JP4533042B2 - How to determine diabetes and abnormal glucose metabolism - Google Patents

Description

  The present invention relates to a method for analyzing the state of glucose metabolism, and more particularly, to a method for accurately determining whether there is abnormal glucose metabolism or diabetes from a fasting blood glucose level and a fasting blood insulin level in a shorter time.

  Diabetes is a syndrome characterized by chronic persistence of a high blood glucose level (blood glucose level) (hyperglycemia), and the relative and absolute lack of action of insulin, a hypoglycemic hormone. Caused by. Symptoms of diabetes include dry mouth, heavy drinking, polyuria, high blood sugar such as weight loss and dullness, and chronic complications. In many cases, the patient is asymptomatic and easily left unattended. However, abnormal glucose metabolism is known to increase the risk of life prognosis as a risk factor for arteriosclerosis that constitutes metabolic syndrome as well as hypertension, hyperlipidemia, abdominal obesity, etc. If the abnormality persists over a long period of time, not only diabetes-related complications such as retinopathy, nephropathy, and neuropathy, but also arteriosclerosis will be promoted. It is also known that risk factors that make up these metabolic syndromes, even if they are minor abnormalities, overlap, the risk of cardiovascular disease is dramatically increased. It is not a mere accidental duplication, and all risk factors have a common pathological condition of visceral fat, obesity and insulin resistance (see Non-Patent Documents 1 to 3). Therefore, in order to prevent the progression of the disease state and reduce the risk of cardiovascular disease, it is important to detect the existence of these risk factors including abnormal glucose metabolism as early as possible and correct the distortion of lifestyle habits. It is believed that.

  Currently, in health examinations conducted by many government agencies and companies, the determination of abnormal glucose metabolism or diabetes is based only on fasting blood glucose levels because of its simplicity. However, it is problematic in terms of accuracy to determine the presence or absence of abnormal glucose metabolism only with this fasting blood glucose level. For example, the oral glucose tolerance test (OGTT) widely known as a reliable criterion for diabetes ), The false negative rate was high, and not only the boundary type exhibiting postprandial hyperglycemia but also the diabetes type glucose metabolism abnormality was often overlooked. On the other hand, although OGTT has no problem in terms of accuracy of judgment, it is used to screen a person with abnormal glucose metabolism from a large number of examinees who have not yet been diagnosed with abnormal glucose metabolism in a health checkup. Has a problem that it takes a long time, is complicated and excessively costly, and the burden on the subject is large.

Therefore, it is suitable for use for early detection of the presence or absence of abnormal sugar metabolism or metabolic syndrome in health checkups, etc. There was a need for a method that could determine the state of the.
Nakamura T. et al. Tsuboro Y. , Et al. : Magneto of suspended multiple risk factors forensic heart disease in Japan employees: A case-control study. Jpn Circ J, 65: 11-17, 2001. Reaven GM: Banting Lecture 1988. Role of insulin resistance in human disease. Diabetes, 37: 1595-1607, 1988. Norman M.M. Kaplan, MD et al. The Deadly Quartet, Upper-Body Obesity, Glucose Intolerance, Hypertriglyceridemia, and Hypertension. Arch International Med. 149: 1514-1520, 1989. D. R. Matthews, J.M. P. Hosker, et al. : Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulative concen- tations in man. Diabetologia, 28: 412-419, 1985. Steven M.M. Haffner, MD, C.I. Gonzalez, MD, et al. A Prospective Analysis of the HOMA Model. Diabetes Care, 19:10, 1138-1141, 1996.

  Therefore, the problem of the present invention is that the time required for early detection of the presence or absence of abnormal sugar metabolism or metabolic syndrome, which does not have the problems of the conventional determination methods for abnormal sugar metabolism or diabetes, is short. It is an object of the present invention to provide a method for analyzing the state of sugar metabolism that is inexpensive, does not burden the subject, and is extremely reliable.

  The present inventor analyzed the distribution of the glucose metabolism state of many subjects in the course of diligent research to solve the above problems, and determined the insulin resistance according to the glucose metabolism state determined based on the analysis result. The inventors have found that the state of sugar metabolism to be analyzed can be easily and accurately determined by using a certain relationship between the index (R) and the β cell function index (β) as a reference, and the present invention has been completed.

That is, the present invention uses the insulin resistance index (R) and the β cell function index (β) to be analyzed based on a certain relationship between the insulin resistance index (R) and the β cell function index (β). Determining the state of sugar metabolism in the subject to be analyzed, the method for analyzing the state of sugar metabolism,
The glucose metabolism status of a number of subjects obtained using a known method for determining a glucose metabolism status, wherein the insulin resistance index (R) and β cell function index (β) The method relates to a method determined from a distribution relating to an insulin resistance index (R) and a beta cell function index (β) of a large number of subjects.

In the present invention, the fixed relationship between the insulin resistance index (R) and the β cell function index (β) is at least the following steps A) to D):
A) Based on a known method for determining the state of sugar metabolism of a large number of subjects, at least a normal type that does not have a sugar metabolism abnormality and a boundary that has a sugar metabolism abnormality but not diabetes Categorizing into 3 or more types of diabetes and diabetes type with abnormal sugar metabolism,
B) From the insulin resistance index (R) and β cell function index (β) of the subjects of the above-mentioned many cases, the insulin resistance index (R) and the β cell function index (β) or 100-β on the plane And plotting the large number of subjects so that the state of glucose metabolism classified in step A) can be identified,
C) dividing the plane according to the state of sugar metabolism visually or statistically from the distribution of the state of sugar metabolism of the majority of subjects plotted in step B);
D) Relational expression between the insulin resistance index (R) and the β cell function index (β) and / or the insulin resistance index (R) and / or β representing each plane portion divided in step C) Calculating a numerical range of the cellular function index (β);
Determined by the process comprising:
The above-mentioned method, which is a relational expression between insulin resistance index (R) and β cell function index (β) and / or a numerical range of insulin resistance index (R) and / or β cell function index (β) About.

  In the present invention, the known method for determining the state of glucose metabolism is a method in which the state of glucose metabolism is determined from the fasting blood glucose level by the oral glucose tolerance test and the blood glucose level 2 hours after the glucose load. It relates to said method.

The present invention also provides that a fixed relationship between the insulin resistance index (R) and the β cell function index (β)
β> a × R + b or β ≦ a × R + b (a and b are each independently a real number), and
k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m and n are each independently a real number),
It relates to said method.

The present invention still further provides that the constant relationship between the insulin resistance index (R) and the β cell function index (β) is:
β ≦ c × R + d, c × R + d <β ≦ c ′ × R + d ′, or β> c ′ × R + d ′ (where c, c ′, d and d ′ are each independently a real number), and
k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m and n are each independently a real number),
It relates to said method.

In the present invention, the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR), the β-cell function index (β) is a homeostasis model assessment β (HOMA-β), wherein a is 40 to 70, b is -10 to 30, k is 0.2 or more, l is 15 or less, m is -500 or more, and n is 2000 or less. Also related.
In the present invention, the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR), the β cell function index (β) is a homeostasis model assessment β (HOMA-β), c is 10 to 40, c ′ is 40 to 70, d is −30 to 20, d ′ is −10 to 30, k is 0.2 or more, l is 15 or less, and m is It also relates to the method as described above, wherein -500 or more and n is 2000 or less.

In the present invention, the constant relationship between the insulin resistance index (R) and the β cell function index (β) reflects the insulin secretion index or the insulin secretion ability calculated from the numerical value related to insulin secretion. The method described above is expressed in a three-dimensional space that further includes a relationship with the index.
The present invention also relates to the above method, wherein the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR), and the β cell function index (β) is a homeostasis model assessment β (HOMA-β). .

The present invention is also a method for examining a health condition,
i) determining that an indicator representing at least one or more health conditions of a subject to be examined is greater than and / or less than a certain reference value; and
ii) determining the state of glucose metabolism of the subject to be examined using the method according to any one of claims 1 to 7;
The method relates to determining whether or not the object to be inspected is a metabolic syndrome.

  Further, in the present invention, the index representing the health state is systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, blood triglyceride value, blood cholesterol value, blood good cholesterol value, blood uric acid value, albumin The present invention relates to a method for examining the health condition, which is excretion rate and blood C-reactive protein concentration.

