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Background: Rifampin (INN, rifampicin), a CYP34A inducer, results in significant interactions when coadministered with combination oral contraceptives that contain norethindrone (INN, norethisterone) and ethinyl estradiol (INN, ethinylestradiol). Little is known about the effects of rifabutin, a related rifamycin.Objectives and methods: The relative effects of rifampin and rifabutin on the pharmacokinetics and pharmacodynamics of ethinyl estradiol and norethindrone were evaluated in a prospective, randomized, double-blinded crossover study in 12 premenopausal women who were on a stable oral contraceptive regimen that contained 35 μg ethinyl estradiol/1 mg norethindrone. Subjects were randomized to receive 14 days of rifampin or rifabutin from days 7 through 21 of their menstrual cycle. After a 1-month washout period (only the oral contraceptives were taken), subjects were crossed over to the other rifamycin.Results: Rifampin significantly decreased the mean area under the plasma concentration–time curve from time 0 to 24 hours [AUC(0-24)] of ethinyl estradiol and the mean AUC(0-24) of norethindrone. Rifabutin significantly decreased the mean AUC(0-24) of ethinyl estradiol and the mean AUC(0-24) of norethindrone. The effect of rifampin was significantly greater than rifabutin on each AUC(0-24). Despite these changes, subjects did not ovulate (as determined by progesterone concentrations) during the cycle in which either rifamycin was administered. Levels of mean follicle-stimulating hormone increased 69% after rifampin.Conclusion: In this study, rifampin (600 mg daily) was a more significant inducer of ethinyl estradiol and norethindrone clearance than rifabutin (300 mg daily), but neither agent reversed the suppression of ovulation caused by oral contraceptives. The carefully monitored oral contraceptive administration and the limited exposure to rifamycins may restrict the application of this study to clinical situations.

Background:Several heart failure (HF) risk factors, including hypertension, diabetes mellitus, obesity, and physical activity, are well described. However, the degree to which optimization of these modifiable risk factors might impact the incidence of HF is not yet fully defined.Hypothesis:We hypothesized more optimal control of major modifiable HF risk factors is associated with progressively lower HF risk.Methods:We performed a prospective analysis of 13,534 ARIC participants (mean age 57, 55% female), examining HF risk associations of different cutpoints of glycemia (HbA1c), systolic blood pressure (SBP), body mass index (BMI) and physical activity (assessed at Visit 2 [1990-92], except for physical activity assessed at Visit 1 (1987-89]). Optimal risk factor control was defined as HbA1c < 7%, SBP < 120 mmHg, BMI 18.5-25 kg/m2, and AHA-recommended activity levels. Severely uncontrolled risk factors were defined as HbA1c > 8%, SBP > 160 mmHg, BMI > 35 kg/m2and no exercise physical activity. Intermediate values were considered mild to moderately uncontrolled. Cox models simultaneously including all risk factors were constructed to assess associations of risk factor levels with incident HF (by discharge codes) after Visit 2 through 2016.Results:There were 2,827 HF events over a median 24 years of follow-up. Risk gradations were seen across categorizations of each risk factor (Table). In the full model, relative to optimal control, HRs were 2.06 for BMI ≥ 35 kg/m2, 1.16 for poor physical activity, 2.31 for HbA1c > 8% and 1.80 for SBP ≥ 160 mmHg. No risk gradient was seen from SBP < 120 to 140 mmHg among hypertensives. Incidence rates (per 1000 PYs) were 7.9 for all optimally controlled risk factors, 14.5 for 3-4 mild to moderately uncontrolled risk factors and 39.5 for 3-4 severely uncontrolled risk factors.Conclusion:Optimal control of modifiable risk factors is strongly linked to lower HF risk. Our findings suggest prioritizing optimization of existing risk factors may be central to successful strategies to prevent HF onset.