Recently, asked me about my opinion on a recent study just published (1).BackgroundTSC1 and TSC2 are a pair of tumor supressor genes, which relevance lies in the inhibition of mTORC1 activity. Design, Setting, and Participants Randomized trial of obese young adults (aged 18-35 years; n = 73) conducted from September 2004 to December 2006 in Boston, Mass, and consisting of a 6-month intensive intervention period and a 12-month follow-up period.
Results Change in body weight and body fat percentage did not differ between the diet groups overall.
Conclusions Variability in dietary weight loss trials may be partially attributable to differences in hormonal response. The main finding of our study is that a simple measure of insulin secretion predicted weight and body fat loss on low-glycemic load and low-fat diets. The results of our outpatient study involving dietary counseling are consistent with 2 short-term feeding studies using hypocaloric diets in obese or overweight participants. With regard to cardiovascular disease risk factors, HDL cholesterol and triglyceride concentrations improved more during the intensive intervention phase of the study for the full cohort on the low-glycemic load diet compared with the low-fat diet.
A methodological concern with most nutrition-related outpatient clinical trials is the possibility of bias because study participants consuming self-prepared diets and the study staff providing education and counseling generally cannot be masked to group assignment. Limitations of this study include self-reporting for assessing diet and reliance on tabulated glycemic index values for quantifying glycemic load.
Statistical issues include the possibility of bias from use of imputed data, the modest sample size (particularly for analyses involving insulin concentration at 30 minutes at the later time points), and the possibility of "overfitting" too many covariates for the sample size.
In conclusion, we found evidence for a diet-phenotype interaction involving insulin secretion.
With prevalence approaching one third of the population, obesity is among the most important medical problems in the United States1 and identification of effective dietary treatment has become a major public health priority.2 Three popular diets?\low fat, low carbohydrate, and low glycemic load?\have recently received much attention. One physiological mechanism that might relate weight loss to dietary composition is individual differences in insulin secretion. The purpose of this study was to determine whether insulin secretion affects body fat loss among obese individuals consuming self-prepared diets. Nutrition education and dietary counseling were provided to participants in both the low-glycemic load and low-fat diet groups. Participants were recruited using posted fliers, newspaper and Internet advertisements, and radio broadcasts that described the study as an opportunity for weight loss.
The same intervention schedule, consisting of a 6-month intensive intervention period and a 12-month follow-up period, was implemented for both groups. Participant adherence was evaluated based on attendance at group workshops and the private session, completion of motivational telephone calls, and self-reported dietary intake. Dietary intake was assessed by a multiple-pass method using the Nutrition Data System for Research Software versions 5.0_35, 2005, and 2006 (Nutrition Coordinating Center, University of Minnesota, Minneapolis).
Dietary glycemic index and glycemic load for each day of self-reported intake were quantified as follows.
At a baseline assessment visit, each participant was given an oral glucose tolerance test using a standard 75-g dose of dextrose. Plasma lipid concentrations were determined in a laboratory certified by the Lipid Standardization Program of the Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute. Baseline demographic characteristics, body composition variables, and cardiovascular disease risk factors were compared between the diet groups using the Fisher exact test for categorical variables and the t test for continuous variables. Dietary intakes and physical activity level over the course of the trial were analyzed by mixed-model analysis of variance.
Missing body composition measures, cardiovascular disease risk factors, and body weight data were imputed by a conservative strategy as follows. The primary end point of the trial, body fat percentage, was analyzed by repeated-measures analysis of variance of the baseline and 6-, 12-, and 18-month measurements, as described above for dietary intakes, with the same covariates and covariance structure.
The power assessment for the primary end point, body fat percentage, was based on a 2-sample t test with 36 participants per diet group and a 5% type I error rate. Baseline characteristics by diet groups and strata for insulin concentration at 30 minutes after a 75-g dose of oral glucose are presented in Table 1.
Insulin concentration at 30 minutes after a dose of oral glucose was not a significant effect modifier for lipids, blood pressure, fasting glucose, and fasting insulin. The information in this report is intended to help clinicians, employers, policymakers, and others make informed decisions about the provision of health care services. This report may be used, in whole or in part, as the basis for the development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. Background: A 50-g oral glucose challenge test (OGCT) is a widely accepted screening test for gestational diabetes mellitus (GDM), but other options are being considered.
Purpose: To systematically review the test characteristics of various screening methods for GDM across a range of recommended diagnostic glucose thresholds.
Data Sources: 15 electronic databases from 1995 to May 2012, reference lists, Web sites of relevant organizations, and gray literature. Study Selection: Two reviewers independently identified English-language prospective studies that compared any screening test for GDM with any reference standard. Data Extraction: One reviewer extracted and a second reviewer verified data from 51 cohort studies. A 50-g oral glucose challenge test (OGCT) is the most widely accepted screening test for gestational diabetes mellitus (GDM) in North America 1.
