
==== Front
Comp Immunol Rep
Comp Immunol Rep
Comparative Immunology Reports
2950-3116
Elsevier

S2950-3116(24)00036-3
10.1016/j.cirep.2024.200169
200169
Article
Examining the dietary effect of insect meals on the innate immune response of fish: A meta-analysis
Chen Yubing y279chen@uwaterloo.ca
ab⁎
Ellis Jennifer a
Huyben David a
a Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
b Department of Biology, University of Waterloo, Waterloo, ON, Canada
⁎ Corresponding author. y279chen@uwaterloo.ca
12 9 2024
12 2024
12 9 2024
7 20016919 6 2024
11 9 2024
11 9 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• A meta-analysis was performed to investigate the effects of insect meal replacement on fish innate immunity.

• 40 % of insect meal replacement resulted in the highest upregulated immune response in fish.

• 87.5–100 % of insect meal replacement was found to significantly decrease the immune gene expression level in fish.

Insect meal inclusion in aquaculture feed has received increased interest as a sustainable alternative to fishmeal and recent evidence has shown additional effects on modulating the immune response. However, lack of effects of insect meal on fish immunity in a few studies have put these beneficial effects into question. The objective of this meta-analysis was to summarize the effects of fishmeal replacement with insect meal on the innate immune response of several fish species based on differential gene expression via qPCR. A systematic literature search was conducted using online databases including Web of Science and ScienceDirect that found 197 studies as of August 2023, however only 20 studies met the criteria of high-quality studies focused on the immune response of fish and were included in this meta-analysis. The most studied insect meal was from black soldier fly, followed by yellow mealworm, and the most commonly analyzed tissue was the liver, followed by the intestine. The effect of fishmeal replacement with insect meal on fish immune responses were examined using a mixed model, with study as the random effect. This meta-analysis found a non-linear (quadratic) relationship (P = 0.017) between immune gene expression fold change and the level of fishmeal replacement with insect meal. Based on the fitted quadratic curve, a 40 % replacement of fishmeal with insect meal resulted in the highest upregulated immune response in fish. Fish taxonomic family was also found to have an effect (P = 0.004) on immune gene expression, where the taxonomic family Moronidae was more affected, with an average fold change of 2.783 (± 0.325). Other variables examined, including the insect type, tissue analyzed, dietary crude protein level, did not affect the immune response (P > 0.05). This meta-analysis also found that dietary lipid had a significant correlation with immune gene expression, and should be taken into consideration in future studies and meta-analyses. These findings are impactful since they provide evidence for the optimal dietary replacement level of insect meals required to significantly affect the innate immune response across several fish species.

Keywords

Immune
Fish
Insect meal
Dietary effect
Meta-analysis
==== Body
pmcIntroduction

To support the sustainable production of aquafeed and facilitate the growth of the aquaculture industry, one of the major challenges in the aquaculture industry is to find alternative protein sources to replace fishmeal due the decreasing availability of forage fish, elevated prices, increased demand from human consumers and unethical use of marine animals as feed ingredients [[1], [2], [3]]. Insect meal is a popular topic and has received increasing attention across the globe due to its suitable nutritional profile, short life cycles and its immune-stimulating activity found across many terrestrial species [4,5]. Insects are considered part of the natural diet of many carnivorous and omnivorous fishes and their inclusion in aquaculture feeds has increased substantially over the past decade [1]. In comparison to fishmeal, production of insect meals can be done locally with few inputs of feed, water and energy, that result in a lower ecological footprint, thus improving the sustainability of aquaculture feeds [1,2,6]. Several studies have found that replacement of fishmeal with insect meal resulted in improved fish growth performance, feed digestibility, and gut bacterial diversity [[7], [8], [9]]. In addition, increasing evidence has suggested the inclusion of insect meal in diets for fish modulates the immune response as reviewed by Aragão et al. [10], and lead to improved disease resistance and higher aquaculture production [11]. However, the dietary inclusion level to elicit this immune response and confounding factors such as fish species, insect type, tissue analyzed and dietary nutritional composition are unknown.

