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The effects of a nutraceutical combination on plasma lipids and glucose: A systematic review and meta-analysis of randomized controlled trials
Pharmacol Res. 2016;110:76-88.
Dyslipidemia and hyperglycemia are associated with an increased risk of ischemic cardiovascular disease. Positive effects of a nutraceutical combination comprising red yeast rice, berberine, policosanol, astaxanthin, coenzyme Q10 and folic acid (NComb) on plasma lipid and glucose levels have been reported in some but not all clinical trials. To address this inconsistency, we tried to estimate the size of lipid- and glucose-lowering effects of NComb through a systematic review and meta-analysis of randomized controlled trials.
A systematic literature search in PubMed-Medline, SCOPUS and Google Scholar databases was conducted to identify randomized controlled trials investigating the effects of NComb on plasma lipids and glucose levels. Inverse variance-weighted mean differences (WMDs) and 95% confidence intervals (CIs) were calculated for net changes in lipid and glucose levels using a random-effects model. Random-effects meta-regression was performed to assess the effect of putative confounders on plasma lipid and glucose levels.
Fourteen trials (1670 subjects in the NComb arm and 1489 subjects in the control arm) met the eligibility criteria for lipid analysis and 10 trials (1014 subjects in the NComb arm and 962 subjects in the control arm) for glucose analysis. Overall, WMDs were significant for the impact of NComb supplementation on plasma levels of total cholesterol (−26.15 mg/dL, p < 0.001), LDL-cholesterol (−23.85 mg/dL, p < 0.001), HDL-cholesterol (2.53 mg/dL, p < 0.001), triglycerides (−13.83 mg/dL, p < 0.001) and glucose (−2.59 mg/dL, p = 0.010). NComb-induced amelioration of lipid profile was not affected by duration of supplementation nor by baseline lipid levels; conversely, a greater glucose-lowering effect of NComb was found with higher baseline glucose levels and longer durations of supplementation.
In conclusion, the present results suggest that NComb supplementation is associated with improvement of lipid and glucose profile. Short-term beneficial effects of NComb supplementation appear to be maintained in the long term.
Abbreviations: SX - astaxanthin, BBR - berberine, BMI - body mass index, CIs - confidence intervals, CMA - comprehensive meta-analysis, CoQ10 - coenzyme Q10, CVD - cardiovascular disease, FA - folic acid, HDL - high-density lipoprotein cholesterol, LDL - low-density lipoprotein cholesterol, NComb - nutraceutical combination, PCS - policosanol, RCTs - randomized controlled trials, RYR - red yeast rice, SD - standard deviation, WMD - weighted mean difference.
Chemical compounds: Monacolin K (PubChem CID: 53232), Berberine (PubChem CID: 2353), Astaxanthin (PubChem CID: 5281224), Coenzyme Q10 (PubChem CID: 5281915), Folic acid (PubChem CID: 6037).
Keywords: Nutraceutical, Red yeast rice, Berberine, Cholesterol, Lipid, Glucose.
Dyslipidemias and hyperglycemia are established risk factors for ischemic cardiovascular disease (CVD)  and . There is a consistent relationship between most of the dyslipidemic phenotypes, such as hypercholesterolemia, hypertriglyceridemia and hypoalphalipoproteinemia, and the risk of CVD  and . The combination of multiple lipid fraction abnormalities is common  and shows a detrimental cumulative impact on CVD risk .
Similar to dyslipidemias, hyperglycemia per se has a negative impact on CVD risk  and , and glucose levels in the diabetic range are associated with an increased CV mortality . The association between fasting blood glucose and CVD burden has also been demonstrated in non-diabetic  and .
The association of dyslipidemia and hyperglycemia, that are inter-related through a mechanistic link where insulin-resistance is involved as a prominent primer, further aggravates their detrimental prognostic significance .
Given the alarmingly high prevalence and unfavourable coexistence of lipid and glucose abnormalities on the one hand  and , and the time-dependent relationship between exposure to these conditions and vascular risk on the other  and , lipid- and glucose-lowering strategies have been proposed to be initiated early before CVD appearance  and .
A quite novel approach, at least in Western countries, to treat dyslipidemias and hyperglycemia involves the use of nutraceuticals . Cholesterol-lowering effects of red yeast rice (RYR) and berberine (BBR), administered as single agents, have been confirmed in some meta-analyses of randomized controlled trials (RCTs)  and . Also, the glucose-lowering effect of BBR has been demonstrated along with the evidence for a potential triglyceride-lowering effect . The role of policosanol (PCS) in reducing cholesterol is controversial and far to be confirmed in studies outside Cuba . Finally, astaxanthin (ASX) and coenzyme Q10 (CoQ10) have demonstrated variable effects on plasma lipids and glucose levels , , , , and . Hence, the possibility to exploit combined lipid- and glucose-lowering effects of multiple nutraceuticals has led to the development of specific nutraceutical combinations  and .
