Depression symptoms are associated with demographic characteristics, nutritional status, and social support among young adults in Chile: a latent class analysis.

BACKGROUND: Depressive disorders are a critical public health concern in Chile. Nonetheless, there is a lack of evidence regarding the identification of depressive symptom clusters. The objective was to identify depressive symptom clusters among Chilean young adults and examine how demographic, and lifestyle factors as well as social support can influence and predict them. METHODS: Cross-sectional study conducted among 1,000 participants from the Limache cohort 2. A latent class analysis (LCA) was performed to identify depressive symptom clusters, using the Patient Health Questionnaire (PHQ-9). Multinomial logistic regression was then applied to explore the associations between identified classes and potential predictors. The models were adjusted by age and sex. RESULTS: Three latent classes of depressive symptoms were identified: minimal (25.7%); somatic (50.7%) and severe (23.6%). In the severe class for eight out nine depressive symptoms the probabilities were above 50%, and the probability of suicidal ideation was almost a third in this class. Being female (Adjusted Odds ratio [AOR], 2.49; 95% confidence interval [CI] [1.63-3.81]), current smoker (AOR, 1.74; 95% CI [1.15-2.65]), having basic education (AOR, 3.12; 95% CI [1.30-7.53]) and obesity (AOR, 2.72; 95% CI [1.61-4.59]) significantly increased the likelihood of belonging to severe class. Higher social support decreased the odds of being in the somatic (OR, 0.96; 95% CI [0.93-0.98]) and severe (OR, 0.92; 95% CI [0.90-0.94]) classes. CONCLUSIONS: These findings highlight the importance of individualized intervention strategies for depression management. Also, the study suggests that nutritional status and social support should be considered when addressing depression in this population.

Determining factors for COVID-19 vaccine hesitancy among Brazilians: a study using structural equation modeling.

OBJECTIVES: to investigate the factors influencing vaccine hesitancy against COVID-19 among Brazilians. METHODS: this research employed an observational and analytical approach, utilizing a web-based survey. Data collection took place in 2020, and data analysis was conducted using structural equation modeling. RESULTS: the prevalence of vaccine hesitancy was found to be 27.5% (1182 individuals). There is a negative correlation between belief in conspiracy theories and social influence. Among the various beliefs associated with vaccination intentions, only conspiracy beliefs exhibited significant predictive value. Thus, the findings suggest that personal beliefs significantly impact hesitancy towards vaccination, and also indicate that trust in governmental bodies is inversely related to hesitancy. CONCLUSIONS: vaccine hesitancy emerges as a multifaceted phenomenon influenced by a complex array of factors, including personal beliefs, trust in governmental bodies, and healthcare systems.

Revisiting Louis Fry's spiritual leadership model in confessional school teachers using structural equation modeling (SEM).

This paper examines the construct validity of the spiritual leadership model proposed by (Fry et al. 2005). The analysis focused on examining the relationships proposed by the model through CFA and structural equation modeling (SEM). A confirmatory factor analysis indicated the SL scale provides acceptable reliability and convergent validity indexes; however, it did not achieve discriminant validity. Model convergence was obtained using MLR (Robust Maximum Likelihood) methods. However, when the robustness indices were analyzed, it was found that some obtained acceptable results and others were deficient, so that an acceptable model fit was not achieved. Regarding the relationship between the hypotheses, it was found that they were significant in all cases except for the reciprocal relationship between vision and altruistic love. In light of this finding, alternative models were developed that also failed to yield significant results. The theoretical and methodological discussion focuses on the relationships of Fry's model and addresses the need to review its causal nature, considering recursive and non-recursive aspects.

Discrimination based on gender identity against transgender women and travestis in Brazil: a latent class analysis and associated factors.

