Global burden, projections, and causal factors of maternal sepsis and other maternal infections: A comprehensive epidemiological and mendelian randomization study.

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Learn more: PMC Disclaimer | PMC Copyright Notice PLoS Negl Trop Dis . 2026 May 27;20(5):e0014374. doi: 10.1371/journal.pntd.0014374 Global burden, projections, and causal factors of maternal sepsis and other maternal infections: A comprehensive epidemiological and mendelian randomization study Anqi Jiang 1, ‡ , Siying Duan 2, ‡ , Shuyun Wu 1 , Wenkui Yu 1, * , Xiaohui Liang 1, * Editor: Vinícius Silva Belo 3 Author information Article notes Copyright and License information 1 Department of Critical Care Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China 2 Emergency Department, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China 3 UFSJ: Universidade Federal de Sao Joao del-Rei, BRAZIL The authors have declared that no competing interests exist. ‡ These authors share first authors. ✉ * E-mail: yudrnj@163.com (WY); 439397013@qq.com (XL) Roles Anqi Jiang : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing Siying Duan : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Visualization, Writing – review & editing Shuyun Wu : Conceptualization, Data curation, Formal analysis, Project administration, Resources, Visualization, Writing – review & editing Wenkui Yu : Conceptualization, Project administration, Supervision, Writing – review & editing Xiaohui Liang : Conceptualization, Project administration, Supervision, Writing – original draft, Writing – review & editing Vinícius Silva Belo : Editor Received 2025 Dec 9; Accepted 2026 May 12; Collection date 2026 May. © 2026 Jiang et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13229374  PMID: 42201989 Abstract Background Maternal sepsis and other maternal infections (MSMI) remain major contributors to global maternal morbidity and mortality. However, the integration of epidemiological trends with causal inference evidence remains limited. Methods Using data from the Global Burden of Disease (GBD) 2021 study, we assessed temporal trends in MSMI burden from 1990 to 2021 and projected future patterns using ARIMA and Bayesian age–period–cohort (BAPC) models. In parallel, we conducted a two-sample multivariable Mendelian randomization (MVMR) analysis to evaluate the causal effects of inflammatory biomarkers and related factors on MSMI risk. Results Although age-standardized rates declined globally, absolute case numbers increased in low-SDI regions, largely driven by population growth. Forecasting results differed between ARIMA and BAPC models, reflecting distinct underlying assumptions regarding temporal dynamics. MVMR analysis identified inflammatory biomarkers, including CRP, IL-13, IL-10, RANTES, and NT-proBNP, as key causal factors associated with MSMI. Conclusions This study provides the first integrated framework combining global disease burden analysis with multivariable MR. By linking population-level trends with causal inference, our findings offer dual evidence to support targeted prevention strategies and advance precision public health interventions for MSMI. Author summary This study found that while global rates have improved overall, the total number of cases has actually grown in poorer regions due to population increases. This tells us that progress is uneven and that future solutions must fit local realities. More importantly, we identified several natural immune-related markers in the blood-such as C-reactive protein and specific signaling molecules-that appear to directly raise the risk of maternal infections. These findings matter to scientists because they offer clear targets for future research and drug development. For non-scientists, especially public health officials and expectant families, our work shows that monitoring inflammation levels could one day help predict or even prevent these dangerous infections. By linking global patterns with genetic evidence, we provide a more complete roadmap for saving mothers’ lives. Introduction Maternal sepsis and other maternal infections (MSMI) are defined as sepsis occurring during the delivery or postpartum period, along with other infectious conditions closely associated with pregnancy. These encompass genitourinary tract infections (excluding sexually transmitted infections), obstetric surgical site infections, and breast infections related to childbirth and lactation. Such infections pose a serious threat to both maternal and neonatal health [ 1 – 2 ]. On the one hand, obstetric infections can lead to chronic pelvic inflammatory disease, ectopic pregnancy, infertility, and even maternal death. Among these outcomes, puerperal sepsis stands as one of the leading causes of maternal mortality. Global estimates indicate that approximately 10.7% of maternal deaths are attributable to puerperal sepsis [ 2 ]. On the other hand, these infections also contribute to short-term neonatal complications, such as intraventricular hemorrhage (IVH), respiratory distress syndrome (RDS), and necrotizing enterocolitis (NEC), and may result in long-term sequelae, including cerebral palsy and intellectual disability. Additionally, they increase the risks of preterm birth and fetal growth restriction. Epidemiological evidence suggests that nearly one million neonatal deaths annually are attributable to maternal infection or sepsis [ 3 ]. Although existing studies have delineated certain epidemiological characteristics of maternal sepsis and related infectious diseases, most findings are confined to specific geographic regions and frequently lack age-standardized methodologies, thereby limiting comparability across different countries and settings [ 4 – 7 ]. Furthermore, due to the complex interplay of age, period, and cohort effects, the independent contribution of each factor to morbidity and mortality risks remains poorly understood. Consequently, accurate prediction of MSMI incidence and mortality trends is of particular importance, as it will furnish essential evidence for optimizing the allocation of medical resource, formulating targeted prevention strategies, and implementing effective clinical interventions. The Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) provides a standardized and systematic assessment of 369 diseases and injuries and 87 risk factors across 204 countries and territories, thus offering essential data support for global public health research [ 8 ]. GBD 2021 data play a central role in numerous studies by facilitating the characterization of disease burden, analysis of temporal trends, future projections, and monitoring of health inequities, thereby forming a critical evidence base for public health policies and intervention strategies [ 9 – 10 ]. Using GBD 2021 data, this study calculated the age-standardized incidence and mortality rates of MSMI among women of reproductive age. Trends from 1990 to 2021 were analyzed, the independent effects of age, period, and birth cohort were evaluated, and the disease burden for the next 25 years was projected. These findings are intended to serve as a comprehensive reference for formulating targeted prevention and control strategies for MSMI at global, regional, and national levels. Disparities in disease burden provide policymakers with an evidence-based foundation for implementing targeted interventions and preventive measures in specific populations. Nevertheless, patients affected by MSMI, along with their families, often seek to understand the underlying causes of these conditions to adopt effective prevention strategies. Therefore, it is essential to thoroughly investigate the risk factors contributing to MSMI for developing effective interventions aimed at reducing associated risks. Unlike traditional observational studies, which are susceptible to residual confounding and various biases, Mendelian randomization (MR) is an epidemiological approach that uses genetic variants-randomly allocated at conception-as instrumental variables to infer causal relationships between exposures and outcomes [ 11 ]. This method substantially reduces reverse causation and confounding biases often introduced by sociodemographic or behavioral factors in conventional observational studies. In this study, we applied a two-sample MR design to examine bidirectional causal relationships between various potential risk factors and MSMI. Furthermore, multivariable MR was employed to assess the independent effects of multiple significantly associated exposures on MSMI outcomes. The findings reveal causal links between several modifiable factors and MSMI, which may inform future preventive strategies and clinical guidance. Materials and methods Ethics statement The FinnGen study protocol was approved by Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (number HUS/990/2017), with all participants providing written informed consent [ 14 ]. Overview and global data sources A schematic overview of the study design is shown in Fig 1 . Data pertaining to MSMI were obtained from the Global Health Data Exchange (GHDx) data query tool ( https://vizhub.healthdata.org/gbd-results/ ). Case definitions for MSMI were aligned with the diagnostic criteria set forth in the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) [ 12 ]. As previously defined [ 12 ], MSMI comprises two distinct entities [ 12 ]. Maternal sepsis is characterized by abnormal core body temperature (＜36°C or >38°C) along with signs of shock, such as systolic hypotension (<90 mmHg) and tachycardia (>120 beats per minute). Other maternal infections refer to any non-AIDS-defining and non-sexually transmitted infectious conditions not considered epidemiologically linked to pregnancy. The latter category encompasses disorders such as gestational urinary tract infections, mastitis, candidiasis, and bacterial vaginosis. Fig 1. Study Overview: Global Burden Analysis and Mendelian Randomization Design. Open in a new tab This flowchart outlines our two-pronged analytical approach. The Global Burden of Disease assessment was summarized in A, quantifying the worldwide impact of MSMI through metrics including incidence, DALYs, mortality, prevalence, YLDs, YLLs and epidemiology trends using EAPC. The MR framework was summarized in B, where genetic variants SNPs significantly associated with exposure phenotypes serve as instrumental variables (IVs) . The MR design rests on three core assumptions: (I) Relevance: SNPs must strongly associate with the exposure; (II) Independence: SNPs must be independent of exposure-outcome confounders; and (III) Exclusion restriction: SNPs must affect the outcome exclusively through the exposure, without alternative pathways. YLDs, Years Lived with Disability; DALYs, Disability-Adjusted Life Years; YLLs, Years of Life Lost. Methodological abbreviations: EAPC, Estimated Annual Percentage Change; ARIMA, autoregressive integrated moving average; BAPC, Bayesian age-period-cohort. We sourced comprehensive global data on health losses attributable to MSMI between 1990 and 2021, spanning 204 countries and territories and 21 regions. Key metrics included incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life years (DALYs), along with their corresponding age-standardized rates (ASIR, ASPR, ASMR, AS-YLD, AS-YLL, and ASDR). DALYs were derived as the sum of YLDs and YLLs. YLDs were calculated by multiplying the prevalence of each disease sequela by its disability weight, while YLLs were estimated based on cause-specific deaths multiplied by the standard life expectancy at the age of death [ 12 ]. Although global standardization efforts under the GBD framework have enhanced data comparability, disparities in socioeconomic status and variations in reproductive-age population structures may still influence data quality. To address potential confounding from these factors, stratified analyses of MSMI-related health loss were conducted by Socio-demographic Index (SDI) and by maternal age group. The SDI is a composite measure of sociodemographic development that integrates income, educational attainment, and fertility rates [ 12 ]. Based on 2021 SDI quintiles, all countries and territories were classified into five development strata: low, low-middle, middle, high-middle, and high, where elevated SDI reflects advanced socioeconomic conditions [ 12 ]. Additionally, temporal trends in MSMI burden were evaluated across detailed age categories as defined in GBD 2021: 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, and 50–54 years [ 13 ]. Complete datasets supporting these analyses are available in S1 - S6 Tables . MR study design A two-sample MR analysis was employed to assess the potential causal effects of multiple exposures on MSMI. The analysis was grounded in three core MR assumptions: (i) the genetic instruments exhibit strong associations with the exposures of interest; (ii) these instruments are independent of potential confounders; and (iii) they influence the risk of MSMI only via the exposure pathways ( Fig 1 ). As all genetic and outcome data were derived from previously published studies with ethical approvals [ 13 ], no additional informed consent was required for the present study. MR date sources Genome-wide association study (GWAS) summary statistics for MSMI were obtained from the FinnGen Consortium [ 14 ]. The analyzed variable factors encompassed five major categories: Basic characteristics : birth weight, body mass index (BMI); Medical history: previous smoking status, history of stillbirth, spontaneous miscarriage or termination, number of pregnancy terminations, medication use for cholesterol, blood pressure, diabetes, or exogenous hormones (e.g., cholesterol-lowering medication); Routine blood and serum metabolic biomarkers: high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), total cholesterol levels or total cholesterol (TC), C-reactive protein levels or C-reactive protein (CRP), vitamin D levels (VitD), N-terminal prohormone of brain natriuretic peptide levels (NT-proBNP); Serum inflammatory cytokines: RANTES levels (RANTES), interleukin-13 levels (IL-13), interleukin-10 levels (IL-10), interleukin-1 receptor antagonist levels (IL-1Ra), interleukin-2 receptor antagonist levels (IL-2Ra), interferon gamma levels (IFN-γ); Serum-specific biomarkers: Pregnancy-specific beta-1-glycoprotein 11 (PSG11), Pregnancy-specific beta-1-glycoprotein 9 (PSG9). Instrumental variables for these factors were sourced from multiple large-scale genetic consortia and databases, including the UK Biobank (UKB) [ 15 ], the Early Growth Genetics (EGG) Consortium [ 16 ], the Genetic Investigation of Anthropometric Traits (GIANT) Consortium [ 17 ], the Global Lipids Genetics Consortium (GLGC) [ 18 ], the NHGRI-EBI Genome-Wide Association Studies Catalog (GWAS Catalog) [ 19 ], the Systematic and Combined Analysis of Olink Proteins (SCALLOP) [ 20 ], and the Within Family GWAS Consortium (WFGWC) [ 21 ]. Detailed data source descriptions are provided in Table 1 . Table 1. Data source of variable factors. id.exposure Exposure