Cardiovascular and metabolic diseases, including obesity, dyslipidaemia, diabetes and hypertension, represent significant global health challenges and contribute to substantial morbidity and mortality worldwide(Reference Silveira Rossi, Barbalho and Reverete de Araujo1). In 2021 alone, these conditions accounted for more than one-third of all deaths globally, underscoring the urgency of effective prevention and management strategies(Reference Mensah, Fuster and Murray2). Among the various approaches to mitigate these risks, dietary interventions have gained particular attention for their potential to improve cardiovascular health and metabolic outcomes(Reference Jafari, Kordkatuli and Mardani3,Reference Jafari, Abbastabar and Alaghi4) .
Functional foods and medicinal plants are emerging as promising tools in dietary strategies(Reference Gutiérrez-Cuevas, López-Cifuentes and Sandoval-Rodriguez5). Okra (Abelmoschus esculentus L.), a member of the mallow family, has garnered interest due to its rich bioactive profile, including mucilage, flavonoids, polyphenols, fibre, vitamins and minerals(Reference Agregán, Pateiro and Bohrer6,Reference Adelakun, Oyelade and Ade-Omowaye7) . These components are associated with a variety of health benefits, such as antioxidant properties, improved glycaemic control, lipid regulation and enhanced liver function(Reference Esmaeilzadeh, Razavi and Hosseinzadeh8). Particularly noteworthy is okra’s high content of soluble fibre, which has been shown to lower cholesterol levels and improve postprandial glycaemic responses(Reference Gemede, Ratta and Haki9).
Recent evidence suggests that okra supplementation may positively influence metabolic and cardiovascular health outcomes(Reference Esmaeilzadeh, Razavi and Hosseinzadeh8), including glycaemic control(Reference Chen, Wang and Sha10), lipid profiles(Reference Bahreini, Saghafi-Asl and Nikpayam11), blood pressure(Reference Tavakolizadeh, Peyrovi and Ghasemi-Moghaddam12) and liver function(Reference Afsharmanesh, Mansourian and Saghaeian Jazi13). However, while its effects on fasting blood glucose and lipid metabolism have been extensively studied, data on its impact on anthropometric measures and blood pressure remain scarce(Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14,Reference Uebelhack, Bongartz and Seibt15) . Furthermore, systematic reviews and meta-analyses to date have primarily focused on glycaemic control and inflammation markers, leaving a critical gap in understanding okra’s comprehensive effects on metabolic and cardiovascular parameters(Reference Nikpayam, Safaei and Bahreini16–Reference Mokgalaboni, Lebelo and Modjadji18).
To address this gap, we conducted a systematic review and dose–response meta-analysis of randomised controlled trials (RCT) using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Our study represents the first effort to evaluate the impact of okra supplementation across a wide range of cardiovascular and metabolic outcomes. By employing advanced statistical techniques such as meta-regression and dose–response analysis, we investigated the relationship between okra dosage, duration and its health effects across diverse populations, including variations in age, gender and health status.
This review aims to provide robust, evidence-based insights into the health benefits of okra. By offering a thorough analysis of current data, we hope to inform dietary recommendations and highlight the potential of okra as a complementary intervention in managing metabolic and cardiovascular disorders. Ultimately, our findings seek to contribute to public health efforts, guiding clinicians and policymakers in leveraging the therapeutic potential of okra to improve overall health and quality of life.
Methods
Protocol and registration
This study was conducted according to a pre-established methodology, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2015 guidelines(Reference Moher, Shamseer and Clarke19) (online Supplementary Table 1). To ensure transparency and uphold high-quality standards, the systematic review and meta-analysis were registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the registration code CRD42024576026.
Search strategy and study selection
A comprehensive literature search was conducted across multiple electronic databases, including PubMed, Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE and SCOPUS, for articles published up to August 2024, with an update in January 2025. Additional searches were performed in ScienceDirect and Google Scholar. Additional studies were identified through manual searches of reference lists from relevant articles, reviews and reputable journals. Additionally, grey literature was searched through sources such as ProQuest Dissertations and Theses, OpenGrey and conference proceedings to identify any unpublished studies relevant to the topic. The search aimed to identify studies that assessed the effects of okra consumption on anthropometric measures, blood pressure, glycaemic control, lipid profiles and liver function tests.
A tailored search strategy was employed for each database to capture relevant studies, using key terms such as ‘okra’, ‘Abelmoschus esculentus’ and ‘clinical trial’, based on EMTREE and Medical Subject Headings (MeSH) tags (online Supplementary Table 2).
Inclusion/exclusion criteria and data extraction
Studies were included if they met the following criteria: RCT involving human adults, with either single-blind or double-blind designs, the presence of control groups and publication in English. Exclusion criteria included non-RCT studies, animal or in vitro research, review articles, case studies, observational studies, editorials, commentaries, letters and studies that did not report sufficient data on the primary outcomes.
Two reviewers (A.J. and A.A.) independently screened the titles and abstracts of all identified articles to determine their eligibility. Full-text reviews were conducted for studies meeting the initial inclusion criteria. Disagreements between the reviewers were resolved through discussion, and when necessary, a third reviewer (A.S.) was consulted to reach a consensus.
Data management was facilitated using EndNote X7 software, which was employed to combine search results and eliminate duplicates. Data extraction was conducted using a standardised form that recorded details such as author information, study year, country, study design, population characteristics, intervention specifics and outcome measures.
Methodological quality assessment and evaluation of the strength of evidence
The methodological quality of the included studies was assessed using the Cochrane risk-of-bias tool. This evaluation was independently performed by two reviewers (A.J. and H.M.), with any discrepancies resolved through discussion or, if needed, the involvement of a third reviewer (A.S.). The assessment criteria focused on sequence generation, allocation concealment, blinding, management of incomplete data, selective reporting and other potential sources of bias.
