KR20170092011A - Method for predicting risk of onset of cardio-metabolic disease - Google Patents
Method for predicting risk of onset of cardio-metabolic disease Download PDFInfo
- Publication number
- KR20170092011A KR20170092011A KR1020160013010A KR20160013010A KR20170092011A KR 20170092011 A KR20170092011 A KR 20170092011A KR 1020160013010 A KR1020160013010 A KR 1020160013010A KR 20160013010 A KR20160013010 A KR 20160013010A KR 20170092011 A KR20170092011 A KR 20170092011A
- Authority
- KR
- South Korea
- Prior art keywords
- fdg
- pet
- disease
- cardio
- breast cancer
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 208000001145 Metabolic Syndrome Diseases 0.000 title claims abstract description 9
- 238000002600 positron emission tomography Methods 0.000 claims description 30
- 201000007741 female breast cancer Diseases 0.000 claims description 9
- 201000002276 female breast carcinoma Diseases 0.000 claims description 9
- UFTFJSFQGQCHQW-UHFFFAOYSA-N triformin Chemical compound O=COCC(OC=O)COC=O UFTFJSFQGQCHQW-UHFFFAOYSA-N 0.000 claims description 8
- 206010012601 diabetes mellitus Diseases 0.000 claims description 7
- 206010006187 Breast cancer Diseases 0.000 claims description 5
- 208000026310 Breast neoplasm Diseases 0.000 claims description 5
- 206010020772 Hypertension Diseases 0.000 claims description 5
- 208000020854 vein disease Diseases 0.000 claims description 5
- AOYNUTHNTBLRMT-SLPGGIOYSA-N 2-deoxy-2-fluoro-aldehydo-D-glucose Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](F)C=O AOYNUTHNTBLRMT-SLPGGIOYSA-N 0.000 claims description 4
- 201000001320 Atherosclerosis Diseases 0.000 claims description 3
- 208000032928 Dyslipidaemia Diseases 0.000 claims description 3
- 208000017170 Lipid metabolism disease Diseases 0.000 claims description 3
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 201000010099 disease Diseases 0.000 claims description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 3
- 208000026106 cerebrovascular disease Diseases 0.000 claims description 2
- 208000031225 myocardial ischemia Diseases 0.000 claims description 2
- 230000002596 correlated effect Effects 0.000 claims 1
- 238000007918 intramuscular administration Methods 0.000 claims 1
- 210000000936 intestine Anatomy 0.000 abstract description 9
- 210000004185 liver Anatomy 0.000 abstract description 7
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 18
- 230000000968 intestinal effect Effects 0.000 description 17
- 238000002591 computed tomography Methods 0.000 description 15
- 102000015779 HDL Lipoproteins Human genes 0.000 description 11
- 108010010234 HDL Lipoproteins Proteins 0.000 description 11
- 108010007622 LDL Lipoproteins Proteins 0.000 description 10
- 102000007330 LDL Lipoproteins Human genes 0.000 description 10
- 150000003626 triacylglycerols Chemical class 0.000 description 9
- 235000012000 cholesterol Nutrition 0.000 description 7
- 230000002503 metabolic effect Effects 0.000 description 7
- 238000012360 testing method Methods 0.000 description 7
- 208000024172 Cardiovascular disease Diseases 0.000 description 6
- 206010028980 Neoplasm Diseases 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000000611 regression analysis Methods 0.000 description 4
- 238000012315 univariate regression analysis Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 230000002526 effect on cardiovascular system Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 239000008103 glucose Substances 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 230000002285 radioactive effect Effects 0.000 description 3
- 230000000391 smoking effect Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 208000004611 Abdominal Obesity Diseases 0.000 description 2
- 206010065941 Central obesity Diseases 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 150000002632 lipids Chemical class 0.000 description 2
- 239000008155 medical solution Substances 0.000 description 2
- 238000012314 multivariate regression analysis Methods 0.000 description 2
- 238000004451 qualitative analysis Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000007473 univariate analysis Methods 0.000 description 2
- AGEIRNYXMCEJRN-KGLIPLIRSA-N (1r,5s)-5-(4-iodophenyl)-8-methyl-8-azabicyclo[3.2.1]octane Chemical compound C1([C@@]23CC[C@@](CCC2)(N3C)[H])=CC=C(I)C=C1 AGEIRNYXMCEJRN-KGLIPLIRSA-N 0.000 description 1
- UXCAQJAQSWSNPQ-ZIVQXEJRSA-N 1-[(2r,4s,5r)-4-fluoranyl-5-(hydroxymethyl)oxolan-2-yl]-5-methylpyrimidine-2,4-dione Chemical compound O=C1NC(=O)C(C)=CN1[C@@H]1O[C@H](CO)[C@@H]([18F])C1 UXCAQJAQSWSNPQ-ZIVQXEJRSA-N 0.000 description 1
