Skip to main content

The effects of coconut oil on the cardiometabolic profile: a systematic review and meta-analysis of randomized clinical trials

Abstract

Background

Despite having a 92% concentration of saturated fatty acid composition, leading to an apparently unfavorable lipid profile, body weight and glycemic effect, coconut oil is consumed worldwide. Thus, we conducted an updated systematic review and meta-analysis of randomized clinical trials (RCTs) to analyze the effect of coconut oil intake on different cardiometabolic outcomes.

Methods

We searched Medline, Embase, and LILACS for RCTs conducted prior to April 2022. We included RCTs that compared effects of coconut oil intake with other substances on anthropometric and metabolic profiles in adults published in all languages, and excluded non-randomized trials and short follow-up studies. Risk of bias was assessed with the RoB 2 tool and certainty of evidence with GRADE. Where possible, we performed meta-analyses using a random-effects model.

Results

We included seven studies in the meta-analysis (n = 515; 50% females, follow up from 4 weeks to 2 years). The amount of coconut oil consumed varied and is expressed differently among studies: 12 to 30 ml of coconut oil/day (n = 5), as part of the amount of SFAs or total daily consumed fat (n = 1), a variation of 6 to 54.4 g/day (n = 5), or as part of the total caloric energy intake (15 to 21%) (n = 6). Coconut oil intake did not significantly decrease body weight (MD -0.24 kg, 95% CI -0.83 kg to 0.34 kg), waist circumference (MD -0.64 cm, 95% CI -1.69 cm to 0.41 cm), and % body fat (-0.10%, 95% CI -0.56% to 0.36%), low-density lipoprotein cholesterol (LDL-C) (MD -1.67 mg/dL, 95% CI -6.93 to 3.59 mg/dL), and triglyceride (TG) levels (MD -0.24 mg/dL, 95% CI -5.52 to 5.04 mg/dL). However, coconut oil intake was associated with a small increase in high-density lipoprotein cholesterol (HDL-C) (MD 3.28 mg/dL, 95% CI 0.66 to 5.90 mg/dL). Overall risk of bias was high, and certainty of evidence was very-low. Study limitations include the heterogeneity of intervention methods, in addition to small samples and short follow-ups, which undermine the effects of dietary intervention in metabolic parameters.

Conclusions

Coconut oil intake revealed no clinically relevant improvement in lipid profile and body composition compared to other oils/fats. Strategies to advise the public on the consumption of other oils, not coconut oil, due to proven cardiometabolic benefits should be implemented.

Registration

PROSPERO CRD42018081461.

Background

Cardiovascular disease (CVD), particularly coronary heart disease and stroke, is a major public health problem, being responsible for one-third of deaths worldwide [1,2,3,4]. Despite the great effort of different scientific organizations to fight against the burden of major risk factors for CVD, it is estimated that 11 million deaths and 255 million disability-adjusted life-years are attributable to dietary risk factors [5,6,7,8].

The impact of different types of dietary fats on health has been studied, and its contribution to the development of diseases, causing major burden, such as diabetes, cardiovascular diseases and cancer has been debated [6, 9]. A recent report from the American Heart Association based on different prospective cohort studies, randomized clinical trials (RCTs), and meta-analyses estimated that replacing 5% of energy intake of saturated fatty acids (SFAs) with the same intake of polyunsaturated fatty acids (PUFAs) or monounsaturated fatty acids (MUFAs) was associated with a 25 and 15% lower risk of coronary heart disease, respectively [6]. In light of this evidence, the most recent Dietary Guidelines for Americans recommend a reduction in SFAs to less than 10% of calories and their replacement with unsaturated fats [10]. Additionally, recent data from long-term prospective cohorts and meta-analyses have shown that these recommendations are associated with weight gain prevention and reduction of insulin resistance and risk for diabetes [11,12,13,14,15].

Despite that, coconut oil, which is more than 90% SFA, has been widely recommended on social media for the management of obesity, diabetes, and lipid disorders, broadening its consumption all over the world [16,17,18]. In increasing demand, the estimated consumption of coconut oil in the United States reached 400,000 tons in 2010 [19]. Nonetheless, before the recent rise in coconut oil consumption in western countries, it was only mainly present in some Asian populations’ diets [20,21,22].

A recent systematic review showed that lauric, myristic, and palmitic fatty acids—the major components of coconut oil—are responsible for the highest increase in low-density lipoprotein cholesterol (LDL-C) levels, which is a major risk factor for CVD [19]. Unlike other types of oils which were consistently proven to prevent weight gain, diabetes, CVD, and mortality [23,24,25], studies that analyzed how coconut oil intake affects weight, lipid and glycemic levels are mostly based on small, short-term observational studies and clinical trials [16, 26, 27]. In addition, there are meta-analyses including RCTs that have even demonstrated that coconut oil intake increased LDL-C in comparison to non-tropical vegetable and animal oils and did not observe differences in TG levels [28, 29].

Due to the popularity of coconut oil as a “healthy” food, its broad dietary consumption has risen all over the world. This has led to increasing difficulties to translate medical and nutritional science into adequate recommendations for physicians and health workers as well as laymen. Given this context, we conducted an updated systematic review and meta-analysis of RCTs investigating the effects of coconut oil intake on body weight and composition, lipid profile, glycemic status, blood pressure, and subclinical inflammation in adults.

Methods

This systematic review and meta-analysis was prospectively registered on PROSPERO (CRD42018081461) and was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [30].

Search methods for the identification of studies

We searched the MEDLINE, EMBASE, and LILACS databases to identify studies analyzing the effects of coconut oil intake on weight, lipid and glycemic profiles, blood pressure, and subclinical inflammation in adults from inception to April, 2022, and searched www.clinicaltrials.gov for potentially available unpublished results (Supplementary Appendix I). The references of relevant systematic reviews were screened manually to identify further relevant citations. When an article did not present the results of interest, we contacted the authors by email requesting the data.