The present invention also provides a computer for analyzing the state of sugar metabolism of a subject to be analyzed,
1) Regarding the insulin metabolism index (R) and β-cell function index (β) of each of the multiple subjects, the glucose metabolism status of the multiple subjects obtained using a known method for determining the status of glucose metabolism Means for storing in the storage unit when a fixed relationship between the insulin resistance index (R) and the β cell function index (β) determined from the distribution is input;
2) When the numerical values related to the insulin resistance index (R) and β cell function index (β) to be analyzed are input, the calculation unit calculates the insulin resistance index (R) and β cell function index (β). means,
3) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are the insulin resistance index (R) and β cell function index (1) stored in the storage unit in 1) a means for calculating whether or not to satisfy a certain relationship with β) in the calculation unit and determining the certain relationship that the object to be analyzed satisfies;
4) Means for determining the state of sugar metabolism of the object to be analyzed based on the fixed relationship satisfied by the object to be analyzed determined in 3) 5) Means for outputting the state of sugar metabolism of the object to be analyzed from the output unit ,
Related to the program to function as.

The present invention also provides a computer for analyzing the state of sugar metabolism of a subject to be analyzed,
I) Based on a known method for determining the state of sugar metabolism, the sugar metabolism state is at least a normal type having no abnormal sugar metabolism, a boundary type having abnormal sugar metabolism but not diabetic, and a diabetic type having abnormal sugar metabolism. When numerical values relating to the state of glucose metabolism of a large number of subjects classified into three or more types and the insulin resistance index (R) and β cell function index (β) of the large number of subjects are input, Means for calculating the insulin resistance index (R) and β cell function index (β) of a large number of subjects in a calculation unit;
II) The insulin resistance index (R) is obtained by statistically processing the distribution of the state of glucose metabolism of a large number of subjects in the arithmetic unit based on the insulin resistance index (R) and the β cell function index (β). ) And β-cell function index (β), a relational expression between insulin resistance index (R) and β-cell function index (β) and / or insulin resistance index according to the state of glucose metabolism. (R) and / or means for determining a numerical range of the β cell function index (β) and storing it in the storage unit,
III) When numerical values relating to the insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are input, the insulin resistance index (R) and β cell function index (β) are calculated by the calculation unit. means,
IV) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are stored in II), the insulin resistance index (R) and β cell function index (β) Means for calculating whether or not the relational expression and / or the numerical range for each state of sugar metabolism satisfying the certain relationship of the metabolic metabolism is satisfied, and determining the relational expression and / or the numerical range that the object satisfies,
V) Subject to be analyzed based on a relational expression and / or numerical range that is a fixed relationship between the insulin resistance index (R) and β-cell function index (β) satisfied by the subject determined in step IV) To determine the state of sugar metabolism in food
VI) Means for outputting the state of sugar metabolism to be analyzed from the output unit,
Related to the program to function as.
Furthermore, the present invention relates to the above program, wherein the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR) and the β cell function index (β) is a homeostasis model assessment β (HOMA-β). .

  The present invention is based on the result of analyzing the relationship between the insulin resistance of a subject to be analyzed and the dynamics of pancreatic β-cell function using the above configuration as an index of the subject's insulin resistance and pancreatic β-cell function as an index. Thus, it is possible to accurately determine the state of sugar metabolism of the subject to be analyzed. Insulin resistance index (R) and β, which can be easily calculated from test values and measured values obtained by simple tests and measurements, as indicators of insulin resistance and pancreatic β cell function to be analyzed By using the cell function index (β), it became possible to analyze the state of sugar metabolism economically and easily.

According to the method of the present invention, the state of sugar metabolism to be analyzed can be determined very accurately, the false negative rate is extremely low, so that the reliability is high and not only diabetes but also a boundary that does not lead to diabetes. Since there is almost no oversight of the type, especially the boundary type that exhibits postprandial hyperglycemia, it is possible to detect a subject having an abnormal glucose metabolism at an early stage. Further, according to the method of the present invention, it is possible to make a judgment with excellent accuracy comparable to OGTT without performing OGTT regarding the state of sugar metabolism to be analyzed. Furthermore, since abnormal glucose metabolism is one of arteriosclerosis risk factors constituting metabolic syndrome, by detecting abnormal glucose metabolism by the method of the present invention, it is combined with the determination of the presence or absence of other risk factors. The presence or absence of a syndrome can also be determined more accurately.
Moreover, according to the method of the present invention, the state of sugar metabolism can be analyzed in a very short time without cost and without taking complicated procedures. For example, when the program according to the method of the present invention is used, insulin resistance can be obtained simply by inputting a fasting blood glucose level (FPG), a fasting blood insulin level (FIRI), etc. measured by blood sampling into a computer or the like. An index (R), a β cell function index (β), and the like are calculated, and the state of sugar metabolism to be analyzed can be automatically output on a display such as a computer.
Therefore, the method of the present invention that exhibits such effects can be used, for example, in general medical examinations and the like, and the state of glucose metabolism can be accurately determined without imposing an economic, time, and physical burden on the examinee. In addition to being able to determine, the risk of the cardiovascular disease of the examinee can be determined accurately at an early stage by associating it with other various health conditions, disease test results, and the like.

  Furthermore, according to the present invention, the state of sugar metabolism to be analyzed can be displayed as a point or the like on a plane or a three-dimensional space classified into categories according to the state of sugar metabolism. The current state and future risks can be visually confirmed from the current position or surrounding pathological conditions. In addition, in the object analyzed by the method of the present invention over time, the change over time of the point can also be shown as a vector, so it is easy to understand the degree of improvement and progress from the past, future risks, etc. Awareness of the subject can be improved to improve habits.

  That is, the method of the present invention can determine the state of sugar metabolism of a subject to be analyzed very accurately and inexpensively in a short time, and can quickly find a subject having abnormal sugar metabolism or further having metabolic syndrome. In addition, by visually expressing the state of the subject's glucose metabolism, it can be used to improve the lifestyle of the subject. It has been realized.

In the present invention, “the state of glucose metabolism” refers to the metabolic state of glucose in the body of the subject, and has two types of normal and abnormal glucose metabolism including diabetes (DM), or normal type and abnormal glucose metabolism. 3 types: non-diabetic border type, and diabetic type (DM), normal type (NGT), border type showing fasting hyperglycemia (IFG), border type showing postprandial hyperglycemia (IGT), fasting There are five types of border type (IFG / IGT) exhibiting hyperglycemia and postprandial hyperglycemia, and diabetic type (DM), or those obtained by further subdividing each of the above-mentioned classifications.
As a method for performing such classification, a known method may be used. For example, when an oral glucose tolerance test (OGTT) is used, fasting blood glucose level is 110 mg / dl according to the criteria of Japan Diabetes Society. If the blood glucose level is less than 140 mg / dl after 2 hours of glucose load and less than 140 mg / dl, the blood glucose level is 126 mg / dl or more after fasting and / or if the blood glucose level is 200 mg / dl after 2 hours of glucose load, the diabetes type, and Those that do not belong to any of the normal type and the diabetic type are classified as border type. Further, according to the above criteria, the boundary type is IFG, fasting blood glucose level 110 mg if fasting blood glucose level 110 mg / dl or more to less than 126 mg / dl and blood glucose level 2 hours after glucose load is less than 140 mg / dl IGT, fasting blood glucose level 110 mg / dl to less than 126 mg / dl and blood glucose level 2 hours after glucose load 140 mg / dl or more If it is less than ˜200 mg / dl, it can be finely classified as IFG / IGT or the like. Furthermore, each said classification | category may be subdivided suitably according to the other known method and reference | standard as needed.

In the present invention, the constant relationship between the insulin resistance index (R) and the β-cell function index (β) is obtained by obtaining a state of sugar metabolism of a large number of subjects by a known method. Distributions regarding the insulin resistance index (R) and β-cell function index (β) of the majority subject are created and determined based on this distribution. Typically, the following steps A) to D):
A) Based on a known method for determining the state of sugar metabolism of a large number of subjects, at least a normal type that does not have a sugar metabolism abnormality and a boundary that has a sugar metabolism abnormality but not diabetes Categorizing into 3 or more types of diabetes and diabetes type with abnormal sugar metabolism,
B) From the insulin resistance index (R) and β cell function index (β) of the subjects of the above-mentioned many cases, the insulin resistance index (R) and the β cell function index (β) or 100-β on the plane And plotting the multiple examples of subjects so that the state of glucose metabolism classified in step A) can be identified,
C) dividing the plane according to the state of glucose metabolism, visually or statistically, from the distribution of the large number of subjects plotted in step B);
D) Relational expression between the insulin resistance index (R) and the β cell function index (β) and / or the insulin resistance index (R) and / or β representing each plane portion divided in step C) Calculating a numerical range of the cellular function index (β);
As a relational expression between the insulin resistance index (R) and the β cell function index (β) and / or the numerical range of the insulin resistance index (R) and / or the β cell function index (β). ,It is determined.