- MKXBOPXRKXGSTI-PJKMHFRUSA-N 1-[(2s,4s,5r)-2-fluoro-4-hydroxy-5-(hydroxymethyl)oxolan-2-yl]-5-methylpyrimidine-2,4-dione Chemical compound O=C1NC(=O)C(C)=CN1[C@]1(F)O[C@H](CO)[C@@H](O)C1 MKXBOPXRKXGSTI-PJKMHFRUSA-N 0.000 description 1
- -1 18 F Chemical class 0.000 description 1
- ZQAQXZBSGZUUNL-BJUDXGSMSA-N 2-[4-(methylamino)phenyl]-1,3-benzothiazol-6-ol Chemical compound C1=CC(N[11CH3])=CC=C1C1=NC2=CC=C(O)C=C2S1 ZQAQXZBSGZUUNL-BJUDXGSMSA-N 0.000 description 1
- ZCXUVYAZINUVJD-AHXZWLDOSA-N 2-deoxy-2-((18)F)fluoro-alpha-D-glucose Chemical compound OC[C@H]1O[C@H](O)[C@H]([18F])[C@@H](O)[C@@H]1O ZCXUVYAZINUVJD-AHXZWLDOSA-N 0.000 description 1
- 238000012935 Averaging Methods 0.000 description 1
- 208000014644 Brain disease Diseases 0.000 description 1
- 206010058838 Enterocolitis infectious Diseases 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 206010020880 Hypertrophy Diseases 0.000 description 1
- 208000022559 Inflammatory bowel disease Diseases 0.000 description 1
- 206010022489 Insulin Resistance Diseases 0.000 description 1
- 108010028554 LDL Cholesterol Proteins 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 238000011497 Univariate linear regression Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 210000000709 aorta Anatomy 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- 230000008033 biological extinction Effects 0.000 description 1
- 230000037396 body weight Effects 0.000 description 1
- 210000004534 cecum Anatomy 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 208000003167 cholangitis Diseases 0.000 description 1
- 210000001072 colon Anatomy 0.000 description 1
- 230000000112 colonic effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000012631 diagnostic technique Methods 0.000 description 1
- 235000014113 dietary fatty acids Nutrition 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 210000001198 duodenum Anatomy 0.000 description 1
- 235000006694 eating habits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229930195729 fatty acid Natural products 0.000 description 1
- 239000000194 fatty acid Substances 0.000 description 1
- 150000004665 fatty acids Chemical class 0.000 description 1
- 125000005816 fluoropropyl group Chemical group [H]C([H])(F)C([H])([H])C([H])([H])* 0.000 description 1
- 125000002791 glucosyl group Chemical group C1([C@H](O)[C@@H](O)[C@H](O)[C@H](O1)CO)* 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 230000001631 hypertensive effect Effects 0.000 description 1
- 208000027139 infectious colitis Diseases 0.000 description 1
- 208000027866 inflammatory disease Diseases 0.000 description 1
- 238000011221 initial treatment Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 210000002429 large intestine Anatomy 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- FFEARJCKVFRZRR-JJZBXVGDSA-N methionine c-11 Chemical compound [11CH3]SCC[C@H](N)C(O)=O FFEARJCKVFRZRR-JJZBXVGDSA-N 0.000 description 1
- 238000010202 multivariate logistic regression analysis Methods 0.000 description 1
- 238000011227 neoadjuvant chemotherapy Methods 0.000 description 1
- 238000009206 nuclear medicine Methods 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 239000012857 radioactive material Substances 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 210000002966 serum Anatomy 0.000 description 1
- 210000000813 small intestine Anatomy 0.000 description 1
- 210000001685 thyroid gland Anatomy 0.000 description 1
- 210000004881 tumor cell Anatomy 0.000 description 1
- 208000001072 type 2 diabetes mellitus Diseases 0.000 description 1
- 210000000689 upper leg Anatomy 0.000 description 1
- 230000035899 viability Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/037—Emission tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/502—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of breast, i.e. mammography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/503—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the heart
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Veterinary Medicine (AREA)
- Animal Behavior & Ethology (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Optics & Photonics (AREA)
- High Energy & Nuclear Physics (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Cardiology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
Description
The present invention relates to a method for providing information for predicting a subject having a high risk of developing a cardiovascular disease.
