Evaluating whole fruit feijoa powder for type 2 diabetes risk prevention: the FERDINAND study

Evaluating whole fruit feijoa powder for type 2 diabetes risk prevention: the FERDINAND RCT | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Evaluating whole fruit feijoa powder for type 2 diabetes risk prevention: the FERDINAND RCT I.A. Mohamed , I.R. Sequeira-Bisson , J.J. Lim , L.D. Plank , R. Murphy , S.D. Poppitt , J.L. Miles-Chan doi: https://doi.org/10.1101/2025.06.18.25329000 I.A. Mohamed 1 Human Nutrition Unit, School of Biological Sciences, Faculty of Science, University of Auckland , Auckland, New Zealand 2 High Value Nutrition National Science Challenge , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site I.R. Sequeira-Bisson 1 Human Nutrition Unit, School of Biological Sciences, Faculty of Science, University of Auckland , Auckland, New Zealand 2 High Value Nutrition National Science Challenge , Auckland, New Zealand 3 Riddet Institute, Massey University , Palmerston North, New Zealand. Find this author on Google Scholar Find this author on PubMed Search for this author on this site J.J. Lim 1 Human Nutrition Unit, School of Biological Sciences, Faculty of Science, University of Auckland , Auckland, New Zealand 2 High Value Nutrition National Science Challenge , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site L.D. Plank 4 Department of Surgery, University of Auckland , Auckland, New Zealand. Find this author on Google Scholar Find this author on PubMed Search for this author on this site R. Murphy 2 High Value Nutrition National Science Challenge , Auckland, New Zealand 5 Department of Medicine, University of Auckland , Auckland, New Zealand 6 Auckland Diabetes Centre , Health New Zealand , Te Whatu Ora, Auckland, New Zealand. 7 Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site S.D. Poppitt 1 Human Nutrition Unit, School of Biological Sciences, Faculty of Science, University of Auckland , Auckland, New Zealand 2 High Value Nutrition National Science Challenge , Auckland, New Zealand 5 Department of Medicine, University of Auckland , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site J.L. Miles-Chan 1 Human Nutrition Unit, School of Biological Sciences, Faculty of Science, University of Auckland , Auckland, New Zealand 2 High Value Nutrition National Science Challenge , Auckland, New Zealand 3 Riddet Institute, Massey University , Palmerston North, New Zealand. 7 Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland , Auckland, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: j.miles-chan{at}auckland.ac.nz Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Low energy diets (LEDs) are effective for body weight (BW) loss and improvement of type 2 diabetes (T2D) risk biomarkers. Emerging evidence suggests that whole fruit feijoa powder, rich in polyphenols and abscisic acid, may further support T2D prevention. The FERDINAND study investigated whether daily consumption of a whole fruit feijoa powder enhanced LED-driven improvements in fasting plasma glucose (FPG), BW, and other metabolic markers. Methods At in-clinic screening, 97 participants were enrolled into the trial and randomised to receive 1.15 g/day of whole fruit feijoa powder (Fx, n=48) or Placebo treatment (n=49) for 6 months. All participants underwent 2 months of LED-induced weight loss, followed by 4 months of dietary advice for weight loss maintenance. BW, FPG, and secondary outcomes including blood pressure (BP), were assessed at baseline (M0), end of month 2 (M2), 4 (M4), and 6 (M6). Data were analysed using linear mixed-effects models as intention-to-treat (ITT) with imputation for missing not-at-random data points. Results BW significantly decreased in both treatment groups during the 2 month LED, with a gradual upward trajectory over the subsequent 4 months (time 0-6m : P<0.001). There was no difference in BW between Fx and Placebo over the 6 month intervention (treatment×time 0-6m : P=0.74). FPG followed the weight loss trajectory in both treatment groups (time 0-6m : P<0.001), but with no significant between-group interaction over 6 months (treatment×time 0-6m : P=0.09, n=97). Systolic BP (SBP), but not diastolic BP, declined significantly over the 6 months between treatments (treatment×time 0-6m : P=0.01) in all participants, with SBP in Fx lower than Placebo at M4 and M6 (P<0.05, both). Conclusions In adults with overweight and prediabetes, Fx supplementation may enhance blood pressure improvement achieved through BW loss. The polyphenolic matrix of whole fruit feijoa, which contains a high proportion of catechins, may have contributed to vascular benefits. Introduction Type 2 diabetes (T2D), which comprises 90-95 % of all diabetes cases ( 1 ), has become an ever-burgeoning strain on global health. An estimated 589 million adults worldwide currently have diabetes, with a 45% increase in cases projected by 2050 ( 2 ). In the last two decades, there has been a three-fold increase in T2D-related global health expenditure, which now exceeds USD 1 trillion ( 2 ). The number of individuals with prediabetes is even greater. Prediabetes is a state of dysglycaemia, whereby blood glucose levels are elevated above normal cut-offs, but below threshold for diabetes diagnosis. Over 1 billion adults worldwide have prediabetes, with cases expected to approach 1.5 billion by 2050 ( 2 ). Therefore, implementing targeted T2D prevention, especially during prediabetes, is critical in alleviating global health burden. Extensive evidence points to excess adiposity being the biggest risk factor for T2D ( 3 – 10 ). This coupled with the increasing prevalence of obesity, makes body weight (BW) loss key in T2D prevention. Bariatric surgery and pharmaceutical agents are widely used for BW loss. However, these tools come with several drawbacks, such as limited availability and prohibitive costs. A less invasive method for achieving BW loss is through commercial low or very low energy diets (LED or VLED), implemented using nutritionally complete meal replacement products ( 11 – 13 ). LEDs encompass a daily energy intake between 3347-5021 kJ, while energy intake ≤3347 kJ characterises VLEDs ( 12 , 14 ). A large body of evidence shows that LEDs and VLEDs, combined with behavioural support, are effective for BW loss and improving T2D biomarkers ( 15 – 23 ). Dietary composition plays an important role in T2D, with risk reduction associated with lowered refined grain intake and increased intake of whole grains, fruits, vegetables, legumes, and nuts ( 24 – 26 ). Consequently, identifying foods which may enhance proven strategies, such as LEDs, is of great importance. Feijoa ( Acca sellowiana ), also known as pineapple guava, is a fruit originating in South America and widely grown in New Zealand ( 27 , 28 ), with biochemical properties of promise in lowering T2D risk. Specifically, it is abundant in polyphenols and abscisic acid (ABA, a plant compound named for its role in abscission), particularly in the skin ( 29 , 30 ). Evidence of polyphenols eliciting T2D risk reduction has been well established ( 31 – 42 ), with evidence for ABA emerging recently ( 43 – 46 ). Mechanistically, ABA exhibits binding to pertussis toxin-sensitive G protein-coupled receptor (GPCR) ( 47 ), peroxisome proliferator-activated receptor gamma (PPAR-γ) ( 48 ), and lanthionine synthetase C-like protein 2 (LANCL2) ( 49 , 50 ). Activation of these targets promote intracellular signalling cascades involving cyclic adenosine monophosphate, cyclic adenosine diphosphate ribose, and protein kinase B phosphorylation, which enhance insulin secretion, glucose uptake, and translocation of glucose transporter type 4 (GLUT4) to the plasma membrane ( 51 – 56 ). To-date, only 2 studies (1 pre-clinical, 1 clinical) have investigated the effect of feijoa on BW and T2D biomarkers. In leptin deficient ob/ob mice, intake of feijoa extract (300 mg/kg BW/day) significantly decreased BW gain, liver organ weight, and circulating total cholesterol ( 57 ). In a small sample randomised controlled trial (RCT) of 34 adults with T2D, conducted in Iran, 150 mg/day of feijoa extract for 12 weeks significantly lowered fasting plasma glucose (FPG), glycated haemoglobin (HbA 1c ), and triglycerides (TAG), compared to placebo ( 58 ). We hypothesised that whole fruit feijoa powder when combined with meal replacement LED, would produce greater benefits than LED alone on