Application of acylcarnitine spectrum or detection reagent thereof in prediction of onset risk of type 2 diabetes mellitus

A type 2 diabetes and acylcarnitine technology, applied in the medical field, can solve problems such as the lack of a simple and reliable type 2 diabetes prediction system

Active Publication Date: 2017-09-01
SHANGHAI INST OF BIOLOGICAL SCI CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, a simple and reliable prediction system for

Method used

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  • Application of acylcarnitine spectrum or detection reagent thereof in prediction of onset risk of type 2 diabetes mellitus
  • Application of acylcarnitine spectrum or detection reagent thereof in prediction of onset risk of type 2 diabetes mellitus
  • Application of acylcarnitine spectrum or detection reagent thereof in prediction of onset risk of type 2 diabetes mellitus

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0119] Baseline Characteristics Study

[0120] During the 6-year follow-up, there were 507 (24.1%) new-onset T2DM patients. Baseline characteristics and plasma acylcarnitine concentrations were compared in newly-onset diabetic patients and controls (Table 3). Compared with controls, there were more Beijing residents and family history of diabetes among new-onset diabetes patients. They had higher baseline BMI, systolic blood pressure, fasting blood glucose, HbA1c and insulin (P<0.05). In terms of baseline plasma acylcarnitines, patients with new-onset diabetes had higher C0, C3DC, C4, C5, C5OH, C8:1, C10, C14OH, C14:1OH, C16:1, C16:2, C18, C18OH, C18:1, C18:2, C20 and C20:4 acylcarnitines with lower levels of 3-dehydroxycarnitine, 3-dehydrocarnitine, C7DC, C10DC, C12, C12OH and C12:1 acylcarnitines.

[0121] Table 3 Comparison of baseline characteristics and plasma acylcarnitine profiles of new-onset type 2 diabetes cases-controls.

[0122]

[0123]

[0124] Data are...

Embodiment 2

[0126] Acylcarnitines and Metabolic Profile

[0127] For correlations between baseline acylcarnitines see figure 1 . Although most acylcarnitines were correlated, the most correlated acylcarnitines (r>0.7) were medium and long chain species. Interestingly, when the correlation between baseline acylcarnitines and metabolic profile was analyzed, it was found to be more strongly associated with follow-up fasting glucose than with baseline fasting glucose (Table 4). Likewise, baseline acylcarnitines were also more strongly associated with 6-year change in fasting blood glucose than with baseline fasting blood glucose. In contrast, the associations of baseline acylcarnitines with HbA1c and insulin were similar at baseline and follow-up.

[0128] Table 4 Correlation of baseline acylcarnitines with fasting blood glucose, HbA1c, and insulin.

[0129]

[0130]

[0131] Gender, age, region and rural-urban were controlled. *P

Embodiment 3

[0133] Type 2 Diabetes Prediction Model

[0134] During the 6-year follow-up, the risk of type 2 diabetes was correlated with C0, C3DC, medium-chain (C8:1, C10, C14OH, C14:1OH) and long-chain (C16:1, C16:2, C18, C18OH, C18: 1, C18:2, C20, C20:4) acylcarnitines are positively correlated with carnitine precursors (3-dehydroxycarnitine, 3-dehydrocarnitine), medium-chain dicarboxylic acids (C10DC, C12DC) and C12 (C12, C12OH, C12:1) was negatively correlated with acylcarnitines (Table 5). When acylcarnitines were classified according to chain length, only long-chain acylcarnitines were significantly associated with the risk of type 2 diabetes (P<0.001).

[0135] Table 5 Relationship between single acylcarnitines and the risk of developing type 2 diabetes.

[0136]

[0137]

[0138] Age, sex, region, urban and rural areas, smoking, alcohol consumption, physical activity, family history, BMI, fasting blood glucose, HbA1c, and systolic blood pressure were controlled. *Signifi...

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Abstract

The invention provides use of acylcarnitine or detection reagent thereof. Specifically, through 6 years of follow-up study on 2103 residents, a targeted metabonomic strategy is employed to determine that an acylcarnitine spectrum, especially a long-chain acylcarnitine spectrum can be used as the marker for effective prediction of the onset risk of type 2 diabetes mellitus, and also can significantly improve the prediction ability of traditional risk factors for new-onset type 2 diabetes mellitus, thus providing favorable guidance for clinical diagnosis and treatment.

Description

technical field [0001] The invention relates to the field of medicine, and more specifically relates to the application of acylcarnitine spectrum or its detection reagent in the risk prediction of type 2 diabetes. Background technique [0002] Diabetes is a group of metabolic diseases characterized by hyperglycemia. Hyperglycemia is caused by defective insulin secretion or impaired biological action, or both. The long-term high blood sugar in diabetes leads to chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. According to statistics, as of 2010, the number of diabetic patients in the world has reached 285 million. In my country, the prevalence of diabetes among adults over 20 years old is 9.7%, and the total number reaches 94.2 million. [0003] Type 2 diabetes is currently the most common type of diabetes in my country, and more than 90% of diabetic patients belong to type 2 diabetes. The global prevalence ...

Claims

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Application Information

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IPC IPC(8): G01N33/53
CPCG01N33/53G01N2800/042
Inventor 林旭曾嵘吴家睿孙亮
Owner SHANGHAI INST OF BIOLOGICAL SCI CHINESE ACAD OF SCI
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