  As known methods for determining the state of glucose metabolism, measurement of fasting blood glucose level and occasional blood glucose level, oral glucose tolerance test (OGTT), measurement of hemoglobin (Hb) A1c, etc. are known. Any or a combination of these can be used in the method of the present invention. That is, any method that can accurately determine abnormal sugar metabolism or diabetes can be used in the method of the present invention without any particular limitation. A highly reliable method used in this case is preferable, and specifically, it is particularly preferable that the determination is based on a fasting blood glucose level by an oral glucose tolerance test (OGTT) and a blood glucose level 2 hours after the glucose load.

  As used herein, “subjects for multiple cases” refers to an unspecified number of healthy subjects and abnormal glucose metabolism including diabetes, which provides data serving as a basis for creating the distribution of the state of glucose metabolism. The ratio of healthy individuals in a large number of subjects and those with abnormal glucose metabolism including diabetes is not particularly limited, but there are at least 50 healthy subjects and 50 or more glucose metabolic abnormal subjects including diabetes. It is preferable that there are at least 200 people. Further, the data used as a basis for creating the distribution of the state of sugar metabolism may be data newly collected by a known method such as the above-described OGTT, or data obtained from a database on the Internet or the like. .

により算出することができる。
また、β細胞機能指数（β）は、膵β細胞のインスリン分泌機能を評価するための指標を意味し、特に、初期と後期の２相性のピークからなる分泌相をとることが知られている食後のインスリン分泌動態について、これら初期および後期の両分泌相を合わせた、全体的な膵β細胞のインスリン分泌能を反映する指標であることが好ましい。かかる指標は、既知の方法、例えば、空腹時血糖値や空腹時インスリン値の測定、ブドウ糖負荷後の血糖値やインスリン値の測定等により、膵β細胞のインスリン分泌機能を評価できる数値として求めることができるものであれば、特に限定されることなく本発明に用いることができ、β細胞機能指数（β）としては、上記方法による測定値やその測定値に基づく計算後の数値や解析後の数値を用いることができる。例えば、本発明に用いることができるβ細胞機能指数（β）の好ましい具体例としては、ホメオスタシスモデルアセスメントβ（homeostasis model assessment β（HOMA-β））が挙げられ、空腹時血糖値（ＦＰＧ）および空腹時インスリン値（ＦＩＲＩ）を用いて下記式

により算出される。 In the present invention, the insulin resistance index (R) means an index for evaluating insulin resistance, and is a known method such as measurement of fasting blood glucose level or fasting insulin level, insulin level after glucose load. Any method can be used for the method of the present invention without particular limitation as long as it can be obtained as a numerical value capable of evaluating insulin resistance by measurement of SSPG (steady state plasma glucose), glucose clamp method, etc. As the insulin resistance index (R), a measured value by the above method, a numerical value after calculation based on the measured value, or a numerical value after analysis can be used. For example, a preferred specific example of the insulin resistance index (R) that can be used in the present invention includes homeostasis model assessment ratio (HOMA-IR), and measured fasting blood glucose level ( FPG) and fasting insulin level (FIRI)

Can be calculated.
The β cell function index (β) is an index for evaluating the insulin secretory function of pancreatic β cells, and in particular, it is known to take a secretory phase consisting of a biphasic peak in early and late phases. The post-prandial insulin secretion kinetics is preferably an index reflecting the overall insulin secretion ability of pancreatic β cells, which is a combination of both the early and late secretion phases. Such an index is obtained as a numerical value that can evaluate the insulin secretion function of pancreatic β cells by a known method, for example, measurement of fasting blood glucose level or fasting insulin level, measurement of blood glucose level or insulin level after glucose load, etc. Can be used in the present invention without any particular limitation, and the β cell function index (β) is a measured value by the above method, a numerical value after calculation based on the measured value, or a value after analysis. Numerical values can be used. For example, preferable specific examples of β cell function index (β) that can be used in the present invention include homeostasis model assessment β (HOMA-β), fasting blood glucose level (FPG) and Using fasting insulin level (FIRI)

Is calculated by

  In the present invention, the fixed relationship between the insulin resistance index (R) and the β cell function index (β) is a relational expression between the insulin resistance index (R) and the β cell function index (β), and / or Or the relationship determined based on the numerical range of an insulin resistance index (R) and / or a beta cell function index (β). Further, since the certain relationship is used to classify the state of sugar metabolism to be analyzed, it is different for each state of sugar metabolism. Therefore, the insulin resistance index (R) and β The relational expression with the cell function index (β) and / or the numerical range of the insulin resistance index (R) and / or the β cell function index (β) are also different for each state of sugar metabolism. Here, the relational expression between the insulin resistance index (R) and the β cell function index (β) is not particularly limited as long as R and β can be expressed by a function included in the formula, A function having a low order of R and β in the formula is preferable, and a linear function is more preferable.

Therefore, the constant relationship between the insulin resistance index (R) and the β cell function index (β) is, for example, β> a × R + b or β ≦ a × R + b (a and b are each independently a real number), And k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m and n are each independently a real number), specifically Β> a × R + b (k ≦ R <l) is normal, β ≦ a × R + b (k ≦ R <l) is glucose metabolism abnormality including diabetes, k is a lower limit value that R can take, and l is R , M can be a lower limit value that β can take, and n can be an upper limit value that β can take. When the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR) and the β cell function index (β) is a homeostasis model assessment β (HOMA-β), in the above formula, a is It is preferably 40 to 70, b is -10 to 30, k is 0.2 or more, l is 15 or less, m is -500 or more, and n is 2000 or less.
The constant relationship between the insulin resistance index (R) and the β cell function index (β) is β ≦ c × R + d, c × R + d <β ≦ c ′ × R + d ′, or β> c ′ × R + d ′. (C, c ′, d and d ′ are each independently a real number), and k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m And n are each independently a real number), specifically, β ≦ c × R + d is diabetic type (k ≦ R <l), c × R + d <β ≦ c ′ × R + d ′ (k ≦ R R <l) is a boundary type, β> c ′ × R + d ′ (k ≦ R <l) is a normal type, k is a lower limit that R can take, l is an upper limit that R can take, and m is β Lower limit value, and n can be an upper limit value that β can take. When the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR) and the β cell function index (β) is a homeostasis model assessment β (HOMA-β), in the above formula, c is 10 to 40, c ′ is 40 to 70, d is −30 to 20, d ′ is −10 to 30, k is 0.2 or more, l is 15 or less, and m is −500. As mentioned above, it is preferable that n is 2000 or less.
It should be noted that k> l, m and n are appropriately selected and combined with k ≦ R and / or R <l and / or m ≦ β and / or β <n, so that β> a × R + b or Classification according to β ≦ a × R + b, or classification according to β ≦ c × R + d, c × R + d <β ≦ c ′ × R + d ′, or β> c ′ × R + d ′ may be further subdivided.