Study selection and data extraction

Study inclusion and data extraction were conducted independently (A.C.D., C.R.A., and C.A.). Reviewers resolved discrepancies by discussion and, when necessary, with adjudication by a third party (F.G.). Inter-rater agreement was assessed using the Kappa statistic and percentage of agreement. Kappa statistic was calculated with SPSS software (version 18.03; Chicago, USA). Data extracted were reviewed and double checked by two independent authors (B.F.S. and E.N.M.), who were blinded to the objectives of the meta-analysis.

A standard protocol for data extraction was used, including the following variables: number of participants, study design, duration of the study, interventions, demographic data, age and sex, chronic disease status, as well as exposures of interest before and after the interventions. Data was extracted to assess the effects of coconut oil on anthropometric profile (body weight, body mass index, waist circumference and body composition), lipid profile (LDL-C, HDL-C, total cholesterol [TC], TC/HDL ratio and triglycerides), glycemic profile (glucose, insulin, the homeostasis model assessment [HOMA] β and HOMA-S, HOMA-IR and glycated hemoglobin [HbA1c]), inflammatory profile (ultra-sensitive c-reactive protein [US-CRP], fibrinogen, total homocysteine [tcHcy], interleukins [IL] 1β, IL-6, IL-8 and interferon-gamma [IFN- γ]) and blood pressure (systolic blood pressure and diastolic blood pressure).

Inclusion and exclusion criteria

Since our aim was to evaluate the isolated effect of coconut oil with no influence of dietary pattern, we considered eligible only RCTs (both parallel group or crossover randomized trials) which analyzed the effects of coconut oil intake in comparison to other fats, oils, or placebos on weight, lipid and glycemic profile, blood pressure, and subclinical inflammation of adults (≥ 18 years) published in all languages. We excluded non-randomized trials or studies with follow-ups shorter than seven days. Studies including patients with illnesses which affect metabolism, studies on animals or in vitro, and studies testing coconut products different from oils for intake were also excluded.

Assessment of risk of bias and quality of evidence

Two pairs of authors independently assessed the risk of bias of each included trial using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2) [31]. RoB 2 plots were generated using the Risk-of-bias VISualization (robvis) tool [32]. The overall certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluations (GRADE) [33].

Statistical synthesis

Data were synthesized both qualitatively and quantitatively. To uniformly summarize the exposure data extracted, we standardized the units of concentration by applying standard conversion factors [34, 35]. Mean differences were calculated for continuous outcomes. For data collection, we prioritized intention-to-treat outcomes. Articles that expressed results as standard error had the results transformed into standard deviation. When a study did not express its results in a change from baseline manner, changes from baseline were calculated by subtracting final values from baseline values in each group and change from baseline standard deviations were imputed using a correlation coefficient calculated from the most similar study reported in considerable detail, in accordance with Cochrane Collaboration recommendations [36]. Where possible (that is, when parallel RCTs provided the baseline and final values of each outcome and when the crossover RCTs provided the order of different interventions and the measures of the variables of interest before and after each intervention), data were pooled using a meta-analytic approach. A random-effects model, with DerSimonian and Laird’s variance estimator, was used, and mean differences with 95% confidence intervals were calculated. A p value ≤ 0.05 was considered statistically significant. We used I2 statistics to assess the consistency of effects among studies [37]. We did not assess publication bias with a statistical test or funnel plot because such assessment is not recommended for sample sizes of less than 10 studies [38]. We used the statistical software R version 4.0.5 with the meta-version 4.18–1 package for meta-analysis.

We planned to perform subgroup analyses regarding the following factors: amount of coconut oil used, type of control group, sex, age, body mass index, geographical region where the study was conducted, studies in overweight/obesity subjects or in those with dyslipidemia, time of follow-up, and study sample size. When subgroup analysis of any forementioned factors was not possible due to the low number of studies – thus precluding our ability to quantitatively investigate the sources of heterogeneity –, this analysis was not performed and, therefore, is not mentioned in the results section of this text.

Results

After screening 1,160 potentially relevant studies, 17 fulfilled the selection criteria, of which seven studies were included in the meta-analysis. Inter-rater agreement assessed by the Kappa coefficient was 0.36 (% agreement: 91.1%) and –0.09 (% agreement: 84%) for the record screening and fulltext assessment stages, respectively. Details of the study selection are presented in Fig. 1 and Table S1.

Fig. 1
figure 1

Flow diagram of study selection

The studies comprise 721 patients (age 18–68 years, 52% females) and follow-ups varied from one week to two years. The studies were performed in Europe (n = 2), Asia (n = 3), New Zealand (n = 1), the United States of America (n = 7), and Brazil (n = 4). In four studies, coconut oil was compared predominantly to MUFAs (olive and canola) [39,40,41,42], in 11 studies predominantly to PUFAs (soybean, chia, safflower, sunflower, and corn) [16, 17, 26, 42,43,44,45,46,47,48,49], and in six studies predominantly to SFAs (lard, butter, and palm oil) [39, 40, 44, 50, 51], followed by comparisons with soybean oil + psyllium, transgenic soybean, hydrogenated soybean, and a placebo in one study each [18, 26, 45, 50]. The amount of coconut oil consumed varied and is expressed differently among studies (Table 1): 12 to 30 ml of coconut oil/day (n = 5), as part of the amount of SFAs or total daily consumed fat (n = 1), a variation of 6 to 54.4 g/day (n = 5), or as part of the total caloric energy intake (15 to 21%) (n = 6). Seven studies included healthy individuals [18, 26, 39, 45, 48, 50, 51]; two included subjects with hypercholesterolemia [41, 44]; four, with abdominal obesity, overweight, or obesity [16, 42, 43, 49]; one, in postmenopausal women [46]; and one, individuals with CVD [17]. The key characteristics of all included studies are in Supplementary Tables S2, S3, S4, S5, S6 and summarized in Table 1.

Table 1 Characteristics of the included studies

We contacted authors from 12 trials, of whom three shared data with us (see Acknowledgments).

Trials reporting the effects of coconut oil on LDL-C to HDL-C ratio, TG, TC/HDL-C ratio, glycemic control (fasting glucose and HbA1c levels) and blood glucose regulation (insulin sensitivity and β-cell function), blood pressure, and subclinical inflammation profile are described in the Supplementary Appendix II. No differences in these parameters were found between coconut oil intake and the different control oils/fats. A summary of findings of the systematic review and meta-analysis is presented in Table 2.

