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.

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 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.

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.

Abstract 17220: Risk Factors for Recurrent Peripartum Cardiomyopathy in 783 Women in California

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Punag Divanji ◽  
Gregory Nah ◽  
Ian Harris ◽  
Anu Agarwal ◽  
Nisha I Parikh
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Regression Models ◽  
Subsequent Pregnancy ◽  
Peripartum Cardiomyopathy ◽  
Lv Function ◽  
Logistic Regression Models ◽  
Grand Multiparity ◽  
Adjusted Logistic Regression

Introduction: Characterized by significant left ventricular (LV) dysfunction and clinical heart failure (HF), peripartum cardiomyopathy (PPCM) has an incidence of approximately 1/2200 live births (0.04%). Prior studies estimate that approximately 25% of those with recovered LV function will have recurrent clinical PPCM during subsequent pregnancies, compared to 50% of those without recovered LV function. Specific predictors of recurrent PPCM have not been studied in cohorts with large numbers. Methods: From 2005-2011, we identified 1,872,227 pregnancies by International Classification of Diseases, 9th Revision (ICD-9) codes in the California Healthcare Cost and Utilization Project (HCUP) database, which captures over 95% of the California hospitalized population. Excluding 15,765 women with prior cardiovascular disease (myocardial infarction, coronary artery disease, stroke, HF, valve disease, or congenital heart disease), yielded n=1,856,462 women. Among women without prior cardiovascular disease, we identified index and subsequent pregnancies with PPCM to determine episodes of recurrent PPCM. We considered the following potential predictors of PPCM recurrence in both univariate and age-adjusted logistic regression models: age, race, hypertension, diabetes, smoking, obesity, chronic kidney disease, family history, pre-eclampsia, ectopic pregnancy, income, and insurance status. Results: In HCUP, n=783 women had pregnancies complicated by PPCM (mean age=30.8 years). Among these women, n=133 had a subsequent pregnancy (17%; mean age=28.1 years), with a mean follow-up of 4.34 years (±1.71 years). In this group of 133 subsequent pregnancies, n=14 (10.5%) were complicated by recurrent PPCM, with a mean time-to-event of 2.2 years (±1.89 years). Among the risk factors studied, the only univariate predictor of recurrent PPCM was grand multiparity, defined as ≥ 5 previous deliveries (odds ratio: 22; 95% confidence interval 4.43-118.22). The other predictors we studied were not significantly associated with recurrent PPCM in either univariate or multivariable models. Conclusion: In a large population database in California with 783 cases of PPCM over a 6-year period, 17% of women had a subsequent pregnancy, of which 10.5% had recurrent PPCM. In age-adjusted logistic regression models, grand multiparity was the only statistically significant predictor of recurrent PPCM.

scholarly journals Coffee Consumption, Genetic Polymorphisms, and the Risk of Type 2 Diabetes Mellitus: A Pooled Analysis of Four Prospective Cohort Studies

2020 ◽  
Vol 17 (15) ◽  
pp. 5379
Author(s):  
An Na Kim ◽  
Hyun Jeong Cho ◽  
Jiyoung Youn ◽  
Taiyue Jin ◽  
Moonil Kang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Regression Models ◽  
Coffee Consumption ◽  
Pooled Analysis ◽  
Random Effects Model ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Logistic Regression Models

The association between coffee consumption and the risk of type 2 diabetes may vary by genetic variants. Our study addresses the question of whether the incidence of type 2 diabetes is related to the consumption of coffee and whether this relationship is modified by polymorphisms related to type 2 diabetes. We performed a pooled analysis of four Korean prospective studies that included 71,527 participants; median follow-up periods ranged between 2 and 13 years. All participants had completed a validated food-frequency questionnaire (FFQ) at baseline. The odds ratios (ORs) and 95% confidence intervals (CIs) for type 2 diabetes were calculated using logistic regression models. The ORs were combined using a fixed or random effects model depending on the heterogeneity across the studies. Compared with 0 to <0.5 cups/day of coffee consumption, the OR for type 2 diabetes was 0.89 (95% CI: 0.80–0.98, p for trend = 0.01) for ≥3 cups/day of coffee consumption. We did not observe significant interactions by five single nucleotide polymorphisms (SNPs) related to type 2 diabetes (CDKAL1 rs7756992, CDKN2A/B rs10811661, KCNJ11 rs5215, KCNQ1 rs163184, and PEPD rs3786897) in the association between coffee and the risk of type 2 diabetes. We found that coffee consumption was inversely associated with the risk of type 2 diabetes.

scholarly journals Metabolic syndrome in patients with type 2 diabetes and atherosclerotic cardiovascular disease: a post hoc analyses of the EMPA-REG OUTCOME trial

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
João Pedro Ferreira ◽  
Subodh Verma ◽  
David Fitchett ◽  
Anne Pernille Ofstad ◽  
Sabine Lauer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Metabolic Syndrome ◽  
Trial Registration ◽  
World Health ◽  
Atherosclerotic Cardiovascular Disease ◽  
Renal Outcomes ◽  
Outcome Trial

Abstract Background Patients with type 2 diabetes (T2D) and metabolic syndrome (MetS) are at greater cardiovascular risk than those with T2D without MetS. In the current report we aim to study the characteristics, cardio-renal outcomes and the effect of empagliflozin in patients with MetS enrolled in the EMPA-REG OUTCOME trial. Methods A total of 7020 patients with T2D and atherosclerotic cardiovascular disease were treated with empagliflozin (10 mg or 25 mg) or placebo for a median of 3.1 years. The World Health Organization MetS criteria could be determined for 6985 (99.5%) patients. We assessed the association between baseline MetS and multiple cardio-renal endpoints using Cox regression models, and we studied the change in the individual component over time of the MetS using mixed effect models. Results MetS at baseline was present in 5740 (82%) patients; these were more often white and had more often albuminuria and heart failure, had lower eGFR and HDL-cholesterol, and higher blood pressure, body mass index, waist circumference, and triglycerides. In the placebo group, patients with MetS had a higher risk of all outcomes including cardiovascular death: HR = 1.73 (95% CI 1.01–2.98), heart failure hospitalization: HR = 2.64 (95% CI 1.22, 5.72), and new or worsening nephropathy: HR = 3.11 (95% CI 2.17–4.46). The beneficial effect of empagliflozin was consistent on all cardio-renal outcomes regardless of presence of MetS. Conclusions A large proportion of the EMPA-REG OUTCOME population fulfills the criteria for MetS. Those with MetS had increased risk of adverse cardio-renal outcomes. Compared with placebo, empagliflozin improved cardio-renal outcomes in patients with and without MetS. Trial registration Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT 01131676

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