Increasingly, research has been conducted to examine the effects of partial or full replacement of fishmeal with insect meal on the immune responses of different fish species. Pro-inflammatory cytokines, such as interleukin-1 beta (IL1b), tumour necrosis factor (TNF) and interferons (IFNs), are important regulators of the innate immune system, controlling the cell-to-cell communication related to immunity, and regulating the host defense network, which are essential in antagonizing pathogens and resisting disease in fish [12,13]. Henry et al. [4] found that dietary inclusion of yellow mealworm (Tenebrio molitor) meal showed significant improvement of anti-bacterial and anti-parasitical defences in European sea bass (Dicentrarchus labrax); Alves et al. [14] found 15 % and 30 % replacement of superworm larvae (Zophobas morio) meal increased the lysozyme activity in both the serum and liver in Nile tilapia (Oreochromis niloticus); yellow catfish (Pelteobagrus fulvidraco) fed with 18 % and 27 % of yellow mealworm were found to have enhanced immune responses and disease resistance by examining the plasma parameters including superoxide dismutase (SOD), lysozyme and immunoglobulin M (IgM), and the expression levels of immune related genes such as interleukin-1 and cyclophilin A [15]. In addition, a higher yellow mealworm inclusion of 50 % in fish feed was also found to have higher survival rates and reduced immunosuppression when challenged with the pathogenic bacteria Vibrio parahaemolyticus [16]. However, some studies have reported no significant effect of substituting fish meal with insect meal on the innate immune responses in fish. For instance, incorporating 5 % of black soldier fly larvae (Hermetia illucens) into the diet did not enhance the innate immune responses or growth performance of lake whitefish (Coregonus clupeaformis) [17]. Similarly, zebrafish fed with 25 % or 50 % of black soldier fly larvae replacement for fishmeal did not show significant differences in interleukin-1-beta and tumour necrosis factor-alpha expression [18]. There discrepancies make it challenging to compare studies due to differences in insect meal replacement levels, fish species, tissue types analyzed and diet formulations. Therefore, it is important to examine the optimal insect meal replacement level to elicit a significant innate immune response, which could enhance disease resistance and overall production in the aquaculture industry.

The quantitative meta-analysis method has become a popular and useful approach in many scientific fields since it summarizes results from many published studies and presents a more conclusive outcome with increased accuracy and reduced bias [[19], [20], [21]]. Therefore, the objective of this study was to perform a quantitative meta-analysis to investigate the effects (and optimal dietary replacement level) of insect meal over fish meal, on the innate immune response of fish in order to better formulate aquaculture feeds and fish production.

Materials and methods

Literature search and dataset development

A systematic literature search was conducted in August 2023 using the Web of Science database, the ScienceDirect database, and hand-searching. The search keywords included: insect, diet, fish, immune response, gene expression, diet replacement and fish immunity. Hand-searching was conducted by reviewing the references provided in each publication associated with insect meal induced immune responses in several fish species. In the end, the search resulted in 197 related articles. The titles and abstracts of 157 articles were screened after removal of 40 duplicates. For inclusion in the dataset, the paper must have used fish as the subject species and included insect meal as partial or complete replacement of fishmeal. In addition, the articles were required to include immune responses, to be specific, immune gene expression levels for each dietary group. No limitation was set for the year of publication. Papers were excluded from further analysis if they were not primary research articles or not about innate immune gene expression. A literature funnel was constructed to document the search results and inclusion/exclusion of papers to focus in on relevant and high quality studies (Fig. 1). After selection criteria were applied, 20 studies were retained for analysis (Table 1).Fig. 1 Literature funnel for this meta-analysis using a PRISMA flow diagram adapted from Moher et al. [58].

Fig. 1

Table 1 Summary of studies used to assess the dietary effect of insect meal replacement on the expression of immune genes using RT-qPCR in various tissues of several fish species.