Nutraceutical combinations have been used as therapeutic strategies especially for those patients whose lipid and glucose levels were marginally high but not enough to warrant the prescription of either lipid- or glucose-lowering medications  and . A specific low-dose combination of nutraceuticals (NComb), containing RYR, BBR, PCS, ASX, CoQ10 and folic acid (FA) has been reported to be effective in reducing total and low-density lipoprotein (LDL) cholesterol levels in most , , , , , , , , , , , , and  but not all trials . The magnitude of cholesterol reduction using NComb, and its influence on plasma triglycerides, high-density lipoprotein (HDL) cholesterol and glucose levels varied across trials, which could be partly atrtibuted to the variable durations and sample sizes of trials exploring lipid- and glucose-lowering effects of NComb.
Despite combination of lipid- and glucose-lowering nutraceuticals like RYR, BBR, PCS, ASX and CoQ10 seems attractive to target patients with mild dyslipidemias and hyperglycemia in their early stages, there is substantial uncertainty about the net effect of this NComb on plasma lipid and glucose levels. The present study aimed to explore this uncertainty through a systematic review and meta-analysis of clinical trials investigating the effects of NComb on plasma lipid and glucose levels.
2.1. Search strategy
This study was designed according to the guidelines of the 2009 preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement . PubMed-Medline and SCOPUS databases were searched using the following search terms in titles and abstracts: (“cholesterol” OR “LDL” OR “HDL” OR “triglyceride” OR “glucose” OR “glycemia”) AND (“red yeast rice” OR “monacolin” OR “armolipid”). Also, the following search terms were used in Google Scholar: (cholesterol AND berberine AND policosanol AND nutraceutical). The wild-card term “*” was used to increase the sensitivity of the search strategy. The search was limited to articles published in English language. The literature was searched from inception to February 10, 2016.
2.2. Study selection
Original studies were included if they met the following inclusion criteria: (i) being a clinical trial with either parallel or cross-over design, (ii) investigating the impact of low-dose NComb [RYR extract 200 mg (equivalent to 3 mg monacolin), BBR 500 mg, PCS 10 mg, ASX 0.5 mg, CoQ10 2 mg and FA 0.2 mg] on serum/plasma concentrations of total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides or glucose, (iii) presentation of sufficient information on lipid an glucose concentrations at baseline and at the end of follow-up in each group or providing the net change values. Exclusion criteria were (i) non-interventional studies, (ii) uncontrolled studies, and (iii) observational studies with case-control, cross-sectional or cohort design.
2.3. Data extraction
Eligible studies were reviewed and the following data were abstracted: (1) first author’s name; (2) year of publication; (3) country were the study was performed; (4) study design; (5) number of participants in the NComb and control groups; (6) intervention assigned to the control group (placebo, no active treatment or active treatment); (7) treatment duration; (8) age, gender and body mass index (BMI) of study participants; (9) baseline and end-trial values for total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides and glucose concentrations; (10) systolic and diastolic blood pressures.
2.4. Quality assessment
A systematic assessment of bias in the included studies was performed using the Cochrane criteria . The items used for the assessment of each study were as follows: adequacy of sequence generation, allocation concealment, blinding, addressing of dropouts (incomplete outcome data), selective outcome reporting, and other potential sources of bias. According to the recommendations of the Cochrane Handbook, a judgment of “yes” indicated low risk of bias, while “no” indicated high risk of bias. Labeling an item as “unclear” indicated an unclear or unknown risk of bias.
2.5. Quantitative data synthesis
Meta-analysis was conducted using Comprehensive Meta-Analysis (CMA) V2 software (Biostat, NJ) . Net changes in measurements (change scores) were calculated for parallel and cross-over trials, as follows: (measure at the end of follow-up in the treatment group−measure at baseline in the treatment group)−(measure at the end of follow-up in the control group−measure at baseline in the control group). For single-arm cross-over trials, net change in plasma concentrations of lipids and/or glucose were calculated by subtracting the value after control intervention from that reported after treatment. All values were collated to mg/dL. Standard deviations (SDs) of the mean difference were calculated using the following formula: SD = square root [(SDpre-treatment)2 + (SDpost-treatment)2–(2R × SDpre-treatment × SDpost-treatment)], assuming a correlation coefficient (R) = 0.5. If the outcome measures were reported in median and range (or 95% confidence interval [CI]), mean and SD values were estimated using the method described by Wan et al. . Where standard error of the mean (SEM) was only reported, SD was estimated using the following formula: SD = SEM × sqrt (n), where n is the number of subjects.
