Estimation of the heritability of latent variables which are included in a structural model for metabolic syndrome

A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families

In a quantitative trait locus (QTL) study, it is usually not feasible to select families with offspring that simultaneously display variability in more than one phenotype. When multiple phenotypes are of interest, the sample will, with high probability, contain 'non-segregating' families, i.e. families with both parents homozygous at the QTL. These families potentially reduce the power of regression-based methods to detect linkage. Moreover, follow-up studies in individual families will be inefficient, and potentially even misleading, if non-segregating families are selected for the study. Our work extends Haseman-Elston regression using a latent class model to account for the mixture of segregating and non-segregating families. We provide theoretical motivation for the method using an additive genetic model with two distinct functions of the phenotypic outcome, squared difference (SqD) and mean-corrected product (MCP). A permutation procedure is developed to test for linkage; simulation shows that the test is valid for both phenotypic functions. For rare alleles, the method provides increased power compared to a 'marginal' approach that ignores the two types of families; for more common alleles, the marginal approach has better power. These results appear to reflect the ability of the algorithm to accurately assign families to the two classes and the relative weights of segregating and non-segregating families to the test of linkage. An application of Bayes rule is used to estimate the family-specific probability of segregating. High predictive value positive values for segregating families, particularly for MCP, suggest that the method has considerable value for identifying segregating families. The method is illustrated for gene expression phenotypes measured on 27 candidate genes previously demonstrated to show linkage in a sample of 14 families.

Parental history of type 2 diabetes mellitus, metabolic syndrome, and cardiometabolic risk factors in Asian Indian adolescents

The objective was to study the influence of parental history of type 2 diabetes mellitus on prevalence of the metabolic syndrome (MS) and other cardiometabolic risk factors in Asian Indian adolescents. Adolescents aged 12 to 19 years (N = 321) were recruited from the Chennai Urban Rural Epidemiology Study. Based on parental diabetic status, 3 groups were studied: group 1, offspring of parents with normal glucose tolerance (n = 105); group 2, offspring of 1 diabetic parent (n = 114); and group 3, offspring of 2 diabetic parents (n = 102). Subjects underwent blood pressure and anthropometric measurements as well as an oral glucose tolerance test and a fasting lipid profile. Metabolic syndrome was diagnosed using the International Diabetes Federation definition. Body mass index (P < .001) and waist and hip circumference (P < .05 for group 2 and P < .001 for group 3) were significantly higher in groups 2 and 3 compared with group 1. High-density lipoprotein cholesterol was significantly lower in groups 2 and 3 compared with group 1 (P < .05). Serum triglycerides were significantly higher in group 3 (P < .05) compared with the other 2 groups. Adolescents in group 3 (P < .001) and group 2 (P < .05) were significantly more overweight and had more abdominal obesity compared with those in group 1. Impaired fasting glucose and impaired glucose tolerance were also significantly higher in group 3 compared with the other 2 groups. High blood pressure showed an increasing trend from group 1 to group 3 (P for trend < .05). Two metabolic abnormalities were present in 7.6%, 14.9%, and 22.5% of adolescents in groups 1, 2, and 3, respectively (trend chi(2): 9.04, P = .003). Prevalence of MS was higher in groups 2 and 3 compared with group 1 but did not reach statistical significance because of small numbers. The cardiometabolic profile of the parents was similar to that of the adolescents. Parental history of type 2 diabetes mellitus increases risk of not only glucose intolerance but also other cardiometabolic risk factors like overweight, low high-density lipoprotein cholesterol, and high blood pressure in Asian Indian adolescents.

Population-based family case-control proband study on familial aggregation of metabolic syndrome: finding from Taiwanese people involved in Keelung community-based integrated screening (KCIS no. 5)

A population-based case-control proband study was undertaken to elucidate familial aggregation, independent environmental factors, and the interaction between them. A total of 7308 metabolic syndrome (MET-S) cases were identified from the Keelung community-based integrated screening programme between 1999 and 2002. The study has a case-control/family sampling design. A total of 1417 case probands were randomly selected from 3225 metabolic syndrome cases and the corresponding 2458 controls selected from 16,519 subjects without metabolic syndrome by matching on sex, age (+/-3 years) and place of residence. The generalized estimation equation model was used to estimate odds ratios and corresponding 95% confidence intervals. The risk for having metabolic syndrome among family members for cases versus control probands was 1.56-fold (1.29-1.89) after controlling for significant environmental factors. Higher risk of metabolic syndrome was found in parents than spouse. Low education against high education had 2.06-fold (1.36-3.13) risk for metabolic syndrome. Betel quid chewing was positively associated with the risk of MET-S, with 1.99-fold (1.13-3.53) risk for 1-9 pieces and 1.76-fold (0.96-3.23) risk for >or=10 pieces compared with non-chewer. Moderate and high intensity of non-occupational exercise led to 21.0% (OR=0.79 (0.63-0.98)) and 26.0% (OR=0.74 (0.59-0.94)) reduction in the risk for metabolic syndrome, respectively. The frequent consumption of vegetable reduced 24.0% (OR=0.76 (0.62-0.92)) risk for MET-S. The frequent consumption of coffee was associated the increased risk for metabolic syndrome (OR=1.32 (1.07-1.64)). The present study confirmed the risk of metabolic syndrome not only has the tendency towards familial aggregation but is affected by independent effect of environmental or individual correlates.

Integrating Genetic and Gene Expression Data to Study the Metabolic Syndrome and Diabetes in Mice

Increasingly, the mouse is becoming the standard model for mammalian physiology and disease. It can be genetically analyzed and manipulated with relative ease. Moreover, the endogenous genetic variation that exists among inbred mouse strains can be exploited to identify genetic control of complex physiologic processes involved in diabetes and the metabolic syndrome, among other conditions relevant to human disease. Recent advances in genetics and gene expression technology have greatly increased the knowledge to be derived from this approach when applied to traditional genetic studies.

Body size as a latent variable in a structural equation model: thermal acclimation and energetics of the leaf-eared mouse

Body size is one of the most important determinants of energy metabolism in mammals. However, the usual physiological variables measured to characterize energy metabolism and heat dissipation in endotherms are strongly affected by thermal acclimation, and are also correlated among themselves. In addition to choosing the appropriate measurement of body size, these problems create additional complications when analyzing the relationships among physiological variables such as basal metabolism, non-shivering thermogenesis, thermoregulatory maximum metabolic rate and minimum thermal conductance, body size dependence, and the effect of thermal acclimation on them. We measured these variables in Phyllotis darwini, a murid rodent from central Chile, under conditions of warm and cold acclimation. In addition to standard statistical analyses to determine the effect of thermal acclimation on each variable and the body-mass-controlled correlation among them, we performed a Structural Equation Modeling analysis to evaluate the effects of three different measurements of body size (body mass, m(b); body length, L(b) and foot length, L(f)) on energy metabolism and thermal conductance. We found that thermal acclimation changed the correlation among physiological variables. Only cold-acclimated animals supported our a priori path models, and m(b) appeared to be the best descriptor of body size (compared with L(b) and L(f)) when dealing with energy metabolism and thermal conductance. However, while m(b) appeared to be the strongest determinant of energy metabolism, there was an important and significant contribution of L(b) (but not L(f)) to thermal conductance. This study demonstrates how additional information can be drawn from physiological ecology and general organismal studies by applying Structural Equation Modeling when multiple variables are measured in the same individuals.