Introduction

Cortisol, a glucocorticoid hormone produced by the adrenal cortex, serves as a pivotal regulator of various physiological processes essential for maintaining homeostasis1. Its secretion is intricately governed by the hypothalamic–pituitary–adrenal (HPA) axis, responding dynamically to stressors encountered by an organism 2. Additionally, beyond its role in stress response, cortisol influences immune function3 and inflammation4,5. Dysregulation of cortisol has been implicated in several diseases, including Cushing syndrome, Addison disease, osteoporosis, hypertension, diabetes mellitus, and the risk of weight gain and subsequent obesity6,7,8. Previous studies have demonstrated a significant association between cortisol levels and the severity of various infections. For instance, elevated cortisol levels at the time of admission have been linked to increased severity of dengue fever9. Similarly, increased cortisol levels have been observed in individuals infected with the Influenza B virus (IBV)10 and those with Coronavirus disease 2019 (COVID-19)11. Furthermore, patients with severe COVID-19 infections have been found to exhibit higher serum cortisol levels12.

Malaria, caused by Plasmodium parasites transmitted through the bites of infected female Anopheles mosquitoes13, remains a formidable global health challenge, particularly prevalent in tropical and subtropical regions14. The clinical manifestations of malaria range from nonspecific symptoms such as fever, chills, and malaise to severe complications, posing substantial morbidity and mortality burdens worldwide13. Despite extensive efforts aimed at malaria control and eradication, the disease continues to exact a heavy toll on vulnerable populations, underscoring the urgency for a deeper understanding of its pathogenesis and associated physiological responses.

Previous studies suggest that malaria-induced immune activation and inflammatory processes may perturb the finely orchestrated regulation of the HPA axis, resulting in alterations in cortisol production, secretion patterns, and responsiveness15,16. These alterations, in turn, could modulate the host’s immune competence and susceptibility to severe malaria outcomes17,18. Understanding the physiological changes, including alterations in cortisol levels, during Plasmodium infections is crucial for several reasons. First, elevated cortisol levels may indicate how the body responds to the stress of infection, potentially influencing disease severity and patient outcomes. Second, cortisol's impact on immune function can affect the host's ability to combat infection, potentially altering susceptibility to severe malaria outcomes. Third, cortisol levels could serve as a biomarker for disease severity, aiding in the timely identification of patients at risk of severe complications. Fourth, understanding the cortisol-malaria relationship could guide the development of targeted treatments that modulate cortisol levels to improve patient outcomes. This systematic review and meta-analysis aimed to investigate the relationship between Plasmodium infections and cortisol levels by consolidating existing evidence of alterations in cortisol levels during such infections. The findings from this review have the potential to inform clinical practice, guide future research directions, and enhance strategies for malaria prevention and management globally.

Methods

Registration and reporting guideline

The study protocol for assessing cortisol levels in malaria patients was registered in PROSPERO (CRD42024496578), and systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines19.

Systematic review question

The systematic review questions followed the Population, Exposure, Comparator, Outcome (PECO) framework20. The “P” are patients with suspected malaria; “E” is the Plasmodium infection; “C” is Plasmodium-uninfected individuals; “O” is blood cortisol levels.

Definitions

The definition of severe malaria, according to the World Health Organization (WHO), includes clinical or laboratory evidence of vital organ dysfunction. This can manifest as impaired consciousness, prostration, multiple convulsions, severe anemia, respiratory distress in relation to metabolic acidosis, hypoglycemia, renal impairment, jaundice, significant bleeding, shock, and clinical or laboratory evidence of vital organ dysfunction. There are also specific criteria for hyperparasitemia, which differ for P. falciparum (> 5% of red blood cells infected) and P. knowlesi (hyperparasitemia defined as > 100,000 parasites per µL)21. Non-severe malaria is defined as the presence of malaria parasites without evidence of WHO criteria for severe malaria.