A random-effects model (using DerSimonian-Laird method) and the generic inverse variance method were used to compensate for the heterogeneity of studies in terms of study design, treatment duration, and the characteristics of populations being studied . Inter-study heterogeneity was assessed using Cochran Q test and I2 index. In order to evaluate the influence of each study on the overall effect size, sensitivity analysis was conducted using leave-one-out method, i.e. iteratively removing one study each time and repeating the analysis  and .
A weighted random-effects meta-regression using unrestricted maximum likelihood model was performed to assess the association between the overall estimate of effect size with duration of NComb supplementation and baseline concentrations of lipids and glucose as potential moderator variables.
2.7. Publication bias
Potential publication bias was explored using visual inspection of Begg’s funnel plot asymmetry, and Begg’s rank correlation and Egger’s weighted regression tests. Duval and Tweedie “trim and fill” and “fail-safe N” methods was used to adjust the analysis for the effects of publication bias .
3.1. Flow and characteristics of included studies
After multiple database searches, 326 published studies were identified and the abstracts were reviewed. Of these, 80 were non-original articles and were excluded. The remaining 125 studies were eliminated because they did not test the effects of NComb and 102 studies were not performed in humans. Then, 19 full text articles were carefully assessed and reviewed; of which 5 studies were excluded for not meeting the inclusion/exclusion criteria. Finally, 14 studies were eligible and included in the systematic review and meta-analysis of the effect of NComb on lipid levels. Ten out of these 14 studies were elegible for evaluation of the effect of NComb on glucose levels. The study selection process is shown in Fig. 1.
Data were pooled from 14 eligible studies (meta-analysis on lipid levels) comprising 28 treatment arms which included 3159 subjects, with 1670 in the NComb arm and 1489 in the control arm.
Also, data were pooled from 10 elegible trials (meta-analysis on glucose levels) comprising 20 treatment arms which included 1976 subjects, with 1014 in the NComb arm and 962 in the control arm.
Included studies were published between 2007 and 2015. All the clinical trials used the same formulation of NComb. The range of intervention periods was from 4 weeks  to 12 months . Study design of 13 out of 14 included studies was parallel-group , , , , , , , , , , , , and  and one study had a cross-over design . Selected studies enrolled subjects with moderate mixed hyperlipidemia , hypercholesterolemia , , , , , and , dyslipidemia  and , metabolic syndrome  and , overweight , coronary heart disease with statin intolerance  and familial combined hyperlipidemia . Baseline anthropometric, clinical and biochemical characteristics of the evaluated studies are presented in Table 1.