Screening tests for GDM are generally administered earlier in gestation for women at high risk for GDM (that is, those with multiple risk factors) and are repeated at 24 to 28 weeks' gestation if results of initial surveillance are normal. The key question for this review was developed by the USPSTF to inform guideline review and development. Two reviewers independently assessed the methodological quality of studies and resolved discrepancies by consensus. We constructed 2 × 2 tables and calculated sensitivity, specificity, and positive and negative likelihood ratios (LRs).
The Results section is organized by type of screening test (for example, OGCT) and is further grouped by the diagnostic criteria used to confirm GDM. The Agency for Healthcare Research and Quality (AHRQ) and the USPSTF suggested the initial questions but did not participate in the literature search, data analysis, or interpretation of the results. From 14,398 citations, 51 prospective cohort studies provided data (Appendix Figure 1) 4-6, 8-11, 16, 17, 21-62.
Studies assessed several screening tests, including the 50-g OGCT, measurement of fasting plasma glucose or HbA1c level, and risk factor–based screening. Figure 2 shows 2 HSROCs with the 95% confidence ellipse using pairs of sensitivity and specificity of the studies that provided data for the 2 glucose thresholds. One study (n = 749) provided data on screening for GDM in the first and second trimesters; GDM was confirmed using the Japan Society of Obstetrics and Gynecology criteria 35.
Seven studies 4-7, 24, 38, 52 assessed measurement of fasting plasma glucose level to screen for GDM, which was confirmed using Carpenter–Coustan criteria. This review included 51 cohort studies that assessed the test characteristics of various screening methods for GDM. Measurement of fasting plasma glucose level has been suggested as an alternative to the OGCT.
Glycated hemoglobin level has poorer test characteristics than fasting plasma glucose level or the OGCT. Although we found limited evidence for GDM screening at less than 24 weeks' gestation, there is clinical justification for early screening in women at high risk for overt diabetes.
Our review did not identify compelling evidence for or against risk factor–based screening.
The IADPSG has proposed the elimination of a screening test in favor of proceeding directly to a diagnostic test for GDM. One of the challenges in comparing studies of screening tests for GDM is the plethora of glucose thresholds and the different glucose loads used for the OGTT (Table 1).
Recently published systematic reviews in this area are more limited in terms of study designs included 71, 72 or tests examined 73, 74. Disclaimer: The findings and conclusions in this document are those of the authors, who are responsible for its content, and do not necessarily represent the views of AHRQ. Appendix Figure 1 is a flow diagram that outlines the study retrieval and selection process. Figure 1 contains two bar graphs that report the assessment of risk of bias and applicability, respectively, using the QUADAS-2 tool to evaluate the methodological quality of studies of screening tests for gestational diabetes. Appendix Figure 2 is a forest plot depicting the sensitivity and specificity of risk factor screening for gestational diabetes by several diagnostic criteria. The HSROC with the 95% confidence ellipse graphically compares the sensitivity and specificity for all studies comparing a particular screening test with GDM diagnostic criteria. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al; International Association of Diabetes and Pregnancy Study Groups Consensus Panel. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, et al; HAPO Study Cooperative Research Group.
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al; QUADAS-2 Group. Perea-Carrasco R, Perez-Coronel R, Albusac-Aguilar R, Lombardo-Grifol M, Bassas-Baena de Leon E, Romero-Diaz C. Serum insulin concentration at 30 minutes after a 75-g dose of oral glucose was determined at baseline as a measure of insulin secretion. Reducing glycemic load may be especially important to achieve weight loss among individuals with high insulin secretion. For individuals with a low insulin concentration at 30 minutes after a 75-g dose of oral glucose, both diets produced comparable results. However, we believe that this possibility has been minimized in our study for several reasons. Underreporting of dietary intake is a well-recognized phenomenon, common to all studies that aim to collect process data under free-living conditions, although adjusting other dietary variables for energy intake may partially correct for underreporting.47-48 With regard to tabulated glycemic index values,28 many were derived from studies conducted in countries where foods may differ in quality from those consumed in the United States.
For obese individuals with high insulin concentration at 30 minutes during an oral glucose tolerance test, a low-glycemic load diet may promote more weight and body fat loss than a low-fat diet.
However, clinical trials have produced inconsistent findings, with some suggesting that one diet is superior for weight loss3-8 and others indicating no difference between diets.9-11 This inconsistency may arise from methodological problems both within and between trials, such as different treatment intensity between groups, inadequate attention to treatment fidelity, variable nutrition education and dietary counseling strategies, and confounding by dietary and nondietary factors. Prior to random assignment of participants to groups, a 75-g oral glucose tolerance test was conducted and serum was stored for later analysis of insulin. Inclusion criteria included age between 18 and 35 years, body mass index (calculated as weight in kilograms divided by height in meters squared) of 30 and above, and medical clearance from a primary care provider. Participants were counseled to consume low-glycemic load foods (particularly nonstarchy vegetables, legumes, and temperate fruits) and to limit intake of high-glycemic load foods (such as refined grains, starchy vegetables, fruit juices, and sweets). Participants were counseled to consume low-fat grains, vegetables, fruits, and legumes and to limit intake of added fats, sweets, and high-fat snacks. There were 23 group workshops (1 hour each), 1 private counseling session (1 hour), and 5 motivational telephone calls (30 minutes each). Principles of nonformal adult education25 and participant-centered counseling26 were applied to promote adherence to the diets. Dietitian adherence to the intervention protocols was conceptualized as treatment fidelity, a term encompassing integrity and differentiation.27 Integrity is the degree to which treatment is implemented according to established procedures, and differentiation is the extent to which interventions are distinct from one another.
