Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) are acute metabolic complications of diabetes mellitus that can occur in patients with both type 1 and 2 diabetes mellitus. Timely diagnosis, comprehensive clinical and biochemical evaluation, and effective management is key to the successful resolution of DKA and HHS. Critical components of the hyperglycemic crises’ management include coordinating fluid resuscitation, insulin therapy, and electrolyte replacement along with the continuous patient monitoring using available laboratory tools to predict the resolution of the hyperglycemic crisis. Understanding and prompt awareness of potential special situations such as DKA or HHS presentation in the comatose state, possibility of mixed acid-base disorders obscuring the diagnosis of DKA, and risk of brain edema during therapy are important to reduce the risks of complications without affecting recovery from hyperglycemic crisis. Identification of factors that precipitated DKA or HHS during the index hospitalization should help prevent subsequent episode of hyperglycemic crisis. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG .

ObjectiveTo examine the effects of an intensive lifestyle intervention (ILI) on cardiovascular disease (CVD), the Action for Health in Diabetes (Look AHEAD) trial randomized 5,145 participants with type 2 diabetes and overweight/obesity to a ILI or diabetes support and education. Although the primary outcome did not differ between the groups, there was suggestive evidence of heterogeneity for prespecified baseline CVD history subgroups (interaction P = 0.063). Event rates were higher in the ILI group among those with a CVD history (hazard ratio 1.13 [95% CI: 0.90‐1.41]) and lower among those without CVD (hazard ratio 0.86 [95% CI: 0.72‐1.02]).MethodsThis study conducted post hoc analyses of the rates of the primary composite outcome and components, adjudicated cardiovascular death, nonfatal myocardial infarction (MI), stroke, and hospitalization for angina, as well as three secondary composite cardiovascular outcomes.ResultsInteraction P values for the primary and two secondary composites were similar (0.060‐0.064). Of components, the interaction was significant for nonfatal MI (P = 0.035). This interaction was not due to confounding by baseline variables, different intervention responses for weight loss and physical fitness, or hypoglycemic events. In those with a CVD history, statin use was high and similar by group. In those without a CVD history, low‐density lipoprotein cholesterol levels were higher (P = 0.003) and statin use was lower (P ≤ 0.001) in the ILI group.ConclusionsIntervention response heterogeneity was significant for nonfatal MI. Response heterogeneity may need consideration in a CVD‐outcome trial design.

