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Plasmodium falciparum genetic diversity and multiplicity of infection based on msp-1, msp-2, glurp and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis

Abstract

Background

In sub-Saharan Africa (SSA), Plasmodium falciparum causes most of the malaria cases. Despite its crucial roles in disease severity and drug resistance, comprehensive data on Plasmodium falciparum genetic diversity and multiplicity of infection (MOI) are sparse in SSA. This study summarizes available information on genetic diversity and MOI, focusing on key markers (msp-1, msp-2, glurp, and microsatellites). The systematic review aimed to evaluate their influence on malaria transmission dynamics and offer insights for enhancing malaria control measures in SSA.

Methods

The review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Two reviewers conducted article screening, assessed the risk of bias (RoB), and performed data abstraction. Meta-analysis was performed using the random-effects model in STATA version 17.

Results

The review included 52 articles: 39 cross-sectional studies and 13 Randomized Controlled Trial (RCT)/cohort studies, involving 11,640 genotyped parasite isolates from 23 SSA countries. The overall pooled mean expected heterozygosity was 0.65 (95% CI: 0.51–0.78). Regionally, values varied: East (0.58), Central (0.84), Southern (0.74), and West Africa (0.69). Overall pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 61%, 44%, and 40%, respectively, while msp-2 I/C 3D7 and FC27 alleles were 61% and 55%. Central Africa reported higher frequencies (K1: 74%, MAD20: 51%, RO33: 48%) than East Africa (K1: 46%, MAD20: 42%, RO33: 31%). For msp-2, East Africa had 60% and 55% for I/C 3D7 and FC27 alleles, while West Africa had 62% and 50%, respectively. The pooled allele frequency for glurp was 66%. The overall pooled mean MOI was 2.09 (95% CI: 1.88–2.30), with regional variations: East (2.05), Central (2.37), Southern (2.16), and West Africa (1.96). The overall prevalence of polyclonal Plasmodium falciparum infections was 63% (95% CI: 56–70), with regional prevalences as follows: East (62%), West (61%), Central (65%), and South Africa (71%).

Conclusion

The study shows substantial regional variation in Plasmodium falciparum parasite genetic diversity and MOI in SSA. These findings suggest a need for malaria control strategies and surveillance efforts considering regional-specific factors underlying Plasmodium falciparum infection.

Background

Plasmodium falciparum presents a significant public health challenge in sub-Saharan Africa (SSA), constituting the majority of reported malaria cases. In 2022, out of the 249 million malaria cases recorded globally, 233 million occurred in SSA, contributing to an estimated 580,000 out of the 608,000 malaria-related deaths worldwide [1]. While the development of a robust immune response is necessary for controlling Plasmodium falciparum infection [2], timely diagnosis and the administration of effective treatments [3] are required to control symptomatic infection and reduce transmission.

The control of Plasmodium falciparum is hindered by the high propensity for genetic diversity of parasites infecting individuals and the frequency of multiplicity of infection (MOI) within individual infections. These factors favour immune evasion, may contribute to malaria pathology, and could promote the emergence of variants resistant to anti-malarial drugs [4]. Moreover, genetic diversity, particularly involving protein-coding genes targeted by diagnostic tests such as histidine-rich protein 2/3 (HRP2/3) [5], which have become important tools for malaria diagnosis and surveillance, could have significant implications for malaria surveillance and control.

Genetic diversity and MOI are emerging as relevant biomarkers of Plasmodium falciparum transmission. Plasmodium falciparum genetic diversity arises from genetic recombination during the parasite lifecycle in the mosquito [6], while MOI results from infection by multiple distinct parasite genotypes [7]. Infection by distinct parasite genotypes occurs either when an individual is bitten by different mosquitoes carrying unique parasite strains (superinfection) or when bitten by a single mosquito carrying multiple distinct genotypes (co-transmission) [8, 9].

The genetic diversity and MOI of Plasmodium falciparum may be assessed by targeted genotyping of markers such as msp-1, msp-2, and glurp, which are coding and therefore targets for immune evasion [10] or microsatellite markers, which are not targets for immune evasion [11]. High-throughput methods, including molecular (DNA) barcodes, targeted deep sequencing, and genome-wide variation analysis, have also been utilized [12, 13], but these are expensive. Although more labour-intensive and subject to some biases, such as amplification efficiency bias due to size differences between msp-1, msp-2, and glurp alleles [14, 15], genotyping of these markers is cheaper and more readily available in resource-limited settings in SSA [12].

The mean values of parasite genetic diversity and MOI are higher in areas with high malaria transmission intensity [16, 17] and lower in those with low transmission intensity [18]. Additionally, mean Plasmodium falciparum genetic diversity and MOI apparently decreased following the suppression of Plasmodium falciparum transmission intensity in areas of Ethiopia [19] and Senegal [20]. In other studies, mean values of Plasmodium falciparum genetic diversity were higher among individuals with symptomatic infections [21, 22] and lower in those with asymptomatic infections [23], and were inversely correlated with parasite density and patient age [24].

Data on Plasmodium falciparum genetic diversity and MOI are relatively sparse, making it difficult to easily identify relevant patterns in SSA. Some studies have focused solely on MOI but not genetic diversity [25, 26], while others have been conducted within a single country [27], or utilized a single genetic marker [28]. This study collated published data on Plasmodium falciparum genetic diversity and MOI in SSA and summarized this data for symptomatic and asymptomatic individuals using a few genetic markers that are widely utilised for parasite genotyping in SSA. The aim of the study was to generate a systematic summary that can inform public health initiatives for malaria control in different regions of SSA.

Methods

Study design and protocol registration

The systematic review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [29]. The review protocol is registered in PROSPERO (#CRD42021267661).

Review question

The study reviewed data on Plasmodium falciparum genetic diversity and MOI in SSA based on msp-1, msp-2, glurp, and microsatellite genetic markers from articles published from January 2000 through May 2023. This period was chosen because access to malaria genomic technologies was reasonably high, yielding representative data in the regions sampled [30]. Also, the period coincides with rapid decline in malaria incidence in SSA [31]. The objectives of the review were to: a) characterize the geographical distribution of Plasmodium falciparum genetic diversity in SSA; b) determine the prevalence of Plasmodium falciparum polyclonal infections in SSA; and c) identify factors associated with Plasmodium falciparum genetic diversity and MOI in SSA.

Search strategy and information sources

A systematic search, conducted by an experienced librarian (AAK), utilized PubMed, EMBASE, EBSCOhost, Web of Science, and the first 50 pages of Google Scholar after searching several pages and found no more relevant studies Additionally, citation lists of the identified articles were searched for additional relevant articles [32,33,34,35,36,37]. The search terms included keywords such as 'Plasmodium falciparum,' 'P. falciparum genetic diversity,' 'P. falciparum multiplicity of infection,' and 'sub-Saharan Africa' (Additional file 1).

Eligibility

Inclusion criteria

The review considered:

  • Articles published in English,

  • Study design, i.e., observational (cross-sectional/survey, case control, and cohort) or randomized clinical trials (RCTs),

  • Minimum required data elements: country, sample size, calendar year(s) when the study was conducted, and detailed laboratory methods used to genotype markers for genetic diversity or MOI,

  • Detailed methods for determining Plasmodium falciparum genetic diversity and MOI, including mean expected heterozygosity, allele frequencies, and mean MOI or percentage of multiple infections.