The strength of evidence for each outcome was assessed using the GRADE framework(Reference Guyatt, Oxman and Vist20). This approach categorises evidence into four levels: high, moderate, low or very low. Two independent reviewers (H.M. and B.P.) conducted this assessment, considering factors such as study design, risk of bias, consistency, precision, directness and the potential for publication bias. A third author (A.J.) was available to adjudicate any disagreements.
Statistical analysis
Statistical analyses were conducted using Stata software, version 15.0 (StataCorp). The primary objective was to analyse data from the included studies to evaluate the effects of okra consumption on various health outcomes. We systematically extracted pre- and post-intervention means, sd and sample sizes for each outcome from the RCT included in the meta-analysis. Key outcomes assessed included anthropometric measures, blood pressure, glycaemic control, lipid profiles and liver function tests. In cases where means and sd were not directly reported, we derived them from available data or contacted the study authors for missing information.
To quantify the effect of okra consumption, we calculated standardised mean differences (SMD) between the intervention and control groups for each outcome. The SMD approach was chosen to standardise results across studies with varying measurement scales(Reference Chandler, Cumpston and Li21). We calculated 95 % CI for each outcome to assess the precision of the estimated effect sizes(Reference Chandler, Cumpston and Li21). Given the anticipated variability among the included studies, a random-effects model was applied using the DerSimonian–Laird method, accounting for both within-study and between-study variability to provide a more generalised estimate of the effect(Reference Borenstein, Hedges and Higgins22). Heterogeneity was assessed using the I 2 statistic, with values of 25, 50 and 75 % indicating low, moderate and high heterogeneity, respectively(Reference Higgins and Thompson23).
To identify potential sources of heterogeneity, subgroup analyses were conducted based on factors such as geographical location, baseline health status, okra dosage, age groups, baseline BMI, intervention duration and sample size. Meta-regression analyses were also performed to explore the influence of okra dosage and duration on cardiovascular and metabolic risk factors, aiming to identify dose–response relationships and the effects of varying supplementation periods on health outcomes(Reference Orsini, Bellocco and Greenland24). A non-linear model was employed to examine the dose–response relationship between okra supplementation and health outcomes(Reference Xu and Doi25), providing insights into how different dosages and durations impact results and identifying any optimal dose for maximum benefit.
An influence analysis was conducted to determine the impact of individual studies on the overall effect size by assessing the consistency of results when specific studies were excluded. Publication bias was evaluated through visual inspection of funnel plots and statistical tests, including Egger’s and Begg’s tests, to determine if the observed results might be influenced by the selective publication of studies with positive outcomes(Reference Egger, Smith and Schneider26).
Results
Study selection
The selection process of the included studies is outlined in Fig. 1. A total of 2458 studies were identified through database searches, including PubMed (n 146), ISI Web of Science (n 578), Scopus (n 873), Embase (n 789) and Cochrane Library (n 72). After removing 728 duplicates, 198 irrelevant studies and 144 animal studies, 1388 studies remained for title and abstract screening. Of these, 1356 studies were excluded due to irrelevance, leaving 32 full-text studies for further evaluation. Ultimately, five studies were excluded due to reporting non-relevant outcomes. Online Supplementary Table 3 provides details on the studies excluded after full-text review and the reasons for their exclusion. As a result, 12 studies involving 770 participants were included in the systematic review and meta-analysis(Reference Chen, Wang and Sha10–Reference Uebelhack, Bongartz and Seibt15,Reference Khodija, Wiboworini and Kartikasari27–Reference Damayanthi, Dewi and Aries32) .
Flow chart of study selection for inclusion trials in the systematic review.
Study characteristics
The characteristics of the included studies are summarised in Table 1. The SMD and 95 % CI for BMI, fat-free mass (FFM), fat mass (FM), hip circumference (HC), waist circumference (WC), body weight, diastolic blood pressure (DBP), systolic blood pressure (SBP), fasting blood insulin, fasting blood sugar (FBS), HbA1c, homeostatic model assessment of insulin resistance (HOMA-IR), HDL-cholesterol, LDL-cholesterol, total cholesterol (TC), TAG, alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine, along with their changes, are presented in Figs. 2–6. The studies were published between 2019 and 2024 and were conducted in Iran, Germany, Indonesia and China. Participants in the intervention group had a mean age ranging from 21·7 to 62 years. The dosage of okra administered varied from 125 mg/d to 40 000 mg/d, with intervention durations ranging from 2 to 12 weeks. The sample size in the intervention groups ranged from 10 to 50 participants. One study included only female participants(Reference Salarfard, Abedian and Mazlum31), while the others included both genders. The study populations comprised individuals with diabetic nephropathy(Reference Bahreini, Saghafi-Asl and Nikpayam11,Reference Nikpayam, Safaei and Bahreyni29) , overweight and moderately obese individuals(Reference Uebelhack, Bongartz and Seibt15,Reference Peng, Cooper and De Costa30) , prediabetes(Reference Afsharmanesh, Mansourian and Saghaeian Jazi13), type 2 diabetes mellitus(Reference Tavakolizadeh, Peyrovi and Ghasemi-Moghaddam12,Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14,Reference Moradi, Tarrahi and Ghasempour28) , healthy individuals(Reference Damayanthi, Dewi and Aries32), gestational diabetes mellitus(Reference Salarfard, Abedian and Mazlum31), impaired glucose tolerance(Reference Chen, Wang and Sha10) and type 2 diabetes mellitus with hypercholesterolaemia(Reference Khodija, Wiboworini and Kartikasari27) subjects.