Date source Population ieu-a-27 Birth weight EGG European ebi-a-GCST90095034 Body mass index GWAS Catalog Hispanic or Latin American ieu-a-94 Body mass index GIANT European ieu-b-4815 Body mass index Within family GWAS consortium European ukb-a-224 Smoking status: Previous UBK European ukb-b-12621 Ever had stillbirth, spontaneous miscarriage or termination UBK European ukb-a-355 Number of pregnancy terminations UBK European ukb-a-448 Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication UBK European ukb-b-17805 Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication UBK European ukb-b-18009 Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication UBK European ebi-a-GCST000755 HDL cholesterol GWAS Catalog European ebi-a-GCST000759 LDL cholesterol GWAS Catalog European ebi-a-GCST005068 LDL cholesterol GWAS Catalog European ebi-a-GCST90018741 LDL cholesterol GWAS Catalog East Asian ieu-a-781 LDL cholesterol GLGC Mixed ieu-b-4845 LDL cholesterol Within family GWAS consortium European ebi-a-GCST000758 Triglycerides GWAS Catalog European ieu-a-783 Triglycerides GLGC Mixed ieu-b-4849 Triglycerides Within family GWAS consortium European ieu-b-4850 Triglycerides Within family GWAS consortium European ebi-a-GCST90018754 Total cholesterol levels GWAS Catalog East Asian ebi-a-GCST90101747 Total cholesterol levels GWAS Catalog Sub-Saharan African ieu-a-782 Total cholesterol GLGC Mixed ebi-a-GCST005067 C-reactive protein levels GWAS Catalog European ebi-a-GCST90018730 C-reactive protein GWAS Catalog East Asian ebi-a-GCST005367 Vitamin D levels GWAS Catalog European ebi-a-GCST90012082 N-terminal prohormone brain natriuretic peptide levels GWAS Catalog European ebi-a-GCST004431 RANTES levels GWAS Catalog European ebi-a-GCST004443 Interleukin-13 levels GWAS Catalog European ebi-a-GCST004444 Interleukin-10 levels GWAS Catalog European ebi-a-GCST004447 Interleukin-1-receptor antagonist levels GWAS Catalog European ebi-a-GCST004454 Interleukin-2-receptor antagonist levels GWAS Catalog European ebi-a-GCST004456 Interferon gamma levels GWAS Catalog European prot-a-2400 Pregnancy-specific beta-1-glycoprotein 11 SCALLOP European prot-a-2408 Pregnancy-specific beta-1-glycoprotein 9 SCALLOP European Open in a new tab Instrumental variable selection Based on GWAS summary statistics, single-nucleotide polymorphisms (SNPs) associated with each exposure of interest were selected as instrumental variables. Independent SNPs were identified through a clumping procedure under strict linkage disequilibrium (LD) criteria ( P <  5  ×  10 ⁻ ⁸, r ²  <  0.001, clumping window  =  10,000 kb). For unavailable SNPs in the outcome dataset, proxy variants were identified from the 1000 Genomes European reference panel using a high LD threshold ( r ²  >  0.8), excluding SNPs without suitable proxies. Additionally, palindromic SNPs with intermediate allele frequencies were removed to prevent strand ambiguity. To enhance analytical robustness, SNPs with a minor allele frequency (MAF) < 0.01 were further excluded, thereby minimizing potential bias arising from low-frequency variants that often yield unreliable estimates in GWAS. The proportion of variance explained (R 2 ) for each risk factor by individual SNPs was quantified [ 13 ], and the strength of instrumental variables was evaluated using the F-statistic [ 22 ]. The F-statistic was calculated as F = R² × (N − 2) / (1 − R²), where R² represents the proportion of variance explained by the genetic instruments and N is the sample size. Statistical analysis Spearman’s rank correlation analysis was employed to investigate the association between the Socio-demographic Index (SDI) and the burden of MSMI. The SDI is a composite indicator of a region’s development level, ranging from 0 (lowest) to 1 (highest), based on lag-distributed income per capita, average educational attainment, and total fertility rate. Given the non-normal distribution of burden indicators across countries, Spearman’s rank correlation was selected as it is a non-parametric method that does not assume linearity or normality, and is robust to outliers. All correlation analyses were performed at the country level, including all 204 countries and territories in the GBD study for the year 1990 and 2021. For each pair of variables (SDI vs. each burden indicator), Spearman’s correlation coefficient (r) and the corresponding two-tailed p-value were calculated. A p-value < 0.05 was considered statistically significant. All statistical analyses were conducted using R software (version 4.5.3) with the “cor.test” function [ 12 ]. Decomposition analysis was performed to quantify the contributions of demographic factors and epidemiological changes to the disparities in the burden of MSMI across different SDI quintiles in 2021. Using the Das Gupta decomposition method, the difference in outcome measure between each SDI quintile (low, low-middle, middle, high-middle, and high SDI) and the global average was partitioned into three components: (1) population structure (aging), reflecting differences in the age composition of populations; (2) population size (growth), reflecting differences in total population across SDI quintiles; and (3) epidemiological changes, reflecting differences in age-specific rates of the outcome measure. Absolute contributions (the number of cases attributable to each factor) and relative contributions (the percentage of the total disparity explained by each factor) were calculated for each SDI quintile. All decomposition analyses were conducted using R software (version 4.5.3) with the “ Dasgupta” function from the “ decompose” package [ 12 ]. The Estimated Annual Percentage Change (EAPC) serve as a key indicator for quantifying temporal trends in epidemiological metrics-such as incidence, mortality, or DALYs-over a specified period. It reflects the average annual rate of change, expressed as a percentage. In this study, the EAPC was applied to analyze trends in the ASR of MSMI from 1990 to 2021, based on data from the GBD 2021 database. EAPC was derived using a linear regression model applied to the natural logarithm of ASR values collected over consecutive years. The use of ASR mitigates confounding from temporal shifts in population age structure over time. The regression model is specified as: ln (ASR) = α + β × year + ε , where ln (ASR) is the natural log-transformed ASR, year represents the time variable, α is the intercept, β is the regression coefficient for time, and ε denotes the random error term. EAPC is calculated using the estimated slope β with the formula: EAPC = (e^β - 1) × 100 . The 95% confidence interval (CI) is obtained from the standard error (SE) of β : Upper limit = {exp (β + 1.96 × SE_ β ) - 1} × 100, Lower limit = {exp ( β - 1.96 × SE_ β ) - 1} × 100. A statistically significant increasing trend in ASR is concluded if both the EAPC and the lower limit of its 95% CI exceed zero, whereas a decreasing trend is inferred when the EAPC and the upper limit of the 95% CI are below zero. To project future disease burden, two complementary modeling approaches were employed, including the autoregressive integrated moving average (ARIMA) model and the Bayesian age-period-cohort (BAPC) model. As a well-established method for epidemiological forecasting [ 23 ], the ARIMA model was fitted to historical time-series data from GBD 2021. After ensuring stationarity through differencing, the “auto.arima()” function was used to identify the optimal model configuration based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Model adequacy was confirmed via white noise testing (P > 0.05 for residuals). The “forecast” package was employed to predict future MSMI-related metrics at α = 0.05 significance. Simultaneously, the BAPC model captured complex temporal structures by integrating age, period, and cohort effects through Bayesian methodology, INLA algorithm for efficient prior and posterior distribution updating. A log-linear regression model was fitted for MSMI metrics as follows: ln (ASR) = α + β_age × age + β_period × period + β_cohort × cohort + ε , with Bayesian parameter estimated via the integrated nested Laplace approximation (INLA) algorithm for efficient prior and posterior distribution updating [ 24 ]. A two-sample MR framework was applied to investigate the causal relationships between variable factors and MSMI. After harmonizing effect alleles and directions using the “ harmonise_data ” function from the TwoSampleMR package in R software (version 4.1.0), MR analyses were conducted through the “ mr ” function, incorporating five complementary methods: MR-Egger regression (MR-Egger) [ 25 ], weighted median (WM) [ 25 ], inverse-variance weighted (IVW) [ 26 ], simple mode, and weighted mode [ 26 ]. MR analyses were performed separately for each exposure factor, with the IVW method serving as the primary analytical approach for causal inference. Heterogeneity among instrumental variables was assessed using Cochran’s Q test via the “ mr_heterogeneity ” function. P-values > 0.05 indicated absence of significant heterogeneity, supporting the use of fixed-effect inverse-variance weighted (IVW) estimates. In this study, all Q test results were non-significant (P > 0.05), confirming homogeneity across variables [ 27 ]. Horizontal pleiotropy was evaluated using MR-Egger regression and MR-PRESSO through the “ mr_pleiotropy_test ” function. P > 0.05 in both tests indicated no evidence of directional pleiotropy, thereby ensuring causal estimate reliability [ 25 , 28 ]. Finally, causal directionality was verified using MR-Steiger analysis, which confirmed the presumed direction (exposure → outcome) by demonstrating greater variance explained in exposure than outcome. Sensitivity analysis was conducted using the “ mr_leaveoneout ” function, which removes each SNP one by one and evaluates its impact on the overall results. Notably, if the results change significantly after removing a particular SNP, this indicates that the SNP is sensitive. In this study, no SNPs with significant deviation were found, indicating that the analysis results are stable and reliable. Given that genetic variations may influence multiple correlated exposures, multivariable Mendelian randomization (MVMR) was performed to delineate the direct causal effects of individual variable factors on MSMI. This approach isolates the independent contribution of each exposure to disease risk [ 29 ]. To mitigate potential collinearity among exposures, the least absolute shrinkage and selection operator (LASSO) was applied for variable selection and dimensionality reduction. This method effectively identified a subset of influential exposures by retaining factors significantly associated with MSMI while excluding those with negligible effects [ 30 ]. The optimal tuning parameter (λ) was determined using 10-fold cross-validation, selecting the value that minimized the mean cross-validated error. Variables with non-zero coefficients were retained for subsequent multivariable MR analysis. Results The global and regional burden of MSMI in 2021 Globally, the country-specific burden of MSMI in 2021 is detailed in Figs 2 – 3 and 5 . Fig 2 presents the absolute numbers of incidence, prevalence, mortality, DALYs, YLDs, and YLLs for each country in 2021. The findings reveal that China, India, and Pakistan reported the highest number of incidence cases and prevalence cases, each exceeding 1,000,000 incidence cases and 150,000 prevalence cases. In terms of mortality attributable to MSMI, India, Nigeria, and the Democratic Republic of the Congo recorded the highest mortality, each experiencing over 1,500 annual deaths. Countries with elevated mortality rates were largely concentrated in Asia and Africa. India, Nigeria, and the Democratic Republic of the Congo also reported the highest number of DALY cases. Furthermore, the assessment of ASR metrics revealed that the highest values for ASIR, ASMR, ASDR, and ASPR in 2021 were predominantly observed in African and South American countries ( Fig 3 ). Within Asia, Afghanistan and Pakistan shouldered the most substantial MSMI burden, whereas the ASR metrics of other Asian countries were generally comparable to those of Europe, North America, and Oceania. At the regional level, Western Sub-Saharan Africa was observed to have the highest number of mortality, DALYs and YLLs in 2021, followed by Sourth Asia, Eastern Sub-Saharan Africa and Central Latin America, whereas Sourth Asia reported the highest number of YLDs, prevalence and incidence in 2021, followed by Western Sub-Saharan Africa, Eastern Sub-Saharan Africa and Central Latin America ( Fig 5A ). In contrast, six key metrics via age-standardized exhibited different regional trends. Specifically, Eastern Sub-Saharan Africa reported the highest age-standardized rates for mortality, DALYs and YLLs, Andean Latin America exhibited the highest age-standardized rates for YLDs and incidence, and Central Sub-Saharan Africa demonstrated the highest age-standardized rates for prevalence ( Fig 5B ). Fig 2. Global Burden of MSMI in 2021. Open in a new tab Spatial distribution of MSMI case burden across 204 countries and territories is presented as follows: (A) Incidence cases, (B) Mortality cases, (C) YLDs, (D) Prevalence cases, (E) DALYs, and (F) YLLs. These maps visualize geographical disparities in disease impact, highlighting regions with the highest burden across different health metrics. YLDs, Years Lived with Disability; DALYs, Disability-Adjusted Life Years; YLLs, Years of Life Lost. The map layer (country/region boundaries) was obtained from Natural Earth ( https://www.naturalearthdata.com/ ), which is in the public domain (Terms of use: https://www.naturalearthdata.com/about/terms-of-use/ ). The basemap was created using the “rnaturalearth” package in R software. Map boundaries are only for illustrative purposes and do not imply any political statement. Fig 3. Global Age-Standardized Burden of MSMI in 2021. Open in a new tab Geographic distribution of age-standardized MSMI burden metrics across 204 countries and territories: (A) Age-Standardized Incidence Rate (ASIR), (B) Age-Standardized Mortality Rate (ASMR), (C) Age-Standardized YLDs Rate (AS-YLD), (D) Age-Standardized Prevalence Rate (ASPR), (E) Age-Standardized DALYs Rate (ASDR), and (F) Age-Standardized YLLs Rate (AS-YLL). These maps enable cross-national comparisons of disease burden, independent of population age structure differences. YLDs, Years Lived with Disability; DALYs, Disability-Adjusted Life Years; YLLs, Years of Life Lost. The map layer (country/region boundaries) was obtained from Natural Earth ( https://www.naturalearthdata.com/ ), which is in the public domain (Terms of use: https://www.naturalearthdata.com/about/terms-of-use/ ). The basemap was created using the “rnaturalearth” package in R software. Map boundaries are only for illustrative purposes and do not imply any political statement. Fig 5. Regional Burden of MSMI in 2021. Open in a new tab (A) Absolute number of MSMI cases by region, representing the actual magnitude of disease burden across different populations. (B) Age-standardized burden rates of MSMI across geographic regions, facilitating direct comparison of disease intensity while accounting for population age structures. 