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. 2026 Sep 5;91(9):e71417. doi: 10.1111/1750-3841.71417

Postprandial Blood Glucose Lowering Effect of Urtica cannabina L. and Its Chemical Profiling via UPLC–Triple‐TOF MS/MS

Zhen Li 1,2, Nina Yang 1,2, Mingting Xu 1,2, Luya Wang 1,2, Liying Liu 1,2, Xiaohui Zhao 1, Huilan Yue 1,✉
PMCID: PMC13545549  PMID: 42698298

ABSTRACT

Reducing postprandial blood glucose (PBG) constitutes a core objective in the clinical management of diabetes mellitus. Urtica cannabina L., a traditional medicinal and edible homologous plant, is widely distributed in China; however, research on its pharmacological activity and chemical composition remains relatively scarce. In the present study, the PBG‐lowering potential of U. cannabina L. was confirmed both in vitro and in vivo, and its chemical constituents were preliminarily identified by UPLC‐triple‐TOF‐MS/MS for the first time. Notably, acarbose (8 mg/kg), a clinically approved first‐line α‐glucosidase inhibitor, and UCE20 (20% ethanol extract of U. cannabina L.) at 10 mg/kg both significantly reduced PBG levels. Furthermore, UPLC–triple‐TOF MS/MS analysis combined with correlation analysis has preliminarily indicated that hydroxycinnamoyl quinates and flavonoid C‐glycosides may serve as the primary bioactive components responsible for the PBG‐lowering effect. These findings suggested U. cannabina L. holds potential as a medicinal ingredient or health food to assist in the regulation of PBG levels.

Keywords: α‐glucosidase, flavonoid C‐glycosides, hydroxycinnamoyl quinates, postprandial blood glucose, Urtica cannabina L., UPLC–triple‐TOF MS/MS

1. Introduction

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by sustained hyperglycemia, primarily resulting from absolute or relative insulin deficiency and pancreatic β‐cell dysfunction (Deshmukh and Jain 2015). Driven by environmental shifts, refined dietary patterns, and sedentary lifestyles, DM prevalence has increased steadily worldwide, constituting a major public health challenge (Seuring et al. 2015). The International Diabetes Federation estimates that the global diabetic population will reach 783 million by 2045, marking a 60% increase from 2021 levels (Cho et al. 2018). Postprandial hyperglycemia and glucose variability are key contributors to macrovascular complications, target‐organ dysfunction, and increased mortality in DM (Tripathi and Srivastava 2006). Accordingly, inhibition of α‐glucosidase (α‐Glu), a critical enzyme in intestinal carbohydrate digestion, represents an effective strategy to attenuate postprandial blood glucose (PBG) excursions and improve glycemic control. This approach is particularly relevant in Asian regions where high‐glycemic staples such as rice and wheat predominate, and α‐Glu inhibitors (AGIs) have remained widely used first‐line agents (Derosa and Maffioli 2012). Clinically used AGIs, such as acarbose and voglibose, effectively blunt PBG rises, reduce insulin demand, and lower the risk of hypoglycemic episodes (Alssema et al. 2021; DiNicolantonio et al. 2015; Yang et al. 2019), with potential benefits in delaying chronic diabetic complications through long‐term optimization of postprandial glycemia (Ceriello et al. 2004). However, their broader application is limited by dose‐dependent gastrointestinal adverse effects and clinically relevant drug–drug interactions that may increase hypoglycemia risk when combined with insulin or sulfonylureas (Hedrington and Davis 2019). These limitations underscore the need for alternative AGIs with improved tolerability and safety (Supporting Information).

Natural products have emerged as a promising reservoir of AGIs, often offering favorable safety profiles and multi‐target pharmacological properties compared with synthetic agents (Surya et al. 2014). Increasing evidence indicates that diverse phytochemicals—particularly tannins, phenolic acids, and flavonoids—exhibit robust α‐Glu inhibitory activity and can effectively mitigate postprandial hyperglycemia (Proença et al. 2021; Subhan et al. 2025; Xiao et al. 2015). Thus, systematic discovery of potent and safe AGIs from medicinal plants remains of substantial translational value.

Urtica cannabina L. (genus Urtica, family Urticaceae) is an herbaceous annual or perennial species distributed across temperate and subtropical regions of the Northern Hemisphere (Kregiel et al. 2018). In China, U. cannabina is broadly distributed in the wild, with major occurrence clustered in the North China–Western Plateau ecozone (e.g., Hebei, Sichuan, Qinghai, and Xinjiang) according to GBIF spatial mapping. Urtica species (nettles) are documented in the Compendium of Materia Medica (Bencao Gangmu) as medicinal plants with pungent flavor and warming properties, traditionally used for rheumatism, sprain‐related pain, pruritus, and chronic bronchitis, and also reported to have therapeutic potential in hypertension and diabetes without obvious toxicity or severe adverse effects (Farzami et al. 2003; Gülçin et al. 2004; Riehemann et al. 1999; Tahri et al. 2000). Modern applications further support their nutraceutical value: U. dioica extracts have been developed into commercial supplements, and nettle leaves are widely consumed in traditional foods such as pies and functional porridges with potential metabolic and immunomodulatory benefits (Bhusal et al. 2022). Phytochemical studies indicate that nettles are rich in phenolic acids and flavonoids—rutin, quercetin, and 5‐O‐caffeoylquinic acid being among the dominant constituents—consistent with broad bioactivities including antioxidant, anti‐inflammatory, antimicrobial, and cardioprotective effects (Jan et al. 2016; Tarasevičienė et al. 2023). Pharmacological evidence suggests that multiple nettles possess hypoglycemic and hypolipidemic activities (Dhouibi et al. 2020; A. Li et al. 2023; Zhang et al. 2011; Zhang et al. 2023). Notably, polyphenols and flavonoids from U. dioica have been reported to reduce blood glucose and cardiovascular risk, partly via α‐Glu inhibition (El Haouari and Rosado 2019).

Nevertheless, existing hypoglycemic investigations have largely centered on U. dioica (Ahangarpour and Dianat 2012; Bnouham et al. 2003), while research related to biological activity and chemical composition for U. cannabina remains limited (Long et al. 2020). Herein, this study aimed to systematically evaluate the in vitro α‐Glu inhibitory activity and in vivo PBG‐lowering effects of U. cannabina, as well as identify its major bioactive constituents. These findings offer credible experimental evidence for the further exploitation of U. cannabina as a safe and promising natural source of postprandial hypoglycemic agents.

2. Materials and Methods

2.1. Chemicals and Reagents

All organic solvents used in this experiment were of chromatographic grade and purchased from Chengdu Jinshan Chemical Reagent Co. Ltd. HPD‐600 macroporous adsorption resin, maltose, sucrose, and alloxan were obtained from Solarbio (Beijing, China). α‐Glu was procured from Sigma‐Aldrich (Shanghai, China). Starch and glucose were purchased from Macklin Biochemical Co. Ltd. (Shanghai, China). Acarbose, rutin, and gallic acid were supplied by Yuanye Bio‐Technology Co. Ltd. (Shanghai, China). pNPG (p‐Nitrophenyl‐β‐D‐glucopyranoside) was acquired from Sigma‐Aldrich (St. Louis, MO, USA). Glucose assay kit was sourced from Zhongsheng Beikong Bio‐Technology Co. Ltd. (Beijing, China).

2.2. Experimental Animals

Eighty male Kunming mice (16–18 g, 4 weeks old) were purchased from the Xi'an Branch of Chongqing Tengxin Biotechnology Co. Ltd. (China; License No.: SCXK [Jing] 2024‐0001). Animals were maintained under controlled conditions (24 ± 1°C, 50 ± 10% relative humidity) with a 12 h light/dark cycle (lights on 9 a.m.–9 p.m.). Standard chow and water were provided ad libitum, and environmental disturbances were minimized to ensure uniform experimental conditions. At the end of the experiment, all mice were euthanized. All animal procedures were performed in accordance with the ethical guidelines approved by the Institutional Animal Care and Use Committee of the Chinese Academy of Sciences (ethical approval no.: 2024–32).