As dietary habits and lifestyle changes and aging, diabetes related diseases are increasing steadily every year. Heart disease is consistently high in the cause of death in Korea, and according to the National Statistical Office (NSO) survey, it ranked second after cancer.
Cardio-metabolic risk (CMR) is a factor that increases the risk of developing atherosclerosis-related diseases. Coronary risk factors include factors such as abdominal obesity, body mass index (BMI), age, sex, hypertension, insulin resistance, cholesterol, triglycerides, LDL, HDL, dyslipidemia and smoking as measured by waist circumference.
On the other hand, positron emission tomography (PET) is a method of detecting radioactive isotopes that emit positron to various basic metabolites and detecting extinction radiation generated by interaction between positron and substance after administration to human body It means a technique to create a tomographic image.
Radioisotopes used for PET is 18 F-FDG (Fluorodeoxy glucose) , 18F-FLT (Fluorothymidine), 18 F-FP-CIT (Fluoropropyl carbomethoxy-3b- (4-iodophenyltropane)), 11 C- methionine, 11 C -Acetate, 11 C-PIB (Pittsburgh compound B), 13 N-ammonia, and 82 Rb, among which 18 F-FDG is the most widely used.
PET was originally developed for the visualization of tumors because tumor cells absorb more 18 F-FDG than normal cells. However, the application of PET is gradually expanding and is now being used in non-tumor pathology physiology. Accordingly, there is a growing interest in the diversity of absorption of 18 F-FDG in general subjects.
In view of this background, the present inventors have for the first time discovered that there is a correlation between intestinal intake of 18 F-FDG and cardiovascular risk factors, and completed the present invention.
It is an object of the present invention to provide an information providing method for predicting a subject having a high risk of developing a cardio-metabolic disease.
In one aspect of the present invention, when the intake of 18 F-FDG (Fluorodeoxy glucose) in a subject's PET (Positron emission tomography) data is higher than the intake of 18 F-FDG in the liver, And determining that the risk of developing cardio-metabolic disease is high, comprising the step of determining the risk of developing cardio-metabolic disease.
In the information providing method of the present invention, 18 F-FDG refers to a compound having a structure represented by the following general formula (1) wherein oxygen at the 2-position of glucose is substituted with 18 F.
[Chemical Formula 1]
In the information providing method of the present invention, PET (positron emission tomography) examination can be used for the measurement of 18 F-FDG in the intestines or liver, and preferably PET / CT (computed tomography) Can be used.
The PET test is a nuclear medicine testing method that can display physiological, chemical, and functional images of the human body in three dimensions using a radioactive material that emits positron. Specifically, PET uses radioactive isotope-labeled compounds such as 18 F, 11 C, 13 N, and 15 O as radioactive isotopes to trace the biochemical changes of the body using compounds such as glucose, amino acid, fatty acid, It is an invasive diagnostic technique. This PET test is mainly used for evaluating cancer test, brain disease and the like, but the present invention is used to provide information for predicting the risk of developing a deep vein disease in a subject.
In addition, the PET / CT inspection is an inspection method for obtaining a more accurate image by attenuating and correcting a PET image by CT. The CT scan is an inspection method for constructing a cross-sectional image by exposing a subject to a trace amount of X-rays and then calculating a relative X-ray absorption coefficient of each tissue using a computer.
In the information providing method of the present invention, the subject refers to all animals including humans, preferably a breast cancer patient, more preferably a female breast cancer patient, most preferably suffering from a deep vein disease It can be a female breast cancer patient who is not.
