This study employed a retrospective cohort design utilizing archived biological samples and clinical data from a randomized controlled trial (RCT) evaluating the efficacy of the AJ in elderly patients (≥60 years) with Grade 1 essential hypertension (SBP ≥140 mmHg and DBP ≥90 mmHg) (Writing Group of Chinese Guidelines for Hypertension Prevention and Control, 2024). The original trial was conducted at Xiamen Traditional Chinese Medicine Hospital. For the current analysis, 85 participants were selected based on the strict availability of paired blood samples at baseline and the 8-week follow-up. The study protocol was approved by the Institutional Review Board (IRB) of Xiamen Traditional Chinese Medicine Hospital (Approval No. XMTCM-2024.003) and strictly adhered to the principles of the Declaration of Helsinki. Informed consent for the secondary use of stored biological samples and anonymized data was obtained from all participants upon initial enrollment.

Primary clinical outcomes, including SBP and DBP, were measured using an automated oscillometric device (Omron HEM-907XL). The mean of the last two of three consecutive measurements was recorded. Vascular function was assessed via Flow-Mediated Dilation (FMD), Pulse Wave Velocity (PWV), and Carotid Intima-Media Thickness (cIMT) following standardized ultrasound protocols. Left Ventricular Mass Index (LVMI) was determined via echocardiography. Biomarker quantification was performed on serum samples stored at −80 °C. Inflammatory cytokines (IL-6, TNF-α, CRP) and oxidative stress markers (8-iso-PGF2α, SOD, MDA) were quantified using specific enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Minneapolis, MN, United States; Abcam, Cambridge, United Kingdom; Elabscience, Wuhan, China). Reactive oxygen species (ROS) levels were assessed using a fluorescence-based Dihydroethidium (DHE) assay. All assays were conducted in duplicate by technicians blinded to clinical metadata.

A haematoxylin and eosin (HE) staining kit was purchased from Servicebio Biotechnology Co., Ltd. (G1005; Wuhan, China). ELISA kits for ACE (E-EL-R2401), REN (E-EL-R3075), AngII (E-EL-R0125), ALD (E-EL-0070), TNF-α (E-EL-R2856, E-EL-M3063), IL-6 (E-EL-R0015, E-EL-M0044), and IL-1β (E-EL-R0012, E-EL-M0037) were obtained from Elabscience Biotechnology Inc. (Wuhan, China). ELISA kits for SOD (A001-3-2) and MDA (A003-1-2) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). AngII (05-23-0101) was purchased from Sigma‒Aldrich (St. Louis, MO, United States). The anti-Iba1 antibody was purchased from Abcam (AB221933; Cambridge, United Kingdom). The Dihydroethidium (DHE) assay kit (KGAF019), Cell Counting Kit-8 (CCK-8, KGA317), BCA protein assay kit (KGP903), and Hoechst 33342 (KGA212-1) were purchased from Jiangsu KeyGEN BioTECH Corp., Ltd. (Nanjing, China). TRIzol reagent (9190) and a PrimeScript RT Kit (RR820 A) were purchased from Takara Bio, Inc. (Tokyo, Japan). Antibodies against NQO1 (67240-1-Ig), Nox1 (17772-1-AP), Nox4 (67681-1-Ig), JUN (24909-1-AP), ROCK2 (66633-1-Ig), CREB1 (67927-1-Ig), and GAPDH (10494-1-AP) were obtained from Wuhan Sanying Biotechnology Co., Ltd. (Wuhan, China).

The AJ comprises Ziziphi Spinosae Semen, Gastrodiae Rhizoma, Margaritifera Concha, Poria, Paeoniae Radix Alba, Bupleuri Radix, Achyranthis Bidentatae Radix, Aurantii Fructus, and Glycyrrhizae Radix et Rhizoma blended at a weight ratio of 12:9:15:20:8:6:9:6:5. All botanical drug materials were obtained from the pharmacy of Xiamen Hospital of Beijing University of Chinese Medicine. The botanical drug were weighed according to the specified ratio, soaked in ultrapure water (1:6 w/v) for 30 min, and boiled for 30 min. The residue underwent a second extraction (1:4 w/v). Combined filtrates were concentrated via rotary evaporation and lyophilized to obtain the final powder. The detailed taxonomic validation and composition of the Anjiang Formula are listed in Table 1. To ensure reproducibility, the chemical profile of the AJ extract was characterized using HPLC/UPLC analysis.

