Participant Eligibility and Recruitment

Twenty healthy adults, aged 18–65 were recruited via flyer in Fort Collins, Colorado. Since no previous crossover trials have evaluated insect consumption impacts on microbiota, a sample size of 20, with balanced design and ten people per treatment, was selected for this pilot study using a 5% significance level (one-sided) to obtain 90% power. A crossover design was appropriate to address research questions and because intra-individual variances are lower than inter-individual variances. Eligibility was determined using an in-person eligibility screening questionnaire at an initial visit after which informed consent was obtained. Participants were excluded if they met any of the following criteria: (a) younger than 18 or older than 65, (b) BMI outside of the 18.5–29.9 range, (c) pregnant or breastfeeding, (d) use of antibiotics in the last 2 months, (e) regular use of prebiotics or probiotics, (f) any intestinal or metabolic disease, cancer, liver or kidney disease g) self-reported presence of food allergies, (h) unwillingness to limit alcohol consumption to 1–2 drinks per day, no more than 7 per week, or (i) current medication or dietary supplement use that may impact gut microbiota. These conditions are known to affect baseline microbiota populations. Additionally, only healthy volunteers were selected because standard care practices for people with various medical conditions would confound measured endpoints, including microbiota composition. This would include taking statins, metformin, NSAIDs, MAO inhibitors, and botanical supplements that target the GI tract or gut microbiota. Healthy individuals are more likely to comply with study requirements and experience fewer adverse events. To be eligible, participants had to confirm willingness to eat one prepared breakfast per day (treatment or control) at home for a total of 28 days (two treatment periods of 14 days each), attend three clinic visits, and provide three blood and stool samples.

Study Design and Dietary Intervention

The study was conducted at Colorado State University between February and May 2017. The study protocol and documents were approved by the Institutional Review Board (IRB) for Human Subjects Research at Colorado State University, CSU protocol #16-6966 H and all participants provided written informed consent prior to beginning the study. All experiments were also performed in accordance with relevant guidelines and regulations. The study is also registered at clinicaltrials.gov as NCT03383341 on December 26, 2017.

The study was conducted as a randomized, double-blind, crossover trial, with two 14-day intervention periods and a 14-day washout period between treatments for a total duration of 42 days. Each study participant was randomly assigned to starting group, cohort 1 or 2, by the study coordinator. A laboratory volunteer that was not involved in study design or conduct assigned codes to treatment foods. Study personnel remained blinded to treatment assignment until after all data were collected and analyzed.

During each intervention period, study participants were provided with a breakfast that included a muffin and a dry breakfast shake mix, which they were instructed to combine with the milk or liquid of their choice and drink after shaking vigorously. The nutrient contents of the study breakfasts are outlined in Table 1. Participants were asked to return packaging and any uneaten portion of the foods to assess compliance.

Table 1 Estimated Nutrient Composition of Study Breakfasts. Full size table

After enrollment, participants were randomly allocated to one of two sequential treatment arms. The first (Cohort 1) received the control breakfast meal for 14 days, followed by the cricket breakfast meal. Cohort 2 received the treatments in reverse order. Both had a washout period of 14-days between each intervention period. All meals were provided to volunteers in identical mylar bags labeled with an alphanumerical code unique to the treatment and participant.

Subjects reported to the Human Performance Clinical Research Laboratory (HPCRL) at Colorado State University three times during the trial to provide a fasting blood sample, a stool sample, and to complete the gastrointestinal (GI) questionnaire. The first was at baseline (Day 0), before beginning the intervention. The second was after the first intervention period (Day 14), and the third was at the end after the second intervention period (Day 42). GI questionnaires were used to collect information on side effects from the intervention. The complete study design is outlined in Fig. 1.