Table 2 Summary of findings of the systematic review and meta-analysis

Coconut oil consumption and health outcomes

Anthropometric profile

Body weight

Nine studies analyzed the effects of coconut oil on body weight [16,17,18, 26, 39, 42, 43, 46, 51]. These studies included 533 participants (56.5% females, 18 to 68 years).

Six studies [16, 17, 39, 42, 43, 51] were included in the meta-analysis. Overall, weight loss was similar for those receiving coconut oil in comparison to those receiving other oils or fat (Fig. 2a).

Fig. 2
figure 2

Forest plots of RCTs analyzing the effects of coconut oil intake on the anthropometric profile. a body weight, kg; b waist circumference, cm. Individual trial-specific estimates and their 95% CIs are indicated by the black dots and the horizontal line, respectively. The center of the diamonds indicates the pooled estimates and the width of the diamonds indicate the corresponding 95% CI

The changes in body weight with coconut oil were also not significantly different in comparison to PUFA-rich oils, SFA-rich oils/fats, and MUFA-rich oils (Fig. 2a).

We also performed subgroup analyses considering the type of control group, gender, geographical region, studies in subjects with overweight/obesity, time of follow-up (< 1 year vs. ≥ 1 year), and the presence of co-interventions. These analyses did not explain the heterogeneity between groups and showed no changes in the direction of the results (Supplementary Figures S1, S2, S3, S4, S5, S6, S7, S8).

Additionally, two crossover studies found no differences between the consumption of coconut oil and other oils and fats on body weight [26, 46].

Waist circumference

Seven studies analyzed the effects of coconut oil on waist circumference [16, 39, 40, 42, 43, 46, 49]. These studies included 347 participants (80.1% females, 23 to 66 years).

Five studies were included in the meta-analysis [16, 39, 42, 43, 49]. Overall, the effect of coconut oil on waist circumference was not different in comparison to other interventions (Fig. 2b).

In order to understand the heterogeneity found, we performed a subgroup analysis. A small yet significant reduction in waist circumference is perceived while comparing the consumption of coconut oil with PUFA-rich oils,) but not with MUFA-rich oils (Supplementary Figure S9).

We also performed subgroup analyses considering the type of control group, gender, geographical region, studies in subjects with overweight/obesity, and the presence of co-interventions. These analyses did not explain the heterogeneity between groups and showed no changes in the direction of the results (Supplementary Figures S9, S10, S11, S12, S13, S14, S15).

In one crossover study, the consumption of coconut oil decreased waist circumference in comparison to safflower oil [40].

Body composition

Six studies analyzed the effect of coconut oil on body fat distribution [16, 17, 39, 40, 46, 49]. These studies included 460 participants (35.4% females, 29 to 68 years).

Five studies [16, 17, 39, 42, 49] were included in the meta-analysis. Overall, the effect of coconut oil intake on total body fat did not differ in comparison to other oils or fats (Supplementary Figure S16). Additionally, in comparison to PUFA- and MUFA-rich oils, the effect on total body fat was not different (Supplementary Figure S17).

Only one crossover study analyzed the effect of coconut oil on fat mass, including only postmenopausal women (n = 12, 100% females, 57.8 ± 3.7 years) [46]. The comparator was safflower, and there was no difference in body fat distribution between groups.

Two studies analyzed the effect of coconut oil on lean mass (n = 41, 29% females, 35–61 years) [46, 49]. The comparators were safflower and soybean oils, and, once again, coconut oil did not cause changes in lean mass in comparison with other oils (Supplementary Table S2).

Lipid profile

LDL-C

Seventeen studies analyzed the effects of coconut oil on LDL-C [16,17,18, 26, 39,40,41,42,43,44,45,46,47,48,49,50,51]. These studies included 515 participants (50% females, 18 to 68 years).

Seven studies [16, 17, 39, 42, 43, 49, 51] were included in the meta-analysis. Overall, the intake of coconut oil did not change LDL-C in comparison to other oils/fats (Fig. 3).

Fig. 3
figure 3

Forest plots of RCTs analyzing the effects of coconut oil intake on the lipid profile. a LDL-C, mg/dL; b HDL-C, mg/dL; c Triglycerides, mg/dL. Individual trial-specific estimates and their 95% CIs are indicated by the black dots and the horizontal line, respectively. The center of the diamonds indicates the pooled estimates and the width of the diamonds indicate the corresponding 95% CI

Coconut oil intake did not increase LDL-C as compared to PUFA-rich oils, SFA-rich oils/fats, and MUFA-rich oils (Fig. 3a).

We performed subgroup analyses considering the type of control group, gender, geographical region, studies in subjects with overweight/obesity, time of follow-up (< 1 year vs. ≥ 1 year), and the presence of co-interventions. These analyses did not explain the heterogeneity between groups and showed no changes in the direction of the results (Supplementary Figures S18, S19, S20, S21, S22, S23, S24, S25).

When analyzing the results of crossover studies, we observed that the intake of coconut oil increases LDL-C levels in comparison to butter, lard, and other oils [18, 26, 40, 41, 44,45,46,47,48, 50].

HDL-C

Seventeen studies analyzed the effects of coconut oil on HDL-C [16,17,18, 26, 39,40,41,42,43,44,45,46,47,48,49,50,51]. These studies included 515 participants (50% females, 18 to 68 years).

Seven studies [16, 17, 39, 42, 43, 49, 51] were included in the meta-analysis. Overall, the intake of coconut oil increased HDL-C by 3.28 mg/dL (Fig. 3).

We also performed subgroup analyses considering the type of control group, gender, geographical region, time of follow-up (< year vs. ≥ 1 year), and studies in overweight/obesity subjects (Figures S26, S27, S28, S29, S30, S31, S32). These analyses did not explain the heterogeneity between groups. However, when analyzed in different comparisons, the relative type of oil in the control group and studies only conducted in women, the significant increase in levels of HDL-C no longer existed. An additional subgroup analysis demonstrated that a significant increase in levels of HDL-C with coconut oil intake in comparison to other oils/fats was only identified in studies that included lifestyle interventions (Figure S33).