The publisher regrets that Table 1, Table 2 in the article were not the correct tables for this article. Table 1, Table 2 as submitted by the author are shown here.Table 1Baseline clinical and metabolic characteristics of the participants.AllWomenMenp-value N⁎HDL subfractions measured in a subset of 716 men and 1269 women.30702072998Age (years)50.0 [43.3, 57.9]48.6 [42.4, 55.7]53.3 [46.4, 62.0]<0.001Phys activity (met-hrs/wk)9.9 [3.9, 20.7]8.1 [3.2, 17.0]14.9 [5.8, 28.4]<0.001Alcohol<0.001 % None1682(55.8%)1274 (62.7%)408 (41.6%) % <1 drink/wk573(19.0%)390 (19.2%)183 (18.7%) % 1 drink /wk. to <1/day613(20.3%)310 (15.2%)303 (30.9%) % ≥1 drink/day146(4.8%)59 (2.9%)87 (8.9%)Diet saturated fat (g)23.9 [16.1, 35.1]23.0 [15.6, 34.0]25.8 [17.1, 37.8]<0.001BMI (kg/m2)32.8 [29.0, 37.4]33.8 [29.6, 38.7]30.9 [28.0, 34.4]<0.001Waist (cm)103.9 [94.8, 113.6]102.5 [93.0, 112.7]106.0 [98.8, 115.3]<0.001HbA1c (%)5.9 [5.6, 6.2]5.9 [5.6, 6.2]5.9 [5.6, 6.2]0.55Log 1/FI (pmol/L)-5.1 [−5.4, −4.7]-5.1 [−5.5, −4.8]-5.1 [−5.4, −4.7]0.435Statin use (%)4.2%5.8%3.5%0.002Lipid-lowering meds (%)4.9%7.0%4.0%0.002Triglyceride (mmol/L)1.6 [1.1, 2.3]1.6 [1.1, 2.2]1.7 [1.2, 2.4]<0.001HDL-C (mmol/L)1.1 [1.0, 1.3]1.2 [1.0, 1.4]1.0 [0.9, 1.2]<0.001Large HDL-P (μmol/L)⁎HDL subfractions measured in a subset of 716 men and 1269 women.3.4 [2.2, 5.4]4.2 [2.8, 6.1]2.4 [1.7, 3.5]<0.001Medium HDL-P (μmol/L)⁎HDL subfractions measured in a subset of 716 men and 1269 women.11 [8.1, 14.7]12.3 [8.9, 16.5]9.2 [6.5, 12]<0.001Small HDL-P (μmol/L)⁎HDL subfractions measured in a subset of 716 men and 1269 women.19.1 [16.1, 22.3]18.5 [15.4, 21.9]20.1 [17.3, 22.9]<0.001Total HDL-P (μmol/L)⁎HDL subfractions measured in a subset of 716 men and 1269 women.34.4 [30.8, 38.8]35.9 [32.0, 40.2]32.3 [29.1, 35.5]<0.001HDL size (nm)⁎HDL subfractions measured in a subset of 716 men and 1269 women.8.8 [8.6, 9.1]8.8 [8.7, 9.28.6 [8.5, 8.8]<0.001Adiponectin (ng/ml)7.3 [1.7, 7.6]7.8 [6.0, 10.3]6.3 [4.9, 8.0]<0.001tPA (ng/ml)10.9 [8.7, 13.4]10.4 [8.3, 12.7]12.0 [9.9, 14.6]<0.001Fibrinogen (mg/dL)374.0 [328.0, 432.0]388.0 [340.0, 448.0]348.0 [307.0, 397.0]<0.001CRP (mg/L)3.7 [1.7, 7.6]5.0 [2.5, 9.2]2.0 [0.9, 3.8]<0.001IL6 (pg/ml)1.9 [1.3, 3.0]2.1 [1.4, 3.2]1.6 [1.1, 2.5]<0.001MCP1 (pg/ml)138.0 [115.7, 167.1]135.3 [113.2, 165.4]143.7 [121.0, 169.6]<0.001sICAM (ng/ml)247.0 [209.6, 288.8]251.4 [212.0, 293.4]239.8 [203.6, 278.3]<0.001sE selectin (ng/ml)44.0 [32.9, 56.7]43.7 [32.3, 56.2]44.5 [34.2, 57.7]0.046Data are expressed in median [Interquartile range] or number (%). HDL subfractions measured in a subset of 716 men and 1269 women. Open table in a new tab Table 2Characteristics after 1 year by treatment group.PlaceboMetforminLifestylep-value BMI (kg/m2)−0.16 (−0.26, −0.05)−0.97 (−1.07, −0.87)−2.42 (−2.57, −2.27)<0.001,†,‡ Log 1/FI (pmol/L)−0.03 (−0.06, 0.01)0.16 (0.13, 0.19)0.25 (0.21, 0.28)<0.001,†,‡ HDL-C (mmol/L)0 00(−0.01, 0.01)0.02 (0.01, 0.03)0.03 (0.02, 0.04)<0.001,† Large HDL-P (μmol/L)0.22 (0.09, 0.34)0.54 (0.42, 0.67)0.97 (0.82, 1.12)<0.001,†,‡ Medium HDL-P (μmol/L)0.26 (−0.15, 0.67)−0.55 (−0.98, −0.11)0.65 (0.19, 1.11)0.0005,‡ Small HDL-P (μmol/L)0.19 (−0.20, 0.58)1.04 (0.62, 1.46)−1.35 (−1.79, −0.90)<0.001,†,‡ Total HDL-P (μmol/L)0.67 (0.31, 1.02)1.03 (0.67, 1.40)0.28 (−0.12, 0.67)0.019‡ HDL size (nm)0.02 (−0.01, 0.04)0.05 (0.03–0.08)0.13 (0.18, 0.34)<0.001†,‡ Log CRP (mg/L)−0.02 (−0.07, 0.02)−0.14 (−0.19, −0.10)−0.39 (−0.44, −0.35)<0.001,†,‡ sICAM (ng/ml)−4.67 (−7.40, −1.93)−14.84 (−17.88, −11.80)−19.87 (−22.75, −16.99)<0.001,†,‡ MAQ physical activity (met-hr./wk)1.1 [−0.8, 3.0]1.4 [−0.5, 3.3]7.3 [5.8, 8.7}<0.001†,‡ % Statin use9.2%7.8%5.8%0.01† % Lipid lowering meds10%8.6%6.5%0.02† Waist (cm)$Men−0.55 (−1.05, −0.04)−2.15 (−2.65, −1.65)−7.63 (−8.37, −6.89)<0.001,†,‡Women−0.78 (−3.75, 2.60)−2.24 (−5.55, 1.07)−5.77 (−9.75, −1.40)<0.001,†,‡ Log Triglyceride (mmol/L)$Men−0.06 (−0.10, −0.02)−0.04 (−0.08, 0)−0.23 (−0.28, −0.19)<0.001†,‡Women−0.07 (−0.10,−0.04)−0.05 (−0.08, −0.02)−0.13 (−0.16, −0.11)<0.001†,‡ Adiponectin (ng/ml)$Men0.15 (0.02, 0.28)0.12 (−0.02, 0.25)1.06 (0.85, 1.27)<0.001†,‡Women0.08 (−0.02, 0.18)0.29 (0.16, 0.41)0.71 (0.58, 0.84)<0.001†,‡ tPA (ng/ml)$Men−0.86 (−1.20, −0.53)−2.40 (−2.78, −2.03)−3.26 (−3.69, −2.84)<0.001,†,‡Women−0.68 (−0.93, −0.44)−1.89 (−2.13, −1.66)−2.18 (−2.43, −1.93)<0.001†,‡ sE selectin (ng/ml)$Men1.41 (0.07, 2.76)0.27 (−0.88, 1.41)−5.64 (−7.03, −4.25)<0.001†,‡Women−0.88 (−1.82, 0.05)−0.50 (−1.32, 0.32)−4.10 (−4.79, −3.42)<0.001†,‡ Dietary saturated fat (g)$Men−5.02 (−6.79, −3.25)−3.61 (−4.82, −2.40)−12.57 (−14.30, −10.84)<0.001†,‡Women−4.17 (−5.31, −3.03)−5.62 (−6.65, −4.58)−11.01 (−12.09, −9.93)<0.001†,‡ HbA1c$Men0.11 (0.07, 0.15)−0.01 (−0.04, 0.02)−0.14 (−0.17, −0.10)<0.001,†,‡Women0.08 (0.05, 0.11)0.01 (−0.01, 0.03)−0.07 (−0.09, −0.05)<0.001,†,‡*Data are presented as mean change (95%CI) or percent. $ p < 0.05 for interaction of treatment group x sex; ⁎ p < 0.05 for Placebo vs Metformin; † p < 0.05 for Placebo vs Lifestyle; ‡ p < 0.05 for Metformin vs Lifestyle. Open table in a new tab Data are expressed in median [Interquartile range] or number (%). *Data are presented as mean change (95%CI) or percent. $ p < 0.05 for interaction of treatment group x sex; ⁎ p < 0.05 for Placebo vs Metformin; † p < 0.05 for Placebo vs Lifestyle; ‡ p < 0.05 for Metformin vs Lifestyle. The publishers would like to apologize for any inconvenience caused. Change in adiponectin explains most of the change in HDL particles induced by lifestyle intervention but not metformin treatment in the Diabetes Prevention ProgramMetabolism - Clinical and ExperimentalVol. 65Issue 5PreviewIn addition to slowing diabetes development among participants in the Diabetes Prevention Program (DPP), intensive lifestyle change and metformin raised HDL-cholesterol (HDL-C) compared to placebo treatment. We investigated the lifestyle and metabolic determinants as well as effects of biomarkers of inflammation, endothelial dysfunction and coagulation and their changes resulting from lifestyle and metformin interventions on the increase in HDL-C in the DPP. Full-Text PDF

ABSTRACT Objective Genomewide association studies (GWAS) have identified consistent associations with obesity, with a number of studies implicating eating behavior as a primary mechanism. Few studies have replicated genetic associations with dietary intake. This study evaluates the association between obesity susceptibility loci and dietary intake. Methods Data were obtained as part of the Diabetes Prevention Program (DPP), a clinical trial of diabetes prevention in persons at high risk of diabetes. The