Figure 1 Study design. Cricket Tx = Breakfast with 25 g Cricket Powder; Control = Breakfast without Cricket Powder. Full size image

Dietary Intervention

Dried, roasted cricket powder was provided to the research team by Entomo Farms (Ontario, Canada). Participants received 14 prepared study breakfast meals that either included cricket powder (25 g/day) or that did not include cricket powder (control) at the beginning of each treatment period. They were asked to consume one prepared breakfast every day during the intervention periods but were able to consume their normal diet the rest of the day. The breakfasts included a pumpkin spice muffin (roughly equivalent to 80 grams) and one dry mix chocolate malt shake (Table 1). A 25 g/day cricket serving size was selected to provide approximately 15 grams protein, similar to many protein rich breakfast drinks, and as a feasible dose for incorporation into palatable foods. Nutrient composition of the cricket powder alone is shown in Table 2.

Table 2 Estimated Nutrient Composition of Cricket Powder per serving (25 g). Full size table

The control and the cricket intervention breakfasts (muffins and shake) were similarly matched in their macro- and micronutrient content, but the control did not contain any cricket powder. In both the control and cricket breakfast shakes, chocolate malt was used as a strong flavoring, and a small amount of instant pudding mix added to keep insoluble ingredients in suspension. The meals were identical in ingredients, with the exception of the following amendments made to the control breakfasts: purple cornmeal (2 tbsp.) was added to the smoothie to mimic the texture of the cricket powder, and (~0.27 tbsp./muffin) cocoa powder was added to the control muffin to mimic the color of the cricket powder and supply dietary fiber. Like chitin, cocoa powder contains insoluble dietary fiber, but it is not novel in the American diet. Cocoa powder used in these muffins was about 33% dietary fiber. The remainder of the participants’ diets was not controlled.

The nutrient content of dried, roasted cricket powder was determined by two commercial laboratories (Covance Laboratories, Madison, Wisconsin and Maxxam Analytics, Ontario, Canada). Total dietary fiber was estimated to be 2.12 grams per 25 grams of cricket powder, with about 87% of it composed of insoluble fiber. Insoluble chitin is considered the most common form of fiber in insects25. Each intervention breakfast shake contained 10 g of cricket powder, and the muffins contained 15 g for a total daily intake of 25 g.

GI Questionnaire

Using a digestive health questionnaire developed by Metagenics (see Supplemental Materials; Fig. S1) participants self-reported feelings related to digestive health at baseline, after treatment period 1 and after treatment period 2. Participants were asked to reflect on the previous two weeks to gauge changes in digestive health over the course of the study.

Blood Chemistry

Three blood samples (~10 mL) were collected from each participant by venipuncture after an overnight fast (12 ± 2 hours) at baseline (day 0), the end of intervention period 1 (day 14), and the end of intervention period 2 (day 42). Samples were collected in lithium heparin and ethylenediaminetetraacetic acid EDTA tubes. Plasma was collected by centrifugation from the EDTA tubes and stored at −80 °C prior to analyses of circulating inflammatory markers. Two-hundred microliters of lithium heparin whole blood was analyzed immediately using the Comprehensive Metabolic Panel (CMP) (Abaxis Global Diagnostics; Union City, CA) on a Piccolo Xpress Chemistry Analyzer (Abbott; Princeton, NJ). The CMP included assessment of blood levels of sodium (Na+ mmol/L), potassium (K+ mmol/L), carbon dioxide (tCO 2 mmol/L), chloride (Cl− mmol/L), glucose (GLU mg/dL), calcium (CA mg/dL), blood urea nitrogen (BUN mg/dL), creatine (CRE mg/dL), alkaline phosphatase (ALP U/L), alanine aminotransferase (ALT U/L), aspartate aminotransferase (AST U/L), bilirubin (T-BIL mg/dL), albumin (ALB g/dL), and total protein (TP g/dL).

DNA Extraction and Sequencing

Fecal samples were self-collected using a stool sampling kit within 24 hours of scheduled clinic visits and delivered refrigerated or frozen to the clinic coordinator. Once returned to the clinic coordinator, samples were stored at −80 °C until analyzed. Stool samples were subsampled with sterile cotton swabs. Fecal DNA was extracted using FastDNA® KIT (MP Biomedical; Santa Ana, CA; cat#116540400) following manufacturer’s instructions and including additional wash steps. Quantification and dilution of isolated DNA PCR was pooled for library preparation. Sample DNA was stored at −20 °C prior to generation of sequencing libraries.