OBJECTIVE: To identify groups of transgender women and travestis (TGW) with specific patterns of gender-based discrimination (GBD) and analyze the factors associated with GBD. METHODS: A cross-sectional study was conducted with TGW recruited through respondent-driven sampling in five Brazilian cities (2019-2021). Latent class analysis was used to characterize GBD (low, medium, and high) using 14 observable variables. Descriptive analysis was performed, and associations between predictor variables and GBD were estimated by adjusted odds ratios (aOR) using ordinal logistic regression. RESULTS: Out of a total of 1,317 TGW, 906 (68.8%) answered questions about GBD. Most were under 34 years old, single, and had a Brown race/skin color. GBD was classified as "low," "medium," and "high," with estimates of 41.7, 44.5, and 13.8%, respectively. Variables positively associated with higher intensity of GBD included living in Manaus compared to São Paulo, being ≤34 years old compared to >34, being homeless compared to living in one's own house or rented apartment, not having legally changed one's name compared to those who had, and reporting physical or sexual violence compared to those who did not report. Variables negatively associated with higher intensity of GBD included having a Brown or Asian race/skin color compared to White and a monthly income ≥1 minimum wage compared to ³1. CONCLUSION: A high proportion of GBD was observed in Brazilian TGW, with this outcome associated with more vulnerable sociodemographic characteristics and a history of violence.

Multimorbidity patterns and associated factors in a megacity: a cross-sectional study.

OBJECTIVE: To identify empirical patterns of multimorbidity and quantify their associations with socioeconomic, behavioral characteristics, and health outcomes in the megacity of São Paulo. METHODS: This was a cross-sectional study conducted through household interviews with residents aged 20 years or older in urban areas (n = 3,184). Latent class analysis was used to identify patterns among the co-existence of 22 health conditions. Age-adjusted prevalence ratios were estimated using Poisson regression. RESULTS: The analysis of latent classes showed 4 patterns of multimorbidity, whereas 58.6% of individuals were classified in the low disease probability group, followed by participants presenting cardiovascular conditions (15.9%), respiratory conditions (12.8%), and rheumatic, musculoskeletal, and emotional conditions (12.8%). Older individuals, with lower schooling and lower household income, presented higher multimorbidity prevalence in cardiovascular, respiratory, rheumatic, musculoskeletal, and emotional conditions patterns compared with the low disease probability pattern. CONCLUSION: The results showed four distinct patterns of multimorbidity in the megacity population, and these patterns are clinically recognizable and theoretically plausible. The identification of trends between patterns would make it feasible to estimate the magnitude of the challenge for the organization of health care policies.

Psychological distress, health-promoting lifestyle and sociodemographic factors in Honduran university students: a structural equation model.

This study sought to analyze an explanatory model on the relationship among sociodemographic factors, health-promoting lifestyle behaviors and psychological distress (depression, anxiety and stress) in college students. This is an observational, analytical and cross-sectional study conducted on a national sample of 4203 students who entered a macro university in Honduras in 2021, 2022 and 2023. We used a sociodemographic survey, the Health-Promoting Lifestyle Profile (HPLP-II) and the Depression, Anxiety and Stress Scales (DASS-21). Univariate analysis and a multivariate structural equation model were conducted. The average HPLP-II score was 117.45 (±â€…23.41), and the average DASS-21 score was 20.06 (±â€…14.16). The multivariate model showed a good data fit (comparative fit index = 0.951; Tucker-Lewis index = 0.957; root mean square error of approximation = 0.067 [90% CI = 0.067-0.068]). Results indicate that being a woman (ß = 0.11; p < 0.001) and being enrolled in biological and health sciences (ß = 0.09; p < 0.001) significantly predict HPLP-II scores. Furthermore, being a woman (ß = 0.17; p < 0.001), age (ß = 0.10; p < 0.001) and having pre-existing medical conditions (ß = 0.16; p < 0.001) significantly explain part of the variance of DASS-21. A significant reverse relationship between health-promoting behavior and psychological distress was shown (r = -0.36; p < 0.001). This study identifies protective and risky sociodemographic factors linked to health-promoting lifestyle behaviors and psychological distress. Our findings have implications for developing comprehensive intervention policies and strategies to promote health in higher education settings.

Factors associated with body mass index in a cohort of older adults: Structural equation modeling analysis.