2.3. Plant Materials and Preparation of Crude Extract

U. cannabina was collected in August 2024 from the Huzhu Beishan Nature Reserve (Qinghai Province, China). The plant material was taxonomically authenticated by Prof. Li‐Juan Mei based on standard morphological characteristics. A voucher specimen (TMR‐2024‐08) was deposited in the Key Laboratory of Tibetan Medicine Research of the Northwest Institute of Plateau Biology.

Naturally air‐dried aerial parts (1 kg) were milled to 80‐mesh powder and extracted with 70% ethanol at a solid‐to‐liquid ratio of 1:20 (w/v) by maceration for 12 h followed by three hot‐reflux extraction cycles at 50°C. The combined extracts were concentrated under reduced pressure at 40°C to obtain 180 g of crude U. cannabina extract (UCE). UCE (180 g) was dissolved in 1800 mL ultrapure water (18.2 MΩ·cm, 25°C, TOC ≦ 10 ppb) and centrifuged (954 × g, 20 min); the supernatant was collected, adjusted to pH 3.72 with 5% HCl, and prepared at a final concentration of 100 mg/mL. The resulting solution was loaded onto an HPD‐600 macroporous adsorption resin column (10 × 95 cm, 1 bed column volume [BV] was equivalent to 7.5 L) preconditioned with 95% ethanol. Gradient elution was then performed at a constant flow rate of 1.5 BV/h using an acidified buffer system (pH 3.1–3.5) with stepwise ethanol concentrations (5%, 20%, 40%, 60%, and 95%; 3 BV each), yielding five fractions designated UCE5 (21.12 g), UCE20 (23.98 g), UCE40 (14.61 g), UCE60 (8.57 g), and UCE95 (9.16 g) (Figure S1).

2.4. Inhibitory Effects on α‐Glu In Vitro

In vitro inhibitory activities against α‐Glu (α‐Glu; sucrase and maltase) were evaluated according to L. Liu et al. (2023). Rat small‐intestinal (RSI) and yeast enzymes, test samples, and substrates were prepared in 0.1 M phosphate‐buffered saline (PBS, pH 6.8). For the sucrase inhibition assay, 50 µL of invertase (diluted 1:8) and UCE fractions were added to ice‐cooled 48‐well plates and pre‐incubated at 37°C with shaking (600 rpm) for 10 min. The reaction was initiated by adding sucrose (final concentration, 50 mM) and allowed to proceed for 20 min, after which it was terminated by heating in a 100°C dry bath. The maltase inhibition assay was conducted in parallel using maltase (diluted 1:7) and maltose as the substrate (final concentration, 1.0 mM). After incubation, glucose production was quantified by adding a glucose assay reagent, and absorbance was measured at 505 nm using a spectrophotometer.

In addition, inhibition of yeast α‐Glu was determined in 96‐well plates using α‐Glu (0.5 U/mL) and p‐nitrophenyl‐β‐D‐glucopyranoside (pNPG, 0.5 mM) as the substrate. The reaction was stopped by adding 0.1 M Na2CO3, and absorbance was recorded at 409 nm. Acarbose served as the positive control. All assays were performed in triplicate, including sample, baseline, positive control, and blank groups. α‐Glu inhibitory activity was expressed as percent inhibition and calculated as follows:

Inhibitionrate%=1−Aexperimental−Abackground/Acontrol−Ablank×100 (1)

2.5. Animal Model and Experimental Design

Experimental mice were acclimated for 1 week under specific pathogen free (SPF) conditions, fasted for 12 h, and then injected with alloxan (60 mg/kg) via the tail vein to establish a diabetic model (Lenzen 2007). This model exhibits impaired postprandial glucose tolerance, making it suitable for evaluating AGIs targeting postprandial hyperglycemia. Fasting blood glucose (FBG) was measured 72 h after alloxan administration, and mice with FBG values of 250–360 mg/dL were considered successfully diabetic and enrolled for subsequent experiments. Oral sucrose, maltose, starch, and glucose tolerance tests were performed as previously described by our group (Yue et al. 2022).

Diabetic mice were randomly assigned to five groups (n = 10) based on FBG and body weight, with no significant differences among groups at baseline. The groups were as follows: model group (3.0 g/kg sucrose/maltose/starch/glucose), acarbose group (8.0 mg/kg acarbose + 3.0 g/kg sucrose/maltose/starch/glucose), and UCE20 low‐, medium‐, and high‐dose groups (10, 20, and 30 mg/kg UCE20, respectively, each combined with 3.0 g/kg sucrose/maltose/starch/glucose). After a 12 h fast (water available ad libitum), baseline blood glucose (0 min) was measured the next morning. Mice then received the corresponding treatments by oral gavage, followed immediately by administration of sucrose, maltose, starch, or glucose. PBG was recorded at 30, 60, and 120 min after dosing.

2.6. Chemical Composition Analysis of UCE20

The chemical constituents of UCE20 were profiled using ultra‐performance liquid chromatography coupled with triple quadrupole time‐of‐flight tandem mass spectrometry (UPLC–triple‐TOF MS/MS). All analyses were performed by Tianjin Booming Biotech Pharmaceutical Co. Ltd.

The ultra‐high performance liquid chromatography system was Waters Acquity UPLC equipped with a binary gradient high‐pressure pump, autosampler, column oven, and photodiode array detector. The mass spectrometer was a Triple TOF 5600 high‐resolution mass spectrometer (AB SCIEX, USA). Separation was performed on an HSS T3 C18 column (2.1 mm × 150 mm, 1.8 µm). Mobile phases A and B consisted of 0.1% formic acid aqueous solution and 0.1% formic acid acetonitrile solution, respectively. The gradient elution program was set as follows: 0 min, 5% B; 17 min, 30% B; 22 min, 95% B; and 23 min, 5% B, at a flow rate of 0.3 mL/min. The column temperature was maintained at 50°C, the detection wavelength was 254 nm, and the injection volume was 3 µL.

Chromatographic signals were simultaneously recorded by a UV detector, while mass spectral data were acquired via electrospray ionization in both positive and negative ion modes. The ion spray voltages were set at 5500 V for positive mode and 4500 V for negative mode. The ion source temperatures were maintained at 600°C (positive ion mode) and 550°C (negative ion mode). The pressures of nebulizing gas (GS1, GS2) and curtain gas (CUR) were 55 and 35 psi, respectively. The declustering potential was 100 V and the focusing potential was 10 V. Data acquisition was performed with full scan MS coupled with information‐dependent acquisition (IDA) to obtain MS/MS spectra. The collision energy was set to 40 ± 20 eV, and the mass range was scanned m/z 100–2000.

2.7. Quantitative Analysis of Chemical Constituents in UCE Fractions

2.7.1. High‐Performance Liquid Chromatography Analysis

To evaluate the separation performance of UCE fractions, an Essentia LC‐16 high‐performance liquid chromatography (HPLC) system (Shimadzu, Kyoto, Japan) was employed. The system consisted of an LC‐16 solvent delivery unit, an in‐line membrane degasser, an SIL‐16 autosampler, a CTO‐16 column oven, and an SPD‐16 dual‐wavelength UV–Vis detector. Chromatographic data were acquired and processed with LabSolutions Essentia software. The UCE fractions were dissolved in methanol to prepare stock solutions (10 mg/mL), filtered through sterile 0.22 µm nylon membrane filters, and analyzed on a Nucifera C18A reversed‐phase column (4.6 mm × 250 mm, 5 µm). The mobile phase consisted of (A) 0.2% (v/v) formic acid in water and (B) acetonitrile, delivered at a flow rate of 1.0 mL/min under the following gradient: 0 min, 5% B; 60 min, 40% B; 100 min, 90% B. After the gradient elution program, a 10 min post‐run time was set to re‐equilibrate the column to the initial mobile phase composition, stabilize column pressure and baseline, and eliminate cross‐interference between consecutive sample injections. The column temperature was maintained at 35°C, UV detection was set at 300 nm, and the injection volume was 20 µL.

