The classification of diabetes mellitus and the tests used for its diagnosis were brought into order by the National Diabetes Data Group of the USA and the second World Health Organization Expert Committee on Diabetes Mellitus in 1979 and 1980. Apart from minor modifications by WHO in 1985, little has been changed since that time. There is however considerable new knowledge regarding the aetiology of different forms of diabetes as well as more information on the predictive value of different blood glucose values for the complications of diabetes. A WHO Consultation has therefore taken place in parallel with a report by an American Diabetes Association Expert Committee to re-examine diagnostic criteria and classification. The present document includes the conclusions of the former and is intended for wide distribution and discussion before final proposals are submitted to WHO for approval. The main changes proposed are as follows. The diagnostic fasting plasma (blood) glucose value has been lowered to > or =7.0 mmol l(-1) (6.1 mmol l(-1)). Impaired Glucose Tolerance (IGT) is changed to allow for the new fasting level. A new category of Impaired Fasting Glycaemia (IFG) is proposed to encompass values which are above normal but below the diagnostic cut-off for diabetes (plasma > or =6.1 to or =5.6 to <6.1 mmol l(-1)). Gestational Diabetes Mellitus (GDM) now includes gestational impaired glucose tolerance as well as the previous GDM. The classification defines both process and stage of the disease. The processes include Type 1, autoimmune and non-autoimmune, with beta-cell destruction; Type 2 with varying degrees of insulin resistance and insulin hyposecretion; Gestational Diabetes Mellitus; and Other Types where the cause is known (e.g. MODY, endocrinopathies). It is anticipated that this group will expand as causes of Type 2 become known. Stages range from normoglycaemia to insulin required for survival. It is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.

Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

D iabetes mellitus is a chronic illness that requires continuing medical care and ongoing patient self-management education and support to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payers, and other interested individuals with the components of diabetes care, general treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. Specifically titled sections of the standards address children with diabetes, pregnant women, and people with prediabetes. These standards are not intended to preclude clinical judgment or more extensive evaluation and management of the patient by other specialists as needed. For more detailed information about management of diabetes, refer to references 1–3. The recommendations included are screening, diagnostic, and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A large number of these interventions have been shown to be cost-effective (4). A grading system (Table 1), developed by the American Diabetes Association (ADA) andmodeled after existingmethods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E. These standards of care are revised annually by the ADA’s multidisciplinary Professional Practice Committee, incorporating new evidence. For the current revision, committee members systematically searched Medline for human studies related to each subsection and published since 1 January 2010. Recommendations (bulleted at the beginning of each subsection and also listed in the “Executive Summary: Standards of Medical Care in Diabetesd2012”) were revised based on new evidence or, in some cases, to clarify the prior recommendation or match the strength of the wording to the strength of the evidence. A table linking the changes in recommendations to new evidence can be reviewed at http:// professional.diabetes.org/CPR_Search. aspx. Subsequently, as is the case for all Position Statements, the standards of care were reviewed and approved by the ExecutiveCommittee of ADA’s Board ofDirectors, which includes health care professionals, scientists, and lay people. Feedback from the larger clinical community was valuable for the 2012 revision of the standards. Readers who wish to comment on the “Standards of Medical Care in Diabetesd2012” are invited to do so at http://professional.diabetes.org/ CPR_Search.aspx. Members of the Professional Practice Committee disclose all potential financial conflicts of interest with industry. These disclosures were discussed at the onset of the standards revisionmeeting. Members of the committee, their employer, and their disclosed conflicts of interest are listed in the “Professional PracticeCommitteeMembers” table (see pg. S109). The AmericanDiabetes Association funds development of the standards and all its position statements out of its general revenues and does not utilize industry support for these purposes.

https://www.science-open.com/document_file/bb04d9d0-b3df-4133-9494-e71e300f4279/PubMedCentral/bb04d9d0-b3df-4133-9494-e71e300f4279.pdf

Standards of Medical Care in Diabetes—2012

https://www.scienceopen.com/document_file/57689460-a74a-45be-b1a0-a3e9cd0c5d62/PubMedCentral/57689460-a74a-45be-b1a0-a3e9cd0c5d62.pdf