The degree of intestinal 18 F-FDG intake and the level of 18 F-FDG uptake in the liver are preferably determined by measuring the standardized uptake value (SUV) in the PET data of the subject. The measurement of SUV is described, for example, in Med. Phys. 39 (6), June 2012, and the like, but the present invention is not limited thereto.
Specifically, the SUV is a software calculation of the metabolic hypertrophy portion detected in a PET image, which means the radiation emitted per gram of tissue divided by the amount of radioactivity administered per kilogram of body weight.
In the present invention, the cardiovascular disease may be cardiovascular disease or diabetes, and specifically, it may be any one selected from the group consisting of hypertension, ischemic heart disease, atherosclerosis, cerebrovascular disease, dyslipidemia and diabetes.
Prediction in the present invention means predicting a medical result in advance, and means for predicting the onset of a deep vein disease for the purpose of the present invention.
In the present invention, the cardio-metabolic risk (CMR) factor refers to a group of factors that increase the risk of cardiovascular disease and diabetes, including age; gender; BMI (body mass index); Abdominal obesity; Blood sugar testing (BST); Or serum triglycerides, cholesterol, high density lipoprotein (HDL), or low density lipo protein (LDL).
In a specific embodiment of the present invention embodiments, by comparing the 18 F-FDG uptake in the liver and the intestine 18 F-FDG uptake of the subject, classifying the intake of the 18 F-FDG in the entire lowest patients with low-fed groups and he said at least one sheet fraction within the 18 F-FDG uptake is higher than 18 F-FDG uptake in the liver or the equivalent intake patients were divided into groups. Was in the univariate regression results of the analysis, the center metabolic risk factors, BMI (body mass index) and triglycerides are enteric 18 F-FDG uptake and a significant positive correlation (positive correlation) against the shim metabolic risk factors of the subject .
In another embodiment of the present invention, the different lengths of the fractions were placed in a three-dimensional volume of interest (VOI) and the Standardized Uptake Value (SUV) was measured in different long segments. In addition, total bowel (TB) SUVs were measured by averaging different SUVs in different intestinal fractions. Multiple regression analysis showed that BMI and triglycerides were independent predictors of TB maximum SU ( max ) and TB mean SU (SUV).
The maximum standard intake factor (SUV max ) means a maximum value of the standard intake factor, and the average standard intake factor (SUV mean ) means a threshold value limit of 40% SUV max .
The 18 F-FDG uptake pattern of the present invention has a significant positive correlation with cardiovascular risk factors, particularly BMI and triglycerides, and thus can be useful for predicting subjects at high risk for the development of cardiovascular disease.
According to the present invention, it is possible to provide information that can predict a subject having a high risk of developing a deep vein disease.
Fig. 1 (a) is an image of PET / CT images of 18 F-FDG uptake in women with low-dose breast cancer, and (b) PET / CT images of 18 F-FDG uptake in female breast cancer patients .
FIG. 2 is a graph showing correlation between TB SUV max and coronary risk factors. FIG. 2B is a graph showing correlation between a, b, B, c, triglyceride, cholesterol, LDL and f HDL.
Hereinafter, preferred embodiments of the present invention will be described in order to facilitate understanding of the present invention. However, these embodiments are provided only for the purpose of easier understanding of the present invention, and the scope of the present invention is not limited by the following description.
EXAMPLES Thyroid metabolic risk factors and intestinal 18 Correlation between F-FDG uptake
1. Selection of experiment subjects
We retrospectively reviewed the FDG PET / CT database of Ewha Womans University Women's Cancer Center and selected a breast cancer group to obtain a sufficient number of subjects. This experiment was approved by the institutional review board (IRB) of Mokdong Hospital, Ewha Womans University. All the procedures, including human participation, in this experiment followed the IRB Code of Ethics and followed the 1964 Helsinki Declaration and subsequent amendments or equivalent ethical codes. All patients received consent documents via IRB, and all data were anonymized before analysis.
A database of 425 female breast cancer patients who had undergone 18 F-FDG PET / CT for initial screening between 2012 and 2015 was used as a database and the results were compared with those of 18 F-FDG The purpose of this study was to investigate the association between intestinal intake and cardio-metabolic risk (CMR) factors, so 99 patients with diabetes or hypertension were excluded.