2.7.2. Determination of Total Phenolic and Total Flavonoid Contents

Total phenolic content (TPC) and total flavonoid content (TFC) in UCE fractions were determined using modified Folin‐Ciocalteu and NaNO2‐Al(NO3)3 colorimetric methods, respectively, as described by Omer and Mohammed (2024).

For TPC determination, aliquots of the gallic acid standard (0.5–3.0 mL, at 0.5 mL intervals) were brought to 3.0 mL with distilled water, followed by the addition of 0.5 mL Folin–Ciocalteu reagent. After 5 min, 2.0 mL of 7.5% Na2CO3 was added, and the mixture was incubated in the dark for 30 min. The absorbance value was detected using a UV–Vis spectrophotometer at 765 nm.

For TFC determination, aliquots of the rutin standard (0–1.5 mL; prepared in 0.1–0.3 mL increments) were diluted to 1.5 mL with distilled water, then sequentially mixed with 0.25 mL of 5% NaNO2 (6 min), 0.25 mL of 10% Al(NO3)3 (6 min), and 2.5 mL of 4% NaOH to a final volume of 6.25 mL. After incubation for 15 min, absorbance was recorded at 508 nm.

Calibration curves were generated by plotting absorbance against the concentrations of gallic acid (for TPC) or rutin (for TFC), using deionized water as the blank. All measurements were performed in triplicate. Results were expressed as milligrams of gallic acid equivalents (GAE) or rutin equivalents (RE) per gram of sample (mg/g), calculated as:

TPC/TFC=C×V×DF/m (2)

where TPC/TFC represents the total phenolic/flavonoid content (mg per g of UCE fraction), C is the concentration (mg/mL) of gallic acid or rutin determined from the calibration curve, V is the volume of the sample solution, DF is the dilution factor, and m is the mass (g) of the extract.

2.8. Data Processing

All data were analyzed using GraphPad Prism 9.5.0. PBG‐time curves were generated for each group, and the area under the curve (AUC) was calculated using the trapezoidal method. All experiments were independently repeated at least three times. Data are presented as mean ± standard deviation. p <0.05 was considered statistically significant. One‐way analysis of variance (ANOVA) was performed on data from principal component analysis (PCA) and correlation analysis, followed by Fisher's least significant difference (LSD) post‐hoc test.

3. Results and Discussion

3.1. Inhibitory Effects on α‐Glu In Vitro

The five UCE fractions obtained by macroporous resin chromatography were evaluated to identify the fraction with the strongest in vitro α‐Glu inhibitory activity. The inhibitory effects of UCE fractions at different concentrations against sucrase and maltase (from rat small intestine, RSI) as well as yeast α‐Glu were determined, and IC50 values were calculated for each group.

UCE5 and UCE60 showed only weak inhibition of sucrase and maltase (IC50> 1 mg/mL). In contrast, UCE, UCE20, and UCE40 exhibited pronounced, concentration‐dependent inhibition of both enzymes, with IC50 values of 0.763 mg/mL (maltase) and 1.077 mg/mL (sucrase) for UCE; 0.052 mg/mL and 0.088 mg/mL for UCE20; and 0.333 mg/mL and 0.434 mg/mL for UCE40, respectively (Figure 1A,B). Acarbose, used as the positive control, showed IC50 values of 0.020 µg/mL for sucrase and 0.043 µg/mL for maltase (Figure 1D,E).

FIGURE 1.

FIGURE 1

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In vitro α‐Glu inhibitory activity assay from two sources. The IC50 values of UCE fractions against sucrase, maltase (A, B) and α‐Glu (C), acarbose against the three α‐Glu (D–F).

For yeast α‐Glu, UCE20 and UCE95 displayed marked inhibitory activity, with IC50 values of 0.464 and 0.927 mg/mL, respectively (Figure 1C), comparable to that of acarbose (IC50 = 0.450 mg/mL) (Figure 1F). Due to the comparatively low activity of other fractions, their IC50 values were not determined in this study. Notably, UCE20 consistently produced the lowest IC50 values across all three α‐Glu assays, indicating that the major α‐Glu inhibitory constituents of U. cannabina are likely enriched in this fraction.

3.2. Effects of UCE20 on Reducing PBG In Vivo

In vitro α‐Glu screening showed that UCE20 was the most active fraction among all UCE fractions, exhibiting the lowest IC50 values against all three tested α‐Glu. Therefore, UCE20 was administered to alloxan‐induced diabetic mice at low, medium, and high doses (10, 20, and 30 mg/kg; UCE20‐L/M/H) to systematically evaluate its in vivo efficacy in attenuating postprandial hyperglycemia in carbohydrate challenge tests (maltose, sucrose, starch, and glucose).

Following administration of maltose, sucrose, or starch, all UCE20‐treated groups significantly reduced PBG at 30 min compared with the model group, with blood glucose levels trending toward normalization by 120 min. Consistently, all treatment groups exhibited significant reductions in the AUC relative to the model group (Figure 2A–F). After glucose loading, only acarbose and UCE20‐L produced significant decreases in AUC compared with the model group (Figure 2G,H). This indicates that the PBG‐lowering activity of UCE20 is not solely attributed to α‐Glu inhibition, suggesting that additional mechanisms beyond direct α‐Glu inhibition may contribute to its hypoglycemic activity.

FIGURE 2.

FIGURE 2

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Effects of UCE20 on reducing PBG. The glycemic curves (A, C, E, G) and the AUC values (B, D, F, H) of diabetes mice within 120 min after oral sucrose, maltose, starch, and glucose administration, respectively. Data are expressed as the mean ± standard deviation (n = 10). Statistically significant differences compared to the model are indicated by *, **, ***, and **** for p < 0.05, p < 0.01, p < 0.001, and p < 0.0001, respectively.

3.3. Compositional Analysis of UCE20 Chemical Constituents

This study demonstrated that UCE20 exerts strong hypoglycemic activity in vitro and in vivo. To clarify the chemical basis underlying this activity, UCE20 was subjected to preliminary characterization and tentative component identification using UPLC–triple‐TOF MS/MS, in combination with retention‐time comparison, MS/MS fragmentation analysis (TOF MS–product ion–IDA mode), and literature‐supported verification. In total, 18 compounds were tentatively identified in UCE20 (Figure 3A), including benzoic acid derivatives (Compound 2), eight hydroxycinnamoyl quinic acids (Compounds 1, 3, 5, 7, 10, 13, 17, and 19), three hydroxycinnamoyl aspartic acids (Compounds 4, 11, and 12), three hydroxycinnamoyl succinic acids (Compounds 6, 8, and 10), flavonoid C‐glycosides (Compounds 16 and 18), and glucosyl alcohol derivatives (Compound 15) (Figure 3B, Table 1).

FIGURE 3.

FIGURE 3

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Compositional analysis of UCE20. UPLC chromatogram (A) and chemical structures (B).

TABLE 1.

UPLC–triple‐TOF MS/MS identification of phenolic acids and flavonoids in UCE20.

