Water and food sources play a major role in the distribution and transfer of microsporidia infection to animals and humans. So, this systematic review and meta-analysis aimed to assess the status and genetic diversity of microsporidia infection in water, vegetables, fruits, milk, cheese, and meat. The standard protocol of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines was followed. Scopus, PubMed, Web of Science, and Google Scholar were searched from 1 January 2000 and 1 February 2023. The point estimates and 95% confidence intervals (CIs) were calculated using a random-effects model. Of the 1,308 retrieved studies, 35 articles were included in the final meta-analysis. The pooled prevalence of microsporidia infection in mixed water, mixed fruits, mixed vegetables, and milk was 43.3% (95% CI, 33–54.2%; I2, 94.86%), 35.8% (95% CI, 5.3–84.8%; I2, 0), 12% (95% CI, 4.9–26.6%; I2, 96.43%), and 5.8% (95% CI, 2.7–12%; I2, 83.72%), respectively. Considering the genotypes, microsporidia with genotype D in water sources and genotype CD6 in vegetables/fruits were the highest reported genotypes. Given the relatively high prevalence of microsporidiosis (especially in water sources), designing strategies for control, and prevention of microsporidia infection in these sources should be recommended.

  • Among the potential resources of microsporidia, water, and food sources play a major role in the distribution and transfer of microsporidia infection to animals and humans.

  • The pooled prevalence of microsporidia infection in mixed water, mixed vegetables, mixed fruits, and milk was 43.3, 35.8, 12, and 5.8%, respectively.

Microsporidia are fungal-related protozoa known as spore-forming intracellular organisms; these worldwide distributed eukaryotes are the One Health matters (Taghipour et al. 2021a). Different species of microsporidia have been isolated from the environment, vertebrate/invertebrate hosts, etc. (Malysh et al. 2019; Han et al. 2021). In addition to the zoonotic importance of microsporidia, human anthropozoonotic, and enteropathogens can be obtained from contaminated food and water supplies, vegetables, milk, and dairy products such as cheese (Taghipour et al. 2021b). Human infections can range from self-limiting gastrointestinal complications in adequate immunity individuals to life-threatening problems in immunocompromised individuals and children as well as the elderly (Shabani et al. 2022; Taghipour et al. 2022a). Although complications such as myositis and hepatitis, ocular and renal even systematic forms are reported, the main clinical index of microsporidiosis is diarrhea; among the 200 identified genera, the well-known genus Enterocytozoon bieneusi and Encephalitozoon spp. are responsible for most of the human clinical manifestations (Taghipour et al. 2020a, 2022b).

There are numerous reports of contamination of environmental factors like soil, drinking water, dairy, and vegetable sources from all over the world, due to the need for processing in each of mentioned sources, untreated drinking water, pasteurization, sterilization, and/or coagulation steps of milk and cheese, as well as proper washing steps of vegetables, it reminds us of the challenges and hidden gaps in the process of these highly consumed substances (Hoch & Solter 2017; Yildirim et al. 2020; Vecková et al. 2021). Thick-walled spores tolerate harsh environmental conditions and in initially entering the human body orally, frequently settle in the intestinal tract (Steele & Bjørnson 2014; Hosseini Parsa et al. 2021). It seems that at least 15 of 1,400 identified fungi-sister microsporidia species have been associated with human infections (Mhaissen & Flynn 2018). Microscopic and polymerase chain reaction-based molecular approaches are used to detect and characterize the Microsporidia spp.; in the former, modified trichrome stain (chromotrope 2R) is used and in the latter, the SSU rRNA genes are targeted (Park & Poulin 2021). It should be noted that the identification, removal, and inactivation of microsporidia spores from water and vegetables use current technologies (Said 2012; Javanmard et al. 2018). Lack of attention to the contamination of the mentioned sources can cause emerging outbreaks, especially in low-income and non-developed communities (Wang et al. 2018). There are various sporadic reports on the contamination of water, vegetables, fruits, milk, cheese, and meat, but no comprehensive report is available on the level of contamination of these sources. Therefore, the present systematic and meta-analysis investigated the contamination of these sources in order to deepen the explorations into the unknown sources of microsporidia fields.

Information sources and systematic search

The present meta-analysis study was systematically done consistent with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement (Moher et al. 2009). Literature searches for published studies on the prevalence of microsporidia infection in water, vegetables, fruits, milk, cheese, and meat were retrieved via four international databases (Scopus, PubMed, Web of Science, and Google Scholar) searching, between 1 January 2000 and 1 February 2023. The searching process was accomplished using MeSH terms alone or in the following combination: (‘Microsporidiosis’ OR ‘Microsporidia’ OR ‘Microsporidium’ OR ‘Microspora’ OR ‘Enterocytozoon bieneusi’ OR ‘Encephalitozoon spp.’) AND (‘Prevalence’ OR ‘Epidemiology’) AND (‘Water’ OR ‘Vegetables’ OR ‘Fruits’ OR ‘Milk’ OR ‘Cheese’ OR ‘Meat’). Moreover, the bibliographic list of all selected records and their citations was searched manually to retrieve extra pertinent reports.

Inclusion/exclusion criteria, study selection, and data extraction

Initial screening was accomplished for eligible records by title and abstract. Duplicate studies were detected and removed with the EndNote X8 software (Thomson Reuters, New York, USA). Then, the full text of potential studies was retrieved and evaluated by two independent expert researchers (S.R. and S.B.). Thereupon, recovered studies data were extracted by another independent investigator (A.T.) and double-checked by others (S.R. and S.B.). Any doubt or disagreement in any of the mentioned processes was resolved by consensus of opinions and discussion. For meta-analysis, studies that met all of the following pre-established criteria were selected: (1) full-texts or abstracts published in English without geographical limitation; (2) peer-reviewed original research articles, short reports, or letters to the editors that studied the prevalence of microsporidia in water, vegetables, fruits, milk, cheese, and meat; (3) papers published online from the inception up to 1 February 2023; and (4) those papers that provided the exact total sample size and positive samples. Studies were excluded which, in addition to not having one or more inclusion criteria, had other factors such as: (1) the studies that did not clearly mention the total studied samples and/or positive cases and (2) all types of review articles, case reports, and case series, as well as local reports in a non-English language. Next, the finalized studies data and variables were collected, which included the last name of the first author, year of publication, geographical region (continent and country) diagnostic method, type of source, totally examined sample size, and positive (isolated) sample numbers.