While analyzing the crossover studies [17, 25, 39, 40, 43,44,45,46,47, 49], we observed that the intake of coconut oil increases HDL-C in comparison to butter, lard, and other oils (data not shown).

Triglycerides

Seventeen studies analyzed the effects of coconut oil on TG levels [16,17,18, 26, 39,40,41,42,43,44,45,46,47,48,49,50,51]. These studies included 515 participants (50% females, 18 to 68 years).

Seven studies [16, 17, 39, 42, 43, 49, 51] were included in the meta-analysis. Overall, the intake of coconut oil did not change TG levels (Fig. 3).

The effect of coconut oil on TG was also not significant in comparison to MUFA-rich oils, PUFA-rich oils, and SFA-rich oils/fats (Fig. 3c).

We performed subgroup analyses considering the type of control group, gender, geographical region, studies in overweight/obesity subjects, time of follow-up (< 1 year vs. ≥ 1 year), and the presence of co-interventions. These analyses did not explain the heterogeneity between groups and showed no changes in the direction of the results (Supplementary Figures S34, S35, S36, S37, S38, S39, S40, S41).

Crossover studies [17, 25, 39, 40, 43,44,45,46,47, 49] showed that the intake of coconut oil increases TG levels in comparison to butter, lard, and other oils.

Risk of bias and certainty of evidence

Detailed results of the assessment of risk of bias are summarized in Supplementary Figures S42, S43, S44, S45, S46. RCTs were overall rated either as having a high risk of bias or presenting some concerns in all analyzed outcomes. Risk of bias arose mainly from poor reporting of the randomization process and from deviations from intended interventions, in addition to carryover effects in crossover trials.

The certainty of evidence was rated as very low due to risk of bias and inconsistency in all analyzed outcomes, as follows (Supplementary Table S7).

Discussion

This systematic review and meta-analysis of RCTs shows that, compared with the dietary consumption of other types of oils and fats, the intake of coconut oil is not superior in reducing body weight or abdominal circumference nor in changing body composition, LDL-C levels, TG, and TC/HDL-C ratio. Subgroup analyses comparing coconut oil with different types of oils based on their fatty acid composition have also confirmed our findings. However, increased levels of HDL-C were observed with the intake of coconut oil in comparison with that of other oils and fats.

Regarding the outcomes that were not included in meta-analyses, only two [17, 43] of the seven studies included in the systematic review that assessed glycemic control had the appropriate minimum follow-up time to analyze changes in HbA1c measures, given that the optimal timeframe to analyze alterations of HbA1c after dietary interventions is 12 weeks. Individual data from these studies do not suggest an impact of coconut oil intake on fasting glycemia, HbA1c, and estimates of β-cell function and insulin sensitivity, in line with findings from other previously published meta-analyses [29, 52].

Unlike other meta-analyses [28, 29, 52], we included studies that evaluated the effect of coconut oil on arterial blood pressure and we observed higher levels of systolic and diastolic blood pressure when coconut oil was compared with a placebo. When comparing coconut oil with olive oil and butter, only diastolic blood pressure levels increased [18, 39]. Despite scarce data addressing the effect of coconut oil on blood pressure, a cross-sectional study conducted in Southern India using a seven-day food survey found that the intake of coconut oil is associated with a higher risk of hypertension [53]. Although the mechanisms related to this finding remain unclear, foods rich in SFAs, such as coconut oil, can induce the development of central adiposity and insulin resistance, both phenomena related to the development of hypertension, which might explain these findings [11,12,13,14, 54].

Regarding markers of subclinical inflammation, as in other published meta-analyses [29, 52], we did not find a reduction in US-CRP with the intake of coconut oil in comparison to soybean oil, olive oil, and butter. Studies that evaluated the antioxidant potential effect of coconut oil are mostly performed in vitro, and their data should not be extrapolated to clinical practice [55]. Among the SFAs, lauric acid, which is roughly 50% of coconut oil composition, has the greatest inflammatory potential, resulting in an unfavorable rationale for conducting experimental studies evaluating the effect of the dietary consumption of coconut oil for this aim [56].

In line with previously published meta-analyses [28, 29, 52], we observed an increase in HDL-C levels with coconut oil in comparison with other oils and fats, which was also confirmed while comparing coconut oil intake with oils rich in MUFAs and PUFAs. These findings may be a result of its composition being predominantly made up of SFAs, resulting in a superior increase in HDL-C levels compared to oils/fats rich in MUFAs and PUFAs [29, 57]. However, neither Mendelian randomization analyses looking at genetic variants related to higher HDL levels [58], nor a meta-analysis of 108 RCTs evaluating the effects of different interventions that increase HDL-C levels [59] demonstrated that this increment protects against cardiovascular disease. In fact, dietary fat both increases transport rate and decreases the fractional catabolic rate of HDL cholesterol esther and apo A-I, intensifying the reverse cholesterol transport, only as an adaptation to the high load of a high fat diet [60, 61]. However, the consumption of SFA-rich oils, such as coconut oil, may not increase the apolipoprotein E-rich sub-fractions, which are mediators of cholesterol’s reverse transport, a main mechanism by which HDL-C exerts its cardio-protective effects [62]. Thus, it does not seem reasonable to advise the intake of coconut oil based on a possible protection against CVD derived from its effect on HDL-C.

An increase in plasmatic levels of HDL-C was observed with coconut oil intake compared with other oils while analyzing different studies (Fig. 3b), which could be explained by the fact that, in most of those studies, participants were exposed to co-interventions, including diet [16, 42, 43, 49, 51] and physical activity [16, 43] – which may have a significant impact on HDL-C levels [58, 59]. In fact, in one of these studies, participants significantly lost more weight, and, in two of them, there was a greater reduction in waist circumference with coconut oil compared to soybean oil. These results may have been driven by the real impact of coconut oil on HDL-C levels and may explain the heterogeneity that was found [16, 43].