Peak no. RT (min) [M−H]−

Error

(×10−6)

 MS fragments (m/z) Proposed compounds Molecular formula
1 6.37 353.0885 1.98 191.0571 ([M−H−162]−), 179.0350 ([M−H−174]−), 135.0455 ([M−H−174−44]−), 85.0297 Caffeoylquinic acid isomers C16H18O9
2 7.64 137.0257 4.49 108.0215, 92.0270 p‐Hydroxybenzoic acid C7H6O3
3 8.28 337.0932 3.89 191.0562 ([M−H−146]−), 163.0405 ([M−H−174]−), 119.0502 ([M−H−174−44]−), 93.0340 Coumaroylquinic acid isomers C16H18O8
4 8.59 451.1363 — 407.1476 ([M−H−44]−), 335.1253, 292.1203 ([M−H−44−115]−), 173.0722, 132.0303, 88.0404 Aspartic acid derivative —
5 8.94 353.0881 2.50 707.1841 ([2M−H]−), 191.0581 ([M−H−162]−), 85.0303 caffeoylquinic acid isomers C16H18O9
6 9.27 325.0582 3.22 193.0515 ([M−H−132]−), 149.0609 ([M−H−132−44]−), 134.0380 ([M−H−132−44−15]−) Feruloyl dihydroxysuccinic acid C14H14O9
7 9.54 353.0883 1.42 707.1842 ([2M−H]−), 191.0676 ([M−H−162]−), 135.0646 ([M−H−174−44]−), 93.0351 caffeoylquinic acid isomers C16H18O9
8 10.13 499.1465 — 367.0635 ([M−H−132]−), 295.0496, 179.0359 ([M−H−320]−), 135.0460, 134.0378 Caffeoyl dihydroxysuccinic acid derivative —
9 10.48 305.0706 — 225.1131, 96.9602, 79.9568, 59.0141 — —
10 10.79 295.0468 1.05 163.0402 ([M−H−132]−), 119.0503 ([M−H−132−44]−), 87.0088, 59.0144 Coumaroyl dihydroxysuccinic acid C13H12O8
11 11.02 250.0731 — 132.0302 ([M−H−128−), 115.0031, 88.0410, 71.0140 Methylbenzoyl aspartic acid —
12 11.33 308.0775 −3.24 193.0506 ([M−H−115]−), 149.0607 ([M−H−115−44]−), 134.0372 ([M−H−115−44−15]−), 115.0032, 88.0401 Feruloyl aspartic acid C14H15NO7
13 11.78 337.0932 1.89 295.0465, 191.0564 ([M−H−146]−), 163.0386 ([M−H−174]−), 119.0505 ([M−H−174−44]−), 93.0343, 85.0295 Coumaroylquinic acid isomers C16H18O8
14 11.95 520.1464 — 384.0913 ([M−H−136]−), 358.0970 ([M−H−136−26]−), 222.0410 ([M−H−136−26−136]−), 178.0507 ([M−H−136−26−44]−), 135.0450 — —
15 12.22 385.1872 — 431.1915, 223.1344 ([M−H−162]−), 205.1238 ([M−H−180]−), 153.0920, 101.0244, 59.0138 Hydroxy epoxy ionol glucoside C19H30O8
16 12.51 593.1568 2.44 503.1195 ([M−H−90]−), 473.1102 ([M−H−120]−), 395.0773, 383.0779 ([M−H−120−90]−), 353.0691 ([M−H−120−120]−), 297.0777 Apigenin‐di‐C‐glucoside C27H30O15
17 13.00 367.1037 0.44 193.0512 ([M−H−174]−), 173.0466 ([M−H−194]−), 134.0379, 93.0350, 67.0194 Feruloylquinic acid isomers C17H20O9
18 13.65 725.1942 1.65 593.1516 ([M−H−132]−), 473.1085 ([M−H−132−120]−), 353.0666 ([M−H−132−120−120]−) Apigenin‐di‐C‐glucoside arabinoside C32H38O19
19 14.08 335.0773 — 173.0463 ([M−H−162]−), 161.0249 ([M−H−174]−), 133.0296, 93.0346 Quinic acid derivative —
20 14.55 377.1813 — 163.0411 ([M−H−214]−), 119.0499 ([M−H−214−44]−), 93.0356 Coumaric acid derivative —
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Note: “—” represents unknown compounds.

Peak 2 exhibited a deprotonated molecular ion [M−H]− at m/z 137, with characteristic fragment ions [M−H‐29]− at m/z 108 (corresponding to the loss of a formyl radical) and [M−H−45]− at m/z 92 (indicative of a carboxyl group elimination), which led to its identification as p‐hydroxybenzoic acid (Orčić et al. 2014). Mass spectrometric characterization indicated that Peaks 1, 5, and 7 all exhibited deprotonated molecular ions [M−H]− at m/z 353. The diagnostic fragment ions were as follows: [M−H−162]− at m/z 191, which corresponds to the quinic acid moiety; [M−H−174]− at m/z 179, assigned to the caffeic acid moiety; and [M−H−174−44]− at m/z 135, derived from decarboxylated caffeic acid. Based on the fragmentation patterns characteristic of caffeoylquinic acid (CQA) isomers, Peaks 1, 5, and 7 were tentatively identified as CQA isomers (Spinoso‐Castillo et al. 2020).

Peak 3 showed a deprotonated molecular ion [M−H]− at m/z 337, accompanied by characteristic fragment ions: [M−H−146]− at m/z 191, [M−H−174]− at m/z 163, and [M−H−174−44−26]− at m/z 93. These fragments were sequentially attributed to quinic acid (generated via ester bond cleavage), coumaric acid, and the product of coumaric acid undergoing decarboxylation and acetylene elimination (with mass losses of 44 and 26 Da, respectively). Peak 13 exhibited the same deprotonated molecular ion [M−H]− at m/z 337 as Peak 3, but displayed a distinct fragmentation pattern, with [M−H−146]− at m/z 191 as its base peak. Thus, Peaks 3 and 13 were tentatively identified as coumaroylquinic acid isomers (Wang 2023). Peak 17 was tentatively identified as feruloylquinic acid isomers based on the presence of a deprotonated molecular ion peak [M−H]− at m/z 367. In addition, fragment ions at m/z 193 (corresponding to the ferulic acid moiety) were observed, which were attributed to the [M−H−174]− fragment. Another fragment ion at m/z 173, corresponding to the [M−H−194]− species, was also detected, indicating the loss of the ferulic acid moiety (Jaiswal et al. 2011).

Peak 6 exhibited a deprotonated molecular ion [M−H]− at m/z 325, with fragment ions including [M−H−132]− at m/z 193, [M−H−176]− at m/z 149, and [M−‐H−132−44−15]− at m/z 134. These fragments correspond to ferulic acid generated by ester bond cleavage, tartaric acid, and the product of ferulic acid after decarboxylation (CO2) and demethylation (•CH3), respectively. Consequently, it was tentatively identified as feruloyl dihydroxysuccinic acid. Peak 10 showed a deprotonated molecular ion [M−H]− at m/z 295, with fragment ions [M−H−132]− at m/z 163 and [M−H−132−44]− at m/z 119, representing coumaric acid and its decarboxylated product, respectively. Based on this evidence, the compound was tentatively identified as coumaroyl dihydroxysuccinic acid (Farag et al. 2013; Pinelli et al. 2008).

Peaks 4, 11, and 12 exhibit identical fragment ions at m/z 115, a characteristic feature of the aspartic acid moiety, strongly indicating their classification as aspartic acid derivatives (W. Liu et al. 2024). Peak 12 showed a deprotonated molecular ion [M−H]− at m/z 308, with fragment ions [M−H−115]− at m/z 193, and [M−H−175−18]− m/z 115, representing ferulic acid moiety and aspartic acid moiety, respectively (Z. Li et al. 2018). Peak 11 was tentatively assigned as methylbenzoyl aspartic acid according to the presence of the molecular ion [M−H]− at m/z 250, and the major fragment ions [M−H−128]− at m/z 132 and [M−H−135]− at m/z 115.

Peak 15, which showed a deprotonated molecular ion [M−H]− at m/z 385, along with characteristic fragment ions [M−H−162]− at m/z 223 (corresponding to the loss of one glucose moiety) and [M−H−180]− at m/z 205, exhibited fragmentation pathways consistent with the typical behavior of C13‐norisoprenoid glucosides. By comparative analysis with published reference data, Peak 15 was identified as hydroxy epoxy ionol glucoside (Neugebauer and Schreier 1995).