association of 31 genomewide association studies identified obesity risk alleles with dietary intake, measured through a food frequency questionnaire, was investigated in 3,180 participants from DPP at baseline. Results The minor allele at BDNF , identified as protective against obesity, was associated with lower total caloric intake (β = −106.06, SE = 33.13; p = .0014) at experimentwide statistical significance ( p = .0016), whereas association of MC4R rs571312 with higher caloric intake reached nominal significance (β = 61.32, SE = 26.24; p = .0194). Among non-Hispanic white participants, the association of BDNF rs2030323 with total caloric intake was stronger (β = −151.99, SE = 30.09; p < .0001), and association of FTO rs1421085 with higher caloric intake (β = 56.72, SE = 20.69; p = .0061) and percentage fat intake (β = 0.37, SE = 0.08; p = .0418) was also observed. Conclusions These results demonstrate with the strength of independent replication that BDNF rs2030323 is associated with 100 to 150 greater total caloric intake per allele, with additional contributions of MC4R and, in non-Hispanic white individuals, FTO . As it has been argued that an additional 100 kcal/d could account for the trends in weight gain, prevention focusing on genetic profiles with high dietary intake may help to quell adverse obesity trends. Clinical Trial Registration: Clinicaltrials.gov, NCT00004992.

To determine the efficacy of pioglitazone to prevent type 2 diabetes in older compared to younger adults with pre-diabetes. Six hundred two participants with impaired glucose tolerance (IGT) were randomized in double blind fashion to placebo or pioglitazone for diabetes prevention in the ACT NOW study (NEJM 364:1104–1115, 2011). Cox proportional hazard regression was used to compare time to development of diabetes over a mean of 2 years between older (≥61 years) and younger participants. We compared effects of pioglitazone versus placebo on metabolic profiles, inflammatory markers, adipokines, β cell function (disposition index), insulin sensitivity (Matsuda index), and body composition by ANOVA. Diabetes incidence was reduced by 85 % in older and 69 % in younger subjects ( p = 0.41). β cell function (disposition index) increased by 35.0 % in the older and 26.7 % in younger subjects ( p = 0.83). Insulin sensitivity (Matsuda index) increased by 3.07 (5.2-fold) in older and by 2.54 (3.8-fold) in younger participants ( p = 0.58). Pioglitazone more effectively increased adiponectin in older versus younger subjects (22.9 ± 3.2 μg/mL [2.7-fold] vs. 12.7 ± 1.4 μg/mL [2.2-fold], respectively; p = 0.04). Younger subjects tended to have a greater increase in whole body fat mass compared to older subjects (3.6 vs. 3.1 kg; p = 0.061). Younger and older subjects had similar decreases in bone mineral density (0.018 ± 0.0071 vs. 0.0138 ± 0.021 g/cm 2 ). Younger and older pre-diabetic adults taking pioglitazone had similar reductions in conversion to diabetes and older adults had similar or greater improvements in metabolic risk factors, demonstrating that pioglitazone is useful in preventing diabetes in older adults.