In this review, changes in body weight were similar between coconut oil and other oils. In only one study, the group receiving coconut oil lost more body weight [16]. This result might be explained by the introduction of systematic error due to an imbalance of co-interventions, which might have been introduced as a result of lack of blinding of the staff who applied the lifestyle interventions. Similary, among the five studies which analyzed the impact of coconut oil in comparison to other oils/fats on central obesity, the two studies which demonstrated that the coconut oil group had a more significant reduction in waist circumference also applied lifestyle co-interventions in a similar manner, possibly resulting in the same forementioned systematic error [16, 43]. Subgroup analyses for studies regarding co-interventions have shown no differences in changes of body weight and waist circumference between coconut oil and diet interventions with other oils (Supplementary Figure S33 and S15).

Previous meta-analyses [29, 52] found higher LDL-C levels with the consumption of coconut oil in comparison with the intake of other oils and fats. These two reviews included crossover trials, and, in one of them [29], oils used in different arms causing very distinct responses in LDL-C levels were grouped as a single intervention against coconut oil [16]. We believe that this may explain the differences in findings between our meta-analysis and previously published ones. In line with our findings, Teng et al. (2020), in their analysis comparing coconut oil to other oils, did not find differences in levels of LDL-C, either [28]. Similar to what was found previously [29, 52], we also did not identify differences in changes of TG levels, TC/HDL-C ratio, LDL-C/HDL-C ratio, and body composition between the consumption of coconut oil and other oils/fats.

LDL-C concentration is one of the main targets for cardiovascular protection. However, some subtypes of LDL-C, especially the slow dense LDL-Cs, have been associated with a higher risk of atherogenesis [63]. Lipoprotein (a) (Lp[a]), a genetic variant of LDL, has also gained attention because of its considerable dyslipidemic potential [64]. There is still no clear evidence that reducing Lp(a) levels results in protection for cardiovascular outcomes [65], nor do we know how nonpharmacological treatments affect Lp(a) [66]. It seems that a healthy lifestyle can promote favorable changes in subclasses of lipoproteins [67], and that the characteristics of fatty acids could influence these changes [68].

None of the studies included in this systematic review assessed the subclasses of lipoproteins or Lp(a). However, a crossover trial including 31 women evaluated the effect of three different margarines, one of them containing 80% coconut oil, on plasma postprandial levels of some hemostatic variables and on fasting Lp(a). Data from only 11 subjects were evaluated, and there was a statistically significant reduction in Lp(a) in the margarine with coconut oil per se, and the total dietary composition (especially carbohydrates and total fat) was different between groups, which can influence the results [66]. New RCTs with higher methodological rigor are needed to confirm the potential of coconut oil in reducing Lp(a).

It is important to highlight that published meta-analyses about the topic included crossover studies with methodological limitations. In this meta-analysis, we only included crossover RCTs when it was possible to determine the order of the interventions and where the baselines and final averages of each arm were available. We then obtained the initial and final values of each outcome in each arm of the study before the participant was allocated to the other arm. This reduces the chance of the residual effects (carry-over) of the former intervention on the next one [36]. We contacted the authors of crossover studies and received these data from the authors of one study, which we included in our analysis [51]. In addition to that, we included two new RCTs [42, 49] that had not been included in the most recent meta-analysis [52].

This systematic review has some limitations. Generally, studies presented a small sample size with a short follow-up, which limits the analysis of the effects of a dietary intervention on cardiometabolic parameters. Therefore, the results must be interpreted with caution. Moreover, there was a limited number of studies analyzing the effect of the consumption of coconut oil on parameters other than lipid profile and body weight, such as body composition and glycemic and inflammatory profiles. The included studies also differ considerably from each other regarding population size and gender composition, time of follow-up, daily quantity of coconut oil consumed, type of coconut oil (virgin, extra virgin), product/vehicle for consumption (e.g.: as a capsule, as a supplement, heated as oil to cook with, or in preparations such as for muffins or crackers). Although this makes it difficult to compare different interventions, we were able to perform subgroup analyses comparing coconut oil with oils/fats with different fatty acid content in their compositions: SFA-, MUFA-, and PUFA-rich oils/fats. We also performed subgroup analyses according to the presence of other dietary interventions and/or physical activity, which may influence the effects attributed to coconut oil on the cardiometabolic parameters which were analyzed.

Up until now, the scientific community has lacked studies with a long-term follow-up and with a significant number of participants that evaluate the effect of coconut oil consumption on cardiovascular outcomes.

Conducting new RCTs examining cardiovascular safety comparing coconut oil with PUFA- and MUFA-rich oils evaluating traditional markers does not seem to be justifiable even though coconut oil is part of the diet in South Asian countries [20,21,22]. Moreover, in Western countries, stimulating the consumption of SFA-rich oils to the detriment of PUFA- and MUFA-rich oils may lead to an excessive intake of SFAs in populations that already have a diet rich in them [69].

Conclusions

The dietary consumption of coconut oil instead of the consumption of PUFA- and MUFA-rich oils with well-established cardio-protective effects should not be encouraged in societies that are not used to consuming it. Moreover, educational strategies should be implemented to make populations, especially those used to consuming coconut oil, aware of the potential risks related with this intake. These populations should also be informed and encouraged to replace it with cardio-metabolically healthy options linked with a reduction in rates of CVD.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CVD:

Cardiovascular disease

GRADE:

Grading of Recommendations, Assessment, Development and Evaluations

HDL-C:

High-density lipoprotein cholesterol

HOMA:

The homeostasis model assessment

HbA1c:

Glycated hemoglobin

IL:

Interleukins

IFN-g:

Interferon-gamma

LDL-C:

Low-density lipoprotein cholesterol

Lp(a):

Lipoprotein [a]

MUFA:

Monounsaturated fatty acid

PRISMA:

Preferred Report Items for Systematic Reviews and Meta-Analysis

PUFA:

Polyunsaturated fatty acid

RCTs:

Randomized clinical trials

RoB:

The risk of bias

Robvis:

Risk-of-bias VISualization

SFA:

Saturated fatty acid

TC:

Total cholesterol

TcHc:

Total homocysteine

TG:

Triglycerides

US-CRP:

Ultra-sensitive c-reactive protein

References

  1. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart disease and stroke statistics-2017 update: a report from the American heart association. Circulation. 2017;135(10):e146–603.

