Juan F. Ascaso

Bio: Juan F. Ascaso is an academic researcher from University of Valencia. The author has contributed to research in topics: Insulin resistance & Familial hypercholesterolemia. The author has an hindex of 27, co-authored 231 publications receiving 4123 citations. Previous affiliations of Juan F. Ascaso include Menéndez Pelayo International University & Carlos III Health Institute.

Lipoprotein ratios: Physiological significance and clinical usefulness in cardiovascular prevention

Abstract: Low-density lipoprotein (LDL) cholesterol concentration has been the prime index of cardiovascular disease risk and the main target for therapy. However, several lipoprotein ratios or "atherogenic indices" have been defined in an attempt to optimize the predictive capacity of the lipid profile. In this review, we summarize their pathophysiological aspects, and highlight the rationale for using these lipoprotein ratios as cardiovascular risk factors in clinical practice, specifying their cut-off risk levels and a target for lipid-lowering therapy. Total/high-density lipoprotein (HDL) cholesterol and LDL/HDL cholesterol ratios are risk indicators with greater predictive value than isolated parameters used independently, particularly LDL. Future recommendations regarding the diagnosis and treatment of dyslipidemia, including instruments for calculating cardiovascular risk or action guidelines, should include the lipoprotein ratios with greater predictive power which, in view of the evidence-based results, are none other than those which include HDL cholesterol.

Diagnosing Insulin Resistance by Simple Quantitative Methods in Subjects With Normal Glucose Metabolism

Abstract: OBJECTIVE —To identify a reliable yet simple indirect method for detection of insulin resistance (IR). RESEARCH DESIGN AND METHODS —A total of 65 subjects (44 men and 21 women aged 30–60 years) were selected by a simple random sampling method. Inclusion criteria were voluntary participation from staff and hospital personnel, absence of abnormal glucose tolerance, and normal results of lipid profile and basic blood chemistry. A blood sample was taken after a 12-h overnight fast to determine plasma lipid, glucose, and insulin levels. An intravenous glucose tolerance test with administration of insulin after 20 min and extraction of multiple blood samples for glucose and insulin measurements and calculation of the minimal model approximation of the metabolism of glucose (MMAMG) S i value were performed. Three indirect indexes used to predict insulin sensitivity or IR were calculated, and metabolic syndrome was diagnosed using the Adult Treatment Panel III (ATP III) criteria. All results were correlated with those of the MMAMG. RESULTS —The 75th percentile value as the cutoff point to define IR corresponded with a fasting plasma glucose level of 12 mU/l, a homeostasis model assessment of 2.6, a 25th percentile for S i value of 21, and QUICKI (quantitative insulin sensitivity check index) and McAuley indexes of 0.33 and 5.8, respectively. The S i index correlated ( P CONCLUSIONS —When compared with the S i index, the most sensitive and specific indirect method was the score proposed by McAuley et al. (specificity 0.91, sensitivity 0.75, 9.2 probability ratio of a positive test), followed by the existence of metabolic syndrome (specificity 0.91, sensitivity 0.66, 7.8 probability ratio of a positive test).

Cuantificación de insulinorresistencia con los valores de insulina basal e índice HOMA en una población no diabética

Abstract: Fundamento Calcular la prevalencia y definir el sindrome de insulinorresistencia mediante la determinacion de insulinemia basal y el indice HOMA, y estudiar su relacion con otros componentes del sindrome metabolico. Sujetos y metodo Estudiamos una poblacion de 292 sujetos no diabeticos, de ambos sexos y edades entre 20 y 65 anos, seleccionados por un metodo de muestreo simple aleatorio entre los que consultaron durante un ano en un centro de salud (en el area metropolitana de Valencia), mediante un metodo de busqueda oportunista. De ellos se selecciono a un subgrupo formado por 96 sujetos que no tenian caracteristicas clinicas ni analiticas del sindrome de insulinorresistencia, y se estudiaron los lipidos plasmaticos, parametros antropometricos, glucosa e insulina plasmatica y el valor del indice HOMA. Resultados El diagnostico de insulinorresistencia se ha establecido por los cortes del percentil 90 de la subpoblacion sin parametros clinicos ni analiticos del sindrome de insulinorresistencia, considerando una insulina plasmatica basal de 16,7 mU/l o superior, o indice HOMA de 3,8 o mayor. El indice HOMA es mas sensible que la insulina plasmatica para el diagnostico de insulinorresistencia. La prevalencia de insulinorresistencia (HOMA ≥ 3,8) en la poblacion estudiada por nosotros es elevada, 31,8%, siendo mas frecuente en hombres que en mujeres. Conclusion Ademas de los valores plasmaticos de insulina e indice HOMA, los mejores indicadores clinicobioquimicos de insulinorresistencia son los valores de glucemia en ayunas, el indice de masa corporal (IMC) y los trigliceridos plasmaticos. Asi, la razon de probabilidad de tener insulinorresistencia es de 5,9, 2,6 y 2,2, respectivamente para glucemia ≥ 110 mg/dl, IMC ≥ 25 kg/m2 y trigliceridos ≥ 150 mg/dl.

