Background

Levothyroxine (LT4) absorption is affected by concomitant ingestion of certain minerals, medications, and foods. It has been hypothesized that metformin may suppress serum thyrotropin (TSH) concentrations by enhancing LT4 absorption or by directly affecting the hypothalamic-pituitary axis. This study examined the effect of metformin ingestion on LT4 absorption, as assessed by serum total thyroxine (TT4) concentrations.

Methods

A modified Food and Drug Administration LT4 bioequivalence protocol was applied to healthy, metformin-naïve, euthyroid adult volunteers. Following an overnight fast, 600 μg LT4 was administered orally. Serum TT4 concentrations were measured at baseline and at 0.5, 1, 1.5, 2, 4, and 6 h following LT4 administration. Measurements were performed before and after one week of metformin ingestion (850 mg three times daily). Peak serum TT4 concentrations, time to peak TT4 concentrations, and area under the concentration-time curve (AUC) were calculated.

Results

Twenty-six subjects (54% men, 27% white, age 33 ± 10 years) were studied. There were no significant differences in peak serum TT4 concentrations (p = 0.13) and time to peak TT4 concentrations (p = 0.19) before and after one week of metformin use. A trend toward reduced TT4 AUC was observed after metformin ingestion (pre-metformin 3893 ± 568 μg/dL-min, post-metformin 3765 ± 588 μg/dL-min, p = 0.09).

Conclusions

LT4 absorption is unchanged by concomitant metformin ingestion. Mechanisms other than increased LT4 absorption may be responsible for the suppressed TSH concentrations observed in patients ingesting both drugs.

BACKGROUND: Attaining guideline-recommended levels of physical activity is associated with substantially lower risk of cardiometabolic diseases. OBJECTIVES: Although physical activity commonly follows a weekend warrior pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 to 2 days rather than spread more evenly across the week (regular), the effects of activity pattern on imaging-based biomarkers of cardiometabolic health are unknown. METHODS: We analyzed 17,146 UK Biobank participants who wore accelerometers for 1 week, and later underwent cardiac magnetic resonance imaging. Activity was categorized as inactive, regular, or weekend warrior. Associations between activity pattern and magnetic resonance imaging-derived visceral adipose tissue (VAT) and epicardial and pericardial adipose tissue (EPAT) were assessed using multiple linear regression adjusted for confounding factors. RESULTS: Compared to inactive, VAT was progressively lower with weekend warrior (-0.71 L, 95% CI -0.78 to -0.64, P < 0.001) followed by regular activity (-0.96 L, 95% CI -1.04 to -0.88, P < 0.001). Observations were similar for EPAT (weekend warrior activity -2.84 cm2, 95% CI -3.20 to -2.49, P < 0.001; regular activity -3.62 cm2, 95% CI -4.03 to -3.20, P < 0.001). When compared directly, weekend warriors had modestly higher adipose tissue than regular activity (VAT difference 0.25 L, 95% CI 0.17-0.32, P < 0.001; EPAT 0.78 cm2, 95% CI 0.40-1.15, P < 0.001). No differences were observed after adjustment for total moderate-to-vigorous physical activity minutes (VAT 0.07 L, 95% CI -0.01 to 0.14, P = 0.09; EPAT 0.04 cm2, 95% CI -0.35 to 0.43, P = 0.84). CONCLUSIONS: Guideline-adherent physical activity is associated with favorable quantitative measures of cardiometabolic health, with no differences based on activity pattern for a given activity volume.

Hypertension is a major risk factor for cardiovascular disease (CVD), yet blood pressure is measured intermittently and under suboptimal conditions. We developed a deep learning model to identify hypertension and stratify risk of CVD using 12-lead electrocardiogram waveforms. HTN-AI was trained to detect hypertension using 752,415 electrocardiograms from 103,405 adults at Massachusetts General Hospital. We externally validated HTN-AI and demonstrated associations between HTN-AI risk and incident CVD in 56,760 adults at Brigham and Womens Hospital. HTN-AI accurately discriminated hypertension (internal and external validation AUROC 0.803 and 0.771, respectively). In Fine-Gray regression analyses model-predicted probability of hypertension was associated with mortality (hazard ratio per standard deviation: 1.47 [1.36-1.60], p < 0.001), HF (2.26 [1.90-2.69], p < 0.001), MI (1.87 [1.69-2.07], p < 0.001), stroke (1.30 [1.18-1.44], p < 0.001), and aortic dissection or rupture (1.69 [1.22-2.35], p < 0.001) after adjustment for demographics and risk factors. HTN-AI may facilitate diagnosis of hypertension and serve as a digital biomarker of hypertension-associated CVD.