により算出される。
したがって、インスリン抵抗性指数（Ｒ）とβ細胞機能指数（β）との一定の関係は、Ｒ、βおよびI. I.との関係式、および／または、インスリン抵抗性指数（Ｒ）および／またはβ細胞機能指数（β）および／またはインスリン分泌指数 ( I. I.)の数値範囲に基づいて決まる関係であってもよい。前記関係式は、Ｒ、βおよびI. I.が式中に含まれる関数で表すことができるものであればよく、特に限定されないが、式中におけるＲ、βおよびI. I.の次数が低い関数が好ましく、１次関数であることがさらに好ましい。また、上記I. I.の代わりに、インスリン分泌に関連する測定値から算出されるインスリン分泌能を反映する他の指数を用いてもよく、かかる指数は、I. I.のように、特に、初期と後期の２相性のピークからなる分泌相をとることが知られている食後のインスリン分泌動態のうち、初期のインスリン分泌能を反映する指標であることが好ましい。これは、食後高血糖を呈する境界型（ＩＧＴ）などでは、この初期インスリン分泌能が障害されることが多いからである。 Furthermore, the fixed relationship between the insulin resistance index (R) and the β cell function index (β) is not only the fixed relationship between R and β, but also the insulin secretion index or a measurement related to insulin secretion. It may be expressed on a three-dimensional space including a relationship with another index that reflects the insulin secretion ability calculated from the value. Here, “insulin secretion index” means insulinogenic index (II), blood insulin level (IRI) after 30 minutes of glucose load, fasting insulin level (FIRI), blood glucose level after 30 minutes of glucose load, And fasting blood glucose level (FPG),

Is calculated by
Therefore, a certain relationship between the insulin resistance index (R) and the β cell function index (β) is the relational expression of R, β and II, and / or the insulin resistance index (R) and / or β cell. The relationship may be determined based on a numerical range of the function index (β) and / or the insulin secretion index (II). The relational expression is not particularly limited as long as R, β, and II can be expressed by a function included in the formula, but a function having a low order of R, β, and II in the formula is preferable. More preferably, it is a next function. Further, instead of the above II, another index reflecting the insulin secretion ability calculated from the measurement value related to insulin secretion may be used. Among the postprandial insulin secretion kinetics known to have a secretory phase consisting of a compatibility peak, it is preferably an index reflecting the initial insulin secretion ability. This is because the initial insulin secretion ability is often impaired in the boundary type (IGT) that exhibits postprandial hyperglycemia.

  In the present invention, when the plane is divided visually or statistically according to the state of glucose metabolism from the distribution of the plotted subjects of multiple cases, “visual” means the multiple plotted on the plane. It means that the trend or bias of the distribution of each state on the plane is visually determined from the distribution of the example object, and the plane is artificially divided according to the state of sugar metabolism based on the visual determination. Similarly, “statistical” refers to the trend or bias of the distribution of each state on the plane from the distribution of multiple target objects plotted on the plane by statistical processing. It means that the plane is divided according to the state of sugar metabolism by the numerical value or relational expression based on the result. For such statistical processing, a known method may be used. For example, an approximate straight line or an approximate curve is obtained as a boundary for optimally separating a state from another state from the numerical characteristics of the distribution of each state. Such that approximate lines and curves are similar to a state classified by a known method for a large number of objects classified into a certain state by their boundaries. The number of people in a large number of subjects (ie, the number of subjects in a large number of subjects that are correctly classified) and the number of subjects in a large number of instances that do not match the state classified by a known method (ie, the number that will be misclassified) It is possible to request that the number of subjects (examples) be minimized.

  In the present invention, metabolic syndrome refers to a disease state in which risk factors for arteriosclerosis and cardiovascular disease such as abnormal glucose metabolism, obesity, abnormal lipid metabolism, and hypertension are closely related to each other due to their origin. . The judgment criteria for each risk factor can be used without particular limitation as long as they are widely known, and the judgment criteria for metabolic syndrome are other than abnormal sugar metabolism and abnormal sugar metabolism. As long as it can be determined as the pathological condition having 2 or 3 or more of each risk factor, it can be used without particular limitation. Accordingly, it is possible to determine that the metabolic syndrome is caused by the presence of an abnormal glucose metabolism and an index representing a health condition used for the determination of hypertension, obesity, and abnormal lipid metabolism being greater than or less than a certain reference value. In addition, it may be determined as metabolic syndrome based on the fact that it has glucose metabolism abnormality including insulin or insulin resistance and has two of hypertension, obesity and lipid metabolism abnormality. In the present invention, the `` index indicating health condition '' means, for example, systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, blood triglyceride value, blood cholesterol value, blood good cholesterol value, blood uric acid value, It also means numerical values and classifications used for judgment of diseases and illnesses in various tests such as albumin excretion rate and blood CRP concentration by high sensitivity CRP (C-reactive protein).

The method for analyzing the state of sugar metabolism according to the present invention can be appropriately executed by a computer by a program. Such programs are typically
To analyze the state of glucose metabolism of the subject to be analyzed,
1) Regarding the insulin metabolism index (R) and β-cell function index (β) of each of the multiple subjects, the glucose metabolism status of the multiple subjects obtained using a known method for determining the status of glucose metabolism Means for storing in the storage unit when a fixed relationship between the insulin resistance index (R) and the β cell function index (β) determined from the distribution is input;
2) When the numerical values related to the insulin resistance index (R) and β cell function index (β) to be analyzed are input, the calculation unit calculates the insulin resistance index (R) and β cell function index (β). means,
3) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are the insulin resistance index (R) and β cell function index (1) stored in the storage unit in 1) a means for calculating whether or not to satisfy a certain relationship with β) in the calculation unit and determining the certain relationship that the object to be analyzed satisfies;
4) Means for determining the state of sugar metabolism of the object to be analyzed based on the fixed relationship satisfied by the object to be analyzed determined in 3) 5) Means for outputting the state of sugar metabolism of the object to be analyzed from the output unit ,
And a method for analyzing the state of sugar metabolism is executed by executing the program. In addition, the program should just perform said 5 steps as a whole, and a commercially available program can be utilized suitably for each means.

Next, the above five stages of the program will be described.
“1) Insulin resistance index (R) and β-cell function index (β) of each of the multiple cases of the glucose metabolism state of the multiple cases obtained using a known method for determining the state of glucose metabolism When a certain relationship between the insulin resistance index (R) and the β-cell function index (β), which is determined from the distribution relating to the input, is inputted, means for storing in the storage unit ”
Said fixed relationship is the insulin resistance index (R) and β-cell function index of each said multiple subject of the state of glucose metabolism of the multiple subject obtained using a known method of determining the status of glucose metabolism What is necessary is just to be determined from the distribution regarding (β), and may be determined by the process including the above steps A) to D). Therefore, even if it is artificially determined by visual analysis from the above-mentioned distribution of the state of sugar metabolism in many cases, it may be determined by statistical analysis using a computer or a computer. Good. The predetermined relationship determined for each state of sugar metabolism is appropriately input to a computer using a commercially available program and stored in the storage unit of the computer in correspondence with each state of sugar metabolism. Become.

“2) When the numerical values related to the insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are input, the calculation unit calculates the insulin resistance index (R) and β cell function index (β). Means to do "
In order to execute this means, first, an insulin resistance index required for obtaining an insulin resistance index (R) and a β cell function index (β) by measuring components in a sample to be analyzed or the like. It is necessary to obtain numerical values for (R) and β cell function index (β). That is, in the present invention, the numerical values related to the insulin resistance index (R) and the β cell function index (β) mean values necessary for obtaining the insulin resistance index (R) and the β cell function index (β). Such numerical values may be measured values of raw data obtained by measuring a sample to be analyzed, or may be numerical values after calculation based on the measured values or numerical values after analysis. For example, when the insulin resistance index (R) is the homeostasis model assessment ratio (HOMA-IR) and the β cell function index (β) is the homeostasis model assessment β (HOMA-β), the insulin resistance index ( R) and β-cell function index (β) refer to fasting blood glucose level (FPG) and fasting blood insulin level (FIRI). Therefore, from the sample to be analyzed obtained by blood sampling or the like, fasting blood glucose level is calculated. The value (FPG) and the fasting blood insulin value (FIRI) may be measured.
When the numerical values are input to the computer, R and β are calculated based on equations for calculating R and β stored in the storage unit. Thus, for example, if the insulin resistance index (R) is HOMA-IR and the β-cell function index (β) is HOMA-β, the computer calculates insulin resistance from FPG and FIRI based on the above formula. HOMA-IR is calculated as the sex index (R), and HOMA-β is calculated as the β cell function index (β).

“3) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are 1) the insulin resistance index (R) and β cell function index stored in the storage unit in 1) Means for calculating whether or not a certain relationship with (β) is satisfied by the calculation unit and determining the certain relationship that the object to be analyzed satisfies ”
This means is that the R and β of the target are determined in accordance with each of the sugar metabolism states stored in the storage unit in 1), and which R and β are constant. Whether or not the relationship is satisfied is determined by performing calculation in the calculation unit.