Peaks 16 and 18 generated identical characteristic fragment ions at m/z 353 [Ag+83]−, suggesting these compounds are likely flavonoid C‐glucosides with apigenin as the aglucone. Peak 16, which exhibited a molecular ion [M−H]− at m/z 593, produced typical fragment ions of flavonoid C‐glucoside [M−H−90]− at m/z 503, [M−H−120]− at m/z 473, [M−H−90−120]− at m/z 383, and [M−H−120−120]− at m/z 353. For Peak 18, with a molecular ion peak [M−H]− at m/z 725, a fragment ion [M−H−132]− was detected at m/z 593, which corresponds to the loss of one arabinose moiety. In addition, it produced typical flavonoid C‐glucoside fragment ions, namely [M−H−132−120]− at m/z 473 and [M−H−132−120−120]− at m/z 353. Based on the above mass spectral data, Peaks 16 and 18 were tentatively characterized identified as apigenin‐di‐C‐glucoside and apigenin‐di‐C‐glucoside arabinoside (Singh et al. 2015; J. B. Xiao, Capanoglu, et al. 2015).

The UPLC–triple‐TOF MS/MS structural analysis revealed that UCE20 primarily consists of hydroxycinnamoyl derivatives (predominantly CQAs) and flavonoid C‐glycosides, suggesting its hypoglycemic activity may be mechanistically attributed to phenolic acids and flavonoid constituents.

3.4. Determination of Chemical Components in UCE Fractions and Correlation Analysis

Preliminary UPLC–triple‐TOF MS/MS profiling indicated that phenolic acids and flavonoids are the major constituents of UCE20. To further clarify the constituent distribution across UCE fractions and identify the bioactive compounds underlying the hypoglycemic effects of U. cannabina (in vivo and in vitro), we quantified TPC and TFC in each fraction and analyzed their correlations with inhibitory activity against three α‐Glu isoforms.

HPLC analysis confirmed good separation among the five fractions (UCE5, UCE20, UCE40, UCE60, and UCE95), as reflected by distinct retention times, symmetric peak shapes, and characteristic UV spectra (Figure 4A).

FIGURE 4.

FIGURE 4

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Determination of TFC and TPC in UCE fractions. HPLC chromatogram of UCE fractions (A), gallic acid standard curve (B) and rutin standard curve (C).

TPC and TFC were determined using the Folin–Ciocalteu method (GAE; y = 23.50x + 0.034, R 2 = 0.9984) and the NaNO2‐Al(NO3)3 assay (RE; y = 4.49x + 0.036, R 2 = 0.9953), respectively (Table 2 and Figure 4B,C). Overall, UCE20 exhibited the highest levels of total phenolics and flavonoids, with phenolic acids constituting the predominant components, supporting the conclusion that phenolic acids and flavonoids are likely key contributors to its hypoglycemic activity.

TABLE 2.

Determination of total phenolic content and total flavonoid content in UCE fractions.

Component (mg/g) UCE5 UCE UCE20 UCE40 UCE60 UCE95
TPC 12.01 ± 0.05 13.75 ± 0.23 84.92 ± 0.59 72.32 ± 1.37 35.21 ± 0.69 7.60 ± 0.25
TFC 5.0 ± 0.15 8.86 ± 0.09 61.50 ± 0.93 31.78 ± 0.93 18.12 ± 0.45 4.11 ± 0.26
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Note: All experiments were independently repeated at least three times. Data are presented as mean ± standard deviation.

Spearman correlation analysis showed that sucrase and maltase inhibition (expressed as IC50 values) was significantly and negatively correlated with TFC (sucrase: r = −0.869, p < 0.05; maltase: r = −0.828, p < 0.05), and also negatively correlated with TPC (r = −0.700 and −0.622, respectively). In contrast, yeast α‐Glu IC50 was positively correlated with both TPC (r = 0.664) and TFC (r = 0.784) (Figure 5A). Collectively, these results suggest that TPC and TFC may serve as practical biomarkers for predicting sucrase and maltase inhibitory potency across UCE fractions.

FIGURE 5.

FIGURE 5

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Correlation analysis. Correlation heat map (A) and PCA analysis (B) between α‐Glu inhibitory activity and TFC, TPC. Data are presented as mean ± standard deviation. Statistically significant differences compared to the model are indicated by * for p < 0.05.

PCA indicated that PC1 and PC2 together explained 90.4% of the total variance. Consistent with the correlation analysis, TPC and TFC loaded negatively with the IC50 values of maltase and sucrase, but positively with the IC50 value of α‐Glu. Cluster patterns further showed that UCE20 and UCE40 grouped closely, as did UCE, UCE5, and UCE60, implying similar inhibitory profiles against sucrase and maltase. By contrast, UCE95 was clearly separated from the other fractions, indicating distinct chemical features and stronger α‐Glu inhibition (Figure 5B). Finally, integrating UPLC–triple‐TOF MS/MS profiling suggested that hydroxycinnamoyl derivatives and are flavonoid C‐glycosides likely key contributors to the hypoglycemic activity of UCE20.

4. Disscusion

U. cannabina is a medicinal and edible wild plant with long‐term ethnic consumption history and widespread dietary applications in Northwest China and the Qinghai‐Tibet Plateau (Meng et al. 2023). It possesses antioxidant, antimicrobial, and antihyperglycemic bioactivities (Bhusal et al. 2022; Rehman et al. 2024). However, existing studies lack sufficient evidence for its PBG‐lowering effects in vitro and in vivo and its corresponding active constituents remain uncharacterized. This study firstly reported the differential inhibitory effects of five enriched fractions from U. cannabina on rat intestinal sucrase, maltase, and yeast α‐Glu. UCE20 showed superior inhibitory activity among all fractions, with IC50 values of 0.052, 0.088, and 0.459 mg/mL toward sucrase, maltase and yeast α‐Glu, respectively. In vivo sucrose, maltose, and starch tolerance tests in alloxan‐induced diabetic mice further validated the prominent postprandial hypoglycemic efficacy of UCE20. In addition, although UCE20 displayed weaker in vitro inhibition of the tested α‐Glu (sucrose and maltase) than acarbose, it produced equal or even superior PBG‐lowering effects in vivo. The inconsistent inhibitory performance across different enzyme sources reflects the distinct structural differences between microbial and mammalian α‐Glu isoforms, which may lead to varied binding affinity with UCE20's phenolic and flavonoid components.

Numerous studies have confirmed that various plant extracts can reduce PBG levels by inhibiting the activity of α‐Glu. However, the dosage of plant extracts is relatively high, ranging from a few hundred milligrams per kilogram (Jiang et al. 2021; Jiang et al. 2021; Jia et al. 2022; Kim et al. 2023). Notably, the dosage at which UCE20 exerted remarkable postprandial hypoglycemic effects in the present study was substantially lower than the doses reported in similar prior investigations. Oral administration of UCE20 at doses of 10, 20, and 30 mg/kg significantly reduced PBG levels after sucrose, maltose, and starch loading in alloxan‐induced diabetic mice. Even the 10 mg/kg treatment significantly reduced the AUC values compared with the model group. Long‐term folk use has formed safe intake standards for U. cannabina: fresh samples are suggested at 50–100 g per serving 2–3 times weekly, while the daily oral dose of dried herb for decoctions or tea is 5–10 g ( Zhang et al. 2025). Here, 1 kg dried U. cannabina produced 23.98 g UCE20, so 3–12 g raw herb equals 119.90–239.80 mg UCE20. The human equivalent dose converted from the 10 mg/kg mouse dose was 49 mg, falling below the safe daily intake of UCE20. These results suggest that the tested mouse dose is within a safe range for human consumption.

Flavonoids and phenolic acids from Urtica dioica exhibit potent α‐Glu inhibitory activity (Altamimi et al. 2022). CQAs and their derivatives (neochlorogenic, chlorogenic, and cryptochlorogenic acids) not only modulate glucose and lipid metabolism, mitochondrial function, and bile acid homeostasis through the SIRT1/FXR pathway and ameliorate obesity‐related dysfunction by remodeling gut microbiota ( Liu et al. 2022; Wang et al. 2025; Xu et al. 2025), but also exert strong α‐Glu inhibitory effects, as validated by multiple in vitro studies (Tu et al. 2022; Zheng et al. 2020). Furthermore, flavone C‐glycosides including vitexin and isovitexin exert prominent postprandial hypoglycemic effects (Choo et al. 2012). Consistent with these documented bioactivities, our phytochemical analysis confirms that UCE20 is enriched in hydroxycinnamoyl quinates (predominantly CQAs) and flavone C‐glycosides, which may, therefore, be the key bioactive constituents responsible for its hypoglycemic efficacy.