Table 1Publication (Study #); Reference # in the Reference List	Species	Country	Rearing tempa	Insect typeb	% Insect meal replacement	Sampled tissues	Target Genesc	
Stenberg et al., 2019 (1); Ref # [44]	Atlantic salmon (Salmo salar)	Norway	NA	BSFL	66–100	Head kidney	il1b, il8, tnfa, hsp70	
Kumar et al., 2021 (2); Ref # [45]	Rainbow trout (Oncorhynchus mykiss)	USA	15	BSFL	8–16	Kidney, Intestine	il8, tnfa, irf-1	
Weththasinghe et al., 2021 (3); Ref # [46]	Atlantic salmon (Salmo salar)	Norway	14.8	BSFL	6.25–25	Distal intestine, skin mucus	il1b, ifn-γ	
Zarantoniello et al., 2019 (4); Ref # [18]	Zebrafish (Danio rerio)	Italy	28	BSFL	25–50	Liver	il1b, tnfa,	
Li et al., 2019 (5); Ref # [47]	Atlantic salmon (Salmo salar)	Norway	13.7	BSFL	85	Proximal intestine, distal intestine	hsp70, ifn-γ, il8	
Gu et al., 2022 (6); Ref # [28]	Largemouth bass (Micropterus salmoides)	China	29	YM	11–66	Intestine	tnfa, il1b	
Su et al., 2017 (7); Ref # [15]	Yellow catfish (Pelteobagrus fulvidraco)	China	24	YM	25–75	Liver, spleen, kidney	il1, cypa	
Hender et al., 2021 (8); Ref # [48]	Barramundi (Lates calcarifer)	Australia	27.15	BSFL	30	Liver, distal intestine	il1b, il8, tnfa, hsp70, hsp90	
Abdel-Latif et al., 2021 (9); Ref # [26]	European seabass (Dicentrarchus labrax)	Egypt	27.85	BSFL	25–50	Liver	il1b, hsp70	
Ji et al., 2015 (10); Ref # [27]	Jian carp (Cyprinus carpio var. Jian)	China	27	SW	50–80	Liver	hsp70	
Zhou et al., 2017 (11); Ref # [49]	Mirror carp (Cyprinus carpio var. specularis)	China	28.5	SW	4–16	Hepatopancrea	tnfa, il6	
Kishawy et al., 2022 (12); Ref # [50]	Nile tilapia (Oreochromis niloticus)	Egypt	27	BSFL	25–100	Spleen	tnfa, il1b	
Fan et al., 2023 (13); Ref # [51]	Channel catfish (Ictalurus punctatus)	China	27	C	25–100	Intestine	tnfa, ifn-γ, il1b, il8	
Ge et al., 2023 (14); Ref # [52]	Largemouth bass (Micropterus salmoides)	China	26.2	YM	24–48	Distal intestine	il1b, tnfa	
Huang et al., 2022 (15); Ref # [53]	Pearl gentian grouper (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂)	China	28	BSFL	10–30	Intestine	tnfa	
Y. Chen et al., 2023 (16); Ref # [17]	Lake whitefish (Coregonus clupeaformis)	Canada	8.5	BSFL	5	Liver	il1b, il8, hsp70, hsp90	
H. Chen et al., 2023 (17); Ref # [54]	Largemouth bass (Micropterus salmoides)	China	27	YM	12–48	Liver	il1b, il8, tnfa	
Gaudioso et al., 2021 (18); Ref # [55]	Rainbow trout (Oncorhynchus mykiss)	Italy	13.3	BSFL	10–60	Head kidney, mid gut	il1b	
Vargas-Abúndez et al., 2019 (19); Ref # [56]	Clownfish (Amphiprion ocellaris)	Italy	28	BSFL	25–75	Liver	hsp70	
Carvalho et al., 2023 (20); Ref # [57]	Gilthead sea bream (Sparus aurata)	Spain	21	BSFL	33–66	Posterior gut	hsp90, hsp70, tnfa, il1b	
Notes:.

a Average rearing temperature of the fish in °C, NA = rearing temperature not specified in the study.

b BSFL = Black soldier fly larvae (Hermetia illucens), YM = Yellow mealworm (Tenebrio molitor), SW = Silkworm (Bombyx mori), C = Cricket (Gryllus bimaculatus).

c Genes examined in each study, il1b = interleukin-1-beta, il8 = interleukin-8, tnfa = tumor necrosis factor-alpha, irf-1 = interferon regulatory factor-1, ifn-γ = interferon gamma, hsp70 = heat shock protein 70, hsp90 = heat shock protein 90, cypa = cyclophilin a, il6 = interleukin-6.

Data relating to the effect of insect meal replacement on fish immune response from each study was extracted and recorded in an excel spreadsheet. The excel database template included the information on the study population such as the fish species, taxonomic family (i.e., Salmonidae), rearing water temperature, and initial weight of fish. Information regarding the insect diet were also recorded, including the insect types, the basal diet types, and 8 categories of insect meal replacement levels (as 0–12.5 %, 12.5–25 %, 25–37.5 %, 37.5–50 %, 50–62.5 %, 62.5–75 %, 75–87.5 % and 87.5–100 %). In addition, the tissues analyzed, dietary crude lipid level, dietary crude protein level, the gene names and the immune gene expression levels were also recorded for each study. Nine genes have been selected based on their relevance to the research objectives, with a focus on the innate immune system, including pro-inflammatory responses (interleukin-1-beta, interleukin-6, interleukin-8, tumor necrosis factor-alpha), stress-related responses (heat shock protein 70, heat shcok protein 90) and antiviral-related responses (interferon-gamma, interferon regulatory factor-1, cyclophilin a). Genes related to adaptive immune responses were not selected. All studies provided the gene expression levels using graph format; therefore, Web Plot Digitizer version 4.6 (Pacifica, California, USA) was used to extract the values. All the manuscript screening and data extraction steps were performed by the same individual. Due to the different recording methods in each study, the gene expression level was recoded as the variable Fold change, and it was standardized according to the following equation:FoldChange=RelativegeneexpressionleveloftheinsectdietRelativegeneexpressionlevelofthecontroldiet