Outcomes

The primary aim of the systematic review and meta-analysis was to synthesize the differences in blood cortisol levels between participants infected with malaria and uninfected individuals. The secondary aim is to compare blood cortisol levels between participants who developed severe malaria and those with less severe forms of the disease. The tertiary aim is to examine the association between blood cortisol levels and parasite density.

Search strategy

Searches were conducted across major databases, including Embase, PubMed, Scopus, and Medline, using comprehensive terms related to cortisol and malaria. The general search terms were “(Cortisol OR Cortef OR Glucocorticoid OR “Pregn-4-ene-3,20-dione, 11,17,21-trihydroxy-, (11beta)-” OR “Hydrocortisone, (11 alpha)-Isomer” OR Epicortisol OR “11-Epicortisol” OR “11 Epicortisol” OR “Hydrocortisone, (9 beta,10 alpha,11 alpha)-Isomer” OR Cortifair OR Cortril) AND (malaria OR Plasmodium OR “Plasmodium Infection “ OR “Remittent Fever “ OR “Marsh Fever “ OR Paludism). The search strategy was tailored to each database's specific requirements (Table S1). Additionally, the first 200 results from Google Scholar were reviewed to capture potentially-relevant studies and gray literature, as this method could substantially enhance the clarity and comprehensiveness of systematic reviews22. The searches were not limited by language or publication date and were conducted from December 21 to 31, 2023.

Eligibility criteria

The inclusion criteria for the studies encompassed research conducted in malaria-endemic areas such as Africa, America, and Asia. Eligible study designs included cross-sectional, cohort, case–control, and quasi-experimental studies that reported blood cortisol levels in infected patients. The studies could involve patients with mono or mixed infections of Plasmodium species such as P. falciparum and P. vivax, encompassing adults, children, and pregnant women. Exclusion criteria included animal studies, in vitro experiments, articles without cortisol information, studies measuring cortisol post-treatment or intervention, and repeated participant groups. Non-original articles such as case reports, reviews, and conference abstracts were also excluded to ensure the inclusion of only primary research studies with original data on cortisol levels in malaria patients.

Study selection and data extraction

After retrieving articles from the main databases, duplicates were removed, and the remaining records were screened using the title and abstract. Articles that met the eligibility criteria were included in the review and underwent full text screening, while those that did not meet the criteria were excluded, with reasons for exclusion specified. Also, the first 200 results from a Google Scholar search were reviewed following a similar process. The study selection was independently performed by two authors (KUK, MK), and disagreements were resolved through discussion with a third author to reach an agreement.

Information was extracted using a standardized datasheet, which included the first author’s name, year of publication and study conduction, study design and location, characteristics, and number of both infected and uninfected participants, exposure characteristics (such as Plasmodium species, parasite density, and clinical status) and outcomes of interest (including cortisol levels and qualitative and quantitative data). This also encompassed the methods used for malaria identification and cortisol measurement, including the types of blood samples used. One author (MK) performed data extraction and thoroughly verified by another author (AM).

Risk of bias assessment

Risk of bias assessment was conducted using the Joanna Briggs Institute (JBI) critical appraisal checklists appropriate to each study design, ensuring a standardized evaluation of methodological quality23. The checklist for cross-sectional studies focused on aspects like the clarity of inclusion criteria, detailed descriptions of subjects and settings, validity of exposure and outcome measurements, and handling of confounding factors. The checklist for case–control studies evaluated the comparability of groups, matching of cases and controls, and measurement methods. The cohort study checklist examined the similarity of groups, exposure measurement, identification of confounding factors, and outcome assessment. Lastly, the checklist for quasi-experimental studies assessed the clarity of cause and effect, comparability and treatment of participants, control group presence, outcome measurement methods, and follow-up completeness. Two authors (MK and KUK) performed the risk of bias assessment, and any disagreements were resolved through discussion to reach a consensus.