Study quality assessment

In our meta-analysis, the standard quality assessment checklist, Joanna Briggs Institute (JBI) was used for the included studies’ quality evaluation (JB 2014). This 10-question checklist has four answering options: Yes, No, Unclear, and Not applicable. In summary, each study can be awarded a maximum of one star for each numbered item. The studies that were considered as moderate and strong (high quality) scored 4–6 and 7–10 points, respectively. Based on the obtained score, the authors have decided to include (4–10 points) and exclude (≤3 points) the papers due to the weak studies category.

Data synthesis and statistical analysis

For each included study, the point estimates and their respective 95% confidence intervals (CIs) were calculated using a random-effects model. It should be noted that the random-effects model allows for a distribution of true effect sizes between published studies. To minimize the biases, using sub-group analyses, the pooled prevalence of microsporidia infection was estimated according to the type of source (water, vegetables, fruits, and milk). Forest plot analysis was used to reveal possible heterogeneity among included studies. The I2 statistic was performed to assess the heterogeneity between studies and the values of <50%, 50–80%, and >80% were defined as low, moderate, and high heterogeneity, respectively (Higgins et al. 2003; Taghipour et al. 2020a, 2020b, 2021c). Moreover, small study effects and their publication bias were discerned by Egger's regression test (Egger et al. 1997; Zhang et al. 2017). All analytical functions were applied by comprehensive meta-analysis software (version 2, BIOSTAT, Englewood, NJ, USA) (Nasiri et al. 2015; Taghipour et al. 2020a).

Study characteristics

We identified a total of 1,308 records following the initial search of databases; after duplicate removal, title- and abstract-screening, 35 articles (27 datasets for water, 7 datasets for vegetables/fruits, 4 datasets for milk, 1 study for cheese, and 1 study for meat) were eligible to be included in this systematic review and meta-analysis (Figure 1). The main characteristics of each study along with the quality assessment according to JBI are listed in Tables 13. All the included articles were of suitable quality. According to Egger's regression test, no significant publication bias was found in studies presenting results for water (t = 1.06, P = 0.29), vegetables (t = 0.32, P = 0.76), and fruits (t = 0.33, P = 0.79). Publication bias was found only in the milk (t = 3.04, P = 0.03).
Table 1

Characteristics of included studies on the prevalence of Microsporidia spp. in water sources