Standards of Medical Care in Diabetes—2013

An analyte monitor includes a sensor, a sensor control unit, and a display unit. The sensor has, for example, a substrate, a recessed channel formed in the substrate, and conductive material disposed in the recessed channel to form a working electrode. The sensor control unit typically has a housing adapted for placement on skin and is adapted to receive a portion of an electrochemical sensor. The sensor control unit also includes two or more conductive contacts disposed on the housing and configured for coupling to two or more contact pads on the sensor. A transmitter is disposed in the housing and coupled to the plurality of conductive contacts for transmitting data obtained using the sensor. The display unit has a receiver for receiving data transmitted by the transmitter of the sensor control unit and a display coupled to the receiver for displaying an indication of a level of an analyte. The analyte monitor may also be part of a drug delivery system to alter the level of the analyte based on the data obtained using the sensor.

Analyte Monitoring Device And Methods Of Use

结节病易误诊,据王洪武等~([1])收集国内18篇关于此病误诊的文献,误诊率高达63.2%,当然有误诊就会有误治,如孙永昌等~([2])报道26例结节病在影像学检查诊断的第一印象中拟诊结核5例,其中就有2例完成规范的抗结核治疗,为此应引起临床对本病诊治的重视。

Sarcoidosis

Although the scientific literature contains numerous reports of the statistical accuracy of systems for self-monitoring of blood glucose (SMBG), most of these studies determine accuracy in ways that may not be clinically useful. We have developed an error grid analysis (EGA), which describes the clinical accuracy of SMBG systems over the entire range of blood glucose values, taking into account 1) the absolute value of the system-generated glucose value, 2) the absolute value of the reference blood glucose value, 3) the relative difference between these two values, and 4) the clinical significance of this difference. The EGA of accuracy of five different reflectance meters (Eyetone, Dextrometer, Glucometer I, Glucometer II, Memory Glucometer II), a visually interpretable glucose reagent strip (Glucostix), and filter-paper spot glucose determinations is presented. In addition, reanalyses of a laboratory comparison of three reflectance meters (Accucheck II, Glucometer II, Glucoscan 9000) and of two previously published studies comparing the accuracy of five different reflectance meters with EGA is described. EGA provides the practitioner and the researcher with a clinically meaningful method for evaluating the accuracy of blood glucose values generated with various monitoring systems and for analyzing the clinical implications of previously published data.

Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose

The homeodomain protein IPF1 (also known as IDX1, STF1 and PDX1; see Methods) is critical for development of the pancreas in mice and is a key factor for the regulation of the insulin gene in the beta-cells of the endocrine pancreas. Targeted disruption of the Ipf1 gene encoding IPF1 in transgenic mice results in a failure of the pancreas to develop (pancreatic agenesis). Here, we report the identification of a single nucleotide deletion within codon 63 of the human IPF1 gene (13q12.1) in a patient with pancreatic agenesis. The patient is homozygous for the point deletion, whereas both parents are heterozygotes for the same mutation. The deletion was not found in 184 chromosomes from normal individuals, indicating that the mutation is unlikely to be a rare polymorphism. The point deletion causes a frame shift at the C-terminal border of the transactivation domain of IPF1 resulting in the translation of 59 novel codons before termination, aminoproximal to the homeodomain essential for DNA binding. Expression of mutant IPF1 in Cos-1 cells confirms the expression of a prematurely terminated truncated protein of 16 kD. Thus, the affected patient should have no functional IPF1 protein. Given the essential role of IPF1 in pancreas development, it is likely that this autosomal recessive mutation is the cause of the pancreatic agenesis phenotype in this patient. Thus, IPF1 appears to be a critical regulator of pancreas development in humans as well as mice.

Pancreatic agenesis attributable to a single nucleotide deletion in the human IPF1 gene coding sequence.

Early-onset type-II diabetes mellitus (MODY4) linked to IPF1.