Characteristic of included studies in the meta-analysis
2 h PPG, 2-h postprandial blood glucose; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; B, both sex; BUN, blood urea nitrogen; CON, control group; DB, double-blinded; DBP, diastolic blood pressure; F, female; FBS, fasting blood sugar; FFM, fat free mass; GDM, gestational diabetes mellitus; GGT, gamma-glutamyl transferase; HC, hip circumference; HOMA-IR, homeostatic model assessment of insulin resistance; hs-CRP, high-sensitivity C-reactive protein; IGT, impaired glucose tolerance; INT, intervention group; QUICKI, Quantitative Insulin Sensitivity Check Index; RCT, randomised controlled trial; SBP, systolic blood pressure; SOD, superoxide dismutase activity; T2DM, type 2 diabetes mellitus; TB, triple-blinded; TC, total cholesterol; WC, waist circumference; WHR, waist-to-hip ratio.
Forest plot of the effects of okra supplement on anthropometric measures ((a) BMI, (b) FFM, (c) FM, (d) HC, (e) WC, (f) weight).
Forest plot of the effects of okra supplement on blood pressure ((a) DBP, (b) SBP).
Forest plot of the effects of okra supplement on glycaemic profile ((a) fasting insulin, (b) FBS, (c) HbA1c, (d) HOMA-IR).
Forest plot of the effects of okra supplement on lipid profile ((a) HDL-cholesterol, (b) LDL-cholesterol, (c) TC, (d) TAG).
Forest plot of the effects of okra supplement on liver function tests ((a) ALP, (b) ALT, (c) AST, (d) creatinine).
Sample sizes for the intervention and control groups were as follows: BMI = 465 (intervention: 258, control: 207), FFM = 212 (intervention: 130, control: 82), FM = 212 (intervention: 130, control: 82), HC = 212 (intervention: 130, control: 82), WC = 311 (intervention: 180, control: 131), weight = 327 (intervention: 190, control: 137), DBP = 308 (intervention: 158, control: 150), SBP = 308 (intervention: 158, control: 150), fasting insulin = 214 (intervention: 108, control: 106), FBS = 535 (intervention: 292, control: 243), HbA1c = 415 (intervention: 228, control: 187), HOMA-IR = 214 (intervention: 108, control: 106), HDL-cholesterol = 540 (intervention: 293, control: 247), LDL-cholesterol = 540 (intervention: 293, control: 247), TC = 540 (intervention: 293, control: 247), TAG = 540 (intervention: 293, control: 247), ALP = 266 (intervention: 153, control: 113), ALT = 425 (intervention: 233, control: 192), AST = 365 (intervention: 203, control: 162) and creatinine = 326 (intervention: 183, control: 143).
Qualitative data assessment
According to the Cochrane Risk of Bias Assessment tool, all studies were assessed as having a high risk of bias(Reference Chen, Wang and Sha10–Reference Uebelhack, Bongartz and Seibt15,Reference Khodija, Wiboworini and Kartikasari27–Reference Damayanthi, Dewi and Aries32) (Table 2).
Quality of included studies in the meta-analysis
H, high risk of bias; L, low risk of bias; U, unclear risk of bias.
Effects of okra supplement on anthropometric measures
Okra supplementation demonstrated significant effects on BMI (SMD = −0·70; 95 CI%: −1·23, −0·16; P = 0·011; I 2 = 86·5 %, P < 0·001) (Fig. 2), FM (SMD = −0·74; 95 CI%: −1·13, −0·36; P < 0·001; I 2 = 43·8 %, P = 0·149), HC (SMD = −0·85; 95 CI%: −1·41, −0·28; P = 0·003; I 2 = 72·4 %, P = 0·012) and weight (SMD = −0·77; 95 CI%: −1·42, −0·11; P = 0·022; I 2 = 87·0 %, P < 0·001). However, no significant changes were observed in FFM (SMD = −0·13; 95 CI%: −0·41, 0·15; P = 0·349; I 2 = 0·0 %, P = 0·679) or WC (SMD = −0·57; 95 CI%: −1·15, 0·01; P = 0·054; I 2 = 82·8 %, P < 0·001).
Sensitivity analyses for BMI, FFM, FM and HC revealed that excluding any of the studies did not alter the overall findings. However, exclusion of the studies by Nikpayam et al. (Reference Nikpayam, Safaei and Bahreyni29) (SMD = –0·73, 95 % CI –1·42, –0·05) and Saatchi et al. (Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14) (SMD = –0·75, 95 % CI –1·37, –0·13) notably altered the effect on WC. Similarly, excluding the studies by Uebelhack et al. (Reference Uebelhack, Bongartz and Seibt15) (SMD = –0·73, 95 % CI –1·51, 0·05), Uebelhack et al. (Reference Uebelhack, Bongartz and Seibt15) (SMD = –0·58, 95 % CI –1·24, 0·08) and Peng et al. (Reference Peng, Cooper and De Costa30) (SMD: –0·54, 95 % CI –1·14, 0·54) significantly changed the effect on weight. No evidence of publication bias was detected for FFM (Egger’s P = 0·859), FM (Egger’s P = 0·257), HC (Egger’s P = 0·193) and WC (Egger’s P = 0·094). However, Egger’s test indicated significant asymmetry for BMI (Egger’s P = 0·020) and weight (Egger’s P = 0·009).
Effects of okra supplement on blood pressure
Studies investigating the effects on DBP and SBP showed no significant reductions, with results for DBP (SMD = −0·15; 95 % CI −0·37, 0·08; P = 0·195; I 2 = 0·0 %, P = 0·966) (Fig. 3) and SBP (SMD = −0·13; 95 % CI −0·56, 0·30; P = 0·551; I 2 = 71·9 %, P = 0·014), respectively.