Three telephone-administered 24-hour recall interviews (2 weekdays and 1 weekend day) were conducted at baseline and again at 6, 12, and 18 months to assess diet and physical activity. The participant was prompted to list in sequence the foods and beverages consumed during the previous day, identify omissions in the initial list, and then provide details (eg, portion sizes, brand names) concerning each reported item. First, glycemic index of individual carbohydrate-containing foods was assigned according to published values based on a glucose reference.28 When a published value was not available, the composition of the food was systematically evaluated to impute a value. At the end of the 6-month intensive intervention period, participants responded to a series of questions regarding satisfaction, using 10-cm visual analog scales with appropriate verbal anchors. Weight was measured using an electronic scale (Model 6702, Scale-Tronix, White Plains, NY) and height was measured using a wall-mounted stadiometer (Holtain Limited, Crymych, Wales). Blood for determination of insulin concentration was obtained by indwelling venous catheter; serum from these samples was stored at -80?‹C until assay.
Low-density lipoprotein (LDL) cholesterol level was measured by a homogeneous enzymatic assay (Genzyme Corp, Cambridge, Mass),34 and levels of high-density lipoprotein (HDL) cholesterol and triglycerides were measured using a Hitachi 911 analyzer (Roche Diagnostics, Indianapolis, Ind). Data on insulin concentration at 30 minutes after a 75-g dose of oral glucose were not available for 17 of 73 participants who were randomly assigned to a diet group (8 in the low-glycemic load diet group and 9 in the low-fat diet group), primarily due to hemolysis when drawing timed blood samples. The t test was corroborated by the Mann-Whitney-Wilcoxon 2-sample test in any case of mildly skewed distribution. The 4 time points (baseline and 6, 12, and 18 months) were represented by an arbitrary pattern (3 degrees of freedom) to avoid a global assumption of linearity or other functional form. Among the 56 participants with insulin response assessed at baseline, effect modification was tested by adding a dichotomous variable for insulin concentration at 30 minutes after a 75-g dose of oral glucose to the regression model (above or below median) and testing the 3-way interaction (group x time x insulin concentration at 30 minutes). There were no significant differences between diet groups, with the exception of LDL cholesterol concentration.
The changes were expressed as the mean (95% confidence interval) of 6-, 12-, and 18-month intake compared with baseline (P value by analysis of variance). Weight loss did not differ between diet groups for the full cohort of 73 participants (P = .99).
Body fat percentage decreased more in the low-glycemic load vs low-fat group, only among those in the stratum with high insulin concentration at 30 minutes (Table 3). Among the whole cohort, changes in LDL cholesterol, HDL cholesterol, and triglyceride concentrations differed significantly between diet groups. Typically, an OGCT is initially administered between 24 and 28 weeks' gestation to women in a nonfasting state who are at moderate risk for GDM (those who do not meet all low-risk criteria but lack ≥2 risk factors for GDM). Preventive Services Task Force (USPSTF) conducted an evidence review on screening for GDM and found insufficient evidence to assess the balance of benefits and harms of screening for GDM 1.
A technical expert panel that included representatives from the USPSTF and the Office of Medical Applications of Research provided content and methodological expertise. Full publications of potentially relevant studies were independently assessed by 2 reviewers using a standardized form. We assessed studies by using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) checklist 18.

The HSROC simultaneously compares the sensitivity and specificity (accounting for their correlation) for all studies comparing a particular screening test with GDM diagnostic criteria.
The studies confirmed GDM by using criteria developed by Carpenter and Coustan, ADA (endorsed from 2000–2010), the National Diabetes Data Group (NDDG), WHO, and others. For patient selection, 47% of studies were assessed as having high or unclear risk of bias. All points are clustered in the upper left-hand quadrant, and the 95% confidence ellipse and diagonal null line do not overlap. Sensitivity was 17%, specificity was 100%, and the negative LR was 0.83 (Table 2), thus providing certainty that GDM is present when this threshold is met or exceeded on an OGCT.
The studies compared different fasting plasma glucose thresholds and showed a pattern of increasing positive LR as the threshold increased (Table 2). It is more reproducible than post–glucose load testing 67, easier to administer to women who cannot tolerate a glucose drink, and less time-consuming for women and laboratories and has been directly related to pregnancy outcomes 15, 16.
