Dietary Patterns and Anti-Flavivirus IgG Seroconversion in Colombian Children

The burden of flaviviral infections, especially dengue and Zika, is high in the Americas. Malnutrition affects the risk and response to infections, but the role of diet on flaviviral infection risk is uncertain. The objective of this study was to investigate the relations between dietary patterns adherence and anti-flavivirus IgG seroconversion in children during a Zika epidemic in a dengue-endemic area of Colombia. In 2015-2016, we followed 424 anti-flavivirus IgG seronegative children aged 2 to 12 years for 1 year. Baseline data included children's sociodemographic, anthropometric, and dietary information collected through a 38-item food frequency questionnaire (FFQ). IgG testing was repeated at the end of follow-up. The primary exposure was adherence to each of four dietary patterns (animal foods, traditional, ultraprocessed foods, and prudent) that were identified from the FFQ through principal component analysis. Secondary exposures were intake frequencies of foods contributing to relevant patterns. We estimated risk of seroconversion by quartiles of adherence scores and compared them using relative risks (RR) and 95% CI from Poisson regression adjusted for sex, age, and socioeconomic status indicators. Seroconversion risk was 32.1%. Adherence to the traditional pattern was positively related to seroconversion. RR comparing fourth versus first quartiles of adherence was 1.52 (95% CI: 1.04-2.21; P trend = 0.02). Of the most representative foods in this pattern, potato and sugarcane water intake frequencies were related to increased seroconversion risk. In conclusion, adherence to a traditional foods pattern, including potatoes and sugarcane water, was positively associated with anti-flavivirus IgG seroconversion.

Magnitude and kinetics of the human immune cell response associated with severe dengue progression by single-cell proteomics

Approximately 5 million dengue virus-infected patients progress to a potentially life-threatening severe dengue (SD) infection annually. To identify the immune features and temporal dynamics underlying SD progression, we performed deep immune profiling by mass cytometry of PBMCs collected longitudinally from SD progressors (SDp) and uncomplicated dengue (D) patients. While D is characterized by early activation of innate immune responses, in SDp there is rapid expansion and activation of IgG-secreting plasma cells and memory and regulatory T cells. Concurrently, SDp, particularly children, demonstrate increased proinflammatory NK cells, inadequate expansion of CD16⁺ monocytes, and high expression of the FcγR CD64 on myeloid cells, yet a signature of diminished antigen presentation. Syndrome-specific determinants include suppressed dendritic cell abundance in shock/hemorrhage versus enriched plasma cell expansion in organ impairment. This study reveals uncoordinated immune responses in SDp and provides insights into SD pathogenesis in humans with potential implications for prediction and treatment.

An 8-gene machine learning model improves clinical prediction of severe dengue progression

BACKGROUND

Each year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.

METHODS

We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.

RESULTS

We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.

CONCLUSIONS

The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

Burden of dengue among febrile patients at the time of chikungunya introduction in Piedecuesta, Colombia

Objective

To estimate the age‐specific incidence of symptomatic dengue and chikungunya in Colombia.

Method

A passive facility‐based fever surveillance study was conducted among individuals with undifferentiated fever. Confirmatory diagnostics included serological and molecular tests in paired samples, and surveillance's underreporting was assessed using capture–recapture methods.

Results

Of 839 febrile participants 686 completed the study. There were 33.2% (295/839) dengue infections (51% primary infections), and 35.9% (191/532) of negative dengue cases there were chikungunya cases. On average, dengue cases were younger (median = 18 years) than chikungunya cases (median = 25 years). Thrombocytopaenia and abdominal pain were the main dengue predictors, while presence of rash was the main predictor for chikungunya diagnosis. Underreporting of dengue was 31%; the estimated expansion factors indicate an underreporting rate of dengue cases of threefold for all cases and of almost sixfold for inpatients.

Conclusions

These findings highlight the ongoing coexistence of both arboviruses, a distinct clinical profile of each condition in the study area that could be used by clinicians to generate a differential diagnosis, and the presence of underreporting, mostly among hospitalised cases.