Sensitivity analyses for DBP and SBP indicated that excluding any of the studies did not alter the overall findings. No evidence of publication bias was found for DBP (Egger’s P = 0·661) and SBP (Egger’s P = 0·173).
Effects of okra supplement on glycaemic profile
Significant improvements were observed across all indices of this group, including fasting insulin (SMD = −0·35; 95 % CI −0·63, −0·07; P = 0·013; I 2 = 4·6 %, P = 0·351) (Fig. 4), FBS (SMD = −1·07; 95 % CI −1·75, −0·38; P = 0·002; I 2 = 91·7 %, P < 0·001), HbA1c (SMD = −0·38; 95 % CI −0·71, −0·05; P = 0·023; I 2 = 61·7 %, P = 0·023) and HOMA-IR (SMD = −0·56; 95 % CI −0·84, −0·29; P < 0·001; I 2 = 0·0 %, P = 0·858).
Sensitivity analyses for FBS and HOMA-IR indicated that excluding any of the studies did not change the overall conclusions. However, excluding the studies by Moradi et al. (Reference Moradi, Tarrahi and Ghasempour28) (SMD = –0·38, 95 % CI –0·85, 0·10) and Chen et al. (Reference Chen, Wang and Sha10) (SMD = –0·24, 95 % CI –0·55, 0·08) significantly changed the effect on fasting insulin. Similarly, excluding the studies by Tavakolizadeh et al. (Reference Tavakolizadeh, Peyrovi and Ghasemi-Moghaddam12) (SMD = –0·30, 95 % CI –0·69, 0·84), Saatchi et al. (Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14) (SMD = –0·27, 95 % CI –0·56, 0·03) and Moradi et al. (Reference Moradi, Tarrahi and Ghasempour28) (SMD: –0·39, 95 % CI –0·78, 0·01) significantly altered the effect on HbA1c. No evidence of publication bias was detected for fasting insulin (Egger’s P = 0·333) and FBS (Egger’s P = 0·475). However, significant asymmetry was observed for HbA1c (Egger’s P = 0·030) and HOMA-IR (Egger’s P = 0·006).
Effects of okra supplement on lipid profile
Okra supplementation significantly reduced LDL-cholesterol (SMD = −0·32; 95 % CI −0·52, −0·11; P = 0·003; I 2 = 29·4 %, P = 0·193) (Fig. 5) and TC (SMD = −0·45; 95 % CI −0·74, −0·16; P = 0·003; I 2 = 63·3 %, P = 0·008). In contrast, it did not result in significant changes in HDL-cholesterol (SMD = 0·13; 95 % CI −0·15, 0·41; P = 0·354; I 2 = 59·7 %, P = 0·015) or TAG (SMD = −0·24; 95 % CI −0·50, 0·02; P = 0·069; I 2 = 53·5 %, P = 0·035).
Sensitivity analyses for HDL-cholesterol, LDL-cholesterol and TC confirmed that excluding any study did not alter the overall findings. However, excluding studies by Saatchi et al. (Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14) (SMD = –0·35, 95 % CI –0·54, –0·16) and Chen et al. (Reference Chen, Wang and Sha10) (SMD = –0·29, 95 % CI –0·57, –0·01) notably changed the overall effect on TAG. No evidence of publication bias was found for HDL-cholesterol (Egger’s P = 0·620), LDL-cholesterol (Egger’s P = 0·921), TC (Egger’s P = 0·730) or TAG (Egger’s P = 0·415).
Effects of okra supplement on liver function tests
AST levels were the only parameter significantly affected by okra supplementation in this group (SMD = −0·45; 95 % CI −0·73, −0·17; P = 0·002; I 2 = 39·4 %, P = 0·159) (Fig. 6). However, no substantial changes were observed in ALP (SMD = 0·03; 95 % CI −0·28, 0·34; P = 0·834; I 2 = 33·8 %, P = 0·209), ALT (SMD = −0·29; 95 % CI −0·71, 0·12; P = 0·164; I 2 = 76·3 %, P = 0·001) and creatinine levels (SMD = −0·12; 95 % CI −0·36, 0·12; P = 0·327; I 2 = 10·6 %, P = 0·346).
Sensitivity analysis conducted for ALP, ALT, AST and creatinine revealed that excluding any of the studies did not alter the overall findings. No publication bias was detected for ALP (Egger’s P = 0·198), ALT (Egger’s P = 0·485), AST (Egger’s P = 0·588) or creatinine (Egger’s P = 0·146).
Subgroup analysis
Subgroup analyses based on country (Iran v. other countries such as China, Germany and Indonesia), health status (prediabetic or diabetic v. non-diabetic), age (≤ 50 years v. > 50 years), baseline BMI (healthy weight (≤ 25 kg/m²) v. overweight or obese (> 25 kg/m²)), intervention duration (≤ 8 weeks v. > 8 weeks), dosage (≤ 2000 mg/d v. > 2000 mg/d) and sample size (< 60 v. ≥ 60 participants) are summarised in Table 3.
Description of the analysis and subgroup results of okra supplementation on CVD risk factors
NA, not applicable, The boldface values in table indicate that those metrics are statistically significant.
Okra supplementation showed greater benefits for FBS, HbA1c, LDL-cholesterol, TC and AST in studies conducted in Iran. In contrast, BMI, WC and weight were more significantly affected in studies from other countries. No substantial effects on HDL-cholesterol, TAG, ALP or creatinine levels were observed based on country.
For health status, okra supplementation had a more pronounced effect on FBS, HbA1c, LDL-cholesterol, TC and AST in prediabetic or diabetic individuals. In contrast, BMI, WC and weight were significantly affected in non-diabetic individuals. No significant effects on HDL-cholesterol, TAG, ALT or creatinine were observed in either group.
