Evidence map›Paper›PMID 38550938›Full record

ReviewCambridge prisms. Precision medicine2023

Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.

Sachin Aryal, Ishan Manandhar, Xue Mei, Beng S Yeoh, Ramakumar Tummala, Piu Saha, Islam Osman, Jasenka Zubcevic, David J Durgan, Matam Vijay-Kumar and 1 more

Open access · diamondAbstract readReview
In one paragraph

Review in Cambridge prisms. Precision medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. 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

11 authors at 2 institutions in 1 country.

Sachin AryalCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.ORCID https://orcid.org/0000-0002-1939-6338
Ishan ManandharCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Xue MeiCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Beng S YeohCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Ramakumar TummalaCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Piu SahaCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Islam OsmanCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Jasenka ZubcevicCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
David J DurganIntegrative Physiology & Anesthesiology, Baylor College of Medicine, Houston, TX, USA.
Matam Vijay-KumarCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
Bina JoeCenter for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
University of Toledo · USBaylor College of Medicine · US

Funding

Genetic, Epigenetic and Dietary Salt effects on Microbiota and HypertensionR01HL143082 · NHLBI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI JOE, BINA · 2018 to 2021
$2.6M
Neural mechanisms of host-microbiota interaction in hypertension: a potential for bio-electronic medicineR01HL152162 · NHLBI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI ZUBCEVIC, JASENKA · 2021 to 2025
$2.2M
EXAMINING THE ROLE OF GUT DYSBIOSIS IN OBSTRUCTIVE SLEEP APNEA INDUCED HYPERTENSION.R01HL134838 · NHLBI · BAYLOR COLLEGE OF MEDICINE · PI DURGAN, DAVID J · 2018 to 2022
$2.0M
Interplay between Dietary Fiber and Gut Microbiota in Hepatocellular CarcinomaR01CA219144 · NCI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI VIJAY-KUMAR, MATAM · 2017 to 2021
$1.6M
YAP1, neointima formation, and blood pressure regulationR00HL153896 · NHLBI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI OSMAN, ISLAM · 2023 to 2025
$747k
NCI NIH HHS R01 CA219144NHLBI NIH HHS R00 HL153896NHLBI NIH HHS R01 HL134838NHLBI NIH HHS R01 HL143082NHLBI NIH HHS R01 HL152162
6 · The paper itself

Abstract

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.

Indexed as

genome-based risk scoresgut microbiotahigh blood pressureMachine learningpersonalized medicine

Identifiers

PMID38550938
PMCPMC10953772
OpenAlexW4376955456

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.