The use of HbA1c level in pregnant women should not be dismissed because a markedly elevated level may be a quick and simple screening test for the presence of overt diabetes.
The highest increase in prevalence of diabetes has occurred in women of reproductive age 69, and the highest perinatal mortality rates of all forms of maternal diabetes occur in women with overt diabetes diagnosed during pregnancy 70. Naylor and colleagues 3 used data from the Toronto Trihospital study to develop a risk scoring system for GDM screening using variable glucose thresholds based on age, body mass index, and race. A 2-step approach to GDM screening has been shown to be more cost-effective then a 1-step approach 13, 37.
The studies in this systematic review assessed the performance of screening tests compared with OGTT results rather than pregnancy outcomes. A systematic review published in 2010 had a scope similar to that of our review and reached similar conclusions 75.
No statement in this report should be construed as an official position of AHRQ or the U.S. The risk of bias dimension comprises four domains, listed along the Y-axis: flow and timing, reference standard, index test, and patient selection. Forest plot of sensitivity and specificity of risk factor screening for gestational diabetes, by diagnostic criteria. The HSROC incorporates all study results with the 95% confidence ellipse, with two curves shown, one for each threshold.
Fasting plasma glucose as a screening test for gestational diabetes in a multi-ethnic, high-risk population. Gestational diabetes: utility of fasting plasma glucose as a screening test depends on the diagnostic criteria.
Fasting plasma glucose test at the first prenatal visit as a screen for gestational diabetes.
The comparison of 50 grams glucose challenge test, HbA1c and fructosamine levels in diagnosis of gestational diabetes mellitus.
Gestational diabetes screening of a multiethnic, high-risk population using glycated proteins.
International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Gestational diabetes mellitus screening and diagnosis: a prospective randomised controlled trial comparing costs of one-step and two-step methods.
Impact of increasing carbohydrate intolerance on maternal-fetal outcomes in 3637 women without gestational diabetes. Toward universal criteria for gestational diabetes: the 75-gram glucose tolerance test in pregnancy. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews.
Comparison between 100-g glucose tolerance test and two other screening tests for gestational diabetes: combined fasting glucose with risk factors and 50-g glucose tolerance test. Using fasting plasma glucose concentrations to screen for gestational diabetes mellitus: prospective population based study. Jelly beans as an alternative to a fifty-gram glucose beverage for gestational diabetes screening. The after breakfast 50-g, 1-hour glucose challenge test in urban Mexican pregnant women: its sensitivity and specificity evaluated by three diagnostic criteria for gestational diabetes mellitus.
Comparison of National Diabetes Data Group and World Health Organization criteria for detecting gestational diabetes mellitus. Cut-off value of 1-h, 50-g glucose challenge test for screening of gestational diabetes mellitus in an Iranian population. Screening tests for gestational diabetes in Japan in the 1st and 2nd trimester of pregnancy.
Fasting plasma glucose versus glucose challenge test: screening for gestational diabetes and cost effectiveness. Universal two-step screening strategy for gestational diabetes has weak relevance in French Mediterranean women: should we simplify the screening strategy for gestational diabetes in France?
Gestational diabetes: an evaluation of serum fructosamine as a screening test in a high-risk population. Screening for gestational diabetes mellitus by a model based on risk indicators: a prospective study.
Perinatal significance of diagnosing glucose intolerance during pregnancy with portable glucose meter. Occurrence of gestational diabetes mellitus and the value of different screening indicators for the oral glucose tolerance test. Two-hour postprandial test versus one-hour, fifty-gram glucola test as screening tools for gestational diabetes: a critical analysis. The impact of potential new diagnostic criteria on the prevalence of gestational diabetes mellitus in Australia. The usefulness of glycosuria and the influence of maternal blood pressure in screening for gestational diabetes.
Detection of gestational diabetes mellitus by homeostatic indices of insulin sensitivity: a preliminary study. Comparison of venous plasma glucose and capillary whole blood glucose in the diagnosis of gestational diabetes mellitus: a community-based study. Gestational diabetes in a high-risk population: using the fasting plasma glucose to simplify the diagnostic algorithm. Contribution of first trimester fasting plasma insulin levels to the incidence of glucose intolerance in later pregnancy: Tanaka women's clinic study. 50 gram oral glucose challenge test combined with risk factor-based screening for gestational diabetes.