Statistical model development

All statistical procedures were completed using SAS® Studio 9.4 M8 (SAS Institute Inc., Cary, NC, USA). The following X variables were assessed to model the response in immune gene expression fold change (Y variable): insect replacement (as Yes/No, % of fishmeal replacement, and 8 categories representing increasing levels of insect replacement of fishmeal in the diet), dietary crude lipid%, and dietary crude protein% levels. Additionally, categorical X variables considered included insect type [Black soldier fly larvae (BSFL), Yellow mealworm (YM), Cricket (C), Silkworm (SW)] and sampling tissue (liver, intestine, distal intestine, head kidney, spleen, kidney, hepatopancreas, posterior gut, proximal intestine, skin mucus, mid gut). Descriptive analysis for each variable in the data was calculated using PROC FREQ and PROC CORR in SAS ® Studio (provided in Table 2). The PROC CORR procedure was also used to evaluate the Pearson correlation coefficients between the continuous dependent and independent variables.Table 2 Descriptive statistics of the dependent, continuous independent and categorical independent variables in the meta-analysis.

Table 2Variables	Na	Mean	Standard Deviation	Minimum	Maximum	
Dependent variable						
  Fold change	191	1.2	0.74	0.2	4.3	
Continuous independent variables						
  Insect replacement (%)	191	40.6	28.39	0.0	100.0	
  Crude lipid level (%)	168	14.9	5.53	5.5	29.0	
  Crude protein level (%)	177	43.2	5.31	31.0	53.1	
Categorical independent variables						
  Tissue analysed						
   Liver	51 (26.70 %)					
   Intestine	49 (25.65 %)					
   Distal intestine	23 (12.04 %)					
   Head kidney	13 (6.81 %)					
   Spleen	12 (6.28 %)					
   Kidney	12 (6.28 %)					
   Posterior gut	8 (4.19 %)					
   Hepatopancreas	8 (4.19 %)					
   Mid gut	3 (1.57 %)					
   Skin mucus	6 (3.14 %)					
   Proximal intestine	6 (3.14 %)					
  Insect replacement level (Category)						
   0–12.5 %	42 (21.99 %)					
   12.5–25 %	39 (20.42 %)					
   25–37.5 %	26 (13.61 %)					
   37.5–50 %	28 (14.66 %)					
   50–62.5 %	7 (3.66 %)					
   62.5–75 %	25 (13.09 %)					
   75–87.5 %	13 (6.81 %)					
   87.5–100 %	11 (5.76 %)					
  Insect type						
   Black soldier fly	100 (53.76 %)					
   Yellow mealworm	58 (31.18 %)					
   Cricket	16 (8.60 %)					
   Silkworm	12 (6.45 %)					
Note:

aN is the total number of observations for each variable or category. % of observation was also presented for categorical variables.

A mixed model approach was applied using the PROC GLIMMIX procedure in SAS Studio [21]. A generalized linear mixed model analysis with study as a random effect was used [22]. Models were considered when all fixed effect variables were significant (P < 0.05) and where the residuals and random effect of study were normally distributed. The Cook's D influence analysis was utilized to detect and remove outliers. For each iteration, a Cook's D fixed effect plot was generated in SAS to visually inspect the influence of individual data points. Data points with high Cook's D values were flagged as potential outliers. These outliers were carefully reviewed, and if necessary, removed from the dataset. The analysis was then rerun, and the Cook's D plot was re-inspected to ensure no other data points were unduly influencing the model. In the end, outliers were removed if their fold change values exceeded 3.9; leading to the removal of three observations from the dataset. A forest plot was generated using PROC SGPLOT in SAS to visualize the dataset using the log transformed variable Fold change as the mean difference (Fig. 2).Fig. 2 Forest plot showing the average treatment difference across 20 studies on the effect of fishmeal replacement with insect meal on fish immune gene expression. Blue dots represented the mean immune gene expression in each study. The lines represented the 95 % confidence intervals for the results for each study.

Fig. 2

The general statistical model used was:Yij=X+Trmt+Si+εij

Where Y is the response variable log (fold change), i = 1, …, 20 studies, j = 1, …, ni treatment means, X= the overall mean of the dependent variable, Trmt = the treatment effect as the fixed effect, Si = the random effect of the ith study, and εij is the residual error.

Furthermore, the maximum value of a quadratic equation (y=ax2+bx+c) is calculated using the formula max=c−b24a, where a, b, and c are the coefficients of the quadratic equation.