OBJECTIVE:Remission of pre-diabetes to normal is an important health concern which has had little success in the past. This study objective was to determine the effect on remission of pre-diabetes with a high protein (HP) versus high carbohydrate (HC) diet and effects on metabolic parameters, lean and fat body mass in prediabetic, obese subjects after 6 months of dietary intervention.RESEARCH DESIGN AND METHODS:We recruited and randomized 24 pre-diabetes women and men to either a HP (30% protein, 30% fat, 40% carbohydrate; n=12) or HC (15% protein, 30% fat, 55% carbohydrate; n=12) diet feeding study for 6 months in this randomized controlled trial. All meals were provided to subjects for 6 months with daily food menus for HP or HC compliance with weekly food pick-up and weight measurements. At baseline and after 6 months on the respective diets oral glucose tolerance and meal tolerance tests were performed with glucose and insulin measurements and dual energy X-ray absorptiometry scans.RESULTS:After 6 months on the HP diet, 100% of the subjects had remission of their pre-diabetes to normal glucose tolerance, whereas only 33.3% of subjects on the HC diet had remission of their pre-diabetes. The HP diet group exhibited significant improvement in (1) insulin sensitivity (p=0.001), (2) cardiovascular risk factors (p=0.04), (3) inflammatory cytokines (p=0.001), (4) oxidative stress (p=0.001), (5) increased percent lean body mass (p=0.001) compared with the HC diet at 6 months.CONCLUSIONS:This is the first dietary intervention feeding study, to the best of our knowledge, to report 100% remission of pre-diabetes with a HP diet and significant improvement in metabolic parameters and anti-inflammatory effects compared with a HC diet at 6 months.TRIAL REGISTRATION NUMBER:NCT0164284.

during the first year and 7.3% (95% CI, 6.2%-8.4%) at year 4, compared with 2.0% for the DSE group at both time points (95% CIs, 1.4%-2.6% at year 1 and 1.5%-2.7% at year 4) (P.001 for each). Among ILI participants, 9.2% (95% CI, 7.9%-10.4%), 6.4% (95% CI, 5.3%-7.4%), and 3.5% (95% CI, 2.7%-4.3%) had continuous, sustained remission for at least 2, at least 3, and 4 years, respectively, compared with less than 2% of DSE participants (1.7% [95% CI, 1.2%-2.3%] for at least 2 years; 1.3% [95% CI, 0.8%-1.7%] for at least 3 years; and 0.5% [95% CI, 0.2%-0.8%] for 4 years). Conclusions In these exploratory analyses of overweight adults, an intensive lifestyle intervention was associated with a greater likelihood of partial remission of type 2 diabetes compared with diabetes support and education. However, the absolute remission rates were modest.

The purpose of this study is to identify a diet that facilitates weight loss and conversion of patients with impaired glucose tolerance (IGT) to normal glucose tolerance (NGT). Specific aim was to determine if a High Protein (HP) or High Carbohydrate (HC) diet is more effective in conversion of IGT obese adults to NGT. 18 obese, pre-diabetic adults were randomized to a HP or HC diet for 6 months (mo) with all food provided. The HP diet consisted of 30% protein, 30% fat, 40% carbohydrates while the HC diet consisted of 15% protein, 30% fat, 55% carbohydrates distributed by percentage of daily kcals derived for each subject. An Oral Glucose Tolerance Test (OGTT) was performed at Baseline (BL) and 6 mo to determine IGT/NGT status. A 2 hr glucose between 140 to 199 mg/dl was considered IGT. DXA was done at BL and 6 mo. Food pick up and weight checks were weekly. Both diet groups had weight loss, improvement in insulin sensitivity determined by HOMA IR [HP (BL 4.69 ± 0.26; 6 mo 1.58 ± 0.14)], [HC (4.62 ± 0.26; 6 mo 3.15 ± 0.27)] and decrease in HbA1c [HP (BL 5.99 ± .05; 6 mo 5.53 ± .02)], [HC (BL 5.9 ± .04; 6 mo 5.69 ± .06)]. The HP diet had a 100% (9/9) conversion rate to NGT while the HC diet had a 44.4% (4/9) conversion rate. HP group had a 2.8 ± .4% increase in lean body mass and 2.5 ± 0.4% decrease in fat mass while the HC group had a 2.1 ± 1.1% and 3.5 ± 0.9 % decrease in lean and fat mass with greater overall average weight loss. Both diets resulted in improvement in glucose tolerance and insulin sensitivity but the HP diet was most effective. Our results suggest that lean body mass preservation may be more important than total weight loss in the conversion of IGT to NGT, possibly due to the high insulin sensitivity of muscle cells.