Qualitative analysis was performed using an Ultimate 3000 HPLC system coupled with a Q Exactive™ Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific). Chromatographic separation was achieved on an ACQUITY UPLC HSS T3 column (2.1 × 50 mm, 1.8 µm). The mobile phase consisted of 0.1% formic acid in water (A) and acetonitrile (B). The gradient elution was programmed as follows: 0-2 min, 2% B; 2-4 min, 2%-18% B; 4-15 min, 18%-55% B; 15-16 min, 55%-95% B; 16-20 min, 95% B. The flow rate was 0.3 mL/min with an injection volume of 5 µL. Mass spectrometry was operated in both positive and negative ESI modes with a scan range of m/z 150–1500 Da. Data acquisition and processing were performed using Thermo Xcalibur 3.0.63 software. Compounds were identified by comparing retention times and mass spectra with reference standards or literature data.

To elucidate the molecular interaction between Shinflavanone and ROCK2, we employed an integrated approach comprising in silico docking, in vitro kinetic analysis, and in cellulo thermal stability assays. Molecular docking was performed using the Glide module within the Schrödinger Suite (Release 2023-4). The crystal structure of the ROCK2 kinase domain (PDB ID: 4WZG) was prepared using the Protein Preparation Wizard, with hydrogen atoms added and bond orders assigned. A receptor grid was generated around the ATP-binding pocket (centroid coordinates: x, y, z), and Shinflavanone was docked using Standard Precision (SP) mode with default parameters. Surface Plasmon Resonance (SPR) analyses were conducted on a Biacore T200 instrument (Cytiva). Recombinant ROCK2 protein was immobilized on a Series S Sensor Chip CM5 via amine coupling to a target density of ∼3000 RU. Shinflavanone was injected at varying concentrations (3.125–50 μM) in running buffer (PBS-P+[20 mM phosphate buffer, 2.7 mM KCl, 137 mM NaCl, 0.05% Surfactant P20], 1% DMSO) at a flow rate of 30 μL/min. Kinetic constants (Κ _on , Κ _off ) and affinity (Κ _D) were derived by fitting the sensorgrams to a 1:1 Langmuir binding model using Biacore Evaluation Software. Microscale Thermophoresis (MST) was performed using a Monolith NT.115 (NanoTemper Technologies). Recombinant ROCK2 was labeled with RED-tris-NTA dye and incubated with a serial dilution of Shinflavanone (range: 0.1 nM to 10 μM) in MST buffer. Thermophoresis was measured at medium MST power, and Κ _D values were calculated from the dose-response curves using MO. Affinity Analysis software. Cellular Thermal Shift Assays (CETSA) were performed to validate target engagement in the physiological environment. Cells were treated with Shinflavanone (10 μM) or DMSO vehicle for 1 h, harvested, and resuspended in PBS. The cell suspensions were divided into aliquots and subjected to a temperature gradient (40 °C–67 °C) for 3 min using a thermal cycler, followed by cooling at room temperature for 3 min. Soluble fractions were isolated by centrifugation (20,000 × g, 20 min) and analyzed via Western blotting for ROCK2. Protein band intensities were quantified, normalized, and fitted to a Boltzmann sigmoidal equation to determine the aggregation temperature (T _m ) and thermal shift (ΔT _m ).

Animal procedures complied with the ARRIVE guidelines and were approved by the IACUC of Xiamen Traditional Chinese Medicine Hospital (XMTCM-2024.003). Male Spontaneously Hypertensive Rats (SHRs, n = 20) and Wistar-Kyoto (WKY) controls (n = 5), aged 8 weeks, were obtained from Vital River Laboratories. Sample sizes were determined based on a power analysis to detect a 15 mmHg difference in SBP with 80% power and α = 0.05. Following acclimatization, SHRs were randomized into four groups (n = 5/group): Model (Vehicle), Low-dose AJ (AJ-L, 8.1 g/kg/day), High-dose AJ (AJ-H, 16.2 g/kg/day), and Minocycline (positive control, 50 mg/kg/day). Doses were determined based on human-to-rat body surface area conversion. Treatments were administered daily via oral gavage for 10 weeks. Blood pressure was monitored weekly using a non-invasive tail-cuff system (Kent Scientific), with investigators blinded to treatment allocation.

