Evidence map›Paper›PMID 39838689›Full record

ReviewCurrent hypertension reviews2025

Cross-Section of Hypertensive Molecular Signaling Pathways: Understanding Pathogenesis and Identifying Improved Drug Targets.

Jeyanthi Sankar, Kannan Rajendran, Ling Shing Wong, Karthikeyan Muthusamy

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current hypertension reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Jeyanthi SankarDepartment of Bioinformatics, Pharmacogenomics and CADD Lab, Alagappa University, Karaikudi, Tamil Nadu, India.ORCID 0009-0004-1116-6580
Kannan RajendranDepartment of General Medicine, Saveetha Medical College & Hospital, Saveetha University, Thandalam, Chennai, Tamil Nadu, India.
Ling Shing WongFaculty of Health and Life Sciences, INTI International University, Nilai, Negeri Sembilan, 71800, Malaysia.
Karthikeyan MuthusamyDepartment of Bioinformatics, Pharmacogenomics and CADD Lab, Alagappa University, Karaikudi, Tamil Nadu, India.ORCID 0000-0001-7755-3381

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension is a chronic medical state and a major determining factor for cardiovascular and renal diseases. Both genetic and non-genetic factors contribute to hypertensive conditions among individuals. The Renin-Angiotensin-Aldosterone System (RAAS) is a major genetic target for the anti-hypertension approach. The majority of classical antihypertensive drugs were mainly focused on the RAAS signaling pathways. Though these antihypertensive drugs control Blood Pressure (BP), they have mild to severe life-threatening effects. Unrevealing effective hypertensive targets for BP management is essential. The effective targets could emerge either from RAAS-dependent or RAAS-independent pathways and/or through the cross-talks among them. Analyzing the physiopathological mechanisms of hypertension has the benefit of understanding the interactions between these systems which helps in better understanding of drug targets and the importance of emergence of novel therapeutics. This review is about the signaling pathways involved in hypertension pathogenesis and their cross-talks and it contributes to a better understanding of the etiology of hypertension.

Indexed as

Antihypertensive AgentsBlood PressureHypertensionRenin-Angiotensin SystemSignal TransductionAnimalsHumansMolecular Targeted TherapyAntihypertensive Agentscross-talk.GPCRsHypertensionnovel approachespharmacogenomicsRAAS

Identifiers

PMID39838689

What Socratic holds

Textmetadata
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.