21 GBD regions: Andean Latin America, Australasia, Caribbean, Central Asia, Central Europe, Central Latin America, Central Sub−Saharan Africa, East Asia, Eastern Europe, Eastern Sub−Saharan Africa, High−income Asia Pacific, High−income North America, North Africa and Middle East, Oceania, South Asia, Southeast Asia, Southern Latin America, Southern Sub−Saharan Africa, Tropical Latin America, Western Europe and Western Sub−Saharan Africa. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. Assessment of the MSMI burden across different age groups in 2021 ( Fig 4 ) revealed a progressive decline in all ASR metrics with advancing age, with the notable exception of the 15–19 age group. The highest burden was observed in the 20–24 age group, whereas the lowest values for all burden metrics were recorded in the 45–49 age group. Furthermore, given that the economic impact of disease varies with socioeconomic development, populations were stratified by the SDI to evaluate the MSMI burden across different development strata. As shown in Fig 6 , a decline in the SDI was associated with a gradual increase in the ASIR, ASPR, ASMR, ASDR, AS-YLD, and AS-YLL. Further Spearman correlation analysis showed that SDI was significantly negatively correlated with ASIR, ASPR, ASMR, ASDR, AS-YLD, and AS-YLL (all P < 0.001), whether for 1990 ( S1 Fig ) or 2021( S2 Fig ). Consequently, these results demonstrate a clear negative correlation between SDI and MSMI burden, indicating that the majority of the MSMI burden is concentrated in low-SDI and low-middle-SDI countries and territories. Fig 4. Age-Specific Burden of MSMI in 2021. Open in a new tab (A) Age-stratified patterns of MSMI burden expressed as ASR, including Age-Standardized DALYs Rate, Age-Standardized Mortality Rate, Age-Standardized Incidence Rate, Age-Standardized Prevalence Rate, Age-Standardized YLDs Rate, and Age-Standardized YLLs Rate, highlighting variations in disease impact across the lifespan. (B) Absolute case numbers of MSMI burden, namely DALYs, Mortality, Incidence, Prevalence, YLDs and YLLs, across different age groups, representing the actual population-level disease distribution. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. Fig 6. Burden of MSMI by SDI in 2021. Open in a new tab (A) Age-standardized burden rates of MSMI across SDI categories, revealing gradients in disease intensity relative to socioeconomic development levels. (B) Absolute number of MSMI cases by SDI stratum, demonstrating the actual distribution of disease burden across populations with varying socioeconomic status. (C) Decomposition results of MSMI burden across SDI categories at the SDI components level. Three SDI components include population structure (aging), epidemiological changes, and population size (population). DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. Decomposition analysis of changes in disease burden among MSMI revealed marked heterogeneity across SDI quintiles ( Fig 6C ), as well as similar trends across Cause ( S3 Fig ) and Sex ( S4 Fig ). All of the total change in MSMI burden, including incidence, prevalence, mortality, DALYs, YLDs and YLLs, was predominantly driven by epidemiological change and population growth ( S3 and S4 Figs ). For SDI quintiles, distinct patterns were observed for mortality and morbidity indicators (incidence) ( Fig 6C ). The total change in DALYs among MSMI was predominantly driven by epidemiological change and population growth in lower SDI settings, with opposing effects across the development spectrum. In the Low SDI quintile, population growth contributed substantially to increased DALYs, while epidemiological change exerted a modest negative effect. Conversely, in High SDI and High-middle SDI quintiles, epidemiological change was the dominant driver of DALY reduction, offsetting small increases attributable to aging. The net change (black dots) demonstrated a gradient from high burden in Low SDI to minimal change in High SDI settings. A similar pattern was observed for mortality and YLLs. In Low SDI countries, mortality change was driven primarily by population growth with minimal epidemiological improvement. In contrast, High SDI quintiles experienced mortality reductions through favorable epidemiological change. Notably, aging contributed negligibly to mortality changes across most quintiles except Low SDI, where a marginal aging effect was observed. The decomposition of incidence revealed distinct dynamics. Low SDI countries exhibited the largest increase in incidence cases, driven overwhelmingly by population growth and partially offset by epidemiological improvement. This pattern was attenuated at higher SDI levels, where epidemiological change contributed more substantially to incidence reduction. Aging effects on incidence were minimal across all quintiles. Across all disease burden metrics, three consistent patterns emerged: (1) population growth was the dominant driver of increased burden in Low SDI settings; (2) epidemiological change contributed to burden reduction across all SDI levels, with effect magnitude positively correlated with development status; and (3) aging exerted minimal influence on most indicators, with slight positive contributions to mortality, incidence and YLDs in lower SDI settings. The net change in disease burden (black dots) demonstrated a clear SDI gradient, with Low SDI countries experiencing the largest change in all metrics and High SDI countries approaching zero net change or reductions. Global trends in the burden of MSMI from 1990 to 2021 The global burden trends of MSMI from 1990 to 2021 is presented in Fig 7 . The findings reveal a consistent overall decline in all ASR, including ASIR, ASPR, ASMR, AS-YLD, AS-YLL, and ASDR, over this period. By 2021, each of these ASR metrics showed a substantial decrease compared to its 1990 levels. A parallel reduction was observed in the absolute numbers of incidence, prevalence, mortality, YLLs, YLDs, and DALYs, which were all significantly lower in 2021 than in 1990. Fig 7. Temporal Trends in MSMI Burden from 1990 to 2021. Open in a new tab (A) Mortality counts and ASMR. (B) DALYs and ASDR. (C) YLDs and AS-YLD. (D) YLLs and AS-YLL. (E) Prevalence counts and ASPR. (F) Incidence counts and ASIR. The bar chart represents the absolute cases, and the dotted line chart stands for age-standardized burden rates. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate; ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate. As shown in Fig 8 , the age distribution of MSMI burden from 1990 to 2021 revealed a general decline in ASR (ASIR, ASMR and ASDR) with advancing age, except in the 15–19 age group. The highest values for ASIR, ASMR, ASPR, and ASDR were consistently observed in the 20–24 age group, while all metrics remained at relatively low levels beyond age 40. Furthermore, compared with 1990, all ASR metrics in 2021showed significant reductions among individuals aged 15–29 years. In contrast, only minimal changes or gradual declines were observed for the 30–49 age group. Fig 8. Age-Stratified Burden Trends of MSMI from 1990 to 2021. Open in a new tab (A) Temporal patterns in age-standardized burden rates across age groups, showing how disease intensity has evolved over three decades while controlling for demographic changes. (B) Longitudinal trends in absolute case numbers by age group, revealing shifts in the actual population distribution of MSMI burden across different generations. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. Despite consistent diagnostic criteria for MSMI across countries, disparities in socioeconomic development have led to incomplete uniformity in diagnostic quality, potentially affecting the accuracy of global burden assessments. To address this, health losses attributable to MSMI from 1990 to 2021 were further analyzed by SDI, as shown in Fig 9 . The study found that, although all ASR indicators exhibited a gradual downward trend in low-SDI regions, the absolute numbers of incidence cases, prevalence cases, and YLDs rose significantly. Specifically, these indicators increased from 3,061,689 (95% UI 2,301,567-4,006,663), 353,651 (95% UI: 239,741–520,932), and 12,526 (95% UI: 5,919–22,393) in 1990–4,711,030 (95% UI: 3,557,613–6,151,574), 597,803 (95% UI: 423,736–852,979), and 19,630 (95% UI: 9,178–35,332) in 2021, representing increases of 54%, 69%, and 57%, respectively. In contrast, mortality rate, YLLs, and DALYs remained largely unchanged in this region. Population growth was identified as the primary driver of this phenomenon; approximately 70%–75% of the increase in MSMI cases in low-SDI regions was attributable to population growth. Meanwhile, the burden of MSMI remained consistently low in high-SDI and high-middle-SDI regions over the study period. Fig 9. Socioeconomic Disparities in MSMI Burden from 1990 to 2021. Open in a new tab (A) Trends in age-standardized burden rates across SDI categories, illustrating the evolution of disease intensity in relation to socioeconomic development over three decades. (B) Temporal patterns in absolute case numbers by SDI stratum, revealing how the actual distribution of MSMI burden has shifted across populations with varying socioeconomic status. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. National-level trends in the MSMI burden from 1990 to 2021 revealed divergent patterns across countries, as shown in Fig 10 . Globally, the ASIR of MSMI declined in most countries during this period, with the exception of ten nations, including Australia, Belarus, Georgia, Germany, Latvia, Kazakhstan, Romania, Russia, Samoa and Spain. Saudi Arabia demonstrated the most substantial decrease in ASIR. A similar overall downward trend was observed for ASMR worldwide. However, Kazakhstan experienced a significant increase in ASMR, while Canada, the Democratic Republic of the Congo, Zimbabwe, Thailand, and Papua New Guinea exhibited more modest elevations. Furthermore, assessment of trends in the ASDR revealed that all countries and regions displayed a decline in DALYs, with the exception of five countries, including Australia and Kazakhstan. The most substantial reductions in ASDR were observed in Asian and African nations. Fig 10. Global Trends in Age-Standardized Burden of MSMI from 1990 to 2021. Open in a new tab Spatiotemporal patterns of EAPC across 204 countries and territories are shown for: (A) ASIR, (B) ASPR, (C) ASMR, (D) ASDR, (E) AS-YLD, and (F) AS-YLL. Countries with significantly increasing trends were highlighted below each small chart. These maps visualize geographical heterogeneity in long-term trends, highlighting regions with accelerating or decelerating disease burden. ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate. The map layer (country/region boundaries) was obtained from Natural Earth ( https://www.naturalearthdata.com/ ), which is in the public domain (Terms of use: https://www.naturalearthdata.com/about/terms-of-use/ ). The basemap was created using the “rnaturalearth” package in R software. Map boundaries are only for illustrative purposes and do not imply any political statement. Future trends in the burden of MSMI To forecast future trends in the burden of MSMI, both the ARIMA and BAPC models were employed. Projections from the ARIMA model suggest that ASPR and ASDR of MSMI will continue to decline in the coming years, while the ASIR is projected to rise, and the ASMR is expected to remain stable ( Fig 11 ). Specifically, by 2050, ASPR and ASDR are estimated to decrease from 60.20 to 55.11 and from 28.57 to 7.74, respectively, whereas ASIR is projected to increase from 492.69 to 887.40. In contrast, projections from the BAPC model differ slightly from those of the ARIMA model. All four metrics, including ASIR, ASPR, ASMR, and ASDR, exhibited a consistent decline through 2041, with the magnitude of reduction illustrated by the blue lines ( Fig 12 ), Based on these findings,it is concluded that ASPR and ASDR are likely to continue decreasing in the future. Nevertheless, the trajectory of ASIR remain uncertain, and ASMR is expected to either remain stable or undergo a slow, sustained decline. Fig 11. Projected Trends in MSMI Burden from 2022 to 2050 using ARIMA Model. Open in a new tab (A) Projected trends in incidence counts and ASIR. (B) Projected trends in prevalence counts and ASPR. (C) Projected trends in mortality counts and ASMR. (D) Projected trends in DALYs and ASDR. ARIMA, autoregressive integrated moving average model. The bar chart represents the absolute cases, and the dotted line chart stands for age-standardized burden rates. DALYs, Disability-Adjusted Life Years; ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate. Fig 12. Trend in the burden of MSMI from 2022 to 2041 using BAPC model. Open in a new tab (A) Trend of incidence and ASIR from 2022 to 2041. (B) Trend of prevalence and ASPR from 2022 to 2041. (C) Trend of mortality and ASMR from 2022 to 2041. (D) Trend of DALYs and ASDR from 2022 to 2050. The blue line represents the predicted trend, and the grey-shaded area represents the 95% confidence interval of the predicted values; the grey-dotted vertical line divides the data into real values (1990-2021) and predicted values (2022-2041). BAPC model, Bayesian age-period-cohort. Two-sample MR analysis and multivariable MR analysis To investigate potential causal links, a range of variable factors were systematically evaluated in relation to MSMI. For maternal sepsis, multiple factors showed statistically significant associations ( P < 0.05), as detailed in Table 2 . These included lipid profiles, inflammatory markers, medication use, and pregnancy history variables. Using the inverse variance weighted (IVW) method, we identified several factors with odds ratios (OR) > 1, such as LDL cholesterol, previous smoking, RANTES levels, IL-13 levels, IL-10 levels, birth weight, cholesterol-lowering medications, blood pressure medication, and PSG11, indicating their potential roles as risk factors for the development of maternal sepsis. Conversely, those factor with OR < 1, including HDL cholesterol, TG, TC, IL-1Ra levels, IFN-γ levels, PSG9, and number of pregnancy terminations, emerged as potential protective influences against the development of maternal sepsis. Table 2. Results of the relationship between the variable factors and the risk of maternal sepsis. id.exposure Exposure SNPs Method P value OR Lower 95%CI Upper 95%CI ieu-a-27 Birth weight 13 MR Egger 0.026189844 17.61498355 1.970634157 157.4557329 Weighted median 0.014247002 1.837887361 1.129660277 2.990129 Inverse variance weighted 0.014684896 1.653291065 1.103972225 2.475942133 Simple mode 0.081865576 2.246910894 0.974331178 5.181614508 Weighted mode 0.069196648 2.035202449 1.012728669 4.089988895 ukb-a-224 Smoking status: Previous 151 MR Egger 0.051806864 51.07375485 1.000928611 2606.108373 Weighted median 0.002474608 4.336763944 