Exclusion criteria

  • Absence of key terms ‘Plasmodium falciparum genetic diversity and or MOI’ in the title and or abstract,

  • Studies using experimental animals,

  • Review articles, case reports, case series, or editorials

  • Use of inappropriate laboratory molecular methods (DNA extraction, PCR and then fragment analysis).

Article screening and data extraction

The articles were deduplicated using Endnote software version X9. Subsequently, the unique articles underwent screening by two independent reviewers (AM and RWN), who also performed data abstraction using predetermined review criteria (Additional file 2). Abstracted data were compared, and any disagreements were resolved through discussion.

Harmonized extracted data included:

  • Study characteristics (author name, article title, publication year, country, malaria transmission setting, and study design); participant characteristics (sample size, age group, and malaria clinical category),

  • Malaria diagnosis and genotyping (malaria diagnosis method, name of genotyped markers, and PCR fragment analysis method),

  • Outcome results on Plasmodium falciparum genetic diversity and MOI based on mean expected heterozygosity, allele frequencies of selected genotyped genetic markers, and the prevalence of polyclonal infections or mean MOI, respectively. Data on factors associated with Plasmodium falciparum genetic diversity and MOI were also extracted.

Data analysis

Meta-analysis was performed using the random effects model (DerSimonian and Laird approach) in STATA (version 17, Stata Corporation, College Station, TX). Forest plots included only studies that reported measures of dispersion such as SD or CI for the respective effect sizes (Mean MOI and or Mean He) to enable computation of standard error for use in meta-analysis using STATA. Pooled estimates for Plasmodium falciparum genetic diversity and MOI were generated, sorted by region. Additionally, patterns of Plasmodium falciparum genetic diversity and MOI were assessed according to malaria clinical categories (asymptomatic and symptomatic malaria infection) [38] and specific genetic marker(s) used to evaluate Plasmodium falciparum genetic diversity and MOI [12].

Heterogeneity analysis

Heterogeneity across the studies was assessed using the chi-squared test and Cochran’s Q statistic, with a 5% level of statistical significance [39], and the I-squared (I2) statistic [40]. An I2 statistic of 25% indicates low heterogeneity, 50% indicates moderate heterogeneity, and > 75% indicates high heterogeneity [41].

Risk of bias and quality of evidence assessment

Risk of bias (RoB) in the selected articles was independently evaluated by two reviewers (AM, RWN) using an RoB assessment tool adapted from Joanna Briggs Institute’s (JBI) critical appraisal tools [42]. The quality of evidence was determined by two independent reviewers using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidelines [43]. RoB assessment covered five domains: study design and limitations, inconsistency in selected articles, indirectness of the evidence, imprecision, and publication bias. Studies scoring 0 to 1, 2 to 3, 4 to 5, and at least 6 were judged to be very low, low, moderate, and high-quality studies, respectively.

Publication bias

Publication bias was assessed by visualizing the asymmetry of the funnel plot and examining the presence and distribution of dots in the plot [44]. Egger’s statistical test was performed to assess the asymmetry of the funnel plot. A statistically significant result (p < 0.05) in Egger’s test indicates that the funnel plot asymmetry is due to small-study effects [45]. All data analysis was conducted using STATA version 17 software package (Stata Corporation, College Station, TX).

Missing data

Variables that were missing from included articles were recorded as not reported (NR). Authors of articles with missing data were contacted for additional information, but only a small number (5/12: 41.67%) responded.

Ethics considerations

The study used already published literature with no direct human subject contact and posed no risk to the participants who participated in the primary studies as determined by the Makerere University School of Medicine Institutional Review Board (# Mak-SOMREC-2021-152) and Uganda National Council for Science and Technology (# HS2744ES) (Table 1).

Table 1 The PECOST framework

Results

A total of 1,718 articles were retrieved from the literature search, and an additional 6 articles were found through a search of the bibliographies of the identified articles. Of these, 52 articles met the inclusion criteria and were included in the review analysis (Fig. 1). The articles were from 23 of the 54 countries in SSA, covering a total of 11,640 genotyped parasite isolates from 9,062 symptomatic and 2,578 asymptomatic Plasmodium falciparum infections. Among the 52 articles, 39 (75%) employed cross-sectional study design while 13 (25%) utilized RCT/cohort study designs. A total of 23 studies enrolled both children and adults, while 22 studies enrolled only children, 2 studies enrolled only adults, and 5 studies did not specify the age group of their study population. The predominant genetic markers used to genotype parasites were the antigen-coding loci, especially msp-1 and/or msp-2, in 76.9% (40/52) of the studies, followed by microsatellites markers only in 19.2% (10/52). In one study (1.92%; 1/52), both microsatellites and msp-1 and/or msp-2) were used, while in another one study (1.92%; 1/52), genotyping of Plasmodium falciparum parasites involved the use of both msp-1, msp-2, and single nucleotide polymorphisms (SNPs) (Table 2).

Fig. 1
figure 1

PRISMA. Flow diagram for identification of articles included in the review

Table 2 Summary of P. falciparum genetic diversity and MOI

Plasmodium falciparum genetic diversity in SSA

Across studies, P. falciparum genetic diversity, primarily assessed using antigen-coding loci (msp-1, msp-2, and glurp), and microsatellites, was reported using either allele frequency, mean expected heterozygosity, or both. The frequencies of msp-1 alleles (K1, MAD20, and RO33) were 20.8%, 4.2%, and 4.2%, respectively, in Ethiopia, a country with moderate malaria transmission in East Africa [79]. In high malaria transmission areas of Equatorial Guinea in West Africa, these same alleles had substantially higher frequencies of 96.07%, 96.09%, and 70.78%, respectively [22]. The frequency of the msp-2 gene I/C 3D7 allele ranged from 15.9% to 98.3% in Ethiopia [19, 79], while the FC27 allele frequency ranged from 10.3% in Ethiopia [84] to 98.9% in high malaria transmission areas in Benin [36]. Meanwhile, the frequency of glurp ranged from 39.53% among symptomatic individuals in a high malaria transmission setting in Nigeria [68] to 97.6% among severe malaria cases living in malaria-moderate areas in Uganda [74] (Table 2).

The overall pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 61%, 44%, and 40%, respectively, while the overall polled allele frequencies of msp-2 I/C 3D7 and FC27 alleles were 61% and 55%, respectively, across reviewed studies. Across regions, the pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 46%, 42%, and 31%, respectively, in East Africa; 74%, 51%, and 48%, respectively, in Central Africa; and 67%, 43%, and 44%, respectively, in West Africa. In comparison, the pooled allele frequencies of the msp-2 I/C 3D7 and FC27 alleles were 60% and 55%, respectively, in East Africa, 67% in Central Africa, and 62% and 50%, respectively, in West Africa. For glurp, the overall pooled allele frequency was 66%, with a pooled frequency of 90% and 70% in East and West African regions, respectively. The only two reviewed studies from Southern Africa [58, 65] studied parasite genetic diversity and MOI using only microsatellites and not msp-1, msp-2, or glurp (Table 3).

