The effects of estrogen replacement therapy on prognosis in women with established coronary disease remain uncertain. The authors conducted a retrospective cohort study of 726 women (mean age, 66.2 years) who survived first myocardial infarction to hospital discharge from 1980 through 1991, while enrolled at Group Health Cooperative of Puget Sound in western Washington State. Estrogen replacement therapy after myocardial infarction (122 women) was ascertained from computerized pharmacy records. Reinfarctions (n = 135) and deaths (n = 183) through 1993 were identified, and relative risks were calculated. The relative risk for reinfarction associated with current estrogen replacement therapy after myocardial infarction, adjusting for age and time since infarction, was 0.64 (95% confidence interval (CI) 0.32-1.30), and that for past estrogen replacement therapy was 0.90 (95% CI 0.62-1.31). The relative risk for all-cause mortality associated with current estrogen replacement therapy was 0.50 (95% CI 0.25-1.00), and that for past estrogen replacement therapy was 0.79 (95% CI 0.56-1.09). While estrogen users were less likely than nonusers to have a history of diabetes or congestive heart failure, adjustment for these and additional prognostic factors altered risk estimates only slightly. Estrogen replacement therapy after first myocardial infarction was not associated with increased risk of reinfarction or mortality. This study provides reassurance regarding the safety of estrogen replacement therapy after myocardial infarction in women.

Obesity is associated with insulin resistance, particularly when body fat has a central distribution. However, insulin resistance also frequently occurs in apparently lean individuals. It has been proposed that these lean insulin-resistant individuals have greater amounts of body fat than lean insulin-sensitive subjects. Alternatively, their body fat distribution may be different. Obesity is associated with elevated plasma leptin levels, but some studies have suggested that insulin sensitivity is an additional determinant of circulating leptin concentrations. To examine how body fat distribution contributes to insulin sensitivity and how these variables are related to leptin levels, we studied 174 individuals (73 men, 101 women), a priori classified as lean insulin-sensitive (LIS, n = 56), lean insulin-resistant (LIR, n = 61), and obese insulin-resistant (OIR, n = 57) based on their BMI and insulin sensitivity index (S(I)). Whereas the BMI of the two lean groups did not differ, the S(I) of the LIR subjects was less than half that of the LIS group. The subcutaneous and intra-abdominal fat areas, determined by computed tomography, were 45 and 70% greater in the LIR subjects (P < 0.001) and 2.5- and 3-fold greater in the OIR group, as compared with the LIS group. Fasting plasma leptin levels were moderately increased in LIR subjects (10.8 +/- 7.1 vs. 8.1 +/- 6.4 ng/ml in LIS subjects; P < 0.001) and doubled in OIR subjects (21.9 +/- 15.5 ng/ml; P < 0.001). Because of the confounding effect of body fat, we examined the relationships between adiposity, insulin sensitivity, and leptin concentrations by multiple regression analysis. Intra-abdominal fat was the best variable predicting insulin sensitivity in both genders and explained 54% of the variance in S(I). This inverse relationship was nonlinear (r = -0.688). On the other hand, in both genders, fasting leptin levels were strongly associated with subcutaneous fat area (r = 0.760) but not with intra-abdominal fat. In line with these analyses, when LIS and LIR subjects were matched for subcutaneous fat area, age, and gender, they had similar leptin levels, whereas their intra-abdominal fat and insulin sensitivity remained different. Thus, accumulation of intra-abdominal fat correlates with insulin resistance, whereas subcutaneous fat deposition correlates with circulating leptin levels. We conclude that the concurrent increase in these two metabolically distinct fat compartments is a major explanation for the association between insulin resistance and elevated circulating leptin concentrations in lean and obese subjects.

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P _interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04-1 × 10(-17)). Except for total HDL particles (r = -0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07-0.17, P = 5 × 10(-5)-1 10(-19)). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE ± 0.22 mg/dl/allele, P = 8 × 10(-5), P(interaction) = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE ± 0.22 mg/dl/allele, P = 0.35) or metformin (β = -0.03, SEE ± 0.22 mg/dl/allele, P = 0.90; P(interaction) = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE ± 0.012 ln nmol/L/allele, P = 0.01, P(interaction) = 0.01) but not in the placebo (β = -0.002, SEE ± 0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE ± 0.008 nmol/L/allele, P = 0.12; P(interaction) = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Sex hormone binding globulin (SHBG) levels are reported to be inversely associated with diabetes risk. It is unknown whether diabetes prevention interventions increase SHBG and whether resultant changes in SHBG affect diabetes risk. The purpose of this analysis was to determine whether intensive lifestyle intervention (ILS) or metformin changed circulating SHBG and if resultant changes influenced diabetes risk in the Diabetes Prevention Program (DPP). This is a secondary analysis from the DPP (1996-2001), a randomized trial of ILS or metformin versus placebo on diabetes risk over a mean follow-up of 3.2 years. The DPP was conducted across 27 academic study centers in the USA. Men, premenopausal and postmenopausal women without hormone use in the DPP were evaluated. The DPP included overweight/obese persons with elevated fasting glucose and impaired glucose tolerance. Main outcomes measures were changes in SHBG levels at 1 year and risk of diabetes over 3 years. ILS resulted in significantly higher increases (postmenopausal women: p<0.01) or smaller decrements (men: p<0.05; premenopausal women: p<0.01) in SHBG compared with placebo or metformin. Changes in SHBG were primarily attributable to changes in adiposity. There were no consistent associations of change in SHBG with the risk of diabetes by treatment arm or participant group. Lifestyle intervention may be associated with favorable changes in circulating SHBG, which is largely due to changes in adiposity. Changes in circulating SHBG do not independently predict reductions in diabetes incidence.

Objective

To determine whether metformin or lifestyle modification can lower rates of all-cause and cause-specific mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.

Research design and methods

From 1996 to 1999, 3,234 adults at high risk for type 2 diabetes were randomized to an intensive lifestyle intervention, masked metformin, or placebo. Placebo and lifestyle interventions stopped in 2001, and a modified lifestyle program was offered to everyone, but unmasked study metformin continued in those originally randomized. Causes of deaths through 31 December 2018 were adjudicated by blinded reviews. All-cause and cause-specific mortality hazard ratios (HRs) were estimated from Cox proportional hazards regression models and Fine-Gray models, respectively.

Results

Over a median of 21 years (interquartile range 20-21), 453 participants died. Cancer was the leading cause of death (n = 170), followed by cardiovascular disease (n = 131). Compared with placebo, metformin did not influence mortality from all causes (HR 0.99 [95% CI 0.79, 1.25]), cancer (HR 1.04 [95% CI 0.72, 1.52]), or cardiovascular disease (HR 1.08 [95% CI 0.70, 1.66]). Similarly, lifestyle modification did not impact all-cause (HR 1.02 [95% CI 0.81, 1.28]), cancer (HR 1.07 [95% CI 0.74, 1.55]), or cardiovascular disease (HR 1.18 [95% CI 0.77, 1.81]) mortality. Analyses adjusted for diabetes status and duration, BMI, cumulative glycemic exposure, and cardiovascular risks yielded results similar to those for all-cause mortality.

Conclusions

Cancer was the leading cause of mortality among adults at high risk for type 2 diabetes. Although metformin and lifestyle modification prevented diabetes, neither strategy reduced all-cause, cancer, or cardiovascular mortality rates.