Lack of concordance between the 75-g and 100-g glucose load tests for the diagnosis of gestational diabetes mellitus. Screening for gestational diabetes at antenatal booking in a Malaysian university hospital: the role of risk factors and threshold value for the 50-g glucose challenge test. However, for those with a high insulin concentration at 30 minutes, the low-glycemic load diet was more efficacious for weight loss, consistent with an a priori hypothesis. We analyzed data using the intention-to-treat principle, with conservative methods for imputing missing data. First, considerable effort was made to maintain similar treatment intensity and treatment fidelity between groups. Regardless of insulin secretion, a low-glycemic load diet has beneficial effects on concentrations of HDL cholesterol and triglycerides but not on LDL cholesterol. Body composition, plasma lipid levels, blood pressure, plasma glucose level, and serum insulin level were assessed at baseline and again at 6, 12, and 18 months. To avoid any bias in assigning participants to diet groups, staff conducting recruitment and enrollment were masked to sequence. Attention also was directed toward consuming sources of healthful fat including nuts, seeds, and oils. The target macronutrient composition was 55% of energy from carbohydrate, 20% from fat, and 25% from protein.
Proposed mechanisms for this presumption involve improved access to metabolic fuels on the low-glycemic load diet14, 19 and decreased energy density on the low-fat diet.20-23 An exchange system or a calorie-counting regimen was not used to impose an energy deficit, and participants did not receive any quantitative information regarding macronutrient targets. Six of the group workshops were scheduled during the first 2 months of the intervention period, and the remaining workshops were held on a monthly basis thereafter. As such, respectful consideration of participant perspectives, core values, life experiences, current circumstances, and available resources formed a foundation for education and counseling.
These calls were separate from the motivational telephone calls that were part of the intervention. The same glycemic index value was assigned to any given food every time that it was reported to avoid bias when evaluating differences between groups and changes over time.
Body composition was assessed by dual-energy x-ray absorptiometry using Hologic instrumentation, models QDR 4500 and Discovery A (Hologic Inc, Bedford, Mass). Plasma glucose level was determined by an enzymatic colorimetric assay using a Hitachi 917 analyzer (Roche Diagnostics). Hemolyzed samples were not analyzed in light of the well-documented effects of hemolysis on the accuracy of assays for quantifying insulin concentration.35 Moreover, there is no reason to believe that availability of blood samples for insulin analysis would influence responses to respective dietary interventions.
Within-participant correlation was accounted for by a random effect (repeated measures with compound-symmetric covariance).
For body fat percentage after dropout, either the last measurement obtained or the baseline value, whichever was greater, was imputed. To summarize and compare the changes overall and within high and low strata for insulin concentration at 30 minutes, scalar contrasts were formed from parameters of the fitted model, estimating the 6-month and 18-month changes (eg, [18-month - baseline] in the low-fat, high insulin concentration group).
An arbitrary pattern of variation for the 23 discrete time points was allowed from which contrasts of interest (eg, [6 months - baseline] in the low-glycemic load group - [6 months - baseline] in the low-fat group) and localized estimates of trend (eg, rate of weight loss over baseline to 26 weeks) could be formed. Among those for whom baseline data were available, insulin concentration at 30 minutes after a dose of oral glucose was a significant effect modifier (P = .02 for group x time x insulin concentration at 30 minutes).
Changes in blood pressure, fasting glucose level, and fasting insulin level were not different between diet groups (Table 3). Department of Health and Human Services endorsement of such derivative products may not be stated or implied. A diagnosis of GDM is made when 1 or more glucose values fall at or above the specified thresholds. We searched trial registries, including the World Health Organization (WHO) International Clinical Trials Registry Platform,, and Current Controlled Trials. The decision to restrict studies to those published in English was made in consultation with the panel of technical experts, who believed that most relevant research would be published in English-language reports. One reviewer used a standardized form to extract data; a second reviewer checked the data for accuracy.
Likelihood ratios are used to estimate the increased or decreased probability of disease (such as GDM) for a patient and can be used to refine clinical judgment. Sensitivities, specificities, and LRs and their 95% CIs are presented in summary tables that include all screening tests and diagnostic criteria. The lack of a gold standard to confirm a diagnosis of GDM limited our ability to compare the results of studies that used different diagnostic criteria. We had concerns about applicability for this domain, primarily because 55% of studies were conducted in developing countries and used WHO criteria to diagnose GDM.
This indicates that the ability of the screening test to correctly classify patients with GDM is significantly better than random classification.
These results should be interpreted cautiously because the women diagnosed with GDM in the first trimester had prepregnancy body mass indices that were significantly higher than those in women who did not have GDM. Small increments in fasting plasma glucose level result in clinically significant increases in the probability of GDM being present. Further study is required to determine the best HbA1c threshold to detect overt diabetes in pregnant women and whether gestational age–specific thresholds would help identify overt diabetes in this population. When the system was applied to a validation group, sensitivity (83%) and specificity (83%) were similar to those of universal screening 3. Ideally, the gold standard comparison for GDM screening tests would be a universally agreed-on set of specific pregnancy outcomes. The current systematic review represents an up-to-date and comprehensive summary of existing evidence for all potential approaches to screening for GDM and provides specific recommendations for practice and future research. Because fasting glucose level better predicts fetal overgrowth 16 and such overgrowth can be modified by metabolic management during pregnancy, a practical option may be to offer women their choice of screening with the OGCT or the fasting plasma glucose test.