Nevertheless, the present study has several limitations. First, only the short‐term postprandial hypoglycemic effects of UCE20 were assessed herein. Additional long‐term oral administration trials are required to comprehensively clarify its overall pharmacological performance. Second, correlation analysis and PCA were solely adopted to screen the primary bioactive constituents of UCE20 in this work. Further investigations integrating partial least squares regression (PLSR) and molecular docking are necessary to quantitatively elucidate the correlation between the content of characteristic compounds and α‐Glu inhibitory activity, which will provide theoretical guidance for the targeted development of functional food raw materials. The present study only investigated acute hypoglycemic effects and in vitro enzymatic inhibitory activity, while bioavailability and systematic pharmacokinetic profiles of UCE20 were not characterized. Further pharmacokinetic assessments are required to comprehensively compare its overall pharmacological performance with acarbose.

5. Conclusion

In conclusion, this study is the first to evaluate the PBG‐lowering activity of fraction UCE20 isolated from U. cannabina and tentatively identify its major active ingredients by means of UPLC–triple‐TOF MS/MS combined with correlation analysis. Notably, the PBG‐lowering efficacy of UCE20 at doses of 10–30 mg/kg in diabetic mice was equivalent to, or even superior to, that of the positive control drug acarbose at 8 mg/kg. Furthermore, the oral glucose tolerance test demonstrated that UCE20 reduces PBG not only through α‐Glu inhibition but also via other potential mechanisms. A total of 18 components were tentatively identified, primarily based on characteristic fragment ion analysis, including one benzoic acid derivative, eight hydroxycinnamoyl quinic acids, three hydroxycinnamoylaspartic acids, three hydroxycinnamoyl succinic acids, two flavonoid C‐glycosides, and one glucosyl alcohol derivative. Correlation analysis suggested that flavonoid C‐glycosides and phenolic acids may serve as the key bioactive components in U. cannabina. These findings indicate that U. cannabina, a traditional folk medicinal and edible plant, holds potential for development as a hypoglycemic agent or dietary supplement to assist in PBG management.

Author Contributions

Zhen Li: writing – original draft. Nina Yang: methodology. Mingting Xu: investigation. Luya Wang: formal analysis, methodology. Liying Liu: data curation. Xiaohui Zhao: supervision. Huilan Yue: writing – review and editing, supervision.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supplementary Figure S1: jfds71417‐sup‐0001‐FigureS1.docx

JFDS-91-0-s001.docx (294.9KB, docx)

Acknowledgments

This study was supported by Central Guidance of Local Scientific and Technological Development Funds in Qinghai Province (2025ZY008).