|Author (year)||Study design||Target Population||Treatment duration||n||Study groups||Age, years||Female (n, %)||BMI, (kg/m2)||Systolic blood pressure||Diastolic blood pressure||Total cholesterol (mg/dl)||LDL cholesterol (mg/dl)||HDL cholesterol (mg/dl)||Triglycerides
|Cicero et al. ||Randomized single-blind, active-controlled||Moderate mixed hyperlipidemia||4 weeks||20||NComb||61.0 ± 15.5||12 (60)||ND||ND||ND||265.4 ± 20.1||174.4 ± 21.9||50.5 ± 12.9||202.4 ± 49.2||85.7 ± 13.2|
|20||Berberine 500 mg/day||60.4 ± 14||12 (60)||ND||ND||ND||263.7 ± 14.2||177.8 ± 13.8||48.5 ± 10.1||191.8 ± 37.4||84.3 ± 11.9|
|Affuso et al. ||Randomized double-blind, placebo-controlled||HC||6 weeks||25||NComb||55 ± 8||12 (48)||28 ± 3.8||ND||ND||254.8 ± 28.9||175.6 ± 25.1||57.9 ± 18.1||127.7 ± 71.7||ND|
|25||Placebo||55 ± 7||12 (48)||28 ± 3.3||ND||ND||250.9 ± 30.9||170.6 ± 22.0||52.8 ± 13.9||146.1 ± 63||ND|
|Izzo et al. ||Randomized controlled, multicenter||Dyslipidemia||8 weeks||682||NComb||56.9 ± 10.9||369 (54.1)||ND||134.2 ± 14.8||81.8 ± 7.4||257.9 ± 32.2||ND||50.6 ± 15.6||189.4 ± 59.9||96.3 ± 16.9|
|662||No active treatment||55.7 ± 11.4||388(58.6)||ND||133.8 ± 12.3||81.2 ± 7.5||244.3 ± 26.7||ND||50.1 ± 15||180.7 ± 51.7||95.8 ± 14.1|
|Marazzi et al. ||Randomized single-blind, placebo-controlled||HC with statin-intolerance||12 months||40||Ncomb||82.45 ± 4.44||19 (47.5)||ND||ND||ND||252 ± 23||172 ± 16||44 ± 12||179 ± 48||94 ± 6|
|40||Placebo||82.53 ± 4.89||20 (50)||ND||ND||ND||253 ± 19||173 ± 10||44 ± 8||179 ± 50||91 ± 7|
|Trimarco et al. ||Randomized controlled, multicenter||Dyslipidemia||16 weeks||818||NComb||57.3 ± 0.4||434 (53.1)||27.9 ± 0.2*||134.3 ± 0.5*||81.5 ± 0.3*||255.4 ± 1.1*||170.1 ± 1.1*||50.0 ± 0.5*||190.5 ± 2.1*||99.6 ± 20.8*|
|933||No active treatment||55.6 ± 0.4||518 (55.5)||28.4 ± 0.2*||134.3 ± 0.5*||81.3 ± 0.3*||243.1 ± 1.0*||162.2 ± 1.0*||48.8 ± 0.4*||184.4 ± 1.9*||99.2 ± 20.4*|
|Affuso et al. ||Randomized double-blind, placebo-controlled||Metabolic syndrome||18 weeks||29||NComb||53 ± 7||9 (31)||32.2 ± 4.6||125 ± 13||78 ± 8||208.4 ± 38.6||134.7 ± 7.3||41.7 ± 10||154 ± 77.2||102.9 ± 21.9|
|30||Placebo||50 ± 12||12 (40)||34.7 ± 5.1||125 ± 14||81 ± 8||196.5 ± 39.8||117.7 ± 38.6||45.5 ± 13.5||168 ± 72.6||84.9 ± 12.1|
|Cicero et al. ||Partially randomized||Overweight||12 months||85||NComb||ND||ND||26.95 ± 0.86||134.35 ± 6.2||86.25 ± 6.09||218.26 ± 14.43||134.58 ± 15.23||38.64 ± 4.46||225.20 ± 42.72||109.58 ± 12.03|
|50||Placebo||ND||ND||24.17 ± 0.99||133.24 ± 5.3||84.09 ± 6.82||213.52 ± 16.98||135.98 ± 18.91||38.97 ± 4.27||192.82 ± 44.39||92.22 ± 10.25|
|Pisciotta et al. ||Randomized active-controlled||Primary poligenic HC||6 months||152||NComb||57.3 ± 12.1||90(50)||23.9 ± 2.9||ND||ND||294.52 ± 19.3||206.89 ± 18.53||59.83 ± 13.51||127.75(106.75–166.25)§||ND|
|76||Ezetimibe 10 mg/day||58.3 ± 12.3||45(50)||23.5 ± 2.8||ND||ND||297.99 ± 18.91||206.89 ± 20.07||60.60 ± 13.12||146.12(108.5–185.5)§||ND|
|Pirro et al. ||Randomized open-label, controlled||HC||2 months||35||NComb||56 ± 11||22(63)||24.9 ± 4.1||130 ± 16||ND||259 ± 29||175 ± 29||56 ± 10||111(93–170)§||93 ± 10|
|35||No active treatment||57 ± 13||22(63)||25.1 ± 3.9||131 ± 10||ND||260 ± 27||178 ± 21||58 ± 14||119(63–165)§||93 ± 9|
