Diaphragm Involvement in Heart Failure: Mere Consequence of Hypoperfusion or Mediated by HF-Related Pro-inflammatory Cytokine Storms?

Spießhoefer, Jens; Boentert, Matthias; Tuleta, Izabela; Giannoni, Alberto; Langer, Daniël; Kabitz, Hans‐Joachim

doi:10.3389/fphys.2019.01335

Cited by 9 publications

(16 citation statements)

References 33 publications

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“…Measurement of twPdi is considered the gold standard of respiratory muscle strength testing because it overcomes most of the technical flaws associated with volitional tests such as forced vital capacity (FVC) or maximum inspiratory pressure (PI max ) [7, 8]. Despite progress in the understanding of both the prevalence and clinical significance of diaphragm involvement in HFrEF, underlying mechanisms are still not well understood [9-13].…”

Section: Introductionmentioning

confidence: 99%

“…Chronic HF is associated with morphologic, histologic, and metabolic alterations of skeletal muscle. Loss of muscle mass is present even in non-cachectic patients with HF [9-13]. Probably dependent on disease severity, histologic abnormalities may include decreased capillary density, muscle fiber atrophy, and reduced electromyographic activity [9-13].…”

Section: Introductionmentioning

confidence: 99%

“…Loss of muscle mass is present even in non-cachectic patients with HF [9-13]. Probably dependent on disease severity, histologic abnormalities may include decreased capillary density, muscle fiber atrophy, and reduced electromyographic activity [9-13]. Metabolic changes include hypoperfusion of muscle tissue and impairment of oxidative and glycolytic pathways [9-13].…”

Section: Introductionmentioning

confidence: 99%

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Heart Failure Results in Inspiratory Muscle Dysfunction Irrespective of Left Ventricular Ejection Fraction

Spießhoefer

Henke²,

Kabitz

et al. 2020

Respiration

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Background: Exercise intolerance in heart failure with reduced ejection fraction (HFrEF) or heart failure with preserved ejection fraction (HFpEF) results from both cardiac dysfunction and skeletal muscle weakness. Respiratory muscle dysfunction with restrictive ventilation disorder may be present irrespective of left ventricular ejection fraction and might be mediated by circulating pro-inflammatory cytokines. Objective: To determine lung and respiratory muscle function in patients with HFrEF/HFpEF and to determine its associations with exercise intolerance and markers of systemic inflammation. Methods: Adult patients with HFrEF (n = 22, 19 male, 61 ± 14 years) and HFpEF (n = 8, 7 male, 68 ± 8 years) and 19 matched healthy control subjects underwent spirometry, measurement of maximum mouth occlusion pressures, diaphragm ultrasound, and recording of transdiaphragmatic and gastric pressures following magnetic stimulation of the phrenic nerves and the lower thoracic nerve roots. New York Heart Association (NYHA) class and 6-min walking distance (6MWD) were used to quantify exercise intolerance. Levels of circulating interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) were measured using ELISAs. Results: Compared with controls, both patient groups showed lower forced vital capacity (FVC) (p < 0.05), maximum inspiratory pressure (PImax), maximum expiratory pressure (PEmax) (p < 0.05), diaphragm thickening ratio (p = 0.01), and diaphragm strength (twitch transdiaphragmatic pressure in response to supramaximal cervical magnetic phrenic nerve stimulation) (p = 0.01). In patients with HFrEF, NYHA class and 6MWD were both inversely correlated with FVC, PImax, and PEmax. In those with HFpEF, there was an inverse correlation between amino terminal pro B-type natriuretic peptide levels and FVC (r = −0.77, p = 0.04). In all HF patients, IL-6 and TNF-α were statistically related to FVC. Conclusions: Irrespective of left ventricular ejection fraction, HF is associated with respiratory muscle dysfunction, which is associated with increased levels of circulating IL-6 and TNF-α.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heart Failure Results in Inspiratory Muscle Dysfunction Irrespective of Left Ventricular Ejection Fraction