First authorPublication yearType of sampleType of waterDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Fournier et al. (2000)  2000 Water Surface Water Nested PCR France Europe 25 16 Microsporidia spp.  
Fournier et al. (2002)  2002 Water Swimming Pools PCR France Europe 48 Endoreticulatus schubergi  
Thurston-Enriquez et al. (2002)  2002 Water Irrigation Water PCR USA America 25 Encephalitozoon intestinalis and Pleistophora 
Dowd et al. (2003)  2003 Water Drinking Water PCR Guatemala America 12 E. intestinalis  
Coupe et al. (2006)  2006 Water Surface Water, including recreational areas PCR France Europe 57 Enterocytozoon bieneusi  
Graczyk et al. (2007)  2007 Water Recreational Bathing Water Sugar-phenol flotation + Multiplexed fluorescence in situ hybridization USA America 60 26 E. bieneusi (26) and E. intestinalis (1) 
Kwakye-Nuako et al. (2007)  2007 Water Drinking Water Modified Zielhl-Neelsen Ghana Africa 27 14 Microsporidia spp.  
Masungo et al. (2010)  2010 Water Tap water Magnesium Sulfate Flotation Zimbabwe Africa  
aIzquierdo et al. (2011)  2011 Water Drinking Water Treatment Plants Trichrome stain + PCR Spain Europe 16 Microsporidia spp.  
Izquierdo et al. (2011)  2011 Water Wastewater Treatment Plants Trichrome stain + PCR Spain Europe 16 Microsporidia spp.  
Izquierdo et al. (2011)  2011 Water Recreational River Areas Trichrome stain + PCR Spain Europe E. intestinalis  
Izquierdo et al. (2011)  2011 Water Total (mixed) Trichrome stain + PCR Spain Europe 38 Microsporidia spp.  
aBen Ayed et al. (2012)  2012 Water Raw Wastewater PCR Tunisia Africa 110 86 E. bieneusi Genotypes D and IV 
Ben Ayed et al. (2012)  2012 Water Treated Wastewater PCR Tunisia Africa 110 49 E. bieneusi Genotypes D and IV 
Ben Ayed et al. (2012)  2012 Water Total (mixed) PCR Tunisia Africa 220 135 E. bieneusi  
Li et al. (2012)  2012 Water Raw Wastewater PCR China Asia 386 338 E. bieneusi Genotype D was the most prevalent, being found in 279 of 338 (82.5%) 
aGalván et al. (2013)  2013 Water Drinking Water Treatment Plants PCR Spain Europe 63 17 Microsporidia spp.  
Galván et al. (2013)  2013 Water Wastewater Treatment Plants PCR Spain Europe 112 68 Microsporidia spp.  
Galván et al. (2013)  2013 Water Locations of Influence PCR Spain Europe 48 24 Microsporidia spp.  
Galván et al. (2013)  2013 Water Total (mixed) PCR Spain Europe 223 109 Microsporidia spp.   
Guo et al. (2014)  2014 Water Stormwater PCR USA America 67 39 E. bieneusi W4 (33), W1 (15), W13 (14), W10 (12), W7 (12), W19 (5), W16 (4), W17 (2), W8 (2), W12 (2), W15 (2), W5 (1), and W11 (3) 10 
Hu et al. (2014)  2014 Water River Water PCR China Asia 178 56 E. bieneusi EbpC, EbpA, D, CS-8, PtEb IX, Peru 8, Peru 11, PigEBITS4, EbpB, G, O, and RWSH1 to RWSH6 10 
Ma et al. (2016)  2016 Water Wastewater Treatment Plant PCR China Asia 50 35 E. bieneusi D, EbpC, PigEBITS7, Peru11, Peru8, EbpA, ESH-01 to ESH-05. The predominant genotype was D, being detected in 31 samples. 10 
Saad et al. (2016)  2016 Water Wastewater PCR Egypt Africa 96 10 E. bieneusi (10) and E. intestinalis (3)  
aHuang et al. (2017)  2017 Water Combined Sewer Overflow PCR China Asia 40 37 E. bieneusi D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and one new genotype SHW2 
Huang et al. (2017)  2017 Water Raw Wastewater PCR China Asia 40 40 E. bieneusi D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and two new genotypes SHW1 and SHW2 
Huang et al. (2017)  2017 Water Total (mixed) PCR China Asia 80 77 E. bieneusi  
Karaman et al. (2017)  2017 Water Environmental Waters Light microscopy Turkey Europe 228 38 Microsporidia spp.  
aYamashiro et al. (2017)  2017 Water Wastewater (Raw sewage) Nested PCR Brazil America 18 E. bieneusi (3)  
Yamashiro et al. (2017)  2017 Water Wastewater (Treated effluent) Nested PCR Brazil America 18 E. bieneusi (2)  
Yamashiro et al. (2017)  2017 Water Total (mixed) Nested PCR Brazil America 36 E. bieneusi  
Ye et al. (2017)  2017 Water Raw Wastewater PCR China Asia 108 46 E. bieneusi D, BEB6, I, J, PigEbIX, PigEBITS5, EbpA, Peru6, Peru8, Type IV, HNWW1, HNWW2, HNWW3, HNWW4, and HNWW5 10 
aChen et al. (2018)  2018 Water Reservoirs Nested PCR Taiwan Asia 28 28 Vittaforma-like  
Chen et al. (2018)  2018 Water Rivers Nested PCR Taiwan Asia 22 19 Vittaforma-like  
Chen et al. (2018)  2018 Water Total (mixed) Nested PCR Taiwan Asia 50 47 Vittaforma-like  
Javanmard et al. (2018)  2018 Water Treated Wastewater PCR Iran Asia 12 E. bieneusi (7) and Encephalitozoon spp. (1) Genotypes D and E 
Li et al. (2018)  2018 Water Water Ponds PCR China Asia E. bieneusi SC02 
Chen et al. (2019)  2019 Water Different water (Environmental, Artemia franciscana, Clinical, Curculionidae, Euproctis chrysorrhoea) PCR Taiwan Asia 19 Vittaforma corneae, Microsporidium sp., Enterocytospora artemiae, Uncultured microsporidia clone, Unikaryonidae sp., and Endoreticulatus sp. 
Gad et al. (2019)  2019 Water Irrigation water samples (Ground and surface freshwater) Microscopic examination Egypt Africa 36 Microsporidia spp. 
Jiang et al. (2020)  2020 Water Raw Wastewater PCR China Asia 164 122 E. bieneusi D (97), Peru11 (4), EbpC (6), PigEBITS7 (1), SHW3 (1), SHW5 (1), SHW6 (1), and SHW7 (1), HenanV (n 3), Peru11 (1), Peru8 (1), D + Peru8 (1), D + SHW4 (1), SHW4 (1), and D + Peru11 (2) 10 
aFan et al. (2021)  2021 Water Wastewater Treatment Plants Nested PCR China Asia 238 134 E. bieneusi D (77), Type IV (30), Peru8 (10), Peru11 (2), EbpC (2), Peru6 (1), MWC-m1 (1), GZW1 (1), PtEb IX (1), Type IV + D (7), Type IV + Peru11 (1), and Peru8 + Type IV (1) 10 
Fan et al. (2021)  2021 Water Sewer Samples Nested PCR China Asia 88 23 E. bieneusi D (11), Type IV (5), PtEb IX (2), Type IV + D (2), EbpC (1), GZW2 (1), and GZW3 (1) 10 
Fan et al. (2021)  2021 Water Total (mixed) Nested PCR China Asia 326 157 E. bieneusi  10 