The concern regarding applicability comprises three domains, listed along the Y-axis: reference standard, index test, and patient selection. The summary graphic compares the sensitivity and specificity for all studies and presents separate curves for the two different cutoff values used in the OGCT. TSC2 has a GAP (GTPase activating protein) domain that stimulates the GTPase activity of Rheb.
There were no significant differences in these end points between diet groups for those with insulin concentration at 30 minutes below the median level (n = 28).
Also, we measured body composition using state-of-the-art dual-energy x-ray absorptiometry. Second, process measures demonstrated that the intended changes in diet occurred in both groups, whereas protein and fiber, 2 potential confounders,45-46 did not differ between groups. Moreover, we recognize that the outcomes observed in this study cannot be attributed exclusively to the effects of lowering dietary glycemic load. The SEs were based on pooled variance estimates from the full mixed-model analysis rather than potential underestimates from predominantly imputed values at the later time points. The target macronutrient composition was 40% of energy from carbohydrate, emphasizing low-glycemic index sources, 35% from fat, and 25% from protein. Rather, hunger and satiety cues were discussed and participants were advised as follows: "Eat when you are hungry, before you become famished.
The primary objective of the workshops was to foster knowledge and skills necessary to follow the respective diets, and the purpose of the telephone calls was to enhance motivation for translating knowledge and skills to changes in dietary behaviors.

First, group workshops were scripted and written educational materials were developed to ensure delivery of well-defined nutrition messages for each diet group; otherwise, the format of the workshops and quality of the materials were completely parallel to maintain equal treatment intensity. The recall interviews were unannounced so that the participant did not know the exact dates of the telephone calls in advance.
Dietary variables of interest for this report include carbohydrate, total and saturated fat, protein, fiber, and energy intakes. Second, the glycemic index for each food item was multiplied by the proportion of total carbohydrate contributed by the item to obtain a weighted glycemic index.
Thus, effect modification by insulin concentration at 30 minutes was tested using a sample size of 56 participants (28 participants per diet group). For the cardiovascular disease risk factors after dropout, the last measurement obtained or the baseline value was imputed, whichever was least favorable (eg, higher for blood pressure, lower for HDL cholesterol).
Glycated hemoglobin level had poorer test characteristics than fasting plasma glucose level or the OGCT. Alternative screening options to the OGCT have been investigated—in particular, measurement of the fasting plasma glucose 4-7 and glycated hemoglobin (HbA1c) levels 8-11.
The absence of a universally accepted gold standard for the diagnosis of GDM has resulted in various recommended diagnostic glucose thresholds that have been endorsed by different stakeholders (Table 1).
The larger the positive LR, the greater the accuracy of the test and the greater the likelihood of disease after a positive test result; the smaller the negative LR, the smaller the likelihood of disease after a negative test result 19. Different criteria resulted in different rates of prevalence, regardless of similarities across study settings and patient characteristics.
For the reference standard (the criteria used to confirm a diagnosis of GDM), 80% of studies were assessed as having high or unclear risk of bias because the result of the screening test was used to determine whether patients had further testing for GDM (lack of blinding) or this was unclear. Four studies 8-11 evaluated different HbA1c thresholds, with GDM confirmed using different diagnostic criteria; we saw no clear pattern over the range of thresholds (Table 2). When the harm of missing a diagnosis (false-negative result) is high, as in women with additional risk factors for adverse pregnancy outcomes, screening tests with high sensitivity are preferred at the expense of specificity.
Adverse pregnancy outcomes associated with GDM are not specific to GDM, and much of the risk for such outcomes is attributable to other factors, such as maternal obesity and excessive maternal weight gain. Neither the test characteristics nor our conclusions were affected by inclusion of these studies.
The diagnostic test endorsed by policymakers for GDM will influence which screening test can be used for GDM because there are no existing comparisons of the OGCT and IADPSG diagnostic criteria.
30 additional studies were found by hand searching the reference lists from included studies.
The percentage of studies that were rated as low, high, or unclear are listed along both X-axes at intervals of 20 percentage points. All points are clustered in the upper left-hand quadrant and there is no overlap between the 95% confidence ellipse and the diagonal null line.
Insulin concentration at 30 minutes after a dose of oral glucose was not a significant effect modifier for cardiovascular disease risk factors. Third, other process measures showed that physical activity and participant satisfaction also did not differ between groups. While we aimed to prescribe diets of similar protein and fiber content, other dietary factors (eg, energy density, palatability) may have differed between groups. Dietitians were directive in negotiating goals with participants and empathetic when assisting them in overcoming adherence challenges. Second, flowcharts provided structure for the private session and motivational telephone calls and were used to foster dietitian adherence to a participant-centered counseling model, with adequate flexibility for addressing situations unique to each individual.
Third, the daily glycemic index was calculated by summing the weighted values for each food item: (glycemic index for food item x proportion of total carbohydrate contributed by item).