Data synthesis and analysis

The qualitative synthesis had been applied to explain the difference in cortisol between malaria-infected patients and uninfected individuals and also between patients with severe and non-severe malaria infection. In the quantitative synthesis, the meta-analysis employed the random effects model to calculate the effect estimate as described previously24. The heterogeneity of the included studies was calculated using I2 statistics (if I2 is more than 50%, assuming that there is a substantial heterogeneity)25. If there was a substantial heterogeneity, the meta-regression and subgroup analyses were conducted to explore potential sources of heterogeneity using covariates extracted from each study such as publication years, study design, continent, participants, Plasmodium species, clinical status, method for malaria detection, and method for cortisol measurement. Publication bias was assessed using a funnel plot and Egger’s test26,27, which indicated potential bias. An influential analysis (sensitivity analysis) was conducted to determine whether a single study disproportionately affected the overall result28. The robustness of the findings was further confirmed through outlier detection, with recalculated results remaining significant even after removing outliers. The power analysis, conducted after the meta-analysis, tested whether the study had sufficient power to detect differences in cortisol levels between comparison groups. This analysis underscored that the sample size and number of included studies were adequate to support the review's conclusions.

Results

Search results

Figure 1 outlines the process of study selection for a systematic review. Initially, 2149 records were identified across various databases, including Embase, PubMed, Scopus, and Medline. After removing 579 duplicates, 1570 records were screened using the title and abstract. Of these, 1509 were excluded for not relating to the participants or outcome of interest. Sixty-one full-text reports were sought for retrieval, with 47 excluded for reasons such as animal studies or case reports with 14 studies shortlisted for the review. The search in Google Scholar with the first 200 results identified 31 relevant reports were assessed for eligibility, with 25 excluded for reasons such as being duplicate records or no information on cortisol levels in malaria. Finally, 20 studies were included in the final review and analysis.

Figure 1
figure 1

The diagram illustrates the process of selecting studies for inclusion in the systematic review. The left side of the diagram shows the identification and screening process via databases and registers. The right side of the diagram depicts the identification of studies via Google Scholar.

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Characteristics of studies included in the review

Table S2 details the characteristics of studies included in the systematic review. Briefly, the publication years of included studies span from 1987 to 2023, with the majority (40%) published between 2000 and 2009. Study designs varied, with cross-sectional studies (50%) being the most common, followed by case–control (25%), cohort (15%), and quasi-experimental studies (10%). Geographically, these studies were predominantly conducted in Africa (70%), with significant contributions from Nigeria and Sudan. Studies were also from Asian countries like Vietnam, India, Myanmar, and South America (Brazil). The studies primarily focused on P. falciparum (80%), with varying participant demographics, including adults, children, and pregnant women. In terms of clinical status, most studies (65%) were from symptomatic malaria patients. Microscopy was the predominant method of Plasmodium diagnosis (85%). Cortisol levels are measured using various techniques, with radioimmunoassay (RIA) and enzyme immunoassays (EIA) being the most common. Serum and plasma were the primary blood samples used for cortisol measurement (Table 1).

Table 1 Characteristics of included studies.
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Risk of bias

In the critical appraisal of the included cross-sectional studies using the JBI checklist, most studies18,29,30,31,32,33 provided clear inclusion criteria and detailed descriptions of subjects and settings. The validity and reliability of exposure and outcome measurements were generally well-addressed, with only two studies34,35 presenting some ambiguities in measurement criteria and outcomes. A notable concern in two studies35,36 was the identification and management of confounding factors, which are crucial for establishing causal inferences.

In the critical appraisal of the case–control studies, five studies37,38,39,40,41 uniformly ensured comparability between groups and appropriate matching of cases and controls. They all employed consistent criteria for participant identification and uniformly measured exposure. A significant methodological concern was identified across most studies, except for one study, was the lack of identification and strategies to address confounding factors38. Additionally, the assessment of whether the exposure period was sufficiently long enough to be meaningful was unclear in several studies. Statistical analyses were appropriate in all studies, and the consistent, reliable outcome assessments were appropriate, except for one study41.

