Epidemiology and factor analysis of obesity, type II diabetes, hypertension, and dyslipidemia (syndrome X) on the Island of Kosrae, Federated States of Micronesia

OBJECTIVES: Obesity, type II diabetes, hypertension, and dyslipidemia are major causes of morbidity and mortality throughout the world. Though these disorders often cluster in individuals and families and are collectively known as syndrome X, the basis for this aggregation is not well understood. To further understand the pathogenesis of syndrome X, a comprehensive epidemiological study was undertaken on the Pacific Island of Kosrae, Federated States of Micronesia (FSM). METHODS: The entire adult (>20 years of age) population of Kosrae underwent a clinical evaluation that included a questionnaire that noted the participants' sex, family data including listing of biological parents, siblings, and children, smoking status, village of residence, age and health status. The medical evaluation included: anthropometric measures (weight, height, waist, hip), serum chemistries (leptin, fasting blood sugar (FBS), insulin, total cholesterol (TC), triglycerides (TG), and apolipoproteins B and A-I (apo B and apo A-I) and blood pressure (BP) measurements. RESULTS: Obesity (BMI >/=35) was found in 24%, diabetes (FBS >/=126 or 2-hour oral glucose tolerance test >/=200) in 12%, hypertension (SBP >/=140 or DBP >/=90) in 17%, and dyslipidemia (TC >/=240 or TG >/=200 or apo B >/=120 or apo A-I 相似文献   

Native and modified low-density-lipoprotein interaction with human platelets in normal and homozygous familial-hypercholesterolaemic subjects.

The binding of low-density lipoproteins (LDL) as well as LDL modified by cyclohexanedione (CHD-LDL) to gel-filtered platelets (GFP) and its effect on platelet function were studied in normal and in homozygous familial hypercholesterolaemic (HFH) subjects. Only normal-derived LDL could significantly compete with normal 125I-labelled LDL for binding to normal platelets. When GFP from normal subjects were incubated with normal LDL at concentrations of 25-200 micrograms of protein/ml, platelet aggregation in the presence of thrombin (0.5 i.u./ml) was increased by 65-186%. CHD-LDL, at similar concentrations, caused the opposite effect and decreased platelet aggregation by 26-47%. Both LDL and CHD-LDL (100 micrograms/ml) from HFH patients, when incubated with normal GFP, caused a significant reduction in platelet aggregation (33 and 50% respectively). When HFH-derived platelets were used, both patient LDL and CHD-LDL (but not the normal lipoprotein) could markedly compete with the patient 125I-labelled LDL for binding to the platelets. LDL and CHD-LDL (100 micrograms/ml) from normal subjects decreased aggregation of HFH-platelets by 52 and 85% respectively, while corresponding concentrations of LDL derived from HFH subjects (HFH-LDL) and CHD-LDL derived from HFH subjects (CHD-HFH-LDL) increased platelet aggregation by 165 and 65% respectively. The present results support the following conclusions: platelet activation by LDL in normal subjects is through the arginine-rich apoprotein-binding site; more than one binding site for LDL exists on platelets; under certain circumstances, LDL binding can cause a reduction in platelet activity; specificity for LDL binding to the platelets resides in different regions of the lipoprotein in HFH and in normal subjects. We have thus suggested a model for LDL-platelet interaction in normal and in HFH subjects.     

Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships,gene interaction,linkage, association,disequilibrium, and ascertainment effects for a complex phenotype

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker x marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium.     

Case-control association studies in mixed populations: correcting using genomic control

OBJECTIVE: Case-control association studies in mixed populations can result in spurious disease-marker associations if subpopulation disease prevalence and marker frequencies both differ. Genomic control (GC) uses neutral loci to correct for spurious association (due to population stratification), but how well this works remains undetermined. METHODS: We simulated and mixed populations with different disease and marker frequencies but without marker-disease association. We generated case-control datasets, calculated the chi2 for disease association with each marker, and applied two GC procedures, dividing by the mean chi2 or median-chi2/0.456. RESULTS: Corrections became conservative (false positive rate [FPR] <5%) with increasing subpopulation prevalence and marker differences. The mean correction resulted in FPRs close to 5% at average subpopulation allele frequency differences <0.26, but inclusion of just a few markers with large frequency differences resulted in conservative FPRs. FPRs from the median correction were mostly conservative but became anticonservative when a few markers with large frequency differences were included. CONCLUSION: GC can both lead to a notable loss of power to detect a true association (conservative) in many circumstances or may fail to eliminate the spurious associations (anticonservative). The mean correction factor is useful in certain situations to correct population stratification, but it is difficult to know when those situations exist.     

Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships,gene interaction,linkage, association,disequilibrium, and ascertainment effects for a complex phenotype

Background

In order to confirm a previous finding of linkage to alcoholism on chromosome 1 we have carried out a genetic linkage study.

Methods

DNA from eighteen families, densely affected by alcoholism, was used to genotype a set of polymorphic microsatellite markers at loci approximately 10 centimorgans apart spanning the short arm and part of the long arm of chromosome 1. Linkage analyses were performed using the classical lod score and a model-free method. Three different definitions of affection status were defined, these were 1. Heavy Drinking (HD) where affected subjects drank more than the Royal College of Psychiatrists recommended weekly amount. 2. The Research Diagnostic Criteria for alcoholism (RDCA) 3. Alcohol Dependence Syndrome (ADS) as defined by Edwards and Gross (1976) and now incorporated into ICD10 and DSMIV.

Results

Linkage analyses with the markers D1S1588, D1S2134, D1S1675 covering the cytogenetic region 1p22.1-11.2 all gave positive two point and multipoint lods with a maximum lod of 1.8 at D1S1588 (1p22.1) for the RDCA definition of alcoholism. Another lod of 1.8 was found with D1S1653 in the region 1q21.3-24.2 using the HD affection model.

Conclusion

These results both support the presence of linkage in the 1p22.1-11.2 region which was previously implicated by the USA Collaborative Study of the Genetics of Alcoholism (COGA) study and also suggest the presence of another susceptibility locus at 1q21.3-24.2.