scholarly journals Recommendations for Abnormal Behaviour Ethograms in Monkey Research

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1461
Author(s):  
Andrea Polanco ◽  
Brenda McCowan ◽  
Lee Niel ◽  
David L. Pearl ◽  
Georgia Mason
Keyword(s):  
Macaca Mulatta ◽  
Regression Models ◽  
Convergent Validity ◽  
P Value ◽  
Morphological Similarity ◽  
Abnormal Behaviour ◽  
Logistic Regression Models ◽  
The Third ◽  
Welfare Implications ◽  
Self Stimulation

Laboratory monkey ethograms currently include subcategories of abnormal behaviours that are based on superficial morphological similarity. Yet, such ethograms may be misclassifying behaviour, with potential welfare implications as different abnormal behaviours are likely to have distinct risk factors and treatments. We therefore investigated the convergent validity of four hypothesized subcategories of abnormal behaviours (‘motor’, e.g., pacing; ‘self-stimulation’, e.g., self-sucking; ‘postural’, e.g., hanging; and ‘self-abuse’, e.g., self-biting). This hypothesis predicts positive relationships between the behaviours within each subcategory. Rhesus macaque (Macaca mulatta) data on 19 abnormal behaviours were obtained from indoor-housed animals (n = 1183). Logistic regression models, controlling for sex, age, and the number of observations, revealed that only 1/6 ‘motor’ behaviours positively predicted pacing, while 2/3 ‘self-abuse’ behaviours positively predicted self-biting (one-tailed p-value < 0.05). Furthermore, ‘self-stimulation’ behaviours did not predict self-sucking, and none of the ‘postural’ behaviours predicted hanging. Thus, none of the subcategories fully met convergent validity. Subsequently, we created four new valid subcategories formed of comorbid behaviours. The first consisted of self-biting, self-hitting, self-injurious behaviour, floating limb, leg-lifting, and self-clasping. The second comprised twirling, bouncing, rocking, swinging, and hanging. The third comprised pacing and head-twisting, while the final subcategory consisted of flipping and eye-poking. Self-sucking, hair-plucking, threat-biting, and withdrawn remained as individual behaviours. We encourage laboratories to replicate the validation of these subcategories first, and for scientists working with other species to validate their ethograms before using them in welfare assessments.

Adiponectin and leptin levels in migraineurs: Results from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

2020 ◽  
Vol 3 ◽  
pp. 251581632096696
Author(s):  
Alessandra C Goulart ◽  
Bianca de Almeida-Pititto ◽  
Paulo A Lotufo ◽  
Itamar S Santos ◽  
Sandra RG Ferreira ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Regression Models ◽  
P Value ◽  
Adult Health ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
The Third ◽  
Disease Associations ◽  
Relationship Of ◽  
The Relationship

Background: Relationships of adipokines (ADP) with migraine are not well-established. We examined the relationship of adiponectin and leptin with migraine by aura symptoms. Methods: In a baseline cross-sectional data of Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), associations of ADP levels and migraine were assessed in a sample of 257 nondiabetic subjects, free from cardiovascular disease. Associations of ADP tertiles (dependent variable) and migraine status were tested using logistic regression models. Categories of migraine were created as follows: no headache (reference), migraine with aura (MA), and migraine without aura (MO) in all sample and by sex. Odds ratio (OR) with respective 95% confidence interval (CI) adjusted for age, sex, body mass index, and metabolic syndrome. Results: Among participants (46 years ± SD: 4.8), 47.5% were women and 36.2% had migraine (16.7% MA). Median values of leptin (ng/mL) increased gradually across subgroup: no headache: 9.5 (interquartile range (IQR): 5.5–15.7) versus MO: 17.0 (IQR: 10.9–34.2) versus MA: 20.9 (IQR: 11.7–29.3), overall p value <0.0001, but not for adiponectin levels. After full adjustment, the third of leptin was positively associated with MA (OR 2.89 (1.00–8.4)) and the second of adiponectin was associated with MO (OR 2.76; 95% CI: 1.09–6.96, p = 0.03). Positive associations with MA, second (OR 3.81; 95% CI: 1.07–13.59; p = 0.04) and third tertile of leptin (6.54; 95% CI: 1.74–24.57, p = 0.005), were also observed in women, but not in men. Conclusions: Positive associations between ADP and migraine, particularly between MA and leptin levels in women, raise the possibility of adipocytokines and play a role in migraine pathophysiology.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

scholarly journals The Differential Effects of Caregiving Intensity on Overnight Hospitalization in the Previous 2 Years

