Evidence map›Paper›PMID 39742176›Full record

ReviewCureus2024

A Narrative Review of the Role of Blood Biomarkers in the Risk Prediction of Cardiovascular Diseases.

Lavanya Garady, Ashok Soota, Yogesh Shouche, Komal Prasad Chandrachari, Srikanth K V, Prasan Shankar, Sanketh V Sharma, Kavyashree C, Shrutika Munnyal, Ahalya Gopi and 1 more

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. A sex-specific genome-wide association study of blood lipid levels in All of Us.medRxiv : the preprint server for health sciences · 2025
    Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Lavanya GaradyPublic Health Sciences, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Ashok SootaInformation Technology, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Yogesh ShoucheMicrobiology, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Komal Prasad ChandrachariNeurosurgery, Narayana Institute of Neurosciences/Mazumdar Shaw Medical Center, Bengaluru, IND.
Srikanth K VCardiology, Narayana Institute of Cardiac Sciences, Bengaluru, IND.
Prasan ShankarAyurvedic Medicine, Institute of Ayurveda and Integrative Medicine (I-AIM) Healthcare Center, The University of Trans-Disciplinary Health Sciences and Technology, Bengaluru, IND.
Sanketh V SharmaAyurvedic Medicine, The University of Trans-Disciplinary Health Sciences and Technology, Bengaluru, IND.
Kavyashree CPublic Health Sciences, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Shrutika MunnyalPublic Health Sciences, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Ahalya GopiPublic Health Sciences, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.
Azad DevyaniPublic Health Sciences, Scientific Knowledge for Ageing and Neurological Ailments (SKAN) Research Trust, Bengaluru, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease (CVD) is a global health crisis and a leading cause of morbidities and mortalities. Biomarkers whose evaluation would allow the detection of CVD at an early stage of development are actively sought. Biomarkers are objectively measured as indicators of health, disease, or response to an exposure or intervention, including therapeutic interventions. Hence, this review aims to identify biomarkers that can help predict CVD risk in the healthy population. This helps with risk prediction and is crucial for advancing preventive cardiology and improving clinical outcomes in a wide range of patient populations. Biomarkers such as atherogenic lipoproteins, fibrinogen, homocysteine, and thyroid-stimulating hormone (TSH) have been linked to CVD risk factors, including dyslipidemia, hypertension, diabetes, and obesity. When combined with conventional biomarkers, inflammatory markers such as C-reactive protein (CRP) can enhance risk prediction. However, biomarkers such as high-sensitivity troponin T (hsTnT) and N-terminal proBNP (NT-proBNP) are widely used as diagnostic biomarkers for heart failure (HF) and cardiac dysfunction, as they are released only after one to two hours of cardiovascular event occurrence. Myeloperoxidase (MPO) and procalcitonin (PCT) have developed into promising new biomarkers for the early detection of systemic bacterial infections as inflammatory markers, which are better diagnostic tools than screening. Combining biomarkers can improve test accuracy, but the best combinations for diagnosis or prognosis must be identified.

Indexed as

blood biomarkerscardiovascular disease (cvd)myocardial infarctionrisk predictionstroke

Identifiers

PMID39742176
PMCPMC11688159

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.