Subjects younger than 50 years showed significant changes in BMI, WC, weight and AST, while those aged 50 and above had significant reductions in FBS, HbA1c, LDL-cholesterol, TC and creatinine. No significant effects were observed in either age group for HDL-cholesterol, TAG or ALT.
For baseline BMI, okra supplementation impacted overweight or obese individuals’ BMI, HbA1c, weight and AST, while significant changes in FBS, LDL-cholesterol, TC and TAG were observed in both healthy weight and overweight/obese groups. No significant effects were observed for WC, HDL-cholesterol, ALT or creatinine in either BMI category.
Okra supplementation showed a significant impact on FBS and AST in interventions lasting ≤ 8 weeks, while BMI, WC, weight, LDL-cholesterol, TC and TAG were more significantly affected in studies lasting > 8 weeks. No significant effects on HbA1c, HDL-cholesterol, ALT or creatinine were observed in either duration category.
For dosages ≤ 2000 mg/d, significant effects were seen in BMI, WC, weight and TAG. In contrast, doses > 2000 mg/d significantly impacted FBS, HbA1c, LDL-cholesterol and AST. Significant effects on TC were observed in both dosage groups, but no significant effects were seen for HDL-cholesterol, ALT or creatinine.
In terms of sample size, okra supplementation significantly impacted BMI, WC, weight and TAG in studies with < 60 participants, while studies with ≥ 60 participants showed significant effects on FBS, HbA1c, LDL-cholesterol and AST. Significant effects on TC were observed in both sample size categories, but no significant effects were seen for HDL-cholesterol, ALT or creatinine.
Meta-regression and non-linear dose–response analysis
Meta-regression analysis assessing the impact of okra doses and intervention duration on cardiovascular risk variables is presented in online Supplementary Figs. 1–4. A non-linear dose–response regression model was used to explore the relationship between okra supplementation and cardiovascular outcomes. A significant association was found between okra dose and weight reduction (coefficient = 2·47, P < 0·001). The dose–response curve indicated that the optimal dose for weight reduction is approximately 2000 mg/d. Similarly, a significant association was observed between okra dose and HbA1c reduction (coefficient = 0·52, P = 0·016). The dose–response curve suggested that the optimal dose for reducing HbA1c levels is around 3000 mg/d.
GRADE assessment
The GRADE profile for the outcomes of okra supplementation is presented in Table 4. The quality of evidence was rated as very low for BMI, FFM, HC, WC, weight, DBP, SBP, HbA1c, HOMA-IR, HDL, TAG, ALP, ALT and creatinine and low for FM, fasting insulin, FBS, TC and AST. The quality of evidence for LDL-cholesterol was rated as moderate. While okra supplementation shows promise in clinical practice, further research is needed to confirm these findings across different populations.
GRADE profile of okra supplementation on cardiovascular risk factors
ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate Aminotransferase; CON, control group; DBP, diastolic blood pressure; FBS, fasting blood sugar; FFM, fat-free mass; FM, fat mass; HC, hip circumference; HOMA-IR, homeostatic model assessment of insulin resistance; INT, intervention group; SBP, systolic blood pressure; SMD, standardised mean difference; TC, total cholesterol; WC, waist circumference.
Explanations.
* Downgraded since more than 50 % of the participants were from high risk of bias studies.
† The I 2 value was > 50 % (or heterogeneity among the studies was high).
‡ Publication bias was detected through Egger and Begg’s test (P-value < 0·05).
§ Downgraded since the 95 % CI crosses the threshold of interest.
|| Downgraded since the participants included were less than 400 persons.
¶ Downgraded for indirectness in country.
Discussion
Summary of findings
This is the first GRADE-assessed systematic review and dose–response meta-analysis evaluating the effects of okra supplementation on cardiovascular outcomes in adults. Our analysis included 12 randomised controlled trials with 770 participants. The results indicated that okra supplementation positively affects BMI, FM, HC, weight, fasting insulin, fasting plasma glucose, HbA1c, HOMA-IR, LDL-cholesterol, TC and AST.
In terms of anthropometric indices, subgroup analyses revealed a significant reduction in BMI, WC and weight in studies conducted outside of Iran as well as studies conducted on individuals under the age of 50, without prediabetes or diabetes, with overweight or obesity and those who received a daily dosage of less than 2000 mg of okra for more than 8 weeks.
With respect to glycaemic control, significant improvements in fasting plasma glucose and HbA1c levels were observed in Iranians over the age of 50 with prediabetes or diabetes who received dosages of 2000 mg or more of okra. Significant decreases in LDL-cholesterol, TC and TAG were also noted in this group when given okra for over 8 weeks at doses less than 2000 mg daily. However, no changes in HDL-cholesterol levels were observed in any of the subgroups. AST levels in Iranians under the age of 50 who were overweight or obese, had prediabetes or diabetes and took doses greater than 2000 mg daily for less than 8 weeks dropped significantly. Additionally, our investigation identified no effect of okra on blood pressure.
Comparison with previous studies
Our findings support the hypoglycaemic effects of okra reported in earlier animal and human studies(Reference Khodija, Wiboworini and Kartikasari27,Reference Salarfard, Abedian and Mazlum31,Reference Sabitha, Ramachandran and Naveen33,Reference Wu, Weng and Zheng34) . However, there are discrepancies with other studies, which may be attributed to variations in study design or sample populations. For example, Saatchi et al. reported no significant changes in HDL-cholesterol, LDL-cholesterol, TC, TAG, BMI, WC, AST, ALT, SBP or DBP after 8 weeks of consuming 4000 mg/d of whole okra fruit in type 2 diabetes mellitus subjects, despite significant reductions in HbA1c, FBS and blood sugar levels(Reference Saatchi, Aghamohammadzadeh and Beheshtirouy14). In contrast, another study found significant reductions in serum TC, LDL-cholesterol, ALT and uric acid levels, as well as an increase in HDL-cholesterol levels after 8 weeks of administering 3000 mg of okra to prediabetic patients(Reference Afsharmanesh, Mansourian and Saghaeian Jazi13).