First authorPublication yearType of sampleType of waterDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Fournier et al. (2000)  2000 Water Surface Water Nested PCR France Europe 25 16 Microsporidia spp.  
Fournier et al. (2002)  2002 Water Swimming Pools PCR France Europe 48 Endoreticulatus schubergi  
Thurston-Enriquez et al. (2002)  2002 Water Irrigation Water PCR USA America 25 Encephalitozoon intestinalis and Pleistophora 
Dowd et al. (2003)  2003 Water Drinking Water PCR Guatemala America 12 E. intestinalis  
Coupe et al. (2006)  2006 Water Surface Water, including recreational areas PCR France Europe 57 Enterocytozoon bieneusi  
Graczyk et al. (2007)  2007 Water Recreational Bathing Water Sugar-phenol flotation + Multiplexed fluorescence in situ hybridization USA America 60 26 E. bieneusi (26) and E. intestinalis (1) 
Kwakye-Nuako et al. (2007)  2007 Water Drinking Water Modified Zielhl-Neelsen Ghana Africa 27 14 Microsporidia spp.  
Masungo et al. (2010)  2010 Water Tap water Magnesium Sulfate Flotation Zimbabwe Africa  
aIzquierdo et al. (2011)  2011 Water Drinking Water Treatment Plants Trichrome stain + PCR Spain Europe 16 Microsporidia spp.  
Izquierdo et al. (2011)  2011 Water Wastewater Treatment Plants Trichrome stain + PCR Spain Europe 16 Microsporidia spp.  
Izquierdo et al. (2011)  2011 Water Recreational River Areas Trichrome stain + PCR Spain Europe E. intestinalis  
Izquierdo et al. (2011)  2011 Water Total (mixed) Trichrome stain + PCR Spain Europe 38 Microsporidia spp.  
aBen Ayed et al. (2012)  2012 Water Raw Wastewater PCR Tunisia Africa 110 86 E. bieneusi Genotypes D and IV 
Ben Ayed et al. (2012)  2012 Water Treated Wastewater PCR Tunisia Africa 110 49 E. bieneusi Genotypes D and IV 
Ben Ayed et al. (2012)  2012 Water Total (mixed) PCR Tunisia Africa 220 135 E. bieneusi  
Li et al. (2012)  2012 Water Raw Wastewater PCR China Asia 386 338 E. bieneusi Genotype D was the most prevalent, being found in 279 of 338 (82.5%) 
aGalván et al. (2013)  2013 Water Drinking Water Treatment Plants PCR Spain Europe 63 17 Microsporidia spp.  
Galván et al. (2013)  2013 Water Wastewater Treatment Plants PCR Spain Europe 112 68 Microsporidia spp.  
Galván et al. (2013)  2013 Water Locations of Influence PCR Spain Europe 48 24 Microsporidia spp.  
Galván et al. (2013)  2013 Water Total (mixed) PCR Spain Europe 223 109 Microsporidia spp.   
Guo et al. (2014)  2014 Water Stormwater PCR USA America 67 39 E. bieneusi W4 (33), W1 (15), W13 (14), W10 (12), W7 (12), W19 (5), W16 (4), W17 (2), W8 (2), W12 (2), W15 (2), W5 (1), and W11 (3) 10 
Hu et al. (2014)  2014 Water River Water PCR China Asia 178 56 E. bieneusi EbpC, EbpA, D, CS-8, PtEb IX, Peru 8, Peru 11, PigEBITS4, EbpB, G, O, and RWSH1 to RWSH6 10 
Ma et al. (2016)  2016 Water Wastewater Treatment Plant PCR China Asia 50 35 E. bieneusi D, EbpC, PigEBITS7, Peru11, Peru8, EbpA, ESH-01 to ESH-05. The predominant genotype was D, being detected in 31 samples. 10 
Saad et al. (2016)  2016 Water Wastewater PCR Egypt Africa 96 10 E. bieneusi (10) and E. intestinalis (3)  
aHuang et al. (2017)  2017 Water Combined Sewer Overflow PCR China Asia 40 37 E. bieneusi D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and one new genotype SHW2 
Huang et al. (2017)  2017 Water Raw Wastewater PCR China Asia 40 40 E. bieneusi D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and two new genotypes SHW1 and SHW2 
Huang et al. (2017)  2017 Water Total (mixed) PCR China Asia 80 77 E. bieneusi  
Karaman et al. (2017)  2017 Water Environmental Waters Light microscopy Turkey Europe 228 38 Microsporidia spp.  
aYamashiro et al. (2017)  2017 Water Wastewater (Raw sewage) Nested PCR Brazil America 18 E. bieneusi (3)  
Yamashiro et al. (2017)  2017 Water Wastewater (Treated effluent) Nested PCR Brazil America 18 E. bieneusi (2)  
Yamashiro et al. (2017)  2017 Water Total (mixed) Nested PCR Brazil America 36 E. bieneusi  
Ye et al. (2017)  2017 Water Raw Wastewater PCR China Asia 108 46 E. bieneusi D, BEB6, I, J, PigEbIX, PigEBITS5, EbpA, Peru6, Peru8, Type IV, HNWW1, HNWW2, HNWW3, HNWW4, and HNWW5 10 
aChen et al. (2018)  2018 Water Reservoirs Nested PCR Taiwan Asia 28 28 Vittaforma-like  
Chen et al. (2018)  2018 Water Rivers Nested PCR Taiwan Asia 22 19 Vittaforma-like  
Chen et al. (2018)  2018 Water Total (mixed) Nested PCR Taiwan Asia 50 47 Vittaforma-like  
Javanmard et al. (2018)  2018 Water Treated Wastewater PCR Iran Asia 12 E. bieneusi (7) and Encephalitozoon spp. (1) Genotypes D and E 
Li et al. (2018)  2018 Water Water Ponds PCR China Asia E. bieneusi SC02 
Chen et al. (2019)  2019 Water Different water (Environmental, Artemia franciscana, Clinical, Curculionidae, Euproctis chrysorrhoea) PCR Taiwan Asia 19 Vittaforma corneae, Microsporidium sp., Enterocytospora artemiae, Uncultured microsporidia clone, Unikaryonidae sp., and Endoreticulatus sp. 
Gad et al. (2019)  2019 Water Irrigation water samples (Ground and surface freshwater) Microscopic examination Egypt Africa 36 Microsporidia spp. 
Jiang et al. (2020)  2020 Water Raw Wastewater PCR China Asia 164 122 E. bieneusi D (97), Peru11 (4), EbpC (6), PigEBITS7 (1), SHW3 (1), SHW5 (1), SHW6 (1), and SHW7 (1), HenanV (n 3), Peru11 (1), Peru8 (1), D + Peru8 (1), D + SHW4 (1), SHW4 (1), and D + Peru11 (2) 10 
aFan et al. (2021)  2021 Water Wastewater Treatment Plants Nested PCR China Asia 238 134 E. bieneusi D (77), Type IV (30), Peru8 (10), Peru11 (2), EbpC (2), Peru6 (1), MWC-m1 (1), GZW1 (1), PtEb IX (1), Type IV + D (7), Type IV + Peru11 (1), and Peru8 + Type IV (1) 10 
Fan et al. (2021)  2021 Water Sewer Samples Nested PCR China Asia 88 23 E. bieneusi D (11), Type IV (5), PtEb IX (2), Type IV + D (2), EbpC (1), GZW2 (1), and GZW3 (1) 10 
Fan et al. (2021)  2021 Water Total (mixed) Nested PCR China Asia 326 157 E. bieneusi  10 