Table 3 Pooled proportions of K1, MAD20, RO33, IC/3D7, FC27 and glurp alleles across studies

Based on expected heterozygosity, the mean expected heterozygosity was 0.09 in Ethiopia [19], a country with moderate malaria transmission in East Africa, and 0.93 in the DRC [80], a country with high malaria transmission in Central Africa (Table 2). The overall pooled mean expected heterozygosity across all studies was 0.65 (95% CI: 0.51–0.78). Across regions, the pooled mean expected heterozygosity was 0.58 (95% CI: 0.29–0.86), 0.84 (95% CI: 0.81–0.86), 0.74 (95% CI: 0.73–0.75), and 0.69 (95% CI: 0.62–0.75) in East, Central, Southern, and West African regions, respectively (Fig. 2).

Fig. 2
figure 2

Forest plot representing the pooled mean expected heterozygosity of P. falciparum infection across 17 studies that reported measures of dispersion (CI and SD) for mean expected heterozygosity in malaria-affected countries in SSA, sorted by region

Each blue square bar indicates the estimated mean expected heterozygosity in one study, and the lines through the square represent the confidence interval around the estimate. The red diamond symbols represent the pooled mean expected heterozygosity in each region, while the green diamond symbol represents the overall pooled mean expected heterozygosity across all regions. The x-axis represents the scale for mean expected heterozygosity which ranges between 0 to 1.

Plasmodium falciparum MOI across malaria-affected countries in SSA

Plasmodium falciparum MOI, defined as the number of distinct parasite genotypes co-existing within a given infection. Plasmodium falciparum MOI was reported using mean MOI and or prevalence of polyclonal infection. The mean MOI ranged from 1.09 in Ethiopia in East Africa [19] to 5.51 in Equatorial Guinea in West Africa [22] (Table 2). The overall pooled mean MOI across studies was 2.09 (95% CI: 1.88–2.30). Across regions, the pooled mean MOI was 2.05 (95% CI: 1.83–2.26), 2.37 (95% CI: 1.28–3.46), 2.16 (95% CI: 2.09–2.23), and 1.96 (95% CI: 1.53–2.39) in East, Central, Southern, and West African regions, respectively (Fig. 3).

Fig. 3
figure 3

Forest plot representing the pooled mean MOI of P. falciparum infection across 32 studies that reported measures of dispersion (CI and SD) for mean MOI in malaria-affected countries in SSA, sorted by region. Each blue square bar indicates the estimated mean P. falciparum MOI in one study, and the lines through the square represent the confidence interval around the estimate. The red diamond symbols represent the pooled mean P. falciparum MOI in each region, while the green diamond symbol represents the overall pooled P. falciparum mean MOI across all regions. The x-axis represents the scale for mean MOI

The prevalence of polyclonal infections ranged from 16.3% in Ethiopia in East Africa [19] to 98% in Equatorial Guinea in Central Africa [22] to 98% in Equatorial Guinea in Central Africa [27] (Table 2). The overall pooled prevalence of Plasmodium falciparum polyclonal infections was 63% (95% CI 56–70) across all studies. Across the regions, the pooled prevalence of polyclonal infections was 62% (95% CI: 53–71), 61% (95% CI: 51–71), 65% (95% CI: 43–88), and 71% (95% CI: 63–79) in East, West, Central, and Southern Africa regions, respectively (Fig. 4).

Fig. 4
figure 4

Forest plot representing the pooled prevalence P. falciparum polyclonal infections reported by 48 studies from malaria-affected countries in SSA, sorted by region. Each gray square bar with a black dot indicates the estimated prevalence of P. falciparum polyclonal infections in one study, and the lines through the square represent the confidence interval around the estimate. The diamond symbol represents the pooled prevalence of P. falciparum polyclonal infections

Factors associated with Plasmodium falciparum genetic diversity and MOI across SSA

In three studies [24, 72, 88], a positive association between patient age and parasite density with Plasmodium falciparum genetic diversity and MOI was observed; however, this association was not consistent across other studies [36, 69, 79]. Some studies indicated an association between Plasmodium falciparum genetic diversity and MOI with the use of chemotherapy to suppress malaria infections. For instance, in a study by Huang, B et al. [66], a decrease in genetic diversity was found over a 10-year period following the introduction of artemisinin-based combination therapy (ACT) in an island population, with a 28% decrease for msp-1 (from 32 to 23) and msp-2 (from 29 to 21). MOI declined from 3.11 to 1.63 for msp-1 and from 2.75 to 1.35 for msp-2. The prevalence of polyclonal infection for msp-1 declined from 76.7% to 29.1% (P < 0.01), and for msp-2, it declined from 62.4% to 28.3% (P < 0.01).

Similarly, a study by Tadele et al. [19] reported a decline in Plasmodium falciparum genetic diversity and MOI. Variations in Plasmodium falciparum genetic diversity and MOI were found in both rural and urban settings. MOI was higher in rural than in urban settings; for instance, the mean MOI for rural versus urban areas was 1.88 versus 1.55, while the prevalence of polyclonal infection was 42.2% versus 57.7% (p = 0.04) [71]. However, in a study conducted in an urban setting in Uganda [74], the mean MOI values were even higher (3.0 to 3.7 for severe and mild malaria cases, respectively p = 0.002) than those observed in rural areas elsewhere. High Plasmodium falciparum genetic diversity and MOI were also reported among both symptomatic [72, 74, 89] and asymptomatic malaria cases [16, 90]. Furthermore, a positive association between the genetic diversity and MOI of Plasmodium falciparum with malaria transmission settings, showing higher values in areas with high malaria transmission and lower values in those with low malaria transmission (2.13 in high and 1.29 in low malaria transmission; p < 0.0001) has been reported [70]. Meanwhile, the expected heterozygosity was high (0.49 to 0.62) and low (0.26 to 0.28) in high and low malaria transmission settings, respectively. However, this relationship was not observed elsewhere reported [37].

Subgroup analysis of Plasmodium falciparum genetic diversity and MOI based on malaria clinical category and the genotyped markers

Subgroup analysis of genetic diversity and MOI was conducted using mean expected heterozygosity and mean MOI, respectively. Considering patient phenotype, the pooled mean expected heterozygosity was 0.64 (95% CI 0.515–0.78) in studies that enrolled only individuals with asymptomatic infection and 0.63 (0.42–0.83) in those that enrolled only individuals with symptomatic infection. However, the pooled mean expected heterozygosity was 0.77 (0.75–0.79) in studies that enrolled individuals with either asymptomatic or symptomatic Plasmodium falciparum infections.

Based on antigen-coding loci, msp-1, and/or msp-2 genotypes, the pooled Plasmodium falciparum mean expected heterozygosity was 0.49 (95% CI 0.24–0.74). In comparison, it was 0.76 (95% CI 0.72–0.79) based on microsatellite markers. The pooled mean MOI was 1.90 (95% CI 1.50–2.30) in studies enrolling asymptomatic individuals, 2.16 (95% CI 1.92–2.41) in studies enrolling symptomatic individuals, and 1.85 (95% CI 1.59–2.11) in studies enrolling both asymptomatic and symptomatic cases. In studies using antigen-coding loci msp-1 and/or msp-2 only, the pooled mean MOI was 2.14 (95% CI 1.91–2.38), while in studies using microsatellites, it was 1.63 (95% CI 1.11–2.15).