Rats were euthanized under isoflurane anesthesia. For molecular analysis, PVN tissues were microdissected on ice guided by a stereotaxic atlas and snap-frozen. For histology, animals underwent transcardial perfusion with 4% paraformaldehyde (PFA). Paraffin-embedded heart and aorta sections (4 μm) underwent Hematoxylin and Eosin (H&E) staining to assess morphological remodeling. Immunofluorescence: PVN frozen sections (10 μm) were permeabilized and incubated with anti-Iba1 antibody (1:500, Abcam) to visualize microglia. Images were acquired via confocal microscopy (Nikon) and analyzed using ImageJ to quantify microglial activation intensity. To evaluate systemic and central pathological changes, serum RAAS components (ACE, REN, AngII, ALD) and brain tissue markers of inflammation and oxidative stress (SOD, MDA, TNF-α, IL-6, IL-1β) were quantified using commercial ELISA kits. For the structural assessment of neuroinflammation, microglial activation within the PVN was visualized via immunofluorescence. Cryosections (10 μm) were permeabilized and incubated with anti-Iba1 primary antibody followed by Alexa Fluor 594-conjugated secondary antibody. Fluorescence images were acquired using confocal microscopy and quantified using ImageJ to determine microglial morphology and density.

BV-2 microglia (BeNa Culture Collection, Beijing, China) were cultured in high-glucose DMEM supplemented with 10% FBS at 37 °C. Cell lines were authenticated via STR profiling and tested negative for mycoplasma contamination. To mimic hypertensive neuroinflammation, cells were serum-starved and treated with AJ prior to stimulation with Angiotensin II (100 nM). Cytotoxicity was assessed using the CCK-8 assay. Intracellular and mitochondrial oxidative stress levels were assessed using DHE (1 μM) and MitoSOX™ Red (5 μM) probes, respectively. Fluorescence intensity was captured via confocal microscopy to quantify ROS generation.

Transcriptional and translational regulations of the RhoA/ROCK2 pathway and oxidative stress axes were analyzed via qRT-PCR and Western blotting. Total RNA was extracted and reverse-transcribed, with relative mRNA expression calculated using the 2^−ΔΔCt method normalized to GAPDH. For protein analysis, lysates were resolved by SDS-PAGE and transferred to PVDF membranes. Immunoblotting was performed using specific antibodies against oxidative enzymes (NQO1, Nox1, Nox4), kinase targets (ROCK2), and transcription factors (JUN, CREB1), with band intensities quantified via densitometry.

The sample size for the animal experiments was determined a priori using G*Power 3.1.9.7 based on the F-test family (ANOVA: Fixed effects, omnibus, one-way). The objective was to detect a significant difference in blood pressure among the five experimental groups (Control, Model, AJ-L, AJ-H, Minocycline). The total sample size N was calculated based on the non-centrality parameter λ for the F-distribution: λ = f 2 × N

Where the effect size f is defined as: f = ∑ i = 1 k p i μ i − μ 2 σ 2

k = Number of groups (n = 5).

σ = Standard deviation within groups.

μ _i = Mean of group i.

N was solved iteratively to satisfy the condition where the non-central F-distribution F(df ₁, df ₂, λ) exceeds the critical value F_crit with probability 1-β.

The calculation yielded a minimum total sample size of N = 20 (4 rats per group). To account for potential attrition or technical variability during the 10-week intervention, the sample size was adjusted to n = 5 per group (Total N = 25). This sample size complies with the ARRIVE 2.0 guidelines for minimizing animal usage while maintaining statistical rigor.

Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) for standard hypothesis testing and R software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria) for advanced data visualization and mixed-model computations. Data are presented as mean ± standard deviation (SD) for continuous variables with normal distribution, median [interquartile range, IQR] for non-normally distributed data, and frequencies (n, %) for categorical variables. Normality was rigorously assessed using the Shapiro-Wilk test and visual inspection of Q-Q plots.