References

  1. Ahangarpour, A. , Mohammadian M., and Dianat M.. 2012. “Antidiabetic Effect of Hydroalcholic Urtica dioica Leaf Extract in Male Rats With Fructose‐Induced Insulin Resistance.” Iranian Journal of Medical Sciences 37: 181–186. https://pmc.ncbi.nlm.nih.gov/articles/PMC3470082/. [PMC free article] [PubMed] [Google Scholar]
  2. Alssema, M. , Ruijgrok C., Blaak E. E., et al. 2021. “Effects of Alpha‐Glucosidase‐Inhibiting Drugs on Acute Postprandial Glucose and Insulin Responses: A Systematic Review and Meta‐Analysis.” Nutrition & Diabetes 11: 1–9. 10.1038/s41387-021-00152-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Altamimi, M. A. , Abu‐Reidah I. M., Altamimi A., and Jaradat N.. 2022. “Hydroethanolic Extract of Urtica dioica L. (Stinging Nettle) Leaves as Disaccharidase Inhibitor and Glucose Transport in Caco‐2 Hinderer.” Molecules 27: 8872. 10.3390/molecules27248872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bhusal, K. K. , Magar S. K., Thapa R., et al. 2022. “Nutritional and Pharmacological Importance of Stinging Nettle (Urtica dioica L.): A Review.” Heliyon 8: e09717. 10.1016/j.heliyon.2022.e09717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bnouham, M. , Merhfour F.‐Z., Ziyyat A., Mekhfi H., Aziz M., and Legssyer A.. 2003. “Antihyperglycemic Activity of the Aqueous Extract of Urtica dioica .” Fitoterapia 74: 677–681. 10.1016/S0367-326X(03)00182-5. [DOI] [PubMed] [Google Scholar]
  6. Ceriello, A. , Hanefeld M., Leiter L., et al. 2004. “Postprandial Glucose Regulation and Diabetic Complications.” Archives of Internal Medicine 164: 2090–2095. 10.1001/archinte.164.19.2090. [DOI] [PubMed] [Google Scholar]
  7. Cho, N. H. , Shaw J. E., Karuranga S., et al. 2018. “IDF Diabetes Atlas: Global Estimates of Diabetes Prevalence for 2017 and Projections for 2045.” Diabetes Research and Clinical Practice 138: 271–281. 10.1016/j.diabres.2018.02.023. [DOI] [PubMed] [Google Scholar]
  8. Choo, C. Y. , Sulong N. Y., Man F., and Wong T. W.. 2012. “Vitexin and Isovitexin From the Leaves of Ficus deltoidea With In‐Vivo α‐Glucosidase Inhibition.” Journal of Ethnopharmacology 142: 776–781. 10.1016/j.jep.2012.05.062. [DOI] [PubMed] [Google Scholar]
  9. Derosa, G. , and Maffioli P.. 2012. “Mini‐Special Issue Paper Management of Diabetic Patients With Hypoglycemic Agents α‐Glucosidase Inhibitors and Their Use in Clinical Practice.” Archives of Medical Science 5: 899–906. 10.5114/aoms.2012.31621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Deshmukh, C. D. , and Jain A.. 2015. “Diabetes Mellitus: A Review.” Indian Journal of Pure & Applied Biosciences 3: 224–230. https://www.researchgate.net/publication/351613911. [Google Scholar]
  11. Dhouibi, R. , Affes H., Ben Salem M., et al. 2020. “Screening of Pharmacological Uses of Urtica dioica and Others Benefits.” Progress in Biophysics and Molecular Biology 150: 67–77. 10.1016/j.pbiomolbio.2019.05.008. [DOI] [PubMed] [Google Scholar]
  12. DiNicolantonio, J. J. , Bhutani J., and O'Keefe J. H.. 2015. “Acarbose: Safe and Effective for Lowering Postprandial Hyperglycaemia and Improving Cardiovascular Outcomes.” Open Heart 2: e000327. 10.1136/openhrt-2015-000327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. El Haouari, M. , and Rosado J. A.. 2019. “Phytochemical, Anti‐Diabetic and Cardiovascular Properties of Urtica dioica L. (Urticaceae): A Review.” Mini‐Reviews in Medicinal Chemistry 19: 63–71. 10.2174/1389557518666180924121528. [DOI] [PubMed] [Google Scholar]
  14. Farag, M. A. , Weigend M., Luebert F., Brokamp G., and Wessjohann L. A.. 2013. “Phytochemical, Phylogenetic, and Anti‐Inflammatory Evaluation of 43 Urtica Accessions (Stinging Nettle) Based on UPLC–Q‐TOF‐MS Metabolomic Profiles.” Phytochemistry 96: 170–183. 10.1016/j.phytochem.2013.09.016. [DOI] [PubMed] [Google Scholar]
  15. Farzami, B. , Ahmadvand D., Vardasbi S., Majin F. J., and Khaghani S.. 2003. “Induction of Insulin Secretion by a Component of Urtica dioica Leave Extract in Perifused Islets of Langerhans and Its in Vivo Effects in Normal and Streptozotocin Diabetic Rats.” Journal of Ethnopharmacology 89: 47–53. 10.1016/s0378-8741(03)00220-4. [DOI] [PubMed] [Google Scholar]
  16. Gülçin, İ. , Küfrevioǧlu Ö. İ., Oktay M., and Büyükokuroǧlu M. E.. 2004. “Antioxidant, Antimicrobial, Antiulcer and Analgesic Activities of Nettle (Urtica dioica L.).” Journal of Ethnopharmacology 90: 205–215. 10.1016/j.jep.2003.09.028. [DOI] [PubMed] [Google Scholar]
  17. Hedrington, M. S. , and Davis S. N.. 2019. “Considerations When Using Alpha‐glucosidase Inhibitors in the Treatment of Type 2 Diabetes.” Expert Opinion on Pharmacotherapy 20: 2229–2235. 10.1080/14656566.2019.1672660. [DOI] [PubMed] [Google Scholar]
  18. Jaiswal, R. , Kiprotich J., and Kuhnert N.. 2011. “Determination of the Hydroxycinnamate Profile of 12 Members of the Asteraceae family.” Phytochemistry 72: 781–790. 10.1016/j.phytochem.2011.02.027. [DOI] [PubMed] [Google Scholar]
  19. Jan, K. N. , Zarafshan K., and Singh S.. 2016. “Stinging Nettle (Urtica dioica L.): A Reservoir of Nutrition and Bioactive Components With Great Functional Potential.” Journal of Food Measurement and Characterization 11: 423–433. 10.1007/s11694-016-9410-4. [DOI] [Google Scholar]
  20. Jia, W. , Bi Q., Jiang S., et al. 2022. “Hypoglycemic Activity of Codonopsis Pilosula (Franch.) Nannf. in Vitro and in Vivo and Its Chemical Composition Identification by UPLC‐Triple‐TOF‐MS/MS.” Food & Function 13, no. 5: 2456–2464. 10.1039/D1FO03761G. [DOI] [PubMed] [Google Scholar]
  21. Jiang, S. , Chen C., Dong Q., et al. 2021. “Alkaloids and Phenolics Identification in Fruit of Nitraria Tangutorum Bobr. By UPLC‐Q‐TOF‐MS/MS and Their a‐glucosidase Inhibitory Effects In Vivo and In Vitro.” Food Chemistry 364: 130412. 10.1016/j.foodchem.2021.130412. [DOI] [PubMed] [Google Scholar]
  22. Jiang, S. , Zhao X., Liu C., et al. 2021. “Identification of Phenolic Compounds in Fruits of Ribes Stenocarpum Maxim. By UHPLC‐QTOF/MS and Their Hypoglycemic Effects In Vitro and In Vivo.” Food Chemistry 344: 128568. 10.1016/j.foodchem.2020.128568. [DOI] [PubMed] [Google Scholar]
  23. Kim, K. J. , Park S.‐Y., Kim J. T., et al. 2023. “In Vitro and in Vivo Postprandial Hypoglycemic Effects and Comprehensive Metabolite Profiling of Dangjo Chili Pepper (Capsicum annuum L. cv. Dangjo).” Food Bioscience 51: 102180. 10.1016/j.fbio.2022.102180. [DOI] [Google Scholar]
  24. Kregiel, D. , Pawlikowska E., and Antolak H.. 2018. “Urtica spp.: Ordinary Plants With Extraordinary Properties.” Molecules 23: 1664. 10.3390/molecules23071664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lenzen, S. 2007. “The Mechanisms of Alloxan‐ and Streptozotocin‐Induced Diabetes.” Diabetologia 51, no. 2: 216–226. 10.1007/s00125-007-0886-7. [DOI] [PubMed] [Google Scholar]
  26. Li, A. , La M., Wang H., et al. 2023. “Target‐Guided Isolation and Purification of Antioxidants From Urtica laetevirens Maxim. By HSCCC Combined With Online DPPH‐HPLC Analysis.” Molecules 28: 7332. 10.3390/molecules28217332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Li, Z. , Zhao C., Zhao X., et al. 2018. “Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra‐High‐Performance Liquid Chromatography–High‐Resolution Mass Spectrometry and Its in Silico Database.” Analytical Chemistry 90: 14321–14330. 10.1021/acs.analchem.8b03654. [DOI] [PubMed] [Google Scholar]
  28. Liu, L. , Jia W., Jiang S., et al. 2023. “Inhibitory Activities and Rules of Plant Gallotannins With Different Numbers of Galloyl Moieties on Sucrase, Maltase and α‐Amylase In Vitro and In Vivo.” Phytomedicine 120: 155063. 10.1016/j.phymed.2023.155063. [DOI] [PubMed] [Google Scholar]
  29. Liu, W. , Liu R. Z., Yang H., Gao W., and Li P.. 2024. “Biosynthetic Pathway Analysis Combined With Feature‐Based Molecular Networking to Comprehensively Characterize the Chemical Constituents in Seeds of Sterculia Lychnophora.” Phytochemical Analysis 35: 1358–1370. 10.1002/pca.3369. [DOI] [PubMed] [Google Scholar]
  30. Liu, Y. , Zhong X., Lin S., et al. 2022. “Limosilactobacillus Reuteri and Caffeoylquinic Acid Synergistically Promote Adipose Browning and Ameliorate Obesity‐Associated Disorders.” Microbiome 10: 1–22. 10.1186/s40168-022-01430-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Long, W. Y. , Tang H. C., Zhong X., Wang H. X., and Wang H. G.. 2020. “Hypoglycemic and Lipid‐Lowering Effects of Extracted From the Aerial Part of Urtica cannabina L. on Alloxan‐Induced Hyperglycemic Mice.” IOP Conference Series: Earth and Environmental Science 512: 012097. 10.1088/1755-1315/512/1/012097. [DOI] [Google Scholar]