A total of 326 non-diabetic and non-hypertensive patients were included in the final statistical analysis. All of the 326 patients were suffering from inflammatory diseases such as inflammatory bowel disease, infectious colitis, cholangitis, rheumatism, Because of this problem, no medication was administered. In addition, no patients underwent neoadjuvant chemotherapy prior to PET / CT.
2. Measurement of cardiovascular risk factors
On the medical chart, the social viability of smoking or drinking was examined. Body mass data, including height and weight, were used for PET examinations at the PET center. Body mass index (BMI) was calculated using height and weight.
Standard clinical data were reviewed prior to initial treatment and triglyceride, cholesterol high density lipoprotein (HDL) and low density lipoprotein (LDL) were measured from the lipid profile.
3. Intestines 18 Measurement of F-FDG uptake
For 18 F-FDG PET / CT, we fasted for at least 6 hours, and then injected intravenously with 5.18 MBq / kg FDG and taken 1 hour later. Before the administration of FDG, glucose levels were measured and found to be below 140 mg / dL. Non-contrast CT was first taken and PET images were taken from the cranium to the thigh (Siemens Biograph mCT 128 slice CT) (Siemens Medical Solutions, Erlangen, Germany). Low dose CT (CARE Dose system, Siemens Medical Solutions, Erlagen, Germany) was photographed to perform attenuation correction. The spatial resolution of the PET center was 2.0 mm FWHM (full width at half maximum) in the transverse and axial directions. 3D emission scans and 2 min scan / bed position x 5-7 positions were obtained as parameters for PET images. PET images were acquired using a 3.0 ×
Intestinal 18 F-FDG uptake was evaluated in two read self qualitative and quantitative. The analysis was performed by consensus, and the analysts were strictly blocked from the coronary risk factor data.
For qualitative analysis, the patients were divided into intestinal 18 F-FDG uptake of high and low patient patient. Between the uptake of 18 F-FDG in the entire section above or equal to the patient than the cross-classification, and at least one fraction intestinal 18 F-FDG uptake low patients with low intake group compared with the intake of 18 F-FDG in High intake group.
For quantitative analysis, the maximum and average normalized intake values (SUV max and SUV mean ) in different intestinal fractions were measured after placing in a three-dimensional volume of interest (VOI). The SUV mean was measured as the threshold limit of 40% SUV max . Intestinal 18 F-FDG uptake in the small intestine was measured in the duodenum third region, plant and distal ring president. 18 F-FDG uptake in the large intestine was measured in the cecum, right ascending aorta, left colonic and descending colon-S colon junctions. Then, SUVs at different bowel sites were averaged and measured for total bowel (TB SUV max and TB SUV mean ).
4. Coronary risk factors and intestinal 18 Correlation analysis between F-FDG uptake
4-1. Statistical method
Data were expressed as 95% confidence interval (CI), and significance was determined when p value was less than 0.05. All statistical analyzes were performed using SPSS software version 18.0 (SPSS Inc., Chicago, IL, USA). The association between anthropometric data (key, weight and BMI), human data (age), and clinical test data (BST and lipid profiles) and intestinal 18 F-FDG uptake was assessed using an independent t- Pearson correlation was used for continuous SUV. Regression analysis was used to measure the association between each coronary risk factor and intestinal 18 F-FDG uptake. Multivariate regression analysis was then performed to measure the independent effects of each variable on intestinal 18 F-FDG uptake.
4-2. Patient data
None of the 326 patients had smoking or alcohol history. The mean age of the patients was 47.2 ± 9.2 years (range 28-79) and the mean height, weight, and BMI were 158.3 ± 5.4 cm (range 144.0 - 173.0), 57.6 ± 8.2kg 3.2 (range 15.1 - 34.8). The blood sugar testing (BST) values were 99.0 ± 10.9 mg / dl (range 75.0 - 188.0) and the triglyceride and cholesterol levels were 96.9 ± 54.8 mg / dl (range 10.0 - 393.0) and 191.5 ± 36.6 mg / - 345.0). HDL and LDL levels were 53.2 ± 22.6 mg / dl (range 26.0 to 340.0) and 113.4 ± 36. mg / dl (range 37.0 - 237.0).