In contrast to our findings, Nikpayam et al. found that diabetic nephropathy patients who received 125 mg of dried okra for 10 weeks experienced a decrease in energy and carbohydrate intake without affecting body composition or anthropometric measurements(Reference Nikpayam, Safaei and Bahreyni29). Additionally, Peng et al. reported that supplementation with IQP-AE-103, a blend of dehydrated okra powder and inulin, resulted in changes in microbiota composition and weight loss, highlighting the synergistic effects of these ingredients(Reference Peng, Cooper and De Costa30). Studies in animals also present inconsistencies. Fan et al. observed improved glucose tolerance, decreased TAG levels and altered liver morphology in obese mice, linked to suppressed PPARγ nuclear receptor expression(Reference Fan, Zhang and Sun35). Conversely, Anjani et al. found that neither green nor purple okra extract affected body weight in diabetic rats. However, both extracts were effective in repairing streptozotocin-induced pancreatic β-cell damage, with purple okra extract showing greater antidiabetic potency, likely due to its higher quercetin content(Reference Anjani, Damayanthi and Rimbawan, Handharyani36).
Mechanisms of action
Insulin resistance is a primary mechanism underlying type 2 diabetes mellitus, along with disordered glucose and lipid metabolism(37). Okra, rich in flavonoids such as ursolic acid and quercetin, may enhance glucose absorption and insulin sensitivity by regulating insulin levels(Reference Sabitha, Ramachandran and Naveen33). Additionally, okra modulates PPAR, which are crucial for lipid and glucose homeostasis in the pancreas, potentially improving FBS levels(Reference Erfani Majd, Tabandeh and Shahriari38). Okra’s inhibitory effects on α-glucosidase and α-amylase provide a plausible mechanism for its ability to lower FBS levels(Reference Sabitha, Panneerselvam and Ramachandran39). By inhibiting these carbohydrate-digesting enzymes, okra supports better blood glucose control and reduced HbA1c levels.
The high content of soluble and insoluble fibre in Abelmoschus esculentus L. promotes satiety and reduces overall calorie consumption, aiding weight loss and reducing BMI. Okra’s soluble fibre binds to bile acids in the gut, potentially lowering oxidative stress and cholesterol, which are linked to lipid metabolism(Reference Nikpayam, Safaei and Bahreyni40). Visceral fat, related to WC and HC, is strongly associated with insulin resistance and metabolic syndrome. Okra’s hypoglycaemic properties and weight-loss effects may help reduce HC and WC(Reference Frayn41).
One of okra’s main polysaccharides, AeP-P-1, activates signalling molecules in the PI3K/Akt pathway in liver tissue, restoring partial kidney and liver function in type 2 diabetic mice(Reference Geng, Pan and Sun42). Quercetin and other polyphenols in okra protect the liver from inflammatory and oxidative stress, aiding in its normal function(Reference Khomsug, Thongjaroenbuangam and Pakdeenarong43). Additionally, vitamin C in okra may chelate iron, reducing oxidative stress by limiting the availability of iron to catalyse reactive oxygen species formation, which is a major cause of liver inflammation(Reference Khomsug, Thongjaroenbuangam and Pakdeenarong43).
While the molecular mechanisms remain unclear, previous studies have suggested that reduced lipogenesis due to decreased expression of SREBP1 and FAS genes, enhanced cholesterol breakdown mediated by CYP7A1, reduced cholesterol absorption due to regulation of the of PPAR-NPC1L1 pathway, and interactions with bile acids. The high fibre content of okra plays a critical role in these processes. Soluble fibre binds cholesterol in the intestines, preventing its absorption into the bloodstream(Reference Esmaeilzadeh, Razavi and Hosseinzadeh8). Additionally, okra’s flavonoids and phytosterols can prevent oxidative damage to lipids, thereby reducing atherosclerosis risk. Due to its antioxidant properties and ability to lower lipid levels, okra benefits cardiovascular health(Reference Islam44).
Beyond the established mechanisms, okra’s therapeutic effects likely involve complex interactions with multiple physiological pathways. Recent evidence suggests that okra’s bioactive compounds modulate inflammatory markers, including a reduction in pro-inflammatory cytokines such as TNF-α, IL-6 and IL-1β, while enhancing anti-inflammatory mediators(Reference Panighel, Ferrarese and Lupo45). The gut microbiota plays a crucial role, as okra’s prebiotic components, particularly its rich mucilage content, promote the growth of beneficial bacteria like Bifidobacterium and Lactobacillus species, enhancing metabolic health through improved gut barrier function and reduced inflammation(Reference Zhou, Yang and Qiu46). Hormonal influences extend beyond insulin regulation, affecting adiponectin and leptin levels, which are crucial for energy homeostasis and glucose regulation(Reference Esmaeilzadeh, Razavi and Hosseinzadeh8). Genetic factors, particularly polymorphisms in genes related to glucose metabolism (such as GLUT4) and lipid metabolism (including PPAR-γ and Sterol regulatory element-binding protein 1 (SREBP-1c)), may explain individual variations in response to okra supplementation(Reference Aligita, Muhsinin and Wijaya47). Furthermore, okra’s interaction with other dietary components, such as its potential to enhance the bioavailability of other nutrients and its synergistic effects with other antioxidants, suggests a broader role in metabolic regulation than previously recognised(Reference Gemede, Haki and Beyene48).