1.676947379 11.21533194 Inverse variance weighted 0.017712179 2.176941596 1.14454347 4.140580797 Simple mode 0.175262762 8.157792703 0.39782714 167.2826589 Weighted mode 0.149442164 11.34477924 0.424578392 303.1336929 ukb-a-448 Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication 10 MR Egger 0.169756592 19.87727453 0.40912769 965.7279435 Weighted median 0.02758256 6.095166926 1.220792874 30.43191081 Inverse variance weighted 0.00486639 6.209318177 1.741878343 22.13451494 Simple mode 0.263178546 4.878170969 0.361405876 65.84439704 Weighted mode 0.137482275 6.53350567 0.684122105 62.39631204 ukb-b-18009 Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 20 MR Egger 0.748570165 2.801366397 0.005667237 1384.740787 Weighted median 0.149057338 3.037928239 0.671493829 13.74399525 Inverse variance weighted 0.038760688 3.432065962 1.065747486 11.05240868 Simple mode 0.327438317 4.077770878 0.26312739 63.19454362 Weighted mode 0.413179614 2.861438751 0.243767897 33.5886383 ukb-a-355 Number of pregnancy terminations 28 MR Egger 0.287195296 0.419467804 0.087520754 2.010417297 Weighted median 0.233074067 0.61861041 0.280920143 1.362233534 Inverse variance weighted 0.0210607 0.504499724 0.282100298 0.902232198 Simple mode 0.85021559 1.154661131 0.26328662 5.063843838 Weighted mode 0.967277967 1.026820841 0.29333479 3.594394788 ebi-a-GCST000755 HDL cholesterol 130 MR Egger 0.065685167 0.8253907 0.674018292 1.010758633 Weighted median 0.516823887 0.949153684 0.81061175 1.111373868 Inverse variance weighted 0.005521824 0.882204584 0.807464098 0.963863198 Simple mode 0.039523617 0.605877579 0.377837325 0.971549435 Weighted mode 0.977230465 1.003004879 0.81656924 1.232006714 ebi-a-GCST000758 Triglycerides 143 MR Egger 0.009163818 0.771160325 0.63596414 0.935097137 Weighted median 0.000143682 0.80930529 0.72567046 0.902579186 Inverse variance weighted 0.000008693679 0.859187247 0.796469044 0.926844215 Simple mode 0.023429635 0.712645161 0.533345432 0.952221758 Weighted mode 0.016152045 0.74230209 0.58396573 0.943569742 ieu-a-783 Triglycerides 165 MR Egger 0.026624249 0.811708821 0.676127916 0.974477159 Weighted median 0.006503945 0.868133426 0.784066002 0.961214544 Inverse variance weighted 0.00012649 0.87085792 0.811411194 0.934659915 Simple mode 0.024788914 0.707683813 0.524721496 0.954442278 Weighted mode 0.04316658 0.712038099 0.513623233 0.987101482 ieu-b-4850 Triglycerides 164 MR Egger 0.514636128 0.950092268 0.814772503 1.10788633 Weighted median 0.119103355 0.932661869 0.854377866 1.018118793 Inverse variance weighted 0.005936672 0.914919292 0.858760057 0.974751101 Simple mode 0.874969231 1.019541065 0.801451326 1.296977059 Weighted mode 0.677463558 1.040494333 0.863268529 1.254103932 ebi-a-GCST005068 LDL cholesterol 10 MR Egger 0.542652878 1.105400094 0.811641127 1.505479857 Weighted median 0.067559961 1.174462584 0.988448546 1.395482211 Inverse variance weighted 0.004129546 1.222608152 1.065704536 1.402612677 Simple mode 0.282342085 1.156885747 0.9011695 1.485164146 Weighted mode 0.222616731 1.166143322 0.92658473 1.467637232 ebi-a-GCST90101747 Total cholesterol levels 8 MR Egger 0.35339293 0.788616958 0.496436891 1.25276086 Weighted median 0.03148423 0.727618402 0.544582631 0.972173016 Inverse variance weighted 0.01640527 0.753491828 0.597975198 0.949453986 Simple mode 0.08750503 0.672150117 0.454072204 0.994964624 Weighted mode 0.060587159 0.715926922 0.534015199 0.959806684 ebi-a-GCST004431 RANTES levels 121 MR Egger 0.311158283 1.065319734 0.943031004 1.203466409 Weighted median 0.00018884 1.140730975 1.064543343 1.222371232 Inverse variance weighted 0.000002851483 1.125730823 1.071270839 1.182959378 Simple mode 0.068437547 1.222763384 0.986815698 1.515126175 Weighted mode 0.068815772 1.207212398 0.987374804 1.47599652 ebi-a-GCST004443 Interleukin-13 levels 139 MR Egger 0.471625071 1.035113669 0.942521091 1.136802474 Weighted median 0.108217887 1.061069258 0.987030259 1.140662062 Inverse variance weighted 0.039725047 1.045933268 1.00211159 1.091671238 Simple mode 0.842634635 1.020647571 0.834471163 1.248361249 Weighted mode 0.040195098 1.119939023 1.006104332 1.246653429 ebi-a-GCST004444 Interleukin-10 levels 7 MR Egger 0.193901758 1.254133972 0.932914526 1.685955119 Weighted median 0.004550546 1.258570964 1.073695067 1.475280013 Inverse variance weighted 0.002377289 1.229558639 1.076110241 1.404888077 Simple mode 0.057193262 1.331528912 1.048481215 1.690988085 Weighted mode 0.024490583 1.270998084 1.085806519 1.48777531 ebi-a-GCST004447 Interleukin-1-receptor antagonist levels 105 MR Egger 0.025021545 0.864016141 0.761756259 0.980003619 Weighted median 0.075208055 0.925423377 0.849689706 1.007907264 Inverse variance weighted 0.035943042 0.93825573 0.884011244 0.995828752 Simple mode 0.484748145 0.917567495 0.721443909 1.167007022 Weighted mode 0.388876835 0.907349869 0.727993942 1.130893731 ebi-a-GCST004456 Interferon gamma levels 121 MR Egger 0.74627903 0.975794177 0.841476345 1.131552042 Weighted median 0.001997875 0.840715698 0.753116693 0.938503809 Inverse variance weighted 0.000132407 0.868800803 0.808344967 0.93377811 Simple mode 0.154090401 0.81715626 0.620108128 1.076819225 Weighted mode 0.127668933 0.829193966 0.652710689 1.053395701 prot-a-2400 Pregnancy-specific beta-1-glycoprotein 11 20 MR Egger 0.619351044 1.071932022 0.818832584 1.403263966 Weighted median 0.011313969 1.245484169 1.050902529 1.476093902 Inverse variance weighted 0.000281446 1.261021025 1.112666064 1.429156579 Simple mode 0.352150823 1.146872164 0.865403224 1.519887752 Weighted mode 0.119983579 1.237217813 0.9575263 1.598606655 prot-a-2408 Pregnancy-specific beta-1-glycoprotein 9 33 MR Egger 0.527536053 0.880872417 0.59694879 1.299837154 Weighted median 0.012263072 0.872188274 0.783667809 0.970707711 Inverse variance weighted 0.0000103725911 0.837499446 0.774015506 0.906190271 Simple mode 0.004113414 0.719683357 0.584185695 0.886608725 Weighted mode 0.266778577 0.908061182 0.768210447 1.073371382 Open in a new tab SNPs, single nucleotide polymorphisms; OR, Odds ratio; 95%CI, 95% Confidence Interval. Analysis of other maternal infections revealed distinct causal profiles, as detailed in Table 3 . Statistically significant causal relationships ( P < 0.05) were identified between multiple factors and other maternal infections, including spontaneous miscarriage or termination, BMI, CRP, LDL cholesterol, TC, TG, IL-13 levels, IL-2Ra levels, NT-proBNP, cholesterol-lowering medications, blood pressure medication, and vitamin D levels. These findings indicated their potential causal roles in the development of other maternal infections. Using the IVW method, factors with OR > 1 were classified as potential risk factors of other maternal infections, such as spontaneous miscarriage or termination, CRP, IL-13 levels, NT-proBNP, blood pressure medication, and vitamin D levels. Conversely, the IVW method found that BMI, LDL cholesterol, total cholesterol, triglycerides, IL-2Ra levels, and cholesterol-lowering medication (all with OR < 1) emerged as potential protective factors, suggesting a negative association with infection risk. Table 3. Results of the relationship between the variable factors and the risk of other maternal infection. id.exposure Exposure SNPs Method P value OR Lower 95%CI Upper 95%CI ebi-a-GCST90095034 Body mass index 51 MR Egger 0.891030985 0.915329861 0.259856995 3.224191654 Weighted median 0.512392841 0.864028786 0.557996018 1.337905147 Inverse variance weighted 0.030158307 0.709525287 0.520276669 0.967612356 Simple mode 0.936353471 1.034170713 0.455224881 2.34940819 Weighted mode 0.793326083 0.925320026 0.519354171 1.648618993 ieu-a-94 Body mass index 67 MR Egger 0.89395571 1.08006344 0.349579277 3.336974216 Weighted median 0.52106033 0.875543194 0.583410457 1.313956367 Inverse variance weighted 0.015545607 0.717571317 0.548402385 0.938924792 Simple mode 0.004967441 0.243077147 0.093661257 0.630853154 Weighted mode 0.906160939 0.961885166 0.505350699 1.830853454 ieu-b-4815 Body mass index 59 MR Egger 0.044589208 0.826465969 0.689019628 0.991330248 Weighted median 0.258357482 0.95324337 0.877286591 1.0357766 Inverse variance weighted 0.000997478 0.908197398 0.857582139 0.961800016 Simple mode 0.577389309 1.056845939 0.871014527 1.282324581 Weighted mode 0.979885109 0.997981666 0.853504959 1.166914609 ukb-b-12621 Ever had stillbirth, spontaneous miscarriage or termination 215 MR Egger 0.01644041 16.68877445 1.704534707 163.3966099 Weighted median 0.544530301 1.651096394 0.326136004 8.358841922 Inverse variance weighted 0.023127631 3.359175266 1.180669579 9.557338195 Simple mode 0.413928906 0.132250775 0.00104164 16.79108411 Weighted mode 0.51919405 0.226339319 0.002488983 20.5824982 ukb-a-448 Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Cholesterol lowering medication 10 MR Egger 0.390635006 0.063640621 0.000166036 24.3931167 Weighted median 0.017017921 0.047486031 0.003886562 0.580184555 Inverse variance weighted 0.008457575 0.073335426 0.010488928 0.5127392 Simple mode 0.12270679 0.02929597 0.000504179 1.702280609 Weighted mode 0.124975226 0.043023747 0.001123678 1.647307126 ukb-b-17805 Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Cholesterol lowering medication 28 MR Egger 0.03210947 0.011029112 0.000223011 0.545449518 Weighted median 0.017312032 0.074679913 0.008815745 0.632628247 Inverse variance weighted 0.002837364 0.101992556 0.022779337 0.456663046 Simple mode 0.070572233 0.02811325 0.000682422 1.158161477 Weighted mode 0.11964277 0.069290551 0.002672177 1.796729965 ukb-b-18009 Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 20 MR Egger 0.882245411 0.482545482 0.000035898 6486.521616 Weighted median 0.060928905 10.40175695 0.898111116 120.4712266 Inverse variance weighted 0.009595695 10.71577348 1.780601582 64.4882058 Simple mode 0.101129173 37.18033426 0.608107904 2273.243359 Weighted mode 0.111957585 29.21290536 0.552304716 1545.150377 ebi-a-GCST005067 C-reactive protein levels 17 MR Egger 0.796189563 1.124476056 0.468952759 2.696319355 Weighted median 0.010931496 1.357544545 1.072779351 1.717899575 Inverse variance weighted 0.00502943 1.301375771 1.082599023 1.564363964 Simple mode 0.131685432 1.431396876 0.919601533 2.228026971 Weighted mode 0.111779545 1.431396876 0.942682966 2.173474107 ebi-a-GCST90018730 C-reactive protein 53 MR Egger 0.375498015 1.316761217 0.720300244 2.407135243 Weighted median 0.018942073 1.569382231 1.077087337 2.28668605 Inverse variance weighted 0.000706729 1.571755344 1.209870209 2.041884197 Simple mode 0.305283359 1.362239862 0.758765945 2.445678349 Weighted mode 0.082079074 1.515821229 0.957086853 2.400737184 ebi-a-GCST000759 LDL cholesterol 166 MR Egger 0.437452869 0.905822499 0.706120345 1.162003624 Weighted median 0.00448905 0.77493768 0.649961703 0.923944294 Inverse variance weighted 0.0000026794 0.766586593 0.686055225 0.856570992 Simple mode 0.095937475 0.706177982 0.469961911 1.061122892 Weighted mode 0.263659628 0.846616324 0.63287721 1.132540704 ebi-a-GCST005068 LDL cholesterol 10 MR Egger 0.093097307 0.631242684 0.393311362 1.013109118 Weighted median 0.017423333 0.732439333 0.566626777 0.946773781 Inverse variance weighted 0.003830062 0.733211185 0.594135036 0.904842517 Simple mode 0.111775718 0.703794215 0.476235031 1.04008791 Weighted mode 0.088567329 0.716807367 0.509285883 1.008888756 ebi-a-GCST90018741 LDL cholesterol 85 MR Egger 0.321153962 0.88310166 0.691807659 1.127290991 Weighted median 0.03014286 0.755764737 0.586747845 0.973468148 Inverse variance weighted 0.000857192 0.770547508 0.66106398 0.898163384 Simple mode 0.000345903 0.443534541 0.289361235 0.679852258 Weighted mode 0.022201985 0.764203947 0.609491006 0.958189156 ieu-a-781 LDL cholesterol 133 MR Egger 0.002382081 0.726461582 0.593492795 0.889221292 Weighted median 0.00842772 0.784753714 0.655263274 0.939833524 Inverse variance weighted 0.00001852412 0.78594139 0.703893288 0.877553287 Simple mode 0.298023451 0.825466716 0.576012649 1.182951974 Weighted mode 0.045221485 0.797397264 0.640255824 0.993106776 ieu-b-4845 LDL cholesterol 66 MR Egger 0.029327673 0.753041996 0.586823025 0.966342873 Weighted median 0.000611801 0.723599391 0.601347347 0.870704894 Inverse variance weighted 0.0000131739 0.75144733 0.660805923 0.854521835 Simple mode 0.293977546 0.804099418 0.53689448 1.204288548 Weighted mode 0.053483286 0.735456442 0.541467394 0.99894506 ebi-a-GCST90018754 Total cholesterol levels 139 MR Egger 0.412450582 0.832239099 0.537165041 1.289402445 Weighted median 0.001077177 0.600022601 0.441759261 0.814984888 Inverse variance weighted 0.002170221 0.73657837 0.605805346 0.895580896 Simple mode 0.00502523 0.418196564 0.229669718 0.761477689 Weighted mode 0.004359167 0.553293067 0.370814469 0.825569777 ieu-a-782 Total cholesterol 135 MR Egger 0.179557034 0.818838391 0.612494903 1.094696965 Weighted median 0.202771291 0.887220701 0.738008358 1.066601161 Inverse variance weighted 0.014166294 0.85500402 0.754413863 0.969006416 Simple mode 0.671864424 1.102694593 0.702169846 1.731682686 Weighted mode 0.637025394 0.914112915 0.630045562 1.326257133 ieu-b-4849 Triglycerides 89 MR Egger 0.005978125 0.62604703 0.452010731 0.867091989 Weighted median 0.019493486 0.828472757 0.707471658 0.970169054 Inverse variance weighted 0.001251977 0.826188393 0.735717911 0.927783939 Simple mode 0.208611554 1.318322326 0.859619297 2.021794719 Weighted mode 0.631005081 1.092184966 0.763075994 1.563236178 ebi-a-GCST90012082 N-terminal prohormone brain natriuretic peptide levels 6 MR Egger 0.788167808 1.491910137 0.097353956 22.86292151 Weighted median 0.007506519 1.420002782 1.098101689 1.836267008 Inverse variance weighted 0.001624768 1.410324814 1.138810379 1.746573545 Simple mode 0.08443591 1.424625963 1.031492598 1.967594473 Weighted mode 0.095355076 1.42523893 1.016074909 1.99916954 ebi-a-GCST005367 Vitamin D levels 35 MR Egger 0.683150889 1.264172287 0.414234557 3.858035363 Weighted median 0.043827867 1.910543097 1.018060885 3.585419085 Inverse variance weighted 0.0000760096 2.394771114 1.553775729 3.690962977 Simple mode 0.270035856 1.9766279 0.600670873 6.50449028 Weighted mode 0.191898937 1.765414446 0.764661666 4.075904815 ebi-a-GCST004443 Interleukin-13 levels 139 MR Egger 0.119615633 1.121765321 0.971519749 1.295246378 Weighted median 0.002939323 1.190584222 1.061282521 1.335639438 Inverse variance weighted 0.00000046607 1.184044101 1.108752649 1.264448328 Simple mode 0.094575574 1.248341314 0.964196587 1.61622231 Weighted mode 0.008498061 1.229915264 1.056567366 1.431703841 ebi-a-GCST004454 Interleukin-2 receptor antagonist levels 107 MR Egger 0.010658579 0.839400881 0.735628793 0.957811663 Weighted median 0.00059236 0.819946681 0.732138476 0.918286064 Inverse variance weighted 0.001203954 0.889222525 0.82821925 0.954719054 Simple mode 0.316223361 0.882840218 0.692706704 1.125161408 Weighted mode 0.009743201 0.824355756 0.713933087 0.951857289 Open in a new tab SNPs, single nucleotide polymorphisms; OR, Odds ratio; 95%CI, 95% Confidence Interval. All instrumental variables showed F-statistics greater than 10 ( S7 Table ), indicating a low risk