Plasmodium falciparum heterogeneity in the included studies

Studies were combined and assessed for heterogeneity. Based on mean expected heterozygosity, there was high heterogeneity among studies enrolling asymptomatic individuals (I2 = 99.61%, P < 0.001) and among studies enrolling individuals with symptomatic malaria (I2 = 99.94%, P < 0.001). Similarly, a high level of heterogeneity was observed among studies using antigen-coding loci, namely msp-1 and msp-2 (I2 = 99.87%, P < 0.001), as well as microsatellites (I2 = 98.28%, P < 0.001). Regarding mean MOI, heterogeneity was high in studies enrolling individuals with asymptomatic malaria (I2 = 98.64%, P < 0.001), symptomatic malaria (I2 = 99.60%, P < 0.001), and both asymptomatic and symptomatic cases (I2 = 96.20%, P < 0.001). A high level of heterogeneity was also observed across the genotyped markers, including antigen coding loci, msp-1, and msp-2 (I2 = 99.619%, P < 0.001), and microsatellites (I2 = 96.52%, P < 0.001) (Additional file 3).

Risk of bias in the included studies

Methodological quality and reporting bias were identified as high in 28.8% (15/52) of the studies. There was a potential for selection bias, as successful genotyping was reported to be < 90% in 11.5% (6/52) of the reviewed articles. Additionally, detection bias related to the assessment of confounding factors was noted in 36.5% (19/52) of the included articles (Additional file 4).

Publication bias assessment

Visual inspection of the funnel plots obtained using mean expected heterozygosity and mean MOI revealed an asymmetrical distribution of estimates from the middle line (Additional file 5). Egger’s statistical test showed a coefficient of 2.16, z = 0.54, and P = 0.59 for mean expected heterozygosity, and a coefficient of 1.65, z = 1.28, and P = 0.2 for mean MOI.

Discussion

The systematic review covered studies investigating Plasmodium falciparum genetic diversity and MOI in malaria-affected countries in SSA. Study findings indicate substantial genetic diversity and MOI among parasites circulating in SSA. The substantial regional variation in parasite genetic diversity and MOI identified in this current study likely reflects differences in regional malaria transmission intensity. This suggests that these markers may be useful in evaluating malaria transmission patterns and the effectiveness of control interventions.

Parasite genetic diversity exhibited variations across regions, with msp-1 (K1, MAD20, and RO33) and msp-2 (I/C3D7 and FC27) alleles showing different frequencies in different regions. The finding that high Plasmodium falciparum genetic diversity was reported in both high [51] and low [65] malaria transmission areas in SSA is interesting. Parasite genetic diversity results from genetic recombination in the mosquito [6, 91], and is more likely in areas with high local malaria transmission intensity. The high parasite genetic diversity in some areas is therefore a cause for concern because it may indicate ongoing transmission despite the intensification of malaria control measures [53]. Nonetheless, there are areas with low Plasmodium falciparum genetic diversity, indicating that malaria control and surveillance efforts should be tailored accordingly.

Plasmodium falciparum mean MOI also exhibited wide variations across regions, ranging from 1.09 to 5.51, with an overall pooled mean MOI of 2.09. Meanwhile, the prevalence of polyclonal infection also varied significantly, ranging from 16.3% to 98%, with an overall pooled prevalence of 63% across studies. Previous reports have documented wide variations in Plasmodium falciparum mean MOI (ranging from 1 to 6.1) and the percentage prevalence of polyclonal infections (ranging from 0 to 96%) [25]. High mean MOI and the presence of polyclonal infections serve as key indicators of high malaria transmission intensity [16, 70]. These factors are influenced by increased vector populations, promoting either superinfection or the concurrent transmission of unrelated parasite genotypes [92]. The variations across regions suggest differences in malaria transmission patterns across SSA, emphasizing the need for modifications in malaria vector control and the implementation of customized regional malaria control measures.

The review identified several factors associated with Plasmodium falciparum genetic diversity and MOI, including parasite density, the clinical category of malaria infection, patient age, malaria control interventions, and malaria transmission intensity [16, 19, 24, 48, 70, 72, 89]. These findings extend those from previous studies that reported a positive correlation between parasite density and parasite genetic diversity/MOI [70, 75]. Higher parasite density increases the likelihood of carrying distinct parasite genotypes [70], while an increase in age enhances immunity to malaria [93] Additionally, Plasmodium falciparum genetic diversity and MOI were found to be higher in rural settings [71], although other reports indicated higher genetic diversity in urban settings [74]. Low parasite genetic diversity and MOI have been reported in areas of Ethiopia, suggesting the effectiveness of malaria control interventions [19].

High Plasmodium falciparum genetic diversity and MOI were reported among both symptomatic [72, 74, 89] and asymptomatic malaria cases [16, 48, 90]. The occurrence of multiple Plasmodium falciparum infections could pose a challenge to parasite elimination efforts [16] due to its positive association with antimalarial drug failure [94]. Asymptomatic infection is typically characterized by low parasitaemia [95] and high MOI [38]. Asymptomatic individuals with low parasitaemia often remain undetected, thus forming a reservoir for malaria transmission and its spread [96]. The variability in parasite genetic diversity infection profiles has implications for treatment strategies, as well as the efficacy of antimalarial drugs.

msp-1 and msp-2 are commonly used genetic markers for assessing Plasmodium falciparum genetic diversity and MOI. Studies exclusively employing antigen-coding loci (msp-1 and/or msp-2) reported a higher pooled mean MOI (2.14), while those utilizing microsatellites showed a lower pooled mean MOI (1.63). The abundance and high polymorphism of microsatellites make them more suitable for estimating MOI compared to msp-1, msp-2, and glurp, which are relatively fewer and exhibit lower levels of polymorphism [13]. Moreover, msp-1, msp-2, and glurp are susceptible to immune selection [97]. Another limitation arises from significant size variations among msp-1, msp-2, and glurp alleles, potentially introducing bias in amplification efficiency. In cases of multiclonal infections, this bias may result in the preferential amplification of shorter fragments, leading to the loss of longer alleles [14, 15]. This emphasizes the importance of employing advanced tools such as microsatellite analysis and whole-genome sequencing for accurately assessing Plasmodium falciparum genetic diversity.

Implications for future research and policy

The substantial variations in Plasmodium falciparum genetic diversity and MOI in SSA necessitates continuous genomic surveillance in different malaria transmission settings. Current research predominantly focuses on symptomatic malaria infections in children utilising msp-1 and msp-2 genetic markers for assessing genetic diversity and MOI. Future studies should broaden their focus to include both adults and children across different malaria transmission contexts. Incorporation of advanced tools like microsatellite and whole-genome sequencing, is crucial for accurate assessments of parasite genetic diversity.

Strengths of the study

The review focused on peer-reviewed articles published over an extended period of time to adequately appreciate the genetic diversity and MOI of Plasmodium falciparum parasites circulation in SSA, an area which contributes over 95% of global malaria cases. The review focused on genetic markers (msp-1, msp-2, glurp, and microsatellites) that are more common and readily available in resource-limited settings in SSA. The review was conducted following standard PRISMA-P review guidelines to enhance the reliability of the findings.