    PubMed  PubMed Central  Article  Google Scholar 

  2. Townsend N, Kazakiewicz D, Lucy Wright F, Timmis A, Huculeci R, Torbica A, et al. Epidemiology of cardiovascular disease in Europe. Nat Rev Cardiol. 2022;19(2):133–43.

    PubMed  Article  Google Scholar 

  3. Lopez-Jaramillo P, Joseph P, Lopez-Lopez JP, Lanas F, Avezum A, Diaz R, et al. Risk factors, cardiovascular disease, and mortality in South America: a PURE substudy. Eur Heart J. 2022;43(30):2841–51.

  4. Daurbekov TG, Koysultanov MU, Alyautdinova AS, Sozonov AS, Khalilov NA, Kuzn IV. Features of the organization of activities in the field of prevention of cardiovascular disease. J Complement Med Res. 2022;13(1):49–52.

    Article  Google Scholar 

  5. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958–72.

  6. Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, et al. Dietary fats and cardiovascular disease: a presidential advisory from the american heart Association. Circulation. 2017;136(3):e1–23.

    PubMed  Article  Google Scholar 

  7. Disease C, Management R. Standards of medical care in diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S125–50.

    Google Scholar 

  8. Bassey I, Akpan U, Nehemiah E, Arekong R, Okonkwo O, Udoh A. Cardiovascular disease risk factors and cardiac markers among male cement workers in Calabar, Nigeria. J Chem Health Risks. 2017;7(2).

  9. Khan W, Augustine D, Rao RS, Patil S, Awan KH, Sowmya SV, et al. Lipid metabolism in cancer: a systematic review. J Carcinog. 2021;20:4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. US Department of agriculture and US Department of health and human services. Dietary guidelines for Americans, 2020–2025. https://www.dietaryguidelines.gov/sites/default/files/2021-03/Dietary_Guidelines_for_Americans-2020-2025.pdf; Dec 2020.

  11. Liu X, Li Y, Tobias DK, Wang DD, Manson JE, Willett WC, et al. Changes in types of dietary fats influence long-term weight change in us women and men. J Nutr. 2018;148(11):1821–9.

    PubMed  PubMed Central  Article  Google Scholar 

  12. Wanders AJ, Blom WAM, Zock PL, Geleijnse JM, Brouwer IA, Alssema M. Plant-derived polyunsaturated fatty acids and markers of glucose metabolism and insulin resistance: a meta-analysis of randomized controlled feeding trials. BMJ Open Diabetes Res Care. 2019;7(1): e000585.

    PubMed  PubMed Central  Article  Google Scholar 

  13. Forouhi NG, Imamura F, Sharp SJ, Koulman A, Schulze MB, Zheng J, et al. Association of plasma phospholipid n-3 and n-6 polyunsaturated fatty acids with type 2 diabetes: The epic-interact case-cohort study. PLoS Med. 2016;13(7): e1002094.

    PubMed  PubMed Central  Article  Google Scholar 

  14. Wu JHY, Marklund M, Imamura F, Tintle N, Ardisson Korat AV, de Goede J, et al. Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies. Lancet Diabetes Endocrinol. 2017;5(12):965–74.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Jasim OH, Mahmood MM, AH Ah. Significance of Lipid profile parameters in predicting pre-diabetes. Archives of Razi Institute. 2022;77(1):267–74.

    Google Scholar 

  16. Oliveira-de-Lira L, Santos EMC, de Souza RF, Matos RJB, Silva MCD, Oliveira LDS, et al. Supplementation-dependent effects of vegetable oils with varying fatty acid compositions on anthropometric and biochemical parameters in obese women. Nutrients. 2018;10(7).

  17. Vijayakumar M, Vasudevan DM, Sundaram KR, Krishnan S, Vaidyanathan K, Nandakumar S, et al. A randomized study of coconut oil versus sunflower oil on cardiovascular risk factors in patients with stable coronary heart disease. Indian Heart J. 2016;68(4):498–506.

    PubMed  PubMed Central  Article  Google Scholar 

  18. Chinwong S, Chinwong D, Mangklabruks A. Daily consumption of virgin coconut oil increases high-density lipoprotein cholesterol levels in healthy volunteers: a randomized crossover trial. Evid Based Complement Alternat Med. 2017;2017:7251562.

    PubMed  PubMed Central  Article  Google Scholar 

  19. Eyres L, Eyres MF, Chisholm A, Brown RC. Coconut oil consumption and cardiovascular risk factors in humans. Nutr Rev. 2016;74(4):267–80.

    PubMed  PubMed Central  Article  Google Scholar 

  20. Prior IA, Davidson F, Salmond CE, Czochanska Z. Cholesterol, coconuts, and diet on Polynesian atolls: a natural experiment: the Pukapuka and Tokelau island studies. Am J Clin Nutr. 1981;34(8):1552–61.

    CAS  PubMed  Article  Google Scholar 

  21. DebMandal M, Mandal S. Coconut (Cocos nucifera L.: Arecaceae): in health promotion and disease prevention. Asian Pac J Trop Med. 2011;4(3):241–7.

    PubMed  Article  Google Scholar 

  22. Feranil AB, Duazo PL, Kuzawa CW, Adair LS. Coconut oil is associated with a beneficial lipid profile in pre-menopausal women in the Philippines. Asia Pac J Clin Nutr. 2011;20(2):190–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ros E, Martínez-González MA, Estruch R, Salas-Salvadó J, Fitó M, Martínez JA, et al. Mediterranean diet and cardiovascular health: teachings of the PREDIMED study. Adv Nutr. 2014;5(3):330s-s336.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Beulen Y, Martínez-González MA, Van de Rest O, Salas-Salvadó J, Sorlí JV, Gómez-Gracia E, et al. Quality of Dietary Fat Intake and Body Weight and Obesity in a Mediterranean Population: Secondary Analyses within the PREDIMED Trial. Nutrients. 2018;10(12):2011.

  25. Kastorini CM, Milionis HJ, Esposito K, Giugliano D, Goudevenos JA, Panagiotakos DB. The effect of Mediterranean diet on metabolic syndrome and its components: a meta-analysis of 50 studies and 534,906 individuals. J Am Coll Cardiol. 2011;57(11):1299–313.

