The Role of Obesity in Cardiomyopathy and Nephropathy
Bibliographic record
Abstract
The growing incidence of obesity and related complications such as cardiomyopathy and nephropathy remains a global health challenge. Many pathophysiological factors including inflammation, oxidative stress and endothelial dysfunction are implicated in obesity- induced abnormalities in the heart and kidney. Moreover, obesity and nutrient-overload are associated with the activation of different inflammatory/oxidative signaling pathways such as endoplasmic reticulum stress, nuclear factor-kappaB (NF-κB), toll-like-receptor-4 (TLR4) and the renin-angiotensin-aldosterone system (RAAS). The pathophysiological role of RAAS, TLR4 and NF-κB in perturbing physiological milieu is well acknowledged. Several pharmacological agents have been formulated to target one or more of these pathways. Although significant strides have been made in elucidating mechanisms implicated in obesity-related cardio-renal diseases, much still has to be done. The pathophysiology of cardiomyopathy and nephropathy is complex and multifaceted. Besides NF-κ B, TLR4, RAAS and inflammatory mediators such as cytokines and chemokines, a wide spectrum of different factors including, the environment, diets, lifestyles, genetics and epigenetics are also involved. With such multifactorial etiology, it remains a daunting challenge to identify the factor(s) that initiate the activation and propagation of adverse stimuli that eventually lead to cardiomyopathy and/or nephropathy in obese individuals. Similarly, the mechanisms of such activation and propagation should be clearly elucidated. Should these hurdles be overcome, there would be a greater likelihood for the development of more-effective therapeutic strategies for the prevention, treatment and management of obesity-induced cardiomyopathy and nephropathy. The present review examines the role of inflammation, oxidative stress and endothelial dysfunction in obesity-induced abnormalities in heart and kidney.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".