Statistical model evaluation

Scatter plots of predicted values vs. observed values, and residuals vs. predicted values, were generated to evaluate the model's goodness-of-fit and served as visual assessment of patterns in residuals using PROC SGPLOT in SAS. Further model evaluation was conducted using the predicted Y values by calculating the root mean square prediction error (RMSPE) [23] and the concordance correlation coefficient (CCC) [24] to provide an overall prediction error estimate and indicate the relationship between the observed and predicted values [25].MSPE=∑i=1n(YPred−YObs)2n

Where n is the total number of observations, YPred and YObs are the predicted and observed value, respectively. Root mean square prediction error (RMSPE) was calculated by taking the square root of the MSPE value, and it is expressed as a percentage of the mean of observation values. RMSPE estimates the overall prediction error for the equation and the smaller RMSPE value indicates the better model. RMSPE is further decomposed into the overall bias error (ECT), deviation of the regression slope from unity (ER) and error due to random disturbance (ED), calculated as:ECT=(O¯−P¯)2

ER=(SP−R×SO)2

ED=(1−R2)×(SO)2

where O¯ = observed mean, P¯ = predicted mean, SO = observed standard deviations, SP = predicted standard deviations and R = Pearson correlation coefficient. Furthermore, ECT, ER and ED are usually expressed as percentages of MSPE.

The CCC, concordance correlation coefficient, quantifies both the precision and accuracy of a model. The CCC ranges from −1 to 1, where −1 indicates that the observed and predicted values are perfectly unrelated, 0 indicates no relationship, and 1 indicates the observed and predicted values are perfectly related. The CCC statistics is calculated as:CCC=R×Cb

whereCb=2[v+1v+u2]

And where R is the Pearson correlation coefficient as a measure of precision, and Cb represents the bias correlation as a measure of accuracy. Within the Cb calculation,v=SOSP

u=O¯−P¯(SO×SP)−1/2

where v measures the change in scale shift or standard deviation between the predicted and observed values. Greater variance is shown with a v-value greater than 1, and smaller variance is shown with a v-value less than 1. Where u indicates the location shift that a positive u-value indicates underprediction, and a negative u-value indicates overprediction.

Results

For preliminary analysis of the data, Pearson Correlation Coefficients of all continuous X and Y variables are presented in Table 3. Crude protein and lipid levels were significantly correlated with the variable Fold change, with a R =−0.322 (P < 0.001) and R =−0.205 (P=0.008), respectively. However, the insect meal replacement percentage was not significantly correlated with fold change, with a R =−0.061 (P > 0.05). This represents the correlations across the dataset while ignoring the study ‘blocking’ effect. A Forest Plot was also used to visualize the log transformed fold change values across the dataset, with 95 % confidence intervals provided (Fig. 2). The average log transformed fold change of the fish immune gene expression of six studies were very positive (> 0.15) and in favour of the insect meal treatments, while only two were very negative (< – 0.15) and the remaining 12 studies showed little to no fold change (< 0.15) as shown in Fig. 2. Treatment difference was slightly below 0 for studies 2, 4, 12 and 17, which favours the control. However, the average difference was above 0 for studies 1, 3, 5, 7 – 11, 13 – 16, 18 and 19, which favours the treatment.Table 3 Pearson Correlation Coefficients between dependent variable (fold change), and continuous independent variables (insect meal replacement percentage, crude lipid, and crude protein levels).

Table 3Variables	Fold Change	
Pearson correlation Coefficient	P-value	
Insect meal replacement percentage	0.061	0.405	
Crude lipid	−0.205	0.008	
Crude protein	−0.322	<0.0001	

Prediction equations developed on continuous variables in the dataset are presented in Table 5. Models explored included crude protein level, crude lipid level and insect replacement percentage (linear and quadratic, presented as Perc and Perc2, respectively) as the fixed effects, with study as a random effect. The equations containing crude protein and crude lipid were not significant (P > 0.05). As well, insect diet replacement percentage, as a linear regression, did not display a significant relationship with the immune gene expression fold change (P > 0.05). However, the insect diet replacement percentage was found to present a significant quadratic relationship with the dependent variable, gene expression fold change (P=0.0170). The immune gene expression fold change was shown to peak at 40 % insect meal replacement of fishmeal (Fig. 3), as determined by mathematical calculations based on the predicted best-fit quadratic equation, which is presented as Perc2 in Table 5. To be specific, the Y variable, immune gene expression fold change was predicted to increase from 0 % and reach the maximum response at 40 % of insect meal replacement, then slowly decrease after 40 % replacement level (Fig. 4). Taxonomic family was also found to have an effect (P = 0.004) on immune gene expression fold change (Table 4), where the taxonomic family Moronidae was the most affected with an average fold change of 2.783 (± 0.325). Taxonomic family Ictalurida and Serranidae had the second and third highest fold change of 1.996 (± 0.263) and 1.469 (± 0.406), respectively.Fig. 3 Scatter plot showing the effect of fishmeal replacement with insect meal on fish immune response, with the predicted equation Y=0.976(±0.181)+0.016(±0.007)Perc−0.0002(±0.0001)×Perc2 (P = 0.017), which the gene expression fold changed peaked at 40 % insect diet replacement as determined by the quadratic curve.