    CAS  PubMed  Article  Google Scholar 

  26. Lu Z, Hendrich S, Shen N, White PJ, Cook LR. Low linolenate and commercial soybean oils diminish serum HDL cholesterol in young free-living adult females. J Am Coll Nutr. 1997;16(6):562–9.

    CAS  PubMed  Google Scholar 

  27. Korrapati D, Jeyakumar SM, Putcha UK, Mendu VR, Ponday LR, Acharya V, et al. Coconut oil consumption improves fat-free mass, plasma HDL-cholesterol and insulin sensitivity in healthy men with normal BMI compared to peanut oil. Clin Nutr. 2019;38(6):2889–99.

    CAS  PubMed  Article  Google Scholar 

  28. Teng M, Zhao YJ, Khoo AL, Yeo TC, Yong QW, Lim BP. Impact of coconut oil consumption on cardiovascular health: a systematic review and meta-analysis. Nutr Rev. 2020;78(3):249–59.

    PubMed  Article  Google Scholar 

  29. Neelakantan N, Seah JYH, van Dam RM. The effect of coconut oil consumption on cardiovascular risk factors: a systematic review and meta-analysis of clinical trials. Circulation. 2020;141(10):803–14.

    PubMed  Article  Google Scholar 

  30. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372: n160.

    PubMed  PubMed Central  Article  Google Scholar 

  31. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366: l4898.

    PubMed  Article  Google Scholar 

  32. McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synth Methods. 2021;12(1):55–61.

    PubMed  Article  Google Scholar 

  33. Balshem H, Helfand M, Schünemann H, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011;64(4):401–6.

    PubMed  Article  Google Scholar 

  34. Omni Calculator. https://www.omnicalculator.com/health/cholesterol-units. Accessed 14 Nov 2021.

  35. Mini Webtool. https://miniwebtool.com/br/blood-sugarconverter/?n1=0.48&c1=1. Accessed 14 Nov 2021.

  36. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions version 6.3 (updated Feb 2022): Cochrane; 2022. Available from: www.training.cochrane.org/handbook.

  37. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.

    PubMed  PubMed Central  Article  Google Scholar 

  38. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343: d4002.

    PubMed  Article  Google Scholar 

  39. Khaw KT, Sharp SJ, Finikarides L, Afzal I, Lentjes M, Luben R, et al. Randomised trial of coconut oil, olive oil or butter on blood lipids and other cardiovascular risk factors in healthy men and women. BMJ Open. 2018;8(3): e020167.

    PubMed  PubMed Central  Article  Google Scholar 

  40. Voon PT, Ng TK, Lee VK, Nesaretnam K. Diets high in palmitic acid (16:0), lauric and myristic acids (12:0 + 14:0), or oleic acid (18:1) do not alter postprandial or fasting plasma homocysteine and inflammatory markers in healthy Malaysian adults. Am J Clin Nutr. 2011;94(6):1451–7.

    CAS  PubMed  Article  Google Scholar 

  41. McKenney JM, Proctor JD, Wright JT Jr, Kolinski RJ, Elswick RK Jr, Coaker JS. The effect of supplemental dietary fat on plasma cholesterol levels in lovastatin-treated hypercholesterolemic patients. Pharmacotherapy. 1995;15(5):565–72.

    CAS  PubMed  Article  Google Scholar 

  42. Netto Cândido TL, da Silva LE, Cândido FG, Valente FX, da Silva JS, Gomes Lopes DR, et al. Effect of the ingestion of vegetable oils associated with energy-restricted normofat diet on intestinal microbiota and permeability in overweight women. Food Res Int. 2021;139: 109951.

    PubMed  Article  CAS  Google Scholar 

  43. Assunção ML, Ferreira HS, dos Santos AF, Cabral CR Jr, Florêncio TM. Effects of dietary coconut oil on the biochemical and anthropometric profiles of women presenting abdominal obesity. Lipids. 2009;44(7):593–601.

    PubMed  Article  CAS  Google Scholar 

  44. Cox C, Mann J, Sutherland W, Chisholm A, Skeaff M. Effects of coconut oil, butter, and safflower oil on lipids and lipoproteins in persons with moderately elevated cholesterol levels. J Lipid Res. 1995;36(8):1787–95.

    CAS  PubMed  Article  Google Scholar 

  45. Ganji V, Kies CV. Psyllium husk fiber supplementation to the diets rich in soybean or coconut oil: hypocholesterolemic effect in healthy humans. Int J Food Sci Nutr. 1996;47(2):103–10.

    CAS  PubMed  Article  Google Scholar 

  46. Harris M, Hutchins A, Fryda L. The impact of virgin coconut oil and high-oleic safflower oil on body composition, lipids, and inflammatory markers in postmenopausal women. J Med Food. 2017;20(4):345–51.

    CAS  PubMed  Article  Google Scholar 

  47. Maki KC, Hasse W, Dicklin MR, Bell M, Buggia MA, Cassens ME, et al. Corn oil lowers plasma cholesterol compared with coconut oil in adults with above-desirable levels of cholesterol in a randomized crossover trial. J Nutr. 2018;148(10):1556–63.

    PubMed  PubMed Central  Article  Google Scholar 

  48. Reiser R, Probstfield JL, Silvers A, Scott LW, Shorney ML, Wood RD, et al. Plasma lipid and lipoprotein response of humans to beef fat, coconut oil and safflower oil. Am J Clin Nutr. 1985;42(2):190–7.

    CAS  PubMed  Article  Google Scholar 

  49. Vogel C, Crovesy L, Rosado EL, Soares-Mota M. Effect of coconut oil on weight loss and metabolic parameters in men with obesity: a randomized controlled clinical trial. Food Funct. 2020;11(7):6588–94.

    CAS  PubMed  Article  Google Scholar 

  50. Heber D, Ashley JM, Solares ME, Wang HJ, Alfin-Slater RB. The effects of a palm-oil enriched diet on plasma lipids and lipoproteins in healthy young men. Nutrition Res. 1992;12:S53–S9.

  51. Schwab US, Niskanen LK, Maliranta HM, Savolainen MJ, Kesäniemi YA, Uusitupa MI. Lauric and palmitic acid-enriched diets have minimal impact on serum lipid and lipoprotein concentrations and glucose metabolism in healthy young women. J Nutr. 1995;125(3):466–73.