The substantial heterogeneity observed in several outcomes, particularly in BMI (I² = 86·5 %), WC (I² = 82·8 %), weight (I² = 87·0 %) and FBS (I² = 91·7 %), warrants careful consideration when interpreting our findings. This heterogeneity likely stems from multiple sources, including variations in participant characteristics across studies (such as baseline BMI, age and metabolic status), differences in okra preparation methods (ranging from whole fruit consumption to concentrated extracts) and diverse measurement techniques employed across research centres(Reference Rhodes, Turner and Savović49). Cultural and dietary differences between countries may have influenced baseline nutritional status and dietary patterns, potentially affecting the response to okra supplementation. For instance, studies conducted in Iran showed different patterns of response compared with those in other countries, possibly due to variations in traditional dietary habits and lifestyle factors. Additionally, the lack of standardisation in okra processing methods, including drying techniques, extraction procedures and storage conditions, may have contributed to the observed heterogeneity by affecting the bioavailability and potency of active compounds(Reference Liu, Qi and Luo50,Reference Falade and Omojola51) .
Clinical implications of findings
The findings of this meta-analysis have significant clinical implications for healthcare practitioners managing patients with metabolic disorders. For patients with prediabetes or diabetes, particularly those over 50 years of age, okra supplementation at doses of 2000 mg or higher shows promising effects on glycaemic control, as evidenced by significant reductions in fasting plasma glucose and HbA1c levels. This suggests that okra supplementation could serve as a valuable adjunctive therapy alongside standard diabetes management protocols. Additionally, the observed improvements in lipid profiles, particularly the reduction in LDL-cholesterol and TC with doses below 2000 mg daily over 8-week periods, indicate that okra supplementation might be particularly beneficial for diabetic patients with concurrent dyslipidaemia, potentially reducing their cardiovascular risk burden.
For clinicians treating overweight or obese patients under 50 years of age without diabetes, okra supplementation presents a different therapeutic opportunity. The significant reductions in BMI, WC and body weight observed in this population, particularly with daily doses below 2000 mg over extended periods (> 8 weeks), suggest that okra could be a useful addition to weight management programmes. The absence of significant effects on blood pressure and the favourable liver function profile, as indicated by improved AST levels, suggests that okra supplementation is generally safe and well-tolerated across different patient populations. However, clinicians should note the varying responses between different demographic groups and consider tailoring dosage and duration of okra supplementation based on individual patient characteristics and therapeutic goals.
Strengths and limitations
One of the key strengths of our meta-analysis is the rigorous methodology employed throughout the study. By adhering to the PRISMA guidelines and registering the study with PROSPERO, we ensured transparency and reproducibility, setting a high standard for systematic reviews. The comprehensive search strategy, which included multiple electronic databases and manual screening of reference lists, allowed us to capture a broad spectrum of relevant studies, thereby minimising the risk of missing critical data. Our use of the GRADE framework to assess the strength of evidence further enhances the reliability of our findings, offering a nuanced evaluation of the quality and consistency of the included studies. Additionally, the application of advanced statistical techniques, including meta-regression and dose–response analyses, enabled us to explore the effects of okra supplementation across diverse populations and health outcomes, providing valuable insights into optimal dosing and treatment durations. This robust approach not only reinforces the validity of our conclusions but also contributes significantly to the scientific understanding of okra’s potential therapeutic benefits.
Our study presents several key advantages over the last meta-analysis, which focuses exclusively on the effects of okra on dyslipidaemia(Reference Mokgalaboni, Phoswa and Mokgalabone52). While the earlier study provides valuable insights into the lipid-modulating properties of okra, our meta-analysis extends the scope by evaluating the effects of okra across a broad spectrum of CVD risk factors, including anthropometric measures, blood pressure, glycaemic control, lipid profile and liver function tests. This comprehensive approach offers a holistic view of okra’s potential therapeutic benefits, which were not explored in the earlier study. Additionally, our meta-analysis includes a substantially larger dataset of 770 participants across 12 trials, compared with just 8 trials in the earlier work. This increased sample size enhances the robustness and generalisability of our findings. Furthermore, while the earlier study focused on lipid markers alone, we employed advanced statistical methods, including dose–response analysis and meta-regression, providing a nuanced understanding of the optimal dosage and treatment duration for different populations. Our inclusion of diverse health outcomes and sophisticated methodology strengthens the evidence base for okra as a multifaceted intervention for CVD risk reduction.
In contrast to the recent meta-analysis, which focuses solely on the effects of okra on glycaemic control in prediabetic and type 2 diabetic patients(Reference Mokgalaboni, Lebelo and Modjadji18), our study expands the focus to encompass a wider range of metabolic and cardiovascular health outcomes. While the earlier study provides valuable evidence on glycaemic control, it does not investigate other critical factors like lipid profiles, liver function or anthropometric parameters, which are essential in understanding the full impact of okra on metabolic health. By including a broader set of health outcomes, our study provides a more comprehensive assessment of okra’s therapeutic potential across different disease contexts. Moreover, while the earlier study included 331 patients across eight trials, our meta-analysis evaluates data from a larger number of participants (770 across 12 trials), thus improving the reliability and precision of our findings. Another key advantage is our use of the GRADE framework to assess the quality of the evidence, a methodology not employed in the second study. This approach allows us to provide a more detailed evaluation of the reliability and consistency of the included studies, offering valuable insights for clinical practice. In addition, our study incorporates subgroup analyses that reveal important patterns in treatment response based on factors such as baseline BMI, age and health status – insights that are critical for personalised treatment strategies but were not explored in the earlier analysis.