|Ruscica et al. ||Randomized double-blind, placebo-controlled, crossover||Moderate dyslipidemia and metabolic syndrome||8 weeks||30||NComb||55.4 ± 9.7||7(23)||26.8 ± 2.4||123 ± 12.3||80.7 ± 5.7||239.32 ± 30.88||150.92 ± 23.93||40.14 ± 2.88||213.5 (168.87–280.87)§||87.84 ± 16.2|
|30||Placebo||55.4 ± 9.7||7(23)||26.8 ± 2.4||123 ± 12.3||80.7 ± 5.7||239.32 ± 38.6||140.77 ± 29.34||41.30 ± 7.33||227.5 (170.62–303.62)§||85.68 ± 18.4|
|Gonnelli et al. ||Randomized double-blind, placebo-controlled||HC||24 weeks||30||NComb||46.4 ± 9.7||15(50)||26.9 ± 4.9||ND||ND||238.4 ± 26.9||162.0 ± 22.5||53.1 ± 13.2||132.1 ± 55.2||92.5 ± 8.8|
|30||Placebo||46.4 ± 10.1||16(53)||26.4 ± 4.1||ND||ND||248.1 ± 32.4||165.8 ± 29.0||55.7 ± 14.5||119.0 ± 50.4||94.4 ± 10.0|
|Solà et al. ||Randomized double-blind, placebo-controlled, multicenter||HC||12 weeks||51||NComb||49.91 ± 11.61||33(64.7)||25.36 ± 4.07||122.2 ± 18.1||76.49 ± 12.2||243.61 ± 24.35||155.67 ± 14.57||66.51 ± 21.20||107.20 ± 61.34||90.58 ± 9.27|
|51||Placebo||52.37 ± 11.15||37(72.5)||27.97 ± 8.66||123.7 ± 17.6||76.75 ± 11.2||243.43 ± 19.49||159.28 ± 15.65||61.10 ± 14.05||115.00 ± 56.02||92.77 ± 10.30|
|Marazzi et al.||Randomized single-blind, active-controlled||Coronary heart disease and statin intolerance||3 months||50||NComb||64 ± 11||24(48)||ND||ND||ND||218 ± 15||149 ± 16||36 ± 8||166 ± 31||ND|
|50||Ezetimibe 10 mg/day||63 ± 10||22(44)||ND||ND||ND||219 ± 14||150 ± 8||34 ± 7||171 ± 25||ND|
|Gentile et al. ||Randomized double-blind, placebo-controlled||Familial combined hyperlipidemia||8 weeks||15||NComb||ND||ND||26.0 ± 2.8||123.0 ± 12.3||77.9 ± 8.3||228.8 ± 41.1||134.7 ± 46.5||40.8 ± 6.6||290.3 ± 104.3||91.5 ± 17.5|
|15||Placebo||ND||ND||26.7 ± 2.8||122.5 ± 9.2||78.1 ± 6.9||241.9 ± 42.1||162.8 ± 41.2||38.2 ± 9.1||204.2 ± 80.9||93.0±5.9|
Values are expressed as mean ± SD, mean ± SEM (*) or median and 25th–75th percentiles (§).
Abbreviations: HC, hypercholesterolemia; ND, no data; BMI, body mass index; NComb, nutraceutical combination.
3.2. Risk of bias assessment
Several of the included studies provided insufficient information with respect to sequence generation and allocaion concealment. Moreover, eight trials showed a high risk of bias with respect to blinding of participants, personnel and outcome assessors , , , , , , , and . However, all assessed studies had low risks of bias for incomplete outcome data and selective outcome reporting. Details of the quality of bias assessment are presented in Table 2.
|Blinding of participants, personnel and outcome assessors||Incomplete
|Selective outcome reporting||Other sources of bias|
|Cicero et al. ||U||U||H||L||L||U|
|Affuso et al. ||U||L||L||L||L||U|
|Izzo et al. ||U||U||H||L||L||U|
|Marazzi et al. ||L||U||H||L||L||U|
|Trimarco et al. ||U||U||H||L||L||U|
|Affuso et al. ||U||L||L||L||L||U|
|Cicero et al. ||U||U||H||L||L||U|
|Pisciotta et al. ||L||U||H||L||L||U|
|Pirro et al. ||U||U||H||L||L||U|
|Ruscica et al. ||U||U||L||L||L||U|
|Gonnelli et al. ||U||L||L||L||L||U|
|Solà et al.||L||L||L||L||L||L|
|Marazzi et al. ||L||U||H||L||L||U|
|Gentile et al. ||U||U||U||L||L||U|
L, low risk of bias; H, high risk of bias; U, unclear risk of bias.