Sequencing libraries were constructed by PCR amplification of the V4 region of the 16s rRNA gene using primers 515F and 806R following the protocol for the Earth Microbiome Project (http://www.earthmicrobiome.org/protocols-and-standards/16s/). Amplicons were purified using AxyPrep Mag PCR clean-up beads (Axygen; Corning, NY) quantified with Quanti-iT PicoGreen,dsDNA Assay Kit (Invitrogen; Eugene, OR), and pooled in equimolar ratios prior to sequencing at the Colorado State University Genomics Core facility using a 2 × 250 MiSeq flow cell (Illumina, San Diego, CA)46,47.

Microbiota Analysis

Paired-end sequence reads were concatenated and all combined 16s sequences were filtered, trimmed and processed with the DADA2 (R bioconductor package)48 implementation included in the open source bioinformatics tool myPhyloDB version 1.2.1 (www.myphylodb.org/). Briefly, all primers were removed from each sequence using the open source Python program Cutadapt49 and sequence variants were inferred using the default pipeline in DADA2. Each sequence variant identified in DADA2 was classified to the closest reference sequence contained in the Green Genes reference database (Vers. 13_5_99) using the usearch_global option (minimum identity of 97%) contained in the open source program VSEARCH50. ANCoVA and DiffAbund analyses were conducted in myPhyloDB51, and MicrobiomeAnalyst52 was used to calculate alpha diversity scores and Bray-Curtis distances. The raw sequencing data and associated metadata will be made available upon request.

Changes in Microbial Metabolism (SCFAs and Bile Acids)

Frozen fecal samples were extracted for short chain fatty acids (SCFAs) using acidified water (pH 2.5) containing 5 mM ethylbutyric acid as an internal standard. Samples in acid water were vortexed for 5 minutes, sonicated for 30 minutes and centrifuged (10,000 RPM /10 mins) to remove particulate matter. Supernatant was collected and centrifuged again (10,000 RPM /10 mins) and 100 μl was transferred to a glass insert bottle and analyzed on a GC-FID (Agilent 6890 Plus GC Series, Aglient 7683 Injector series, GC Column: TG-WAXMS A 30mx 0.25 mm × 0.25um) using the GC OpenLab program. Samples were normalized to the internal standard, ethyl butyric acid, and quantified using standard curves generated from dilutions of commercial stocks of acetate, propionate, and butyrate.

Bile acids were quantified using the following methods. Stool samples (25 mg) were homogenized in 500 μL of NH 4 OH, with 5 μL internal standards glycodeoxycholic acid d-4, deoxycholic acid d-4, and taurocholic acid d-5. The mixture was vortexed and incubated at 60 °C for 1 hour followed by sonication for 30 minutes. One mL of HPLC grade water was added and incubated at −80 °C overnight. Samples were centrifuged at 4 °C at 10,000 rpm for 30 minutes and the clear supernatant was transferred to vials for Ultrahigh Pressure Liquid Chromatography-Mass Spectrometry (UPLC-MS) analysis.

Analysis was performed on a Waters Acquity UPLC coupled to a Xevo TQ-S triple quadrupole mass spectrometer (Millford, MA, USA), as described previously53. Chromatographic separations occurred on a Waters HSS T3 stationary phase column (1 × 100 mm, 1.8 µM). The mobile phases were 2 mM ammonium hydroxide (A) and methanol and water with 0.1% formic acid (B). The samples were held at 4 °C and column at 70 °C. The analytical gradient was carried out as follows: At 0 min, 0.1% B; time 0.5 min, 0.1% B; time 2 min, 30% B; time 15 min, 97% B; time 16 min, 97%B; time 16.5 min, 0.1% B; time 21 min, 0.1% B. Flow rate was 210 µL/min and injection volume was 2 µL. The mass spectrometry was operated in selected reaction monitoring (SRM) mode. Inter-channel delay was set to 3 ms and the MS was operated in both negative and positive ionization modes with capillary voltage at 2.1 and 3.2 kV. Source temperature was 150 °C and desolvation temperature was 500 °C with a gas flow rate of 1000 L/hr, cone gas flow 150 L/hr, and collision gas flow 0.2 mL/min. Nebulizer pressure was 7 Bar and argon was used as the collision gas. Waters TargetLynx software was used for peak integration.