acute and/or longer-term weight loss or weight loss maintenance, along with associated glycaemic and other metabolic outcomes. Therefore, the 6-month FERDINAND (evaluating FE ijoa fo R Di abetes prevention in a multi-ethnic N ew Ze A la ND cohort) study investigated daily consumption of 1150 mg/day whole fruit feijoa powder during 2-months of LED-driven weight loss followed by 4-months of weight maintenance, in a multiethnic New Zealand cohort with overweight and prediabetes. Methods Study Design The FERDINAND study was a two-arm, parallel design, double-blind RCT, of 6 months duration ( Figure 1 ). Participants underwent 2-months of LED-driven weight loss (i.e. LED phase) followed by 4-months of weight loss maintenance (i.e. maintenance phase). Participants consumed either whole fruit feijoa powder or Placebo powder every day for the entire study period. The whole fruit feijoa powder consisted of 1,150 mg of microcrystalline cellulose and 1,150 mg of freeze-dried powder with skin included (Feiolix®, Anagenix Ltd, Auckland, New Zealand; Fx). The Placebo powder sachet contained 2,300 mg of microcrystalline cellulose. The study received ethical approval from the New Zealand Health and Disabilities Committee (Reference: 2022 EXP 12032) and was also prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN: 12622000210774). The study was conducted at the Human Nutrition Unit (HNU), University of Auckland, New Zealand, from September 2022 to March 2024. Download figure Open in new tab Figure 1: FERDINAND Study Design. Participants Prospective participants living in the wider Auckland area were pre-screened via an online questionnaire and telephone interview, then invited to the HNU for an on-site screening visit. Prior to any data collection, individuals provided written informed consent. Prediabetes status was determined by FPG, using American Diabetes Association guidelines (ADA, 5.6-6.9 mmol/L) ( 1 ). Hypertension was defined as SBP ≥140 mmHg and/or DBP ≥90 mmHg (uncontrolled hypertension) and/or use of antihypertensive medications (controlled hypertension) ( 59 , 60 ). The other inclusion criteria were body mass index (BMI) ≥26 kg/m 2 , BW ≤150 kg, Finnish Diabetes Risk Score ≥12, otherwise healthy per self-report and able to attend clinical investigation day (CID) visits. Potential participants were excluded if they had current or history of significant disease, including type 1 or type 2 diabetes mellitus, cardiovascular disease, pancreatic disease, digestive diseases (e.g. inflammatory bowel syndrome/disease, ulcerative colitis, Crohn’s disease) and cancer, recent BW loss/gain >10% within previous three months or were taking part in an active diet program, previous bariatric surgery, current medication controlling glycaemia and weight loss, steroid medications (except topical steroids) or atypical antipsychotics within previous three months, were intending to alter physical activity during following twelve months, smoked, vaped or used recreational drugs within previous six months, or were pregnant or breastfeeding within previous six months. Additional exclusions included unwillingness to consume LED meal replacement products or adhere to the food avoidance requirements during the weight maintenance phase or hypersensitivities/allergies to these foods, not prepared to comply with the study protocol, or participation in other clinical intervention studies in the previous six months. Randomisation and Blinding Participants were randomly allocated in a 1:1 ratio to consume either Fx or Placebo powder following a computer-generated randomisation sequence, with the powders distinguished using anonymised codes and blinding conducted by an independent researcher who maintained confidentiality of the codes until the completion of the study. Participants and the study team remained blinded throughout the study period and were unblinded upon completion of data collection. Participants were randomised to treatment and commenced the study in multiple cohorts each consisting of 10-25 eligible participants. Procedures During the LED phase, participants consumed commercial, complete meal replacement products (Cambridge Weight Plan, Corby, UK), whereby 4 energy-controlled sachets, each containing approximately 840 kJ, were consumed every day. Each meal replacement sachet was reconstituted using 250 mL of water only. Furthermore, participants consumed the intervention powder every day with their first LED product of the day. Participants were prohibited from consuming any other foods or beverages (including coffee, tea, and non-caloric beverages apart from water) during the LED phase, to ensure that any observed effects were from Fx, rather than other foods which may contain the same, or additional, bioactive compounds. Participants were also instructed to drink at least 2.25 L water daily. During the LED phase, Benefiber (GlaxoSmithKline, London, UK) was provided upon request, to participants who self-reported constipation and/or bowel discomfort. Upon completion of the LED phase, weight loss maintenance was monitored for 4 months. Restrictions on food and beverage intake were mostly removed during this maintenance phase, with feijoa and feijoa-containing products being the only prohibited items. Participants continued consuming their assigned intervention powder every day with their breakfast meal. Group diet advice sessions were conducted by registered dietitians at baseline and throughout the study ( Figure 1 ). These sessions focused on behavioural strategies to enhance adherence to study protocol and long-term considerations for weight maintenance (e.g. meal planning, dealing with food cravings and how to navigate social gatherings). The advice adhered to the New Zealand Ministry of Health healthy eating guidelines for improving metabolic health ( 61 ). For both intervention powders and meal replacement products, participants were instructed to keep all opened and unopened sachets and return them at their following CID visit. It was assumed that opened sachets equated to consumed sachets. Participants were instructed to contact the study team if sachets were opened but not consumed. Furthermore, participants were contacted regularly throughout the study by a trained researcher, to discuss meal replacement and intervention powder compliance. The following calculations were used to assess compliance: Outcomes BW and FPG, were joint co-primary outcomes, while secondary outcomes comprised body composition, blood pressure, and blood biochemistry. All outcomes were assessed at M0/CID1 (baseline), M2/CID2 (post-LED), M4/CID3, and M6/CID4 (study end). Prior to BW, height, and body composition measurements, participants were asked to remove shoes, heavy clothing, headwear (if applicable), jewellery, and all items from their pockets. BW and height were measured in duplicate using a calibrated digital scale (Mettler Toledo Spider 2, Greifensee, Switzerland) and a wall-mounted stadiometer (Seca Model 222, Hamburg, Germany), respectively, and averaged for data analyses. BMI was then calculated using the average of the BW and height measurements. Waist circumference (WC) was measured using a non-stretchable measuring tape (Abbott Laboratories, Illinois, USA), placed horizontally around the abdomen at the level of the umbilicus. Participants inhaled and exhaled once, with the measurement recorded at the end of expiration in duplicate and averaged for data analysis. Body composition was measured, at the University of Auckland Body Composition Laboratory, by dual energy x-ray absorptiometry (DXA) using an iDXA scanner (GE Healthcare, Wisconsin, USA) and analysed using enCORE software (version 18, GE Healthcare, Wisconsin, USA). The following measures were calculated by proprietary algorithms in the enCORE software: total body fat mass (TBFm), total body fat-free mass (TBFFm), abdominal adipose tissue mass (AATm), visceral adipose tissue mass (VATm), and visceral adipose tissue percentage (VATp). Additional measures were also obtained using the following calculations: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in duplicate using an automated sphygmomanometer (Welch Allyn ProBP 2400, Welch Allyn, New York, USA) and averaged for data analyses. Fasting venous blood samples were collected into BD Vacutainer tubes (Becton, Dickinson and Company, New Jersey, USA). Plasma samples were collected for analysis of FPG and