3.3. Effect of NComb on plasma lipid and glucose concentrations
Overall, the impact of NComb on plasma concentrations of total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides and glucose were reported in 14, 13, 14, 14 and 10 studies, respectively. NComb supplementation was found to significantly reduce plasma concentrations of total cholesterol (WMD: −26.15 mg/dL, 95% CI: −31.89, −20.41, p < 0.001; I2 = 88.70%; Fig. 2), LDL-cholesterol (WMD: −23.85 mg/dL, 95% CI: −30.70, −17.00, p < 0.001; I2 = 90.13%; Fig. 2), triglycerides (WMD: −13.83 mg/dL, 95% CI: −17.10, −10.56, p < 0.001; I2 = 0%; Fig. 2) and glucose (WMD: −2.59 mg/dL, 95% CI: −4.58, −0.61, p = 0.010; I2 = 55.59%; Fig. 2), while increasing HDL-cholesterol levels (WMD: 2.53 mg/dL, 95% CI: 1.77, 3.30, p < 0.001; I2 = 1.39%; Fig. 2). All these effects were robust in the sensitivity analysis (Fig. 3), and the overall estimate of effect size was not significantly driven by a single study.
When the meta-analysis was stratified according to the type of control intervention, significantly greater reductions in plasma levels of total cholesterol (p < 0.001) and LDL-cholesterol (p = 0.002), and significantly greater elevations in plasma levels of HDL-cholesterol (p = 0.005) were found in the subset of trials with placebo or “no active treatment” control group compared with the subset with active control groups (berberine or ezetimibe). However, there was no significant difference in the magnitude of changes in plasma levels of triglycerides (p = 0.554) and glucose (p = 0.538) between the subsets of trials with active or neutral control groups (Fig. 4).
Meta-regression analysis was conducted to evaluate the association between changes in plasma lipid and glucose concentrations and duration of supplementation as a potential confounder. No significant association was found between changes in lipid parameters with either baseline values of lipids or duration of supplementation with NComb (Fig 5 and Fig 6). In contrast, changes in plasma glucose levels were associated with both baseline glucose concentrations (slope: −0.12; 95% CI: −0.18, −0.05; p < 0.001) and duration of supplementation (slope: −0.32; 95% CI: −0.60, −0.05; p = 0.020) (Fig 5 and Fig 6).
3.5. Publication bias
Visual inspection of funnel plots did not suggest presence of publication bias and requirement to “trim and fill” correction for the meta-analyses on total cholesterol, LDL-cholesterol, triglycerides and glucose (Fig. 7). This finding was also confirmed by the results of Egger’s linear regression, Begg’s rank correlation, and “fail safe N” tests. In contrast, the funnel plot of HDL-cholesterol meta-analysis was asymmetric, requiring imputation of 6 potentially missing studies using “trim and fill” correction, though the corrected effect size remained statistically significant (Fig. 7). Presence of publication bias in the meta-analysis of HDL-C was also confirmed by Egger’s linear regression test. The results of Egger’s linear regression, Begg’s rank correlation, and “fail safe N” tests are summarized in Table 3.
|Corrected effect sizea||Begg’s rank correlation test||Egger's linear regression test||Fail safe N test|
|WMD||95% CI||Kendall’s Taua||z-value||p-value||Intercept||95% CI||p-value||nb|
|Total cholesterol||–||–||−0.15||0.77||0.443||−0.83||−3.86, 2.20||0.561||2216|
|HDL-C||2.98||2.03, 3.94||−0.11||0.55||0.584||−1.07||−2.00, −0.15||0.027||61|
a With continuity correction.
b Number of theoretically missing studies to bring the p-value to > 0.05.
Results of the present systematic review and meta-analysis of RCTs suggested that supplementation with NComb improves plasma lipids and glucose levels. It is well known that dyslipidemia and hyperglycemia are major risk factors for the development of CVD  and . Treatment with lipid-lowering drugs prevents primary and/or recurrent CV events , whereas glucose-lowering drugs exert beneficial effects mostly on microvascular rather than macrovascular complications .
Therapeutic lifestyle changes aimed at pursuing an acceptable control of CV risk factors, comprising dyslipidemia and hyperglycemia, are limited by poor adherence and persistence  and . Similarly, reduced long-term compliance to lipid- and glucose-lowering drugs, that is frequently due to side effects, is a matter of crucial importance in the field of cardiovascular prevention  and . Hence, therapeutic strategies which are both effective and safe in reducing both lipid and glucose levels are desirable.
In recent years, combinations of nutraceuticals claiming lipid- and glucose-lowering effects have been tested in different populations , , , and . We performed a meta-analysis of published RCTs to evaluate the efficacy of a combination of RYR, BBR, PCS, ASX, CoQ10 and FA (NComb) on cholesterol, triglyceride and glucose levels. NComb exhibited significantly greater reducing effects on the levels of plasma total cholesterol, LDL-cholesterol, triglycerides, and glucose compared to placebo or no active treatment. Also, a significant increase in plasma HDL-cholesterol levels was observed following NComb supplementation.