of weak instrument bias. The results of harmonization analyses in the Mendelian randomization analysis were available in S8 Table . The results of causal effects using five Mendelian randomization methods were summarized in S9 Table . The validity of MR estimates was systematically evaluated through pleiotropy ( S10 Table ), heterogeneity ( S11 Table ), and directionality tests ( S12 Table ). As summarized in Table 4 , all Cochran’s Q statistics returned P-values greater than 0.05, indicating negligible heterogeneity among instrumental variables and supporting the use of fixed-effects IVW models. Similarly, pleiotropy assessment via MR-Egger and MR-PRESSO showed no evidence of horizontal pleiotropy (all P > 0.05, Table 5 ), confirming the robustness of causal estimates. The detailed results of MR-PRESSO global test were available in S13 Table . Finally, directionality testing verified the assumed causal direction, with outcome variance consistently lower than exposure variance across all analyses ( Table 6 ).The results of the sensitivity analysis showed that no significantly deviating SNPs were found in this study ( S5 Fig for maternal sepsis and S6 Fig for other maternal infections), indicating that the analysis results are stable and reliable. Table 4. MR heterogeneity analyses of associations between variable factors and MSMI. id.exposure Outcome Exposure Method Q-statistic Q-df Q P-value ebi-a-GCST000755 Maternal sepsis HDL cholesterol MR Egger 132.742861 128 0.36903889 Inverse variance weighted 133.274913 129 0.38030986 ebi-a-GCST005068 Maternal sepsis LDL cholesterol MR Egger 1.92050296 8 0.98335275 Inverse variance weighted 2.43014945 9 0.98270334 ebi-a-GCST90101747 Maternal sepsis Total cholesterol levels MR Egger 3.42634417 6 0.7537421 Inverse variance weighted 3.47594974 7 0.83776626 ebi-a-GCST000758 Maternal sepsis Triglycerides MR Egger 78.9947419 141 0.99999448 Inverse variance weighted 80.4237597 142 0.99999272 ieu-a-783 Maternal sepsis Triglycerides MR Egger 107.083447 163 0.99977325 Inverse variance weighted 107.752613 164 0.99978168 ieu-b-4850 Maternal sepsis Triglycerides MR Egger 94.6804383 162 0.99999455 Inverse variance weighted 94.9594172 163 0.99999544 ukb-a-224 Maternal sepsis Smoking status: Previous MR Egger 132.46237 149 0.83069857 Inverse variance weighted 135.00376 150 0.80445374 ebi-a-GCST004431 Maternal sepsis RANTES levels MR Egger 93.9900647 119 0.95609204 Inverse variance weighted 94.9319698 120 0.95572199 ebi-a-GCST004443 Maternal sepsis Interleukin-13 levels MR Egger 127.041642 137 0.71779248 Inverse variance weighted 127.101413 138 0.73686716 ebi-a-GCST004444 Maternal sepsis Interleukin-10 levels MR Egger 2.94596968 5 0.70831557 Inverse variance weighted 2.967531 6 0.81291087 ebi-a-GCST004447 Maternal sepsis Interleukin-1-receptor antagonist levels MR Egger 122.007611 103 0.09741256 Inverse variance weighted 124.509633 104 0.08323955 ebi-a-GCST004456 Maternal sepsis Interferon gamma levels MR Egger 110.356099 119 0.70228183 Inverse variance weighted 113.453303 120 0.65072466 ieu-a-27 Maternal sepsis Birth weight MR Egger 12.8567604 11 0.30279053 Inverse variance weighted 18.2361736 12 0.10870539 ukb-a-448 Maternal sepsis Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication MR Egger 3.50251837 8 0.89899411 Inverse variance weighted 3.88877634 9 0.91857919 ukb-b-18009 Maternal sepsis Medication for cholesterol, MR Egger 4.40320971 18 0.99952805 blood pressure, diabetes, or take exogenous hormones: Blood pressure medication Inverse variance weighted 4.40747778 19 0.99977691 prot-a-2400 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 11 MR Egger 5.61340087 18 0.9975288 Inverse variance weighted 7.39602758 19 0.9917841 prot-a-2408 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 9 MR Egger 17.3492977 31 0.97700756 Inverse variance weighted 17.4167633 32 0.98310086 ukb-a-355 Maternal sepsis Number of pregnancy terminations MR Egger 25.8012946 26 0.47406812 Inverse variance weighted 25.8630926 27 0.52620851 ukb-b-12621 Other maternal infections Ever had stillbirth, spontaneous miscarriage or termination MR Egger 175.286843 213 0.97225125 Inverse variance weighted 177.687787 214 0.96659426 ebi-a-GCST90095034 Other maternal infections Body mass index MR Egger 26.7993475 49 0.99590367 Inverse variance weighted 26.9666765 50 0.99683189 ieu-a-94 Other maternal infections Body mass index MR Egger 61.3858909 65 0.60421154 Inverse variance weighted 61.9210704 66 0.61952872 ieu-b-4815 Other maternal infections Body mass index MR Egger 41.2917887 57 0.94171176 Inverse variance weighted 42.4384028 58 0.93768164 ebi-a-GCST005067 Other maternal infections C-reactive protein levels MR Egger 7.22842614 15 0.95099822 Inverse variance weighted 7.34060883 16 0.96612389 ebi-a-GCST90018730 Other maternal infections C-reactive protein MR Egger 32.8333563 51 0.97744468 Inverse variance weighted 33.2407969 52 0.98009883 ebi-a-GCST000759 Other maternal infections LDL cholesterol MR Egger 93.3720615 164 0.99999813 Inverse variance weighted 95.524593 165 0.99999679 ebi-a-GCST005068 Other maternal infections LDL cholesterol MR Egger 1.43418275 8 0.99374762 Inverse variance weighted 1.91387229 9 0.99275746 ebi-a-GCST90018741 Other maternal infections LDL cholesterol MR Egger 81.2222415 83 0.53470902 Inverse variance weighted 83.1995153 84 0.50416949 ieu-a-781 Other maternal infections LDL cholesterol MR Egger 60.1375626 131 0.99999998 Inverse variance weighted 60.9661753 132 0.99999998 ieu-b-4845 Other maternal infections LDL cholesterol MR Egger 32.6061457 64 0.99962198 Inverse variance weighted 32.6065236 65 0.99973666 ieu-a-782 Other maternal infections Total cholesterol MR Egger 66.6264069 133 0.99999973 Inverse variance weighted 66.7309577 134 0.9999998 ebi-a-GCST90018754 Other maternal infections Total cholesterol levels MR Egger 128.530834 137 0.68512589 Inverse variance weighted 128.904021 138 0.69834992 ieu-b-4849 Other maternal infections Triglycerides MR Egger 46.7446912 87 0.99987418 Inverse variance weighted 49.9355313 88 0.99964051 ebi-a-GCST004443 Other maternal infections Interleukin-13 levels MR Egger 118.312122 137 0.87370069 Inverse variance weighted 118.997614 138 0.87704775 ebi-a-GCST004454 Other maternal infections Interleukin-2 receptor antagonist levels MR Egger 94.79927 105 0.75228244 Inverse variance weighted 95.832303 106 0.75041729 ebi-a-GCST90012082 Other maternal infections N-terminal prohormone brain natriuretic peptide levels MR Egger 0.02316797 4 0.99993342 Inverse variance weighted 0.02480886 5 0.99999489 ukb-a-448 Other maternal infections Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication MR Egger 3.90319485 8 0.86575018 Inverse variance weighted 3.90563851 9 0.91751258 ukb-b-17805 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Cholesterol lowering medication MR Egger 18.1140805 26 0.87158352 Inverse variance weighted 19.5794325 27 0.84793373 ukb-b-18009 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication MR Egger 11.2938293 18 0.88147437 Inverse variance weighted 11.7175694 19 0.89734009 ebi-a-GCST005367 Other maternal infections Vitamin D levels MR Egger 15.8644933 33 0.99485326 Inverse variance weighted 17.346877 34 0.99203558 Open in a new tab MSMI, maternal sepsis and maternal other infections; Q,Cochran’s Q; Q_df,degrees of freedom for Cochran’s Q; Q_pval, p-value of statistical significance for Cochran’s Q . Table 5. MR pleiotropy test of associations between variable factors and MSMI. id.exposure Outcome Exposure Egger intercept Egger P-value MR-PRESSO RSSobs MR-PRESSO global P-value ebi-a-GCST000755 Maternal sepsis HDL cholesterol 0.005672223 0.475129483 135.5979561 0.396 ebi-a-GCST000758 Maternal sepsis Triglycerides 0.009410656 0.233931454 81.63661094 1 ebi-a-GCST005068 Maternal sepsis LDL cholesterol 0.038542093 0.495589402 2.898157208 0.984 ebi-a-GCST90101747 Maternal sepsis Total cholesterol levels -0.008498967 0.831139435 4.173755419 0.852 ieu-a-783 Maternal sepsis Triglycerides 0.006300554 0.414536915 109.5645232 1 ieu-b-4850 Maternal sepsis Triglycerides -0.003925279 0.598094071 96.24572275 1 ukb-a-224 Maternal sepsis Smoking status: Previous -0.025849254 0.113016177 137.0172496 0.786 ebi-a-GCST004431 Maternal sepsis RANTES levels 0.009938388 0.333756893 96.53206568 0.971 ebi-a-GCST004443 Maternal sepsis Interleukin-13 levels 0.001997058 0.807223541 129.4788713 0.734 ebi-a-GCST004444 Maternal sepsis Interleukin-10 levels -0.003905133 0.888997096 3.894627352 0.833 ebi-a-GCST004447 Maternal sepsis Interleukin-1-receptor antagonist levels 0.016208237 0.149166153 126.8630402 0.106 ebi-a-GCST004456 Maternal sepsis Interferon gamma levels -0.014643297 0.080996492 115.2668184 0.681 ieu-a-27 Maternal sepsis Birth weight -0.136888781 0.055090675 21.09228962 0.148 ukb-a-448 Maternal sepsis Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication -0.024727881 0.551567389 4.801907271 0.926 ukb-b-18009 Maternal sepsis Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 0.00287889 0.948631014 4.92575117 0.999 prot-a-2400 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 11 0.041587445 0.198468248 8.238128917 0.993 prot-a-2408 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 9 -0.008816342 0.796780532 18.53511745 0.982 ukb-a-355 Maternal sepsis Number of pregnancy terminations 0.005594817 0.805629574 27.5872085 0.556 ukb-b-12621 Other maternal infections Ever had stillbirth, spontaneous miscarriage or termination -0.015614573 0.122746547 179.5733172 0.961 ebi-a-GCST90095034 Other maternal infections Body mass index -0.014233343 0.684279045 28.00369296 0.996 ieu-a-94 Other maternal infections Body mass index -0.021032711 0.467066726 63.53281603 0.65 ieu-b-4815 Other maternal infections Body mass index 0.024722583 0.288772319 43.8810214 0.936 ebi-a-GCST005067 Other maternal infections C-reactive protein levels 0.019603322 0.742310664 8.452549367 0.965 ebi-a-GCST90018730 Other maternal infections C-reactive protein 0.011413996 0.526127458 34.24911822 0.985 ebi-a-GCST000759 Other maternal infections LDL cholesterol -0.013729077 0.144250098 96.71864105 1 ebi-a-GCST005068 Other maternal infections LDL cholesterol 0.057373017 0.508165114 2.204985768 0.999 ebi-a-GCST90018741 Other maternal infections LDL cholesterol -0.015208192 0.163410733 84.91942696 0.526 ebi-a-GCST90018754 Other maternal infections Total cholesterol levels -0.006943867 0.542284304 130.483698 0.71 ieu-a-781 Other maternal infections LDL cholesterol 0.008484727 0.364345832 61.83519629 1 ieu-a-782 Other maternal infections Total cholesterol 0.003766704 0.746943237 67.788827 1 ieu-b-4845 Other maternal infections LDL cholesterol -0.000289938 0.984549717 33.56187092 1 ieu-b-4849 Other maternal infections Triglycerides 0.031192006 0.077535697 51.07842566 1 ebi-a-GCST004443 Other maternal infections Interleukin-13 levels 0.010376436 0.409141706 120.6098671 0.894 ebi-a-GCST004454 Other maternal infections Interleukin-2 receptor antagonist levels 0.012829042 0.311783622 97.39915124 0.763 ebi-a-GCST90012082 Other maternal infections N-terminal prohormone brain natriuretic peptide levels -0.010254851 0.969629419 0.035985447 1 ukb-a-448 Other maternal infections Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication 0.003016256 0.96178583 4.878109826 0.922 ukb-b-17805 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Cholesterol lowering medication 0.036947558 0.236970417 20.83373081 0.866 ukb-b-18009 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 0.043963533 0.523300433 12.97198381 0.915 ebi-a-GCST005367 Other maternal infections Vitamin D levels 0.026331916 0.232036799 18.41384171 0.988 Open in a new tab MR-PRESSO, MR pleiotropy residual sum and outlier; MSMI, maternal sepsis and maternal other infections; RSSobs, residual sum of square observed;MR-PRESSO global P-value, p-value of statistical significance for MR-PRESSO global test . Table 6. MR Steiger directionality test. id.exposure Outcome Exposure snp_r2.exposure snp_r2.outcome correct_causal_direction Steiger_pval ebi-a-GCST000755 Maternal sepsis HDL cholesterol 0.172766974 0.00116295 TRUE 0 ebi-a-GCST000758 Maternal sepsis Triglycerides 0.260936468 0.000789068 TRUE 0 ebi-a-GCST005068 Maternal sepsis LDL cholesterol 0.13670689 8.77472251754687e-05 TRUE 4.48934246922421e-289 ebi-a-GCST90101747 Maternal sepsis Total cholesterol levels 0.032949807 7.60439910408359e-05 TRUE 4.82117443703251e-138 ieu-a-783 Maternal sepsis Triglycerides 0.375332955 0.001008277 TRUE 0 ieu-b-4850 Maternal sepsis Triglycerides 0.417524705 0.000844268 TRUE 0 ukb-a-224 Maternal sepsis Smoking status: Previous 0.018538819 0.001157995 TRUE 1.14026032633963e-207 ebi-a-GCST004431 Maternal sepsis RANTES levels 0.672725925 0.000962158 TRUE 0 ebi-a-GCST004443 Maternal sepsis Interleukin-13 levels 0.872902105 0.001081431 TRUE 0 ebi-a-GCST004444 Maternal sepsis Interleukin-10 levels 0.090442191 0.000100461 TRUE 1.19379412532228e-143 ebi-a-GCST004447 Maternal sepsis Interleukin-1-receptor antagonist levels 0.526073036 0.001068639 TRUE 0 ebi-a-GCST004456 Maternal sepsis Interferon gamma levels 0.294069903 0.001054506 TRUE 0 ieu-a-27 Maternal sepsis Birth weight 0.018501074 0.000224668 TRUE 5.71534446473855e-73 ukb-a-448 Maternal sepsis Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication 0.010561014 9.73078824485904e-05 TRUE 2.93924236218571e-139 ukb-b-18009 Maternal sepsis Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 0.010065199 7.14657434896656e-05 TRUE 4.24151069540903e-153 prot-a-2400 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 11 0.13747845 0.000169513 TRUE 6.93527095697716e-101 prot-a-2408 Maternal sepsis Pregnancy-specific beta-1-glycoprotein 9 0.285192338 0.000303507 TRUE 1.50002556130939e-235 ukb-a-355 Maternal sepsis Number of pregnancy terminations 0.011390177 0.000256786 TRUE 6.17487302983546e-71 ukb-b-12621 Other maternal infections Ever had stillbirth, spontaneous miscarriage or termination 0.015735548 0.00152237 TRUE 2.81990950863449e-135 ebi-a-GCST90095034 Other maternal infections Body mass index 0.039895709 0.000263659 TRUE 1.53542408021117e-290 ieu-a-94 Other maternal infections Body mass index 0.052128823 0.000564291 TRUE 0 ieu-b-4815 Other maternal infections Body mass index 0.072172921 0.00044353 TRUE 0 ebi-a-GCST005067 Other maternal infections C-reactive protein levels 0.078534254 0.000126633 TRUE 3.90595461583497e-149 ebi-a-GCST90018730 Other maternal infections C-reactive protein 0.070119209 0.000372273 TRUE 0 ebi-a-GCST000759 Other maternal infections LDL cholesterol 0.240555508 0.000978787 TRUE 0 ebi-a-GCST005068 Other maternal infections LDL cholesterol 0.13670689 8.5562584656784e-05 TRUE 5.19993733888866e-289 ebi-a-GCST90018741 Other maternal infections LDL cholesterol 0.203102753 0.000785247 TRUE 0 ebi-a-GCST90018754 Other maternal infections Total cholesterol levels 0.134441683 0.001151513 TRUE 0 ieu-a-781 Other maternal infections LDL cholesterol 0.325527243 0.000660266 TRUE 0 ieu-a-782 Other maternal infections Total cholesterol 0.250530156 0.000605703 TRUE 0 ieu-b-4845 Other maternal infections LDL cholesterol 0.317324796 