The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.

/pdf/homeostasis-model-assessment-insulin-resistance-and-beta-119fe0y1fg.pdf

Homeostasis model assessment : insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man

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.

ABCD
: Appropriate Blood pressure Control in Diabetes
ABI
: ankle–brachial index
ABPM
: ambulatory blood pressure monitoring
ACCESS
: Acute Candesartan Cilexetil Therapy in Stroke Survival
ACCOMPLISH
: Avoiding Cardiovascular Events in Combination Therapy in Patients Living with Systolic Hypertension
ACCORD
: Action to Control Cardiovascular Risk in Diabetes
ACE
: angiotensin-converting enzyme
ACTIVE I
: Atrial Fibrillation Clopidogrel Trial with Irbesartan for Prevention of Vascular Events
ADVANCE
: Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation
AHEAD
: Action for HEAlth in Diabetes
ALLHAT
: Antihypertensive and Lipid-Lowering Treatment to Prevent Heart ATtack
ALTITUDE
: ALiskiren Trial In Type 2 Diabetes Using Cardio-renal Endpoints
ANTIPAF
: ANgioTensin II Antagonist In Paroxysmal Atrial Fibrillation
APOLLO
: A Randomized Controlled Trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly People
ARB
: angiotensin receptor blocker
ARIC
: Atherosclerosis Risk In Communities
ARR
: aldosterone renin ratio
ASCOT
: Anglo-Scandinavian Cardiac Outcomes Trial
ASCOT-LLA
: Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm
ASTRAL
: Angioplasty and STenting for Renal Artery Lesions
A-V
: atrioventricular
BB
: beta-blocker
BMI
: body mass index
BP
: blood pressure
BSA
: body surface area
CA
: calcium antagonist
CABG
: coronary artery bypass graft
CAPPP
: CAPtopril Prevention Project
CAPRAF
: CAndesartan in the Prevention of Relapsing Atrial Fibrillation
CHD
: coronary heart disease
CHHIPS
: Controlling Hypertension and Hypertension Immediately Post-Stroke
CKD
: chronic kidney disease
CKD-EPI
: Chronic Kidney Disease—EPIdemiology collaboration
CONVINCE
: Controlled ONset Verapamil INvestigation of CV Endpoints
CT
: computed tomography
CV
: cardiovascular
CVD
: cardiovascular disease
D
: diuretic
DASH
: Dietary Approaches to Stop Hypertension
DBP
: diastolic blood pressure
DCCT
: Diabetes Control and Complications Study
DIRECT
: DIabetic REtinopathy Candesartan Trials
DM
: diabetes mellitus
DPP-4
: dipeptidyl peptidase 4
EAS
: European Atherosclerosis Society
EASD
: European Association for the Study of Diabetes
ECG
: electrocardiogram
EF
: ejection fraction
eGFR
: estimated glomerular filtration rate
ELSA
: European Lacidipine Study on Atherosclerosis
ESC
: European Society of Cardiology
ESH
: European Society of Hypertension
ESRD
: end-stage renal disease
EXPLOR
: Amlodipine–Valsartan Combination Decreases Central Systolic Blood Pressure more Effectively than the Amlodipine–Atenolol Combination
FDA
: U.S. Food and Drug Administration
FEVER
: Felodipine EVent Reduction study
GISSI-AF
: Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico-Atrial Fibrillation
HbA1c
: glycated haemoglobin
HBPM
: home blood pressure monitoring
HOPE
: Heart Outcomes Prevention Evaluation
HOT
: Hypertension Optimal Treatment
HRT
: hormone replacement therapy
HT
: hypertension
HYVET
: HYpertension in the Very Elderly Trial
IMT
: intima-media thickness
I-PRESERVE
: Irbesartan in Heart Failure with Preserved Systolic Function
INTERHEART
: Effect of Potentially Modifiable Risk Factors associated with Myocardial Infarction in 52 Countries
INVEST
: INternational VErapamil SR/T Trandolapril
ISH
: Isolated systolic hypertension
JNC
: Joint National Committee
JUPITER
: Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin
LAVi
: left atrial volume index
LIFE
: Losartan Intervention For Endpoint Reduction in Hypertensives
LV
: left ventricle/left ventricular
LVH
: left ventricular hypertrophy
LVM
: left ventricular mass
MDRD
: Modification of Diet in Renal Disease
MRFIT
: Multiple Risk Factor Intervention Trial
MRI
: magnetic resonance imaging
NORDIL
: The Nordic Diltiazem Intervention study
OC
: oral contraceptive
OD
: organ damage
ONTARGET
: ONgoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial
PAD
: peripheral artery disease
PATHS
: Prevention And Treatment of Hypertension Study
PCI
: percutaneous coronary intervention
PPAR
: peroxisome proliferator-activated receptor
PREVEND
: Prevention of REnal and Vascular ENdstage Disease
PROFESS
: Prevention Regimen for Effectively Avoiding Secondary Strokes
PROGRESS
: Perindopril Protection Against Recurrent Stroke Study
PWV
: pulse wave velocity
QALY
: Quality adjusted life years
RAA
: renin-angiotensin-aldosterone
RAS
: renin-angiotensin system
RCT
: randomized controlled trials
RF
: risk factor
ROADMAP
: Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention
SBP
: systolic blood pressure
SCAST
: Angiotensin-Receptor Blocker Candesartan for Treatment of Acute STroke
SCOPE
: Study on COgnition and Prognosis in the Elderly
SCORE
: Systematic COronary Risk Evaluation
SHEP
: Systolic Hypertension in the Elderly Program
STOP
: Swedish Trials in Old Patients with Hypertension
STOP-2
: The second Swedish Trial in Old Patients with Hypertension
SYSTCHINA
: SYSTolic Hypertension in the Elderly: Chinese trial
SYSTEUR
: SYSTolic Hypertension in Europe
TIA
: transient ischaemic attack
TOHP
: Trials Of Hypertension Prevention
TRANSCEND
: Telmisartan Randomised AssessmeNt Study in ACE iNtolerant subjects with cardiovascular Disease
UKPDS
: United Kingdom Prospective Diabetes Study
VADT
: Veterans' Affairs Diabetes Trial
VALUE
: Valsartan Antihypertensive Long-term Use Evaluation
WHO
: World Health Organization