Spießhoefer

Henke²,

Kabitz

et al. 2020

Respiration

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“…Chronic systemic inflammation causes coronary microvascular endothelial inflammation, leading to interstitial fibrosis and cardiac hypertrophy (2,3), which ultimately leads to diastolic dysfunction and development of HFpEF. Recently, Spiesshoefer et al 2019, have speculated that respiratory muscle function may be affected in HFpEF by inflammatory cytokines (23), however, inflammatory markers were not measured in our retrospective study. Arterial HTN is the most prevalent comorbidity in HFpEF, and is thought to induce HFpEF through microvascular inflammation (3).…”

Section: Discussionmentioning

confidence: 62%

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Kaptein

Karagodin²,

Zuo

et al. 2020

Preprint

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Background Subclinical dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF.Methods Clinical, laboratory, and echocardiographic data were retrospectively collected for 81 patients without HF and 81 matched patients with HFpEF at the time of first documentation of subclinical diastolic dysfunction. Density-based clustering or hierarchical clustering to group patients based on 65 total variables including 19 categorical and 46 numerical variables. Logistic regression analysis was conducted on the entire study population as well as each individual cluster to identify independent predictors of HFPEF.Results Unsupervised clustering identified 3 subgroups which differed in gender composition, severity of cardiac hypertrophy and aortic stenosis, NT-proBNP, percentage of patients who progressed to HFpEF, and timing of disease progression from diastolic dysfunction to HFpEF to death. Clusters that had higher percentages of women had progressively milder cardiac hypertrophy, less severe aortic stenosis, lower NT-proBNP, were diagnosed at an older age with HFpEF, and survived to an older age.Independent predictors of HFpEF for the entire cohort included diabetes, chronic kidney disease, atrial fibrillation, and diuretic use, with additional predictive variables found for each cluster.Conclusions Cluster analysis can identify phenotypically distinct subgroups of patients with diastolic dysfunction. Clusters differ in HFpEF and mortality outcome. In addition, the variables that correlate with and predict HFpEF outcome differ among clusters.

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“…Chronic systemic inflammation causes coronary microvascular endothelial inflammation, leading to interstitial fibrosis and cardiac hypertrophy [ 2 , 3 ], which ultimately leads to diastolic dysfunction and development of HFpEF. Recently, Spiesshoefer et al 2019, have speculated that respiratory muscle function may be affected in HFpEF by inflammatory markers [ 19 ], however these were not measured in our study. Arterial HTN is the most prevalent comorbidity in HFpEF, and is thought to induce HFpEF through microvascular inflammation [ 3 ].…”

Section: Discussionmentioning

confidence: 65%

Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning

Kaptein

Karagodin

Zuo

et al. 2020

BMC Cardiovasc Disord

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Background: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF. Methods: Clinical, laboratory, and echocardiographic data were retrospectively collected for 81 patients without HF and 81 matched patients with HFpEF at the time of first documentation of subclinical diastolic dysfunction. Densitybased clustering or hierarchical clustering to group patients was based on 65 total variables including 19 categorical and 46 numerical variables. Logistic regression analysis was conducted on the entire study population as well as each individual cluster to identify independent predictors of HFpEF. Results: Unsupervised clustering identified 3 subgroups which differed in gender composition, severity of cardiac hypertrophy and aortic stenosis, NT-proBNP, percentage of patients who progressed to HFpEF, and timing of disease progression from diastolic dysfunction to HFpEF to death. Clusters that had higher percentages of women had progressively milder cardiac hypertrophy, less severe aortic stenosis, lower NT-proBNP, were diagnosed at an older age with HFpEF, and survived to an older age. Independent predictors of HFpEF for the entire cohort included diabetes, chronic kidney disease, atrial fibrillation, and diuretic use, with additional predictive variables found for each cluster. Conclusions: Cluster analysis can identify phenotypically distinct subgroups of patients with diastolic dysfunction. Clusters differ in HFpEF and mortality outcome. In addition, the variables that correlate with and predict HFpEF outcome differ among clusters.

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Diaphragm Involvement in Heart Failure: Mere Consequence of Hypoperfusion or Mediated by HF-Related Pro-inflammatory Cytokine Storms?

Cited by 9 publications

References 33 publications

Heart Failure Results in Inspiratory Muscle Dysfunction Irrespective of Left Ventricular Ejection Fraction

Heart Failure Results in Inspiratory Muscle Dysfunction Irrespective of Left Ventricular Ejection Fraction

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning

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