In the critical appraisal of the cohort studies, three studies17,42,43 ensured that the two groups were similar and recruited from the same population. Exposures were measured consistently, the measurement of exposure was conducted validly and reliably, participants were free of the outcome at the study’s start, and outcomes were measured validly and reliably. Adequate follow-up time, completeness, and management of incomplete follow-up are well-addressed in all studies. Appropriate statistical analyses were utilized across all studies. Two studies17,42 also successfully identified confounding factors and stated strategies to deal with them. However, one study43 did not identify or articulate strategies for confounding factors.

In the critical appraisal of the quasi-experimental studies, two studies44,45 excelled across all the criteria. Both studies clearly defined causal relationships, ensured comparable treatment across groups, included control groups, and conducted multiple outcome measurements. They also managed follow-up processes thoroughly and employed consistent and reliable outcome measurement methods. Appropriate statistical analyses were conducted, affirming the strength of their conclusions (Table S3).

Qualitative synthesis

The comparison of cortisol levels between malaria cases and uninfected individuals showed that most studies reported higher cortisol levels in infected patients17,18,30,31,32,33,35,37,38,39,44,45,46 (Table 2). Conversely, some studies found no significant difference29,34,36,40,41,43. When comparing cortisol levels between severe and non-severe malaria cases, four studies reported significantly higher cortisol levels in severe cases17,18,33,44. In contrast, one study found no significant difference between cerebral and uncomplicated malaria31. Regarding the relationship between parasitemia and cortisol levels, four studies observed a significant positive correlation18,39,41,46, whereas three studies did not find a significant correlation29,36,42.

Table 2 Blood cortisol levels in infected patients in relation to uninfected individuals, parasite density, and disease severity.
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Regionally, studies from Africa37,38 and Asia33,44 reported higher cortisol levels in infected adults compared to uninfected cases. In pregnant women, studies from Sudan34,38 and Gabon39, also found elevated cortisol levels correlated with parasite load. Contrastingly, studies from Ghana29 and Nigeria40 reported no significant differences. In children, increased cortisol levels in severe malaria were reported in Ghana29, Nigeria30, Cameroon18, and India17.

Meta-analysis

The difference in blood cortisol levels between infected patients and uninfected individuals was estimated using the quantitative data on cortisol as reported by 15 studies17,29,30,31,32,33,34,36,37,38,40,41,44,45,46. The results showed significantly higher cortisol levels in infected patients compared to uninfected individuals (P < 0.0001, standardized mean difference (SMD): 1.354, 95% CI: 0.913 to 1.795, I2: 88.3%, across 15 studies, Fig. 2).

Figure 2
figure 2

The plot presents the aggregated data on blood cortisol levels from 15 studies, comparing infected patients with uninfected individuals. The Y-axis represents cortisol levels, while the X-axis categorizes the groups compared. The blue square signifies the SMD of cortisol level reported by a study, with error bars depicting the 95% CI. The gray diamond represents the combined SMD with a 95% CI is 1.354 (0.91–1.80), indicating significantly higher cortisol levels in malaria patients (P < 0.0001). The high level of heterogeneity among the studies is denoted by an I2 value of 88.3%. The number of participants across all studies totaled 1053. Abbreviations: SMD, standardized mean difference; CI, confidence interval.

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The meta-regression analysis of several covariates suggested that none of the covariates—publication years, study design, continent, participants, Plasmodium species, clinical status, method for malaria detection, and method for cortisol measurement—significantly explain the heterogeneity (high I2 values near or above 90% and low R-squared values at 0%), as indicated by the high residual heterogeneity (P < 0.0001) and non-significant P-values for the test of moderators (ranging from 0.206 to 0.681) for all but two covariates (Table 3). The ‘method for cortisol measurement’ and ‘blood sample for cortisol measurement’ covariates are exceptions. These two covariates demonstrated a significant impact on the between-study variance (with tau2 values of 0.415 and 0.387, respectively) and explained a considerable amount of heterogeneity (32.21% and 36.69%, respectively, as indicated by R-squared), with a significant test of moderators P-values (0.04 and 0.026, respectively). This suggests that the method by which cortisol is measured, and the type of blood sample used for cortisol measurement are significant moderators in the analysis and account for some of the variability in the effect sizes across studies.