### 1.1 Principles

The 2013 guidelines on hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology …

/pdf/2013-esh-esc-guidelines-for-the-management-of-arterial-4ydvs5ifsx.pdf

2013 ESH/ESC Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC).

Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)

the growth of knowledge regarding the etiology and pathogenesis of diabetes has led many individuals and groups in the diabetes community to express the need for a revision of the nomenclature, diagnostic criteria, and classification of diabetes. As a consequence, it was deemed essential to develop an appropriate, uniform terminology and a functional, working classification of diabetes that reflects the current knowledge about the disease. (1)

Report of the expert committee on the diagnosis and classification of diabetes mellitus

Aims Insulin sensitivity and acute insulin response measure key components of Type 2 diabetes aetiology that contribute independently to risk in the Insulin Resistance Atherosclerosis Study. As insulin sensitivity and acute insulin response are not routinely measured in a clinical setting, we evaluated three fasting biomarker models, homeostasis model assessment of insulin sensitivity (HOMA-%S), β-cell function (HOMA-%B) and a Diabetes Risk Score, as potential surrogates for risk associated with insulin sensitivity, acute insulin response and the interaction of these two measures, the disposition index.



Methods  Models were calculated from baseline plasma biomarker concentrations for 664 participants who underwent a frequently sampled intravenous glucose tolerance test. To assess relationships among biomarker models and test measures, we calculated improvement in risk estimation gained by combining each fasting measure with each frequently sampled intravenous glucose tolerance test measure using logistic regression.



Results  The strongest correlates of acute insulin response, insulin sensitivity and disposition index were HOMA-%B (rs2 = 0.23), HOMA-%S (rs2 = 0.48) and Diabetes Risk Score (rs2 = 0.34), respectively. Individual areas under the curves for prediction of diabetes were 0.549 (HOMA-%B), 0.694 (HOMA-%S), 0.700 (insulin sensitivity), 0.714 (acute insulin response), 0.756 (Diabetes Risk Score) and 0.817 (disposition index). Models combining acute insulin response with Diabetes Risk Score (area under the curve 0.798) or HOMA-%S (area under the curve 0.805) nearly equalled disposition index, outperforming other individual measures (P < 0.05). Insulin sensitivity plus Diabetes Risk Score (area under the curve 0.760) was better than insulin sensitivity (P = 0.03), but not better than Diabetes Risk Score alone. HOMA-%S plus insulin sensitivity (area under the curve 0.704) was not significantly better than either alone.



Conclusions  The Diabetes Risk Score and HOMA-%S were excellent surrogates for insulin sensitivity, capturing the predictive power of insulin sensitivity. Diabetes Risk Score captured some of the additional predictive power of acute insulin response, but the HOMA models did not. No fasting model was as predictive as disposition index, but the Diabetes Risk Score was the best surrogate.

/pdf/biomarker-models-as-surrogates-for-the-disposition-index-in-324p1t1tfc.pdf

Biomarker models as surrogates for the disposition index in the Insulin Resistance Atherosclerosis Study.