aA study that includes several datasets based on the type of water.

QA, quality assessment.

Table 2

Characteristics of included studies on the prevalence of Microsporidia spp. in vegetables and fruits

First authorPublication YearType of sampleType of vegetables/fruitsDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Javanmard et al. (2018)  2018 Vegetables Total (mixed) PCR Iran Asia 12 Enterocytozoon bieneusi (5), Encephalitozoon hellem (1) Genotypes D (3) and E (2) for E. bieneusi 
aGad et al. (2020)  2020 Vegetables Lettuce Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al.(2020)  2020 Vegetables Parsley Acid-fast trichrome stain Egypt Africa 25 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Watercress Acid-fast trichrome stain Egypt Africa 22 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Dill Acid-fast trichrome stain Egypt Africa 26 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables White radish Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Green onion Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Tomatoes Acid-fast trichrome stain Egypt Africa 28 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Carrot Acid-fast trichrome stain Egypt Africa 25 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Cucumber Acid-fast trichrome stain Egypt Africa 24 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Total (mixed) Acid-fast trichrome stain Egypt Africa 219 40 Microsporidia spp. 
aSaid (2012)  2012 Vegetables Rocket Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 22 Microsporidia spp. 
Said (2012)  2012 Vegetables Lettuce Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 25 Microsporidia spp. 
Said (2012)  2012 Vegetables Parsley Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 10 Microsporidia spp. 
Said (2012)  2012 Vegetables Leek Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 Microsporidia spp. 
Said (2012)  2012 Vegetables Green onion Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 11 Microsporidia spp. 
Said (2012)  2012 Vegetables Total (mixed) Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 300 76 Microsporidia spp. 
aLi et al. (2019a)  2019 Vegetables Lettuce PCR China Asia 200 13 E. bieneusi (13) CM8 (2), CD6 (7), EbpA (3), and Henan-IV (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Coriander PCR China Asia 152 E. bieneusi (1) CM8 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Celery PCR China Asia 70 E. bieneusi (1) EbpA (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Baby bok choy PCR China Asia 59 E. bieneusi (1) CHV3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Chinese cabbage PCR China Asia 47 10 
Li et al. (2019a)  2019 Vegetables Leaf lettuce PCR China Asia 44 E. bieneusi (1) CHG19 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Water spinach PCR China Asia 28 E. bieneusi (3) CD6 (1), BEB8 (1), and CTS3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Crown daisy PCR China Asia 27 10 
Li et al. (2019a)  2019 Vegetables Fennel plant PCR China Asia 26 E. bieneusi (1) EbpC (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Endive PCR China Asia 25 E. bieneusi (1) Henan-IV (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Spinach PCR China Asia 20 10 
Li et al. (2019a)  2019 Vegetables Schizonepeta PCR China Asia 20 10 
Li et al. (2019a)  2019 Vegetables Cabbage PCR China Asia 18 10 
Li et al. (2019a)  2019 Vegetables Leaf mustard PCR China Asia 11 10 
Li et al. (2019a)  2019 Vegetables Chinese chive PCR China Asia 132 E. bieneusi (5) CD6 (1), EbpA (2), EbpC (1), and CHV1 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Chive PCR China Asia 128 E. bieneusi (4) CD6 (2), CHV2 (1), and CTS3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Cucumber PCR China Asia 41 E. bieneusi (1) CD6 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Potato PCR China Asia E. bieneusi (1) CHV4 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Bean PCR China Asia 28 E. bieneusi (4) CD6 (4) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Green chili PCR China Asia 10 
Li et al. (2019a)  2019 Vegetables Total (mixed) PCR China Asia 1084 37 E. bieneusi (37) 10 
Li et al. (2019a)  2019 Fruits Watermelon PCR China Asia 15 E. bieneusi (1) CD6 (1) for E. bieneusi 10 
aJedrzejewski et al. (2007)  2007 Fruits Strawberries Fluorescence in situ hybridization Poland Europe 15 E. intestinalis (3) 
Jedrzejewski et al. (2007)  2007 Fruits Raspberries Fluorescence in situ hybridization Poland Europe 10 E. intestinalis (1) and E. bieneusi (2) 
Jedrzejewski et al. (2007)  2007 Fruits Total (mixed) Fluorescence in situ hybridization Poland Europe 25 E. intestinalis (4) and E. bieneusi (2) 
Jedrzejewski et al. (2007)  2007 Vegetables Mung bean Fluorescence in situ hybridization Poland Europe E. bieneusi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Alfalfa Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Radishes Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Sprouts (mixed) Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Parsley leaves Fluorescence in situ hybridization Poland Europe E. cuniculi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Arugula lettuce Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Curly lettuce Fluorescence in situ hybridization Poland Europe E. bieneusi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Iceberg lettuce Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Red radish Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Leek Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Dill Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Total (mixed) Fluorescence in situ hybridization Poland Europe 55 E. cuniculi (1) and E. bieneusi (2) 
aSalamandane et al. (2021)  2021 Vegetables Coriander Nested PCR Mozambique Africa 39 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Parsley Nested PCR Mozambique Africa 45 
Salamandane et al. (2021)  2021 Vegetables Portuguese Cabbage Nested PCR Mozambique Africa 45 
Salamandane et al. (2021)  2021 Vegetables Pointed White Cabbage Nested PCR Mozambique Africa 45 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Carrot Nested PCR Mozambique Africa 18 
Salamandane et al. (2021)  2021 Vegetables Tomato Nested PCR Mozambique Africa 42 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Lettuce Nested PCR Mozambique Africa 45 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Green Pepper Nested PCR Mozambique Africa 42 
Salamandane et al. (2021)  2021 Vegetables Total (mixed) Nested PCR Mozambique Africa 321 E. bieneusi (4) 
aMasungo et al. (2010)  2010 Fruits Mangoes Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Apples Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Peaches Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Guavas Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Total (mixed) Magnesium Sulfate Flotation Technique Zimbabwe Africa 40 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Black Nightshade Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Cabbage Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Rape Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Pumpkin Leaves Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Total (mixed) Magnesium Sulfate Flotation Technique Zimbabwe Africa 40 15 Microsporidia spp. 
First authorPublication YearType of sampleType of vegetables/fruitsDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Javanmard et al. (2018)  2018 Vegetables Total (mixed) PCR Iran Asia 12 Enterocytozoon bieneusi (5), Encephalitozoon hellem (1) Genotypes D (3) and E (2) for E. bieneusi 
aGad et al. (2020)  2020 Vegetables Lettuce Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al.(2020)  2020 Vegetables Parsley Acid-fast trichrome stain Egypt Africa 25 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Watercress Acid-fast trichrome stain Egypt Africa 22 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Dill Acid-fast trichrome stain Egypt Africa 26 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables White radish Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Green onion Acid-fast trichrome stain Egypt Africa 23 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Tomatoes Acid-fast trichrome stain Egypt Africa 28 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Carrot Acid-fast trichrome stain Egypt Africa 25 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Cucumber Acid-fast trichrome stain Egypt Africa 24 Microsporidia spp. 
Gad et al. (2020)  2020 Vegetables Total (mixed) Acid-fast trichrome stain Egypt Africa 219 40 Microsporidia spp. 
aSaid (2012)  2012 Vegetables Rocket Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 22 Microsporidia spp. 
Said (2012)  2012 Vegetables Lettuce Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 25 Microsporidia spp. 
Said (2012)  2012 Vegetables Parsley Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 10 Microsporidia spp. 
Said (2012)  2012 Vegetables Leek Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 Microsporidia spp. 
Said (2012)  2012 Vegetables Green onion Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 60 11 Microsporidia spp. 
Said (2012)  2012 Vegetables Total (mixed) Modified Zeihl–Neelsen stain and modified trichrome stain Egypt Africa 300 76 Microsporidia spp. 
aLi et al. (2019a)  2019 Vegetables Lettuce PCR China Asia 200 13 E. bieneusi (13) CM8 (2), CD6 (7), EbpA (3), and Henan-IV (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Coriander PCR China Asia 152 E. bieneusi (1) CM8 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Celery PCR China Asia 70 E. bieneusi (1) EbpA (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Baby bok choy PCR China Asia 59 E. bieneusi (1) CHV3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Chinese cabbage PCR China Asia 47 10 
Li et al. (2019a)  2019 Vegetables Leaf lettuce PCR China Asia 44 E. bieneusi (1) CHG19 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Water spinach PCR China Asia 28 E. bieneusi (3) CD6 (1), BEB8 (1), and CTS3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Crown daisy PCR China Asia 27 10 
Li et al. (2019a)  2019 Vegetables Fennel plant PCR China Asia 26 E. bieneusi (1) EbpC (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Endive PCR China Asia 25 E. bieneusi (1) Henan-IV (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Spinach PCR China Asia 20 10 
Li et al. (2019a)  2019 Vegetables Schizonepeta PCR China Asia 20 10 
Li et al. (2019a)  2019 Vegetables Cabbage PCR China Asia 18 10 
Li et al. (2019a)  2019 Vegetables Leaf mustard PCR China Asia 11 10 
Li et al. (2019a)  2019 Vegetables Chinese chive PCR China Asia 132 E. bieneusi (5) CD6 (1), EbpA (2), EbpC (1), and CHV1 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Chive PCR China Asia 128 E. bieneusi (4) CD6 (2), CHV2 (1), and CTS3 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Cucumber PCR China Asia 41 E. bieneusi (1) CD6 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Potato PCR China Asia E. bieneusi (1) CHV4 (1) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Bean PCR China Asia 28 E. bieneusi (4) CD6 (4) for E. bieneusi 10 
Li et al. (2019a)  2019 Vegetables Green chili PCR China Asia 10 
Li et al. (2019a)  2019 Vegetables Total (mixed) PCR China Asia 1084 37 E. bieneusi (37) 10 
Li et al. (2019a)  2019 Fruits Watermelon PCR China Asia 15 E. bieneusi (1) CD6 (1) for E. bieneusi 10 
aJedrzejewski et al. (2007)  2007 Fruits Strawberries Fluorescence in situ hybridization Poland Europe 15 E. intestinalis (3) 
Jedrzejewski et al. (2007)  2007 Fruits Raspberries Fluorescence in situ hybridization Poland Europe 10 E. intestinalis (1) and E. bieneusi (2) 
Jedrzejewski et al. (2007)  2007 Fruits Total (mixed) Fluorescence in situ hybridization Poland Europe 25 E. intestinalis (4) and E. bieneusi (2) 
Jedrzejewski et al. (2007)  2007 Vegetables Mung bean Fluorescence in situ hybridization Poland Europe E. bieneusi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Alfalfa Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Radishes Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Sprouts (mixed) Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Parsley leaves Fluorescence in situ hybridization Poland Europe E. cuniculi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Arugula lettuce Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Curly lettuce Fluorescence in situ hybridization Poland Europe E. bieneusi (1) 
Jedrzejewski et al. (2007)  2007 Vegetables Iceberg lettuce Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Red radish Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Leek Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Dill Fluorescence in situ hybridization Poland Europe 
Jedrzejewski et al. (2007)  2007 Vegetables Total (mixed) Fluorescence in situ hybridization Poland Europe 55 E. cuniculi (1) and E. bieneusi (2) 
aSalamandane et al. (2021)  2021 Vegetables Coriander Nested PCR Mozambique Africa 39 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Parsley Nested PCR Mozambique Africa 45 
Salamandane et al. (2021)  2021 Vegetables Portuguese Cabbage Nested PCR Mozambique Africa 45 
Salamandane et al. (2021)  2021 Vegetables Pointed White Cabbage Nested PCR Mozambique Africa 45 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Carrot Nested PCR Mozambique Africa 18 
Salamandane et al. (2021)  2021 Vegetables Tomato Nested PCR Mozambique Africa 42 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Lettuce Nested PCR Mozambique Africa 45 E. bieneusi (1) 
Salamandane et al. (2021)  2021 Vegetables Green Pepper Nested PCR Mozambique Africa 42 
Salamandane et al. (2021)  2021 Vegetables Total (mixed) Nested PCR Mozambique Africa 321 E. bieneusi (4) 
aMasungo et al. (2010)  2010 Fruits Mangoes Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Apples Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Peaches Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Guavas Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Fruits Total (mixed) Magnesium Sulfate Flotation Technique Zimbabwe Africa 40 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Black Nightshade Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Cabbage Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Rape Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Pumpkin Leaves Magnesium Sulfate Flotation Technique Zimbabwe Africa 10 Microsporidia spp. 
Masungo et al. (2010)  2010 Vegetables Total (mixed) Magnesium Sulfate Flotation Technique Zimbabwe Africa 40 15 Microsporidia spp. 