To account for the repeated-measures design and potential missing data (missing at random), longitudinal changes in blood pressure and biomarkers (IL-6, 8-iso-PGF2α, CRP) were analyzed using Linear Mixed-Effects Models (LMM). The model included “Group” (AJ vs. Control), “Time” (Baseline vs. Week 8), and the “Group × Time” interaction as fixed effects, with “Subject ID” entered as a random intercept to account for within-subject correlation. Treatment effects are reported as the Least Squares Mean (LSM) difference with 95% Confidence Intervals (CI). For the analysis of the inflammatory biomarker panel, the Benjamini-Hochberg procedure was applied to control the False Discovery Rate (FDR) at 5%. Additionally, to evaluate the clinical relevance of mechanistic targets, multivariate logistic regression was employed to assess the association between biomarker kinetics (changes in IL-6, 8-iso-PGF2α, SOD, and ROCK proxy) and the likelihood of achieving Blood Pressure Control (defined as SBP <140 mmHg and DBP <90 mmHg). Odds Ratios (OR) with 95% CIs were calculated, adjusting for potential confounders including age, sex, and BMI. Associations between continuous variables (e.g., ΔBiomarker vs. ΔSBP) were assessed using Spearman’s rank correlation coefficient to capture monotonic relationships.

For mechanistic studies involving multiple groups (e.g., cell viability, ROS quantification, animal tissue analysis), comparisons were conducted using one-way Analysis of Variance (ANOVA) followed by Tukey’s post hoc test for normally distributed data. For datasets with unequal variances (confirmed by Levene’s test), Welch’s ANOVA with Dunnett’s T3 post hoc test was utilized. Non-parametric data were analyzed using the Kruskal-Wallis test followed by Dunn’s multiple comparison test. All statistical tests were two-tailed, with a significance threshold set at p < 0.05.

To fully clarify the chemical basis of the Anjiang Formula, UPLC-HRMS analysis was conducted. The Total Ion Chromatograms (TIC) are shown in Supplementary Figure S2. A total of 166 compounds were identified, including flavonoids, triterpenoid saponins, and phenolic acids. Among them, 45 major constituents—including Naringin, Hesperidin, Paeoniflorin, Saikosaponins (A, C, D), and Glycyrrhizic acid—were unequivocally verified by comparison with authentic reference standards (Supplementary Table S1). This extensive profiling confirms the consistency and complexity of the botanical formulation.

We conducted a retrospective analysis of 85 hypertensive patients (Figure 1a). While baseline characteristics were balanced between groups (Supplementary Data), and AJ treatment induced a profound leftward shift in the SBP distribution, with a mean difference of −10.2 mmHg compared to the control group (mean reduction: 23.1 mmHg vs. −12.9 mmHg; p < 0.001, Figure 1b). Crucially, Individual Responder Analysis—visualized via Waterfall plots—revealed that 93% of patients in the AJ group achieved a clinically meaningful BP reduction (responder cutoff >10 mmHg), whereas only 62% of control patients met this threshold (Figure 1c). Subgroup analyses also demonstrated consistent treatment benefits across stratified risk factors, including age, sex, and BMI, with no significant interaction heterogeneity (p _interaction > 0.05, Figure 1d).

We next investigated whether the hemodynamic improvements were accompanied by structural vascular recovery and systemic anti-inflammatory effects. In a dedicated imaging sub-cohort (Figure 2a), AJ treatment significantly ameliorated endothelial function, increasing FMD from 5.1% ± 2.1% to 6.6% ± 2.2%, resulting in a net between-group difference of +1.3% (95% CI: +0.6 to +2.0, p < 0.01; Figure 2b). Concurrently, arterial stiffness was significantly reduced, as evidenced by a marked decrease in PWV in the AJ group (mean change: 125 cm/s; 95% CI: 190 to −60, p < 0.05; Figure 2c). While cIMT and cardiac remodeling indices (LVMI, E/e') showed favorable downward trends, these structural changes did not reach statistical significance over the 8-week period (p > 0.05; Figures 2d–g). Importantly, blinded re-read analysis confirmed high reproducibility for all imaging metrics (ICC ≥0.85; Figure 2f), ensuring data reliability.

Analysis of systemic biomarkers over the 8-week intervention revealed that the AJ significantly reprogrammed the inflammatory-oxidative axis relative to the control group (Figure 3). Linear mixed-effects modeling demonstrated that AJ treatment elicited a profound reduction in serum IL-6 (β _GT = −0.90 pg/mL, p = 0.0003; Figure 3A) and the oxidative stress marker 8-iso-PGF2α (β _GT = −45 pg/mL, p = 0.008; Figure 3B), while concurrently restoring antioxidant SOD activity (β _GT = +19 U/mL, p = 0.012; Figure 3C). Furthermore, peripheral levels of the mechanistic target proxy, ROCK2, were significantly suppressed in the AJ group compared to controls (β _GT = −0.26 ng/mL, p = 0.035; Figure 3D).

