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Background and Aims: Type 2 diabetes is associated with a heightened risk of cardiovascular complications, including myocardial steatosis. Fasting-mimicking diets (FMDs) may mimic the metabolic benefits of fasting, while being less intensive than fasting. This study aims to investigate the effect of following an FMD program on myocardial triglyceride content (MTGC), as assessed by Magnetic Resonance Spectroscopy (MRS), in patients with type 2 diabetes., Methods and Results: 100 patients with type 2 diabetes, who used metformin as the only glucose-lowering drug or no medication were randomly assigned to either an FMD group or a control group. The FMD group received the FMD program for 5 consecutive days a month alongside usual care, while the control group received usual care only. Both groups underwent baseline, 6-months and 12-months examinations, including single voxel cardiac 1H-MRS to assess MTGC. N = 13 participants of the FMD and n = 13 of the control group had complete data at baseline and twelve month follow-up. The FMD group exhibited a significant reduction in MTGC over the twelve month period (-0.235 % MTGC, p = 0.027), while the control group saw no significant change (0.143 % MTGC, p = 0.236). The decrease of MTGC in the FMD group was statistically different (p = 0.018) from control., Conclusion: Following an FMD program reduces MTGC, which indicates a favorable effect on cardiac metabolism and thereby may be an effective strategy to reduce the cardiovascular risk in patients with type 2 diabetes., Clinical Trials Registration Number: NCT03811587., Trial Registration: ClinicalTrials.gov; NCT03811587, submitted January 13th, 2019., (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Depending on age, sex, and familial longevity, alterations in thyroid status occur frequently and often co-occur with differences in other hormonal axes. However, studies that explore the effects of thyroid status modulation on other hormonal axes remain scarce. We aimed to determine the effects of thyroid status modulation on prolactin, IGF-1, cortisol, LH, testosterone, and SHBG levels. We also explored whether effects differed depending on type of challenge, sex, and familial longevity., Methods: Data were gathered from two single-arm challenge studies comprising an intramuscular injection of 0.1 mg recombinant human TSH (rhTSH, N = 29) or 100 µg T3 orally (N = 27) in healthy older individuals. Changes in hormone concentration profiles relative to baseline were determined for 4 and 5 days, respectively., Results: IGF-1 increased with a maximum of 6.3% (SEM = 1.6%, p = 0.002) in the rhTSH challenge and 8.8% (SEM = 1.6%, p < 0.001) in the T3 challenge, while LH (19.3% [SEM = 6.6%, p = 0.048]), testosterone (13.8% [SEM = 4.7%, p = 0.048]), and SHBG (11.8% [SEM = 3.5%, p = 0.02]) increased significantly in the T3 challenge only. Moreover, prolactin significantly decreased in both rhTSH and T3 challenges (-8.8% [SEM = 3.4%, p = 0.048] and -12.0% [3.3%, p = 0.004], respectively) as did cortisol (-14.8% [SEM = 3.6%, p < 0.001] and -15.6% [SEM = 3.5%, p < 0.001]). There was no significant interaction with type of challenge, sex, or familial longevity, except for prolactin in the rhTSH challenge (p = 0.004) which decreased significantly in men only., Conclusions: Upon modulation of thyroid status, changes were observed in IGF-1, prolactin, and cortisol. In the T3 challenge, LH, testosterone, and SHBG increased in men. Observed changes are hypothesized to be driven by (f)T3., Introduction: Depending on age, sex, and familial longevity, alterations in thyroid status occur frequently and often co-occur with differences in other hormonal axes. However, studies that explore the effects of thyroid status modulation on other hormonal axes remain scarce. We aimed to determine the effects of thyroid status modulation on prolactin, IGF-1, cortisol, LH, testosterone, and SHBG levels. We also explored whether effects differed depending on type of challenge, sex, and familial longevity., Methods: Data were gathered from two single-arm challenge studies comprising an intramuscular injection of 0.1 mg recombinant human TSH (rhTSH, N = 29) or 100 µg T3 orally (N = 27) in healthy older individuals. Changes in hormone concentration profiles relative to baseline were determined for 4 and 5 days, respectively., Results: IGF-1 increased with a maximum