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 76-76
Author(s):  
Kylie Meyer ◽  
Zachary Gassoumis ◽  
Kathleen Wilber
Keyword(s):  
Health Services ◽  
Fixed Effects ◽  
Regression Models ◽  
P Value ◽  
Logistic Regression Models ◽  
Spousal Caregiving ◽  
Health Insurance Status ◽  
Race Ethnicity ◽  
Self Assessed Health ◽  
Retirement Study

Abstract Caregiving for a spouse is considered a major stressor many Americans will encounter during their lifetimes. Although most studies indicate caregiving is associated with experiencing diminished health outcomes, little is known about how this role affects caregivers’ use of acute health services. To understand how spousal caregiving affects the use of acute health services, we use data from the Health and Retirement Study. We apply fixed effects (FE) logistic regression models to examine odds of experiencing an overnight hospitalization in the previous two years according to caregiving status, intensity, and changes in caregiving status and intensity. Models controlled for caregiver gender, age, race, ethnicity, educational attainment, health insurance status, the number of household residents, and self-assessed health. Overall, caregivers were no more likely to experience an overnight hospitalization compared to non-caregivers (OR 0.92; CI 0.84 to 1.00; p-value=0.057). However, effects varied according to the intensity of caregiving and the time spent in this role. Compared to non-caregivers, for example, spouses who provided care to someone with no need for assistance with activities of daily living had lower odds of experiencing a hospitalization (OR 0.77; CI 0.66 to 0.89). In contrast, caregivers who provided care to someone with dementia for 4 to &lt;6 years had 3.29 times the odds of experiencing an overnight hospitalization (CI 1.04 to 10.38; p-value=0.042). Findings indicate that, although caregivers overall appear to use acute health services about as much as non-caregivers, large differences exist between caregivers. Results emphasize the importance of recognizing diversity within caregiving experiences.

Trends in the prevalence of metabolic syndrome and its components in Mexican adults, 2006-2018

2021 ◽  
Vol 63 (6, Nov-Dic) ◽  
pp. 713-724
Author(s):  
Rosalba Rojas-Martínez ◽  
Carlos A Aguilar-Salinas ◽  
Martín Romero-Martínez ◽  
Lilia Castro-Porras ◽  
Donaji Gómez-Velasco ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Metabolic Syndrome ◽  
Regression Models ◽  
Significant Risk ◽  
P Value ◽  
Logistic Regression Models ◽  
Mexican Adults ◽  
Diabetes Risk Score ◽  
Linear Trends

Objective. To examine trends in the prevalence of metabolic syndrome (MS) and its components. Materials and methods. Data from 27 800 Mexican adults who participated in Ensanut 2006, 2012, 2016 and 2018 were analyzed. Linear regression was used across each Ensanut period to assess temporal linear trends in the prevalence of MS. Logistic regression models were obtained to calculate the percentage change, p-value for the trend and the association between the presence of MS and the risk of developing type 2 diabetes mellitus (T2DM) over 10 years using the Finnish Diabetes Risk Score (FINDRISC) and cardiovascular disease (CVD) using Globorisk. Results. The prevalence of MS in Mexican adults according to the harmonized definition was: 40.2, 57.3, 59.99 and 56.31%, in 2006, 2012, 2016 and 2018 respectively (p for trend <0.0001). In 2018, 7.62% of metabolic syndrome cases had a significant risk for incident DM2 and 11.6% for CVD. Conclusion. It is estimated that there are 36.5 million Mexican adults living with metabolic syndrome, of which 2 million and 2.5 million have a high risk of developing T2DM or cardiovascular disease respectively, over the next 10 years.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Outcomes of COVID-19: disparities by ethnicity

