Effect of body composition at selection on reproductive development in large white gilts

Fifty-four Large White gilts were used to determine the effect of body composition at selection (145 d of age) on the onset of puberty and subsequent reproductive development until 202 d of age. Gilts were assigned to one of three groups based on their backfat depth at selection: 10 to 12 mm (L), 13 to 15 mm (M), and 16 to 18 mm (F). All of the F gilts, 92% of the M gilts, and 67% of the L gilts reached puberty by slaughter at 202 d of age. Data from a subgroup (first 67% to reach puberty in each group; L = Lp, M = Mp, and F = Fp) was also used. The M (Mp) and F (Fp) gilts reached puberty at 172 d (166 d) and 170 d (166 d) of age, respectively, but the L (Lp) gilts at 184.5 d were 12 d (18 d) older than M(P < .05), Mp(P < .001), and F(P < .01), Fp (P < .001) gilts. The Lp (97.68 kg) and Mp (98.33 kg) gilts were lighter (P < .01) than Fp (108.72 kg) gilts at puberty. There were no differences (P < .05) among the L, M, and F gilts in terms of backfat depth or weight at puberty. The L (Lp) gilts had a mean of 1.16 (1.75) estrous cycles, which was lower (P < .01) than for M (Mp) and (P < .01) F (Fp) gilts, with 1.96 (2.29) and 2.25 (2.33) cycles, respectively. L (Lp) gilts had fewer (P < .05) follicles, 13.14 (12.63), than either M (Mp), 19.08 (18.71), or F (Fp), 18.25 (17.42) gilts. The number of corpora lutea was not influenced (P > .05) by grouping at selection, but Fp gilts had fewer (P < .05) corpora lutea than Mp or Fp gilts. Live weight at slaughter was not influenced (P > .10) by grouping at selection or subgrouping at puberty. The L gilts with a mean of 18.05 mm of backfat at slaughter were leaner (P < .05) than the F (21.66 mm) but not (P > .10) the M gilts (19.41 mm). Subgrouping had no effect. Fat deposition and protein deposition were higher (P < .05) in those animals that attained puberty. We conclude that the rate of fat and protein deposition seems to be one of the determinants of puberty attainment.

Sensitivity and uncertainty analyses for burden of disease and risk factor estimates

Epidemiological studies report confidence or uncertainty intervals around their estimates. Estimates of the burden of diseases and risk factors are subject to a broader range of uncertainty because of the combination of multiple data sources and value choices. Sensitivity analysis can be used to examine the effects of social values that have been incorporated into the design of the disability–adjusted life year (DALY). Age weight, where a year of healthy life lived at one age is valued differently from at another age, is the most controversial value built into the DALY. The discount rate, which addresses the difference in value of current versus future health benefits, also has been criticized. The distribution of the global disease burden and rankings of various conditions are largely insensitive to alternate assumptions about the discount rate and age weighting. The major effects of discounting and age weighting are to enhance the importance of neuropsychiatric conditions and sexually transmitted infections. The Global Burden of Disease study also has been criticized for estimating mortality and disease burden for regions using incomplete and uncertain data. Including uncertain results, with uncertainty quantified to the extent possible, is preferable, however, to leaving blank cells in tables intended to provide policy makers with an overall assessment of burden of disease. No estimate is generally interpreted as no problem. Greater investment in getting the descriptive epidemiology of diseases and injuries correct in poor countries will do vastly more to reduce uncertainty in disease burden assessments than a philosophical debate about the appropriateness of social value

Body size and ovarian cancer: case-control study and systematic review (Australia)

Objective: Although increased body mass is an established risk factor for a variety of cancers, its relation with cancer of the ovary is unclear. We therefore investigated the association between measures of body mass index (BMI) and ovarian cancer risk. Methods: Data from an Australian case-control study of 775 ovarian cancer cases and 846 controls were used to examine the association with BMI. We have also summarized the results from a number of other studies that have examined this association. Results: There was a significant increased risk of ovarian cancer with increasing BMI, with women in the top 15% of the BMI range having an odds ratio (OR) of 1.9 (95% confidence interval (CI), 1.3-2.6) compared with those in the middle 30%. Stratifying by physical activity showed a stronger effect among inactive women (OR = 3.0, 95% CI 1.3-6.9). The overall effect was consistent with the findings of most prior population-based case-control studies, while cohort studies reported positive effects closer to the null. Hospital-based studies gave variable results. Conclusions: Taken together, the evidence is in favor of a small to moderate positive relation between high BMI and occurrence of ovarian cancer.