Despite the strengths of our meta-analysis, several limitations must be acknowledged. The relatively small number of included studies and the variation in their design and methodology introduce a degree of heterogeneity that could influence the overall results. Even with the use of a random-effects model to address this variability, it is not possible to eliminate the likelihood of remaining confounding. Furthermore, the exclusion of non-English articles may have led to language bias, limiting the generalisability of our findings across various cultural and geographical settings. Despite our efforts to reduce this through comprehensive search strategies and statistical tests, another limitation is the reliance on reported data from published studies, which may be subject to publication bias.
Furthermore, another limitation of our study was the use of the SMD instead of the weighted mean difference, which was necessitated by the heterogeneous units reported for key outcomes, such as insulin levels, inflammatory markers and liver function tests. Although the weighted mean difference would have provided more clinically interpretable results, the lack of access to raw data for unit conversion made the use of SMD the most scientifically appropriate choice for ensuring comprehensive inclusion of relevant studies.
Additionally, okra supplementation can involve different parts of the plant, each offering distinct health benefits. The lack of specificity regarding the plant part that was used in the included studies introduces another layer of uncertainty to the outcomes. It is also important to note that some original studies used okra extract, while others used the crude okra plant. This inconsistency in the form of okra supplementation poses a potential limitation, as the doses of okra extract and crude plant are not directly comparable. Therefore, the interpretation and generalisation of the dose–response results should be approached with caution. Future studies should provide more detailed information on the okra intervention methods, and future meta-analyses focusing on these aspects are needed to validate and refine our dose–response findings. Lastly, while our analysis included a range of health outcomes, the specific effects of okra on certain parameters, such as long-term cardiovascular risk, remain underexplored due to the short duration of most included trials. These limitations highlight the need for further large-scale, high-quality RCT to confirm and extend our findings.
Future research directions
Future research should prioritise investigating the optimal dosing strategies for different patient populations, particularly considering the varying effects observed between diabetic and non-diabetic individuals, age groups and BMI categories. Long-term randomised controlled trials spanning beyond 8 weeks are needed to establish the sustained efficacy and safety of okra supplementation, especially in patients with multiple comorbidities. Additionally, studies should explore potential drug interactions between okra supplements and commonly prescribed medications for diabetes, hypertension and dyslipidaemia, as these could significantly impact clinical recommendations.
The mechanisms underlying okra’s differential effects on various metabolic parameters warrant further investigation through well-designed molecular studies. Future research should focus on identifying specific bioactive compounds responsible for okra’s beneficial effects and their molecular targets, particularly in relation to glucose metabolism and lipid homeostasis. Studies examining the role of gut microbiota modulation in okra’s therapeutic effects could provide valuable insights into its mechanism of action and potentially lead to more targeted therapeutic approaches.
Investigation into the potential synergistic effects of okra with other natural compounds or conventional medications could open new therapeutic possibilities. Given the observed variations in efficacy between different populations, studies examining genetic polymorphisms and their influence on individual responses to okra supplementation could help develop personalised treatment approaches. Additionally, research comparing different forms of okra supplementation (whole fruit, extract or specific compounds) could help optimise delivery methods and enhance therapeutic outcomes.
There is also a critical need for research examining okra’s effects on specific subpopulations not well-represented in current studies, such as individuals with metabolic syndrome, gestational diabetes or concurrent autoimmune conditions. While most research has focused on okra’s impact on glycaemic control, other potential benefits, such as its anti-cancer, liver-protective and kidney-protective effects, have not been adequately investigated. Future clinical trials should explore the effects of specific plant parts on various health outcomes. Moreover, expanding research to include molecular mechanisms by which okra exerts its beneficial effects is essential. Advanced molecular biology and omics approaches could help identify the specific bioactive compounds in okra and their interactions with other dietary components. Exploring the potential incorporation of okra into functional foods or nutraceuticals through multidisciplinary studies could inform the formulation of practical dietary guidelines and interventions. Furthermore, cost-effectiveness analyses comparing okra supplementation with conventional treatments would provide valuable information for healthcare policymakers and insurance providers considering coverage for complementary therapies.
Conclusions
In conclusion, this meta-analysis provides strong evidence that okra supplementation offers significant health benefits for cardiometabolic health. These benefits include improved glycaemic control, lipid profile and liver function. Employing statistical methods to combine data from various RCT, our results indicated that okra has therapeutic potential and can serve as a dietary intervention for preventing and managing conditions such as diabetes, obesity and dyslipidaemia. The dose–response insights gained are crucial for formulating effective supplementation guidelines. Incorporating okra into dietary approaches has significant implications for public health and clinical practice. This comprehensive evaluation enhances the reliability of our findings, making them highly valuable for future research and practical applications in treating metabolic and cardiovascular disorders.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114525000303.
Acknowledgements
We acknowledge the use of ChatGPT (GPT-4, OpenAI’s large-scale language-generation model) for assistance in editing and improving the writing style of this article. The AI was used solely for language enhancement and not for creating the content or references of the manuscript. The authors carefully reviewed, edited and revised all AI-assisted texts and take full responsibility for the content and accuracy of this article.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Conceptualisation: A. J. Data curation: A. J., H. M., A. A. Formal analysis: A. J., A. S. Investigation: A. J., H. M., B. P. N., A. A., A. S. Methodology: A. J., A. S. Project administration: A. J., A. S. Software: A. J., H. M., A. S. Visualisation: A. J. Supervision: A. S. Validation: A. J., H. M., B. P. N., A. S. Writing – original draft: A. J., H. M., B. P. N. Writing – review and editing: A. J., A. A., A. S.
The authors whose names are listed in this article certify that they have no affiliations with or involvement in any organisation or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership or other equity interest; and expert testimony or patent-licencing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
All relevant data are provided within the manuscript and supplementary file. Additionally, data analysed for this study are available upon request from the corresponding author.