Limitations of the study

The present study has several limitations. Firstly, reliance on peer-reviewed published articles may have introduced potential publication bias. Secondly, studies that did not explicitly mention Plasmodium falciparum genetic diversity and/or MOI in the title may have been missed. Additionally, the geographical coverage of articles was not comprehensive, as they did not encompass all countries in SSA, thereby impacting the generalizability of findings within the region. For instance, only two of the reviewed studies originated from Southern Africa [58, 65], focusing solely on microsatellites rather than msp-1, msp-2, and glurp, which limits inferences about marker distribution in this region. Furthermore, the exclusion of studies lacking measures of dispersion (CI and SD) affected the meta-analysis of mean expected heterozygosity and mean MOI.

Conclusion

This systematic review reveals considerable variations in Plasmodium falciparum genetic diversity and MOI across malaria-affected countries in SSA. Despite control efforts, the high observed parasite genetic diversity and MOI emphasize the necessity for customized, region-specific malaria control strategies, and continuous surveillance.

Availability of data and materials

The datasets generated and/or analysed in this review are available from the corresponding author upon reasonable request.

Abbreviations

GRADE:

Grading of Recommendations, Assessment and Development and Evaluations

HRP2:

Histidine rich protein 2

JBI:

Joanna Briggs Institute’s

MOI:

Multiplicity of infection

MSP:

Merozoite surface protein

NR:

Not reported

PCR:

Polymerase chain reaction

PRISMA-P:

Reporting Items for Systematic Review and Meta-Analysis for Protocols

RoB:

Risk of bias

STREGA:

STrengthening the REporting of Genetic Association Studies

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

References

  1. WHO. World Malaria Report 2023. Geneva: World Health Organization; 2023.

    Google Scholar 

  2. Hu W-C. Human immune responses to Plasmodium falciparum infection: molecular evidence for a suboptimal THαβ and TH17 bias over ideal and effective traditional TH1 immune response. Malar J. 2013;12:392.

    Article  PubMed  PubMed Central  Google Scholar 

  3. White NJ. The role of anti-malarial drugs in eliminating malaria. Malar J. 2008;7(Suppl 1):S8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Su X-Z, Lane KD, Xia L, Sá JM, Wellems TE. Plasmodium genomics and genetics: new insights into malaria pathogenesis, drug resistance, epidemiology, and evolution. Clin Microbiol Rev. 2019;32:00019–19.

    Article  Google Scholar 

  5. Baker J, McCarthy J, Gatton M, Kyle DE, Belizario V, Luchavez J, et al. Genetic diversity of Plasmodium falciparum histidine-rich protein 2 (PfHRP2) and its effect on the performance of PfHRP2-based rapid diagnostic tests. J Infect Dis. 2005;192:870–7.

    Article  CAS  PubMed  Google Scholar 

  6. Mu J, Awadalla P, Duan J, McGee KM, Joy DA, McVean GA, et al. Recombination hotspots and population structure in Plasmodium falciparum. PLoS Biol. 2005;3: e335.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pinkevych M. Understanding the relationship between Plasmodium falciparum growth rate and multiplicity of infection. J Infect Dis. 2015;211:1121–7.

    Article  PubMed  Google Scholar 

  8. Alizon S. Parasite co-transmission and the evolutionary epidemiology of virulence. Evolution. 2013;67:921–33.

    Article  PubMed  Google Scholar 

  9. Wong W, Griggs AD, Daniels RF, Schaffner SF, Ndiaye D, Bei AK, et al. Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès. Senegal Genome Med. 2017;9:5.

    Article  PubMed  Google Scholar 

  10. Patel P, Bharti PK, Bansal D, Raman RK, Mohapatra PK, Sehgal R, et al. Genetic diversity and antibody responses against Plasmodium falciparum vaccine candidate genes from Chhattisgarh, Central India: implication for vaccine development. PLoS Biol. 2017;12: e0182674.

    Google Scholar 

  11. Ghansah A, Tiedje KE, Argyropoulos DC, Onwona CO, Deed SL, Labbé F, et al. Comparison of molecular surveillance methods to assess changes in the population genetics of Plasmodium falciparum in high transmission. Front Parasitol. 2023;2:1067966.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Apinjoh TO, Ouattara A, Titanji VPK, Djimde A, Amambua-Ngwa A. Genetic diversity and drug resistance surveillance of Plasmodium falciparum for malaria elimination: is there an ideal tool for resource-limited sub-Saharan Africa? Malar J. 2019;18:217.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhong D, Koepfli C, Cui L, Yan G. Molecular approaches to determine the multiplicity of Plasmodium infections. Malar J. 2018;17:172.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Messerli C, Hofmann NE, Beck HP, Felger I. Critical evaluation of molecular monitoring in malaria drug efficacy trials and pitfalls of length-polymorphic markers. Antimicrob Agents Chemother. 2016;61:e01500-e1516.

    PubMed  PubMed Central  Google Scholar 

  15. Porter KA, Burch CL, Poole C, Juliano JJ, Cole SR, Meshnick SR. Uncertain outcomes: adjusting for misclassification in antimalarial efficacy studies. Epidemiol Infect. 2011;139:544–51.

    Article  CAS  PubMed  Google Scholar 

  16. Touray AO, Mobegi VA, Wamunyokoli F, Herren JK. Diversity and multiplicity of Plasmodium falciparum infections among asymptomatic school children in Mbita, Western Kenya. Sci Rep. 2020;10:5924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Simpson SV, Nundu SS, Arima H, Kaneko O, Mita T, Culleton R, Yamamoto T. The diversity of Plasmodium falciparum isolates from asymptomatic and symptomatic school-age children in Kinshasa province, Democratic Republic of Congo. Malar J. 2023;22:102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mohd Abd Razak MR, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. Genetic diversity of Plasmodium falciparum populations in malaria declining areas of Sabah East Malaysia. PLoS Biol. 2016;11:e0152415.

    Google Scholar 

  19. Tadele G, Jaiteh FK, Oboh M, Oriero E, Dugassa S, Amambua-Ngwa A, et al. Low genetic diversity of Plasmodium falciparum merozoite surface protein 1 and 2 and multiplicity of infections in western Ethiopia following effective malaria interventions. Malar J. 2022;21:383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sane R, Talla C, Diouf B, Sarr FD, Diagne N, Faye J, et al. Low genetic diversity and complexity of submicroscopic Plasmodium falciparum infections among febrile patients in low transmission areas in Senegal. PLoS Biol. 2019;14: e0215755.

    CAS  Google Scholar 

  21. Somé AF, Bazié T, Zongo I, Yerbanga RS, Nikiéma F, Neya C, et al. Plasmodium falciparum msp1 and msp2 genetic diversity and allele frequencies in parasites isolated from symptomatic malaria patients in Bobo-Dioulasso, Burkina Faso. Parasit Vectors. 2018;11:323.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen JT, Li J, Zha GC, Huang G, Huang ZX, Xie DD, et al. Genetic diversity and allele frequencies of Plasmodium falciparum msp1 and msp2 in parasite isolates from Bioko Island, Equatorial Guinea. Malar J. 2018;17:458.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Agonhossou R, Akoton R, Lagnika H, Djihinto OY, Sovegnon PM, Saizonou HD, et al. Plasmodium falciparum msp1 and msp2 genetic diversity in Plasmodium falciparum single and mixed infection with Plasmodium malariae among the asymptomatic population in Southern Benin. Parasitol Int. 2022;89: 102590.

