Publications

We investigated the associations between a polymorphism in the melanocortin 4 receptor (MC4R) gene and changes in body size and composition from childhood to adulthood in the Québec Family Study. Ninety-one subjects (43 males) less than 18 years of age (mean age 13.5 +/- 2.4 years; range 8.4-17.8 years) at baseline were re-measured 11.2 years later on average. The anthropometric variables analyzed were height, weight, body mass index, percent body fat, sum of skinfolds, fat mass index, and fat free mass index (FFMI). All variables were adjusted for age and sex. The subjects were genotyped for the MC4R C-2745T polymorphism. Forty-five subjects were homozygotes for the common allele (C/C), 36 were heterozygotes (C/T) and 10 were homozygotes for the rare allele (T/T). The rare allele was associated with increased height at baseline as well as at the follow-up visit. Although FFMI tended to increase more in subjects carrying the rare allele, no significant differences were found for the changes over time for the other phenotypes. These results suggest that DNA sequence variation in the MC4R locus may contribute to the gain in body height from childhood to adulthood.

80 We examined the relationship between fitness level, body composition, fasting insulin concentration, and glucose area-under-the-curve (GAUC) from an oral glucose tolerance test (OGTT) in healthy older adults. Subjects were 19 females (F) who were not on estrogen (age=67±4.9 yr, Ht=163.0±5.3 cm, Wt=71.2±11.6 kg) and 29 males (M) (age=66.9±4.2 yr, Ht=177.3±7.0 cm, Wt=85.9±11.1 kg) (Mean±SD). Body density was measured by hydrostatic weighing and percent body fat was calculated using the Siri equation. Abdominal visceral fat (AVF) cross-sectional area was determined using single slice CT at L4-L5. Fasting insulin concentration was measured at −30 min. Glucose concentrations during the OGTT (75 g dextrose) were measured every 30 min from −30 to 180 min. GAUC was calculated using the trapezoidal rule. Simple linear regression analysis was performed on each gender separately. In males, the following results were observed: 1) GAUC was not significantly related to AVF, AVF adjusted for fat mass, percent body fat, fat mass, BMI or fitness [as assessed by VO2 peak (ml/kg/min)], 2) fasting insulin level was significantly related to AVF (r=0.49, P=0.007) and fitness (r=−0.38, P=0.04), but not to AVF adjusted for fat mass, BMI, percent body fat, GAUC or fat mass. In females, the following results were observed: 1) GAUC was significantly related to AVF (r=0.54, P=0.02), but not to AVF adjusted for fat mass, percent body fat, fat mass, BMI, or fitness, 2) fasting insulin level was significantly related to AVF (r=0.78, P=0.0002), AVF adjusted for fat mass (r=0.69, P=0.0003), BMI (r=0.7, P=0.0002), fat mass (r=0.69, P=0.003), and GAUC (r=0.47, P=0.05), but not to percent body fat or fitness. We conclude that in healthy older adults, fasting insulin concentration is more strongly related to measures of body composition and fitness than GAUC. Supported in part by NIH grant R01AG10997 and by GCRC grant NIH RR00847

Background: The peroxisome proliferator-activated receptor-γ (PPARG) Pro12Ala and the PPARG co-activator-1α (PPARGC1A) Gly482Ser polymorphisms (SNPs) have been associated with type 2 diabetes mellitus (T2DM) risk. We hypothesized that independent and interactive effects of the PPARG Pro12Ala and PPARGC1A Gly482Ser polymorphisms influence T2DM intermediate phenotypes.

Lamarche, Benoit; Després, Jean-Pierre; Pouliol, Marie-Christine; Moorjani, Sital; Lupien, Paul J.; Thériault, Germain; Tremblay, Angélo; Nadeau, André; Bouchard, Claude FACSM Author Information

Background: Plasma proteins can be biomarkers of cardiovascular health status but also physiologic effectors that mediate health benefits. Cardiorespiratory fitness (CRF) is an integrative measure of cardiovascular and metabolic health and independent predictor of future cardiovascular disease (CVD) and mortality risk, however limited knowledge exists regarding its molecular transducers. We sought to expand upon prior proteomic studies of CRF using an antibody-based technology (Olink TM ). Hypothesis: Olink proteomic profiling will identify new markers and potential mediators of CRF. Methods: We measured plasma proteins (N=1,472) using Olink’s platform in a pilot study within the HERITAGE Family Study (N=209 participants, mean age=34 years, 56% female, 36% Black) before and after 20 weeks of endurance exercise training (ET). We performed multivariable linear regression to measure the association between protein levels and CRF measured by CPET (VO 2 max in ml*kg -1 *min -1 ) adjusting for age and sex. Lean body mass was measured by hydrostatic weighing. Protein changes after ET were assessed using paired Student’s t-tests. Results: We identified 70 proteins significantly associated (FDR q <0.05) with VO 2 max. Among these, 14 have not previously been measured in the context of CRF, including perilipin-1, a modulator of lipid homeostasis in adipocytes (beta=-3.8, q=2.4x10 -4 ); latent-transforming growth factor beta-binding protein 3 (beta=-2.6, q=1.3x10 -3 ), a key regulator of TGF-beta activation; and hydroxysteroid 11-beta dehydrogenase 1 (beta=3.2, q=1.2x10 -3 ), which regulates cortisol metabolism. Carbonic anhydrase (CA) XIV (CA14; beta=5.4, q=1.3x10 -8 ), a member of the CA family with extracellular activity, had the strongest positive association with VO 2 max in our primary model as well as after further adjusting VO 2 max for lean body mass. Further, CA14 levels increased significantly after ET (log2 fold change: 0.14, q=0.007). Conclusions: Antibody-based plasma proteomics profiling identified new markers of CRF, including a CA isoform that increases after ET. These findings motivate further study of CA14’s mechanistic role in CA14 and expanded proteomics profiling to investigate molecular transducers of CRF.

