pancreatic cancer neural invasion model (INV) has been reported as an animal model of advanced pancreatic cancer cachexia in immunodeficient mice with human pancreatic cancer cells implanted into the sciatic nerve, resulting in increased systemic catabolism (Non-Patent Document 17). Compared to a control subcutaneous transplant model of the same cells (pancreatic cancer subcutaneous tumor mice (SC) with the human pancreatic cancer cells implanted subcutaneously), this model is characterized by significant weight loss without a decrease in food intake and systemic inflammation initiated by neural inflammation.

[0088] When the fasting blood amino acid concentrations of this model were measured, it was found that the branched-chain amino acid (BCAA) concentrations of leucine, isoleucine, and valine were higher in this model than in the control model (FIG. 13).

[0089] Furthermore, the mechanism of increased blood BCAA concentrations in this model was investigated from the following two perspectives: 1. Evaluation of the phosphorylation level (expression amount) of hepatic BCKDH, the rate-limiting enzyme in BCAA metabolism; 2. Evaluation of leucine metabolic flux using stable isotope-labeled leucine.

[0090] The results of evaluation in 1 ( FIG. 14 ) and 2 ( FIG. 15 ) confirmed that this model exhibited increased hepatic BCKDH phosphorylation levels and decreased leucine metabolic flux ( FIG. 16 ). Figure 14 shows the hepatic BCKDH phosphorylation levels (P-BCKDH / BCKDH ratio) of a pancreatic cancer neural invasion mouse model (INV) and a pancreatic cancer subcutaneous tumor mouse model (SC). Figure 15 also shows the leucine metabolic flux of a severely cachectic pancreatic cancer neural invasion mouse model (INV type severe), a moderately cachectic pancreatic cancer neural invasion mouse model (INV type moderate), and a pancreatic cancer subcutaneous tumor model (SC). Using the plasma BCAA concentration in each individual in the INV model, the animals were divided into two groups, a high BCAA concentration group and a low BCAA concentration group, using the k-means method. The high BCAA concentration group was designated the severe group, and the low BCAA concentration group was designated the moderate group. Figure 16 shows a scatter plot of the liver BCKDH phosphorylation level (P-BCKDH / BCKDH ratio) of the pancreatic cancer neural invasion mouse model (INV) and pancreatic cancer subcutaneous tumor-bearing mice (SC) on the X-axis, and the leucine metabolic flux of INV and SC on the Y-axis.

[0091] These confirmed results, combined with the finding that elevated hepatic BCKDH phosphorylation levels in patients with clinically advanced pancreatic cancer are associated with poor prognosis (Example 1), suggest that 1. BCAA metabolic disorders exist as a pathological condition of advanced pancreatic cancer, and 2. the BCAA metabolic disorders are associated with prognosis.

[0092] High-protein nutritional supplements and BCAA-containing nutritional supplements are used as palliative therapies for advanced pancreatic cancer cachexia, and early intervention has been reported to alleviate skeletal muscle mass loss. While there is currently insufficient evidence regarding the impact of nutritional therapy on cancer cachexia prognosis, multidisciplinary treatments combined with exercise therapy and drug therapy are expected to restore physical function and improve prognosis, and are being tested. In this study, elevated hepatic BCKDH phosphorylation levels observed in advanced pancreatic cancer models and patients were associated with impaired BCAA metabolism and poor prognosis. This finding suggests that nutritional therapy for advanced pancreatic cancer cachexia may also be useful as a marker for stratifying patients for improved physical function and prognosis.

[0093] Sixty-eight elderly patients with advanced pancreatic cancer or non-small cell lung cancer (22 patients with pancreatic cancer (median age 75.5 years) and 46 patients with non-small cell lung cancer (median age 76.0 years)) before the start of initial systemic chemotherapy, different from the 48 patients with advanced pancreatic cancer with liver metastases before initial treatment described in Example 3, were randomly assigned to a nutritional and exercise therapy intervention group and a non-intervention group. The nutritional and exercise therapy intervention group underwent a nutritional and exercise intervention (duration: 12 weeks) in parallel with the initial systemic chemotherapy. The nutritional therapy intervention consisted of nutritional guidance using the Mini Nutritional Assessment Form (MNA (registered trademark)) and prescription of Inner Power (registered trademark) (Otsuka Pharmaceutical Co., Ltd.), which is rich in BCAAs. Blood samples were collected from all patients before the start of treatment and 12 weeks after. In addition, medical information was collected from all patients, including patient background information, symptoms such as loss of appetite, and body composition such as skeletal muscle mass.

[0094] Blood samples from 65 of the 68 subjects were assessed for acylcarnitine concentrations by enzymatic cycling.

[0095] Analysis was performed using medical information for all patients whose acylcarnitine blood levels were measured. Specifically, the median acylcarnitine level of all patients was used as the cutoff value, and all patients were classified into two groups: those whose acylcarnitine blood levels were above the cutoff value (high group) and those whose acylcarnitine blood levels were below the cutoff value (low group).

[0096] Figure 17 shows the overall survival time curves for the low and high value groups in the nutrition / exercise intervention and non-intervention groups. Overall survival was defined as the period from the start date of initial systemic chemotherapy to the date of death. In the non-intervention group, the overall survival period for the low value group tended to be shorter than that of the high value group, while in the nutrition / exercise intervention group, the overall survival period for the low value group was longer than that of the high value group (Figure 17). These results demonstrate that the effectiveness of nutritional therapy for patients with advanced pancreatic cancer or non-small cell lung cancer can be predicted using acylcarnitine blood levels.

