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Record W4402091390 · doi:10.1002/ptr.8313

Polyphenols in clinical trials: Current trends

2024· letter· en· W4402091390 on OpenAlexaboutno aff
Francisco Alejandro Lagunas‐Rangel

Bibliographic record

VenuePhytotherapy Research · 2024
Typeletter
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsnot available
Fundersnot available
KeywordsPolyphenolBioavailabilityGentisic acidBiologyBiochemistryChemistryFood sciencePharmacologyAntioxidant

Abstract

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Polyphenols are natural compounds abundant in plants and currently of great interest to the scientific community because of their potential health benefits (Lagunas-Rangel & Bermúdez-Cruz, 2020). These compounds are commonly found in a wide variety of plant-based foods, such as fruits, vegetables, and whole grains. They are also present in beverages made from these plants, such as tea, chocolate and wine (Rana et al., 2022). Currently, more than 500 diverse polyphenols have been identified in a broad spectrum of more than 400 foods. These compounds are abundantly synthesized as part of the secondary metabolism of plants through the shikimate pathway or the polyketide pathway (Shen et al., 2022). Some polyphenols play essential roles in plant physiological functions, others serve in defense mechanisms against various stress factors and stimuli such as soil, water, and light conditions (Marranzano et al., 2019). Polyphenols are capable of regulating numerous physiological processes, such as cellular redox potential, enzyme activity, cell proliferation, and signal transduction pathways (Lagunas-Rangel, 2023). Despite their pivotal role, it is important to recognize that polyphenols often have low oral bioavailability. This is mainly due to their extensive biotransformation, facilitated by phase I and phase II reactions in enterocytes and liver, as well as interactions with the intestinal microbiota. However, despite these difficulties, its metabolites play an important role in providing health benefits (Luca et al., 2020). Polyphenols have been associated with a lower risk of stroke, myocardial infarction, and diabetes, along with improvements in various health markers, such as lipid profiles, blood pressure, insulin sensitivity, and systemic inflammation. In particular, the flavonoid quercetin and the stilbene resveratrol stand out for their positive effects on cardiometabolic health. Finally, although polyphenols have been associated with an increase in cerebral blood flow that could have a benefit on cognition, this remains doubtful with the available evidence (Fraga et al., 2019). Polyphenols have a wide range of chemical structures, which has led to their classification into different groups. The four main families of polyphenols are flavonoids, lignans, stilbenes, and phenolic acids (Tsao, 2010). In this context, the present study aimed to investigate the number and characteristics of clinical studies conducted on polyphenols, with special attention to the identification of trends in their research. Several polyphenols were identified in the natural products section of the International Union of Basic and Clinical Pharmacology (IUPHAR)/British Pharmacological Society (BPS) Guide to Pharmacology (Harding et al., 2024). Subsequently, a comprehensive analysis of clinical trials included in the ClinicalTrials.gov database (Zarin et al., 2016) was conducted for these polyphenols. The titles and full texts of all identified clinical trials (up to April 2024) were reviewed to collect relevant data, including the type of polyphenol used, the countries participating in the study, the status of the study, and the medical context of the research. In addition, efforts were made to identify and eliminate any duplicate studies or erroneous results. Overall, 1258 clinical trials studying the actions of polyphenols in different contexts were identified (Table 1), of which the majority (60.02%) were completed studies (Figure 1a). The main subgroups of polyphenols studied were flavanols (37.36%), stilbenes (16.93%), and other polyphenols (39.19%) (Figure 2). In particular, curcumin and resveratrol became the most investigated polyphenols, together accounting for 43.8% of all polyphenol-related studies. It should be noted that the United States leads the research on polyphenols, with almost one third of the studies (33.06%), followed by Italy (5.64%), the United Kingdom (4.53%), Canada (3.66%), and Egypt (3.34%) (Figure 1b). Regarding the objectives of clinical studies, a considerable portion is devoted to cancer research (18.20%), closely followed by research on the safety and pharmacokinetics of polyphenol use (14.55%). Other notable areas are research on inflammatory processes (9.78%), cardiovascular diseases (7.15%), diabetes (6.36%), and obesity (5.41%) (Figure 1c). Clinical trials within the flavonoid subclass cover a number of compounds (Figure 2). For example, assays were identified for flavonols (27.87%) such as quercetin, myricetin, and kaempferol; isoflavonoids (6.38%) such as daidzein; flavones (7.66%) such as nobiletin, luteolin, baicalein, and apigenin; dihydrochalcones (0.21%) such as phloretin; and flavanols (57.87%) such as catechin, epicatechin, epicatechin gallate, epigallocatechin, epigallocatechin gallate, theaflavin, and theaflavin gallate. In particular, no clinical studies on anthocyanins, chalcones, dihydroflavonols, or flavanones were found. Notably, research on these compounds is mainly focused on cancer (27%), safety and pharmacokinetics (16%), cardiovascular disease (8%), obesity (8%), and inflammation (7%). Within the subgroup of phenolic acids, clinical trials have been conducted with several compounds (Figure 2). For example, trials were identified for hydroxybenzoic acids (79.25%) such as gallic acid, ellagic acid, and benzoic acid, as well as for hydroxycinnamic acids (20.75%) such as verbascoside, rosmarinic acid, and caffeic acid. However, no clinical studies were found for hydroxyphenylacetic, hydroxyphenylpropanoic, or hydroxyphenylpentanoic acids within this section. Research on these compounds is mainly focused on safety and pharmacokinetics (37%), followed by cancer (18%), inflammation (7%), obesity (7%), and diabetes (6%). Within the lignan subgroup (0.48%), only sesamin was identified, while resveratrol was found within the stilbene subgroup (16.93%). The few studies on lignans predominantly addressed cardiovascular and coronary heart disease, along with influenza infection. Meanwhile, research on stilbenes (entirely resveratrol) explored various aspects such as safety and pharmacokinetics (14.83%), diabetes (12.17%), cardiovascular disease (9.13%), obesity (8.37%), inflammation (8.37%), and cancer (7.22%). Finally, other polyphenols considered in the analysis include curcuminoids (68.56%) such as curcumin, furanocoumarins (0.41%) such as bergaptene, hydroxybenzaldehydes (1.62%) such as vanillin, hydroxyphenylpropenes (18.26%) such as eugenol and gingerol, phenolic terpenes (7.30%) such as thymol and carvacrol, and tyrosols (3.85%) such as oleuropein. Most of the clinical studies on these compounds focus mainly on cancer (31%), safety and pharmacokinetics (13%), inflammation (12%), cardiovascular disease (8%), and diabetes (7%) (Figure 2). Curcumin and resveratrol are the most researched polyphenols, accounting for 43.8% of all studies related to polyphenols. Quercetin follows as the third most studied polyphenol, with 9.7% of the clinical studies focused on it. Figure 3 illustrates the main topics of the clinical trials related to these three compounds and indicates the phases of the trials. In summary, polyphenols are among the most studied natural compounds in clinical trials, which have evaluated their efficacy in various conditions such as cancer, cardiovascular disease, diabetes, obesity, and inflammation, among many others. Even in the midst of the COVID-19 pandemic, some were considered as prophylactic measures to prevent SARS-CoV-2 infection and as potential treatment adjuvants. Thus, its broad spectrum of health benefits has attracted a great deal of attention in the scientific community. In general, its metabolic benefits often outweigh its potential in the fight against cancer, mainly because reaching the high concentrations needed for the latter objective is hampered by its low bioavailability. Francisco Alejandro Lagunas-Rangel: Conceptualization; data curation; formal analysis; investigation; writing – original draft; writing – review and editing. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The author declares no conflict of interest. Data available on request from the author.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.012
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.508
GPT teacher head0.599
Teacher spread0.091 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations2
Published2024
Admission routes1
Has abstractyes

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