"4) Means for determining the state of sugar metabolism of the analysis target based on the fixed relationship satisfied by the analysis target determined in 3)"
Since the certain relationship between R and β is determined for each state of sugar metabolism and is stored in the storage unit in 1), this means that the target R and β satisfy Decide which glucose metabolism state corresponds to a certain relationship.

"5" Means for outputting the state of sugar metabolism to be analyzed from the output unit "
In this means, the determined state of sugar metabolism to be analyzed is output on a computer display or the like.

In addition, the program can determine the relationship between the insulin resistance index (R) and the β-cell function index (β) by using a known method for determining the state of glucose metabolism. Each step of determining from a distribution relating to the insulin resistance index (R) and β-cell function index (β) of each of said multiple subjects. That is, a program that includes such a stage is typically
To analyze the state of glucose metabolism of the subject to be analyzed,
I) Based on a known method for determining the state of sugar metabolism, the sugar metabolism state is at least a normal type having no abnormal sugar metabolism, a boundary type having abnormal sugar metabolism but not diabetic, and a diabetic type having abnormal sugar metabolism. When numerical values relating to the state of glucose metabolism of a large number of subjects classified into three or more types and the insulin resistance index (R) and β cell function index (β) of the large number of subjects are input, Means for calculating the insulin resistance index (R) and β cell function index (β) of a large number of subjects in a calculation unit;
II) The insulin resistance index (R) is obtained by statistically processing the distribution of the state of glucose metabolism of a large number of subjects in the arithmetic unit based on the insulin resistance index (R) and the β cell function index (β). ) And β-cell function index (β), a relational expression between insulin resistance index (R) and β-cell function index (β) and / or insulin resistance index according to the state of glucose metabolism. (R) and / or means for determining a numerical range of the β cell function index (β) and storing it in the storage unit,
III) When numerical values relating to the insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are input, the insulin resistance index (R) and β cell function index (β) are calculated by the calculation unit. means,
IV) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are stored in II), the insulin resistance index (R) and β cell function index (β) Means for calculating whether or not the relational expression and / or the numerical range for each state of sugar metabolism satisfying the certain relationship of the metabolic metabolism is satisfied, and determining the relational expression and / or the numerical range that the object satisfies,
V) Subject to be analyzed based on a relational expression and / or numerical range that is a fixed relationship between the insulin resistance index (R) and β-cell function index (β) satisfied by the subject determined in step IV) To determine the state of sugar metabolism in food
VI) Means for outputting the state of sugar metabolism to be analyzed from the output unit,
By executing such a program, the method for analyzing the state of sugar metabolism according to the present invention is executed, and as a result, the state of the target sugar metabolism to be analyzed is displayed on a computer display or the like. Is done. As a whole, such a program may perform the above six steps, and a commercially available program can be used as appropriate for each step.

Further, each step of I) and II) of the program will be described.
“I) Based on a known method for determining the state of sugar metabolism, the sugar metabolism state is at least a normal type having no abnormal sugar metabolism, a boundary type having abnormal sugar metabolism but not diabetes, and a diabetic type having abnormal sugar metabolism When the numerical values related to the state of glucose metabolism of a large number of subjects and the insulin resistance index (R) and β cell function index (β) of the large number of subjects classified into three or more types of Means for calculating the insulin resistance index (R) and β cell function index (β) of the subject of the majority in the calculation unit ”
To determine a fixed relationship between the insulin resistance index (R) and β-cell function index (β), the status of glucose metabolism, insulin resistance index (R) and β-cell function index (β) in many subjects First, in this means, the state of glucose metabolism, insulin resistance index (R), and β cell function index (β) of a large number of subjects classified by a known method with high reliability are obtained by this means. Therefore, numerical values related to the insulin resistance index (R) and the β cell function index (β) need to be input. Then, the insulin resistance index (R) and the β cell function index (β) are calculated by the calculation unit from the inputted numerical values related to the insulin resistance index (R) and the β cell function index (β) of the subjects. . In addition, numerical values related to the state of glucose metabolism and the insulin resistance index (R) and β-cell function index (β) of many subjects may be newly collected data by a known method. The acquired data may be used.
For example, when the insulin resistance index (R) is a homeostasis model assessment ratio (HOMA-IR) and the β-cell function index (β) is a homeostasis model assessment β (HOMA-β), the insulin resistance Since the numerical values for the index (R) and β-cell function index (β) refer to fasting blood glucose level (FPG) and fasting blood insulin level (FIRI), fasting blood glucose level (FPG) and fasting blood insulin level A value (FIRI) may be input. When the numerical values are input to the computer, R and β are calculated based on equations for calculating R and β stored in the storage unit. Therefore, for example, when the insulin resistance index (R) is HOMA-IR and the β-cell function index (β) is HOMA-β, the insulin resistance index ( HOMA-IR is calculated as R), and HOMA-β is calculated as β cell function index (β).

"II) By processing statistically the distribution of the state of glucose metabolism of a large number of subjects in the arithmetic unit based on the insulin resistance index (R) and the β cell function index (β), the insulin resistance index ( R) and β cell function index (β) as a certain relationship, depending on the state of glucose metabolism, the relational expression between insulin resistance index (R) and β cell function index (β) and / or insulin resistance Means for determining the numerical range of the index (R) and / or the β cell function index (β) and storing them in the storage unit ”
In this means, as a fixed relation between R and β, a relational expression between R and β and / or a numerical range of R and / or β is determined and stored. If the determination of the certain relationship between R and β is performed by statistical processing that examines the tendency or bias of the distribution of each state from the distribution of the sugar metabolism states of the subjects in many cases, in particular, It is not limited. Thus, for example, from the R and β of the majority object, the majority object is plotted on a virtual plane with R and β or 100-β as axes so that the state of glucose metabolism can be identified, and the plot is plotted. In addition, from the distribution of the glucose metabolism state of a large number of subjects, the plane is divided according to the glucose metabolism state based on the result of statistical processing, and the insulin resistance index (R) representing each divided plane portion. ) And the β cell function index (β) and / or by calculating the numerical range of the insulin resistance index (R) and / or β cell function index (β).
In addition, since said III)-VI) correspond to said 2) -5), it is understood similarly to said 2) -5).

  In the present invention, the input in each of the above steps is performed by a conventionally used method via a numeric keypad, keyboard, mouse, various recording media, a network, etc., and the output is displayed on a display, printed out, and recorded on various recording media. It can be performed by a conventionally used method such as writing.

Furthermore, the method of the present invention can be executed by a system constituted by means for executing each of the above steps as appropriate.
Such a system is typically
1) Regarding the insulin metabolism index (R) and β-cell function index (β) of each of the multiple subjects, the glucose metabolism status of the multiple subjects obtained using a known method for determining the status of glucose metabolism Means for storing in the storage unit when a fixed relationship between the insulin resistance index (R) and the β cell function index (β) determined from the distribution is input;
2) When the numerical values related to the insulin resistance index (R) and β cell function index (β) to be analyzed are input, the calculation unit calculates the insulin resistance index (R) and β cell function index (β). means,
3) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are the insulin resistance index (R) and β cell function index (1) stored in the storage unit in 1) a means for calculating whether or not to satisfy a certain relationship with β) in the calculation unit and determining the certain relationship that the object to be analyzed satisfies;
4) Means for determining the state of sugar metabolism of the object to be analyzed based on the fixed relationship satisfied by the object to be analyzed determined in 3) 5) Means for outputting the state of sugar metabolism of the object to be analyzed from the output unit ,
Including or
I) Based on a known method for determining the state of sugar metabolism, the sugar metabolism state is at least a normal type having no abnormal sugar metabolism, a boundary type having abnormal sugar metabolism but not diabetic, and a diabetic type having abnormal sugar metabolism. When numerical values relating to the state of glucose metabolism of a large number of subjects classified into three or more types and the insulin resistance index (R) and β cell function index (β) of the large number of subjects are input, Means for calculating the insulin resistance index (R) and β cell function index (β) of a large number of subjects in a calculation unit;
II) The insulin resistance index (R) is obtained by statistically processing the distribution of the state of glucose metabolism of a large number of subjects in the arithmetic unit based on the insulin resistance index (R) and the β cell function index (β). ) And β-cell function index (β), a relational expression between insulin resistance index (R) and β-cell function index (β) and / or insulin resistance index according to the state of glucose metabolism. (R) and / or means for determining a numerical range of the β cell function index (β) and storing it in the storage unit,
III) When numerical values relating to the insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are input, the insulin resistance index (R) and β cell function index (β) are calculated by the calculation unit. means,
IV) The calculated insulin resistance index (R) and β cell function index (β) of the subject to be analyzed are stored in II), the insulin resistance index (R) and β cell function index (β) Means for calculating whether or not the relational expression and / or the numerical range for each state of sugar metabolism satisfying the certain relationship of the metabolic metabolism is satisfied, and determining the relational expression and / or the numerical range that the object satisfies,
V) Subject to be analyzed based on a relational expression and / or numerical range that is a fixed relationship between the insulin resistance index (R) and β-cell function index (β) satisfied by the subject determined in step IV) Means for determining the state of sugar metabolism of
VI) Means for outputting the state of sugar metabolism to be analyzed from the output unit,
It is possible to determine the state of sugar metabolism to be analyzed by cooperating each means in an integrated manner. In this method, the analysis method of the state of glucose metabolism can be realized not only by an integrated computer but also by the entire system using a database on the Internet.