Blood pressure was determined using an automated system (Model Pro 400, Dinamap, Tampa, Fla) with the participant sitting quietly.
For body weight after dropout, an increase of 1 kg per year was imputed,36 starting from the last available measurement. The overall diet effect was tested by the group x time interaction and the presence of effect modification (group x time x insulin concentration at 30 minutes). No studies compared the OGCT with International Association of the Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria. These have been proposed because the values are comparatively easy to obtain and the tests require a shorter time commitment from the women having them. These criteria reflect changes that have occurred in laboratory glucose measurements over the years, as well as evidence that links glucose values with pregnancy outcomes 15-17. We extracted study and patient characteristics, inclusion and exclusion criteria, and index test and reference standard characteristics. A positive LR greater than 10 indicates a large and often conclusive probability that the condition is present, whereas a negative LR less than 0.10 suggests a large and often conclusive probability that the condition is not present. Eight studies that examined risk factor–based screening used different diagnostic criteria and could not be pooled 22, 41-43, 46, 59, 62, 64.
However, if the harm of an incorrect diagnosis (false-positive result) is high, screening tests with high specificity are preferred at the expense of sensitivity. Variable glucose thresholds based on known risk factors would provide a sound scientific approach to GDM screening and may help clinicians align the intensity of clinical care according to patient risk. Although data show a continuous positive relationship between glucose levels and various maternal and neonatal outcomes of varying importance, no clear inflection point exists 14. Second, 80% of studies were assessed as having high or unclear risk of diagnostic review bias, in which interpretation of the reference standard may have been influenced by the knowledge of the results of the index test. Measurement of HbA1c is not a good screening test for GDM, but further study may demonstrate its potential value for identifying overt diabetes in pregnancy.
Using the detailed selection criteria, 151 studies met the inclusion criteria and 469 were excluded. The domain of flow and timing was assessed as low risk of bias, the index test was generally rated as low risk, the reference standard was generally rated as unclear risk, and patient selection was rated as low risk. This indicates that the ability of the test to correctly classify patients with gestational diabetes is significantly better than random classification.
Moreover, dietitians delivered the interventions without knowing which individuals were in the low-concentration and high-concentration insulin strata at 30 minutes after a dose of oral glucose. Participants were asked to keep one 3-day food diary prior to each workshop, particularly during the intensive intervention period, as a strategy for monitoring goal attainment. The group x time interaction term (3 degrees of freedom) provided a test of the hypothesis that the 2 dietary intervention groups did not differ across the study period. To summarize and compare the changes, scalar contrasts were formed from the fitted weight model representing net change at 18 months (eg, [18 months - baseline] in the low-fat, high insulin concentration group).
Diagnostic Characteristics of Screening Tests for Gestational Diabetes MellitusAppendix Table 2.
Some stakeholders have recommended a 1-step diagnostic test for GDM because it results in a more rapid diagnosis of affected women 12; however, this approach has not been shown to be cost-effective 13. An LR of 1 means that a positive or negative result is equally probable in a patient with or without the disease. However, 18% were assessed as having high risk of partial verification bias because not all patients received a confirmatory reference standard if the screening test result was below a certain threshold. Sensitivity and specificity varied widely across the studies, and no conclusions could be drawn (Appendix Table 2). A third concern relates to patient selection and the possibility of spectrum bias; 82% of studies were assessed as having high or unclear concerns about applicability. Of the 151 studies, 26 were identified as companion publications and 125 were unique studies.
Concern about applicability of the reference standard was assessed as low, the index test was also assessed as low, and patient selection was assessed as having high concerns about applicability. Dietitians prepared written feedback on submitted diaries, highlighting successes and providing advice for correcting deviations from diet prescriptions. The private session and telephone calls were digitally recorded, such that the study director and lead dietitian could monitor deviations from the protocol and provide feedback to dietitians as necessary.
Scalar contrasts also were constructed and compared for the linear trend in weight over each phase of the trial (baseline to 26 weeks, 26-50 weeks, and 50-74 weeks).
Diagnostic Characteristics of Other Screening Tests for Gestational Diabetes MellitusAppendix Figure 2. In addition, other less common tests, such as measurement of serum fructosamine and adiponectin, were assessed using different diagnostic criteria. By accepting a low cutoff for ruling out GDM and a high cutoff for diagnosing the disease on a screening test, the time and cost of a 2-step approach for diagnosis are reduced. This was primarily because the studies were conducted in developing countries and used the WHO criteria to diagnose GDM.
Of the 125 unique studies, 28 were further excluded during data extraction due to a lack of comparison or outcome of interest, leaving the total number of included studies at 97. The interplay between these proteins is shown below (3):In response to growth factors, Akt phosphorylates TSC2 directly on four or five residues (Ser939, Ser981, Ser1130, Ser1132 and Thr1462). The duration of the private session and each telephone call also was monitored as an indicator of integrity with regard to treatment intensity. To corroborate these results, effect modification was tested using baseline insulin concentration at 30 minutes after a dose of oral glucose as a continuous variable, log-transformed to reduce the influence of extreme observations. Forest plot of sensitivity and specificity of risk factor screening for gestational diabetes, by diagnostic criteria.Figure 2. Although patient preference may be an important consideration in the choice of screening test, it is important to note that there is evidence of population differences in the frequency of fasting or post–glucose load elevations in pregnancy 68.