aA study that includes several datasets based on the type of vegetables or fruits.

QA, quality assessment.

Table 3

Characteristics of included studies on the prevalence of Microsporidia spp. in milk, cheese, and meat

First authorPublication yearType of sampleType of animalsDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Lee (2008)  2008 Milk  Cows PCR South Korea Asia 180 15 E. bieneusi (15) CEbB (1), CEbB (1), CEbA (2), CEbD (1), CEbA (1), CEbC (1), CMITS1 (1), CEbB (1), CEbA (1), CMITS1 (1), CEbA (1), CEbD (1), and CEbA (2) 10 
Kváč et al. (2016)  2016 Milk Lactating Multiparous Holstein Cows Nested PCR Czech Republic Europe 50 E. cuniculi (1) 
aYildirim et al. (2020)  2020 Milk Cattle Nested PCR Turkey Asia 200 Enterocytozoon bieneusi (9) ERUSS1 (2), BEB6 (2), TREb1 (1), ERUSS1 (2), BEB6 (1), and ERUSS1 (1) 10 
aYildirim et al. (2020)  2020 Milk Sheep Nested PCR Turkey Asia 200 36 E. bieneusi (36) ERUSS1 (7), BEB6 (4), TREb2 (2), TREb3 (1), TREb4 (1), ERUSS1 (7), BEB6 (4), ERUSS1 (5), BEB6 (3), TREb3 (1), TREb5 (1) 10 
aYildirim et al. (2020)  2020 Milk Water buffalo Nested PCR Turkey Asia 50 E. bieneusi (1) TREb6 (1) 10 
bVecková et al. (2021)  2021 Milk Goat Molecular detection Czech Republic Europe 99 E. cuniculi Genotype II (3) 
bVecková et al. (2021)  2021 Cheese Goat Molecular detection Czech Republic Europe Encephalitozoon cuniculi Genotype I and II 
Sak et al. (2019)  2019 Meat (from the shoulder, belly, and ham) Pigs Nested PCR Czech Republic Europe 50 E. cuniculi (2) Genotype II (2) 
First authorPublication yearType of sampleType of animalsDiagnostic methodCountryContinentSample sizeInfected by MicrosporiaSpeciesGenotypesQA
Lee (2008)  2008 Milk  Cows PCR South Korea Asia 180 15 E. bieneusi (15) CEbB (1), CEbB (1), CEbA (2), CEbD (1), CEbA (1), CEbC (1), CMITS1 (1), CEbB (1), CEbA (1), CMITS1 (1), CEbA (1), CEbD (1), and CEbA (2) 10 
Kváč et al. (2016)  2016 Milk Lactating Multiparous Holstein Cows Nested PCR Czech Republic Europe 50 E. cuniculi (1) 
aYildirim et al. (2020)  2020 Milk Cattle Nested PCR Turkey Asia 200 Enterocytozoon bieneusi (9) ERUSS1 (2), BEB6 (2), TREb1 (1), ERUSS1 (2), BEB6 (1), and ERUSS1 (1) 10 
aYildirim et al. (2020)  2020 Milk Sheep Nested PCR Turkey Asia 200 36 E. bieneusi (36) ERUSS1 (7), BEB6 (4), TREb2 (2), TREb3 (1), TREb4 (1), ERUSS1 (7), BEB6 (4), ERUSS1 (5), BEB6 (3), TREb3 (1), TREb5 (1) 10 
aYildirim et al. (2020)  2020 Milk Water buffalo Nested PCR Turkey Asia 50 E. bieneusi (1) TREb6 (1) 10 
bVecková et al. (2021)  2021 Milk Goat Molecular detection Czech Republic Europe 99 E. cuniculi Genotype II (3) 
bVecková et al. (2021)  2021 Cheese Goat Molecular detection Czech Republic Europe Encephalitozoon cuniculi Genotype I and II 
Sak et al. (2019)  2019 Meat (from the shoulder, belly, and ham) Pigs Nested PCR Czech Republic Europe 50 E. cuniculi (2) Genotype II (2) 

