Changes in the PQRST intervals and heart rate variability associated with rewarming in two newborns undergoing hypothermia therapy.

BACKGROUND: Little is known about the effects of hypothermia therapy and subsequent rewarming on the PQRST intervals and heart rate variability (HRV) in term newborns with hypoxic-ischemic encephalopathy (HIE). OBJECTIVES: This study describes the changes in the PQRST intervals and HRV during rewarming to normal core body temperature of 2 newborns with HIE after hypothermia therapy. METHODS: Within 6 h after birth, 2 newborns with HIE were cooled to a core body temperature of 33.5 degrees C for 72 h using a cooling blanket, followed by gradual rewarming (0.5 degrees C per hour) until the body temperature reached 36.5 degrees C. Custom instrumentation recorded the electrocardiogram from the leads used for clinical monitoring of vital signs. Generalized linear mixed models were calculated to estimate temperature-related changes in PQRST intervals and HRV. Results: For every 1 degrees C increase in body temperature, the heart rate increased by 9.2 bpm (95% CI 6.8-11.6), the QTc interval decreased by 21.6 ms (95% CI 17.3-25.9), and low and high frequency HRV decreased by 0.480 dB (95% CI 0.052-0.907) and 0.938 dB (95% CI 0.460-1.416), respectively. CONCLUSIONS: Hypothermia-induced changes in the electrocardiogram should be monitored carefully in future studies.

An investigation of the association between obesity, acanthosis nigricans, and fasting serum insulin level in 6--9 year old children living on the El Paso-Juarez U.S.-Mexico border

The objectives of this study were to investigate the relationship between fasting serum insulin levels and Acanthosis Nigricans (AN) (a dermatological condition characterized by hyperpigmentation and thickening of the skin in specific body areas such as the neck and knuckles) and obesity among 6 to 9 year old children. Children were selected at random from a pediatric clinic located on the U.S.-Mexico border. Because none of the children participants had a weight for height at or above the 97th percentile of the CDC growth charts, obesity was defined as weight for height at or above the 95th percentile and at risk of overweight between the 85 th and 95th percentiles of the CDC growth charts. Anthropometrics, blood samples for fasting serum insulin and blood glucose, and a picture of the neck were obtained at baseline (n = 85) and 6 months later (n = 49). None of the children partipating had high fasting serum insulin levels and only 2 children had AN degree 2 (moderately severe). At baseline children with a weight for height at or above the 95th, percentile had 15 units less of insulin than children who weighed less. However, 6 months later this was not confirmed, thus the baseline result is considered to be an anomaly. Eventhough statistical significance was not reached, results showed that children without AN had 5 percentiles lower weight for height than children with AN. The most important recommendation from this study is the need to monitor longitudinal growth in children to characterize the individual child's growth pattern. AN seems to be related to longitudinal growth changes. ^

AROUSAL, FATIGUE AND MENTAL PERFORMANCE: THE EFFECTS OF FASTING ON COGNITION

This study examined the effects of skipping breakfast on selected aspects of children's cognition, specifically their memory (both immediate and one week following presentation of stimuli), mental tempo, and problem solving accuracy. Test instruments used included the Hagen Central/Incidental Recall Test, Matching Familiar Figures Test, McCarthy Digit Span and Tapping Tests. The study population consisted of 39 nine-to eleven year old healthy children who were admitted for overnight stays at a clinical research setting for two nights approximately one week apart. The study was designed to be able to adequately monitor and control subjects' food consumption. The design chosen was the cross-over design where randomly on either the first or second visit, the child skipped breakfast. In this way, subjects acted as their own controls. Subjects were tested at noon of both visits, this representing an 18-hour fast.^ Analysis focused on whether or not fasting for this period of time affected an individual's performance. Results indicated that for most of the tests, subjects were not significantly affected by skipping breakfast for one morning. However, on tests of short-term central and incidental recall, subjects who had skipped breakfast recalled significantly more of the incidental cues although they did so at no apparent expense to their storing of central information. In the area of problem-solving accuracy, subjects skipping breakfast at time two made significantly more errors on hard sections of the MFF Test. It should be noted that although a large number of tests were conducted, these two tests showed the only significant differences.^ These significant results in the areas of short-term incidental memory and in problem solving accuracy were interpreted as being an effect of subject fatigue. That is, when subjects missed breakfast, they were more likely to become fatigued and in the novel environment presented in the study setting, it is probable that these subjects responded by entering Class II fatigue which is characterized by behavioral excitability, diffused attention and altered performance patterns. ^

Fasting blood glucose and 8-year mortality in the hypertension detection follow-up program population

This study analyzed the relationship between fasting blood glucose (FBG) and 8-year mortality in the Hypertension Detection Follow-up Program (HDFP) population. Fasting blood glucose (FBG) was examined both as a continuous variable and by specified FBG strata: Normal (FBG 60–100 mg/dL), Impaired (FBG ≥100 and ≤125 mg/dL), and Diabetic (FBG>125 mg/dL or pre-existing diabetes) subgroups. The relationship between type 2 diabetes was examined with all-cause mortality. This thesis described and compared the characteristics of fasting blood glucose strata by recognized glucose cut-points; described the mortality rates in the various fasting blood glucose strata using Kaplan-Meier mortality curves, and compared the mortality risk of various strata using Cox Regression analysis. Overall, mortality was significantly greater among Referred Care (RC) participants compared to Stepped Care (SC) {HR = 1.17; 95% CI (1.052,1.309); p-value = 0.004}, as reported by the HDFP investigators in 1979. Compared with SC participants, the RC mortality rate was significantly higher for the Normal FBG group {HR = 1.18; 95% CI (1.029,1.363); p-value = 0.019} and the Impaired FBG group, {HR = 1.34; 95% CI (1.036,1.734); p-value = 0.026,}. However, for the diabetic group, 8-year mortality did not differ significantly between the RC and SC groups after adjusting for race, gender, age, smoking status among Diabetic individuals {HR = 1.03; 95% CI (0.816,1.303); p-value = 0.798}. This latter finding is possibly due to a lack of a treatment difference of hypertension among Diabetic participants in both RC and SC groups. The largest difference in mortality between RC and SC was in the Impaired subgroup, suggesting that hypertensive patients with FBG between 100 and 125 mg/dL would benefit from aggressive antihypertensive therapy.^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Sources of familial aggregation and co-aggregation of fasting blood glucose and nutritional factors among Mexican -Americans in Starr County, Texas

The purpose of this study was to determine the effects of nutrient intake, genetic factors and common household environmental factors on the aggregation of fasting blood glucose among Mexican-Americans in Starr County, Texas. This study was designed to determine: (a) the proportion of variation of fasting blood glucose concentration explained by unmeasured genetic and common household environmental effects; (b) the degree of familial aggregation of measures of nutrient intake; and (c) the extent to which the familial aggregation of fasting blood glucose is explained by nutrient intake and its aggregation. The method of path analysis was employed to determine these various effects.^ Genes play an important role in fasting blood glucose: Genetic variation was found to explain about 40% of the total variation in fasting blood glucose. Common household environmental effects, on the other hand, explained less than 3% of the variation in fasting blood glucose levels among individuals. Common household effects, however, did have significant effects on measures of nutrient intake, though it explained only about 10% of the total variance in nutrient intake. Finally, there was significant familial aggregation of nutrient intake measures, but their aggregation did not contribute significantly to the familial aggregation of fasting blood glucose. These results imply that similarities among relatives for fasting blood glucose are not due to similarities in nutrient intake among relatives. ^