    Article  CAS  PubMed  Google Scholar 

  24. Sondo P, Derra K, Rouamba T, Nakanabo Diallo S, Taconet P, Kazienga A, et al. Determinants of Plasmodium falciparum multiplicity of infection and genetic diversity in Burkina Faso. Parasit Vectors. 2020;13:427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lopez L, Koepfli C. Systematic review of Plasmodium falciparum and Plasmodium vivax polyclonal infections: impact of prevalence, study population characteristics, and laboratory procedures. PLoS Biol. 2021;16: e0249382.

    CAS  Google Scholar 

  26. Eldh M, Hammar U, Arnot D, Beck HP, Garcia A, Liljander A, et al. Multiplicity of asymptomatic Plasmodium falciparum infections and risk of clinical malaria: a Systematic review and pooled analysis of individual participant data. J Infect Dis. 2020;221:775–85.

    Article  PubMed  Google Scholar 

  27. Opute AO, Akinkunmi JA, Funsho AO, Obaniyi AK, Anifowoshe AT. Genetic diversity of Plasmodium falciparum isolates in Nigeria. A review. Egypt J Med Hum Genetics. 2022;23:129.

    Article  Google Scholar 

  28. Chaudhry S, Singh V. A systematic review on genetic diversity of var gene DBL1α domain from different geographical regions in Plasmodium falciparum isolates. Infect Genet Evol. 2021;95: 105049.

    Article  CAS  PubMed  Google Scholar 

  29. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;10:89.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Neafsey DE, Taylor AR, MacInnis BL. Advances and opportunities in malaria population genomics. Nat Rev Genet. 2021;22:502–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. WHO. World Malaria Report 2022. Geneva: World Health Organization; 2022.

    Google Scholar 

  32. Bendixen M, Msangeni HA, Pedersen BV, Shayo D, Bødker R. Diversity of Plasmodium falciparum populations and complexity of infections in relation to transmission intensity and host age: a study from the Usambara Mountains, Tanzania. Trans R Soc Trop Med Hyg. 2001;95:143–8.

    Article  CAS  PubMed  Google Scholar 

  33. Barry AE, Schultz L, Senn N, Nale J, Kiniboro B, Siba PM, et al. High levels of genetic diversity of Plasmodium falciparum populations in Papua New Guinea despite variable infection prevalence. Am J Trop Med Hyg. 2013;88:718–25.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Mita T, Jombart T. Patterns and dynamics of genetic diversity in Plasmodium falciparum: what past human migrations tell us about malaria. Parasitol Int. 2015;64:238–43.

    Article  PubMed  Google Scholar 

  35. Mobegi VA, Loua KM, Ahouidi AD, Satoguina J, Nwakanma DC, Amambua-Ngwa A, et al. Population genetic structure of Plasmodium falciparum across a region of diverse endemicity in West Africa. Malar J. 2012;11:223.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ogouyèmi-Hounto A, Gazard DK, Ndam N, Topanou E, Garba O, Elegbe P, et al. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in Plasmodium falciparum isolates from children in South of Benin. Parasite. 2013;20:37.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Oyebola MK, Idowu ET, Olukosi YA, Iwalokun BA, Agomo CO, Ajibaye OO, et al. Genetic diversity and complexity of Plasmodium falciparum infections in Lagos, Nigeria. Asian Pac J Trop Biomed. 2014;4(Suppl 1):S87-91.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sarah-Matio EM, Guillochon E, Nsango SE, Abate L, Ngou CM, Bouopda GA, et al. Genetic diversity of Plasmodium falciparum and distribution of antimalarial drug resistance mutations in symptomatic and asymptomatic infections. Antimicrob Agents Chemother. 2022;66: e0018822.

    Article  PubMed  Google Scholar 

  39. Cooper H, Hedges LV, Valentine JC. The handbook of research synthesis and meta-analysis 2nd edition. 2009. Russell Sage Foundation

  40. Bowden J, Tierney JF, Copas AJ, Burdett S. Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics. BMC Med Res Methodol. 2011;11:41.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11:193–206.

    Article  PubMed  Google Scholar 

  42. Joanna Briggs Institute. Critical appraisal tools. https://jbi.global/critical-appraisal-tools

  43. Schünemann HJ, Brożek J, Guyatt G, Oxman A. Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach. 2013;15. https://gdt.gradepro.org/app/handbook/handbook.html

  44. Simmonds M. Quantifying the risk of error when interpreting funnel plots. Syst Rev. 2015;4:24.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001;54:1046–55.

    Article  CAS  PubMed  Google Scholar 

  46. Chekol T, Alemayehu GS, Tafesse W, Legesse G, Zerfu B, File T, et al. Genetic diversity of Merozoite Surface Protein-1 and -2 Genes in Plasmodium falciparum isolates among asymptomatic population in Boset and Badewacho districts, Southern Ethiopi. a J Parasitol Res. 2022;2022:7728975.

    PubMed  Google Scholar 

  47. Amoah LE, Abukari Z, Dawson-Amoah ME, Dieng CC, Lo E, Afrane YA. Population structure and diversity of Plasmodium falciparum in children with asymptomatic malaria living in different ecological zones of Ghana. BMC Infect Dis. 2021;21:439.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Abukari Z, Okonu R, Nyarko SB, Lo AC, Dieng CC, Salifu SP, et al. The diversity, multiplicity of infection and population structure of Plasmodium falciparum parasites circulating in asymptomatic carriers living in high and low malaria transmission settings of Ghana. Genes (Basel). 2019;10:434.

    Article  CAS  PubMed  Google Scholar 

  49. Mulenge FM, Hunja CW, Magiri E, Culleton R, Kaneko A, Aman RA. Genetic diversity and population structure of Plasmodium falciparum in lake Victoria islands, a region of intense transmission. Am J Trop Med Hyg. 2016;95:1077–85.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Gnagne AP, Konate A, Bedia-Tanoh AV, Amiah-Droh M, Menan HIE, N’Guetta AS, et al. Dynamics of Plasmodium falciparum genetic diversity among asymptomatic and symptomatic children in three epidemiological areas in Cote d’Ivoire. Pathog Glob Health. 2019;113:133–42.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Nabet C, Doumbo S, Jeddi F, Konaté S, Manciulli T, Fofana B, et al. Genetic diversity of Plasmodium falciparum in human malaria cases in Mali. Malar J. 2016;15:353.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Apinjoh TO, Tata RB, Anchang-Kimbi JK, Chi HF, Fon EM, Mugri RN, et al. Plasmodium falciparum merozoite surface protein 1 block 2 gene polymorphism in field isolates along the slope of mount Cameroon: a cross - sectional study. BMC Infect Dis. 2015;15:309.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gatei W, Gimnig JE, Hawley W, Ter Kuile F, Odero C, Iriemenam NC, et al. Genetic diversity of Plasmodium falciparum parasite by microsatellite markers after scale-up of insecticide-treated bed nets in western Kenya. Malar J. 2015;13(Suppl 1):495.