aAn article with three datasets (classification of milk based on the type of animal).

bAn article with two datasets (divided by milk and cheese).

QA, quality assessment.

Figure 1

PRISMA flow diagram describing included/excluded studies.

Figure 1

PRISMA flow diagram describing included/excluded studies.

Close modal

Status of microsporidia in water

Twenty-seven studies (43 datasets) were involved to investigate microsporidia in water sources (Fournier et al. 2000, 2002; Thurston-Enriquez et al. 2002; Dowd et al. 2003; Coupe et al. 2006; Graczyk et al. 2007; Kwakye-Nuako et al. 2007; Masungo et al. 2010; Cheng et al. 2011; Izquierdo et al. 2011; Ben Ayed et al. 2012; Li et al. 2012, 2018; Galván et al. 2013; Guo et al. 2014; Hu et al. 2014; Ma et al. 2016; Huang et al. 2017; Karaman et al. 2017; Yamashiro et al. 2017; Ye et al. 2017; Chen et al. 2018, 2019; Javanmard et al. 2018; Gad et al. 2020; Jiang et al. 2020; Fan et al. 2021). Considering the type of water, these studies were classified into 43 datasets (Table 1). These studies were carried out in four continents including Asia (China, Iran, and Taiwan), Europe (France and Spain), America (Brazil, USA, and Guatemala), and Africa (Egypt, Tunisia, Ghana, and Zimbabwe) during the years 2000–2021. The pooled prevalence of microsporidia infection in mixed water sources was calculated at 43.3% (95% CI, 33–54.2%; I2, 94.86%) (Figure 2), which were mostly based on molecular-based studies data (38/43 datasets), because of the high sensitivity and specificity, the results are more reliable. Likewise, the genus and species of microsporidia, along with the genotype, are embedded in Table 1. The E. bieneusi was the most isolated species (23 studies). Also, the most identified genotypes were genotype D (12 reports).
Figure 2

The pooled prevalence of Microsporidia spp. in water sources based on the random-effects model.

Figure 2

The pooled prevalence of Microsporidia spp. in water sources based on the random-effects model.

Close modal

Status of microsporidia in vegetables and fruits

A total of seven articles including 73 datasets were included on the status of microsporidia in vegetables and fruits (Jedrzejewski et al. 2007; Masungo et al. 2010; Said 2012; Javanmard et al. 2018; Li et al. 2019a; Gad et al. 2020; Salamandane et al. 2021) (Table 2). In this regard, the studies were classified into 64 vegetable datasets and 9 fruit datasets (Table 2). These studies were conducted in three continents (Africa, Asia, and Europe) during the years 2007–2021 (Table 2). The status of microsporidia infection in vegetables and fruits according to the type of vegetables/fruits is presented in Table 2. Likewise, the genus and species of microsporidia, along with the genotype, are revealed in Table 2. The pooled prevalence of microsporidia infection in mixed fruits and vegetables was 35.8% (95% CI, 5.3–84.8%; I2, 0) and 12% (95% CI, 4.9–26.6%; I2, 96.43%), respectively (Figure 3). Among 73 datasets, 32 were used to investigate microsporidia using molecular methods. Most reports were related to E. bieneusi (26 datasets). Also, CD6 has been the predominant genotype that is reported in seven studies.
Figure 3

The pooled prevalence of Microsporidia spp. in vegetables and fruits based on the random-effects model.

Figure 3

The pooled prevalence of Microsporidia spp. in vegetables and fruits based on the random-effects model.

Close modal

Status of microsporidia in milk, cheese, and meat

Four articles (six datasets) were included to investigate microsporidia in milk (Lee 2008; Kváč et al. 2016; Yildirim et al. 2020; Vecková et al. 2021) and one article each for cheese (Vecková et al. 2021) and meat (Sak et al. 2019). The studies were conducted in Asia and Europe during the years 2008–2021 (Table 3). All studies used molecular diagnostic methods. The status of microsporidia infection in milk, cheese, and meat according to the type of animal is embedded in Table 3. Also, the genus and species of microsporidia, considering the genotype, are shown in Table 3. The pooled prevalence of microsporidia infection in milk was 5.8% (95% CI, 2.7–12%; I2, 83.72%) (Figure 4). Of the six reports of milk contamination, four were associated with E. bieneusi. On the other hand, because the number of meat- and cheese-related studies did not reach the quorum for analysis, these studies were collected and shown only for observation.
Figure 4

The pooled prevalence of Microsporidia spp. in milk based on the random-effects model.

Figure 4

The pooled prevalence of Microsporidia spp. in milk based on the random-effects model.

