Perbedaan Asupan Lemak, Lingkar Pinggang dan Persentase Lemak Tubuh pada Wanita Dislipidemia dan Non Dislipidemia

Differences of fat intake, waist circumference and percentage of bodt fat in dyslipidemia and non dyslipidemia adult women: Heart disease is the leading cause of death in several countries in the world . One of the major risk factors for heart disease is dyslipidemia . Dyslipidemia is a disorder of lipid metabolism characterized by an increase or decrease in plasma lipid fractions . Dyslipidemia has a strong relationship with the occurrence of central obesity . The purpose of this study was to analyze differences in the intake of fat , waist circumference and body fat percentage in dyslipidemia and non dyslipidemia adult women. This research is analytic study with cross sectional approach . The population in this study were adult women who examined their lipid profile in December 2013 in the Clinical Laboratory Cito Indraprasta Semarang . The total sample was 32 people . Independent test analysis of the differences using t-test for variables waist circumference and Mann Whitney test for variable fat intake and body fat percentage to 95 % and a significance level of 5% error. The results showed 17 adult women ( 53.1 % ) and 15 female adult dyslipidemia ( 46.9 % ) non- dyslipidemia. Average intake of fat, waist circumference and percentage body fat in adult women dyslipidemia higher than non dyslipidemia in adult women. Analysis of statistical tests showed difference in fat intake , waist circumference and body fat percentage in women adult dyslipidemia and non dyslipidemia (p value, respectively p = 0.002, p = 0.0001 and p = 0.0001

Agency and aging EXP1 & EXP2

Agency and aging EXP1 & EXP

Concentration (median and range) for individual protein biomarkers measured in the serum of cancer and control patients.

<p>Concentration (median and range) for individual protein biomarkers measured in the serum of cancer and control patients.</p

Study Design^a.

a<p>A total of 97 unique patients participated in the study including study and validation groups.</p>b<p>Validation Groups had no overlapping patients with Study Groups that underwent the same analysis with one exception (please see the next footnote). For Analysis 3, for example, no patients were found in both Study Group 2 and Validation Groups 3 and 4.</p>c<p>Eight of the 17 patients in Validation Group 2 were excluded from analysis 5 because they were already included with Study Group 3 leaving 9 patients for the validation and mutual consistency testing (see also text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#pone-0042748-t007" target="_blank">Table 7</a>).</p

Analysis Descriptions.

a<p>We examined between-biomarker correlations to help interpret results of multivariate models involving multiple potential biomarkers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#pone.0042748.s006" target="_blank">Table S4</a>).</p

Patient Characteristics^a.

a<p>Results are median (interquartile range) unless noted.</p>b<p>This group of patients provided two samples each, one from a stable state and one from an APE state at admission for a hospitalization. Data shown here are derived from the time point of the stable sample collection for each individual.</p>c<p>The 26 patients that gave paired samples necessarily suffered an APE during the study in order to give the necessary APE state sputums. This criterion selected patients with significantly lower lung function, <i>t</i>-test <i>p</i> = 0.005, increased incidence of CF-related diabetes, χ-square <i>p</i><0.001, decreased 5-year predicted survival, <i>t</i>-test <i>p</i> = 0.01 and more frequent APE (differences not tested due to confounding) than the other patients in the study.</p>d<p>Patients in Validation Group 1 had higher FEV₁% and 5-year predicted survival and remarkably no incidence of CF-related diabetes. Despite these differences, the coefficients for HMGB-1 reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#pone-0042748-t007" target="_blank">Table 7</a> for Validation Groups 1 and 2 are quite similar to those for Study Group 1 patients and pass testing for mutual consistency.</p>e<p>The 5-year predicted survival is a clinically useful composite estimate of overall disease state in CF but may be difficult to use in interpretation of inflammatory states. Similar to lung function and other clinical markers of disease, it may require years to see a change <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#pone.0042748-Liou1" target="_blank">[2]</a>.</p

Testing Validation Results for Mutual Consistency.

a<p>Weighted least squares analysis.</p>b<p>Study Group 2 patients, Analysis 3, n = 26.</p>c<p>Study Group 3 patients, Analyses 4 and 5, n = 76.</p>d<p>Validation Group 2 patients not included in Group 3, n = 9 with 1 death, 1 lung transplant.</p

Patient Comparisons with the 2006 CF Foundation Patient Registry^a.