0.000429552 TRUE 0 ieu-b-4849 Other maternal infections Triglycerides 0.288618339 0.000502453 TRUE 0 ebi-a-GCST004443 Other maternal infections Interleukin-13 levels 0.872902105 0.00120224 TRUE 0 ebi-a-GCST004454 Other maternal infections Interleukin-2 receptor antagonist levels 0.722869916 0.000885187 TRUE 0 ebi-a-GCST90012082 Other maternal infections N-terminal prohormone brain natriuretic peptide levels 0.035021417 8.28959349926104e-05 TRUE 3.48384787578417e-132 ukb-a-448 Other maternal infections Medication for cholesterol blood pressure diabetes or take exogenous hormones: Cholesterol lowering medication 0.010561014 9.02500143124042e-05 TRUE 2.02207235991357e-139 ukb-b-17805 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Cholesterol lowering medication 0.018753757 0.000237196 TRUE 3.13531143849049e-266 ukb-b-18009 Other maternal infections Medication for cholesterol, blood pressure, diabetes, or take exogenous hormones: Blood pressure medication 0.010065199 0.000153414 TRUE 1.85145527787838e-139 ebi-a-GCST005367 Other maternal infections Vitamin D levels 0.130750127 0.000274771 TRUE 0 Open in a new tab snp, single nucleotide polymorphisms; Steiger_pval, p value of Steiger directionality test . LASSO regression retained key inflammatory biomarkers, including RANTES, IL-10, CRP, IL-13, and NT-proBNP, for subsequent multivariable MR analysis (MVMR). MVMR analysis was employed to evaluate the independent causal roles of specific inflammatory biomarkers in MSMI. For maternal sepsis, genetically predicted elevations in both RANTES and IL-10 levels demonstrated significant positive associations with disease risk ( Table 7 ). Relatively, in the analysis of other maternal infections, higher levels of CRP, IL-13, and NT-proBNP were causally linked to increased infection risk ( Table 7 ). These results highlight distinct inflammatory pathways contributing to different forms of maternal infection and underscore the potential value of these biomarkers in risk stratification. Table 7. Results of multivariate mendelian randomization (dimension reduction with LASSO) for maternal sepsis and other maternal infections. Outcome Exposure SNPs P value OR Lower 95%CI Upper 95%CI Exposure1 Exposure2 Maternal sepsis RANTES levels 1 0 1.44802918 1.43913011 1.45698328 RANTES levels Interleukin-10 levels Interleukin-10 levels 2 0 1.30709749 1.30369878 1.31050506 RANTES levels Interleukin-10 levels Maternal other infections Interleukin-13 levels 1 0.00656167 1.16173727 1.04270804 1.29435416 C-reactive protein levels Interleukin-13 levels C-reactive protein levels 4 0.00000023385433 1.59615066 1.33687051 1.90571707 C-reactive protein levels Interleukin-13 levels Maternal other infections C-reactive protein levels 4 1.37781487119777e-10 1.60932196 1.39167948 1.86100118 C-reactive protein levels NT-proBNP NT-proBNP 1 0.00000300542091 1.53884312 1.28421102 1.84396342 C-reactive protein levels NT-proBNP Open in a new tab LASSO, least absolute shrinkage and selection operator; SNPs, single nucleotide polymorphisms; OR, Odds ratio; 95%CI, 95% Confidence Interval; NT-proBNP, N-terminal prohormone brain natriuretic peptide levels . Discussion This comprehensive study firstly provides a detailed assessment of the global burden, trends, and causal determinants of MSMI from 1990 to 2021, with projections extending to 2041. Our findings reveal persistent disparities, identify modifiable risk factors, and highlight critical areas for future intervention and research. The disproportionate burden in low- and middle-income countries, particularly in South Asia and sub-Saharan Africa, aligns with the inverse equity hypothesis proposed by Victora et al. [ 31 ], which posits that health interventions often benefit higher-resource populations first, thereby widening disparities. For example, increasing mortality was observed in Kazakhstan, a phenomenon that may be related to limited healthcare access and uneven resource distribution. In contrast, a marked decline was demonstrated in Saudi Arabia, likely reflecting improvements in maternal healthcare services. Additionally, increasing trends observed in Australia may be associated with advanced maternal age and a higher prevalence of comorbidities. The strong negative correlation between SDI and MSMI burden ( Fig 6 ) underscores how structural determinants, including limited healthcare access, sanitation, and education-perpetuate inequitable maternal health outcomes [ 32 ]. Moreover, as a composite indicator, the SDI encompasses components such as educational attainment and fertility rate, which may also influence the burden of infections among populations with MSMI [ 33 – 34 ]. Notably, with respect to fertility, primiparous women tend to face greater difficulties in newborn care compared with multiparous women [ 35 ]. The observed age distribution pattern, characterized by a peak burden in the 20–24 age group and elevated risk in adolescents (15–19 years), reflects complex biological and social vulnerabilities. Younger mothers face increased physiological risks due to reproductive immaturity while simultaneously experiencing greater barriers to healthcare utilization [ 36 ]. Thus, this pattern emphasizes the need for life-course approaches to maternal health that address specific risk factors across different age groups. The overall global decline in MSMI burden since 1990 likely reflects improvements in maternal healthcare, antibiotic accessibility, and infection control practices [ 37 ]. Nevertheless, divergent trends between high- and low-SDI regions ( Fig 9 )-where absolute case numbers increased in low-SDI areas despite declining ASR-suggest that population growth is the main driver of the increasing absolute burden, offsetting the decline in ASR, and highlights how persistent structural barriers can undermine progress. Consequently, this finding supports the concept of the epidemiological polarization described by Bawah et al [ 38 ], where improvements in health indicators mask persistent or worsening absolute burdens in disadvantaged populations. The MR analyses provide novel causal evidence linking specific biomarkers to MSMI risk. The identification of inflammatory markers (RANTES, IL-10, CRP) as risk factors reinforces the central role of dysregulated immunity in sepsis pathogenesis [ 39 ]. Importantly, the differential associations observed in the MR analysis may reflect distinct immunopathological mechanisms underlying MSMI. RANTES and IL-10, identified as independent risk factors for maternal sepsis, are closely involved in immune regulation and anti-inflammatory processes. Elevated IL-10 levels, in particular, may indicate an excessive compensatory anti-inflammatory response, leading to immune suppression and impaired pathogen clearance during sepsis progression [ 40 ]. Similarly, RANTES plays a key role in leukocyte recruitment and immune cell activation, and its dysregulation may contribute to maladaptive immune responses in severe infections [ 41 ]. In contrast, CRP and IL-13, which were associated with other maternal infections, are more strongly linked to systemic inflammatory responses and host defense mechanisms. CRP is a well-established marker of acute inflammation, reflecting the intensity of the innate immune response [ 42 ], while IL-13 is involved in Th2-mediated immune pathways and may influence susceptibility to certain infectious processes [ 43 ]. Although not retained as primary factors in the MVMR analysis, some protective biomarkers such as HDL and IL-1Ra may still possess potential translational relevance. HDL has been widely recognized for its anti-inflammatory and immunomodulatory properties [ 44 ], while IL-1Ra functions as a natural inhibitor of IL-1–mediated inflammatory signaling [ 45 ]. These findings suggest that modulation of inflammatory pathways may represent a potential avenue for prevention or intervention. Nevertheless, further studies are required to validate these effects in clinical settings. Importantly, the MR findings can be partially contextualized within the observed global epidemiological patterns. Regions with higher MSMI burden, particularly low- and middle-SDI countries in South Asia and sub-Saharan Africa, are characterized by increased exposure to infection, poor nutritional status, and limited access to healthcare, all of which may contribute to chronic low-grade inflammation [ 46 – 47 ]. This observation is consistent with our MR findings, which identified inflammatory biomarkers, including CRP, IL-13, and RANTES, as previously reported causal risk factors for MSMI [ 48 – 50 ]. The concordance between epidemiological patterns and genetic evidence suggests that systemic inflammation may represent a key biological pathway linking adverse socioeconomic environments to increased maternal infection risk. Furthermore, in low- and middle-income countries where diagnostic capacity is often limited, the integration of biomarker-based approaches may improve early detection and risk stratification of maternal infections [ 51 ]. Thus, these findings suggest that maternal sepsis may be characterized by immune dysregulation and suppression, whereas other maternal infections may be driven more by heightened inflammatory activation. This distinction highlights the potential need for tailored prevention and therapeutic strategies targeting different immune pathways. The burden estimates presented herein are largely consistent with previous Global Burden of Disease studies, which have shown declining but unevenly distributed maternal infection rates [ 52 ]. However, this study adds significant granularity by revealing how socioeconomic stratification affects both absolute and relative burden metrics-a nuance often overlooked in aggregate global analyses. The causal role of inflammatory biomarkers identified through MR extends previous observational studies that reported associations but could not establish causality [ 53 ]. Our finding that HDL cholesterol may be protective offers new insights into potential modifiable factors, complementing experimental evidence suggesting that HDL particles can neutralize bacterial toxins and modulate immune responses. Notably, the divergent projections between ARIMA and BAPC models ( Figs 11 - 12 ) echo ongoing methodological debates about forecasting infectious diseases. The difference between ARIMA and BAPC results is mainly attributable to their model structures. ARIMA only considers time trends, whereas BAPC incorporates age, period, and cohort effects. Therefore, BAPC may better reflect long-term changes in population structure. Thus, ARIMA is more suitable for short-term prediction, while BAPC provides more reliable long-term estimates. Both models demonstrated good performance in diagnostic tests. The ARIMA model fit the data well based on AIC/BIC and residual tests, while the BAPC model showed stable results in posterior checks. Future studies should prioritize several key areas. First, prospective validation of the identified causal biomarkers in diverse populations is essential, particularly in high-burden regions of Africa and Asia [ 54 ]. Research exploring the mechanisms through which lipid metabolism and specific cytokines influence maternal infection susceptibility could reveal new therapeutic targets. Methodologically, developing integrated forecasting models that incorporate climate change, antimicrobial resistance patterns, and healthcare investment scenarios would enhance prediction accuracy. Implementation research examining context-specific strategies for reducing the burden in low-SDI regions is urgently needed-particularly interventions that address both clinical and social determinants of MSMI [ 55 ]. Finally, expanding genomic studies to include underrepresented populations will improve the generalizability of causal inferences and help advance precision public health approaches to maternal infection prevention. Longitudinal studies tracking how changing SDI modifies MSMI burden could also provide valuable insights for targeting interventions most effectively. Several limitations warrant consideration when interpreting our results. First, heterogeneity in diagnostic capacity and reporting systems across countries may affect the accuracy of MSMI burden estimates, particularly in low-SDI regions. In low-resource settings, limited laboratory capacity and incomplete surveillance systems may lead to underreporting and misclassification, especially for other maternal infections. Such systematic bias may result in underestimation of the true disease burden and may weaken the observed inverse relationship between SDI and MSMI burden. Despite robust quality control, heterogeneity in diagnostic capability and reporting completeness across countries may affect burden estimates, particularly in low-resource settings [ 56 ]. This concern is especially relevant given our finding that diagnostic quality varies with socioeconomic development ( Fig 9 ). Second, the MR analyses assume linearity of causal effects and may not capture threshold or non-linear relationships. Although MR reduces confounding, it relies on key assumptions, including the absence of unmeasured confounding of the instrument–outcome association. For complex exposures such as inflammatory cytokines and behavioral traits, genetic instruments may influence outcomes through multiple biological pathways, introducing potential bias [ 57 ]. Therefore, despite the application of sensitivity analyses to detect pleiotropy, residual bias from unknown or unmeasured pathways cannot be completely excluded. This limitation has been widely recognized in MR studies involving complex traits. Additionally, the majority of genetic instruments were derived from European-ancestry populations, which may limit the generalizability of the findings to other populations with different genetic architectures [ 58 ]. Differences in environmental exposures and gene–environment interactions across populations may further influence the validity of causal estimates. This limitation is particularly relevant for high-burden regions such as Africa and South Asia, where the applicability of the findings may be reduced. The statistical power of the MR analyses may have been insufficient to detect small but clinically meaningful effects, particularly for exposures with a limited number of genome-wide significant SNPs. Although a strict F-statistic threshold (>10) was applied to ensure strong instruments and minimize weak instrument bias, it is important to note that MR studies are inherently constrained by the variance in the exposure explained by the genetic instruments. With a limited number of SNPs, the statistical power to detect modest causal effects is reduced. Thus, null findings should not be interpreted as definitive evidence of no causal relationship, but rather as an indication that any true effect may be smaller than the study was designed to detect. Future investigations leveraging expanded GWAS datasets or employing multi-variable MR approaches to aggregate genetic signals may help overcome these power limitations. It is