glucoregulatory peptides in fluoride oxalate and P800 tubes, respectively. Whole blood was collected in a tripotassium ethylenediaminetetraacetic acid (K 3 -EDTA) tube for HbA 1c analysis. Serum samples were collected for lipid biomarkers and liver function enzymes using a serum separator (SST™ ll Advance) tube. The following clinical blood markers were analysed on the cobas® c311 (Roche Diagnostics Ltd, Mannheim, Germany): FPG, HbA 1c , total cholesterol, TAG, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT). Insulin and C-peptide were analysed on the cobas® e601 (Roche Diagnostics Ltd, Mannheim, Germany). The online Homeostatic Model Assessment (HOMA) Calculator © (The University of Oxford, version 2.2.3) was used to calculate insulin resistance (HOMA2-IR), β-cell function (HOMA2-B), and insulin sensitivity (HOMA2-S), determined based on FPG and fasting insulin. Statistical analysis Power calculations were conducted based on FPG, using data obtained from the New Zealand arm of the PREVIEW diabetes prevention study ( 62 ) conducted in our laboratory in a cohort of adults with overweight and prediabetes, and of mixed ethnicity. The sample size calculations assumed that any FPG improvements achieved during LED phase would be maintained between the treatments during maintenance phase. In a two-arm, parallel-design longitudinal study with baseline FPG of 5.8 mmol/L [standard deviation (SD): 0.6 mmol/L], effect size of 0.5, type I error α=0.05, 80% power, and two-tailed p-value, a clinically significant difference in FPG between treatments of 0.3 mmol/L was expected to be detected as statistically significant in a cohort of 160 participants (80 per treatment), including 20% drop out. Conduct of the trial during the COVID pandemic slowed recruitment however, and re-assessment of sample size showed that a group of n=90 participants (45 per treatment) would still be expected to detect a between-group difference in FPG of 0.3 mmol/L, with SD of baseline FPG 0.5 mmol/L, effect size of 0.6, type I error α=0.05, and power of 80% as significant. Baseline characteristics were summarised using mean and SD for continuous outcomes. Comparison of continuous outcomes, by treatment, were made using Welch’s two-sample t-test. Pearson’s chi-squared test with Yates’ continuity correction was used for comparing categorical outcomes. Attrition rates between the treatments were compared using the likelihood ratio test. In participants who successfully completed the LED phase, compliance to the meal replacements was compared between treatments using Welch’s two-sample t-test. In participants who successfully completed the 6 month intervention, compliance to Fx and Placebo powders was also compared using Welch’s two-sample t-test. Analyses of all outcomes were conducted as repeated measures linear mixed models (LMM). Participants were included as the random effect. Treatment group (Fx, Placebo), time (M0/CID1, M2/CID2, M4/CID3, M6/CID4), treatment×time interaction, and gender were included as fixed effects, while baseline measurement (M0/CID1) was included as a covariate. When main effect of time was significant, post hoc multiple comparisons between the following timepoints were conducted using Tukey’s analyses; M0-M2, M0-M4, M0-M6, M2-M4, M2-M6. To improve normality, the following variables were natural log-transformed prior to conducting LMMs: insulin, HOMA2-IR, TAG, ALT, AST, and GGT. For interpretability, estimated marginal means and SEMs from models involving log-transformed variables were back-transformed by raising them to the power of e . All LMM analyses were conducted using intention-to-treat (ITT) approach, for all participants randomised into the trial at M0/CID1, with imputation for missing-not-at-random (MNAR) data points using the last value carried forward (LVCF) method. Given that 80 % of withdrawals occurred during the LED phase (M0-M2), during which metabolic improvements are typically expected even with partial adherence, the ITT strategy provided a conservative estimate of treatment effects. This method is well established in trials of similar design, including the DROPLET ( 18 ), DiRECT ( 22 ), and DIADEM-I ( 23 ) studies. Noting that Restricted Maximum Likelihood (REML) for LMMs may not adequately handle MNAR data, we also investigated outcomes from the raw data set (no imputation). One extreme FPG outlier (n=1, Placebo; single data point M6/CID4: 9.6 mmol/L, >3 IQR above the upper quartile) was identified, therefore a sensitivity analysis was undertaken where FPG was analysed both with and without this data point. All analyses were conducted using R version 4.3.3 ( 63 ). The level of significance was set at P≤0.05. Results Baseline characteristics, attrition, and compliance The baseline characteristics of the ITT cohort (n=97) are shown in Table 1 . 81.4% of the cohort were female and 18.6% were male. Participants were ethnically diverse, with representation of European Caucasian (45.4%), Māori (17.5%), Pacific Peoples (18.6%), Asian Chinese (12.4%), Asian Indian (9.3%), and Other (16.5%) ethnicities. BW (mean ± SD) was 103.1 ± 21.0 kg and FPG was 6.2 ± 0.6 mmol/L. The percentage of participants who completed the 6 month study in the Fx and Placebo groups was 66.7% and 61.2% respectively. Attrition rates were not significantly different between the groups. Three quarters of withdrawals occurred during the LED weight loss phase ( Figure 2 ). There was 1 adverse event (AE) reported in the Fx group during the LED, whereby the participant experienced intolerance to the meal replacement products. There was also 1 AE reported in the Placebo group during the LED, whereby the participant experienced intolerance to the placebo powder. These participants were withdrawn from the study, upon review by the independent medical monitor. Of those who completed the LED phase, >80% compliance to the meal replacements was achieved in both treatments. Furthermore, the mean ± SD BW loss (M0-M2) in these LED completers was 10.2 ± 4.4 kg, or 10.0 ± 3.5 % (raw data, no imputation). Compliance to Fx and Placebo powders throughout the 6 month study was also >80%, with higher compliance in Placebo (92 ± 11 %) vs Fx (82 ± 22 %; P=0.03). View this table: View inline View popup Download powerpoint Table 1: Baseline characteristics of ITT cohort at M0/CID1, in all participants and by treatment. Download figure Open in new tab Figure 2: CONSORT flowchart describing progress of participants through the study. M: month, CID: clinical investigation day, LED: low energy diet, CONSORT: Consolidated Standards of Reporting Trials. Primary outcomes – BW, FPG (all and sub-cohort) BW BW was closely matched between treatment groups at baseline M0/CID1, with mean BMI above the cut point for obesity in both groups (Fx: 37.2 kg/m 2 ; Placebo: 37.1 kg/m 2 ). In the ITT cohort, no significant between-treatment difference in trajectory from baseline was observed (treatment×time 0-6m : P=0.74). Fx treatment did not result in significantly greater decrease in BW vs. Placebo over 6 months. There was a significant change in BW over the study period, independent of treatment group (time 0-6m : P<0.001), with a mean decrease of 7.2 kg during the 2 month LED phase in all participants (P<0.001). Whilst an increase was observed in all participants during the weight maintenance phase (ΔBW 2-6m : 2.1 kg; P<0.001), BW remained lower than baseline at study end (ΔBW 0-6m : -5.1 kg; P<0.001). BMI followed the same trajectory from baseline and between treatment groups. FPG, all, n=97 In the full ITT cohort (n=97), there was a significant difference in trajectory from baseline between the two treatment groups (treatment×time 0-6m : P=0.05). However, Fx was not significantly lower than Placebo at any timepoint. In addition, and importantly, sensitivity analysis showed that removal of the single extreme FPG outlier from the Placebo group at the end of intervention timepoint (M6/CID4, FPG: 9.6 mmol/L) resulted in loss of significance ( Figure 3A , treatment×time 0-6m : P=0.09). There was a significant change in FPG between baseline and M6/CID4, independent of treatment group (time 0-6m : P<0.001). In line with BW, post hoc analysis revealed that FPG in all participants decreased during the LED weight loss phase (ΔFPG 0-2m : -0.5 mmol/L; P<0.001). Despite an increase during the weight maintenance phase (ΔFPG 2-6m : 0.3 mmol/L; P<0.001), FPG in all participants