Phosphorylation of TSC2 by Akt impairs its ability to inhibit Rheb, thereby blocking the inhibitory effect of Rheb on mTORC1.
Third, weekly staff meetings provided an opportunity for continued discussion on intervention delivery, particularly strategies for assisting individual participants without compromising differentiation between diets.
For graphical presentation, the raw weights were converted to changes from the participant's baseline measurements and repeated-measures analysis was performed on the resulting 22 discrete time points. In particular, fasting glucose level did not diagnose GDM as frequently in Asian women as in non-Asian women.
Further study is required to confirm whether glucose outcome relationships differ across populations. All outcome variables were analyzed untransformed with the exception of plasma triglyceride concentration, which showed a marked skew in distribution.
This promotes the association (by SH2 domains in the p85 regulatory subunit) and activation of PI3K. PtdIns (3,4,5)P3 binds to the PH domain of Akt and promotes its translocation to the plasma membrane. PI3K-dependent kinase 1 (PDK1) then phosphorylates Akt on Thr308 and PDK2 phosphorylates Ser473.
Phosphorylated Akt, as previously mentioned, phosphorylates and inactivates TSC2 and PRAS40 promoting mTORC1 activation. The net result is promoting proliferation and cell survival, hallmarks of cellular malignancy development and progression.Discussion of the studyThe basis for the utilization of glucose restriction for treating TSC related tumors can be easily inferred from the above explanation.
Contrary to what was expected, in vivo experiments showed that tumor size and growth rate were highest in the CF group and 2-DG supressed tumor growth independently of diet. These results also contradicted the observed standard uptake values (SUV) during the FDG-PET scan (presented as the maximum SUV within each tumor).
Theoretically, as these tumors are sensitive to glucose deprivation, there should be a correlation between glucose uptake (measured by uptake values of FDG) and tumor size (increased tumor size should show increased SUV).
However, there was no correlation between these parameters, as the CF+2-DG group showed the minimum mean SUV but the largest tumor size. To further complicate things, ketonemia was not developed in CF mice, but beta-hydroxybutyrate levels were higher with the Western +2-DG diet. Testing the effects of fatty acids in vitro showed that palmitic acid induced necrosis and oleic acid induced proliferation. Addition of rapamycin reduced cell-size, in contrast with 2-DG, which decreased proliferation.
Finally, there was increased activation of mTORC1 (measured by phospho-S6) and low levels of phosphorylated Akt (secondary to feedback inhibition) in all groups, with no differences between groups.InterpretationFirst, the results confirm the potent anti-tumor activity of 2-DG. Second, the CF group failed to establish ketosis, and the Western group had increased levels of beta-hydroxybutyrate, as well as reduced tumor size.
This (despite the observed growth-promoting properties of acetate and beta-hydroxybutyrate in vitro, see below) can be interpreted as an inhibitory effect of ketonemia on cancer growth. The comparison of glucose and beta-hydroxybutyrate levels is shown below:The diet which resulted in lower glucose levels and higher ketone bodies was associated with reduced tumor size, and the diet which produced greater glucose levels and lower ketone bodies was associated with increased tumor size. This underscores the importance of the phenotype of the tumor being treated, an important factor that is not taken into account by some "low-carb" advocates who think that restricting dietary glucose will magically cure all cancers.Another important factor to take into consideration is that mice were not calorie restricted, and more importantly, that the CF diet was high in protein.
AMPK inhibits mTORC1 activity by TSC2 dependent and independent mechanisms (possibly by phosphorylation of Raptor) (12).
2-DG also increases intracellular AMP levels (activating AMPK), which would explain the benefits of its utilization observed in this model (13). Supporting the role of AMPK as a target for cancer treatment, the combination of metformin and 2-DG seems to be more toxic to cancer cells than either by itself (14). Interestingly, AMPK activity was not changed in response to 2-DG in this model, which suggests that there are other mechanisms mediating the anti-proliferative effect of 2-DG.Summing upCancer is a very complex disease which treatment has to be personalized depending on the phenotype. With the increase knowledge in cancer molecular biology and genetics, therapies should be designed depending on specific markers evaluated. This complexity explains why not all cancers can be treated just by restricting glucose and making such statement is ludicrous.

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  1. 04.04.2014 at 19:23:40

    Doesn't take enough insulin before eating, the glucose their writing about meal frequency, fasting and.

    Author: Keremcem
  2. 04.04.2014 at 19:27:56

    Benefit from exercise stress testing before.

    Author: 18_USHAQ_ATASI