Table 3 Meta-regression analysis (15 studies).
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The subgroup analysis of the meta-analysis revealed several insights into how different factors impact cortisol levels in infected patients compared to uninfected individuals (Table 4). The publication year of studies did not significantly affect cortisol differences (P = 0.527), suggesting consistency in findings over time. However, the study design played a notable role, with quasi-experimental studies reporting the highest cortisol level differences (P = 0.046), indicating that research methodology may influence outcomes. Geographical differences between Africa and Asia did not significantly alter cortisol levels, although the effect was larger in studies from Asia (P = 0.194). Notably, participant demographics were influential (P = 0.014), with studies on children showing a significant increase in cortisol compared to adults. The differences between Plasmodium species (P. falciparum or P. vivax) did not significantly alter cortisol levels (P = 0.095). The methods used for both Plasmodium detections did not significantly alter cortisol levels (P = 0.324). The methods used for cortisol measurement affect the reported cortisol levels (P < 0.0001), with enzyme-linked fluorescent assays yielding the highest differences. Similarly, the type of blood sample used for cortisol measurement was significant (P < 0.0001), with plasma samples showing higher cortisol levels compared to serum.

Table 4 Subgroup analyses of covariates in the meta-analysis of the differences in cortisol levels between malaria patients and uninfected controls.
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A cumulative meta-analysis shows that after each new study is incorporated, the results of the meta-analysis did not significantly change, suggesting that further studies might not substantially alter the conclusion (Fig. 3).

Figure 3
figure 3

Cumulative analysis of cortisol levels in infected patients versus uninfected individuals. The Y-axis measures cortisol levels, while the X-axis categorizes the studies included in the analysis. Each gray squared box represents data from a single study. The gray diamond represents the combined effect size calculated across all studies. Error bars may indicate confidence intervals, demonstrating the precision of the estimated effect sizes. The heterogeneity of the included studies is quantified by the I2 statistic. Abbreviations: SMD, standardized mean difference; CI, confidence interval.

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Publication bias

There is an asymmetry in the funnel plot (Fig. 4); this might suggest the presence of publication bias or other biases, such as methodological differences between smaller and larger studies or true heterogeneity. The Egger’s test confirmed the presence of significant publication bias (P = 0.013).

Figure 4
figure 4

The funnel plot in the context of the meta-analysis compared cortisol levels in infected patients compared to uninfected individuals. Gray dots, effect estimate of a single study.

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Influential analysis

The leave-one-out meta-analysis on cortisol levels in infected patients versus uninfected individuals indicated that omitting any single study does not substantially alter the overall conclusion that cortisol levels are significantly higher in infected patients compared to uninfected individuals (Supplementary file 1). This is evidenced by the consistently significant P-value of less than 0.0001 across all iterations. The robustness of the pooled SMD suggests that the meta-analysis findings are stable and not overly reliant on any single study.

Outlier detection

The outlier detection in the meta-analysis, focusing on cortisol levels in infected patients versus uninfected individuals, identifies four studies as outliers33,34,40,46 (Supplementary file 2). After removing these outliers, the meta-analysis was recalculated with the remaining 11 studies. The results after removing outliers showed a highly significant P-value (< 0.0001), indicating that the effect is robust even after removing outliers.

Power analysis

The power analysis showed a power of 100%. This analysis was based on a hypothesized effect size (d) of 1.354, with 15 studies included (k = 15) and sample sizes of 589 (n1) and 464 (n2) for the two comparative groups. The significance level was set at 5% (P = 0.05), and a high heterogeneity was assumed. A power of 100% indicates that the meta-analysis was highly likely to detect the specified effect size if it truly exists in the population. This high-power level is primarily due to the large cumulative sample size and the number of studies included, providing robust capability to identify the effect.

