The etiology of familial resemblance for systolic (SBP) and diastolic (DBP) blood pressure, both within a single time point as well as across time points, was assessed to determine how familial etiologies underlying a trait may change across time. SBP and DBP measurements were taken roughly 12 years apart in family members participating in the longitudinal Québec Family Study. A longitudinal (bivariate) familial correlation model yields 3 types of correlations: intraindividual cross-time (e.g., father's BP at time 1 with his own BP at time 2); interindividual within-time (e.g., father time 1 with child time 1); and interindividual cross-time (e.g., father time 1 with child time 2). In addition, the change in BP across time (i.e., time 1-time 2) is examined using a univariate family correlation model. This combined method is useful in assessing the degree to which the same familial factors are operating across time (interindividual cross-time correlations), as well as the degree to which different heritable components are involved across time (change score). Maximal heritabilities for SBP were about 70% at each time point, while for DBP the heritability was larger at time 1 (87%) than time 2 (39%). Both the change scores (48% for SBP and 54% for DBP) and the cross-time comparisons (58% to 72% for SBP and 63% to 65% for DBP) evidenced significant familial resemblance. These results illustrate how simple methodologies can be used to specify how familial etiologies underlying a trait may change across time. For BP, the model includes unique familial factors that are specific to each time measurement, and an additional familial factor which is common to both time points. The factors leading to differences in longitudinal familial resemblance for BP (i.e., the unique factors) may be primarily genetic in origin, while those leading to stability across time may include both genetic and familial environmental effects. Sex and/or age interactions with the genotypes are also suggested.
Approximately 13% of San Bernardino County, California residents live in poverty. San Bernardino County has implemented the anti-hunger project, “Community Network to Provide Food Assistance” to coordinate private/public food assistance resources to reduce gaps in and duplication of services to the needy. Coordination is achieved through: a county-wide, toll free “Hotline for Food” a directory listing over 340 food assistance agencies; staff training about food assistance resources and making referrals; Food Access Advisory Committee to direct project activities; “Regional Networks” of emergency food providers who resolve hunger issues in their communities; and disseminating nutrition materials. The hotline has referred over 10,341 callers to emergency food and long-term food programs. As a result of networking, the Nutrition, Head Start, and Social Services programs are collaborating on a nutrition education grant. In addition, many private food providers now give nutrition education to their clients that incorporates project developed nutrition education materials. To evaluate this project, information is collected using the computer linked, hotline intake questionnaire. Also, hotline referral cards are color-coded to track agencies making referrals and identify areas needing outreach. This project has enhanced food access in San Bernardino County and can be easily duplicated in any community.
Almost one-quarter of U.S. children are now obese, a dramatic increase of over 20% in the past decade. It is intriguing that the increase in prevalence has been occurring while overall fat consumption has been declining. Body mass and composition are influenced by genetic factors, but the actual heritability of juvenile obesity is not known. A low physical activity (PA) is characteristic of obese children and adolescents, and it may be one cause of juvenile obesity. There is little evidence, however, that overall energy expenditure is low among the obese. There is a strong association between the prevalence of obesity and the extent of TV viewing. Enhanced PA can reduce body fat and blood pressure and improve lipoprotein profile in obese individuals. Its effect on body composition, however, is slower than with low-calorie diets. The three main dietary approaches are: protein sparing modified fast, balanced hypocaloric diets, and comprehensive behavioral lifestyle programs. To achieve long-standing control of overweight, one should combine changes in eating and activity patterns, using behavior modification techniques. However, the onus is also on society to reduce incentives for a sedentary lifestyle and over-consumption of food. To address the key issues related to childhood weight management, the American College of Sports Medicine convened a Scientific Roundtable in Indianapolis.
Objective: To explore cross‐sectional associations between short sleep duration and variations in body fat indices and leptin levels during adulthood in a sample of men and women involved in the Québec Family Study. Research Methods and Procedures: Anthropometric measurements, plasma lipid‐lipoprotein profile, plasma leptin concentrations, and total sleep duration were determined in a sample of 323 men and 417 women ages 21 to 64 years. Results: When compared with adults reporting 7 to 8 hours of sleep per day, the adjusted odds ratio for overweight/obesity was 1.38 (95% confidence interval, 0.89 to 2.10) for those with 9 to 10 hours of sleep and 1.69 (95% confidence interval, 1.15 to 2.39) for those with 5 to 6 hours of sleep, after adjustment for age, sex, and physical activity level. In each sex, we observed lower adiposity indices in the 7‐ to 8‐hour sleeping group than in the 5‐ to 6‐hour sleeping group. However, all of these significant differences disappeared after statistical adjustment for plasma leptin levels. Finally, the well‐documented regression of plasma leptin levels over body fat mass was used to predict leptin levels of short‐duration sleepers (5 and 6 hours of sleep), which were then compared with their measured values. As expected, the measured leptin values were significantly lower than predicted values. Discussion: There may be optimal sleeping hours at which body weight regulation is facilitated. Indeed, short sleep duration predicts an increased risk of being overweight/obese in adults and is related to a reduced circulating leptin level relative to what is predicted by fat mass. Because sleep duration is a potentially modifiable risk factor, these findings might have important clinical implications for the prevention and treatment of obesity.
The risk of becoming obese is higher in some families than in others. The risk (the lambda coefficient) is two to three fold for moderate obesity, but up to five to eight fold for severe obesity. Several genes exhibit mutations that can cause early onset severe obesity. These mutations are rare and account for only a small fraction of the cases of obesity. At this time, more than fifty genes have been shown in various studies to influence the energy balance, nutrient partitioning, or the age of onset of obesity. The results of these studies are generally disappointing and often contradictory. One approach is to scan the genome with a high number of polymorphic markers to identify chromosomal regions harboring genes implicated in the development of obesity. Such studies can be helpful in defining new targets to explore.
Objective: In the present study, we undertook a two‐step fine mapping of a 20‐megabase region around a quantitative trait locus previously reported on chromosome 15q26 for abdominal subcutaneous fat (ASF) in an extended sample of 707 subjects from 202 families from the Quebec Family Study. Research Methods and Procedure: First, 19 microsatellites (in addition to the 7 markers initially available on 15q24‐q26; total = 26) were genotyped and tested for linkage with abdominal total fat, abdominal visceral fat, and ASF assessed by computed tomography and with fat mass (FM) using variance component‐based approach on age‐ and sex‐adjusted phenotypes. Second, 16 single nucleotide polymorphisms (SNPs) were genotyped and tested for association using family‐based association tests. Results: After the fine mapping, the peak logarithm of odds ratio (LOD) score (marker D15S1004) increased from 2.79 to 3.26 for ASF and from 3.52 to 4.48 for FM, whereas for abdominal total fat, the peak linkage (marker D15S996) decreased from 2.22 to 1.53. No evidence of linkage was found for abdominal visceral fat. Overall, for genotyped SNPs, three variants located in the putative MCTP2 gene were significantly associated with FM and the three abdominal fat phenotypes ( p ≤ 0.05). The major allele and genotype of rs1424695 were associated with higher adiposity values ( p < 0.004). The same trend was found for the two other polymorphisms ( p < 0.05). None of the other SNPs was associated with adiposity phenotypes. The linkage for FM became non‐significant (LOD = 0.84) after adjustment for the MCTP2 polymorphisms, whereas the one for ASF remained unchanged. Discussion: These results suggest that the MCTP2 gene, located on chromosome 15q26, influences adiposity. Other studies will be needed to investigate the function of the MCTP2 gene and its role in obesity.