  32. Meng, X. , Dang B., Zhang W., Zhang J., Dong H., and Bai K.. 2023. “Effects of Different Drying Methods on Nutritional Components and Antioxidant Activity of Nettle Leaves.” Food Indus 44, no. 08: 161–166. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2023&filename=SPGY202308038. [Google Scholar]
  33. Neugebauer, W. , and Schreier P.. 1995. “Identification and Enantiodifferentiation of C13 Norisoprenoid Degradation Products of Glycosidically Bound 3‐Hydroxy‐.Alpha.‐Ionol From Stinging Nettle (Urtica dioica L.).” Journal of Agricultural and Food Chemistry 43: 1647–1653. 10.1021/jf00054a044. [DOI] [Google Scholar]
  34. Omer, G. A. , and Mohammed L. Y.. 2024. “Total Phenolic, Flavonoids and Vitamin C Contents With Antioxidant Activity of Urtica dioica L. leaves Growing in Zakho, Kurdistan Region‐Iraq.” Baghdad Science Journal 21: 1592–1608. 10.21123/bsj.2023.8658. [DOI] [Google Scholar]
  35. Orčić, D. , Francišković M., Bekvalac K., et al. 2014. “Quantitative Determination of Plant Phenolics in Urtica dioica Extracts by High‐Performance Liquid Chromatography Coupled With Tandem Mass Spectrometric Detection.” Food Chemistry 143: 48–53. 10.1016/j.foodchem.2013.07.097. [DOI] [PubMed] [Google Scholar]
  36. Pinelli, P. , Ieri F., Vignolini P., Bacci L., Baronti S., and Romani A.. 2008. “Extraction and HPLC Analysis of Phenolic Compounds in Leaves, Stalks, and Textile Fibers of Urtica dioica L.” Journal of Agricultural and Food Chemistry 56: 9127–9132. 10.1021/jf801552d. [DOI] [PubMed] [Google Scholar]
  37. Proença, C. , Ribeiro D., Freitas M., and Fernandes E.. 2021. “Flavonoids as Potential Agents in the Management of Type 2 Diabetes Through the Modulation of α‐Amylase and α‐Glucosidase Activity: A Review.” Critical Reviews in Food Science and Nutrition 62: 3137–3207. 10.1080/10408398.2020.1862755. [DOI] [PubMed] [Google Scholar]
  38. Rehman, G. , Khan I., Rauf A., et al. 2024. “Antidiabetic Properties of Caffeoylmalic Acid, a Bioactive Natural Compound Isolated From Urtica dioica .” Fitoterapia 176: 106024. 10.1016/j.fitote.2024.106024. [DOI] [PubMed] [Google Scholar]
  39. Riehemann, K. , Behnke B., and Schulze‐Osthoff K.. 1999. “Plant Extracts From Stinging Nettle (Urtica dioica), an Antirheumatic Remedy, Inhibit the Proinflammatory Transcription Factor NF‐κB.” FEBS Letters 442: 89–94. 10.1016/S0014-5793(98)01622-6. [DOI] [PubMed] [Google Scholar]
  40. Seuring, T. , Archangelidi O., and Suhrcke M.. 2015. “The Economic Costs of Type 2 Diabetes: A Global Systematic Review.” Pharmacoeconomics 33: 811–831. 10.1007/s40273-015-0268-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Singh, A. , Kumar S., Bajpai V., Reddy T. J., Rameshkumar K. B., and Kumar B.. 2015. “Structural Characterization of Flavonoid C‐ and O‐Glycosides in an Extract of Adhatoda Vasica Leaves by Liquid Chromatography With Quadrupole Time‐of‐Flight Mass Spectrometry.” Rapid Communications in Mass Spectrometry 29: 1095–1106. 10.1002/rcm.7202. [DOI] [PubMed] [Google Scholar]
  42. Spinoso‐Castillo, J. L. , Escamilla‐Prado E., Aguilar‐Rincón V. H., Corona‐Torres T., García‐de los Santos G., and Morales‐Ramos V.. 2020. “Quantitative Comparison of Three Main Metabolites in Leaves of Coffea Accessions by UPLC‐MS/MS.” European Food Research and Technology 247: 375–384. 10.1007/s00217-020-03631-8. [DOI] [Google Scholar]
  43. Subhan, M. , Sanachai K., Sungthong B., Datham S., Ratha J., and Puthongking P.. 2025. “Comparison In Vitro and In Silico Studies of Phenolic Acids and Flavonoids on α‐Glucosidase Inhibition.” Journal of Pharmacy & Pharmacognosy Research 13: 311–323. 10.56499/jppres24.2081_13.1.311. [DOI] [Google Scholar]
  44. Surya, S. , Salam A. D., Tomy D. V., Carla B., Kumar R. A., and Sunil C.. 2014. “Diabetes Mellitus and Medicinal Plants‐A Review.” Asian Pacific Journal of Tropical Disease 4: 337–347. 10.1016/s2222-1808(14)60585-5. [DOI] [Google Scholar]
  45. Tahri, A. , Yamani S., Legssyer A., et al. 2000. “Acute Diuretic, Natriuretic and Hypotensive Effects of a Continuous Perfusion of Aqueous Extract of Urtica dioica in the Rat.” Journal of Ethnopharmacology 73: 95–100. 10.1016/S0378-8741(00)00270-1. [DOI] [PubMed] [Google Scholar]
  46. Tarasevičienė, Ž. , Vitkauskaitė M., Paulauskienė A., and Černiauskienė J.. 2023. “Wild Stinging Nettle (Urtica dioica L.) Leaves and Roots Chemical Composition and Phenols Extraction.” Plants 12: 309. 10.3390/plants12020309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Tripathi, B. K. , and Srivastava A. K.. 2006. “Diabetes Mellitus: Complications and Therapeutics.” Medical Science Monitor 12: RA130–147. https://www.researchgate.net/publication/6975011. [PubMed] [Google Scholar]
  48. Tu, J. , Liu G., Jin Y., et al. 2022. “Enrichment of γ‐Aminobutyric Acid in Mulberry Leaves and the Inhibitory Effects of the Water Extract on ACE and α‐Glucosidase Activity.” Industrial Crops and Products 177: 114485. 10.1016/j.indcrop.2021.114485. [DOI] [Google Scholar]
  49. Wang, Y. 2023. Structure Identification Method of Polyphenols Based on LC‐MS and the Application in Traditional Chinese Medicine. Yunnan University of Chinese Medicine. 10.27460/d.cnki.gyzyc.2023.000230. [DOI] [Google Scholar]
  50. Wang, Y. , Sun J., Xue L., et al. 2025. “Chlorogenic Acid Improves High‐Fat Diet‐Induced Skeletal Muscle Metabolic Disorders by Regulating Mitochondrial Function and Lactate Metabolism.” Journal of Agricultural and Food Chemistry 73: 10347–10357. 10.1021/acs.jafc.5c03967. [DOI] [PubMed] [Google Scholar]
  51. Xiao, J. B. , Capanoglu E., Jassbi A. R., and Miron A.. 2015b. “Advance on the Flavonoid C‐Glycosides and Health Benefits.” Critical Reviews in Food Science and Nutrition 56: S29–S45. 10.1080/10408398.2015.1067595. [DOI] [PubMed] [Google Scholar]
  52. Xiao, H. Z. , Liu B. G., Mo H. Z., and Liang G. Z.. 2015a. “Comparative Evaluation of Tannic Acid Inhibiting α‐Glucosidase and Trypsin.” Food Research International 76: 605–610. 10.1016/j.foodres.2015.07.029. [DOI] [PubMed] [Google Scholar]
  53. Xu, J. , Xue Q., Xiong A., et al. 2025. “Chlorogenic Acid Attenuates Pyrrolizidine Alkaloid‐Induced Liver Injury Through Modulation of the SIRT1/FXR Signaling Pathway.” Chinese Medicine 20: 1–15. 10.1186/s13020-025-01077-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Yang, H. K. , Lee S.‐H., Shin J., et al. 2019. “Acarbose Add‐on Therapy in Patients With Type 2 Diabetes Mellitus With Metformin and Sitagliptin Failure: A Multicenter, Randomized, Double‐Blind, Placebo‐Controlled Study.” Diabetes & Metabolism Journal 43: 287. 10.4093/dmj.2018.0054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Yue, H. , Jiang S., Wang L., et al. 2022. “Hypoglycemic Ingredients Identification of Rheum tanguticum Maxim. Ex Balf. By UHPLC‐triple‐TOF‐MS/MS and Interrelationships Between Ingredients Content and Glycosidase Inhibitory Activities.” Industrial Crops and Products 178: 114595. 10.1016/j.indcrop.2022.114595. [DOI] [Google Scholar]
  56. Zhang, X. Y. , Lu X., Feng B. M., Xi Y. F., Gao C. C., and Feng W. X.. 2023. “Phytochemical and Chemotaxonomic Studies on Four Plants From Genus of Urtica L. (Urticaceae).” Biochemical Systematics and Ecology 106: 104561. 10.1016/j.bse.2022.104561. [DOI] [Google Scholar]
  57. Zhang, H. Y. , Yan X. J., Jiang Y., Han Y. S., and Zhou Y. F.. 2011. “The Extraction, Identification and Quantification of Hypoglycemic Active Ingredients From Stinging Nettle (Urtica angustifolia).” African Journal of Biotechnology 10: 9428–9437. 10.5897/ajb10.1981. [DOI] [Google Scholar]
  58. Zhang, J. Z. , Zhang Q. A., Zhang X. Q., et al. 2025. “Nettle (Urtica cannabina L.) Polysaccharides as a Novel Dietary Supplement: Enhancing Systemic Antioxidant Status via Modulation of the Gut–Liver Axis.” Frontiers in Pharmacology 16: 1692189. 10.3389/fphar.2025.1692189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Zheng, Y. , Tian J., Yang W., et al. 2020. “Inhibition Mechanism of Ferulic Acid Against α‐Amylase and α‐Glucosidase.” Food Chemistry 317: 126346. 10.1016/j.foodchem.2020.126346. [DOI] [PubMed] [Google Scholar]

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