We further explored whether the observed molecular changes translated into physiological recovery and clinical benefits. Integrated correlation and regression analyses revealed that systemic reductions in inflammatory (IL-6) and oxidative (8-iso-PGF2α) biomarkers were strongly predictive of improvements in autonomic function (Figure 4). specifically, the reduction in serum IL-6 was positively correlated with improved Blood Pressure Variability (BPV, indicated by ΔSBP-CV) and negatively correlated with enhanced Heart Rate Variability (HRV, indicated by ΔSDNN), establishing a direct link between inflammation suppression and autonomic stabilization (Figures 4a–c). Crucially, to determine the drivers of clinical efficacy, we performed a multivariable logistic regression analysis. This model identified AJ treatment (OR 3.45, p = 0.002), ΔIL-6 reduction (OR 2.10, p = 0.015), and ΔROCK inhibition (OR 1.85, p = 0.030) as significant independent predictors of achieving Blood Pressure Control (<140/90 mmHg) at Week 8, whereas baseline age and BP severity were not predictive (Figure 4d).

To define the precise molecular mechanism of the AJ, we identified Shinflavanone as a direct ligand for ROCK2 through an integrated structural and biophysical approach. Molecular docking simulations predicted that Shinflavanone occupies the ATP-binding cleft of the ROCK2 kinase domain, forming a stable complex via hydrogen bonds with residues Met172, Asp185, and Lys121 (Figure 5a). This prediction was validated by SPR analysis, which demonstrated rapid association and stable dissociation kinetics, yielding a high-affinity equilibrium dissociation constant (Κ _D) of 20.0 nM (Figure 5b). This nanomolar affinity was independently corroborated in solution using MST, which returned a comparable Κ _D of 22.5 ± 3.1 nM (Figures 5c,d). Furthermore, to confirm target engagement within the physiological cellular environment, CETSA revealed that Shinflavanone treatment significantly enhanced the thermal stability of endogenous ROCK2, resulting in a melting temperature shift (ΔT _m ) of +5.2 °C compared to vehicle control (Figure 5c). Collectively, these data show Shinflavanone as a potent, direct binder of ROCK2.

To assess the antihypertensive effects of AJ, we then measured tail vein blood pressure in SHR rats treated with AJ at 8.1, or 16.2 g/kg/day. Compared with the control group, treatment with AJ resulted in a sustained reduction in systolic, diastolic, and mean arterial blood pressure over the 10-week period compared to the untreated model group, achieving levels comparable to the positive control Minocycline (Figure 6a). Compared with the control group, histological examination revealed that AJ treatment effectively reversed hypertension-induced pathological remodeling, specifically attenuating aortic wall thickening (Figure 6b) and reducing cardiomyocyte hypertrophy and cellular disarray in the left ventricle (Figure 6c).

The AJ significantly attenuates neuroinflammation within the hypothalamic PVN and modulates the systemic RAAS in SHRs. Immunofluorescence analysis revealed that AJ treatment potently suppressed microglial activation in the PVN, evidenced by a marked, dose-dependent reduction in Iba1 fluorescence intensity and a morphological shift from an activated state to a resting phenotype compared to the untreated model group (Figure 7a). This central anti-inflammatory effect was further substantiated by the significant downregulation of proinflammatory cytokines, including TNF-α, IL-6, and IL-1β, within the PVN tissues of AJ-treated rats (Figure 7b). Concurrently, AJ treatment rebalanced dysregulated systemic RAAS metabolites, significantly lowering serum levels of Renin (PRN), Angiotensin II (Ang II), and Aldosterone (ALD) while increasing ACE levels compared to the model group, thereby linking central neuroimmune modulation with systemic blood pressure regulation (Figure 7c).

We determined the optimal dosing for AJ using a CCK-8 assay, selecting concentrations of 6.25 μg/mL and 12.5 μg/mL for subsequent experiments. To evaluate the antioxidant efficacy of AJ, we utilized an Ang II-stimulated BV-2 microglial model. DHE and MitoSOX Red staining revealed that Ang II stimulation significantly increased intracellular ROS (Figures 8a,c) and mitochondrial superoxide levels (Figures 8b,d), while ELISA assays confirmed elevated MDA levels and reduced SOD activity (Figure 8e). AJ treatment dose-dependently reversed these oxidative phenotypes, with the 12.5 μg/mL dose showing superior efficacy in reducing ROS, mitochondrial superoxide, and MDA levels compared to the 6.25 μg/mL dose. Furthermore, Western blot analysis demonstrated that AJ treatment significantly upregulated the antioxidant enzyme NQO1 and downregulated the oxidative enzymes Nox1 and Nox4 (Figure 8f), confirming its ability to mitigate Ang II-induced oxidative stress through the modulation of key redox-regulating proteins.