OBJECTIVE To prospectively evaluate the frequency and severity of hypoglycemic episodes in IDDM subjects who declare themselves to have reduced awareness of hypoglycemia, to validate their self-designations in their natural environment, and to determine objectively the presence or absence of autonomic and neuroglycopenic symptoms associated with their low blood glucose (BG) levels. RESEARCH DESIGN AND METHODS A total of 78 insulin-dependent diabetes mellitus (IDDM) subjects (mean age 38.3 ± 9.2 years; duration of diabetes 19.3 ± 10.4 years) completed two sets of assessments separated by 6 months. The assessments included reports of frequency and severity of low BG, symptoms associated with low BG, and a BG symptom/estimation trial using a hand-held computer (HHC). Diaries of hypoglycemic episodes were kept for the intervening 6 months. HbA 1 levels were determined at each assessment. RESULTS Of the subjects, 39 declared themselves as having reduced awareness of hypoglycemia (reduced-awareness subjects). There were no differences between these reduced-awareness subjects and aware subjects with regard to age, sex, disease duration, insulin dose, or HbA 1 . During the HHC trials, reduced-awareness subjects were significantly less accurate in detecting BG P = 0.001) and had significantly fewer autonomic (0.41 ± 0.82 vs. 1.08 ± 1.22, P = 0.006, reduced-awareness vs. aware) and neuroglycopenic (0.44 ± 0.85 vs. 1.18 ± 1.32, P = 0.004, reduced-awareness vs. aware) symptoms per subject. Prospective diary records revealed that reduced-awareness subjects experienced more moderate (351 vs. 238, P = 0.026) and severe (50 vs. 17, P = 0.0062) hypoglycemic events. The second assessment results were similar to the first and verified the reliability of the data. CONCLUSIONS IDDM subjects who believe they have reduced awareness of hypoglycemia are generally correct. They have a history of more moderate and severe hypoglycemia, are less accurate at detecting BG

Reduced Awareness of Hypoglycemia in Adults With IDDM: A prospective study of hypoglycemic frequency and associated symptoms

OBJECTIVE —The objective of this study was to introduce continuous glucose–error grid analysis (CG-EGA) as a method of evaluating the accuracy of continuous glucose-monitoring sensors in terms of both accurate blood glucose (BG) values and accurate direction and rate of BG fluctuations and to illustrate the application of CG-EGA with data from the TheraSense Freestyle Navigator. RESEARCH DESIGN AND METHODS —We approach the design of CG-EGA from the understanding that continuous glucose sensors (CGSs) allow the observation of BG fluctuations as a process in time. We account for specifics of process characterization (location, speed, and direction) and for biological limitations of the observed processes (time lags associated with interstitial sensors). CG-EGA includes two interacting components: 1 ) point–error grid analysis (P-EGA) evaluates the sensor’s accuracy in terms of correct presentation of BG values and 2 ) rate–error grid analysis (R-EGA) assesses the sensor’s ability to capture the direction and rate of BG fluctuations. RESULTS —CG-EGA revealed that the accuracy of the Navigator, measured as a percentage of accurate readings plus benign errors, was significantly different at hypoglycemia (73.5%), euglycemia (99%), and hyperglycemia (95.4%). Failure to detect hypoglycemia was the most common error. The point accuracy of the Navigator was relatively stable over a wide range of BG rates of change, and its rate accuracy decreased significantly at high BG levels. CONCLUSIONS —Traditional self-monitoring of BG device evaluation methods fail to capture the important temporal characteristics of the continuous glucose-monitoring process. CG-EGA addresses this problem, thus providing a comprehensive assessment of sensor accuracy that appears to be a useful adjunct to other CGS performance measures.

https://care.diabetesjournals.org/content/diacare/27/8/1922.full.pdf

Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors: Continuous glucose–error grid analysis illustrated by TheraSense Freestyle Navigator data

William L. Clarke

Papers

Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose

Pancreatic agenesis attributable to a single nucleotide deletion in the human IPF1 gene coding sequence.

Early-onset type-II diabetes mellitus (MODY4) linked to IPF1.

Reduced Awareness of Hypoglycemia in Adults With IDDM: A prospective study of hypoglycemic frequency and associated symptoms

Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors: Continuous glucose–error grid analysis illustrated by TheraSense Freestyle Navigator data