The prognosis of diabetic ketoacidosis has undergone incredibly remarkable evolution since the discovery of insulin nearly a century ago. The incidence and economic burden of diabetic ketoacidosis have continued to rise but its mortality has decreased to less than 1% in good centers. Improved outcome is attributable to a better understanding of the pathophysiology of the disease and widespread application of treatment guidelines. In this review, we present the changes that have occurred over the years, highlighting the evidence behind the recommendations that have improved outcome. We begin with a discussion of the precipitants and pathogenesis of DKA as a prelude to understanding the rationale for the recommendations. A brief review of ketosis-prone type 2 diabetes, an update relating to the diagnosis of DKA and a future perspective are also provided.

BACKGROUND:The relative effectiveness of 3 approaches to blood pressure control-(i) an intensive lifestyle intervention (ILI) focused on weight loss, (ii) frequent goal-based monitoring of blood pressure with pharmacological management, and (iii) education and support-has not been established among overweight and obese adults with type 2 diabetes who are appropriate for each intervention. METHODS:Participants from the Action for Health in Diabetes (Look AHEAD) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) cohorts who met criteria for both clinical trials were identified. The proportions of these individuals with systolic blood pressure (SBP) <140 mm Hg from annual standardized assessments over time were compared with generalized estimating equations. RESULTS:Across 4 years among 480 Look AHEAD and 1,129 ACCORD participants with baseline SBPs between 130 and 159 mm Hg, ILI (OR = 1.46; 95% CI = [1.18-1.81]) and frequent goal-based monitoring with pharmacotherapy (OR = 1.51; 95% CI = [1.16-1.97]) yielded higher rates of blood pressure control compared to education and support. The intensive behavioral-based intervention may have been more effective among individuals with body mass index >30 kg/m2, while frequent goal-based monitoring with medication management may be more effective among individuals with lower body mass index (interaction P = 0.047). CONCLUSIONS:Among overweight and obese adults with type 2 diabetes, both ILI and frequent goal-based monitoring with pharmacological management can be successful strategies for blood pressure control. CLINICAL TRIALS REGISTRY:clinicaltrials.gov identifiers NCT00017953 (Look AHEAD) and NCT00000620 (ACCORD).