Context Premenopausal women have blunted counter-regulatory hormone responses (CRR) to hypoglycemia compared to men. Postmenopausal women have CRR similar to men; the premenopausal pattern can be restored by estrogen. However, glucagon and pancreatic polypeptide (PP) responses remain lower in postmenopausal women than in men. Since hyperandrogenemia contributes to the metabolic phenotype of polycystic ovary syndrome (PCOS), we hypothesize that CRR to hypoglycemia especially of glucagon and PP is exaggerated in premenopausal women with PCOS compared to premenopausal control women. Study Subjects and Methods Ten obese women with PCOS and 9 control women of similar ethnicity, age and BMI underwent determination of CRR in response to hypoglycemia during 180-min 60 mU/m 2 /min insulin dose hypoglycemic clamp with isotopic assessment of endogenous glucose production (EGP). To assess CRR to hypoglycemia, glucagon, cortisol, growth hormone (GH), epinephrine, norepinephrine, PP, lactate, free fatty acid (FFA), β-hydroxybutyrate, and glycerol levels were sampled at 15-min intervals throughout the clamp. Main Findings Incremental glucagon levels were ~ 3-fold higher during hypoglycemia ( P  = 0.03) in PCOS. Postabsorptive, steady-state and incremental GH, cortisol, epinephrine, norepinephrine, PP, FFA, glycerol and β-hydroxybutyrate did not differ. At target glucose levels of ~ 52 mg/dL, insulin mediated glucose disposal (IMGD) was decreased by ~ 40% ( P  = 0.02) in PCOS, compared to control women, despite ~ 20% higher steady-state insulin levels ( P  = 0.03). Neither postabsorptive nor steady-state EGP differed. However, postabsorptive lactate levels were ~ 50% higher ( P  = 0.02). PCOS status ( P  = 0.04) and IMGD ( P  = 0.02) predicted the differential glucagon response to hypoglycemia in separate regression models, however, neither parameter remained an independent predictor in a combined model. Principal Conclusions Glucagon responses were increased in PCOS, whereas other CRR did not differ. Women with PCOS were insulin resistant under hypoglycemic conditions and higher postabsorptive lactate levels in PCOS were consistent with this finding. Insulin resistance may have contributed to exaggerated glucagon response to hypoglycemia in PCOS.

/pdf/exaggerated-glucagon-responses-to-hypoglycemia-in-women-with-4dc7oal03s.pdf

Exaggerated glucagon responses to hypoglycemia in women with polycystic ovary syndrome.

and anaplerosis in heart: validation with syntactic model

In the 1950’s, Dr. I. Arthur Mirsky first recognized the possible importance of insulin degradation changes to the pathogenesis of type 2 diabetes. While this mechanism was ignored for decades, insulin degradation is now being recognized as a possible factor in diabetes risk. After Mirsky, the relative importance of defects in insulin release and insulin resistance were recognized as risk factors. The hyperbolic relationship between secretion and sensitivity was introduced, as was the relationship between them, as expressed as the disposition index (DI). The DI was shown to be affected by environmental and genetic factors, and it was shown to be differentiated among ethnic groups. However, the importance of differences in insulin degradation (clearance) on the disposition index relationship remains to be clarified. Direct measure of insulin clearance revealed it to be highly variable among even normal individuals, and to be affected by fat feeding and other physiologic factors. Insulin clearance is relatively lower in ethnic groups at high risk for diabetes such as African Americans and Hispanic Americans, compared to European Americans. These differences exist even for young children. Two possible mechanisms have been proposed for the importance of insulin clearance for diabetes risk: in one concept, insulin resistance per se leads to reduced clearance and diabetes risk. In a second and new concept, reduced degradation is a primary factor leading to diabetes risk, such that lower clearance (resulting from genetics or environment) leads to systemic hyperinsulinemia, insulin resistance, and beta-cell stress. Recent data by Chang and colleagues appear to support this latter hypothesis in Native Americans. The importance of insulin clearance as a risk factor for metabolic disease is becoming recognized and may be treatable.

/pdf/the-physiology-of-insulin-clearance-3win3116.pdf

The Physiology of Insulin Clearance

Weiss et al. (Circ. Res. 70: 392-408, 1992) proposed a model of the citric acid cycle (CAC) in myocytes and a system of 17 differential equations that can be used to describe the changes over time ...

Richard N. Bergman

Papers

Biomarker models as surrogates for the disposition index in the Insulin Resistance Atherosclerosis Study.

Exaggerated glucagon responses to hypoglycemia in women with polycystic ovary syndrome.

and anaplerosis in heart: validation with syntactic model

The Physiology of Insulin Clearance

Estimation of TCA cycle flux, aminotransferase flux, and anaplerosis in heart: validation with syntactic model.