BACKGROUND: Considering the interrelationship between the multiple determinants of nutritional status, analyses are needed to estimate direct and mediated effects between the variables that influence body mass index (BMI) in older adults. We aimed to verify the direct and indirect effects of sociodemographic, behavioral and health conditions on the BMI of older adults in the community. METHODS: This is a longitudinal study based on data collection in 2015 and 2020. Descriptive analysis and Structural Equation Modeling were performed, considering p<0.05. RESULTS: The sample consisted of 220 older adults with a mean baseline age of 68.86 years (± 7.10). Direct associations of higher BMI value with younger age, higher monthly family income, greater functional disability to perform instrumental activities of daily living and greater number of morbidities were found. In addition, education, gender, moderate to vigorous physical activity were indirectly associated with high BMI. CONCLUSION: These findings provide insights into the complex relationship of multiple determinants of nutritional status in older adults and support the design of public health policies that consider the specificities of this population group.

Evaluation of nasal swab and nasal wipe for detection of Influenza A in swine using Bayesian latent class analysis.

Influenza A virus (IAV) is an important pathogen in Brazilian swine herds, and monitoring the viral circulation is essential to control and reduce the transmission. Surveillance programs for IAV are often based on individual piglets level sampling, making the evaluation of the available diagnostic tools crucial to assessing IAV circulation in herds. Thus, two sample collection methodologies were compared in pig herds in southern Brazil to detect IAV by RT-qPCR: nasal swab (NS) and nasal wipe (NW). A Bayesian latent class model (BLCM) was set for two tests and two populations. The NW and NS used are more specific (higher than 95 % for both) than sensitive. The sensitivity for NW was lower than the NS, 84.14 % (70 % - 95 %; posterior probability interval (PPI): 95 %) and 87.15 % (73 % - 97 %; PPI: 95 %), respectively, and the specificity was 95 % (90 % - 99 %; PPI: 95 %) and 99 % (96 % - 100 %; PPI: 95 %), respectively. Although the wipe sample collection loses both sensitivity and specificity compared with nasal swab, differences in test performance were very limited and PPIs largely overlapped. Therefore NW can also be considered a valuable tool. The decision about the use of both techniques should be based on the trade-off between their performance limitations and feasibility in routine monitoring.

Profiles of mental illness in college students and associated factors: A latent class analysis.

Mental illness among university students poses a pressing challenge for educational institutions, urging the need for strategies that foster health and mitigate mental distress, with an emphasis on preventing suicide. Our study sought to discern the profiles of mental illness among college students and explore the factors associated with them. We examined data from 918 students at a Brazilian Federal Institute, utilizing Latent Class Analysis and multinomial regression for our analyses. We identified three distinct mental illness profiles: Anxiety with Low Suicide Risk; Mental Illness with Moderate Suicide Risk; and Mental Illness with High Suicide Risk. We observed a reduced association of these profiles with religious beliefs. Conversely, there was a heightened association with cisgender women, individuals identifying as LGBTQI+, those with learning disabilities, and victims of sexual violence. Our findings underscore the importance of tailored prevention and health promotion programs to enhance student well-being. There's a compelling need to devise mental health strategies tailored to the specific needs of the identified groups, particularly students from the LGBTQI + community, survivors of sexual abuse, and those grappling with learning disabilities.

Association of parental level of education and child factors on length-for-age indicator among socially vulnerable children aged 6-24 months from a Brazilian state using structural equation modelling.

This cross-sectional study employs structural equation modelling (sEM) to explore both direct and indirect effects of parental level of education and child individual factors on the length-for-age outcomes in children aged 6-24 months assisted by the Bolsa Família Program in the State of Alagoas. A total of 1448 children were analysed by the sEM technique. A negative standardised direct effect (sDE) of the children's younger age (sDE: -0·06; P = 0·017), the use of bottle feeding (sDE: -0·11; P < 0·001) and lack of a minimum acceptable diet (sDE: -0·09; P < 0·001) on the length-for-age indicator was found. Being female (SDE: 0·08; P = 0·001), a higher birth weight (SDE: 0·33; P < 0·001), being ever breastfed (sdE: 0·07; P = 0·004) and a higher level of parental education (SDE: 0·09; P < 0·001) showed a positive SDE effect on the child's length-for-age. The model also demonstrated a negative standardised indirect effect (SIE) of the sweet beverage consumption (SIE: -0·08; P = 0·003) and a positive effect of being ever breastfed (SIE: 0·06; P = 0·017) on the child's length-for-age through parental level of education as a mediator. This research underscores the crucial role of proper feeding practices and provides valuable insights for the development of targeted interventions, policies and programmes to improve nutritional well-being and promote adequate linear growth and development among young children facing similar challenges.