2020 ◽  
Author(s):  
Hamad Ali ◽  
Abdullah Alshukry ◽  
Sulaiman K Marafie ◽  
Monera AlRukhayes ◽  
Yaseen Ali ◽  
...  
Keyword(s):  
South Asian ◽  
Regression Models ◽  
South Asians ◽  
P Value ◽  
Multivariate Logistic Regression ◽  
Death Rates ◽  
Logistic Regression Models ◽  
Asian Patients ◽  
Worse Prognosis

AbstractObjectivesTo investigate the role of ethnicity in COVID-19 outcome disparities in a cohort in Kuwait.MethodsThis is a retrospective analysis of 405 individuals infected with SARS-CoV-2 in Kuwait. Outcomes such as symptoms severity and mortality were considered. Multivariate logistic regression models were used to report the odds ratios (OR) for ICU admission and dying from COVID-19.ResultsThe cohort included 290 Arabs and 115 South Asians. South Asians recorded significantly higher COVID-19 death rates compared to Arabs (33% vs. 7.6%, P value<0.001). When compared to Arabs, South Asians also had higher odds of being admitted to the ICU (OR = 6.28, 95% CI: 3.34 – 11.80, p < 0.001). South Asian patients showed 7.62 (95% CI: 3.62 – 16.02, p < 0.001) times the odds of dying from COVID-19.ConclusionCOVID-19 patients with South Asians ethnicity are more likely to have worse prognosis and outcome when compared to patients with Arab ethnicity. This suggest a possible role for ethnicity in COVID-19 outcome disparities and this role is likely to be multifactorial.

Neuroinflammatory and Neurodegenerative Markers in Non-Hodgkin’s Lymphoma and Hematologic Malignancies with Central Nervous System Involvement

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2829-2829
Author(s):  
Weihoa Tang ◽  
Kevin H.M. Kuo ◽  
Alejandro Lazo-Langner ◽  
Stephen H. Pasternak ◽  
Anargyros Xenocostas
Keyword(s):  
Central Nervous System ◽  
Regression Models ◽  
Systemic Chemotherapy ◽  
Central Nervous System Involvement ◽  
Hematologic Malignancies ◽  
Intrathecal Chemotherapy ◽  
Myelogenous Leukemia ◽  
P Value ◽  
Cns Disease ◽  
Logistic Regression Models