  Each of the above means is usually composed of a computer or the like, a central processing unit (CPU) as a network interface or control means, a memory such as a RAM, a storage device such as a hard disk drive (HDD), a display device, or a numeric keypad And an input device such as a keyboard, a mouse, and an auxiliary recording device.

Hereinafter, a preferred embodiment of the program of the present invention will be described based on the flowchart of FIG. 1, but the present invention is not limited to this.
In order to execute the program of the present invention, a computer including an input unit, a storage unit, a calculation unit, and an output (display) unit is required.

Step 1 and 2
In order to execute the program, the user of the program firstly, the insulin resistance of each said multiple subject of the state of sugar metabolism of the multiple subject obtained using a known method for determining the state of glucose metabolism. A constant relationship between the insulin resistance index (R) and the β-cell function index (β), determined from the distribution with respect to the index (R) and the β-cell function index (β), ie, R by glucose metabolism state The relational expression and / or numerical range of β is input to the computer.
The predetermined relationship is stored in the storage unit of the computer in association with each sugar metabolism state. In addition, as shown in FIG. 3, the storage unit includes a target identification number for specifying a sample, and numerical values related to an insulin resistance index (R) and a β cell function index (β), for example, an insulin resistance index ( A table may be stored so that data, such as measured values obtained from the object to be analyzed, required to determine R) and β cell function index (β) can be entered.

Steps 3 and 4
When data such as measured values necessary for obtaining the insulin resistance index (R) and β cell function index (β) to be analyzed are input to the table or the like, the data is stored and stored in the storage unit. R and β are calculated by the calculation unit based on the above equations for calculating R and β.

Steps 5 and 6
The calculation unit calculates which of the predetermined relationships between R and β stored in the storage unit R and β is satisfied by the calculation unit, and determines the certain relationship that the target satisfies.

Steps 7 and 8
The computer determines which glucose metabolism state the determined relationship corresponds to based on the data stored in the storage unit in step 1 and determines the determined glucose metabolism state to be analyzed by the computer. It is displayed on the display. For example, such display may be made on the table stored in step 1 as shown in FIG. 4 or on a plane having R and β as axes as shown in FIG.

  The program of the present invention may also take an embodiment as shown in the flowchart of FIG. In this form, the user of the program can determine from a large number of examples of data obtained by himself / herself with a certain relationship between R and β. Steps w to z are for determining the predetermined relationship and storing them in the storage unit of the computer. Subsequent steps 3 'to 8' are understood in the same manner as steps 3 to 8 in FIG.

Step w
Users of the program are able to determine the status of glucose metabolism and the numerical values regarding insulin resistance index (R) and β-cell function index (β), ie insulin resistance, in a large number of subjects classified according to reliable and known methods. A measurement value or the like to be analyzed necessary for obtaining the index (R) and the β cell function index (β) is input. For example, when the insulin resistance index (R) is the homeostasis model assessment ratio (HOMA-IR) and the β cell function index (β) is the homeostasis model assessment β (HOMA-β), the insulin resistance index ( Since the numerical values related to R) and β-cell function index (β) refer to fasting blood glucose level (FPG) and fasting blood insulin level (FIRI), both values may be input.

Step x and y
The computer calculates the insulin resistance index (R) and the β cell function index (β) from the input numerical values related to the insulin resistance index (R) and the β cell function index (β) in the calculation unit. To do. In addition, by processing statistically the distribution of the sugar metabolism states of the subjects in many cases based on R and β in the calculation unit, the tendency or bias of the distribution of each state is examined and judged, and R and β As a certain relationship, a relational expression between R and β and / or a numerical range of R and / or β is determined for each state of sugar metabolism. For example, from R and β of the majority object, as shown in FIG. 6, the majority object is plotted on a virtual plane with R and β or 100-β as axes so that the state of glucose metabolism can be identified. Then, from the plotted distribution of glucose metabolism states of the subjects, as shown in FIG. 7, the plane is divided according to the state of glucose metabolism based on the result of statistical processing, and the division is performed. May be determined by calculating a relational expression between R and β and / or a numerical range of R and / or β representing each planar portion made.

Step z
In order to perform the following steps 3 ′ to 8 ′, a certain relationship between R and β determined in steps w to y, that is, a relational expression and / or numerical range of R and β according to the state of sugar metabolism Are stored in the storage unit of the computer in association with each sugar metabolism state. Further, any of steps w to z prior to step 3 ′ may be performed, but as shown in FIG. 3, the storage unit may include an object identification number for identifying a sample, an insulin resistance index (R), and β. A table so that numerical values relating to the cell function index (β), for example, measured values for obtaining the insulin resistance index (R) and β cell function index (β) obtained from the analysis target can be input. May be stored.

  Thereafter, in steps 3 ′ to 8 ′, as in steps 3 to 8, when the user inputs numerical values related to the insulin resistance index (R) and β cell function index (β) to be analyzed, R and β Is calculated by the calculation unit, and the relational expression and / or numerical range of R and β satisfying the object to be analyzed is calculated and determined, and the sugar metabolism state of the object to be analyzed is determined from the result, and finally the computer It is displayed on the display.

EXAMPLES Hereinafter, although this invention is demonstrated more concretely based on an Example, this invention is not limited to a following example at all.
A large number of 236 people who received OGTT since September 2001 were included. The majority of subjects included those who had already undergone drug treatment for hypertension, ischemic heart disease, and hyperlipidemia, but all of diabetes was untreated. Moreover, the measured value before a treatment was used for the blood triglyceride value and the blood good cholesterol level. The body mass index BMI (Body mass index) used at the time of OGTT was used. The breakdown of the majority subjects is 134 males and 102 females, the male / female ratio is 1.31, and the age composition is male 59.3 ± 13. 4 (25-92) and female 63.8 ± 15.0 (22-93).

  The blood glucose level is determined by the glucose hexokinase method (HK method, N-assay Glu-UL Knitbo, Nittobo Co., Ltd.), the blood insulin level is determined by the EIA double antibody method (Axim Insulin Dynapack, Abbott Japan), the blood triglyceride ( TG) value using CK-glycerol-3-phosphate oxidase method (Pure Auto TG-N, Daiichi Kagaku), blood good cholesterol (HDL-C) value using direct method (Cholestest NHDL, Daiichi Kagaku) Measured respectively. Each numerical value is shown as mean ± SD. The comparison between the two groups was performed using Student's t-test, and the statistical significance levels were all 5% or less.

  From the results of fasting blood glucose level based on OGTT and blood glucose level 2 hours after glucose load of many subjects, fasting blood glucose level is less than 110 mg / dl, 110 mg / dl or more to less than 126 mg / dl and 126 mg / dl or more Many subjects were classified into 9 blocks, 3 blocks and 3 blocks with blood glucose levels of less than 140 mg / dl, 140 mg / dl to 200 mg / dl and 200 mg / dl for 2 hours after glucose loading. The number of people was determined (FIG. 8). In addition, the above-mentioned subjects of many cases are determined from the criteria based on the OGTT of the Japan Diabetes Society from diabetes type (DM) [fasting blood glucose level 126 mg / dl or higher and / or blood glucose level 200 mg / dl or higher after 2 hours of glucose load], boundary type (IFG [fasting blood glucose level 110 mg / dl or more to less than 126 mg / dl and glucose level less than 140 mg / dl after 2 hours of glucose load], IGT [fasting blood glucose level less than 110 mg / dl and glucose level 2 hours after glucose load 140 mg / dl dl or more to less than 200 mg / dl], IFG / IGT [fasting blood glucose level of 110 mg / dl to less than 126 mg / dl and glucose level after 2 hours of glucose loading 140 mg / dl to less than 200 mg / dl]), normal type (NGT [Fasting blood glucose level of less than 110 mg / dl and glucose load after 2 hours of blood glucose level of less than 140 mg / dl])) Calculate the ratio by dividing into 5 groups did. The result is shown in FIG.

