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We investigated the associations of low handgrip strength (HGS, i.e., a marker of muscular fitness) with liver fat content (LFC) and serum liver enzymes in a population‐based setting. We used data from 2700 participants (51.7% women), aged 21–90 years, from two independent cohorts of the population‐based Study of Health in Pomerania (SHIP‐START‐2 and SHIP‐TREND‐0). Cross‐sectional, multivariable adjusted regression models were performed to examine the associations of HGS with LFC, measured by magnetic resonance imaging and serum liver enzymes. We found significant inverse associations of HGS with both LFC and serum liver enzymes. Specifically, a 10‐kg lower HGS was associated with a 0.59% (95% confidence interval [CI]: 0.24–0.94; p = 0.001) higher LFC, a 0.051 µkatal/L (95% CI: 0.005–0.097; p = 0.031) higher gamma‐glutamyltransferase (GGT) concentration and a 0.010 µkatal/L (95% CI: 0.001–0.020; p = 0.023) higher aspartate aminotransferase (AST) concentration. The adjusted odds‐ratio for prevalent hepatic steatosis (defined by a MRI‐PDFF ≥5.1%) per 10‐kg lower HGS was 1.21 (95% CI: 1.04–1.40; p = 0.014). When considering only obese individuals, those with low HGS had a 1.58% (95% CI: 0.18–2.98; p = 0.027) higher mean LFC and higher chance of prevalent hepatic steatosis (adjusted OR 1.74, 95% CI: 1.15–2.62; p = 0.009) compared to individuals with high HGS. We found similar associations in individuals with overweight, but not in those with normal weight. Lower HGS was strongly associated with both higher LFC and higher serum GGT and AST concentrations. Future studies might clarify whether these findings reflect adverse effects of a sedentary lifestyle or aging on the liver. Highlights: We used magnetic resonance imaging, the most accurate and sensitive noninvasive diagnostic tool for determination of hepatic steatosisLower handgrip strength was strongly associated with both higher liver fat content and higher serum GGT and AST concentrationsEspecially overweight and obese individuals with low handgrip strength had a significant higher risk of prevalent hepatic steatosis [ABSTRACT FROM AUTHOR]

Findings on the association between cardiorespiratory fitness (CRF) and moderate-to-vigorous physical activity (MVPA) may be distorted if patterns of accumulated MVPA over a week exist but are ignored. Our aim was to identify MVPA patterns and to associate them to CRF. Two hundred twenty-four 40-75-year-old adults wore accelerometers for 7 days. CRF was measured by peak oxygen uptake (V'O.sub.2,peak) assessed on a cycle ergometer via standardized cardiopulmonary exercise testing. Growth mixture modeling indicated four MVPA patterns: 'low/stable' (57%, Mean MVPA time (M) = 21 min day.sup.-1), 'medium/stable' (20%, M = 46 min day.sup.-1), 'medium/weekend high' (14%, M = 47 min day.sup.-1), and 'high/weekend low' (9%, M = 71 min day.sup.-1). V'O.sub.2,peak was higher for persons with 'high/weekend low' and 'medium/weekend high' patterns compared to 'low/stable' and 'medium/stable' (p values < 0.001). The same total amount of MVPA may have greater benefit if performed on fewer days during the week but with a longer duration than if performed every day but with a lower duration., Author(s): Sophie Baumann [sup.1] [sup.2] [sup.3] , Diana Guertler [sup.2] [sup.3] , Franziska Weymar [sup.2] [sup.3] [sup.4] , Martin Bahls [sup.3] [sup.5] , Marcus Dörr [sup.3] [sup.5] , Neeltje van [...]

The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG. A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC). NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death. Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice. [ABSTRACT FROM AUTHOR]

Previous studies provided evidence for the importance of cardiac structure abnormalities, in particular greater left ventricular (LV) mass, for brain aging, but longitudinal studies are lacking to date. We included 926 individuals (median age 48 years; 53% women) from the TREND cohort of the Study of Health in Pomerania (SHIP) without reduced ejection fraction or a history of myocardial infarction. LV mass index (LVMI) was determined by echocardiography at baseline. Brain morphometric measurements were derived from magnetic resonance images at baseline and 7‐year follow‐up. Direct effects of baseline LVMI on brain morphometry at follow‐up were estimated using linear regression models with adjustment for baseline brain morphometry. At baseline, median LVMI was 40 g/m2.7 and 241 individuals (26%) met the criterion of LV hypertrophy. After correction for multiple testing, baseline LVMI was directly associated with reduced global cortical thickness and increased cortical brain age at follow‐up independent from hypertension and blood pressure. Exposure‐outcome relations were nonlinear and significantly stronger in the upper half of the exposure distribution. Specifically, an increase in baseline LVMI from the 50% quantile to the 95% quantile was associated additional 2.7 years (95% confidence interval = [1.5 years, 3.8 years]) of cortical brain age at follow‐up. Additional regional analyses yielded bilateral effects on multiple frontal cortical regions. Our findings highlight the role of cardiac structure in brain aging. LVMI constitutes an easily measurable marker that might help to identify persons at risk for cognitive impairment and dementia. [ABSTRACT FROM AUTHOR]

This review critiques the literature supporting clinical assessment and management of cardiovascular disease and cardiovascular disease risk stratification with brachial-ankle pulse wave velocity (baPWV). First, we outline what baPWV actually measures—arterial stiffness of both large central elastic arteries and medium-sized muscular peripheral arteries of the lower limb. Second, we argue that baPWV is not a surrogate for carotid-femoral pulse wave velocity. While both measures are dependent on the properties of the aorta, baPWV is also strongly dependent on the muscular arteries of the lower extremities. Increased lower-extremity arterial stiffness amplifies and hastens wave reflections at the level of the aorta, widens pulse pressure, increases afterload, and reduces coronary perfusion. Third, we used an established evaluation framework to identify the value of baPWV as an independent vascular biomarker. There is sufficient evidence to support (1) proof of concept; (2) prospective validation; (3) incremental value; and (4) clinical utility. However, there is limited or no evidence to support (5) clinical outcomes; (6) cost-effectiveness; (8) methodological consensus; or (9) reference values. Fourth, we address future research requirements. The majority of the evaluation criteria, (1) proof of concept, (2) prospective validation, (3) incremental value, (4) clinical utility and (9) reference values, can be supported using existing cohort datasets, whereas the (5) clinical outcomes and (6) cost-effectiveness criteria require prospective investigation. The (8) methodological consensus criteria will require an expert consensus statement. Finally, we finish this review by providing an example of a future clinical practice model. [ABSTRACT FROM AUTHOR]