OBJECTIVE:The objective was to test the clinical utility of Quantose M(Q) to monitor changes in insulin sensitivity after pioglitazone therapy in prediabetic subjects. Quantose M(Q) is derived from fasting measurements of insulin, α-hydroxybutyrate, linoleoyl-glycerophosphocholine, and oleate, three nonglucose metabolites shown to correlate with insulin-stimulated glucose disposal.RESEARCH DESIGN AND METHODS:Participants were 428 of the total of 602 ACT NOW impaired glucose tolerance (IGT) subjects randomized to pioglitazone (45 mg/d) or placebo and followed for 2.4 years. At baseline and study end, fasting plasma metabolites required for determination of Quantose, glycated hemoglobin, and oral glucose tolerance test with frequent plasma insulin and glucose measurements to calculate the Matsuda index of insulin sensitivity were obtained.RESULTS:Pioglitazone treatment lowered IGT conversion to diabetes (hazard ratio = 0.25; 95% confidence interval = 0.13-0.50; P < .0001). Although glycated hemoglobin did not track with insulin sensitivity, Quantose M(Q) increased in pioglitazone-treated subjects (by 1.45 [3.45] mg·min(-1)·kgwbm(-1)) (median [interquartile range]) (P < .001 vs placebo), as did the Matsuda index (by 3.05 [4.77] units; P < .0001). Quantose M(Q) correlated with the Matsuda index at baseline and change in the Matsuda index from baseline (rho, 0.85 and 0.79, respectively; P < .0001) and was progressively higher across closeout glucose tolerance status (diabetes, IGT, normal glucose tolerance). In logistic models including only anthropometric and fasting measurements, Quantose M(Q) outperformed both Matsuda and fasting insulin in predicting incident diabetes.CONCLUSIONS:In IGT subjects, Quantose M(Q) parallels changes in insulin sensitivity and glucose tolerance with pioglitazone therapy. Due to its strong correlation with improved insulin sensitivity and its ease of use, Quantose M(Q) may serve as a useful clinical test to identify and monitor therapy in insulin-resistant patients.

The hadron ratios measured in central Au-Au collisions are analysed by means of Hadron Resonance Gas (HRG) model over a wide range of nucleon-nucleon center-of-mass energies, √s NN = 7.7–200 GeV as offered by the RHIC Beam Energy Scan I (BES-I) (STAR Collaboration). The temperature and baryon chemical potential are deduced from fits of experimental ratios to thermal model calculations assuming chemical equilibrium. We find that the resulting freeze-out parameters using single hard-core value and point-like constituents of HRG are identical. This implies that the excluded-volume comes up with no effect on the extracted parameters. We compare the results with other studies and with the lattice QCD calculations. Various freeze-out conditions are confronted with the resulting data set. The effect of including new resonances is also analysed. At vanishing chemical potential, a limiting temperature was estimated, T lim = 158.5 ± 3 MeV.

CONTEXT Steroid sex hormones and SHBG may modify metabolism and diabetes risk, with implications for sex-specific diabetes risk and effects of prevention interventions. OBJECTIVE This study aimed to evaluate the relationships of steroid sex hormones, SHBG and SHBG single-nucleotide polymorphisms (SNPs) with diabetes risk factors and with progression to diabetes in the Diabetes Prevention Program (DPP). DESIGN AND SETTING This was a secondary analysis of a multicenter randomized clinical trial involving 27 U.S. academic institutions. PARTICIPANTS The study included 2898 DPP participants: 969 men, 948 premenopausal women not taking exogenous sex hormones, 550 postmenopausal women not taking exogenous sex hormones, and 431 postmenopausal women taking exogenous sex hormones. INTERVENTIONS Participants were randomized to receive intensive lifestyle intervention, metformin, or placebo. MAIN OUTCOMES Associations of steroid sex hormones, SHBG, and SHBG SNPs with glycemia and diabetes risk factors, and with incident diabetes over median 3.0 years (maximum, 5.0 y). RESULTS T and DHT were inversely associated with fasting glucose in men, and estrone sulfate was directly associated with 2-hour post-challenge glucose in men and premenopausal women. SHBG was associated with fasting glucose in premenopausal women not taking exogenous sex hormones, and in postmenopausal women taking exogenous sex hormones, but not in the other groups. Diabetes incidence was directly associated with estrone and estradiol and inversely with T in men; the association with T was lost after adjustment for waist circumference. Sex steroids were not associated with diabetes outcomes in women. SHBG and SHBG SNPs did not predict incident diabetes in the DPP population. CONCLUSIONS Estrogens and T predicted diabetes risk in men but not in women. SHBG and its polymorphisms did not predict risk in men or women. Diabetes risk is more potently determined by obesity and glycemia than by sex hormones.