remained significantly lower than baseline at all timepoints (P<0.001, all). Download figure Open in new tab Figure 3: Linear mixed-effects model for FPG, SBP, and DBP in the ITT cohort (A, C & E), and sub-cohorts with prediabetes (B) and normotension (D & F) confirmed at CID1/baseline. Data is presented as estimated marginal mean ± SEM. $ Extreme outlier removed prior to analysis (n=1, Placebo, M6/CID4, FPG: 9.6 mmol/L). Post hoc differences shown as following superscripts; a P≤0.05 compared to M0/CID1 in Fx group, b P≤0.05 between treatments at same timepoint. ITT: intention-to-treat, M: month, CID: clinical investigation day, SEM: standard error of the mean, BW: body weight, FPG: fasting plasma glucose, Fx: whole fruit feijoa powder, SBP: systolic blood pressure, DBP: diastolic blood pressure. FPG, sub-cohort (prediabetes only), n=69 The mean ± SD delay between in-clinic screening/enrolment and baseline assessment was 33 ± 8 days, during which FPG in 16 participants lowered to normoglycaemia (FPG ≤5.5 mmol/L, Fx: n=8, Placebo: n=8) and 12 participants worsened to T2D (FPG ≥7.0 mmol/L, Fx: n=9, Placebo: n=3). Analyses of the sub-cohort with confirmed prediabetes at baseline (n=69, extreme outlier removed) showed that no significant difference in change in FPG over time was apparent between the treatment groups (treatment×time 0-6m : P=0.10). Sensitivity analysis showed that inclusion of the single extreme outlier datapoint at the M6/CID4 timepoint in this sub-cohort (n=69) did not change this outcome. The significant main effect of time on FPG independent of treatment group persisted ( Figure 3B , time 0-6m : P<0.001). Post hoc analyses showed a significant decrease in FPG during the LED weight loss phase in all participants, with FPG remaining lower than baseline at all timepoints (P<0.001, all). Secondary outcomes There was a significant change between baseline and M6/CID4, independent of treatment group, in all other anthropometry, body composition, and blood biochemistry outcomes ( Table 2 , time 0-6m : P<0.01, all) except SATp and the liver enzyme ALP, with consistent improvements during the LED weight loss phase. There were no significant effects of Fx vs. Placebo treatment over the 6 month intervention for any of these secondary variables ( Table 2 , treatment×time 0-6m : P>0.05, all). View this table: View inline View popup Download powerpoint Table 2: Primary and secondary outcomes in the ITT cohort (n=97). SBP, DBP Notably, however, there was a significant treatment×time interaction for SBP ( Figure 3C , treatment×time 0-6m : P=0.01), reflecting the difference in trajectory from baseline of SBP between treatment groups over 6 months . Post hoc analyses showed SBP to be significantly lower in Fx treatment compared to Placebo at M4/CID3 and M6/CID4 (P≤0.05, both). Fx treatment resulted in significantly greater improvement in SBP vs. Placebo over 6 months. There was no significant interaction for DBP over the 6 month intervention ( Figure 3E , treatment×time 0-6m : P=0.12). BP, sub-cohort (normotension only) 47% of participants (Fx, n=24; Placebo, n=22) had hypertension at baseline. In the sub-cohort of 51 normotensive participants, there was no significant interaction and hence no significant difference in the change in trajectory of SBP or DBP between treatment groups over 6 months (treatment×time 0-6m , SBP: P=0.20, DBP: P=0.38). Contrary to the ITT cohort, Fx did not result in significantly greater decrease in SBP over 6 months In this normotensive sub-cohort. A significant change in SBP and DBP over 6 months, independent of treatment group, persisted ( Figure 3D & 3F, time 0-6m : P<0.001, both). Raw data (no imputation) Analyses from all observed data (i.e. without imputation) were consistent with ITT analyses across all primary and secondary outcomes (results not shown). Discussion The FERDINAND RCT investigated the effects of Fx supplementation on BW and T2D biomarkers in a multi-ethnic cohort of adults living with overweight and obesity, during and following 8 weeks of LED-induced weight loss. Participants were enrolled into the intervention with overweight and dysglycaemia, and with a desire to achieve and maintain significant weight loss of ∼8-10 %. The study comprised an acute 2-month phase of LED meal replacement-driven weight loss, followed by a 4-month phase of healthy eating lifestyle advice aiming to maintain the weight lost. Compliance to the meal replacements and intervention powder demonstrated the successful completion of the treatments over the study period. Regarding the co-primary outcomes, when analysed independent of treatment group, BW changed significantly over the 6 months as expected, with a post-LED decrease over 2 months followed by rebound during the weight maintenance phase. BW was not significantly different between the treatments throughout the study, with no evidence of greater weight loss in the Fx treatment group. The hypothesised enhancement of Fx on weight loss was grounded on proposed anti-obesity mechanisms attributed to polyphenols. Experimental evidence suggests that polyphenols may inhibit adipocyte differentiation ( 64 ), increase fatty acid oxidation ( 65 ), and modulate gut microbiota composition in ways that favour improved metabolic regulation ( 66 , 67 ). However, translation of these mechanistic insights into clinically meaningful weight loss in humans has been inconsistent. A recent meta-analysis of 40 RCTs administering polyphenol supplementation (31-3000 mg/day for 1-12 months) reported BW loss of 0.36 kg (95 % CI: -0.68 to -0.05; P=0.02) ( 68 ). Although statistically significant, the magnitude of weight loss due to supplementation alone is unlikely to be clinically meaningful, particularly when contrasted with the ≥5 % BW loss commonly used to define clinical relevance ( 69 ). Furthermore, the substantial energy deficit imposed during the LED phase of FERDINAND likely exerted a dominant physiological effect on BW. It is therefore plausible that any incremental influence of polyphenols on adiposity was comparatively small and effectively masked by the magnitude of LED-induced weight loss. This interpretation is consistent with previous trials that have combined polyphenol supplementation with an LED, which similarly reported no additional effects on BW or body composition ( 70 – 73 ). FPG followed the weight loss trajectory and decreased and then rebounded post-LED in both treatment groups. Whilst borderline significant improvement in FPG was observed in the Fx vs. Placebo group, this effect was lost when sensitivity analysis was undertaken. This highlights that the effect of Fx on FPG was statistically weak and influenced by a single outlier point in the Placebo group at the end of study assessment. Furthermore, these observations included 28 participants in whom FPG either reverted to normoglycaemia or progressed to T2D between the screening visit and start of the intervention, i.e. were no longer prediabetic. Removal of these participants from the analysis eliminated the borderline difference between the two treatments, irrespective of the single extreme outlier, but notably also decreased power to detect any change as statistically significant. Nevertheless, sustained lowering of FPG in the Fx group is plausible when considering the bioactive profile of the whole fruit feijoa powder. Approximately 60 % of the powder’s known polyphenolic content consisted of catechins (Anagenix Ltd, personal communication, March 19 2026). A recent meta-analysis by Wan and colleagues, encompassing 28 RCTs administering catechin supplementation (63-1344 mg/day for 1-12 months), reported a mean FPG lowering of 0.10 mmol/L (95 % CI: -0.18 to - 0.03; P<0.001) ( 74 ). Although FPG did not differ significantly between treatment arms at study end in FERDINAND (-0.13 mmol/L, 95 % CI: -0.49 to 0.23), the model-estimated differences were numerically comparable to the pooled effect size reported in the meta-analysis ( 74 ). At first glance, this comparison appears incongruent given the markedly lower known catechin dose delivered in FERDINAND (0.60 mg/day), which was over 100-fold lower than the lowest dose included in Wan and colleagues’ meta-analysis (63 mg/day). In addition, FERDINAND incorporated a structured LED regimen, whereas only two trials within Wan and colleagues’ meta-analysis included an LED component ( 70 , 72 ). It could be argued that