Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate.

To assess the accuracy of BMI compared to directly measured dual-energy X-ray absorptiometry percent body fat (DEXA %BF) among a worker compensation population.

Methods

The agreement between BMI and DEXA %BF was assessed by the Pearson correlation coefficient among 1394 evaluable patients over a 5-year period. Sensitivity and specificity were calculated to measure how well BMI can identify true obese and nonobese individuals.

Results

Using at least 30kg/m² to identify obesity, BNI had a specificity of 0.658 and a sensitivity of 0.735. The correlation was better in females at 0.66, compared to males at 0.55, and weaker in older age groups at 0.42, as compared to the youngest age group at 0.59. Overall, 29.8% of the population was reclassified based on their DEXA %BF measures.

Conclusions

In a 5-year cohort worker compensation population, BMI was an inaccurate measure of true obesity.

Abstract Introduction The objective of the present study was to assess heritability of clinical and radiographic features of hand osteoarthritis (OA) in affected patients and their siblings. Methods A convenience sample of patients with clinical and radiographic hand OA and their siblings were evaluated by examination and radiography. Radiographs were scored for hand OA features by radiographic atlas. The heritability of hand OA phenotypes was assessed for clinical and radiographic measures based on anatomic locations and radiographic characteristics. Phenotypic data were transformed to reduce non-normality, if necessary. A variance components approach was used to calculate heritability. Results One hundred and thirty-six probands with hand OA and their sibling(s) were enrolled. By anatomic location, the highest heritability was seen with involvement of the first interphalangeal joint (h 2 = 0.63, P = 0.00004), the first carpometacarpal joint (h 2 = 0.38, P = 0.01), the distal interphalangeal joints (h 2 = 0.36, P = 0.02), and the proximal interphalangeal joints (h 2 = 0.30, P = 0.03) with osteophytes. The number and severity of joints with osteophyte involvement was heritable overall (h 2 = 0.38, P = 0.008 for number and h 2 = 0.35, P = 0.01 for severity) and for all interphalangeal joints (h 2 = 0.42, P = 0.004 and h 2 = 0.33, P = 0.02). The severity of carpometacarpal joint involvement was also heritable (h 2 = 0.53, P = 0.0006). Similar results were obtained when the analysis was limited to the Caucasian sample. Conclusions In a population with clinical and radiographic hand OA and their siblings, the presence of osteophytes was the most sensitive biomarker for hand OA heritability. Significant heritability was detected for anatomic phenotypes by joint location, severity of joint involvement with osteophytes as well as for overall number and degree of hand OA involvement. These findings are in agreement with the strong genetic predisposition for hand OA reported by others. The results support phenotyping based on severity of osteophytes and a joint-specific approach. More specific phenotypes may hold greater promise in the study of genetics in hand OA.

Objective

To assess the relationship of physical activity with bone mineral density (BMD) at various sites and examine potential modifying metabolic factors.

Methods

Responses from physical activity questionnaires were used to determine total physical activity (PA), moderate physical activity (mod-PA), and sedentary time. Regression analyses were performed to evaluate association of activity traits with insulin sensitivity by euglycemic clamp, adiponectin, C-reactive protein (CRP), and plasminogen activator inhibitor-1 (PAI-1) in 741 healthy subjects.

Results

The cohort was relatively sedentary. Activity level was associated with arm, pelvis, and leg BMD in univariate analyses. In multivariate association analyses of arm BMD, only female sex (β = -0.73, P < 0.0001) and adiponectin (β = -0.076, P = 0.0091) were significant. Multivariate analyses of pelvis BMD found independent associations with body mass index (BMI) (β = 0.33, P < 0.0001), adiponectin (β = -0.10, P = 0.013), female sex (β = -0.18, P < 0.0001), sedentary time (β = -0.088, P = 0.034), PA (β = 0.11, P = 0.01), and mod-PA (β = 0.11, P = 0.014). Age (β = -0.10, P = 0.0087), female sex (β = -0.63, P < 0.0001), BMI (β = 0.24, P < 0.0001), and mod-PA (β = 0.10, P = 0.0024) were independently associated with leg BMD.

Conclusions

These results suggest that BMD increases with physical activity in the arms, legs, and pelvis and is inversely related to sedentary time in the pelvis and legs; these associations may be modified by age, sex, BMI, and adiponectin, depending on the site, with physical activity being more important to pelvis and leg BMD than arm BMD and sedentary time being important for pelvis BMD. Moreover, we demonstrated that CRP, PAI-1, and insulin sensitivity play a minor role in BMD.

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.

Objective

Patients with type 2 diabetes mellitus have an increased risk of fracture despite normal or increased bone mineral density (BMD). Studies on the relationship of glucose homeostasis with BMD phenotypes have been inconclusive because distinguishing the roles of insulin resistance and hyperglycaemia in bone remodelling is challenging. In this study, we sought to define the relationship of site-specific BMD with glucose homeostasis traits and anthropometric traits.

Design/patients/measurements

In a cross-sectional study, we examined 787 subjects from the Mexican-American Coronary Artery Disease (MACAD) cohort who had undergone euglycaemic-hyperinsulinaemic clamps, oral glucose tolerance testing and dual X-ray absorptiometry. Glucose homeostasis traits included insulinogenic index (IGI30), insulin sensitivity (M value), insulin clearance (MCRI), fasting insulin, fasting glucose and 2-hour glucose. Univariate and multivariate analyses were performed to assess the association of glucose homeostasis and anthropometric traits with site-specific BMD.