Fig. 3

Fig. 4 Least square Means (± SE) for the model developed to quantify the effect of fishmeal replacement with insect meal on fish immune response (between the independent categorical variable Perc type and the dependent variable gene expression fold change, P = 0.0835). Different letters on the plot represent significant differences between groups, determined by Perc type least squares means, with adjustments for multiple comparisons using Holm-Simulated (Adj. P).

Fig. 4

Table 4 P-values and F-values for variables against log-transformed fold change immune expression level using univariate models.

Table 4	P-values	F-values	Degrees of freedom	
Taxonomic Family	0.004	4.69	9	
Insect meal	0.317	1.01	1	
Insect type	0.344	1.19	4	
Perc type	0.084	1.84	7	
Tissue	0.332	1.17	10	
Perc	0.894	0.02	1	
Perc2	0.017	5.81	1	
Crude lipid	0.242	1.49	1	
Crude protein	0.238	1.50	1	
Crude lipid*Perc type	0.035	2.22	8	
Crude lipid*Perc	0.120	2.48	1	
Crude lipid*Tissue	0.796	0.60	10	
Crude lipid* Taxonomic Family	0.002	7.48	8	
Crude lipid*Crude protein	0.103	3.03	1	
Crude protein* Taxonomic Family	0.010	5.72	10	
Crude protein*Perc type	0.065	1.92	8	
Crude protein*Perc	0.433	0.62	1	
Crude protein*Tissue	0.849	0.54	10	
Insect type*Tissue	0.606	0.88	19	
Insect type*Perc type	0.065	1.58	24	
Insect type*Crude lipid	0.546	1.75	42	
Insect type*Crude protein	<0.0001	2.93	49	
Insect type* Taxonomic Family	0.005	4.17	11	
Insect type*Perc	0.189	1.33	42	
Perc type* Taxonomic Family	0.035	1.87	38	
Perc type*Tissue	0.194	1.24	41	
Perc* Taxonomic Family	0.0006	4.22	10	
Perc*Tissue	0.713	0.72	11	
Tissue* Taxonomic Family	0.308	1.78	22	

Table 5 Prediction equations for the crude protein model, crude lipid model, Perc model and the Perc2 model (quadratic relationship) with the corresponding significance values.

Table 5Equation ID	Prediction equations	P	
Crude protein	Y=2.321(±0.915)−0.026(±0.021)×Crudeprotein(%)	0.238	
Crude lipid	Y=1.641(±0.351)−0.027(±0.022)×Crudelipid(%)	0.242	
Perc	Y=1.229(±0.147)−0.0003(±0.002)×Perc	0.894	
Perc2	Y=0.976(±0.181)+0.016(±0.007)×Perc−0.0002(±0.0001)×Perc2	0.017	

When explored, the categorical variables sampling tissue and insect type did not have a significant effect on the dependent variable (fold change) (P=0.3316 and P=0.3443). In addition, the categorical variable insect diet replacement level (grouped), did not have an overall significant effect on the dependent variable (P=0.0835). However, it was observed that fish fed with 37.5–50 %, 50–62.5 % and 62.5–75 % of insect meal have significantly higher upregulated gene expression compared to fish fed with 87.5–100 % of insect meal (P=0.022,0.032,0.003, respectively) (Fig. 4). Specifically, fish fed with 50–62.5 % of insect meal have the highest upregulated gene expression (1.472 ± 0.266). Insect meal replacement levels of 62.5–75 % showed the second highest gene expression (1.462 ± 0.177), and the 37.5–50 % group showed the third highest gene expression (1.311 ± 0.165). Additionally, the highest insect replacement group, 87.5–100 %, was found to significantly decrease the immune gene expression level (P=0.003), which resulted in the lowest downregulated gene expression fold change (0.7505 ± 0.245) among all groups.

Model evaluation statistics and plots provided a metric of each model's goodness-of-fit and are presented in Table 6 and Supplementary Figure 1. Higher CCC value were found in the insect meal replacement Perc grouping model and the Perc2 model, considering the conditional predictions (based on both the fixed and random components of the model), indicating high accuracy (Cb) and precision (R) describing the dataset. Slightly lower CCC, Cb and R values were found in the linear Perc model. Model evaluation also showed the lowest rMSPE% of 0.544 in the Perc type model compared to other models, indicating the best model in this meta-analysis with most of the error coming from the regression (ER% = 96.066 ). The Perc2 model also has a relatively low rMSPE% of 0.558, and the majority of the error coming from random sources (ED% = 98.621). The predicted vs. residual plots of these models are illustrated in Supplementary Figure 1, where the predicted Y values are adjusted Y values, which are conditional prediction based on the fixed and random parts of the model. The plots show little bias for the Perc Model and Perc2 Model.Table 6 Model evaluation statistics for models included in this meta-analysis.