    Article  PubMed  Google Scholar 

  54. Oyedeji SI, Awobode HO, Anumudu C, Kun J. Genetic diversity of Plasmodium falciparum isolates from naturally infected children in north-central Nigeria using the merozoite surface protein-2 as molecular marker. Asian Pac J Trop Med. 2013;6:589–94.

    Article  CAS  PubMed  Google Scholar 

  55. Agomo CO, Lameed O, Ajibaye O. Genetic diversity in merozoite surface protein 1 of Plasmodium falciparum isolates from Igbogbo-Bayeku, Lagos. Nigeria Ife J Sci. 2022;24:025–34.

    Google Scholar 

  56. File T, Golassa L, Dinka H. Plasmodium falciparum clinical isolates reveal analogous circulation of 3D7 and FC27 allelic variants and multiplicity of infection in urban and rural settings: the case of Adama and its surroundings, Oromia. Ethiopia J Parasitol Res. 2022;2022:5773593.

    PubMed  Google Scholar 

  57. Ajogbasile FV, Kayode AT, Oluniyi PE, Akano KO, Uwanibe JN, Adegboyega BB, et al. Genetic diversity and population structure of Plasmodium falciparum in Nigeria: insights from microsatellite loci analysis. Malar J. 2021;20:236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gwarinda HB, Tessema SK, Raman J, Greenhouse B, Birkholtz LM. Parasite genetic diversity reflects continued residual malaria transmission in Vhembe district, a hotspot in the Limpopo Province of South Africa. Malar J. 2021;20:96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Agaba BB, Anderson K, Gresty K, Prosser C, Smith D, Nankabirwa JI, et al. Genetic diversity and genetic relatedness in Plasmodium falciparum parasite population in individuals with uncomplicated malaria based on microsatellite typing in Eastern and Western regions of Uganda, 2019–2020. Malar J. 2021;20:242.

    Article  PubMed  Google Scholar 

  60. Oyedeji SI, Bassi PU, Oyedeji SA, Ojurongbe O, Awobode HO. Genetic diversity and complexity of Plasmodium falciparum infections in the microenvironment among siblings of the same household in North-Central Nigeria. Malar J. 2020;19:338.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Papa Mze N, Bogreau H, Diedhiou CK, Herdell V, Rahamatou S, Bei AK, et al. Genetic diversity of Plasmodium falciparum in Grande Comore Island. Malar J. 2020;19:320.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ndiaye T, Sy M, Gaye A, Ndiaye D. Genetic polymorphism of Merozoite Surface Protein 1 (msp1) and 2 (msp2) genes and multiplicity of Plasmodium falciparum infection across various endemic areas in Senegal. Afr Health Sci. 2019;19:2446–56.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Mohammed H, Hassen K, Assefa A, Mekete K, Tadesse G, Taye G, et al. Genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria based on msp-1 and msp-2 genes in Gublak. North West Ethiopia Malar J. 2019;18:413.

    CAS  PubMed  Google Scholar 

  64. Nderu D, Kimani F, Karanja E, Thiong’o K, Akinyi M, Too E, et al. Genetic diversity and population structure of Plasmodium falciparum in Kenyan-Ugandan border areas. Trop Med Int Health. 2019;24:647–56.

    Article  PubMed  Google Scholar 

  65. Roh ME, Tessema SK, Murphy M, Nhlabathi N, Mkhonta N, Vilakati S, et al. High genetic diversity of Plasmodium falciparum in the low-transmission setting of the Kingdom of Eswatini. J Infect Dis. 2019;220:1346–54.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Huang B, Tuo F, Liang Y, Wu W, Wu G, Huang S, et al. Temporal changes in genetic diversity of msp-1, msp-2, and msp-3 in Plasmodium falciparum isolates from Grande Comore Island after introduction of ACT. Malar J. 2018;17:83.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Niang M, Thiam LG, Loucoubar C, Sow A, Sadio BD, Diallo M, et al. Spatio-temporal analysis of the genetic diversity and complexity of Plasmodium falciparum infections in Kedougou, southeastern Senegal. Parasit Vectors. 2017;10:33.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kolawole OM, Mokuolu OA, Olukosi YA, Oloyede TO. Population genomics diversity of Plasmodium falciparum in malaria patients attending Okelele Health Centre, Okelele, Ilorin, Kwara State. Nigeria Afr Health Sci. 2016;16:704–11.

    Article  PubMed  Google Scholar 

  69. Mahdi Abdel Hamid M, Elamin AF, Albsheer MM, Abdalla AA, Mahgoub NS, Mustafa SO, et al. Multiplicity of infection and genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria in Gezira State. Sudan Parasit Vectors. 2016;9:362.

    Article  PubMed  Google Scholar 

  70. Kateera F, Nsobya SL, Tukwasibwe S, Mens PF, Hakizimana E, Grobusch MP, et al. Malaria case clinical profiles and Plasmodium falciparum parasite genetic diversity: a cross sectional survey at two sites of different malaria transmission intensities in Rwanda. Malar J. 2016;15:237.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Mawili-Mboumba DP, Mbondoukwe N, Adande E, Bouyou-Akotet MK. Allelic diversity of MSP1 gene in Plasmodium falciparum from rural and urban areas of Gabon. Korean J Parasitol. 2015;53:413–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Bouyou-Akotet MK, M’Bondoukwé NP, Mawili-Mboumba DP. Genetic polymorphism of merozoite surface protein-1 in Plasmodium falciparum isolates from patients with mild to severe malaria in Libreville. Gabon Parasite. 2015;22:12.

    Article  PubMed  Google Scholar 

  73. Ahmedou Salem MS, Ndiaye M, OuldAbdallahi M, Lekweiry KM, Bogreau H, Konaté L, et al. Polymorphism of the merozoite surface protein-1 block 2 region in Plasmodium falciparum isolates from Mauritania. Malar J. 2014;13:26.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Kiwuwa MS, Ribacke U, Moll K, Byarugaba J, Lundblom K, Färnert A, et al. Genetic diversity of Plasmodium falciparum infections in mild and severe malaria of children from Kampala. Uganda Parasitol Res. 2013;112:1691–700.

    Article  PubMed  Google Scholar 

  75. Hamid MM, Mohammed SB, El Hassan IM. Genetic diversity of Plasmodium falciparum field isolates in central Sudan inferred by PCR genotyping of Merozoite Surface Protein 1 and 2. N Am J Med Sci. 2013;5:95–101.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Olasehinde GI, Yah CS, Singh R, Ojuronbge OO, Ajayi AA, Valecha N, et al. Genetic diversity of Plasmodium falciparum field isolates from south western Nigeria. Afr Health Sci. 2012;12:355–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Awaga KL, Missihoun TD, Karou SD, Djadou KE, Chabi NW, Akati A, et al. Genetic diversity and genotype multiplicity of Plasmodium falciparum infections in symptomatic individuals in the maritime region of Togo. Trop Med Int Health. 2012;17:153–60.