  As shown in FIG. 8, as a result of OGTT, abnormal glucose metabolism was observed in 174 (73.7%) of 236 patients, of which 89 (37.7%) were diabetic (DM) and borderline (diabetic Reserve Army (IFG, IGT, IFG / IGT) was 85 (36.0%). The breakdown of borderline diabetic reserves was IFG 16 (6.8%), IGT 47 (19.9%), and IFG / IGT 22 (9.3%).

In this example, the homeostasis model assessment ratio (HOMA-IR) is used as the insulin resistance index (R), and the homeostasis model assessment β (HOMA-β) is used as the β cell function index (β). Fasting blood glucose level (FPG) and fasting blood insulin level (FIRI) were used as numerical values for the sex index (R) and β-cell function index (β).
Therefore, from the fasting blood glucose level (FPG) and the fasting blood insulin level (FIRI) of all the majority subjects, HOMA-IR as an insulin resistance index (R) and β cell function index (β) HOMA-β is calculated, and the classification of the five groups based on the result of OGTT can be identified on a plane in which the vertical axis is (100-β (HOMA-β)) and the horizontal axis is R (HOMA-IR). All the subjects were plotted. The result is shown in FIG.
On the created plane, the deviation of the distribution of the state of each sugar metabolism is judged by visual observation, and two straight lines (β = 52.61 × R + 12.14, β = 31.17 × R−3.47) are visually determined. The boundary line between the normal type and the glucose metabolism abnormality was β = 52.61 × R + 12.14, and the boundary line between the boundary type and the diabetes type among the sugar metabolism abnormalities was β = 31.17 × R−3.47. The result is shown in FIG.

  As shown in FIG. 10, IFG and IFG / IGT exist on the boundary line between the boundary type and the diabetes type (β = 31.17xR-3.47), and IFG is limited to the region where β <75. did. Moreover, IGT mostly overlapped with the region of NGT, but showed a slightly higher R value than NGT, and was distributed with diversity in the region between the two straight lines. Based on this distribution, the plane was divided, and the localization of each glucose metabolism abnormality group was conceptually represented in FIG. 11 as a schematic diagram. FIG. 11 is a schematic diagram to the last, and the present invention is not limited to this.

  Next, for the purpose of further studying the boundary type (IGT) exhibiting postprandial hyperglycemia showing diversity, as shown in FIG. 12, R is less than 1.2 and 100-β is 40 or more (β <60). Is defined as “region 1”, R is 1.4 or more and 100−β is less than 30 (β ≧ 70) is defined as “region 2”, and NGT included in “region 1” , IGT, and DM are respectively NGT1, IGT1, and DM1, and “region 2” is also subdivided as NGT2, IGT2, and DM2. In addition, in FIG. 13 and FIG. 14, each subdivided group of “1 region” and “2 region” is conceptually represented as a schematic diagram on a plane. 13 and 14 are schematic views only, and the present invention is not limited to this.

  In addition, an axis of insulin secretion index (I. I.) representing insulin secretion ability is further provided in the above plan view, and R value, β value and I.I. I. Insulin secretion index (II) of each group was measured to create a three-dimensional space diagram with the value as an axis, whereas IGT1 and DM1 showed significantly lower values compared to NGT , IGT2, DM2 I. Value and β value approximate NGT, while NGT2 I.D. I. Was significantly higher than these (Table 1). Therefore, in the three-dimensional space diagram, NGT1, IGT1, and DM1 and NGT2, IGT2, and DM2 that were somewhat overlapped in the plan view were clearly separated. A conceptual schematic diagram thereof is shown in FIG. As shown in FIG. 15, it was suggested from the similarity of kinetics of sugar metabolism that the abnormal sugar metabolism follows the progress as shown by the arrows in the figure. From the result that each group was separable in the three-dimensional space diagram, I.D. I. It became clear that an analysis method using a three-dimensional space including an index axis that reflects the secretory ability of insulin, such as the above, can make a more detailed and reliable determination.

  In addition, II, β (HOMA-β), R (HOMA-IR), body mass index (BMI), blood triglyceride level (TG), good cholesterol level (HDL-C) for all majority subjects were calculated, and All subjects were classified into 5 groups of NGT, IFG, IFG / IGT, IGT, and DM, and the average value ± SD of each value for each group was calculated and subjected to statistical analysis (Table 1). . Further, NGT1 and NGT2 of NGT, IGT1 and IGT2 of IGT, and DM1 and DM2 of DM, which were further classified by the above-described subclassification, were similarly analyzed (Table 1). Moreover, the hypertension treatment rate (HT) of each group was described in the same table. In this example, as a standard of metabolic syndrome, as shown in FIG. 16, HOMA-IR used as type 2 diabetes (DM), IGT, and insulin resistance index (R) is 1.4 or more. Have at least one of these, and have high blood pressure (systolic blood pressure 140 mmHg or higher or diastolic blood pressure 90 mmHg or higher), obesity (BMI 25 or higher), and lipid metabolism abnormality (TG 150 mg / dl or higher or HDL-C 40 mg / dl or lower) Subjects with at least two of the three were considered metabolic syndrome (MS) and the proportion of MS included in each group was calculated. The results are shown in Table 1.

  As shown in Table 1, the MS content rate is clearly higher in the “2 region” than in the “1 region” even in the same sugar metabolism state, and all of the NGT-containing subjects belong to NGT2. In addition, it became clear that there are many people with MS in NGT2, IGT2, and DM2 compared to other groups.

  Next, with respect to the above 236 subjects, as a comparative example, a blood glucose level of less than 110 mg / dl is normal, 110 mg based on a method of analyzing glucose metabolism based only on fasting blood glucose, which has been widely used in the past. / Dl or more is determined to be a glucose metabolism abnormality, and among the sugar metabolism abnormalities, 110 mg / dl or more to less than 126 mg / dl is determined to be a boundary type, and 126 mg / dl or more is determined to be diabetes, and the above method according to the present invention is performed. As an example, R (HOMA-IR) and β (HOMA-β) were calculated from the fasting blood glucose level and the fasting insulin level of many subjects, and two straight lines (β = 52.61 × R + 12) obtained as described above were obtained. .14, β = 31.17xR-3.47), based on the relational expression of R and β, β ≦ 52.61xR + 12.14 (0.2 ≦ R <15, −500 ≦ β <2000) It is determined as an abnormal sugar metabolism, and among the abnormal sugar metabolism, 31.17xR-3.47 <β ≦ 52.61xR + 12.14 (0.2 ≦ R <15, −500 ≦ β <2000) is defined as a boundary type, β ≦ 31.17xR−3.47 (0.2 ≦ R <15, −500 ≦ β <2000) is determined to be diabetes mellitus, based on the determination result by OGTT as a standard, abnormal glucose metabolism discovery rate, false negative rate , False positive rate, borderline (reserve) discovery rate, diabetes discovery rate, MS discovery rate. The results are shown in Table 2.

  As is apparent from the results of Table 2, in the comparative example, the false negative rate was 38.5%, and over 1/3 of the subjects actually having abnormal glucose metabolism were overlooked. More than half of subjects who are military), more than 1/5 of subjects who actually have diabetes, were missed, while in the examples, all were missed less than 1/10, It became clear that the analysis method was extremely reliable. Further, regarding the MS discovery rate, only less than 40% of the comparative examples could be found, whereas 90% or more of the examples could be found.

  From the above results, the method of the present invention is a numerical value relating to the insulin resistance index (R) and β cell function index (β), such as fasting blood glucose level and fasting insulin level, which can be easily measured by a single blood sampling or the like. From the above, it has been clarified that by calculating the insulin resistance index (R) and the β cell function index (β), it is possible to easily and extremely accurately find a subject having abnormal glucose metabolism and MS. In addition, due to its simplicity and accuracy, the method according to the present invention allows early detection of those with abnormal sugar metabolism or MS from a large number of subjects who have not yet been determined to have abnormal sugar metabolism. Therefore, it was suggested that this is an extremely suitable analytical method.