relative to the studies included in Wan and colleagues’ meta-analysis, the FPG findings in FERDINAND were primarily driven by the structured LED rather than by polyphenol exposure. However, comparison with the two trials within the meta-analysis that also incorporated LEDs does not fully support this assertion. Despite administering catechin doses that were orders of magnitude higher than those used in FERDINAND (1125 mg/day and 300 mg/day), the magnitude of FPG change reported in these trials was comparable to that observed in FERDINAND. Furthermore, the whole fruit feijoa powder contained a matrix of bioactive compounds, including procyanidins and ellagic acid, which may exert additive or synergistic metabolic effects. In vitro and animal studies suggest that combinations of polyphenols can produce synergistic improvements in inflammation ( 75 ) and oxidative stress ( 76 , 77 ), mechanisms closely linked to glycaemic regulation. Additionally, the potential role of ABA in the sustained FPG lowering in the Fx group must also be considered. The 1150 mg of whole fruit feijoa powder used in the FERDINAND study provided 54 μg/day of ABA (Anagenix Ltd, personal communication, March 19 2026). RCTs investigating daily ABA supplementation have utilised a dose of 55 μg/day of ABA for 75 days in healthy adults (n=10) ( 46 ), and alternatively, 2 g/day of dwarf peach powder, known to contain ABA, over a 3-month period in individuals with prediabetes (n=65) ( 78 ). While the ABA dosage was not explicitly reported in the latter trial ( 78 ), its use of a fruit-derived ABA source in a population with prediabetes makes it one of the few studies to examine such an intervention in this at-risk group, and thus a particularly relevant comparator to FERDINAND. Despite these differences, both studies reported improvements in fasting glycaemia by the end of the supplementation period ( 46 , 78 ). Amongst the secondary outcomes, only SBP in the Fx group significantly improved compared to Placebo over the 6 month intervention. Since there was no BP exclusion criteria for the trial, a large number of participants with overweight and associated biomarkers of poor metabolic health were enrolled with previously undiagnosed hypertension at baseline. When these participants were removed from the data analyses, the significant effect of Fx vs. Placebo over 6 months was lost. Whilst this may be in part due to the decrease in sample size, it is possible that the observed differences in SBP between the treatments may be driven by the sub-cohort of participants with uncontrolled and/or controlled hypertension. From a mechanistic perspective, flavonoids such as catechins, the most abundant polyphenolic class in whole fruit feijoa powder, have established vasoprotective properties. Flavonoids have been shown to enhance endothelial nitric oxide bioavailability, inhibit angiotensin-converting enzyme activity, and attenuate oxidative stress ( 79 ). Consistent with these mechanisms, a recent meta-analysis of 31 RCTs implementing catechin supplementation (20-1344 mg/day for 2-24 weeks) reported a mean SBP lowering of 1.56 mmHg (95 % CI: -2.75 to -0.37; P<0.001) ( 74 ). Notably, the model-estimated mean SBP lowering observed in FERDINAND (-5.93 mmHg, 95 % CI: -15.42 to 3.56) was numerically greater than that reported in 28 of the 31 trials included in this meta-analysis ( 74 ), despite the substantially lower catechin dose administered. Furthermore, when compared to the only trial within the meta-analysis that incorporated an LED ( 70 ), the magnitude of SBP lowering in FERDINAND was comparable. One potential explanation for these findings is the combined influence of LED-induced metabolic improvements and the polyphenolic profile of whole fruit feijoa. Experimental evidence suggests that polyphenolic compounds may exert synergistic vascular effects. For example, Zhang and colleagues demonstrated that combined curcumin and luteolin exposure produced greater inhibition of TNF-α-induced vascular inflammation than either compound alone in both human and murine models ( 80 ). It is therefore plausible that the polyphenolic matrix of whole fruit feijoa contributed to vascular benefits beyond what might be expected from catechin content alone. As noted previously, the intervention conducted by Taghavi and colleagues in Iran remains the only prior clinical trial to investigate whole fruit feijoa supplementation in humans ( 58 ). Whilst the FERDINAND study is the first to investigate supplementation with a whole fruit feijoa powder for T2D prevention, it was not without some limitations. Owing to a number of challenges in recruiting participants into this study during the COVID pandemic, underpinned by many individuals with prediabetes being unaware of their adverse glycaemic status ( 81 ), and in the absence of validated predictive tools to pinpoint individuals with prediabetes amongst many with overweight yet normoglycaemia ( 82 ), the sample size was below the a priori target. Hence the study may not have detected small yet clinically important differences in FPG between treatments. Additionally, a sub-cohort of participants reverted to normoglycaemia or progressed to T2D between enrolment and baseline assessment, with the average time between these visits being 33 days. Hence the cohort with confirmed prediabetes at baseline was smaller than planned. Regarding attrition, a 20 % dropout rate is typically expected in LED and VLED interventions ( 15 ). In contrast, the dropout rate in FERDINAND was 36 %, of which 4 out of 5 of these participants withdrew during the 2 month LED. It must be considered that FERDINAND implemented a strict total meal replacement strategy, including restrictions to non-caloric beverage consumption, which may have contributed to the higher than expected dropout rate. Nevertheless, results that were generated from observed data (i.e. no imputation) were consistent with ITT analyses, further supporting the use of LVCF for imputing missing data. In conclusion, despite no significant effect on BW or FPG beyond that achieved through a commercial LED alone, the FERDINAND study provides evidence to support Fx supplementation during weight loss for improvement in SBP in adults with overweight plus current/recent history of prediabetes. Further studies in larger cohorts are warranted to fully evaluate the contribution that whole fruit feijoa powder may make to improved blood pressure. Data Availability The data produced in the present study may be available upon reasonable request to the corresponding author. However, individual participant data will not be made available. This is in accordance with Southern Health and Disabilities Ethics Committee (New Zealand) application that all data generated will only be used for this study. Declarations Ethics statement Ethics approval was received from the New Zealand Health and Disability Ethics Committee (2022 EXP 12032) and prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN: 12622000210774). Study participants were provided with all study details, explained to them in writing and in person before they provided written informed consent. Data availability statement The datasets generated from this study will be restricted in line with ethical approvals. Requests to access the datasets should be directed to Jennifer Miles-Chan ( j.miles-chan{at}auckland.ac.nz ). Individual participant data will not be made available. This is in accordance with National Health and Disability Ethics Committees application that all data generated will only be used for the purpose of this study. Funding This study was funded by the New Zealand National Science Challenge High Value Nutrition (NSC-HVN) Program, Ministry for Business, Innovation and Employment (MBIE, grant no. 3710040). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Conflict of interests Anagenix Ltd supplied the whole fruit feijoa powder (Feiolix®) and matched placebo. Anagenix Ltd had no involvement in the study design, data collection, statistical analysis, interpretation of findings, or manuscript preparation. The authors declare no other commercial or financial relationships that could be construed as a potential conflict of interest. Author contributions IM: Investigation, Project administration, Statistical analysis, Writing – original draft. IS-B: Conceptualisation, Methodology, Supervision, Statistical analysis, Writing – review & editing. JL: Investigation, Writing – review & editing. LP: Conceptualisation, Methodology, Supervision, Writing – review & editing. RM: Conceptualisation, Supervision, Writing – review & editing. SP: Conceptualisation, Funding acquisition, Methodology, Supervision, Writing – review & editing. JM-C: Conceptualisation, Funding acquisition, Methodology, Supervision, Writing – review & editing. Acknowledgements We thank Dr William Zhu for collection and processing of blood samples; Dr Shakeela Jayasinghe for conducting and maintaining confidentiality of intervention blinding; Registered Dietitians Dr Amy Liu and Shivaanee Ram for reviewing the diet design and administering group dietary consultation meetings with participants; Christine Keven at the Analytical and Molecular Research Platform, Liggins Institute, for blood biochemical measurements; Associate Professor Beatrix Jones and Jessica McLay at the Department of Statistics, University of Auckland, for their statistical advice. We also appreciate the participants who took part in this study. Footnotes ↵ † These authors share senior authorship. Additional outcome variables added and statistical approach unified. References 1. ↵ ElSayed NA , Aleppo G , Aroda VR , Bannuru RR , Brown FM , Bruemmer D , et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023 . Diabetes Care . 2023 Jan ; 46 (Suppl 1 ): S19 – 40 . doi: 10.2337/dc23-S002 PubMed PMID: 36507649 ; PubMed Central PMCID: PMC9810477 . OpenUrl CrossRef PubMed 2. ↵ International Diabetes Federation . IDF Diabetes Atlas: 11th Edition . 11th ed . Brussels, Belgium : International Diabetes Federation ; 2025 . 3. ↵ Abdullah A , Stoelwinder J , Shortreed S , Wolfe R , Stevenson C , Walls H , et al. The duration of obesity and the risk of type 2 diabetes . Public Health Nutr . 2011 Jan; 14 ( 1 ): 119 – 26 . doi: 10.1017/S1368980010001813 PubMed PMID: 20587115 . OpenUrl CrossRef PubMed Web of Science 4. Ley SH , Schulze MB , Hivert MF , Meigs JB , Hu FB . Risk Factors for Type 2 Diabetes . In: Cowie CC , Casagrande SS , Menke A , Cissell MA , Eberhardt MS , Meigs JB , et al. , editors. Diabetes in America [Internet] . 3rd ed. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US) ; 2018 [cited 2024 Jun 21 ]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK567966/ PubMed PMID: 33651531 . OpenUrl PubMed 5. Narayan KMV , Boyle JP , Thompson TJ , Gregg EW , Williamson DF . Effect of BMI on Lifetime Risk for Diabetes in the U.S . Diabetes Care . 2007 Jun 1 ; 30 ( 6 ): 1562 – 6 . doi: 10.2337/dc06-2544 PubMed PMID: 17372155 . OpenUrl Abstract / FREE Full Text 6. Saponaro C , Gaggini M , Carli F , Gastaldelli A . The Subtle Balance between Lipolysis and Lipogenesis: A Critical Point in Metabolic Homeostasis . Nutrients . 2015 Nov 13; 7 ( 11 ): 9453 – 74 . doi: 10.3390/nu7115475 OpenUrl CrossRef PubMed 7. Schienkiewitz A , Schulze MB , Hoffmann K , Kroke A , Boeing H . Body mass index history and risk of type 2 diabetes: results from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study . Am J Clin Nutr . 2006 Aug ; 84 ( 2 ): 427 – 33 . doi: 10.1093/ajcn/84.1.427 PubMed PMID: 16895894 . OpenUrl Abstract / FREE Full Text 8. Sniderman AD , Bhopal R , Prabhakaran D , Sarrafzadegan N , Tchernof A . Why might South Asians be so susceptible to central obesity and its atherogenic consequences? The adipose tissue overflow hypothesis . Int J Epidemiol . 2007 Feb 1 ; 36 ( 1 ): 220 – 5 . doi: 10.1093/ije/dyl245 OpenUrl CrossRef PubMed Web of Science 9. Tchernof A , Després JP . Pathophysiology of Human Visceral Obesity: An Update . Physiol Rev . 2013 Jan 1 . Located at: Bethesda, MD . doi: 10.1152/physrev.00033.2011 OpenUrl CrossRef PubMed 10. ↵ Vazquez G , Duval S , Jacobs DR , Silventoinen K . Comparison of Body Mass Index, Waist Circumference, and Waist/Hip Ratio in Predicting Incident Diabetes: A Meta-Analysis . Epidemiol Rev . 2007 May 2 ; 29 ( 1 ): 115 – 28 . doi: 10.1093/epirev/mxm008 OpenUrl CrossRef PubMed Web of Science 11. ↵ Delbridge E , Proietto J . State of the science: VLED (Very Low Energy Diet) for obesity . Asia Pac J Clin Nutr . 2006 ; 15 Suppl: 49 – 54 . PubMed PMID: 16928661 . OpenUrl PubMed 12. ↵ Gow ML , Pham-Short A , Jebeile H , Varley BJ , Craig ME . Current Perspectives on the Role of Very-Low-Energy Diets in the Treatment of Obesity and Type 2 Diabetes in Youth . Diabetes Metab Syndr Obes Targets Ther . 2021 Jan 18 ; 14 : 215 – 25 . doi: 10.2147/DMSO.S238419 PubMed PMID: 33500642 ; PubMed Central PMCID: PMC7822089 . OpenUrl CrossRef PubMed 13. ↵ Mustajoki P , Pekkarinen T . Very low energy diets in the treatment of obesity . Obes Rev . 2001 ; 2 ( 1 ): 61 – 72 . doi: 10.1046/j.1467-789x.2001.00026.x OpenUrl CrossRef PubMed 14. ↵ Saris WHM . Very-Low-Calorie Diets and Sustained Weight Loss . Obes Res . 2001 ; 9 ( S11 ): 295S - 301S . doi: 10.1038/oby.2001.134 OpenUrl CrossRef PubMed 15. ↵ Parretti HM , Jebb SA , Johns DJ , Lewis AL , Christian-Brown AM , Aveyard P . Clinical effectiveness of very-low-energy diets in the management of weight loss: a systematic review and meta-analysis of randomized controlled trials . Obes Rev . 2016 ; 17 ( 3 ): 225 – 34 . doi: 10.1111/obr.12366 OpenUrl CrossRef PubMed 16. Sellahewa L , Khan C , Lakkunarajah S , Idris I . A Systematic Review of Evidence on the Use of Very Low Calorie Diets in People with Diabetes . Curr Diabetes Rev . 2017 ; 13 ( 1 ): 35 – 46 . doi: 10.2174/1573399812666151005123431 PubMed PMID: 26435354 . OpenUrl CrossRef PubMed 17. Churuangsuk C , Hall J , Reynolds A , Griffin SJ , Combet E , Lean MEJ . Diets for weight management in adults with type 2 diabetes: an umbrella review of published meta-analyses and systematic review of trials of diets for diabetes remission . Diabetologia . 2022 Jan ; 65 ( 1 ): 14 – 36 . doi: 10.1007/s00125-021-05577-2 OpenUrl CrossRef PubMed 18. ↵ Astbury NM , Aveyard P , Nickless A , Hood K , Corfield K , Lowe R , et al. Doctor Referral of Overweight People to Low Energy total diet replacement Treatment (DROPLET): pragmatic randomised controlled trial . BMJ . 2018 Sep 26 ; k3760 . doi: 10.1136/bmj.k3760 OpenUrl Abstract / FREE Full Text 19. Astbury NM , Piernas C , Hartmann-Boyce J , Lapworth S , Aveyard P , Jebb SA . A systematic review and meta-analysis of the effectiveness of meal replacements for weight loss . Obes Rev . 2019 ; 20 ( 4 ): 569 – 87 . doi: 10.1111/obr.12816 OpenUrl CrossRef PubMed 20. Noronha JC , Nishi SK , Braunstein CR , Khan TA , Blanco Mejia S , Kendall CWC , et al. The Effect of Liquid Meal Replacements on Cardiometabolic Risk Factors in Overweight/Obese Individuals With Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials . Diabetes Care . 2019 Mar 28 ; 42 ( 5 ): 767 – 76 . doi: 10.2337/dc18-2270 OpenUrl Abstract / FREE Full Text 21. Christensen P , Meinert Larsen T , Westerterp-Plantenga M , Macdonald I , Martinez JA , Handjiev S , et al. Men and women respond differently to rapid weight loss: Metabolic outcomes of a multi-centre intervention study after a low-energy diet in 2500 overweight, individuals with pre-diabetes (PREVIEW) . Diabetes Obes Metab . 2018 Dec 1 ; 20 ( 12 ): 2840 – 51 . doi: 10.1111/dom.13466 OpenUrl CrossRef 22. ↵ Lean ME , Leslie WS , Barnes AC , Brosnahan N , Thom G , McCombie L , et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial . The Lancet . 2018 Feb ; 391 ( 10120 ): 541 – 51 . doi: 10.1016/S0140-6736(17)33102-1 OpenUrl CrossRef 23. ↵ Taheri S , Zaghloul H , Chagoury O , Elhadad S , Ahmed SH , Khatib NE , et al. Effect of intensive lifestyle intervention on bodyweight and glycaemia in early type 2 diabetes (DIADEM-I): an open-label, parallel-group, randomised controlled trial . Lancet Diabetes Endocrinol . 2020 Jun 1; 8 ( 6 ): 477 – 89 . doi: 10.1016/S2213-8587(20)30117-0 PubMed PMID: 32445735 . OpenUrl CrossRef PubMed 24. ↵ Imamura F , O’Connor L , Ye Z , Mursu J , Hayashino Y , Bhupathiraju SN , et al. Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction . BMJ . 2015 Jul 21 ; 351 : h3576 . doi: 10.1136/bmj.h3576 PubMed PMID: 26199070 ; PubMed Central PMCID: PMC4510779 . OpenUrl Abstract / FREE Full Text 25. InterAct Consortium , Romaguera D , Norat T , Wark PA , Vergnaud AC , Schulze MB , et al. Consumption of sweet beverages and type 2 diabetes incidence in European adults: results from EPIC-InterAct . Diabetologia . 