4-3. Qualitative analysis
Intestinal 18 to F-FDG uptake based on a visual rating, were included in the group are low intake of patients 100 (30.7%), a patient of 226 patients (69.3%) and was included in the intake group. In the core metabolic risk factors, age (p = 0.004), when the BMI (p <0.001), and TG (p <0.001), there was a significant difference depending on the visual rating of the intestine 18 F-FDG uptake. Detailed data are shown in Table 1.
Table 1 enteric 18 F-FDG-core metabolic risk factors according to a visual rating of the intake
In univariate regression analysis, high age ( p = 0.009), high BMI ( p <0.001) and high triglyceride ( p = 0.001) were significant factors for the increase of intestinal 18F-FDG uptake in PET. Cholesterol levels showed ambiguous significance ( p = 0.077).
Detailed data are shown in Table 2. BMI and triglycerides were independent predictors of intestinal 18 F-FDG uptake ( p = 0.027, p < 0.05 ), respectively, when multiple regression analysis was performed for three significant variables and univariate analysis was performed for ambiguous variables p = 0.023). A typical example of the core according to the difference in the metabolic risk factors visual rating of the intestine 18 F-FDG uptake in PET are shown in Fig. Fig. 1 (a) is a 18- F-FDG PET / CR image of a 57-year-old female breast cancer patient, and the intake of 18 F-FDG in the intestine was lower than the liver. The BMI of this patient was 20.0 and the triglyceride level was 45 mg / dL. Fig. 1 (b) is a 18- F-FDG PET / CT image of a 64-year-old female breast cancer patient with high 18 F-FDG uptake in the intestine. The patient had a BMI of 27.3 and a triglyceride level of 393 mg / dL.
[Table 2] Univariate and multivariate logistic regression analysis
4-4. Quantitative analysis
The overall TB SUV max is 2.0 ± 0.5 (range 1.2 to 4.0) and the TB SUV mean is 1.7 ± 0.4 (range 1.0 to 3.1). According to Pearson correlation, TB SUV max is age (r = 0.203 and p <0.001, Fig. 2a), BMI (r = 0.373 and p <0.001, Figure 2b), triglycerides (r = 0.338 and p <0.001, Fig. 2c), cholesterol (r = 0.148 and p = 0.008, Fig. 2d) and LDL (r = 0.143 and p = 0.024, Fig. 2e) and exhibited a significant positive correlation, HDL (r = -0.147 p = 0.022 and , Fig. 2f). In addition, the TB SUV mean was significantly lower in the age group (r = 0.175 and p = 0.001), BMI (r = 0.333 and p <0.001), triglyceride (r = 0.321 and p <0.001), cholesterol (r = 0.151 and p = And LDL (r = 0.136 and p = 0.0330), respectively, and showed a significant negative correlation with HDL (r = -0.131 and p = 0.040).
Univariate regression analysis, high age (p <0.001), higher BMI (p <0.001), high triglycerides (p <0.001), high cholesterol (p = 0.008), low HDL (p = 0.022), and high LDL (p = 0.024) was a significant factor in the increase of TB SUV max in PET. Further, in the TB SUV mean, age (p = 0.001), BMI ( p <0.001), TG (p <0.001), cholesterol (p = 0.007), HDL ( p = 0.040), and LDL (p = 0.033) This was a significant factor. BMI and triglycerides were higher in the TB SUV max ( p = 0.006 and p = 0.004) and TB SUV mean ( p = 0.007 and p = 0.004) in PET, respectively, when multiple regression analysis was performed on the six significant variables in the univariate analysis 0.017). Detailed data are shown in Table 3.