Clustering of health behaviors and their associations with cardiometabolic risk factors among adults at high risk for type 2 diabetes in India: A latent class analysis.

BACKGROUND: We aimed to identify clusters of health behaviors and study their associations with cardiometabolic risk factors in adults at high risk for type 2 diabetes in India. METHODS: Baseline data from the Kerala Diabetes Prevention Program (n = 1000; age 30-60 years) were used for this study. Information on physical activity (PA), sedentary behavior, fruit and vegetable intake, sleep, and alcohol and tobacco use was collected using questionnaires. Blood pressure, waist circumference, 2-h plasma glucose, high-density lipoprotein and low-density lipoprotein cholesterol, and triglycerides were measured using standardized protocols. Latent class analysis was used to identify clusters of health behaviors, and multilevel mixed-effects linear regression was employed to examine their associations with cardiometabolic risk factors. RESULTS: Two classes were identified, with 87.4% of participants in class 1 and 12.6% in class 2. Participants in both classes had a high probability of not engaging in leisure-time PA (0.80 for class 1; 0.73 for class 2) and consuming <5 servings of fruit and vegetables per day (0.70 for class 1; 0.63 for class 2). However, participants in class 1 had a lower probability of sitting for >=3 h per day (0.26 vs 0.42), tobacco use (0.10 vs 0.75), and alcohol use (0.08 vs 1.00) compared to those in class 2. Class 1 had a significantly lower mean systolic blood pressure (ß = -3.70 mm Hg, 95% confidence interval [CI] -7.05, -0.36), diastolic blood pressure (ß = -2.45 mm Hg, 95% CI -4.74, -0.16), and triglycerides (ß = -0.81 mg/dL, 95% CI -0.75, -0.89). CONCLUSION: Implementing intervention strategies, tailored to cluster-specific health behaviors, is required for the effective prevention of cardiometabolic disorders among high-risk adults for type 2 diabetes.

Who is getting COVID-19 boosters? A latent class analysis in a nationally representative sample of Mexican adults 60 years and over.

OBJECTIVE: Estimate COVID-19 vaccine booster uptake and identify sociodemographic profiles associated with vaccine booster uptake in Mexican adults aged 60 and older. METHODS: Using data from the 2022 National Health and Nutrition Survey, we estimated COVID-19 booster uptake in Mexican adults 60 and older. We conducted a latent class analysis using sociodemographic characteristics and then estimated group-specific booster prevalence. RESULTS: Adults aged 60 and older with a completed vaccination schedule had 80.3% booster coverage. Two groups showed the lowest coverage: 1) unemployed and informal working men with elementary education with low socioeconomic status (73.8% boosted), and 2) female homekeepers with elementary education or less living in rural areas (77.0% boosted). CONCLUSIONS: Our analysis points to the need to reach out to men and women with elementary education or less who live in rural areas to strengthen booster campaigns in the future.

Persistent Food Insecurity and Material Hardships: A Latent Class Analysis of Experiences among Venezuelan Refugees and Migrants in Urban Colombia.

The causes and conditions of displacement often increase the vulnerability of migrant and refugee populations to food insecurity, alongside other material hardships. We aimed to examine the multidimensional aspects and patterns of food insecurity and other material hardships in a cross-sectional sample of 6221 Venezuelan refugees and migrants in urban Colombia using a latent class analysis. Using multinomial and logistic regression models, we investigated the demographic and migratory experiences associated with identified classes and how class membership is associated with multiple health outcomes among Venezuelan refugees and migrants, respectively. Approximately two thirds of the sample was comprised cisgender women, and the participants had a median age of 32 years (IQR: 26-41). Four heterogeneous classes of food insecurity and material hardships emerged: Class 1-low food insecurity and material hardship; Class 2-high food insecurity and material hardship; Class 3-high income hardship with insufficient food intake; and Class 4-income hardship with food affordability challenges. Class 2 reflected the most severe food insecurity and material hardships and had the highest class membership; Venezuelans with an irregular migration status were almost 1.5 times more likely to belong to this class. Food insecurity and material hardship class membership was independently associated with self-rated health, mental health symptoms, and recent violence victimization and marginally associated with infectious disease outcomes (laboratory-confirmed HIV and/or syphilis infection). Social safety nets, social protection, and other interventions that reduce and prevent material hardships and food insecurity among refugees and migrants, alongside the host community, may improve public health, support development, and reduce healthcare costs. In the long term, regularization and social policies for migrants aimed at enhancing refugees' and migrants' social and economic inclusion may contribute to improving food security in this population.

Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial.

Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this 'typical CFA', leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.

Use of Latent Class Analysis to Predict Intensive Care Unit Admission and Mortality in Children with a Major Congenital Anomaly.

OBJECTIVE: To define major congenital anomaly (CA) subgroups and assess outcome variability based on defined subgroups. STUDY DESIGN: This population-based cohort study used registries in Denmark for children born with a major CA between January 1997 and December 2016, with follow-up until December 2018. We performed a latent class analysis (LCA) using child and family clinical and sociodemographic characteristics present at birth, incorporating additional variables occurring until age of 24 months. Cox proportional hazards regression models estimated hazard ratios (HRs) of pediatric mortality and intensive care unit (ICU) admissions for identified LCA classes. RESULTS: The study included 27 192 children born with a major CA. Twelve variables led to a 4-class solution (entropy = 0.74): (1) children born with higher income and fewer comorbidities (55.4%), (2) children born to young mothers with lower income (24.8%), (3) children born prematurely (10.0%), and (4) children with multiorgan involvement and developmental disability (9.8%). Compared with those in Class 1, mortality and ICU admissions were highest in Class 4 (HR = 8.9, 95% CI = 6.4-12.6 and HR = 4.1, 95% CI = 3.6-4.7, respectively). More modest increases were observed among the other classes for mortality and ICU admissions (Class 2: HR = 1.7, 95% CI = 1.1-2.5 and HR = 1.3, 95% CI = 1.1-1.4, respectively; Class 3: HR = 2.5, 95% CI = 1.5-4.2 and HR = 1.5, 95% CI = 1.3-1.9, respectively). CONCLUSIONS: Children with a major CA can be categorized into meaningful subgroups with good discriminative ability. These groupings may be useful for risk-stratification in outcome studies.

Intimate partner violence among women in Togo: a generalised structural equation modeling approach.

OBJECTIVE: This study aimed to assess the magnitude and identify associated factors with intimate partner violence (IPV) in Togo. DESIGN: Cross-sectional study. SETTING: Togo. PARTICIPANTS: Women of reproductive age (15-49 years). PRIMARY OUTCOME: Intimate partner violence. METHODS: This study used data from the 2013 Togolese Demographic and Health Survey. A total of 4910 married or partnered women were included. A Generalised Structural Equation Model (GSEM) was performed to identify significant factors associated with IPV. Results of the GSEM were reported as adjusted ORs (aOR) with their corresponding 95% CIs. RESULTS: The pooled prevalence of IPV was 35.5% (95% CI: 34.2% to 36.8%). Emotional violence and physical violence were the most reported forms of IPV (29.7% and 20.2%, respectively), while sexual violence was the least common, with a prevalence of 7.5%. Additionally, the results indicated that the following factors related to women, men and households were significantly associated with IPV in Togo: ethnicity, region, religion, wealth index, working status, age at the first union, having attitudes toward wife-beating, participation in household decision-making, education level, alcohol use and controlling behaviour. CONCLUSION: IPV is a complex and multifactorial phenomenon in Togo. The Togo government as well as women's human rights organisations should consider these factors when designing IPV programmes.

Relationships between internet addiction, quality of life and sleep problems: a structural equation modeling analysis.