Although cardiorespiratory fitness (CRF) is prognostic in older adults, the effect of CRF during early adulthood on long-term cardiovascular structure, function, and prognosis is less clear.

This study was performed retrospectively to investigate whether exercise energy expenditure (EE) measured during a standardized treadmill protocol (4.5 km/h at 0% grade) falls below predicted values after body weight loss in obese men. A reference equation was established to predict net exercise EE in a control sample of 83 obese individuals (27 kg/m(2)< or = body mass index <45 kg/m(2)), using age, fat mass and fat-free mass as independent variables. This equation was then used to predict net exercise EE in another group of 11 obese men before and after a 15-week drug-based weight loss programme that was coupled with energy restriction [-2929 kJ/day (-700 kcal/day)]. Body weight and body composition were determined by hydrodensitometry. Net exercise EE, insulin, leptin, 3,3',5-tri-iodothyronine and free thyroxine were measured after an overnight fast at baseline and 2-4 weeks after the end of the programme, when subjects were weight stable. Body weight was significantly reduced (-11%; P <0.01) at the end of the weight loss programme. At baseline, measured net exercise EE was similar to that predicted from the regression equation [19.6 and 19.8 kJ/min (4.69 and 4.74 kcal/min) respectively; not significant]. However, after the end of the intervention, measured net exercise EE was significantly below the predicted value [15.5 and 17.3 kJ/min (3.71 and 4.14 kcal/min) respectively; P <0.01]. The difference between the predicted and the measured fall in net exercise EE was significantly associated with changes in leptin concentration ( r =0.79, P <0.01), even after correction for changes in fat mass and insulin. These observations suggest that net exercise EE falls below predicted values after body weight loss. In addition, this greater than predicted decrease in net exercise EE was associated with changes in leptin.

Background— A genome-wide linkage scan identified a quantitative trait locus for exercise training–induced changes in submaximal exercise (50 W) heart rate (ΔHR50) on chromosome 2q33.3-q34 in the HERITAGE Family Study (n=472). Methods and Results— To fine-map the region, 1450 tag SNPs were genotyped between 205 and 215 Mb on chromosome 2. The strongest evidence of association with ΔHR50 was observed with 2 single-nucleotide polymorphisms (SNPs) located in the 5′ region of the cAMP-responsive element-binding protein 1 ( CREB1 ) gene (rs2253206: P =1.6×10 −5 and rs2360969: P =4.3×10 −5 ). The associations remained significant ( P =0.01 and P =0.023, respectively) after accounting for multiple testing. Regression modeling of the 39 most significant SNPs in the single-SNP analysis identified 9 SNPs that collectively explained 20% of the ΔHR50 variance. CREB1 SNP rs2253206 had the strongest effect (5.45% of variance), followed by SNPs in the FASTKD2 (3.1%), MAP2 (2.6%), SPAG16 (2.1%), ERBB4 (3 SNPs≈1.4% each), IKZF2 (1.4%), and PARD3B (1.0%) loci. In conditional linkage analysis, 6 SNPs from the final regression model ( CREB1, FASTKD2, MAP2, ERBB4, IKZF2 , and PARD3B ) accounted for the original linkage signal: The log of the odds score dropped from 2.10 to 0.41 after adjusting for all 6 SNPs. Functional studies revealed that the common allele of rs2253206 exhibits significantly ( P &lt;0.05) lower promoter activity than the minor allele. Conclusions— Our data suggest that functional DNA sequence variation in the CREB1 locus is strongly associated with ΔHR50 and explains a considerable proportion of the quantitative trait locus variance. However, at least 5 additional SNPs seem to be required to fully account for the original linkage signal.

Objective: To examine whether there is a major gene effect on fasting insulin and pleiotropic loci for fasting insulin, total fat mass (FM), and abdominal visceral fat (AVF). Research Methods and Procedures: A major gene hypothesis for fasting plasma insulin levels was assessed using segregation analyses of data on 495 members in 98 normolipidemic sedentary families of white descent who participated in the HERITAGE Family Study. Results: Segregation analyses were performed on insulin adjusted for age, on insulin adjusted for age and FM, and on insulin adjusted for age and AVF. Before adjustment for AVF and FM, a major gene effect on fasting insulin levels was indicated. The putative locus accounted for 54% of the variance under a recessive inheritance pattern, affecting 11% of the sample (i.e., allele frequency = 0.33). However, after adjusting for the effects of AVF or FM, neither a major effect alone nor a multifactorial component alone could be rejected, and support for a major gene was equivocal, i.e., neither the hypothesis of Mendelian τ values or that of the equal τs were rejected and the equal τ model fit the data better than the Mendelian τ model. This pattern (i.e., major gene evidence for insulin before but not after adjustment for AVF or FM) suggests that there is a putative locus with pleiotropic effects on both insulin and FM and another pleiotropic locus for both insulin and AVF. Discussion: Although these data do not directly support an additional major gene for insulin independent of AVF and FM, such support cannot be ruled out because there is still a significant major effect on FM‐ or AVF‐adjusted insulin (albeit the Mendelian nature of this effect is ambiguous).

No abstract is provided for this article.