Close modal

Foodborne and waterborne diseases occur mainly through many classes of pathogens (i.e., bacteria, viruses, and parasites) excreted in the feces of animals and humans, which are the main cause of disease outbreaks worldwide (Bartlett 1996; Percival et al. 2004; Lynch et al. 2009; Bonadonna & La Rosa 2019). In recent years, a number of foodborne and waterborne epidemics caused by protozoa (i.e., giardiasis, cryptosporidiosis, cyclosporiasis, and toxoplasmosis) have been reported in the world (Mintz et al. 1993; Choi et al. 1997; Quiroz et al. 2000; Strausbaugh & Herwaldt 2000; Efstratiou et al. 2017; E Silva et al. 2019). Among the foodborne and waterborne pathogens, Microsporidia spp. is of special importance due to the spread of spores through the water and food, which can ignite serious adverse impacts on human and animal's health. This systematic review is the first that brings information to reveal the global status of Microsporidia spp. infection in water and food sources. These findings could be helpful for physicians and public health policymakers. Our results indicate that 43.3% mixed water, 35.8% mixed fruits, 12% mixed vegetables, and 5.8% milk were positive for Microsporidia spp. infection, respectively. In this review, most studies have been conducted on the water sources. While there are few studies regarding the contamination of vegetables/fruits (seven articles), milk (four articles), cheese (one study), and meat (one study) with Microsporidia spp., especially in developing countries. Hence, the need for further studies and more attention to Microsporidia spp. infection in these sources is tangible in these countries. Stray animals (cats and dogs) (Taghipour et al. 2020a, 2021c), farm animals (cattle, sheep, goat, pig, and boar) (Taghipour et al. 2021b, 2022a, 2022b), rodents (Taghipour et al. 2021a) and a broad range of birds' (Ruan et al. 2021) access to various environmental resources, including water and vegetable sources, are serious problems in controlling and preventing Microsporidia spp. waterborne and foodborne outbreaks. Thus, monitoring programs for Microsporidia spp. infection should be considered in these animals to prevent water and food contamination with the microsporidia spores. Considering the water contamination (Table 1), the sites contaminated by Microsporidia spp. belonged to surface water, drinking water, and wastewater.

The prevalence of microsporidia in water samples was surprisingly higher than expected, which can be a warning sign of environmental pollution because water, whether drinking or non-drinking water, is a potential source of contamination for humans. In addition to the risk of contaminated drinking water, accidental ingestion of pool water and other water can be considered a possible route of contamination. On the other hand, the aspect of water resource origin animal infections and playing the role of infection reservoir should not be neglected (Afzali et al. 2015; Stentiford et al. 2016). The high prevalence of microsporidia in water becomes even more alarming when we consider its lethality in immunocompromised individuals. Besides, the detection of Microsporidia spp. in drinking water could demonstrate that disinfection processes applied to this source of water are not adequate (Izquierdo et al. 2011). It seems that the sanitization of drinking water and water in close-to-human settlements is necessary, especially in areas with a low level of hygiene.

Contamination of soil and water sources with microsporidia spores probably increases the burden of vegetables/fruit contamination in public places (Dado et al. 2012; Kwok et al. 2013). Therefore, it seems that the cycle of water, fruits, and soil is important for the transmission of microsporidia spores and requires more attention to control and prevent the spread of this microorganism. The significant contamination of fruits and vegetables, directly and indirectly, refers to the contamination of the surrounding soil, as well as their irrigation system defect (Bartosova et al. 2021). Unfortunately, there is no global standard protocol for washing vegetables/fruits and it is diverse in different geographical areas. It seems that the use of natural substances such as concentrations of salt water (saturated), vinegar, and so on, that have the sporicidal effect of microsporidia and are more available can be included in preventive health measures and policies (Leiro et al. 2012; Rodríguez-García et al. 2022).

Consumption of unpasteurized and raw milk can be dangerous for humans because it is a potential source of microbial contamination (Mungai et al. 2015). Although the potential risk of unpasteurized milk and raw milk for transmission of several microbial agents is well known (Mungai et al. 2015), limited knowledge is available concerning the presence of Microsporidia spp. in milk and related risk factors for public health. The limited data analysis has made our interpretation of the actual extent of milk contamination ambiguous, however, at least it can be recommended to consume pasteurized/sterilized milk and dairy products in groups with insufficient immunity as well as infants/children who are fed with non-mother's milk (Firoozeh et al. 2017; Vecková et al. 2021). However, the presence of Microsporidia spp. in raw milk could arise from direct contamination from the environment of dairy farms, infected dairy animals, and food handlers. Also, few studies have been done on cheese and meat (one study for each), so it is suggested to do extensive studies on these products for a deeper understanding of this issue.

The most prevalent genotype which has been detected in humans and animals is genotype D (Li et al. 2019b; Shen et al. 2020). In this systematic review, along with genotype D (in water), genotype CD6 (in vegetables) was also reported; these genotypes have been found in humans and animals (Taghipour et al. 2021a, 2021b). Microsporidia can be considered a One Health issue, the contamination of water and food sources (fruits and vegetables), and livestock products are not only important in terms of human infections but also a potential indicator and representative of environmental contamination and/or animal infections (Taghipour et al. 2020a); therefore, this finding may suggest that a human source or an animal source can infect the environmental sources (e.g., water sources and vegetables).

It is suggested that future studies with more accurate diagnostic methods screen the health of water, milk, dairy products, fruits, and vegetables as well as meat in terms of contamination with resistant microsporidia spores. In measuring the contamination of fruits and vegetables and even water, studies should take into account the possibility of contamination with animal/human feces and improper disposal of wastewater, and in sampling, they should not take samples from places that have a higher probability of contamination.

This study has some limitations and the results presented here should be interpreted with regard to these limitations; we can mention the limited number of reports from the countries of the world, especially developed countries. Limited reporting makes the estimated prevalence an apparent prevalence rather than a true prevalence. Similarly, there were fewer studies and sample/sizes on milk, cheese, and meat than expected. The used methods with different sensitivity and specificity cause variation and make it more difficult to interpret the results. To solve this problem, it is suggested that future studies use standard and high sensitivity and specificity methods for diagnosis. Some studies were published in languages other than English (local languages) and we were unable to include their data; so, it is thought that the publication of articles/reports in English can make us more aware of the situation close to the reality of the prevalence in the world. Nevertheless, from a global perspective, we believe what we had reported here is close to true microsporidia prevalence in water, vegetable, and milk sources.

A relatively high prevalence was found in water sources, which were mainly according to the molecular methods from worldwide, that the filtration and sanitization of drinking water, as well as the disinfection of swimming pools with chlorine and the same substances, seem necessary. Next, the fruits and vegetables contamination probably indicate improper and/or unsanitary irrigation of these products, which seems to require apt washing and the use of a safe and appropriate protocol to eliminate the resistant form (spores) of parasites and other infectious organisms. In the present research, the milk, cheese, and meat contamination, although was estimated less than others, however, it is significant because the reports on these samples are very limited, and it seems that our information is like the tip of the iceberg. Therefore, it is recommended to consume pasteurized and/or sterilized dairy products and avoid raw or undercooked meat.

The authors would like to thank all staff of the Department of Medical Parasitology, Jahrom University of Medical Sciences.

All authors contributed to the study design. A.T. and S.R. contributed to all parts of the study. A.A. and V.M. contributed to the study implementation. A.T. and S.R. collaborated in the analysis and interpretation of data. A.T. and S.B. collaborated on manuscript writing and revision. All the authors commented on the drafts of the manuscript and approved the final version of the article.

None.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Author notes

These authors contributed equally to this work.

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