    Article  CAS  PubMed  Google Scholar 

  78. Mohammed H, Assefa A, Chernet M, Wuletaw Y, Commons RJ. Genetic polymorphisms of Plasmodium falciparum isolates from Melka-Werer, North East Ethiopia based on the merozoite surface protein-2 (msp-2) gene as a molecular marker. Malar J. 2021;20:85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Abamecha A, El-Abid H, Yilma D, Addisu W, Ibenthal A, Bayih AG, et al. Genetic diversity and genotype multiplicity of Plasmodium falciparum infection in patients with uncomplicated malaria in Chewaka district. Ethiopia Malar J. 2020;19:203.

    Article  CAS  PubMed  Google Scholar 

  80. Singana BP, Mayengue PI, Niama RF, Ndounga M. Genetic diversity of Plasmodium falciparum infection among children with uncomplicated malaria living in Pointe-Noire, Republic of Congo. Pan Afr Med J. 2019;32:183.

    PubMed  PubMed Central  Google Scholar 

  81. Mohammed H, Kassa M, Mekete K, Assefa A, Taye G, Commons RJ. Genetic diversity of the msp-1, msp-2, and glurp genes of Plasmodium falciparum isolates in Northwest Ethiopia. Malar J. 2018;17:386.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Mohammed H, Kassa M, Assefa A, Tadesse M, Kebede A. Genetic polymorphism of Merozoite Surface Protein-2 (MSP-2) in Plasmodium falciparum isolates from Pawe District. North West Ethiopia PLoS Biol. 2017;12: e0177559.

    Google Scholar 

  83. Kidima W, Nkwengulila G. Plasmodium falciparum msp-2 genotypes and multiplicity of infections among children under five years with uncomplicated malaria in Kibaha. Tanzania J Parasitol Res. 2015;2015: 721201.

    CAS  PubMed  Google Scholar 

  84. Mohammed H, Mindaye T, Belayneh M, Kassa M, Assefa A, Tadesse M, et al. Genetic diversity of Plasmodium falciparum isolates based on msp-1 and msp-2 genes from Kolla-Shele area, Arbaminch Zuria District, southwest Ethiopia. Malar J. 2015;14:73.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Ibara-Okabande R, Koukouikila-Koussounda F, Ndounga M, Vouvoungui J, Malonga V, Casimiro PN, et al. Reduction of multiplicity of infections but no change in msp2 genetic diversity in Plasmodium falciparum isolates from Congolese children after introduction of artemisinin-combination therapy. Malar J. 2012;11:410.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Sumari D, Hosea KM, Mugasa JP, Abdulla S. Genetic diversity of Plasmodium falciparum strains in children under five years of age in Southeastern Tanzania. Open Trop Med J. 2010;3.

  87. Aubouy A, Migot-Nabias F, Deloron P. Polymorphism in two merozoite surface proteins of Plasmodium falciparum isolates from Gabon. Malar J. 2003;2:12.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Peyerl-Hoffmann G, Jelinek T, Kilian A, Kabagambe G, Metzger WG, von Sonnenburg F. Genetic diversity of Plasmodium falciparum and its relationship to parasite density in an area with different malaria endemicities in West Uganda. Trop Med Int Health. 2001;6:607–13.

    Article  CAS  PubMed  Google Scholar 

  89. Metoh TN, Chen JH, Fon-Gah P, Zhou X, Moyou-Somo R, Zhou XN. Genetic diversity of Plasmodium falciparum and genetic profile in children affected by uncomplicated malaria in Cameroon. Malar J. 2020;19:115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Adjah J, Fiadzoe B, Ayanful-Torgby R, Amoah LE. Seasonal variations in Plasmodium falciparum genetic diversity and multiplicity of infection in asymptomatic children living in southern Ghana. BMC Infect Dis. 2018;18:432.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Jiang H, Li N, Gopalan V, Zilversmit MM, Varma S, Nagarajan V, et al. High recombination rates and hotspots in a Plasmodium falciparum genetic cross. Genome Biol. 2011;12:R33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Nkhoma SC, Banda RL, Khoswe S, Dzoole-Mwale TJ, Ward SA. Intra-host dynamics of co-infecting parasite genotypes in asymptomatic malaria patients. Infect Genet Evol. 2018;65:414–24.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Hviid L. Clinical disease, immunity and protection against Plasmodium falciparum malaria in populations living in endemic areas. Expert Rev Mol Med. 1998;1998:1–10.

    Article  CAS  PubMed  Google Scholar 

  94. Kyabayinze DJ, Karamagi C, Kiggundu M, Kamya MR, Wabwire-Mangen F, Kironde F, et al. Multiplicity of Plasmodium falciparum infection predicts antimalarial treatment outcome in Ugandan children. Afr Health Sci. 2008;8:200–5.

    PubMed  PubMed Central  Google Scholar 

  95. Babiker HA, Gadalla AA, Ranford-Cartwright LC. The role of asymptomatic Plasmodium falciparum parasitaemia in the evolution of antimalarial drug resistance in areas of seasonal transmission. Drug Resist Updat. 2013;16:1–9.

    Article  CAS  PubMed  Google Scholar 

  96. Laishram DD, Sutton PL, Nanda N, Sharma VL, Sobti RC, Carlton JM, et al. The complexities of malaria disease manifestations with a focus on asymptomatic malaria. Malar J. 2012;11:29.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Ferreira MU, da Silva NM, Wunderlich G. Antigenic diversity and immune evasion by malaria parasites. Clin Diagn Lab Immunol. 2004;11:987–95.

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This study was supported by Kabale University, Makerere University through Mak-RIF PhD grants, and EDCTP2 through Infectious Diseases Institute, Makerere University under the Optimizing Malaria Treatment for HIV-Malaria co-infected Individuals by Addressing Drug Interactions between Artemisinin-based Combination Therapies and Antiretroviral Drugs (OPTIMAL) study: Grant ID TMA 2017CDF-1943.

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Authors

Contributions

AM, MO, SLN, JIN, CK, BM, SMK EAO and PBK conceived the idea, planned, and designed the study protocol. AAK and AM designed the search strategy. AM and RWN screened articles and abstracted data. AM performed the analysis and wrote the first draft of the manuscript. PBK, SMM, BC, SLN and EAO advised on the analysis and critically edited the manuscript. AM, PBK and SMM wrote the final draft of the manuscript.

Corresponding author

Correspondence to Alex Mwesigwa.

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The study utilized already published literature with no direct human subject contact, posing no risk to the participants involved in the primary studies, as determined by the Makerere University School of Medicine Institutional Review Board (# Mak-SOMREC-2021–152) and the Uganda National Council for Science and Technology (# HS2744ES).

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Supplementary Information

Additional file 1.

Search strategy.

Additional file 2.

Screening criteria.

Additional file 3.

Subgroup and Heterogeneity analysis.

Additional file 4.

RoB assessment.

Additional file 5.

Publication bias assessment

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Mwesigwa, A., Ocan, M., Musinguzi, B. et al. Plasmodium falciparum genetic diversity and multiplicity of infection based on msp-1, msp-2, glurp and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis. Malar J 23, 97 (2024). https://doi.org/10.1186/s12936-024-04925-y

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