Importance of HDL cholesterol levels and the total/ HDL cholesterol ratio as a risk factor for coronary heart disease in molecularly defined heterozygous familial hypercholesterolaemia

Abstract: Aims To assess the relationship of the lipid profile to coronary heart disease in a group of heterozygous familial hypercholesterolaemic subjects with similar age, sex, body mass index, prevalence of angiotensin converting enzyme DD genotype and type of low density lipoprotein receptor mutation. Methods and Results A total of 66 molecularly defined heterozygous familial hypercholesterolaemic subjects, 33 of whom had coronary heart disease, were studied. Clinical features, cardiovascular risk factors and lipid parameters were compared in both groups. Familial hypercholesterolaemic patients with coronary heart disease showed significantly lower values of mean plasma HDL cholesterol and a higher total/HDL cholesterol ratio as compared with familial hypercholesterolaemic subjects free of coronary heart disease. Total and LDL cholesterol concentrations were higher in patients with coronary heart disease, without reaching statistical significance. No differences in plasma lipoprotein(a) levels on absolute and log-transformed values were observed between the two groups. In the whole familial hypercholesterolaemia group, plasma HDL cholesterol levels were related to plasma triglyceride values and to LDL receptor gene ‘null mutations’. Conclusions In familial hypercholesterolaemic subjects of similar age, gender, body mass index, systolic and diastolic blood pressure, and genetic factors that could influence coronary heart disease risk, plasma HDL cholesterol values and total/HDL cholesterol ratios are two important coronary risk factors. Hence, treatment of familial hypercholesterolaemia should focus not only on lowering total and LDL cholesterol levels, but also on increasing HDL cholesterol values for coronary heart disease prevention. More prospective and intervention trials should be conducted to establish the relationship of HDL cholesterol levels and coronary heart disease in familial hypercholesterolaemia.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education 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, payors, and other interested individuals with the components of diabetes care, 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. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, 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.

The Pharmacological Basis of Therapeutics

Abstract: The Pharmacological Basis of Therapeutics A Textbook of Pharmacology, Toxicology and Therapeutics for Physicians and Medical Students. By Prof. Louis Goodman and Prof. Alfred Gilman. Pp. xiii + 1383. (New York: The Macmillan Company, 1941.) 50s. net.

Classification and diagnosis of diabetes

Abstract: 1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.

Abstract: OBJECTIVE —In recent years, several major organizations have endorsed the concept of the metabolic syndrome and developed working definitions for it. How well these definitions predict the risk for adverse events in people with the metabolic syndrome is only now being learned. The purpose of this study was to summarize the estimates of relative risk for all-cause mortality, cardiovascular disease, and diabetes reported from prospective studies in samples from the general population using definitions of the metabolic syndrome developed by the National Cholesterol Education Program (NCEP) and World Health Organization (WHO). RESEARCH DESIGN AND METHODS —The author reviewed prospective studies from July 1998 through August 2004. RESULTS —For studies that used the exact NCEP definition of the metabolic syndrome, random-effects estimates of combined relative risk were 1.27 (95% CI 0.90–1.78) for all-cause mortality, 1.65 (1.38–1.99) for cardiovascular disease, and 2.99 (1.96–4.57) for diabetes. For studies that used the most exact WHO definition of the metabolic syndrome, the fixed-effects estimates of relative risk were 1.37 (1.09–1.74) for all-cause mortality and 1.93 (1.39–2.67) for cardiovascular disease; the fixed-effects estimate was 2.60 (1.55–4.38) for coronary heart disease. CONCLUSIONS —These estimates suggest that the population-attributable fraction for the metabolic syndrome, as it is currently conceived, is ∼6–7% for all-cause mortality, 12–17% for cardiovascular disease, and 30–52% for diabetes. Further research is needed to establish the use of the metabolic syndrome in predicting risk for death, cardiovascular disease, and diabetes in various population subgroups.

Type 1 diabetes

Abstract: Summary Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.