  As described above, according to the method of the present invention, the state of sugar metabolism of a subject can be determined very accurately and in a short time, and a subject having abnormal glucose metabolism or metabolic syndrome can be found early. Therefore, it is expected to be applied to software and computers used for analyzing the health condition from test values in health examinations.

It is a flowchart which shows the one aspect | mode of the program of this invention. It is a flowchart which shows the one aspect | mode of the program of this invention. It is a figure which shows the one aspect | mode of the table used in the program of this invention. It is a figure which shows the one aspect | mode of the table used in the program of this invention. It is a figure which shows the one aspect | mode of the top view used in the program of this invention. It is a schematic diagram which shows the one aspect | mode of the statistical process used in the program of this invention. It is a schematic diagram which shows the one aspect | mode of the statistical process used in the program of this invention. It is a figure which shows the OGTT result of many example object. It is a figure which shows the OGTT result of many examples object as distribution according to the state of glucose metabolism. It is the figure which classified visually the distribution of the state of sugar metabolism of many example subjects. It is a conceptual schematic diagram which divided | segmented the plane according to the state of glucose metabolism. It is a figure which shows the fine classification of the distribution according to the state of sugar metabolism of many examples object. It is a conceptual schematic diagram subdivided according to the state of sugar metabolism. It is a conceptual schematic diagram subdivided according to the state of sugar metabolism. It is a conceptual schematic diagram showing the classification according to the state of sugar metabolism in a three-dimensional space. It is a figure showing the judgment standard of the metabolic syndrome in a present Example.

Claims (11)

Based on the predetermined relationship homeostasis model assessment ratio (HOMA-IR) (R) and homeostasis model assessment β (HOMA-β) (β ), from HOMA-IR and HOMA-beta in a subject in classification, pre Symbol pairs a how you classify the state of the sugar metabolism in the elephant,
A certain relationship between HOMA-IR and HOMA-β was obtained by using a method in which the state of glucose metabolism was determined from the fasting blood glucose level by the oral glucose tolerance test and the blood glucose level 2 hours after the glucose load . the state of many examples subject sugar metabolism, are determined based on the distribution obtained by regarding the HOMA-IR and HOMA-beta of the many examples subject, said method. A certain relationship between HOMA-IR and HOMA-β is at least the following steps A) to D):
A) Regarding the status of glucose metabolism in each of the multiple subjects, based on the method in which the glucose metabolism status is determined from the fasting blood glucose level by the oral glucose tolerance test and the blood glucose level 2 hours after the glucose load. A step of classifying at least three types of a normal type having no abnormal sugar metabolism, a boundary type having abnormal sugar metabolism but not diabetes, and a diabetic type having abnormal glucose metabolism;
B) From the HOMA-IR and HOMA-β of the majority subject, the sugar metabolism of the majority subject classified in step A) on a plane centered on HOMA-IR and HOMA-β or 100-β Plotting so that the state of can be identified,
C) dividing the plane according to the state of sugar metabolism visually or statistically from the distribution of the state of sugar metabolism of the majority of subjects plotted in step B);
Representing each planar portion divided by D) Step C), the step of calculating relation between HOMA-IR and HOMA-beta, and / or, a numerical range of HOMA-IR and / or HOMA-beta,
Determined by the process comprising:
The method according to claim 1, wherein the relational expression between HOMA-IR and HOMA-β and / or the numerical range of HOMA-IR and / or HOMA-β . The fixed relationship between HOMA-IR and HOMA-β is
β> a × R + b or β ≦ a × R + b (a and b are each independently a real number), and
k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m and n are each independently a real number),
The method according to claim 1 or 2 . The fixed relationship between HOMA-IR and HOMA-β is
β ≦ c × R + d, c × R + d <β ≦ c ′ × R + d ′, or β> c ′ × R + d ′ (where c, c ′, d and d ′ are each independently a real number), and
k ≦ R and / or R <l, and / or m ≦ β and / or β <n (k, l, m and n are each independently a real number),
The method according to claim 1 or 2 . a is 40 to 70, b is -10 to 30, k is 0.2 or more, l is 15 or less, m is -500 or more, n is 2000 or less, to claim 3 The method described. c is 10 to 40, c ′ is 40 to 70, d is −30 to 20, d ′ is −10 to 30, k is 0.2 or more, l is 15 or less, and m is 5. The method of claim 4 , wherein -500 or more and n is 2000 or less. On a three-dimensional space, the relationship between HOMA-IR and HOMA-β further includes a relationship with an insulin secretion index or another index that reflects insulin secretion ability calculated from a measurement related to insulin secretion. represented by a method according to any one of claims 1-6. i) an index representing at least one or more health conditions of the subject to determine, classifies more than a predetermined reference value and / or less than that, and,
ii) classifying the state before Symbol subject to glucose metabolism using the method according to any one of claim 1 to 7
The front Symbol Target is a method for obtaining an index for determining whether a metabolic syndrome, i) an index representing at least one of the health state is classified into more than the reference value in, and ii) In the method, it is determined whether it is classified as diabetic type (DM) or borderline type (IGT) or HOMA-IR is 1.4 or more . The index indicating the health condition is systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, blood triglyceride value, blood cholesterol value, blood good cholesterol value, blood uric acid value, albumin excretion rate, blood C 9. The method of claim 8 , wherein the concentration is reactive protein. To determine the state of glucose metabolism in the subject to be classified ,
1) the state of the oral glucose tolerance test by fasting blood glucose and glucose tolerance after 2 hours after glucose level by the method many examples subject of a glucose metabolism obtained using the state of glucose metabolism is determined, the number Example is determined based on the distribution obtained by regarding the HOMA-IR and HOMA-beta of the target, the fixed relationship between HOMA-IR and HOMA-beta is input, it means for storing in the storage unit,
2) Means for calculating HOMA-IR and HOMA-β in the arithmetic unit when numerical values relating to HOMA-IR and HOMA-β to be classified are input,
3) The calculation unit determines whether the calculated HOMA-IR and HOMA-β to be classified satisfy a certain relationship between HOMA-IR and HOMA-β stored in the storage unit in 1) Means for determining the certain relationship that is satisfied by the object to be classified and calculated by:
4) Means for determining the state of sugar metabolism of the object to be classified based on the fixed relationship satisfied by the object to be classified determined in 3) 5) Means for outputting the state of sugar metabolism of the object to be classified from the output unit ,
Program to function as. To determine the state of glucose metabolism in the subject to be classified ,
I) Based on a method in which the state of glucose metabolism is judged from the fasting blood glucose level by oral glucose tolerance test and the blood glucose level 2 hours after glucose load , the glucose metabolism state is at least a normal type having no abnormal glucose metabolism, The status of glucose metabolism in a large number of subjects classified into three or more types of border type having abnormal glucose metabolism but not diabetic and diabetic types having abnormal glucose metabolism, and HOMA-IR and HOMA of said large number of subjects When figures for -β is input, it means for calculating the HOMA-IR and HOMA-beta of the many examples subject in the calculating portion,
The distribution of the state of glucose metabolism in II) numerous examples subject, by statistically processing the computation unit based on HOMA-IR and HOMA-beta, as a constant relationship between HOMA-IR and HOMA-beta, by the state of glucose metabolism, it means for storing relationship between HOMA-IR and HOMA-beta, and / or, a numerical range of HOMA-IR and / or HOMA-beta, determined, in the storage unit,
III) Means for calculating HOMA-IR and HOMA-β in the calculation unit when numerical values relating to HOMA-IR and HOMA-β to be classified are input,
IV) The above - mentioned relational expression according to the state of sugar metabolism, wherein the calculated HOMA-IR and HOMA-β of the object to be classified is a fixed relationship between HOMA-IR and HOMA-β stored in II) And / or means for calculating whether the numerical value range is satisfied or not by the calculation unit, and determining the relational expression and / or the numerical range that the object satisfies,
V) Determine the state of sugar metabolism of the subject to be classified based on the relational expression and / or numerical range, which is a certain relationship between HOMA-IR and HOMA-β satisfied by the subject, determined in step IV). means
VI) Means for outputting from the output section the state of sugar metabolism to be classified ,
Program to function as.

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