2013 Jul ; 56 ( 7 ): 1520 – 30 . doi: 10.1007/s00125-013-2899-8 PubMed PMID: 23620057 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Ley SH , Hamdy O , Mohan V , Hu FB . Prevention and Management of Type 2 Diabetes: Dietary Components and Nutritional Strategies . Lancet Lond Engl . 2014 Jun 7 ; 383 ( 9933 ): 1999 – 2007 . doi: 10.1016/S0140-6736(14)60613-9 PubMed PMID: 24910231 ; PubMed Central PMCID: PMC4751088 . OpenUrl CrossRef PubMed Web of Science 27. ↵ Sharpe RH , Sherman WB , Miller EP . Feijoa History and Improvement . Proc Fla State Hortic Soc . 1993 ; 106 : 134 – 8 . OpenUrl 28. ↵ Weston RJ . Bioactive products from fruit of the feijoa ( Feijoa sellowiana , Myrtaceae): A review . Food Chem . 2010 Aug 15 ; 121 ( 4 ): 923 – 6 . doi: 10.1016/j.foodchem.2010.01.047 OpenUrl CrossRef 29. ↵ Faller ALK , Fialho E . [Polyphenol availability in fruits and vegetables consumed in Brazil] . Rev Saude Publica . 2009 Apr ; 43 ( 2 ): 211 – 8 . doi: 10.1590/s0034-89102009005000010 PubMed PMID: 19225692 . OpenUrl CrossRef PubMed 30. ↵ Zhu F . Chemical and biological properties of feijoa ( Acca sellowiana ) . Trends Food Sci Technol . 2018 Nov 1 ; 81 : 121 – 31 . doi: 10.1016/j.tifs.2018.09.008 OpenUrl CrossRef 31. ↵ Wedick NM , Pan A , Cassidy A , Rimm EB , Sampson L , Rosner B , et al. Dietary flavonoid intakes and risk of type 2 diabetes in US men and women12345 . Am J Clin Nutr . 2012 Apr 1 ; 95 ( 4 ): 925 – 33 . doi: 10.3945/ajcn.111.028894 OpenUrl Abstract / FREE Full Text 32. Sun Q , Wedick NM , Tworoger SS , Pan A , Townsend MK , Cassidy A , et al. Urinary Excretion of Select Dietary Polyphenol Metabolites Is Associated with a Lower Risk of Type 2 Diabetes in Proximate but Not Remote Follow-Up in a Prospective Investigation in 2 Cohorts of US Women123 . J Nutr . 2015 Jun ; 145 ( 6 ): 1280 – 8 . doi: 10.3945/jn.114.208736 PubMed PMID: 25904735 ; PubMed Central PMCID: PMC4442116 . OpenUrl Abstract / FREE Full Text 33. Zamora-Ros R , Forouhi NG , Sharp SJ , González CA , Buijsse B , Guevara M , et al. The Association Between Dietary Flavonoid and Lignan Intakes and Incident Type 2 Diabetes in European Populations . Diabetes Care . 2013 Dec ; 36 ( 12 ): 3961 – 70 . doi: 10.2337/dc13-0877 PubMed PMID: 24130345 ; PubMed Central PMCID: PMC3836159 . OpenUrl Abstract / FREE Full Text 34. Rienks J , Barbaresko J , Oluwagbemigun K , Schmid M , Nöthlings U . Polyphenol exposure and risk of type 2 diabetes: dose-response meta-analyses and systematic review of prospective cohort studies . Am J Clin Nutr . 2018 Jul ; 108 ( 1 ): 49 – 61 . doi: 10.1093/ajcn/nqy083 OpenUrl CrossRef PubMed 35. Delpino FM , Figueiredo LM . Resveratrol supplementation and type 2 diabetes: a systematic review and meta-analysis . Crit Rev Food Sci Nutr . 2022 Jun 1 ; 62 ( 16 ): 4465 – 80 . doi: 10.1080/10408398.2021.1875980 PubMed PMID: 33480264 . OpenUrl CrossRef PubMed 36. García-Martínez BI , Ruiz-Ramos M , Pedraza-Chaverri J , Santiago-Osorio E , Mendoza-Núñez VM . Hypoglycemic Effect of Resveratrol: A Systematic Review and Meta-Analysis . Antioxid Basel Switz . 2021 Jan 7 ; 10 ( 1 ): 69 . doi: 10.3390/antiox10010069 PubMed PMID: 33430470 ; PubMed Central PMCID: PMC7827898 . OpenUrl CrossRef PubMed 37. Gu W , Geng J , Zhao H , Li X , Song G . Effects of Resveratrol on Metabolic Indicators in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis . Int J Clin Pract . 2022 Jan 31 ;2022: 9734738 . doi: 10.1155/2022/9734738 PubMed PMID: 35685602 ; PubMed Central PMCID: PMC9158797 . OpenUrl CrossRef PubMed 38. Nyambuya TM , Nkambule BB , Mazibuko-Mbeje SE , Mxinwa V , Mokgalaboni K , Orlando P , et al. A Meta-Analysis of the Impact of Resveratrol Supplementation on Markers of Renal Function and Blood Pressure in Type 2 Diabetic Patients on Hypoglycemic Therapy . Mol Basel Switz . 2020 Nov 30; 25 ( 23 ): 5645 . doi: 10.3390/molecules25235645 PubMed PMID: 33266114 ; PubMed Central PMCID: PMC7730696 . OpenUrl CrossRef PubMed 39. Gómez-Martínez S , Díaz-Prieto LE , Vicente Castro I , Jurado C , Iturmendi N , Martín-Ridaura MC , et al. Moringa oleifera Leaf Supplementation as a Glycemic Control Strategy in Subjects with Prediabetes . Nutrients . 2021 Dec 24; 14 ( 1 ): 57 . doi: 10.3390/nu14010057 PubMed PMID: 35010932 ; PubMed Central PMCID: PMC8746299 . OpenUrl CrossRef PubMed 40. Mollace V , Scicchitano M , Paone S , Casale F , Calandruccio C , Gliozzi M , et al. Hypoglycemic and Hypolipemic Effects of a New Lecithin Formulation of Bergamot Polyphenolic Fraction: A Double Blind, Randomized, Placebo-Controlled Study . Endocr Metab Immune Disord - Drug Targets . 2019 Feb 7 ; 19 ( 2 ): 136 – 43 . doi: 10.2174/1871530319666181203151513 OpenUrl CrossRef PubMed 41. Schell J , Betts NM , Lyons TJ , Basu A . Raspberries Improve Postprandial Glucose and Acute and Chronic Inflammation in Adults with Type 2 Diabetes . Ann Nutr Metab . 2019 ; 74 ( 2 ): 165 – 74 . OpenUrl PubMed 42. ↵ Sirvent P , Chavanelle V , Otero YF , Bargetto M , Le Joubioux F , Boisseau N , et al. TOTUM-63, a plant-based polyphenol-rich extract, improves glycaemic control in subjects with prediabetes or early stage newly-diagnosed type 2 diabetes in a randomized, double-blind, placebo-controlled trial . Diabetes Obes Metab . 2022 Dec ; 24 ( 12 ): 2331 – 40 . doi: 10.1111/dom.14817 PubMed PMID: 35837981 ; PubMed Central PMCID: PMC9796323 . OpenUrl CrossRef PubMed 43. ↵ Ameri P , Bruzzone S , Mannino E , Sociali G , Andraghetti G , Salis A , et al. Impaired increase of plasma abscisic Acid in response to oral glucose load in type 2 diabetes and in gestational diabetes . PloS One . 2015 ; 10 ( 2 ): e0115992 . doi: 10.1371/journal.pone.0115992 PubMed PMID: 25723556 ; PubMed Central PMCID: PMC4344322 . OpenUrl CrossRef PubMed 44. Atkinson FS , Villar A , Mulà A , Zangara A , Risco E , Smidt CR , et al. Abscisic Acid Standardized Fig (Ficus carica) Extracts Ameliorate Postprandial Glycemic and Insulinemic Responses in Healthy Adults . Nutrients . 2019 Jul 31 ; 11 ( 8 ): 1757 . doi: 10.3390/nu11081757 OpenUrl CrossRef PubMed 45. Magnone M , Ameri P , Salis A , Andraghetti G , Emionite L , Murialdo G , et al. Microgram amounts of abscisic acid in fruit extracts improve glucose tolerance and reduce insulinemia in rats and in humans . FASEB J . 2015 Dec ; 29 ( 12 ): 4783 – 93 . doi: 10.1096/fj.15-277731 OpenUrl CrossRef PubMed 46. ↵ Magnone M , Leoncini G , Vigliarolo T , Emionite L , Sturla L , Zocchi E , et al. Chronic Intake of Micrograms of Abscisic Acid Improves Glycemia and Lipidemia in a Human Study and in High-Glucose Fed Mice . Nutrients . 2018 Oct 12 ; 10 ( 10 ): 1495 . doi: 10.3390/nu10101495 PubMed PMID: 30322104 ; PubMed Central PMCID: PMC6213903 . OpenUrl CrossRef PubMed 47. ↵ Bruzzone S , Moreschi I , Usai C , Guida L , Damonte G , Salis A , et al. Abscisic acid is an endogenous cytokine in human granulocytes with cyclic ADP-ribose as second messenger . Proc Natl Acad Sci U S A . 2007 Apr 3 ; 104 ( 14 ): 5759 – 64 . doi: 10.1073/pnas.0609379104 PubMed PMID: 17389374 ; PubMed Central PMCID: PMC1832220 . OpenUrl Abstract / FREE Full Text 48. ↵ Bassaganya-Riera J , Guri AJ , Lu P , Climent M , Carbo A , Sobral BW , et al. Abscisic Acid Regulates Inflammation via Ligand-binding Domain-independent Activation of Peroxisome Proliferator-activated Receptor γ . J Biol Chem . 2011 Jan 28 ; 286 ( 4 ): 2504 – 16 . doi: 10.1074/jbc.M110.160077 PubMed PMID: 21088297 ; PubMed Central PMCID: PMC3024745 . OpenUrl Abstract / FREE Full Text 49. ↵ Sato M , Dehvari N , Oberg AI , Dallner OS , Sandström AL , Olsen JM , et al. Improving type 2 diabetes through a distinct adrenergic signaling pathway involving mTORC2 that mediates glucose uptake in skeletal muscle . Diabetes . 2014 Dec ; 63 ( 12 ): 4115 – 29 . doi: 10.2337/db13-1860 PubMed PMID: 25008179 . OpenUrl Abstract / FREE Full Text 50. ↵ Zeng M , van der Donk WA , Chen J . Lanthionine synthetase C–like protein 2 (LanCL2) is a novel regulator of Akt . Mol Biol Cell . 2014 Dec 1; 25 ( 24 ): 3954 – 61 . doi: 10.1091/mbc.E14-01-0004 PubMed PMID: 25273559 ; PubMed Central PMCID: PMC4244203 . OpenUrl Abstract / FREE Full Text 51. ↵ Charles MA , Fanska R , Schmid FG , Forsham PH , Grodsky GM. Adenosine 3′,5′-Monophosphate in Pancreatic Islets: Glucose-Induced Insulin Release . Science . 1973 ; 179 ( 4073 ): 569 – 71 . OpenUrl Abstract / FREE Full Text 52. Furman B , Pyne N . Modulation of cyclic nucleotides and cyclic nucleotide phosphodiesterases in pancreatic islet beta-cells and intestinal L-cells as targets for treating diabetes mellitus . Curr Opin Investig Drugs Lond Engl 2000 . 