[0097] The blood concentrations of carbon-number-specific acylcarnitines were evaluated by tandem mass spectrometry for blood samples collected from six of the 55 subjects shown in Example 2. In addition, the BCKDH phosphorylation levels were evaluated by the method described in Example 1 using normal liver tissue samples collected from the six subjects.

[0098] Using the hepatic pBCKDH phosphorylation level, three patients from whom plasmas 1 to 3 were collected were labeled as the hepatic pBCKDH level low group, and three patients from whom plasmas 4 to 6 were collected were labeled as the hepatic pBCKDH level high group. Labeling was performed based on the expression distribution in subjects whose hepatic pBCKDH was measured as shown in Example 2 ( FIG. 18 ).

[0099] It was found that the blood concentration of 2-carbon acylcarnitine (acetylcarnitine) in the group with high liver pBCDH levels was higher than that in the group with low pBCKDH phosphorylation levels (Figure 19). Note that acetylcarnitine accounts for the majority of blood acylcarnitines.

[0100] The concentrations of acylcarnitines classified by carbon number were evaluated by tandem mass spectrometry for liver tissue samples and muscle tissue samples collected from the pancreatic cancer neural invasion mouse model (INV) and the pancreatic cancer subcutaneous tumor mouse model (SC) shown in Example 4.

[0101] The concentration of carbon-2 acylcarnitine (acetylcarnitine) in skeletal muscle was higher in the INV model than in the SC model (Figure 20). The tissue concentration of carbon-3 acylcarnitine (propionylcarnitine) was higher in the INV model than in the SC model in both liver and skeletal muscle, with its hepatic concentration being significantly higher than that in skeletal muscle. Note that when the propionyl group (CH3-CH2-CO-) of propionylcarnitine is a dicarboxylic acid (-CO-CH2-CO-), it is called malonylcarnitine, but its retention time was short and it could not be evaluated in this assay.

[0102] As described above, the present invention can be widely implemented in many industrial fields, particularly in the fields of pharmaceuticals, food, medicine, etc., and is extremely useful.

[0103] REFERENCE SIGNS LIST 100 Prediction device 102 Control unit 102a Acquisition unit 102b Prediction unit 102b1 Conversion unit 102b2 Generation unit 102b3 Classification unit 102c Result output unit 102d Transmission unit 104 Communication interface unit 106 Storage unit 106a Data file 106b Prediction 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

1. A prediction method comprising a prediction step of predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for said patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH (branched-chain α-keto acid dehydrogenase) in the liver tissue of said patient or the concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of said patient with skeletal muscle loss.

2. The prediction method according to claim 1, wherein the patient with skeletal muscle loss is a patient with advanced cancer.

3. The prediction method according to claim 1 or 2, wherein the prediction step is executed by a control unit of an information processing device that includes a control unit.

4. A prediction device comprising a control unit, wherein the control unit comprises a prediction means for predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH (branched-chain α-keto acid dehydrogenase) in the liver tissue of the patient or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

5. A prediction device as described in claim 4, which is communicatively connected via a network to a terminal device that provides the analysis value or the concentration value, and wherein the control unit comprises: a data receiving means that receives the analysis value or the concentration value transmitted from the terminal device; and a result transmitting means that transmits the prediction result obtained by the prediction means to the terminal device.

6. A prediction program to be executed by an information processing device having a control unit, the prediction program comprising a prediction step for predicting the prognosis of a patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH (branched-chain α-keto acid dehydrogenase) in the liver tissue of the patient or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of the patient with skeletal muscle loss.

7. A computer-readable recording medium on which the prediction program according to claim 6 is recorded.

8. A prediction system comprising a prediction device having a control unit and a terminal device having a control unit connected to each other via a network so that they can communicate with each other, wherein the control unit of the terminal device comprises: a data transmission means for transmitting to the prediction device an analytical value of the phosphorylation state of branched-chain α-keto acid dehydrogenase (BCKDH) in the liver tissue of a patient with skeletal muscle loss or a concentration value of carnitines in the blood, urine, sweat, liver tissue, or muscle tissue of the patient with skeletal muscle loss; and a result receiving means for receiving a prediction result regarding the prognosis of the patient with skeletal muscle loss or a prediction result regarding the effectiveness of nutritional therapy for the patient with skeletal muscle loss, transmitted from the prediction device; and the control unit of the prediction device comprises: a data receiving means for receiving the analytical value or the concentration value transmitted from the terminal device; a prediction means for predicting the prognosis of the patient with skeletal muscle loss or the effectiveness of nutritional therapy for the patient with skeletal muscle loss using the analytical value or the concentration value received by the data receiving means; and a result transmitting means for transmitting the prediction result obtained by the prediction means to the terminal device.

9. A terminal device comprising a control unit, wherein the control unit comprises a result acquisition means for acquiring a prediction result regarding the prognosis of a patient with skeletal muscle loss or a prediction result regarding the effectiveness of nutritional therapy for said patient with skeletal muscle loss, wherein the prediction result is a prediction result of the prognosis of said patient with skeletal muscle loss or the effectiveness of nutritional therapy for said patient with skeletal muscle loss, using an analytical value of the phosphorylation state of BCKDH (branched-chain α-keto acid dehydrogenase) in the liver tissue of said patient with skeletal muscle loss or concentration values ​​of carnitines in the blood, urine, sweat, liver tissue or muscle tissue of said patient with skeletal muscle loss.

Images