of 6.3% (SEM = 1.6%, p = 0.002) in the rhTSH challenge and 8.8% (SEM = 1.6%, p < 0.001) in the T3 challenge, while LH (19.3% [SEM = 6.6%, p = 0.048]), testosterone (13.8% [SEM = 4.7%, p = 0.048]), and SHBG (11.8% [SEM = 3.5%, p = 0.02]) increased significantly in the T3 challenge only. Moreover, prolactin significantly decreased in both rhTSH and T3 challenges (-8.8% [SEM = 3.4%, p = 0.048] and -12.0% [3.3%, p = 0.004], respectively) as did cortisol (-14.8% [SEM = 3.6%, p < 0.001] and -15.6% [SEM = 3.5%, p < 0.001]). There was no significant interaction with type of challenge, sex, or familial longevity, except for prolactin in the rhTSH challenge (p = 0.004) which decreased significantly in men only., Conclusions: Upon modulation of thyroid status, changes were observed in IGF-1, prolactin, and cortisol. In the T3 challenge, LH, testosterone, and SHBG increased in men. Observed changes are hypothesized to be driven by (f)T3., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Background: Coronary artery disease (CAD) comprises one of the leading causes of morbidity and mortality both in the European population and globally. All established clinical risk stratification scores and models require blood lipids and physical measurements. The latest reports of the European Commission suggest that attracting health professionals to collect these data can be challenging, both from a logistic and cost perspective, which limits the usefulness of established models and makes them unsuitable for population-wide screening in resource-limited settings, i.e., rural areas. Therefore, the aim of this study was to develop and externally validate a questionnaire-based risk stratification model on a population scale at minimal cost, i.e., the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) to stratify the 10-year incidence of coronary artery disease., Methods: Cox proportional hazards (CoxPH) and Cox gradient boosting (CoxGBT) models were trained with 10-fold cross-validation using combinations of ten questionnaire variables on the White population of the UK Biobank (n = 448,818) and internally validated the models in all ethnic minorities (n = 27,433). The Lifelines cohort was employed as an independent external validation population (n = 97,770). Additionally, we compared QUES-CAD's performance, containing only questionnaire variables, to clinically established risk prediction tools, i.e., Framingham Coronary Heart Disease Risk Score, American College of Cardiology/American Heart Association pooled cohort equation, World Health Organization cardiovascular disease risk charts, and Systematic Coronary Risk Estimation 2 (SCORE2). We conducted partial log-likelihood ratio (PLR) tests and C-index comparisons between QUES-CAD and established clinical prediction models., Findings: In the external validation set, QUES-CAD exhibited C-index values of CoxPH: 0.692 (95% Confidence Interval [CI]: 0.673-0.71) and CoxGBT: 0.699 (95% CI: 0.681-0.717) for the male population and CoxPH: 0.771 (95% CI: 0.748-0.794) and CoxGBT: 0.759 (95% CI: 0.736-0.783) for the female population. The addition of measurement-based variables and variables that require a prior medical examination (i.e., insulin use, number of treatments/medications taken, prevalent cardiovascular disease [other than CAD, and stroke diagnosed by a doctor]) and the further addition of biomarkers/other measurements (i.e., high-density lipoprotein [HDL] cholesterol, total cholesterol, and glycated haemoglobin) did not significantly improve QUES-CAD's performance in most instances. C-index comparisons and PLR tests showed that QUES-CAD performs and fits the data at least as well as the clinical prediction models., Interpretation: QUES-CAD performs comparably to established clinical prediction models and enables a population-wide identification of high-risk individuals for CAD. The model developed and validated herein relies solely on ten questionnaire variables, overcoming the limitations of existing models that depend on physical measurements or biomarkers., Funding: University Medical Center Groningen., Competing Interests: Declaration of interests PF, SvD, and JCF are employed by Ancora Health B.V. and own shares of Ancora Health B.V. BHRW sits on the medical advisory board of Ancora Health B.V., without being compensated for this position. All other authors have no conflict of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)