acknowledged that overlap between exposure and outcome GWAS samples may introduce bias in two-sample MR analyses, particularly by inflating Type I error rates and underestimating standard errors. In this study, this risk was mitigated by selecting exposure and outcome datasets from independent, large-scale consortium efforts. Specifically, the use of UK Biobank-based GWAS for exposures and data from distinct sources (e.g., the FinnGen consortium for outcomes) represents a strategy to minimize overlap, given their differing study populations, ascertainment strategies, and geographic distributions. Therefore, substantial sample overlap is unlikely in this study. Nevertheless, the possibility of partial overlap due to shared control populations or unreported study affiliations cannot be entirely excluded. Future studies utilizing strictly non-overlapping datasets or applying methods to correct for sample overlap (e.g., MRlap, pseudo-IVW) will be valuable to further validate our findings. Third, the forecasting models, though statistically validated, rely on historical trends and may not account for future healthcare breakthroughs, emerging antimicrobial resistance, or climate change impacts on infection patterns [ 59 ]. Conclusions In summary, this study systematically characterized the global burden and future trends of MSMI and identified key causal risk factors using MVMR. Importantly, by integrating population-level epidemiological analysis with genetic causal inference, this study provides complementary lines of evidence that strengthen the robustness of the findings. The observed discrepancy between temporal trends and absolute burden highlights the critical role of demographic changes, while the identification of inflammatory biomarkers offers potential targets for intervention. To the best of our knowledge, this is the first study to combine GBD-based trend analysis with MVMR in the context of MSMI, thereby providing a novel framework for linking macro-level disease patterns with underlying biological mechanisms. Thus, these findings may inform more precise and evidence-based public health strategies, particularly in high-burden settings. Supporting information S1 Fig. Spearman’s correlation analyses between MSMI Burden and SDI in 1990. (A) ASIR-SDI, (B) ASPR-SDI, (C) ASMR-SDI, (D) ASDR-SDI, (E) AS-YLD-SDI, and (F) AS-YLL-SDI. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate. (PDF) pntd.0014374.s001.pdf (818.6KB, pdf) S2 Fig. Spearman’s correlation analyses between MSMI Burden and SDI in 2021. (A) ASIR-SDI, (B) ASPR-SDI, (C) ASMR-SDI, (D) ASDR-SDI, (E) AS-YLD-SDI, and (F) AS-YLL-SDI. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate. (PDF) pntd.0014374.s002.pdf (819.3KB, pdf) S3 Fig. Decomposition results of MSMI burden across MSMI at the SDI components level. Three SDI components include population structure (aging), epidemiological changes, and population size (population). DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. (PDF) pntd.0014374.s003.pdf (642.7KB, pdf) S4 Fig. Decomposition results of MSMI burden across sex at the SDI components level. Three SDI components include population structure (aging), epidemiological changes, and population size (population). DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. (PDF) pntd.0014374.s004.pdf (562.7KB, pdf) S5 Fig. Leave-one-out plots for the causal association between metabolites and maternal sepsis. The consistency of results remains robust even after excluding individual genetic variants in each analysis, indicating a high level of reliability and stability in our findings. (PDF) pntd.0014374.s005.pdf (210.8KB, pdf) S6 Fig. Leave-one-out plots for the causal association between metabolites and other maternal infections. The consistency of results remains robust even after excluding individual genetic variants in each analysis, indicating a high level of reliability and stability in our findings. (PDF) pntd.0014374.s006.pdf (257.4KB, pdf) S1 Table. All original data of MSMI from the GBD database. (ZIP) pntd.0014374.s007.zip (37.4MB, zip) S2 Table. All original data of MSMI based on age categories from the GBD database. (XLSX) pntd.0014374.s008.xlsx (439.3KB, xlsx) S3 Table. All original data of MSMI at the country level from the GBD database. (XLSX) pntd.0014374.s009.xlsx (17.5MB, xlsx) S4 Table. All original data of MSMI based on age categories and all ages from the GBD database. (XLSX) pntd.0014374.s010.xlsx (531.1KB, xlsx) S5 Table. All original data of MSMI based on SDI quintiles from the GBD database. (XLSX) pntd.0014374.s011.xlsx (470.6KB, xlsx) S6 Table. All original data of MSMI based on GBD region categories from the GBD database. (XLSX) pntd.0014374.s012.xlsx (1.9MB, xlsx) S7 Table. Summary of F-statistics for all exposures in this study. (XLSX) pntd.0014374.s013.xlsx (11.4KB, xlsx) S8 Table. Summary of coordinated results of instrumental variables (SNPs) for each exposure-outcome pair in this study. (XLSX) pntd.0014374.s014.xlsx (891.3KB, xlsx) S9 Table. Summary of causal effects using five Mendelian randomization methods in this study. (XLSX) pntd.0014374.s015.xlsx (43.7KB, xlsx) S10 Table. Summary of horizontal pleiotropy test in Mendelian randomization analysis. (XLSX) pntd.0014374.s016.xlsx (13.8KB, xlsx) S11 Table. Summary of heterogeneity test in Mendelian randomization analysis. (XLSX) pntd.0014374.s017.xlsx (16KB, xlsx) S12 Table. Summary of Steiger test for causal direction in Mendelian randomization analysis. (XLSX) pntd.0014374.s018.xlsx (13.7KB, xlsx) S13 Table. Summary of MR-PRESSO global test in Mendelian randomization analysis. (XLSX) pntd.0014374.s019.xlsx (11.3KB, xlsx) S1 Checklist. The filled checklist is based on the STROBE Statement-Checklist of items that should be included in reports of Mendelian randomization studies ( https://www.strobe-mr.org ). 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Lancet. 2020;396(10258):1223–49. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014374.r001 Decision Letter 0 Vinícius Belo Author information Copyright and License information Roles Vinícius Belo : Academic Editor © 2026 Vinícius Belo This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 24 Mar 2026 -->PNTD-D-25-02202 Global Burden, Projections, and Causal factors of Maternal Sepsis and Other Maternal Infections: A Comprehensive Epidemiological and Mendelian Randomization Study PLOS Neglected Tropical Diseases Dear Dr. Liang, Thank you for submitting your manuscript to PLOS Neglected Tropical Diseases. After careful consideration, we feel that it has merit but does not fully meet PLOS Neglected Tropical Diseases's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript within by May 4 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosntds@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pntd/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: * A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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Kind regards, Vinícius Silva Belo Academic Editor PLOS Neglected Tropical Diseases Song Liang Section Editor PLOS Neglected Tropical Diseases Shaden Kamhawi co-Editor-in-Chief PLOS Neglected Tropical Diseases orcid.org/0000-0003-4304-636XX Paul Brindley co-Editor-in-Chief PLOS Neglected Tropical Diseases orcid.org/0000-0003-1765-0002 Additional Editor Comments: Please carefully address all reviewer comments in a comprehensive revision. Particular attention must be given to substantial improvements in the statistical analysis and its reporting (including justification of methods, robustness/sensitivity analyses, model diagnostics, and power considerations), as well as to ensuring that the conclusions are fully supported by the data. Journal Requirements: 1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full. 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According to PLOS's copyright policy, authors who use figures or other material (e.g., graphics, clipart, maps) from another author or copyright holder must demonstrate or obtain permission to publish this material under the Creative Commons Attribution 4.0 International (CC BY 4.0) License used by PLOS journals. Please respond directly to this email and provide any known details concerning your material's license terms and permissions required for reuse, even if you have not yet obtained copyright permissions or are unsure of your material's copyright compatibility. Once you have responded and addressed all other outstanding technical requirements, you may resubmit your manuscript within Editorial Manager. Potential Copyright Issues: - Figure 1: Please confirm whether you drew the images / clip-art within the figure panels by hand. 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( http://www.planiglobe.com/?lang=enl ) * Natural Earth - All maps are public domain. ( http://www.naturalearthdata.com/about/terms-of-use/ ). Reviewers' Comments: Reviewer's Responses to Questions Key Review Criteria Required for Acceptance? As you describe the new analyses required for acceptance, please consider the following: Methods -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? -Is the study design appropriate to address the stated objectives? -Is the population clearly described and appropriate for the hypothesis being tested? -Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? -Were correct statistical analysis used to support conclusions? -Are there concerns about ethical or regulatory requirements being met? Reviewer #1: -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? NO -Is the study design appropriate to address the stated objectives? YES -Is the population clearly described and appropriate for the hypothesis being tested? YES -Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? NO -Were correct statistical analysis used to support conclusions? NO -Are there concerns about ethical or regulatory requirements being met? NO Reviewer #2: 1. Insufficient consideration of data heterogeneity and robustness validation: Sepsis is a time-dependent condition, and disparities in diagnostic and clinical capacity across healthcare settings and countries may bias global disease burden estimates. While the authors acknowledge this limitation, they did not perform sensitivity analyses or use statistical methods to quantify the impact of such heterogeneity on results, leaving the robustness of conclusions inadequately validated. 2. Limited generalizability of causal inference: Genetic instruments used for Mendelian randomization analyses are predominantly derived from European ancestry populations. Variations in lifestyle, health literacy and cultural contexts across regions can strongly influence sepsis outcomes, yet gene-environment interactions are not explored. These gaps restrict the generalizability of findings to high-burden regions such as Africa and Asia. 3. Insufficient mechanistic discussion and translational relevance: The manuscript lacks in-depth exploration of the molecular mechanisms underlying the associations of lipid metabolism and inflammatory cytokines with maternal infections, and does not integrate existing basic research to support biological plausibility, limiting clinical translational value. Additionally, while targeted interventions are recommended, the study does not propose feasible, evidence-based intervention strategies tailored to key high-risk populations. 4.Academic writing and linguistic issues: The manuscript contains overly lengthy sentences, minor grammatical errors, and imprecise use of academic terminology in several sections, compromising readability and scientific rigor. A thorough linguistic revision is strongly suggested. ********** Results -Does the analysis presented match the analysis plan? -Are the results clearly and completely presented? -Are the figures (Tables, Images) of sufficient quality for clarity? Reviewer #1: -Does the analysis presented match the analysis plan? YES -Are the results clearly and completely presented? NO -Are the figures (Tables, Images) of sufficient quality for clarity? NO Reviewer #2: (No Response) ********** Conclusions -Are the conclusions supported by the data presented? -Are the limitations of analysis clearly described? -Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? -Is public health relevance addressed? Reviewer #1: -Are the conclusions supported by the data presented? NO -Are the limitations of analysis clearly described? NO -Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? NO -Is public health relevance addressed? YES Reviewer #2: (No Response) ********** Editorial and Data Presentation Modifications? Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”. Reviewer #1: (No Response) Reviewer #2: (No Response) ********** Summary and General Comments Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed. Reviewer #1: This study addresses a critical topic in maternal and neonatal health by ambitiously integrating Global Burden of Disease (GBD) trend analysis with bidirectional, multivariable Mendelian Randomization (MR). The scope is impressive and the potential public health impact is significant. However, the manuscript in its current form requires substantial revisions to enhance methodological rigor, depth of analysis, logical coherence, and the clarity of its contributions. The epidemiological and MR sections feel disconnected, key methodological details are omitted, and the discussion lacks the mechanistic depth and integrative synthesis needed to fully realize the study's potential. 1. The conflicting projections for ASIR (increasing by ARIMA vs. decreasing by BAPC) represent a core issue. The current discussion is superficial. A thorough analysis explaining this discrepancy is mandatory. Investigate if the ARIMA model failed to capture age-period-cohort effects. I strongly suggest the author to report model fit statistics and model diagnostics. Conduct formal model comparison to justify which projection is more reliable or under which assumptions each trend is plausible. 2. While Figure 6 shows an inverse correlation, formal statistical testing quantifying the SDI-MSMI burden relationship is absent. Furthermore, SDI is a composite index. A decomposition analysis exploring the relative contributions of its components would provide more precise insights into the social determinants. 3. The observation of increasing absolute cases despite declining ASRs in low-SDI regions attributed to population growth needs quantitative support. Please calculate the proportion of the increase attributable to population growth or present overlayed plots of population growth and case numbers by SDI quintile. 4. Figure 10 reveals substantial between-country heterogeneity. This is not addressed in the discussion. Provide case studies or literature references linking these trends to specific national contexts to ground the global analysis. 5. The potential for diagnostic misclassification and under-reporting in low-resource settings is a critical limitation, especially for "other maternal infections." Expand the discussion on how this systemic bias might have underestimated the true burden and potentially attenuated the observed SDI-burden gradient. 