The reductions in plasma total cholesterol and LDL-cholesterol levels observed in this meta-analysis confirmed the findings in 13 , , , , , , , , , , , , and  out of 14 , , , , , , , , , , , , , and  individual trials for total cholesterol and 12 , , , , , , , , , , , and  out of 13 trials , , , , , , , , , , , , and  for LDL-cholesterol. In addition, it also allowed for greater certainty in the assessment of time-related LDL-cholesterol reductions. In this regard, meta-regression analysis failed to indicate a significant association between duration of NComb supplementation and total and LDL-cholesterol reductions. This finding may ensure that NComb maintains its cholesterol-lowering efficacy over time.
From a clinical perspective, such a reduction of LDL cholesterol (−23.85 mg/dL in the overall analysis and −28.16 mg/dL in trials without active treatment as comparator) might translate into a possible clinical benefit. Accordingly, some trials have demonstrated a beneficial effect of NComb on surrogate markers of CV risk, such as endothelial dysfunction  and aortic pulse wave velocity . In addition, lower absolute LDL-cholesterol reductions (−12.8 mg/dL) obtained with ezetimibe produced a 7.2% lower rate of major vascular events compared to placebo in the IMProved Reduction of Outcomes: Vytorin Efficacy International Trial . However, the hypothetical beneficial effect of NComb on CV risk remains speculative until specific trials exploring this issue will be performed.
Which nutraceutical component of NComb exerted the most beneficial effect on total and LDL- cholesterol levels cannot be inferred from this meta-analysis nor from the individual trials; accordingly, none of the included studies in this meta-analysis explored the cholesterol-lowering effect of the single nutraceuticals over placebo. Also, a possible synergistic cholesterol-lowering effect of the different nutraceuticals cannot be deduced from any of the trials assessed in this meta-analysis.
Irrespective of whether a single nutraceutical of this NComb or synergism between them improved total and LDL-cholesterol levels, convergence of results of published RCTs and studies exploring the mechanism of action of these nutraceuticals, seems to support a more consistent cholesterol-lowering action for RYR and BBR , , , and . In this regard, cholesterol-lowering effects of RYR and BBR, administered as single agents, have been observed in meta-analyses of RCTs  and . Also, there is substantial agreement that monacolin K, whose content is variably found in RYR formulations, is able to reduce cholesterol levels via inhibiting hydroxy-methyl-glutaryl-CoA reductase . Finally, BBR significantly increases hepatic low-density lipoprotein receptor expression and reduces the expression and secretion of proprotein convertase subtilisin/kexin type 9 .
Additional components of this NComb include PCS, ASX, CoQ10 and FA. The cholesterol-lowering efficacy of PCS has been questioned and the circumstances leading to a high level of skepticism on PCS have been the subject of an intense debate . ASX demonstrated a variable effect on cholesterol levels  and , with evidence of a positive effect in some studies , but neutral effect in a recent meta-analysis . Similarly, the cholesterol-lowering impact of CoQ10 as a single supplementation agent is controversial  and .
In this meta-analysis, the large number of patients per treatment group allowed for statistically significant changes to be seen in plasma triglycerides, HDL-cholesterol and glucose levels. Although these changes were mild in absolute terms, they all converge in the same direction of improving the glycolipid profile. In addition, these results were obtained mainly in studies where baseline triglyceride levels were below 200 mg/dL (10 out of 14 studies), HDL-cholesterol levels were above 40 mg/dL (10 out of 13) and glucose levels were below 100 mg/dL (8 out of 10 studies). Whether the overall beneficial effect of this NComb on glycolipid profile might be greater in large populations of patients with atherogenic dyslipidemia or impaired glucose metabolism needs to be demonstrated. In this regard, it should be pointed out that meta-regression analysis showed that NComb-induced amelioration of glycemia was affected by duration of NComb supplementation and baseline glucose levels; specifically, a greater glucose-lowering effect of NComb was found with higher baseline glucose levels and longer durations of supplementation. Hence, according to the present results, a possible greater glucose-lowering effect of this NComb can be hypothesized in patients with higher baseline glucose levels; also, the glucose-lowering effect of NComb might require longer duration of treatment to amplify its beneficial effects.