Abstract Introduction: Central nervous system involvement (CNSI) in hematologic malignancies confers poor prognosis and is difficult to diagnose requiring clinical, radiological and cytological correlates. Neuroimaging studies generally lack specificity and sensitivity, and although cerebral spinal fluid (CSF) cytology is considered the gold standard, it has a very low sensitivity. Several CSF proteins have been studied as possible biomarkers for CNSI to no avail. CSF levels of beta-amyloid peptides (Ab42) and the proteins S100B, Tau, 14-3-3 and Hexosaminidase B are known to be altered by neurodegenerative disease or neuronal injury. Tau levels have also been shown to increase after intrathecal chemotherapy (ITC) in children with lymphoid malignancies. Because of the absence of a reliable biomarker for CNSI, we initiated a study of patients’ CSF to screen for candidate markers of neuronal damage or inflammation. Materials and Methods: Fifty-eight adult patients with hematological malignancies (31 high-grade non-Hodgkin’s lymphoma, 22 acute lymphoblastic leukemia, 1 post-transplant lymphoproliferative disorder, 2 chronic myelogenous leukemia in accelerated phase, and 2 acute myelogenous leukemia) and 6 normal control patients undergoing spinal anaesthesia were included. CSF samples were obtained and frozen at −80°C until analysis. For patients receiving ITC, the CSF sample was withdrawn prior to ITC administration. Clinical information collected included: age, sex, diagnosis, CNS irradiation, ITC/systemic chemotherapy, and presence or absence of CNS disease. Suspected CNS disease was defined as the presence of focal neurological signs or symptoms consistent with leptomeningeal or parenchymal disease, or radiographic evidence of CNSI. Proven CNS disease was defined by the finding of malignant cells in the CSF by cytology or flow cytometry. The control population consisted of patients undergoing spinal anaesthesia with no clinical or pathologic evidence of hematological malignancy or CNS disease. Tau, S100B and Ab42 were quantified by ELISA. 14-3-3 was assayed by Western blotting. Hexosaminidase B was assayed using a fluorogenic substrate. Variables potentially influencing the levels or presence of markers were explored using Mann-Whitney U tests, Student’s t-test, simple linear regression, ANOVA or Pearson ?2 tests, as appropriate. To test correlation with CNSI multiple logistic regression models were constructed. Receiver operating characteristic (ROC) curves and test performance statistics were generated when feasible. Results: One hundred and twenty-eight samples were analyzed from 58 patients and 6 controls. Whereas no difference was observed for S100B or Ab42, increased levels of Tau or positivity for 14-3-3 were associated with diagnosis, CNSI and probably radiotherapy or ITC (Table 1). Multivariate analysis showed that Tau level or 14-3-3 positivity were associated with CNSI after adjusting for confounders (Table 2). For Tau the area under the ROC curve was 0.791. Sensitivity and specificity were 90 and 33% for a cut-off of 200 pg/mL, and 63% and 90% for a cut-off of 500 pg/ml, respectively. Conclusions: Our results suggest that elevated CSF Tau levels and 14-3-3 positivity may correlate with CNSI in patients with hematologic malignancies and might be useful for diagnosis in suspected cases. These findings warrant further prospective studies. Table 1: Univariate analysis exploring variables potentially influencing Tau levels or presence of 14-3-3 protein P value (2 tailed) for differences in Tau levels P value (2-tailed) for differences in expression of 14-3-3 protein Age 0.992 0.792 Sex 0.372 0.715 Systemic chemotherapy 0.485 0.588 Intrathecal Chemotherapy 0.084 0.191 CNS Radiotherapy 0.163 0.022 Diagnosis &lt;0.001 &lt;0.001 Suspected/confirmed CNS infiltration &lt;0.001 &lt;0.001 Table 2. Logistic regression models exploring CNSI with CSF markers Tau level (Per decile increase) 14-3-3 (Positivity) OR (95% CI) P OR (95% CI) P Unadjusted 1.51 (1.29–1.78) &lt;0.001 7.23 (3.12,–16.76) &lt;0.001 Adjusted for CNS radiotherapy and diagnosis 1.40 (1.17–1.68) &lt;0.001 3.85 (1.48–9.99) 0.006

scholarly journals Earlier onset of proteinuria or hypertension is a predictor of progression from gestational hypertension or gestational proteinuria to preeclampsia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mamoru Morikawa ◽  
Michinori Mayama ◽  
Kiwamu Noshiro ◽  
Yoshihiro Saito ◽  
Kinuko Nakagawa-Akabane ◽  
...  
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort ◽  
Regression Models ◽  
Gestational Hypertension ◽  
P Value ◽  
Hypertensive Disorders Of Pregnancy ◽  
Multivariate Logistic Regression ◽  
Logistic Regression Models ◽  
Hypertensive Disorders ◽  
New Onset

AbstractAlthough gestational hypertension (GH) is a well-known disorder, gestational proteinuria (GP) has been far less emphasized. According to international criteria, hypertensive disorders of pregnancy include GH but not GP. Previous studies have not revealed the predictors of progression from GP to preeclampsia or those of progression from GH to preeclampsia. We aimed to determine both sets of predictors. A retrospective cohort study was conducted with singleton pregnant women who delivered at 22 gestational weeks or later. Preeclampsia was divided into three types: new onset of hypertension/proteinuria at 20 gestational weeks or later and additional new onset of other symptoms at < 7 days or at ≥ 7 days later. Of 94 women with preeclampsia, 20 exhibited proteinuria before preeclampsia, 14 experienced hypertension before preeclampsia, and 60 exhibited simultaneous new onset of both hypertension and proteinuria before preeclampsia; the outcomes of all types were similar. Of 34 women with presumptive GP, 58.8% developed preeclampsia; this proportion was significantly higher than that of 89 women with presumptive GH who developed preeclampsia (15.7%). According to multivariate logistic regression models, earlier onset of hypertension/proteinuria (before or at 34.7/33.9 gestational weeks) was a predicator for progression from presumptive GH/GP to preeclampsia (odds ratios: 1.21/1.21, P value: 0.0044/0.0477, respectively).

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