[Table 3] Univariate and Multivariate Regression Analysis Results
Claims (6)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020160013010A KR101894098B1 (en) | 2016-02-02 | 2016-02-02 | Method for predicting risk of onset of cardio-metabolic disease |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020160013010A KR101894098B1 (en) | 2016-02-02 | 2016-02-02 | Method for predicting risk of onset of cardio-metabolic disease |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| KR20170092011A true KR20170092011A (en) | 2017-08-10 |
| KR101894098B1 KR101894098B1 (en) | 2018-08-31 |
Family
ID=59652291
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020160013010A KR101894098B1 (en) | 2016-02-02 | 2016-02-02 | Method for predicting risk of onset of cardio-metabolic disease |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR101894098B1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021215821A1 (en) * | 2020-04-21 | 2021-10-28 | 가톨릭대학교 산학협력단 | Method for predicting neurologic prognosis of post-cardiac arrest syndrome patient |
| WO2023044689A1 (en) * | 2021-09-23 | 2023-03-30 | 苏州大学 | Immunometabolic myocardial infarction patch, preparation method therefor, and application thereof |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102532906B1 (en) * | 2021-02-26 | 2023-05-17 | 주식회사 에이티앤씨 | Method for providing the information for predicting risk of Alzheimer's disease and method for predicting risk of Alzheimer's disease using the same |
| WO2022182058A1 (en) * | 2021-02-26 | 2022-09-01 | 주식회사 에이티앤씨 | Method for providing information for predicting risk of alzheimer's disease, and method for predicting risk of alzheimer's disease and apparatus including artificial intelligence model, which use same |
| KR102543914B1 (en) * | 2021-04-14 | 2023-06-21 | 주식회사 에이티앤씨 | Alzheimer's disease risk prediction device including artificial intelligence model |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090027842A (en) * | 2007-09-13 | 2009-03-18 | 연세대학교 산학협력단 | Ischemic heart disease risk prediction apparatus, method for the same, and computer readable recording medium on which program for the same is recorded |
| KR101351411B1 (en) * | 2011-12-16 | 2014-01-15 | 전북대학교산학협력단 | Method for diagnosing selectively malignant tumor by differentiating malignant tumor and inflammation in F-18 FDG positron emission tomography using pioglitazone |
| KR20150043898A (en) * | 2013-10-15 | 2015-04-23 | 이화여자대학교 산학협력단 | A method to provide information for prognosis of breast cancer |
| KR20160002495A (en) * | 2014-06-30 | 2016-01-08 | 연세대학교 산학협력단 | Methods for Predicting or Determining Cardiovascular Disesase Using Hepatic Enzymes and Plasma Metabolites |
| EP3095382A1 (en) * | 2011-06-03 | 2016-11-23 | Bayer Healthcare LLC | System and method for rapid quantitative dynamic molecular imaging scans |
| KR20160134072A (en) * | 2015-05-14 | 2016-11-23 | 이화여자대학교 산학협력단 | Method for providing information of prediction for recurrence of breast cancer |
| US20170172527A1 (en) * | 2014-04-04 | 2017-06-22 | Bayer Healthcare Llc | Combined radiopharmaceutical imaging system |
-
2016
- 2016-02-02 KR KR1020160013010A patent/KR101894098B1/en active IP Right Grant
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090027842A (en) * | 2007-09-13 | 2009-03-18 | 연세대학교 산학협력단 | Ischemic heart disease risk prediction apparatus, method for the same, and computer readable recording medium on which program for the same is recorded |
| EP3095382A1 (en) * | 2011-06-03 | 2016-11-23 | Bayer Healthcare LLC | System and method for rapid quantitative dynamic molecular imaging scans |
| KR101351411B1 (en) * | 2011-12-16 | 2014-01-15 | 전북대학교산학협력단 | Method for diagnosing selectively malignant tumor by differentiating malignant tumor and inflammation in F-18 FDG positron emission tomography using pioglitazone |
| KR20150043898A (en) * | 2013-10-15 | 2015-04-23 | 이화여자대학교 산학협력단 | A method to provide information for prognosis of breast cancer |
| US20170172527A1 (en) * | 2014-04-04 | 2017-06-22 | Bayer Healthcare Llc | Combined radiopharmaceutical imaging system |
| KR20160002495A (en) * | 2014-06-30 | 2016-01-08 | 연세대학교 산학협력단 | Methods for Predicting or Determining Cardiovascular Disesase Using Hepatic Enzymes and Plasma Metabolites |
| KR20160134072A (en) * | 2015-05-14 | 2016-11-23 | 이화여자대학교 산학협력단 | Method for providing information of prediction for recurrence of breast cancer |
Non-Patent Citations (2)