From a clinical perspective, there is substantial uncertainity on whether these changes in triglycerides, HDL-cholesterol and glucose levels might be translated into a relevant clinical benefit. The beneficial triglyceride-lowering effect of fibrates is not always paralleled by a consistent CV risk reduction . Also, the impact of pharmacologically increased plasma HDL-cholesterol has been strongly questioned in the last years . Finally, the possible advantage of such a slight glucose reduction on macrovascular outcomes is far to be clarified. Irrespective of the speculative assumptions on the possible beneficial effects of mild triglyceride and glucose reductions and HDL-cholesterol increase, there is substantial agreement that high triglyceride and glucose levels and low HDL cholesterol levels have a detrimental influence on CV prognosis , , , and . Therefore, to have a new therapeutic aid that may improve glucose and lipid profile certainly represents an advance in the management of patients with mild dyslipidemia and an initial derangement of glucose metabolism.
As for total and LDL-cholesterol reductions, the relative impact of the single nutraceuticals of NComb on plasma triglycerides, HDL-cholesterol and glucose levels cannot be ascertained from this meta-analysis nor from the individual trials. However, a large body of evidence supports the predominant impact of BBR over the other nutraceuticals . Consequently, meta-analyses of RCTs exploring the lipid and glucose-lowering effects of BBR, concluded that BBR might represent an effective strategy to improve glycolipid profile . Mechanisms explaining this wide metabolic impact of BBR have been comprehensively reviewed by Pirillo and Catapano , reporting that among the pathways through which BBR exerts its metabolic effects, AMP-activated protein kinase plays a central role. Evidence on ASX's effect on atherogenic dyslipidaemia  has been provided; yet the overall impact of ASX on glycolipid profile has not been confirmed in a recent meta-analysis  and the mechanisms of the potential effect has not been uncovered so far. Finally, the sporadic reports of a possible beneficial effect of CoQ10 on lipid profile and glucose levels have been counterbalanced by the net results of meta-analyses showing a neutral effect of CoQ10 on lipid and glucose profile , , and .
This review has some limitations primarly resulting from small population size of most of the included studies. Thus, insufficient data were available to allow separate analysis of effects of NComb in different patients subgroups. Moreover, most of these studies were of suboptimal quality in terms of providing adequate description of allocation concealment and lacking the use of double blinding, thus leading to possible overestimation of NComb’s benefit. On the other hand, three studies reported the use of either ezetimibe or berberine as an active control , , and . Since the cholesterol-lowering effect of ezetimibe is widely recognized  and berberine demonstrates both cholesterol-, triglyceride- and glucose-lowering effects  and , this could lead to a possible underestimation of NComb's benefit. Exclusion of these studies , , and  did confirm a greater impact of NComb on the outcome measures of this meta-analysis. In addition, PCS was included by the manufacturer in the studied NComb because of the initial proposed evidence of its cholesterol-lowering effects; however, as reported by a comprehensive review of Marinangeli et al. , despite Cuban studies claimed that the original PCS supplement was effective at producing significant reductions in cholesterol levels, research groups outside of Cuba have failed to validate the cholesterol-lowering efficacy of PCS in randomized controlled trials. Hence, the possibility that the adjunct of PCS to this NComb might be useless should be considered. The same line of reasoning applies to FA. In a randomized dose-finding trial in patients with ischemic heart disease and healthy volunteers, a FA dose as low as 0.2 mg/daily administered for 6 months effectively reduced homocysteine concentrations . However, despite significant homocysteine-lowering by FA and vitamin B supplementation, this intervention did not reduce CVD risk, thus questioning the role of FA supplementation in CV prevention  and .
Finally, although safety issues related to this NComb did not emerge from any of the individual trials, report of safety data was not complete to address this point in our meta-analysis.
With these limitations in mind, we must recognize that results of this meta-analysis emphasize the usefulness of this NComb in ameliorating plasma total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides and glucose levels. Hence, patients with isolated mild cholesterol elevations, with or without concomitant modest alteration of plasma triglycerides, HDL-cholesterol or glucose levels despite adequate lifestyle intervention, are potential candidates for NComb supplementation.
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a Unit of Internal Medicine, Angiology and Arteriosclerosis Diseases, Department of Medicine, University of Perugia, Perugia, Italy
b Biomedical Research Unit, Mexican Social Security Institute, Durango, Mexico
c Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad 9177948564, Iran
d Metabolic Research Centre, Royal Perth Hospital, School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
⁎ Corresponding author at: Unit of Internal Medicine, Angiology and Arteriosclerosis Diseases University of Perugia, Hospital “Santa Maria della Misericordia”, Piazzale Menghini, 1-06156 Perugia, Italy.
⁎⁎ Corresponding author at: Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad 9177948564, Iran.
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