Table 6X	Typea	rMSPE %b	ER%c	ED%d	ECT%e	CCCf	Cbg	Rh	Ui	Vj	
Perc	COND	0.568	1.504	98.496	6.11E-29	0.544	0.844	0.644	−6.02E-16	1.820	
Perc	RAW	0.739	0.525	99.172	0.303	−0.001	0.024	−0.061	−0.055	82.867	
Percb	COND	0.558	1.379	98.621	6.34E-29	0.568	0.861	0.660	−6.02E-16	1.751	
Percb	RAW	0.739	0.802	98.914	0.284	0.026	0.329	0.080	−0.053	5.901	
Perc type	COND	0.544	1.226	98.774	1.50E-28	0.598	0.881	0.679	−9.04E-16	1.673	
Perc type	RAW	0.742	2.128	97.537	0.335	0.055	0.486	0.113	−0.058	3.849	
Insect type	COND	0.569	0.954	99.046	1.52E-29	0.548	0.856	0.640	−3.01E-16	1.770	
Insect type	RAW	0.679	0.048	99.743	0.209	0.258	0.655	0.394	−0.042	2.678	
Tissue	COND	0.545	0.813	99.187	6.63E-29	0.601	0.889	0.676	−6.02E-16	1.640	
Tissue	RAW	0.728	1.739	98.162	0.099	0.128	0.610	0.210	−0.031	2.939	
Taxonomic family	COND	0.568	0.154	99.846	6.10E-29	0.566	0.887	0.638	−6.02E-16	1.647	
Taxonomic family	RAW	0.603	0.013	99.94	0.047	0.493	0.857	0.575	−0.018	1.766	
Note:.

a RAW; raw predictions, which do not use the predictors of random effects in computing the statistics. COND; conditioned predictions, which use the predictors of random effects in computing the statistics.

b Root mean square prediction error as a percentage of observed mean.

c Error due to regression; % of total MSPE.

d Error due to disturbance; % of total MSPE.

e Error due to mean bias; % of total MSPE.

f Concordance correlation coefficient; CCC=R*Cb.

g Bias correction factor; measure of accuracy.

h Pearson correlation coefficient; measure of precision.

i Location shift.

j Scale shift.

Discussion

The objective of this meta-analysis was to systematically review the published literature and quantify the dietary effects of fish meal replacement with insect meal on the innate immune responses of several fish species. This study provides a synthesis of available data for the determination of optimal level of insect meal replacement across species and will inform insect suppliers, fish feed producers and fish farmers to improve the efficacy of insect-based diets to increase the production and sustainability of the aquaculture industry.

This meta-analysis found that the percentage of insect meal replacement in diets for fish did not display a linear correlation with fold change of immune gene expression. This result is similar compared to what other studies have suggested. For example, Abdel-Latif et al. [26] suggested positive effects of replacing up to 50 % of fishmeal with insect meal in European seabass (Dicentrarchus labrax) that was reflected by the upregulation of il1b, hsp70, il10, higher relative survival rate and phagocytic activity with increasing insect meal percentage in diet. Another study also found that 50 % of fishmeal replacement with silkworm pupae meal resulted in the highest upregulated sod expression in juvenile Jian carp (Cyprinus carpio var. Jian) when compared to 0 %, 60 %, 70 % or 80 % replacement [27]. Therefore, results from previous individual studies that are also included in our meta-analysis displayed similar findings, indicating that the insect meal replacement percentage have a quadratic relationship with immune gene expression, as opposed to a linear relationship.

Similarly, the meta-analysis in the present study found a significant quadratic relationship between insect meal replacement level (%) and the immune responses in fish (P = 0.017). The corresponding predicted quadratic equation showed a peak immune response at 40 % of insect meal replacement. However, our data visually show a peak around 50 % as illustrated in Fig. 3, Fig. 4, and this discrepancy arises from differences in data presentation. Specifically, the 40 % peak is derived from a best-fit quadratic model, which provides an optimized value based on predicted relationships between insect meal inclusion and gene expression, as determined by mathematical calculation. In contrast, Fig. 3, Fig. 4 present the raw data, including standard errors and showing natural variability, which leads to a peak around 50 %. Additionally, this analysis found that fish immune responses were generally stable and not significantly disrupted when insect meal replacement levels exceeded 80 %. This finding is consistent with many studies that examined the effect of different levels of insect meal on fish immune and growth response. For example, Gu et al. [28] conducted a study on largemouth bass (Micropterus salmoides) and found that 44 % and 55 % of insect meal replacement significantly upregulated glutathione peroxidase and glutathione which functions to help protect cells from oxidative damage, suggesting the optimal insect meal replacement percentage that is similar to our results. Another study also found a three-fold hsp70 upregulation in rainbow trout that fed with 50 % replacement of Hermetia illucens prepupae meal [29]. Insect meals can be beneficial to fish since their exoskeletons contain chitin, a mucopolysaccharide polymer consisting of β 1,4-linked N-acetyl-d- glucosamine residues, that can have immunostimulatory effects on fish [[30], [31], [32]].