    CAS  PubMed  Google Scholar 

  52. Jayawardena R, Swarnamali H, Lanerolle P, Ranasinghe P. Effect of coconut oil on cardio-metabolic risk: a systematic review and meta-analysis of interventional studies. Diabetes Metab Syndr. 2020;14(6):2007–20.

    PubMed  Article  Google Scholar 

  53. Beegom R, Singh RB. Association of higher saturated fat intake with higher risk of hypertension in an urban population of Trivandrum in south India. Int J Cardiol. 1997;58(1):63–70.

    CAS  PubMed  Article  Google Scholar 

  54. Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med. 2004;140(12):992–1000.

    PubMed  Article  Google Scholar 

  55. Izar MCO, Lottenberg AM, Giraldez VZR, Santos Filho RDD, Machado RM, Bertolami A, et al. Position statement on fat consumption and cardiovascular health - 2021. Arq Bras Cardiol. 2021;116(1):160–212.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. Lee JY, Sohn KH, Rhee SH, Hwang D. Saturated fatty acids, but not unsaturated fatty acids, induce the expression of cyclooxygenase-2 mediated through Toll-like receptor 4. J Biol Chem. 2001;276(20):16683–9.

    CAS  PubMed  Article  Google Scholar 

  57. Mensink RP, Zock PL, Kester AD, Katan MB. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials. Am J Clin Nutr. 2003;77(5):1146–55.

    CAS  PubMed  Article  Google Scholar 

  58. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012;380(9841):572–80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Briel M, Ferreira-Gonzalez I, You JJ, Karanicolas PJ, Akl EA, Wu P, et al. Association between change in high density lipoprotein cholesterol and cardiovascular disease morbidity and mortality: systematic review and meta-regression analysis. BMJ. 2009;338: b92.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. Hayek T, Ito Y, Azrolan N, Verdery RB, Aalto-Setälä K, Walsh A, et al. Dietary fat increases high density lipoprotein (HDL) levels both by increasing the transport rates and decreasing the fractional catabolic rates of HDL cholesterol ester and apolipoprotein (Apo) A-I. Presentation of a new animal model and mechanistic studies in human Apo A-I transgenic and control mice. J Clin Invest. 1993;91(4):1665–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Santos HO, Lavie CJ. Weight loss and its influence on high-density lipoprotein cholesterol (HDL-C) concentrations: a noble clinical hesitation. Clin Nutr ESPEN. 2021;42:90–2.

    PubMed  Article  Google Scholar 

  62. Morton AM, Furtado JD, Mendivil CO, Sacks FM. Dietary unsaturated fat increases HDL metabolic pathways involving apoE favorable to reverse cholesterol transport. JCI Insight. 2019;4(7):e124620.

  63. Superko HR, Gadesam RR. Is it LDL particle size or number that correlates with risk for cardiovascular disease? Curr Atheroscler Rep. 2008;10(5):377–85.

    CAS  PubMed  Article  Google Scholar 

  64. Yeang C, Witztum JL, Tsimikas S. “LDL-C” = LDL-C + Lp(a)-C: implications of achieved ultra-low LDL-C levels in the proprotein convertase subtilisin/kexin type 9 era of potent LDL-C lowering. Curr Opin Lipidol. 2015;26(3):169–78.

  65. Tsimikas S. A test in context: lipoprotein(a): diagnosis, prognosis, controversies, and emerging therapies. J Am Coll Cardiol. 2017;69(6):692–711.

    CAS  PubMed  Article  Google Scholar 

  66. Enkhmaa B, Petersen KS, Kris-Etherton PM, Berglund L. Diet and Lp(a): Does Dietary Change Modify Residual Cardiovascular Risk Conferred by Lp(a)? Nutrients. 2020;12(7):2024.

  67. Santos HO, Earnest CP, Tinsley GM, Izidoro LFM, Macedo RCO. Small dense low-density lipoprotein-cholesterol (sdLDL-C): analysis, effects on cardiovascular endpoints and dietary strategies. Prog Cardiovasc Dis. 2020;63(4):503–9.

    PubMed  Article  Google Scholar 

  68. Santos HO, Kones R, Rumana U, Earnest CP, Izidoro LFM, Macedo RCO. Lipoprotein(a): current evidence for a physiologic role and the effects of nutraceutical strategies. Clin Ther. 2019;41(9):1780–97.

    CAS  PubMed  Article  Google Scholar 

  69. Santos HO, Howell S, Earnest CP, Teixeira FJ. Coconut oil intake and its effects on the cardiometabolic profile - a structured literature review. Prog Cardiovasc Dis. 2019;62(5):436–43.

    PubMed  Article  Google Scholar 

Download references

Acknowledgements

Thanks to authors Oliveira-de-Lira et al., Schwab et al., and Khaw et al. for providing data from their articles for the preparation of this study.

Funding

This study was supported by the Hospital de Clínicas de Porto Alegre Research Incentive Fund (FIPE-HCPA project 2018–0393), the Division of Research of Universidade Federal do Rio Grande do Sul (PROPESQ-UFRGS), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Research National Council (CNPq) grant: CNPq/MCTI/FNDCT18/2021 (420065/2021–0) and Hospital de Clínicas de Porto Alegre (HCPA). FIPE-HCPA, PROPESQ-UFRGS, CAPES, CNPq and HCPA had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

Authors

Contributions

Concept and design: ACD and FG. Acquisition, analysis, or interpretation of data: ACD, ENM, BFS, CRA, CA, VC and FG. Drafting of the manuscript: ACD. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: ACD. Supervision: VC and FG. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fernando Gerchman.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Supplementary material.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Duarte, A.C., Spiazzi, B.F., Zingano, C.P. et al. The effects of coconut oil on the cardiometabolic profile: a systematic review and meta-analysis of randomized clinical trials. Lipids Health Dis 21, 83 (2022). https://doi.org/10.1186/s12944-022-01685-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-022-01685-z

Keywords

  • Coconut oil
  • Saturated fatty acids
  • Lipid profile
  • Anthropometric profile