Mapping genetic markers of artemisinin resistance in Plasmodium falciparum malaria in Asia: a systematic review and spatiotemporal analysis

Abstract

Background: The increase in artemisinin resistance threatens malaria elimination in Asia by the target date of 2030 and could derail control efforts in other endemic regions. This study aimed to develop up-to-date spatial distribution visualisations of the kelch13 (K13) gene markers of artemisinin resistance in Plasmodium falciparum for policy makers.

Methods: In this systematic review and spatiotemporal analysis we used the WorldWide Antimalarial Resistance Network (WWARN) surveyor molecular markers of artemisinin resistance database. We updated the database by searching PubMed and SCOPUS for studies published between Jan 1, 1990, and March 31, 2021. Articles were included if they contained data on K13 markers of artemisinin resistance from patients' samples in Asia and articles already included in the WWARN database were excluded. Data were extracted from the published articles and authors were contacted when information was missing. We used the lowest administrative unit levels for the sampling locations of all the K13 data to describe the spatiotemporal distribution. The numbers of samples tested and those with each molecular marker in each administrative unit level were aggregated by year to calculate the marker prevalence over time.

Findings: Data were collated from 72 studies comprising K13 markers from 16 613 blood samples collected from 1991 to 2020 from 18 countries. Most samples were from Myanmar (3842 [23·1%]), Cambodia (3804 [22·9%]), and Vietnam (2663 [16·0%]). The median time between data collection and publication was 3·6 years (range 0·9-25·0, IQR 2·7 [2·5-5·2]). There was a steady increase in the prevalence of WHO-validated K13 markers, with the lowest of 4·3% in 2005 (n=47) and the highest of 62·9% in 2018 (n=264). Overall, the prevalence of Cys580Tyr mutation increased from 48·9% in 2002 to 84·9% in 2018.

Interpretation: From 2002 to 2018, there has been a steady increase in geographical locations and the proportion of infected people with validated artemisinin resistance markers. More consistent data collection, over more extended periods in the same areas with the rapid sharing of data are needed to map the spread and evolution of resistance to better inform policy decisions. Data in the literature are reported in a heterogeneous way leading to difficulties in pooling and interpretation. We propose here a tool with a set of minimum criteria for reporting future studies.

Funding: This research was funded in part by the Wellcome Trust.

Figure 1

Study selection K13=kelch13. WWARN=WorldWide Antimalarial Resistance Network.

Figure 2

Distribution of samples by country All samples (n=16 613) obtained from the published K13 studies (n=72) and their distribution, with 13 440 samples from within the Greater Mekong subregion and 3173 samples from outside the Greater Mekong subregion. K13=kelch13.

Figure 3

Spatial distribution of K13 markers in Asia Distribution of the prevalence of K13 markers and the year of the most recent sample collection for each administrative unit. All molecular markers in that year were aggregated in each administrative unit level 1 (Afghanistan, Bangladesh, Cambodia, Indonesia, Laos, Iran, Malaysia, Nepal, Thailand, Vietnam, and Yemen) and administrative level 2 (China, India, Pakistan, and Myanmar). Validated and associated markers were only found in India and the Greater Mekong subregion. K13= kelch13.

Figure 4

Prevalence of K13 markers by year All K13 molecular markers were grouped by category and year independently using two classifications (WHO and WWARN) across all countries. The overall prevalence of each category of molecular markers by year was evaluated. Except for 2019 and 2020, there was a consistent increase of WHO-validated, WHO-associated, and WWARN-associated markers over time. Samples for 2019 (from India and Pakistan) showed wild type parasites, and 2020 samples (from Saudi Arabia) had one WHO-validated mutation (Met476Ile), unevaluated markers, and wild type parasites. WWARN=WorldWide Antimalarial Resistance Network. K13= kelch13.

Figure 5

Temporal trends of individual WHO-validated markers All samples with single nucleotide polymorphisms categorised as WHO validated pooled together by year and their overall proportions. The 2019 and 2020 samples all came from outside the Greater Mekong subregion, with the result for 2020 being from one (1%) of 80 blood samples from Saudi Arabia. Excluding 2019 and 2020, the Cys580Tyr mutation was the most common WHO-validated mutation in almost every year except for 2003 and 2005. *No validated markers reported.