Following AJ treatment, the levels of TNF-α, IL-6, and IL-1β in the cell culture supernatant were measured by ELISA. Compared with those in the control group, the levels of these cytokines in the model group significantly increased. Compared with the model treatment, the AJ treatment reduced the levels of these cytokines in a concentration-dependent manner (Figure 9), with a significant difference observed between the 12.5 μg/mL and 6.25 μg/mL AJ treatment groups.

The mRNA expression levels of selected core targets were assessed by qRT‒qPCR (Figure 10a). Compared with those in the control group, the STAT3 and ROCK2 mRNA levels in the model group were significantly upregulated, whereas the JUN and CREB1 mRNA levels were downregulated. Treatment with AJ resulted in a concentration-dependent reduction in ROCK2 mRNA expression and a corresponding increase in JUN and CREB1 mRNA levels but did not significantly alter STAT3 mRNA expression. Consistently, Western blot analysis confirmed that AJ regulated ROCK2, JUN, and CREB1 protein levels in a manner analogous to changes in their respective mRNAs (Figure 10b).

To definitively confirm whether the RhoA/ROCK2 signaling axis is the direct target of AJ, we assessed the activity of RhoA and ROCK2 in Ang II-stimulated BV-2 microglia. Ang II stimulation significantly increased the levels of GTP-bound RhoA (active form) and downstream ROCK2 activity compared to controls. Treatment with AJ significantly suppressed this activation, showing an efficacy comparable to the specific ROCK2 inhibitor Y-27632. Crucially, co-treatment with the ROCK2 agonist LPA abolished the inhibitory effect of AJ, restoring high levels of RhoA-GTP and ROCK2 activity (Figure 11).

We next investigated whether the antioxidant capacity of AJ is dependent on the modulation of ROCK activity. While AJ treatment effectively reversed AngII-induced oxidative stress—manifested by reduced ROS production (DHE/MitoSox), decreased Nox1/Nox4 expression, and restored NQO1/SOD levels—these beneficial effects were abrogated by the ROCK agonist LPA (Figure 12).

Finally, to verify if the anti-neuroinflammatory properties of AJ were governed by the RhoA/ROCK2 pathway, we analyzed cytokine release. AJ treatment significantly blunted the AngII-triggered surge in TNF-α, IL-6, and IL-1β. However, the addition of LPA reactivated the inflammatory response, neutralizing the suppressive effect of AJ (Figure 13). Therefore, Shinflavanone acted as a direct inhibitor of the RhoA/ROCK2 signaling axis within PVN microglia. By locking ROCK2 in an inactive state, the drug orchestrates a dual protective maneuver: it suppresses the NOX1/NOX4-dependent generation of Reactive Oxygen Species (ROS) and modulates the nuclear transcriptional landscape by downregulating JUN while upregulating CREB1 (Figure 14).

Despite the robustness of our data, several limitations warrant consideration. First, the clinical study employed a retrospective cohort design. Although we utilized propensity matching to minimize baseline variances, inherent selection bias cannot be fully excluded, and unmeasured confounders (e.g., dietary salt intake variations) may influence outcomes (Zhou et al., 2016). Second, the lack of blinding represents a risk for performance bias (Hariton and Locascio, 2018). Third, while we confirmed target engagement in microglia, the cell-type specificity in the brain remains to be mapped using single-cell proteomics to confirm that neurons or astrocytes are not off-target sites (Mowry and Biancardi, 2019). Finally, while we identified Shinflavanone as a high-affinity ligand for ROCK2, AJ is a multi-metabolite botanical formula. A reductionist focus on a single molecule, while necessary for mechanistic target validation, may not fully capture the holistic pharmacology of the intervention. It is highly probable that other constituents within AJ exert synergistic effects—potentially by enhancing the bioavailability of Shinflavanone (Zhou et al., 2026), modulating peripheral vascular resistance via alternative pathways (e.g., Calcium signaling), or providing broader anti-inflammatory coverage. Future 'network pharmacology' studies combined with high-throughput metabolomics are warranted to map this synergistic landscape.