Previous research has found that many fish species have difficulties with chitin digestion due to the differential chitinolytic activity in different fish species with regards to evolutionary changes [33]. Therefore, the higher replacement level of insect meal will increase the corresponding chitin level in the diet, which reduce feed digestibility and growth performance despite immune benefits to the host. For instance, research has found that including 15.4 % of chitin in the feed resulted in significantly lower digestibility in both Nile tilapia (Oreochromis niloticus) and rainbow trout (Oncorhynchus mykiss), compared to feed containing 1.8 % and 2.7 % chitin on a dry matter basis [34]. Thus, the observed negative effects of the highest (87.5–100 %) insect meal replacement level on fish immune response in this meta-analysis could partially be due to the increasing chitin level, which led to decreased digestibility of feed.

These results showed a significant quadratic relationship between the insect meal replacement percentage and immune gene expression across several fish species, however, dietary composition of crude lipid influenced the results as well. A study by Huyben et al. [35] found that higher inclusion of crude lipid increased the gene expression of ifn-y in the head kidney of Atlantic salmon. Higher dietary lipid bring along higher levels of long-chain polyunsaturated fatty acids (LC-PUFA), especially arachidonic acid (ARA) and to a lesser extent eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), that act as precursors to eicosanoid metabolites such as prostaglandins, leukotrienes, thromboxanes, docosanoids, maresins and resolvins, which regulate inflammatory and immune response processes [36]. In a microarray study, Glencross et al. [37] reported a general upregulation of immune pathways in Atlantic salmon with increasing levels of dietary DHA and EPA. Alternations in omega-3 (e.g., DHA, EPA) and omega-6 fatty acid profiles can also impact various biological processes, including the innate immune system [38,39]. For instance, a study found that an omega-6/omega-3 ratio of 2.4 induced higher pro-inflammatory gene expression in salmon head kidney leukocytes compared to a ratio of 0.7 [40]. Therefore, dietary lipid must be taken into consideration when investigating immune gene response of fish.

Some studies have proposed that an upregulation of innate immune genes in fish fed insect meals be an indirect effect via the gut microbiome. Chitin has been found to have immune-stimulating effects, as noted above, however previous studies have found that feeding insects changes the gut microbiome. Rainbow trout fed between 10 and 30 % dietary replacement of black soldier fly meal resulted in changes in composition (e.g. Bacillus spp.) and increased alpha-diversity of gut microbes in several studies [8,41,42]. These authors suggested that chitin acts as a substrate for chitinase producing bacteria that are not commonly found in the fish gut, thereby increasing richness of the gut microbes. Specific bacteria and increased diversity can enhance interactions with gut associated lymphoid tissue (GALT) leading to an upregulation of the immune response [43], however more research in this area is needed.

Conclusions

As the first meta-analysis investigating the effect of insect meal replacement level on fish immunity, this review confirmed that 40 % of insect meal replacement of fishmeal resulted in the upregulation of immune genes in fish despite differences between fish species and diet formulations. This meta-analysis extensively examined eight factors and their corresponding models. However, only 20 studies were included in this meta-analysis due to limited published literature available, and it is also important to note that the meta-analysis cannot consider the effect of control diet formulation in each study and its corresponding effects on fish immune response. The meta-analysis also found that dietary lipid had a significant correlation with immune gene expression and should be taken into consideration in future studies and meta-analyses. This data offers critical information that will assist insect ingredient suppliers, fish feed producers and fish farmers to optimize the inclusion of insects in their diets for improved immune response and disease resistance in farmed finfish.

Funding

This research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Yubing Chen: Writing – review & editing, Writing – original draft, Visualization, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jennifer Ellis: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. David Huyben: Writing – review & editing, Writing – original draft, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Data availability

Data will be made available on request.

Acknowledgements

We would like to thank the Animal Biosciences Department at the University of Guelph for supporting our research. Thank you to Yu-Fen Chien for providing valuable bioinformatic-related suggestions and discussions.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cirep.2024.200169.
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