Discussion

This systematic review and meta-analysis presented a comprehensive compilation of studies investigating the relationship between Plasmodium infection and cortisol levels across different demographics and geographical locations. The systematic review indicated a consistent difference in cortisol levels between infected patients and uninfected cases, with several studies from Sudan, Gabon, Vietnam, Nigeria, India, Cameroon, Tanzania, and Vietnam reporting higher blood cortisol levels in malaria-infected patients compared to uninfected individuals17,18,30,31,32,33,35,37,38,39,44,45,46. Furthermore, the meta-analysis confirms that Plasmodium-infected patients exhibit significantly higher cortisol levels compared to uninfected controls, underscoring a robust association between Plasmodium infection and elevated cortisol levels. This significant increase in cortisol levels was observed across all age groups, with studies involving adults, pregnant women, and children showing similar results. The random effects model analysis showed a substantial and significant effect but also revealed high heterogeneity between studies. This heterogeneity was explored and explained by several variables included in the model, such as differences in study populations, methods of cortisol measurement, and types of blood samples used (serum or plasma).

Although the majority of included studies showed significantly higher cortisol levels in Plasmodium -infected individuals compared to uninfected controls, a few studies (from Sudan, Nigeria, Brazil, and Kenya) reported no significant difference in cortisol levels between infected and uninfected cases29,34,36,40,41,43. The discrepancy in the results of studies included in the systematic review suggests a possible variation in cortisol levels between studies. This variation was confirmed by the fact that the studies included in the meta-analysis showed considerable heterogeneity, as indicated by a high I2 value of 88.3%. Substantial differences in the results of the individual studies could be due to various factors such as study design, participant demographics, geographical areas, methods of cortisol and malaria measurement, or other unidentified variables.

The meta-regression and subgroup analyses revealed that among the various factors assessed, only two covariates were identified as significant moderators including the method for cortisol measurement and the blood sample for cortisol measurement. These covariates not only explained a considerable portion of the heterogeneity (32.2% and 36.7%, respectively) but also significantly impacted the between-study variance, highlighting the importance of the technical aspects of cortisol measurement in interpreting the results. A previous study suggested the immunoassays were reported to be interfered with by cross-reactivity in a complex matrix47. Furthermore, different properties of antibodies used in the reaction can cause differences in the specificity of the immunoassays for detecting cortisol levels48. The multicenter study revealed that when comparing different mass spectrometry and the immunoassays, there was a high inter-assay variation of true cortisol levels49. Mass spectrometry enables precise quantification and significantly minimizes variability among different laboratories50. Although the immunoassay is being used in clinical laboratories due to its simplicity and availability, the evidence from the meta-analysis potentially suggested the cautious interpretation of immunoassays for the quantification of cortisol in patients with malaria. It should be compared with a reference method to accurately interpret cortisol levels.

For other covariates, the subgroup analysis demonstrated that the type of study design, particularly quasi-experimental studies, influenced the reported differences in cortisol levels. While the effect was larger in studies from Asia compared to Africa, this difference was not statistically significant, indicating that geographical factors might not be the primary determinant for the observed cortisol level differences. The age of participants was a significant factor, with studies involving children showing a more pronounced increase in cortisol levels compared to studies involving adults. The type of Plasmodium species (P. falciparum or P. vivax) did not significantly affect the observed cortisol level differences, suggesting a general effect of malaria on cortisol levels regardless of the specific species involved. For the variation across participants’ groups, studies focusing on pregnant women, particularly in Gabon, highlighted intricate patterns. For instance, infected primigravidae exhibited higher cortisol levels compared to uninfected primigravidae and infected multigravidae39. This evidence might suggest a heightened stress response in first-time pregnant women when infected with malaria. Studies involving children and adults, such as those by Armah BNA29 from Ghana and Ibrahim et al.36 from Sudan showed varied results, indicating that age may influence the cortisol response to malaria.