Fecal Triglycerides

Fecal triglycerides were assessed using the Triglycerides Assay Kit (Cayman Chemicals, Ann Arbor, MI). Briefly, 75 mg of homogenized fecal sample was suspended in 1xNP40 reagent containing protease inhibitors. Samples were centrifuged at 4 °C for 10 minutes at 10,000 rpm. Supernatant was diluted 1:5 with 1xNP40 and absorbance at 530–550 nm was measured after incubation for 15 min at room temperature. Triglyceride quantity was determined by fitting to standard curves.

Measures of Inflammation (Fecal secretory immunoglobulin A and cytokine analyses)

To assess changes in oral tolerance and mucosal immunity, fecal secretory immunoglobulin A (sIgA) was analyzed using the Human Secretory IgA ELISA Assay Kit (Eagle Biosciences, Amherst, NH) following manufacturer’s instructions.

System inflammation was assessed by measuring plasma levels of GM-CSF, IFNα, IL-1α, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p70), IL-13, and TNF-α using the Milliplex MAP Human High Sensitivity T Cell panel (Millipore Sigma, Burlington, MA). All samples were processed according to the manufacturers’ protocols and analyzed on a Luminex instrument (LX200; Millipore, Austin, TX).

Statistical Analysis

To estimate the effect of the cricket diet on the various outcome measures, separate linear mixed models were fit for each outcome of interest using the restricted maximum likelihood (REML) criterion in the lme4 package (V 1.1–13) in R (V 3.3.2)54. Each model was adjusted for treatment group, baseline measure and period of treatment while the study participant was modeled as a random effect55. Following the recommendations of Senn et al. in regards to crossover designs, the washout period was assumed to be sufficient (no non-negligible carry-over effects) and an interaction term between period and treatment was omitted from all models56. After the models were fit to the data, semi-parametric, bootstrap 95% confidence intervals (4,000 iterations) were estimated for each model parameter and approximate p-values were calculated using the Satterthwaite approximation as implemented in the lmerTest package (V 2.0–36)57. Given that this study was intended as a pilot, no multiple-testing corrections were applied to model parameter p-values.

A graphical analysis of the residuals for 47 of the 55 models revealed no noteworthy violations of the assumptions of a linear mixed model. For eight of the models however (sIgA, fecal triglycerides, IL-13, ursodeoxycholic acid, 3a_6b_7b-trihydroxycholenoic acid, glycochenodeoxycholic acid, tarurodeoxzycholic acid and taruocholic acid) heteroscedasticity in the error variance could be seen in the residuals, hence the models were refit on the natural logarithm scale (where baseline measure was also log-transformed). Because some participants had sIgA values measured at zero, a small constant (c = 0.1) was added to measured values to allow for the log-transformation. For completeness, both model summaries (adjusted and unadjusted) for each of these outcomes are included in the appendix. No apparent model violations were found in graphical analysis of the residuals for the updated models.

Microbiota data were normalized using Laplace smoothing58 followed by subsampling with replacement (rarefaction (keep) command)51. Two individuals were omitted from the microbiota analyses due to a low number of sequence reads obtained for one or more of their time points. Data were rarefied to 24,150 sequence reads using 100 iterations. An Analysis of Covariance (ANCOVA) model was used to assess taxonomic differences across treatment groups, and genewise negative binomial GLM (DiffAbund; adjusted p-value for statistical significance was set as q < 0.1) was used to determine differential analysis of taxa relative abundance between treatments. Measures of alpha (CHAO1 estimates, Shannon diversity index) and beta diversity (Bray-Curtis distances) were statistically analyzed using non-parametric Kruskal-Wallis tests. All descriptive statistics were calculated using R (V 3.3.3).

Data Availability Statement

The datasets generated during and/or analyzed during the current study are provided in Supplemental Materials or are available from the corresponding author on reasonable request.