Results

Two-hour glucose was negatively associated with arm BMD in women, which remained significant in multivariate analysis (β = -.15, P = .0015). Positive correlations between fasting insulin and BMD at weight-bearing sites, including pelvis (β = .22, P < .0001) and legs (β = .17, P = .001) in women and pelvis (β = .33, P < .0001) in men, lost significance after multivariate adjustment. Lean mass exhibited strong independent positive associations with BMD at multiple sites in both sexes.

Conclusion

Our findings suggest that (i) anabolic effects of insulin might work via mechanical loading from lean mass; (ii) a direct negative effect of increasing glucose might be more prominent at cortical-bone-rich sites in women; and (iii) lean mass is a strong positive predictor of bone mass.

Objective

The worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.

Methods

The cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.

Results

BMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r = 0.78 versus r = 0.51), but not in sex-stratified analyses (men, r = 0.63 versus r = 0.79; women, r = 0.69 versus r = 0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.

Conclusions

BAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI's relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.

Reduction in insulin clearance plays an important role in the compensatory response to insulin resistance. Given the importance of this trait to the pathogenesis of diabetes, a deeper understanding of its regulation is warranted. Our goal was to identify metabolic and cardiovascular traits that are independently associated with metabolic clearance rate of insulin (MCRI). We conducted a cross-sectional analysis of metabolic and cardiovascular traits in 765 participants from the Mexican-American Coronary Artery Disease (MACAD) project who had undergone blood sampling, oral glucose tolerance test, euglycemic-hyperinsulinemic clamp, dual-energy X-ray absorptiometry, and carotid ultrasound. We assessed correlations of MCRI with traits from seven domains, including anthropometry, biomarkers, cardiovascular, glucose homeostasis, lipase activity, lipid profile, and liver function tests. We found inverse independent correlations between MCRI and hepatic lipase (P = 0.0004), insulin secretion (P = 0.0002), alanine aminotransferase (P = 0.0045), total fat mass (P = 0.014), and diabetes (P = 0.03). MCRI and apolipoprotein A-I exhibited a positive independent correlation (P = 0.035). These results generate a hypothesis that lipid and adiposity associated traits related to liver function may play a role in insulin clearance.

Glutamine, glutamate, asparagine, and aspartate are involved in an enzyme-network that controls nitrogen metabolism. Branched-chain-amino-acid aminotransferase-1 (BCAT1) promotes proliferation of gliomas with wild-type IDH1 and is closely connected to the network. We hypothesized that metabolism of asparagine, glutamine, and branched-chain-amino-acids is associated with progression of malignant gliomas. Gene expression for asparagine synthetase (ASNS), glutaminase (GLS), and BCAT1 were analyzed in 164 gliomas from 156 patients [33-anaplastic gliomas (AG) and 131-glioblastomas (GBM), 64 of which were recurrent GBMs]. ASNS and GLS were twofold higher in GBMs versus AGs. BCAT1 was also higher in GBMs. ASNS expression was twofold higher in recurrent versus new GBMs. Five patients had serial samples: 4-showed higher ASNS and 3-higher GLS at recurrence. We analyzed grade and treatment in 4 groups: (1) low ASNS, GLS, and BCAT1 (n = 96); (2) low ASNS and GLS, but high BCAT1 (n = 26); (3) high ASNS or GLS, but low BCAT1 (n = 25); and (4) high ASNS or GLS and high BCAT1 (n = 17). Ninety-one  % of patients (29/32) with grade-III lesions were in group 1. In contrast, 95 % of patients (62/65) in groups 2-4 had GBMs. Treatment was similar in 4 groups (radiotherapy-80 %; temozolomide-30 %; other chemotherapy-50 %). High expression of ASNS, GLS, and BCAT1 were each associated with poor survival in the entire group. The combination of lower ASNS, GLS, and BCAT1 levels correlated with better survival for newly diagnosed GBMs (66 patients; P = 0.0039). Only tumors with lower enzymes showed improved outcome with temozolomide. IDH1(WT) gliomas had higher expression of these genes. Manipulation of amino acid metabolism in malignant gliomas may be further studied for therapeutics development.

Obesity promotes systemic insulin resistance through inflammatory changes that lead to the release of cytokines from activated macrophages. Although the mechanism is unclear, the second messenger cAMP has been found to attenuate macrophage activity in response to a variety of hormonal signals. We show that, in the setting of acute overnutrition, leptin triggers catecholamine-dependent increases in cAMP signaling that reduce inflammatory gene expression via the activation of the histone deacetylase HDAC4. cAMP stimulates HDAC4 activity through the PKA-dependent inhibition of the salt-inducible kinases (SIKs), which otherwise phosphorylate and sequester HDAC4 in the cytoplasm. Following its dephosphorylation, HDAC4 shuttles to the nucleus where it inhibits NF-κB activity over proinflammatory genes. As variants in the Hdac4 gene are associated with obesity in humans, our results indicate that the cAMP-HDAC4 pathway functions importantly in maintaining insulin sensitivity and energy balance via its effects on the innate immune system.