OBJECTIVE: To assess the relationship between internet addiction, quality of life, and sleep problems among adolescents. METHOD: This research was conducted with a representative sample of 875 adolescents. This cross-sectional study used the Internet Addiction Test, Pediatric Quality of Life Inventory™ version 4.0, Pediatric Daytime Sleepiness Scale, and sleep duration. Sociodemographic factors were also analyzed. Structural equation modeling was used to investigate relationships between variables. RESULTS: After adjusting the model for covariances between the latent variables of daytime sleepiness and correlations between the physical and emotional domains of quality of life, the authors obtained satisfactory fit indices (RMSEA = 0.031, CFI = 0.926, TLI = 0.909, SRMR = 0.058). Internet addiction was positively associated with daytime sleepiness (rho = 0.549, p < 0.001) and negatively associated with quality of life (rho = -0.173, p < 0.001). By contrast, sleep duration was negatively associated with daytime sleepiness (rho = -0.089, p = 0.007), positively associated with quality of life (rho = 0.105, p = 0.014), and dependent on school shift (rho = 0.453, p < 0.001). CONCLUSIONS: Adolescents with higher levels of internet addiction had lower perceptions of quality of life and higher daytime sleepiness. Moreover, sleep duration had a positive correlation with quality of life. Given its detrimental effects on quality of life and daytime sleepiness, parents should better supervise internet use in adolescents.

Vitamin D serum concentration is prospectively associated with depressive symptoms in the EpiFloripa Aging Cohort Study: a structural equation modeling approach.

OBJECTIVE: To evaluate the direct, indirect, and total prospective effects of serum concentrations of 25-hydroxycholecalciferol (25[OH]D) on depressive symptoms in older adults. METHODS: Data from the second (2013-2015) and third (2017-2019) waves of the EpiFloripa Aging Cohort Study (= 60 years) were analyzed. Depressive symptoms were assessed with the 15-item Geriatric Depression Scale. 25(OH)D levels were measured using the microparticle chemiluminescence method. A directed acyclic graph was constructed to identify the minimum set of adjustments. Structural equation modeling analysis was used to determine the effects of 25(OH)D on depressive symptoms. RESULTS: Data from 574 older adults (63.1% female) were analyzed. In the follow-up (n=390), 16.2% of them presented severe depressive symptoms (= 6 points). Structural equation modeling analysis revealed that 25(OH)D had a small direct negative effect (ß = -0.11, p < 0.05) and an overall negative effect (ß = -0.13; p < 0.05) on depressive symptoms in wave 3 (increased 25[OH]D led to decreased depressive symptoms). No direct or indirect effect on depressive symptoms was found in wave 2. CONCLUSION: Our findings indicate a prospective association between 25(OH)D and depressive symptoms, suggesting a long-term effect in older adults from southern Brazil.

Subphenotypes of severe early-onset pre-eclampsia at hospital admission. A Latin American single-center exploratory latent class analysis.

OBJECTIVES: To identify distinct subphenotypes of severe early-onset pre-eclampsia in Latin America and analyze biomarker and hemodynamic trends between subphenotypes after hospital admission. METHODS: A single-center prospective cohort study was conducted in Colombia. The latent class analysis identified subphenotypes using clinical variables, biomarkers, laboratory tests, and maternal hemodynamics. Class-defining variables were restricted to measurements at and 24 h after admission. Primary and secondary outcomes were severe maternal and perinatal complications. RESULTS: Among 49 patients, two subphenotypes were identified: Subphenotype 1 (34.7%) had a higher likelihood of an sFlt-1/PlGF ratio ≤ 38, maternal age > 35, and low probability of TPR > 1400, CO <8, and IUGR; Subphenotype 2 (65.3%) had a low likelihood of an sFlt-1/PlGF ratio < 38, maternal age > 35, and high probability of TPR > 1400, CO <8, and IUGR. At 24 h postadmission, 64.7% of subphenotype 1 patients changed to subphenotype 2, while 25% of subphenotype 2 patients were reclassified as subphenotype 1. Subphenotype 1 displayed significant changes in CO and TPR, while subphenotype 2 did not. Maternal complications were more prevalent in subphenotype 2, with an odds ratio of 5.3 (95% CI: 1.3-22.0; P = 0.02), but no significant differences in severe neonatal complications were observed. CONCLUSIONS: We identified two distinct subphenotypes in a Latin American cohort of patients with severe early-onset pre-eclampsia. Subphenotype 2, characterized by higher TPR, sFlt-1, and serum creatinine and lower CO and PlGF at admission, was associated with worse maternal outcomes and appeared less modifiable after in-hospital treatment.