a<p>Results are median (interquartile range) unless noted. CFFPR patients include all sputum-producing adult patients in 2006 but exclude those followed at the Intermountain Adult CF Center.</p>b<p>We used χ-square tests to determine statistical differences in Gender, Infections and Anti-inflammatory Therapy between the Intermountain CF Center and the CFFPR 2006. For all other variables shown, we used Kolmogorov-Smirnov tests because data were not normally distributed.</p

Multivariate models for concurrent outcomes and APE-associated predictions.

a<p>Data from study group 1, n = 56. We found no evidence of two-way interactions or non-linear effects using squared terms for these models. Age, gender, CF-related diabetes, airway infection with either <i>Pseudomonas aeruginosa</i> or <i>Staphylococcus aureus</i> and chronic azithromycin, oral or inhaled steroid use had no significant interactions with any inflammatory marker terms in any multivariate model. Log transformed values of biomarkers were used for modeling outcomes. Concurrent FEV₁% and Weight-for-age <i>z</i>-score models used linear regression. The model for the number of APE occurring in the year prior to initial sputum collection used quasi-Poisson regression.</p>b<p>Data from study group 2, n = 26. Additional adjustment for the stable FEV₁% measurement, sequence of stable and APE time point collections, airway infection with either <i>Pseudomonas aeruginosa</i> or <i>Staphylococcus aureus</i>, use of azithromycin or steroids had no significant effect in these models.</p>c<p>Estimates of the mean change in FEV₁% per unit change in log scale biomarkers. Results from a linear regression model for the associations between difference in FEV₁% between stable and APE time points and GM-CSF (log scale) measured at the APE onset time point. Each univariate representing measurements obtained during clinically stable and APE time points were added in turn to a model containing GM-CSF measured at the APE time point, the only statistically significant univariate. IL-5 (<i>p</i> = 0.006) and IL-10 (<i>p</i> = 0.015) measured at the APE time point and TCC (<i>p = </i>0.012) measured at the stable time point were found to be positively associated with FEV₁% decline independently of GM-CSF. Backward selection of a multivariate model containing GM-CSF (APE), IL-5 (APE), IL-10 (APE), and TCC (Stable) produced the final model presented here.</p>d<p>Estimates of the predicted total number of APE during 5 years of follow up per unit change in log scale biomarkers measured during clinical stability. Results show a quasi-Poisson regression model for the association with number of APE during 5 years of follow-up. HMGB-1 (log scale) was the only significant univariate (<i>p</i><0.05), but CRP, IFN-α and IL-8 (all log scale) had trends toward significance (<i>p</i><0.2). Backwards multivariate model selection retaining adjustment variables for follow-up time and low or high number of APE in the year prior to stable sputum collection (low  =  0 or 1 (reference group), high >1) as an indicator of baseline inflammation, retained only HMBG-1. A 1 unit change in log scale HMGB-1 is associated with a mean change in number of APE of 0.34.</p

Proportional Hazards Models of Time-to-Event^a.

a<p>The table shows results from proportional hazards models for the association between time-to-first APE following sputum collection and HMBG-1 (log scale) measurement from clinically-stable time points, Study Group 2, n = 26, and the association between time-to-lung transplant or death following initial sputum collection and HMGB-1 (log scale) measurements for all patients in the study with sufficient sample to measure HMGB-1, Study Group 3, n = 76. Both analyses shown met the assumption of proportionality for proportional hazards modeling <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#pone.0042748-Grambsch1" target="_blank">[31]</a>. Among the 76 patients, there were 15 events: 9 deaths and 6 listings for lung transplantation. All listed patients were subsequently transplanted. Adjustments for number of APE in the year prior to stable sputum collection were non-significant, and inclusion of variables for use of azithromycin or steroids had no effect on these models. Concurrent FEV₁% and airway infection with either <i>Pseudomonas aeruginosa</i> or <i>Staphylococcus aureus</i> had non-significant associations with time-to-first APE. FEV₁% is confounded as a predictor of time-to-transplant or death (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042748#s4" target="_blank">Discussion</a>). <i>P aeruginosa</i> and <i>S aureus</i> infection are not primarily considered in selection of candidates for transplant and are not potential confounders; they had no effect on time-to-transplant or death. Approximately a 10% increase in HMGB-1 is associated with a 4% increase in the hazard rate for time-to-first APE and a 5% increase in hazard rate for time-to-lung transplant or death.</p