| Title |
|---|
| Journal of the American college of cardiology, Balaji Natarajan et al., Vol.65, Iss. 10S, 2015. 03. 17 |
| Positron Emission Tomography: Basic Science, Dale L. Bailey et al., Springer Science & Business media, 2005. 7.4. |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021215821A1 (en) * | 2020-04-21 | 2021-10-28 | 가톨릭대학교 산학협력단 | Method for predicting neurologic prognosis of post-cardiac arrest syndrome patient |
| KR20210130038A (en) * | 2020-04-21 | 2021-10-29 | 이화여자대학교 산학협력단 | Method for predicting neurological prognosis in a patient with post-cardiac arrest syndrome |
| WO2023044689A1 (en) * | 2021-09-23 | 2023-03-30 | 苏州大学 | Immunometabolic myocardial infarction patch, preparation method therefor, and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| KR101894098B1 (en) | 2018-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18 F-FDG in healthy volunteers | |
| Tan et al. | Total-body PET/CT using half-dose FDG and compared with conventional PET/CT using full-dose FDG in lung cancer | |
| van Sluis et al. | Image quality and semiquantitative measurements on the biograph vision PET/CT system: initial experiences and comparison with the biograph mCT | |
| KR101894098B1 (en) | Method for predicting risk of onset of cardio-metabolic disease | |
| Hu et al. | Total-body 18 F-FDG PET/CT scan in oncology patients: how fast could it be? | |
| Bowen et al. | Initial characterization of a dedicated breast PET/CT scanner during human imaging | |
| Sörensen et al. | Regional distribution and kinetics of [18 F] fluciclovine (anti-[18 F] FACBC), a tracer of amino acid transport, in subjects with primary prostate cancer | |
| Li et al. | Effective radiation dose of 18F-FDG PET/CT: how much does diagnostic CT contribute? | |
| Blomberg et al. | Impact of personal characteristics and technical factors on quantification of sodium 18F-fluoride uptake in human arteries: prospective evaluation of healthy subjects | |
| Marin et al. | Clinical impact of an adaptive statistical iterative reconstruction algorithm for detection of hypervascular liver tumours using a low tube voltage, high tube current MDCT technique | |
| Sanghera et al. | Reproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer | |
| Hérin et al. | Use of Model-Based Iterative Reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study | |
| van Sluis et al. | Shortened duration whole body 18F-FDG PET Patlak imaging on the Biograph Vision Quadra PET/CT using a population-averaged input function | |
| Singhal et al. | 18F-PBR06 versus 11C-PBR28 PET for assessing white matter translocator protein binding in multiple sclerosis | |
| Jauw et al. | Noise-induced variability of immuno-PET with zirconium-89-labeled antibodies: an analysis based on count-reduced clinical images | |
| Alameen et al. | Radiobiological risks in terms of effective dose and organ dose from 18F-FDG whole-body PET/CT procedures | |
| Scagliori et al. | Conflicting or complementary role of computed tomography (CT) and positron emission tomography (PET)/CT in the assessment of thymic cancer and thymoma: our experience and literature review | |
| Hu et al. | Feasibility of ultra-low 18F-FDG activity acquisitions using total-body PET/CT | |
| Degirmenci et al. | Standardized uptake value-based evaluations of solitary pulmonary nodules using F-18 fluorodeoxyglucose-PET/computed tomography | |
| LeBlanc et al. | Thoracic and abdominal organ uptake of 2‐deoxy‐2‐[18F] fluoro‐D‐glucose (18FDG) with positron emission tomography in the normal dog | |
| Hosseini Nasab et al. | Organ equivalent dose and lifetime attributable risk of cancer incidence and mortality associated with cardiac CT angiography | |
| van der Vos et al. | Comparison of a free-breathing CT and an expiratory breath-hold CT with regard to spatial alignment of amplitude-based respiratory-gated PET and CT images | |
| Batallés et al. | Variations of the hepatic SUV in relation to the body mass index in whole body PET-CT studies | |
| Baldassi et al. | Image quality evaluation for a clinical organ-targeted PET camera | |
| Kedves et al. | Predictive value of diffusion, glucose metabolism parameters of PET/MR in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A201 | Request for examination | ||
| E902 | Notification of reason for refusal | ||
| E701 | Decision to grant or registration of patent right | ||
| GRNT | Written decision to grant |