Both Manuka and Non-Manuka Honey Types Inhibit Antibiotic Resistant Wound-Infecting Bacteria

Abstract

Postoperative infections are a major concern in United States hospitals, accounting for roughly 20% of all hospital-acquired infections yearly. Wound-infecting bacteria, in particular, have a high rate of drug resistance (up to 65%), creating life-threatening complications. Manuka honey, native to New Zealand, has been FDA-approved for wound treatment in the United States after studies demonstrated its ability to inhibit a variety of bacterial species and facilitate wound healing. The aim of this study was to identify alternative (non-manuka) honey types that can be specifically used against antibiotic resistance bacteria in wound infections. We utilized a honey-plate method to measure the minimum inhibitory concentration (MIC) of honey to avoid the limitations of agar diffusion, where large, nonpolar polyphenols (which will not diffuse efficiently) play an important role in bioactivity. This study demonstrated that there are several alternative (non-manuka) honey types, particularly fresh raw Arkansas wildflower honeys, that comparably inhibit the growth of the antibiotic-resistant bacterial species specifically implicated in wound infections. Concentrations of 10-30% honey inhibited the growth of the highly antibiotic-resistant organisms colloquially referred to as "superbugs", which the WHO declared in 2017 to be in critical need of new antibiotics. There was no statistical difference between manuka honey and fresh summer Arkansas wildflower honey in overall bacterial inhibition. These results could transform wound care in the United States, where manuka honey can be expensive and difficult to obtain and where antibiotic resistance remains a troubling concern for wound treatment.

Keywords: alternative treatments; antibacterial; antibiotic resistance; honey; minimum inhibitory concentration.

Figure 1

Example of a honey plate MIC assay. Each organism is patched onto the plates in triplicate. The MIC is defined as the lowest concentration of honey at which an organism does not grow. The three values obtained in one experiment are averaged in order to control for differences in inoculation volume that may occur in this dry patch method. Plates are Mueller–Hinton media with honey added to equal 0.5–32% honey, while the control is Mueller–Hinton media with 0% honey.

Figure 2

Comparison of honey type (n = 46) and average MIC for all bacteria species (n = 7). There was a significant effect of honey type on average MIC (Kruskal–Wallis, X² = 102.8, df = 5, p < 0.0001) (mean ± SD). Overall, manuka (n = 12) and summer Arkansas wildflower (n = 9) had the highest efficacy and demonstrated statistical equivalence in comparison to the poorest performing honeys, acacia (n = 5), clover (n = 6), orange blossom (n = 7), and wildflower (n = 7), which were not statistically different from one another. These results highlight how floral source differences impact microbial inhibition and suggest that manuka and summer Arkansas wildflower are comparable in antimicrobial efficacy despite their differences in floral source (Dunn’s, Supplemental Table S1). Columns with equivalent letters (e.g. A and A) are statistically similar while columns with different letters (e.g A and B) are statistically different.

Figure 3

Relationship between a honey’s pH and average MIC against all species tested. There is a weak correlation between MIC and pH (Spearman’s R = −0.2031, p = 0.166, n = 48), such that as pH increases, honey becomes more effective overall (smaller MIC value).

Figure 4

Relationship between a honey’s water content and average MIC against all species tested. There is a significant inverse trend, indicating that a higher water content corresponds to a higher efficacy (lower MIC) (Pearson’s R = −0.543, p < 0.0008, n = 35).

Figure 5

Relationship between a honey’s color (as measured by spectrophotometric analysis) and average MIC against all species tested. There is a moderate correlation (Pearson’s R = −0.581, p < 0.0001, n = 38) where darker colored honeys inhibit bacteria at a lower MIC.

Figure 6

Comparison of average MIC, color, pH, and water content between spring and summer Arkansas wildflower (mean ± SD). (A) Average MIC across all bacterial species (n = 7) varied significantly between spring (n = 91) and summer honeys (n = 56). Spring honeys had a significantly higher average MIC when compared to summer honeys (Mann–Whitney, p = 0.0225, U = 1987). (B) Comparisons of color yielded a significant difference between the two Arkansas wildflower honey harvests (unpaired t-test, p = 0.0167). Spring honeys (n = 9) had a significantly lighter color compared to summer honeys (n = 8). (C) No significant difference between spring honeys (n = 13) and summer honeys (n = 5) was observed when pH was evaluated (unpaired t-test, p = 0.7212). (D) When comparing water content, there was a significant difference between spring and summer (unpaired t-test, p = 0.0302). Spring honeys (n = 2) were significantly lower in water content compared to summer honeys (n = 6). * indicates significant difference.

Figure 7

Comparison of the average MICs for each bacteria species tested (mean ± SD). The MICs of manuka and spring Arkansas honey were significantly different for K. pneumoniae, E. coli, A. baumannii, BORSA, VISA, and E. aerogenes (one-way ANOVA, Tukey; p = 0.0005, p = 0.0002, p = 0.0047, p = 0.0002, p < 0.0001, p = 0.0003, respectively). Manuka is also significantly more effective than summer Arkansas honey for A. baumannii, BORSA, and VISA (one-way ANOVA, Tukey; p = 0.002, p = 0.0043, p = 0.001 respectively), which are generally the most sensitive organisms. Summer Arkansas honey is only significantly different from spring Arkansas honey for E. coli (one-way ANOVA, Tukey p = 0.0238), and there is no significant difference across honey types for P. aeruginosa (one-way ANOVA, p = 0.1450). Additional p-values are in Supplemental Table S2. Columns with equivalent letters (e.g. A and A) are statistically similar while columns with different letters (e.g A and B) are statistically different.