6. The F-statistic calculation is mentioned but not reported. This is a key metric for assessing weak instrument bias. Provide a summary of F-statistics for all exposures either in the main text or supplementary materials. The equation for F-statistic calculation should be provided in the Method section. 7. The use of LASSO for variable selection prior to MVMR lacks detail. How was the tuning parameter (λ) selected? Which variables were selected and what were their coefficients? The selection process directly impacts MVMR conclusions and must be transparent. 8. While MR mitigates confounding, the assumption of no genetic confounding is strong. For complex exposures like cytokines and behavioral traits, unmeasured or unknown confounding remains a concern. Strengthen the discussion of this limitation, citing relevant methodological literature. 9. The over-reliance on European genetic data is a major limitation. The discovered causal relationships may not generalize to other populations due to differences in genetic architecture, environment, and gene-environment interactions. Discuss the implications for the applicability of your findings to high-burden regions. 10. There is no mention of whether the MR analyses were sufficiently powered to detect clinically meaningful effect sizes. This is particularly pertinent for exposures with a low number of instrumental SNPs. Consider adding a power. 11. The bias arising from sample overlap between the exposure and outcome populations was not assessed. It is recommended to supplement the analysis with MRlap to correct for the effects of sample overlap. 12. The number of instrumental variables in the Mendelian randomization analysis is too low, with some exposures having as few as 1 SNP and the highest being only 4. This presents a substantial challenge to statistical power. It is difficult to believe that results based on such a limited number of instrumental variables are statistically significant. Therefore, it is advisable to consider appropriately relaxing the criteria for screening instrumental variables and including more SNPs to enhance statistical power. 13. The paper currently reads as two separate stories. A synthesis is lacking. For instance, MR identifies inflammatory markers as key causal factors. Is there epidemiological evidence suggesting higher levels of these markers in high-burden populations/regions? 14. The biological explanations for MR findings are cursory. Why are RANTES and IL-10 independent risks for sepsis, while CRP and IL-13 are risks for other infections? This suggests potentially distinct immunopathological mechanisms. Deepen the discussion by linking these specific cytokines/chemokines to known roles in pregnancy immunology, anti-infective responses, and sepsis pathophysiology. 15. The protective roles of HDL, IL-1Ra, etc., have significant translational potential, which is underexplored. Do these findings point to novel prevention/therapy targets? Could interventions to raise HDL or agents like IL-1Ra analogues be considered for MSMI prevention in high-risk pregnancies? Adopt a more proactive translational medicine perspective. 16. The current abstract and conclusions are largely descriptive. They need to be rewritten to emphasize the value-added of the integrated approach. State clearly that this is the first study to combine GBD trend analysis with MVMR, providing a dual evidence base for future precision public health interventions. 17. Many global maps are cluttered due to the large number of countries, obscuring key messages. Add regional summary insets or tables to global maps. Highlight countries with significantly increasing trends in Fig. 10. Ensure all figure titles and legends are self-explanatory. 18. The acronym "MSMI" is sometimes spelled out. Please use the acronym consistently after its first definition. 19. The results of harmonization and other sensitivity analyses in the Mendelian randomization analysis should be added to the supplementary materials as supplementary results. 20. Descriptions of the features in each column should be added to all tables in the supplementary materials. 21. There are minor grammatical errors and overly long sentences. A thorough language edit is recommended to enhance clarity, conciseness, and precision throughout the manuscript. Reviewer #2: (No Response) ********** PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . Reviewer #1: Yes: Yunfeng WANG Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] Figure resubmission: --> -->-->While revising your submission, we strongly recommend that you use PLOS’s NAAS tool ( https://ngplosjournals.pagemajik.ai/artanalysis ) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.-->--> After uploading your figures to PLOS’s NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete. If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above. When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.--> Reproducibility: To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols --> PLoS Negl Trop Dis. 2026 May 27;20(5):e0014374. doi: 10.1371/journal.pntd.0014374.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026 May. PMC Copyright notice 22 Apr 2026 Attachment Submitted filename: Response to Reviewers.docx pntd.0014374.s022.docx (22.1KB, docx) PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014374.r003 Decision Letter 1 Vinícius Belo Author information Copyright and License information Roles Vinícius Belo : Academic Editor © 2026 Vinícius Belo This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 12 May 2026 Dear Dr Liang, We are pleased to inform you that your manuscript 'Global Burden, Projections, and Causal factors of Maternal Sepsis and Other Maternal Infections: A Comprehensive Epidemiological and Mendelian Randomization Study' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases. Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests. Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated. IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript. Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS. Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases. Best regards, Vinícius Silva Belo Academic Editor PLOS Neglected Tropical Diseases Song Liang Section Editor PLOS Neglected Tropical Diseases Shaden Kamhawi co-Editor-in-Chief PLOS Neglected Tropical Diseases orcid.org/0000-0003-4304-636XX Paul Brindley co-Editor-in-Chief PLOS Neglected Tropical Diseases orcid.org/0000-0003-1765-0002 *********************************************************** The revisions have improved the manuscript, and the final version is suitable for acceptance and publication. p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; line-height: 16.0px; font: 14.0px Arial; color: #323333; -webkit-text-stroke: #323333}span.s1 {font-kerning: none Reviewer's Responses to Questions Key Review Criteria Required for Acceptance? As you describe the new analyses required for acceptance, please consider the following: Methods -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? -Is the study design appropriate to address the stated objectives? -Is the population clearly described and appropriate for the hypothesis being tested? -Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? -Were correct statistical analysis used to support conclusions? -Are there concerns about ethical or regulatory requirements being met? Reviewer #1: -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? Yes Is the study design appropriate to address the stated objectives? Yes -Is the population clearly described and appropriate for the hypothesis being tested? Yes -Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? Yes -Were correct statistical analysis used to support conclusions? Yes -Are there concerns about ethical or regulatory requirements being met? Yes ********** Results -Does the analysis presented match the analysis plan? -Are the results clearly and completely presented? -Are the figures (Tables, Images) of sufficient quality for clarity? Reviewer #1: -Does the analysis presented match the analysis plan? Yes -Are the results clearly and completely presented? Yes -Are the figures (Tables, Images) of sufficient quality for clarity? Yes ********** Conclusions -Are the conclusions supported by the data presented? -Are the limitations of analysis clearly described? -Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? -Is public health relevance addressed? Reviewer #1: -Are the conclusions supported by the data presented? Yes -Are the limitations of analysis clearly described? Yes -Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? Yes -Is public health relevance addressed? Yes ********** Editorial and Data Presentation Modifications? Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”. Reviewer #1: NA ********** Summary and General Comments Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed. Reviewer #1: The authors have responded to my concerns. The technical limitations of the article have been described in the Discussion section. Although this study has some seemingly fatal flaws, such as too few instrumental variables and failure to consider sample overlap bias, the authors have provided explanations for these limitations in the Discussion section. Overall, the structure of the article is complete and the statistical methods are rigorous. ********** PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . Reviewer #1: Yes: Yunfeng WANG PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0014374.r004 Acceptance letter Vinícius Belo Author information Copyright and License information Roles Vinícius Belo : Academic Editor © 2026 Vinícius Belo This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice Dear Postdoc Liang, We are delighted to inform you that your manuscript, "Global Burden, Projections, and Causal factors of Maternal Sepsis and Other Maternal Infections: A Comprehensive Epidemiological and Mendelian Randomization Study," has been formally accepted for publication in PLOS Neglected Tropical Diseases. We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication. The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly. Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers. For Research Articles, you will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing . Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases. Best regards, Shaden Kamhawi co-Editor-in-Chief PLOS Neglected Tropical Diseases Paul Brindley co-Editor-in-Chief PLOS Neglected Tropical Diseases Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 Fig. Spearman’s correlation analyses between MSMI Burden and SDI in 1990. (A) ASIR-SDI, (B) ASPR-SDI, (C) ASMR-SDI, (D) ASDR-SDI, (E) AS-YLD-SDI, and (F) AS-YLL-SDI. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate. (PDF) pntd.0014374.s001.pdf (818.6KB, pdf) S2 Fig. Spearman’s correlation analyses between MSMI Burden and SDI in 2021. (A) ASIR-SDI, (B) ASPR-SDI, (C) ASMR-SDI, (D) ASDR-SDI, (E) AS-YLD-SDI, and (F) AS-YLL-SDI. DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. ASIR, Age-Standardized Incidence Rate; ASPR, Age-Standardized Prevalence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; AS-YLD, Age-Standardized YLDs Rate; AS-YLL, Age-Standardized YLLs Rate. (PDF) pntd.0014374.s002.pdf (819.3KB, pdf) S3 Fig. Decomposition results of MSMI burden across MSMI at the SDI components level. Three SDI components include population structure (aging), epidemiological changes, and population size (population). DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. (PDF) pntd.0014374.s003.pdf (642.7KB, pdf) S4 Fig. Decomposition results of MSMI burden across sex at the SDI components level. Three SDI components include population structure (aging), epidemiological changes, and population size (population). DALYs, Disability-Adjusted Life Years; YLDs, Years Lived with Disability; YLLs, Years of Life Lost. (PDF) pntd.0014374.s004.pdf (562.7KB, pdf) S5 Fig. Leave-one-out plots for the causal association between metabolites and maternal sepsis. The consistency of results remains robust even after excluding individual genetic variants in each analysis, indicating a high level of reliability and stability in our findings. (PDF) pntd.0014374.s005.pdf (210.8KB, pdf) S6 Fig. Leave-one-out plots for the causal association between metabolites and other maternal infections. The consistency of results remains robust even after excluding individual genetic variants in each analysis, indicating a high level of reliability and stability in our findings. (PDF) pntd.0014374.s006.pdf (257.4KB, pdf) S1 Table. All original data of MSMI from the GBD database. (ZIP) pntd.0014374.s007.zip (37.4MB, zip) S2 Table. All original data of MSMI based on age categories from the GBD database. (XLSX) pntd.0014374.s008.xlsx (439.3KB, xlsx) S3 Table. All original data of MSMI at the country level from the GBD database. (XLSX) pntd.0014374.s009.xlsx (17.5MB, xlsx) S4 Table. All original data of MSMI based on age categories and all ages from the GBD database. (XLSX) pntd.0014374.s010.xlsx (531.1KB, xlsx) S5 Table. All original data of MSMI based on SDI quintiles from the GBD database. (XLSX) pntd.0014374.s011.xlsx (470.6KB, xlsx) S6 Table. All original data of MSMI based on GBD region categories from the GBD database. (XLSX) pntd.0014374.s012.xlsx (1.9MB, xlsx) S7 Table. Summary of F-statistics for all exposures in this study. (XLSX) pntd.0014374.s013.xlsx (11.4KB, xlsx) S8 Table. Summary of coordinated results of instrumental variables (SNPs) for each exposure-outcome pair in this study. (XLSX) pntd.0014374.s014.xlsx (891.3KB, xlsx) S9 Table. Summary of causal effects using five Mendelian randomization methods in this study. (XLSX) pntd.0014374.s015.xlsx (43.7KB, xlsx) S10 Table. Summary of horizontal pleiotropy test in Mendelian randomization analysis. (XLSX) pntd.0014374.s016.xlsx (13.8KB, xlsx) S11 Table. Summary of heterogeneity test in Mendelian randomization analysis. (XLSX) pntd.0014374.s017.xlsx (16KB, xlsx) S12 Table. Summary of Steiger test for causal direction in Mendelian randomization analysis. (XLSX) pntd.0014374.s018.xlsx (13.7KB, xlsx) S13 Table. Summary of MR-PRESSO global test in Mendelian randomization analysis. (XLSX) pntd.0014374.s019.xlsx (11.3KB, xlsx) S1 Checklist. The filled checklist is based on the STROBE Statement-Checklist of items that should be included in reports of Mendelian randomization studies ( https://www.strobe-mr.org ). Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614–1621. doi:10.1001/jama.2021.18236 . (DOCX) pntd.0014374.s020.docx (50KB, docx) Attachment Submitted filename: Response to Reviewers.docx pntd.0014374.s022.docx (22.1KB, docx) Data Availability Statement All data analyzed during this study are included in this published article and its supplementary information files. The publicly available datasets analyzed during the current study are available in Tables and Supplementary Tables. 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