2006 Oct ; 7 ( 10 ): 898 – 905 . PubMed PMID: 17086934 . OpenUrl PubMed 53. Islam MdS . The Ryanodine Receptor Calcium Channel of β-Cells: Molecular Regulation and Physiological Significance . Diabetes . 2002 May 1 ; 51 ( 5 ): 1299 – 309 . doi: 10.2337/diabetes.51.5.1299 OpenUrl Abstract / FREE Full Text 54. Sharp GWG . The Adenylate cyclase-cyclic AMP system in islets of langerhans and its role in the control of insulin release . Diabetologia . 1979 May 1 ; 16 ( 5 ): 287 – 96 . doi: 10.1007/BF01223617 OpenUrl CrossRef PubMed Web of Science 55. Takasawa S , Tohgo A , Noguchi N , Koguma T , Nata K , Sugimoto T , et al. Synthesis and hydrolysis of cyclic ADP-ribose by human leukocyte antigen CD38 and inhibition of the hydrolysis by ATP . J Biol Chem . 1993 Dec 15 ; 268 ( 35 ): 26052 – 4 . PubMed PMID: 8253715 . OpenUrl Abstract / FREE Full Text 56. ↵ Zocchi E , Hontecillas R , Leber A , Einerhand A , Carbo A , Bruzzone S , et al. Abscisic Acid: A Novel Nutraceutical for Glycemic Control . Front Nutr . 2017 ; 4 : 24 . doi: 10.3389/fnut.2017.00024 PubMed PMID: 28660193 ; PubMed Central PMCID: PMC5468461 . OpenUrl CrossRef PubMed 57. ↵ Foo LY , Watson RR . Feijoa Fruit Extract [Internet] . 2013 [cited 2023 Jan 6 ]. Available from: https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2013186680 58. ↵ Taghavi M , Hoseyni R , Panah HR , Razavi M , Watson R . Effect of Feijoa Supplementation in Patients with Type 2 Diabetes . Iran J Endocrinol Metab . 2012 ; 14 ( 3 ): 222 – 5 . OpenUrl 59. ↵ Iqbal AM , Jamal SF . Essential Hypertension . In: StatPearls [Internet] . Treasure Island (FL): StatPearls Publishing ; 2025 [cited 2025 Apr 9 ]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK539859/ PubMed PMID: 30969681 . OpenUrl PubMed 60. ↵ World Health Organization . Hypertension [Internet] . 2023 [cited 2025 Apr 9 ]. Available from: https://www.who.int/news-room/fact-sheets/detail/hypertension 61. ↵ McIntyre L , Anna Jackson , Carr H , Carter MA , Mackay S , Gerritsen S . Eating and activity guidelines for New Zealand adults . Updated 2020. Wellington, New Zealand : Ministry of Health ; 2020 . 62. ↵ Fogelholm M , Larsen TM , Westerterp-Plantenga M , Macdonald I , Martinez JA , Boyadjieva N , et al. PREVIEW: Prevention of Diabetes through Lifestyle Intervention and Population Studies in Europe and around the World. Design, Methods, and Baseline Participant Description of an Adult Cohort Enrolled into a Three-Year Randomised Clinical Trial . Nutrients . 2017 ; 9 ( 6 ): 632 . doi: 10.3390/nu9060632 OpenUrl CrossRef PubMed 63. ↵ R Core Team . R: A language and environment for statistical computing (Version 4.4.2) [Computer software] . R Foundation for Statistical Computing. [Internet] . 2024 [cited 2026 Jan 9 ]. Available from: https://www.R-project.org/ 64. ↵ Min SY , Yang H , Seo SG , Shin SH , Chung MY , Kim J , et al. Cocoa polyphenols suppress adipogenesis in vitro and obesity in vivo by targeting insulin receptor . Int J Obes . 2013 Apr ; 37 ( 4 ): 584 – 92 . doi: 10.1038/ijo.2012.85 OpenUrl CrossRef PubMed 65. ↵ Dulloo AG , Duret C , Rohrer D , Girardier L , Mensi N , Fathi M , et al. Efficacy of a green tea extract rich in catechin polyphenols and caffeine in increasing 24-h energy expenditure and fat oxidation in humans2 . Am J Clin Nutr . 1999 Dec 1; 70 ( 6 ): 1040 – 5 . doi: 10.1093/ajcn/70.6.1040 OpenUrl Abstract / FREE Full Text 66. ↵ Duarte L , Gasaly N , Poblete-Aro C , Uribe D , Echeverria F , Gotteland M , et al. Polyphenols and their anti-obesity role mediated by the gut microbiota: a comprehensive review . Rev Endocr Metab Disord . 2021 Jun 1 ; 22 ( 2 ): 367 – 88 . doi: 10.1007/s11154-020-09622-0 OpenUrl CrossRef PubMed 67. ↵ Yen GC , Cheng HL , Lin LY , Chen SC , Hsu CL . The potential role of phenolic compounds on modulating gut microbiota in obesity . J Food Drug Anal . 2020 ; 28 ( 2 ): 195 – 205 . doi: 10.38212/2224-6614.1054 PubMed PMID: 35696110 ; PubMed Central PMCID: PMC9261860 . OpenUrl CrossRef PubMed 68. ↵ Zhang Y , Balasooriya H , Sirisena S , Ng K . The effectiveness of dietary polyphenols in obesity management: A systematic review and meta-analysis of human clinical trials . Food Chem . 2023 Mar 15 ; 404 : 134668 . doi: 10.1016/j.foodchem.2022.134668 OpenUrl CrossRef 69. ↵ Ryan DH , Yockey SR . Weight Loss and Improvement in Comorbidity: Differences at 5%, 10%, 15%, and Over . Curr Obes Rep . 2017 Jun 1 ; 6 ( 2 ): 187 – 94 . doi: 10.1007/s13679-017-0262-y OpenUrl CrossRef PubMed 70. ↵ Diepvens K , Kovacs EMR , Nijs IMT , Vogels N , Westerterp-Plantenga MS . Effect of green tea on resting energy expenditure and substrate oxidation during weight loss in overweight females . Br J Nutr . 2005 Dec ; 94 ( 6 ): 1026 – 34 . doi: 10.1079/BJN20051580 OpenUrl CrossRef PubMed Web of Science 71. Ibero-Baraibar I , Abete I , Navas-Carretero S , Massis-Zaid A , Martinez JA , Zulet MA . Oxidised LDL levels decreases after the consumption of ready-to-eat meals supplemented with cocoa extract within a hypocaloric diet . Nutr Metab Cardiovasc Dis . 2014 Apr 1 ; 24 ( 4 ): 416 – 22 . doi: 10.1016/j.numecd.2013.09.017 OpenUrl CrossRef PubMed 72. ↵ Mielgo-Ayuso J , Barrenechea L , Alcorta P , Larrarte E , Margareto J , Labayen I . Effects of dietary supplementation with epigallocatechin-3-gallate on weight loss, energy homeostasis, cardiometabolic risk factors and liver function in obese women: randomised, double-blind, placebo-controlled clinical trial . Br J Nutr . 2014 Apr 14 ; 111 ( 7 ): 1263 – 71 . doi: 10.1017/S0007114513003784 OpenUrl CrossRef PubMed 73. ↵ Silver HJ , Dietrich MS , Niswender KD . Effects of grapefruit, grapefruit juice and water preloads on energy balance, weight loss, body composition, and cardiometabolic risk in free-living obese adults . Nutr Metab . 2011 Feb 2 ; 8 ( 1 ): 8 . doi: 10.1186/1743-7075-8-8 OpenUrl CrossRef 74. ↵ Wan S , Luo J , Zhu Y , An P , Luo Y , Xing Q , et al. The Effect of Antioxidant Polyphenol Supplementation on Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis . Nutrients . 2024 Dec 5 ; 16 ( 23 ). doi: 10.3390/nu16234206 OpenUrl CrossRef 75. ↵ Lv L , Cui H , Ma Z , Liu X , Yang L . Recent progresses in the pharmacological activities of caffeic acid phenethyl ester . Naunyn Schmiedebergs Arch Pharmacol . 2021 Jul ; 394 ( 7 ): 1327 – 39 . doi: 10.1007/s00210-021-02054-w PubMed PMID: 33492405 . OpenUrl CrossRef PubMed 76. ↵ Hazewindus M , Haenen GRMM , Weseler AR , Bast A . The anti-inflammatory effect of lycopene complements the antioxidant action of ascorbic acid and α-tocopherol . Food Chem . 2012 May 15 ; 132 ( 2 ): 954 – 8 . doi: 10.1016/j.foodchem.2011.11.075 OpenUrl CrossRef 77. ↵ Neunert G , Górnaś P , Dwiecki K , Siger A , Polewski K . Synergistic and antagonistic effects between alpha-tocopherol and phenolic acids in liposome system: spectroscopic study . Eur Food Res Technol . 2015 Dec 1 ; 241 ( 6 ): 749 – 57 . doi: 10.1007/s00217-015-2500-4 OpenUrl CrossRef 78. ↵ Derosa G , Maffioli P , D’Angelo A , Preti PS , Tenore G , Novellino E . Abscisic Acid Treatment in Patients with Prediabetes . Nutrients . 2020 Sep 24 ; 12 ( 10 ): 2931 . doi: 10.3390/nu12102931 PubMed PMID: 32987917 ; PubMed Central PMCID: PMC7599846 . OpenUrl CrossRef PubMed 79. ↵ Wang Y , Feltham BA , Suh M , Jones PJH. Cocoa flavanols and blood pressure reduction: Is there enough evidence to support a health claim in the United States? Trends Food Sci Technol. 2019 Jan 1 ; 83 : 203 – 10. doi: 10.1016/j.tifs.2018.11.023 OpenUrl CrossRef 80. ↵ Zhang L , Wang X , Zhang L , Virgous C , Si H. Combination of curcumin and luteolin synergistically inhibits TNF-α-induced vascular inflammation in human vascular cells and mice . J Nutr Biochem . 2019 Nov 1 ; 73 : 108222 . doi: 10.1016/j.jnutbio.2019.108222 OpenUrl CrossRef 81. ↵ Centers for Disease Control and Prevention . Diabetes [Internet] . 2024 [cited 2025 May 15 ]. National Diabetes Statistics Report . Available from: https://www.cdc.gov/diabetes/php/data-research/index.html 82. ↵ Barber SR , Davies MJ , Khunti K , Gray LJ. Risk assessment tools for detecting those with pre-diabetes: a systematic review . Diabetes Res Clin Pract . 2014 Jul ; 105 ( 1 ): 1 – 13 . doi: 10.1016/j.diabres.2014.03.007 PubMed PMID: 24694663 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 10, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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