For the impact of malaria severity on cortisol levels, some studies indicated that cortisol levels are not only higher in malaria-infected patients but also correlate with the severity of the disease. For instance, Sivaraman et al.17 from India and Vandermosten et al.18 from Cameroon observed significantly higher cortisol levels in patients with severe malaria compared to those with uncomplicated malaria. Similarly, van Thien et al.33 from Vietnam also observed significantly increased cortisol levels in patients with cerebral malaria compared to uninfected groups. The result was mixed for the correlation of cortisol levels with parasite density. Bouyou-Akotet et al.39 from Gabon and Olaniyan et al.41 from Nigeria found significant correlations, suggesting a possible link between the parasite load and stress response as indicated by cortisol levels. However, other studies, including Armah BNA29 from Ghana and Libonati et al.42 from Brazil, found no significant correlation. These varied results indicated that the association between parasite density and cortisol levels might be influenced by regional differences in malaria endemicity.

High blood cortisol levels may stimulate gluconeogenesis in humans51, suggesting that an increased rate of gluconeogenesis could lead to elevated cortisol levels in patients with malaria. Additionally, cortisol is believed to suppress cell-mediated immunity and may be associated with heightened susceptibility to malaria during pregnancy39. This hypothesis is bolstered by in vitro experiments demonstrating that high cortisol levels diminish NK cell cytotoxicity against P. falciparum46 and downregulate NK cell receptors52. High blood cortisol levels in patients with malaria also indicate an intact hypothalamic–pituitary–adrenal (HPA) axis and may be attributed to cytokine release or stress induced by malaria45. Libonati et al.42 observed that cortisol levels were higher on Day 0 and then declined in tandem with clinical improvement and reduction in parasitemia, suggesting that cytokine production decreases during the convalescence phase of the disease. Furthermore, the elevated cortisol levels in patients with malaria may be attributable to the release of cortisol in response to low blood glucose levels, as hypoglycemia is frequently observed in patients with malaria, particularly in severe cases53,54,55,56.

Understanding the relationship between cortisol levels and malaria can have implications for managing and treating the disease. For instance, if cortisol levels are indeed higher in severe cases, they could potentially serve as a biomarker for disease severity. The mixed results regarding the correlation between cortisol levels and parasite load warrant further investigation to elucidate the underlying mechanisms. The variation in cortisol response based on demographic factors like pregnancy status and age calls for more targeted studies to understand the physiological underpinnings of these differences. In addition, future research should clarify the potential role of cortisol level measurement in clinical settings, particularly whether it can enhance the management and treatment of malaria or predict severe disease outcomes effectively. The findings suggest that cortisol could serve as a supplementary biomarker for understanding the pathophysiological impact of malaria. However, translating these findings into clinical practice requires further validation through targeted studies. Specifically, research should focus on whether interventions that modulate cortisol levels can positively influence patient outcomes and if cortisol measurement can offer actionable insights beyond current diagnostic and prognostic tools.

While a strong and significant effect was shown by the study results, the high levels of heterogeneity suggested that the results should be interpreted with caution, considering the potential variability of effects across different study settings. This heterogeneity could be attributed to differences in study populations, methods of cortisol measurement, and types of blood samples used (serum or plasma), as demonstrated by the subgroup analyses. It is suggested that future research continue to investigate these sources of variability to improve the consistency and reliability of cortisol level measurement in the context of malaria.

Conclusion

The systematic review and meta-analysis confirm that Plasmodium infection was associated with increased cortisol levels, highlighting the intricate relationship between the disease and the host stress response. These findings underscore the potential of cortisol as a supplementary biomarker for understanding the pathophysiological impact of malaria. By providing insights into the stress-related mechanisms of malaria, this comprehensive understanding can inform future research and potentially enhance disease management and treatment strategies, particularly in regions heavily burdened by malaria.