scholarly journals Risk Factors for the Prediction of Hyperglycemia during Pregnancy – A Cohort Study from a Brazilian Public Primary Care Center

2020 ◽  
Author(s):  
Joice Monaliza Vernini ◽  
Bianca Nicolosi Cassetari ◽  
Mariana Alvarez Arantes ◽  
Roberto Araújo Costa ◽  
Claudia Garcia Magalhães ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Body Mass Index ◽  
Cohort Study ◽  
Negative Predictive Value ◽  
Body Mass ◽  
Gestational Age ◽  
Predictive Value ◽  
Fasting Glucose ◽  
Primary Care Center

Abstract BACKGROUND – In Brazil, the prevalence of maternal hyperglycemia is among the highest, costs are elevated and there is no evidence to recommend universal screening. OBJECTIVE – To evaluate the performance of risk factors (RF) for predicting hyperglycemia in pregnancy – Mild Gestational Hyperglycemia (MGH) or gestational Diabetes Mellitus (GDM) in public primary-care centers in Brazil. METHODS – A cohort study, including 514 women with a single pregnancy and no hyperglycemia. Study participants were evaluated at GA (gestational age) < or ≥ 20 weeks, and underwent a 75g-OGTT along with glycemic profile (GP) testing between 24 and 28 weeks. Clinical, anthropometric and laboratory data – fasting glucose (FG), glycated hemoglobin (HbA1c), basal insulin and lipid profile were obtained. The most common RF associations (OR and 95% CI and p <0.05) and different cut-off points were tested for the prediction of MGH-GDM. Predictive performance was assessed by Sensitivity/Specificity, negative predictive value NPV (negative predictive value) and false positive rates (FP; 1-Esp). RESULTS – At GA <20 weeks, age ≥25 years, WC (Waist circumference) ≥ 88 cm, BMI pre ≥25 kg/m 2 (pre gestational body mass index) and BMI gest ≥25 kg/m 2 (gestational body mass index ); at GA (gestational age) ≥20 weeks, age ≥25 years, BMI pre ≥25 kg/m 2 and TG (triglicerides) ≥150 mg/dL showed better performace for predicting MGH-GDM. Irrespective of gestational age, FG (Fasting glucose) ≥ 85 mg/dL, HbA1c ≥5.7% and HOMA-IR ≥2.71 were good predictors to rule out the risk of these complications. CONCLUSION – The results of this study should contribute to define the best diagnostic approach to MGH-GDM in our center and in others with similar characteristics.

scholarly journals Analysis of risk factors for osteopenia and osteoporosis among adults and elderly in primary health care

2020 ◽  
Vol 8 (1) ◽  
pp. 263-274
Author(s):  
Aniandra Karol Gonçalves Sgarbi ◽  
Kátia Gianlupi Lopes ◽  
Márcia Regina Martins Alvarenga
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Health Care ◽  
Body Mass Index ◽  
Primary Health Care ◽  
Body Mass ◽  
Family Health ◽  
Normal Body Mass Index ◽  
Primary Care Center ◽  
Primary Health

Objective: To analyze the distribution of risk factors for osteopenia and osteoporosis among adults and elderly in primary care. Method: sectional study of quantitative approach. Random sample extracted from registered adults and elderly from the five Family Health Strategy units belonging to an Expanded Family Health and Primary Care Center in Dourados, MS. Data collected between March and December 2015. The sample consisted of 44 adults and 103 elderly, of which only 109 performed all examinations. Body mass index, bone densitometry, serum calcium, 25 serum hydroxyvitamin D and a structured questionnaire were used. Results: The factors that were significantly associated with the risk of osteopenia and osteoporosis (p <0,05) were female gender, alcohol consumption and normal body mass index. Conclusion: the identification of these risk factors made it possible to trace their distribution profile, which will be of great value for carrying out health promotion actions and prevention of these diseases in Primary Health Care.

Body mass index and other risk factors for kidney cancer in men: a cohort study in Lithuania

2019 ◽  
Vol 27 (4) ◽  
pp. 272-278
Author(s):  
Rūta Everatt ◽  
Dalia Virvičiūtė ◽  
Abdonas Tamošiūnas
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Cohort Study ◽  
Body Mass ◽  
Kidney Cancer

scholarly journals Impact of body mass index on prevalence of multimorbidity in primary care: cohort study

2013 ◽  
Vol 31 (1) ◽  
pp. 38-43 ◽  
Author(s):  
H. P. Booth ◽  
A. T. Prevost ◽  
M. C. Gulliford
Keyword(s):  
Primary Care ◽  
Body Mass Index ◽  
Cohort Study ◽  
Body Mass

Risk factors for term small for gestational age infants in women with low prepregnancy body mass index

2010 ◽  
Vol 36 (3) ◽  
pp. 506-512 ◽  
Author(s):  
Hiroko Watanabe ◽  
Kazuko Inoue ◽  
Masako Doi ◽  
Momoyo Matsumoto ◽  
Kayoko Ogasawara ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Body Mass ◽  
Gestational Age ◽  
Small For Gestational Age ◽  
Prepregnancy Body Mass Index

scholarly journals the PREGNANCY OUTCOMES DO INFLUENCE BY EXTREMES OF BODY MASS INDEX

2021 ◽  
Vol 71 (2) ◽  
pp. 690-93
Author(s):  
Lubna Razzak ◽  
Ramna Devi ◽  
Sana Tariq ◽  
Anchal Seetlani ◽  
Sara Jamshed
Keyword(s):  
Body Mass Index ◽  
Cohort Study ◽  
Gestational Diabetes ◽  
Cesarean Section ◽  
Body Mass ◽  
Gestational Age ◽  
Retrospective Cohort Study ◽  
Pregnancy Outcomes ◽  
Retrospective Cohort ◽  
Maternal Outcomes

Objective: To investigate whether extreme of body mass index (BMI) is associated with pregnancy outcomes. Study Design: Retrospective cohort study. Place and Duration of Study: Hamdard University Hospital, Karachi, Pakistan, from Feb 2019 to Jan 2020. Methodology: We conducted a retrospective cohort study of 1000 women delivered in between February 2019 to January 2020. BMI is categorized into four groups according to the Asian-Pacific cutoff points as underweight (<18.5 kg/m2), normal weight (18.5–22.9 kg/m2), overweight (23–24.9 kg/m2), and obese (>25 kg/m2). Maternal outcomes measured were pre-eclampsia, gestational diabetes, delivery by cesarean section, instrumental delivery, anemia, postpartum hemorrhage and fetal outcome included small for gestational age and large for gestational age. Logistic regression model was used to adjust the confounder. Maternal outcomes were evaluated with relative risks and 95% confidence intervals. Results: In results, 13%, 54%, 22%, 9% and 2% were underweight, normal body mass index, overweight, obese and morbidly obese categories respectively. The gestational diabetes, pre-eclampsia, labour induction, frequency of cesarean section, postpartum hemorrhage increased linearly with increasing body mass index and expressed as adjusted odds ratio (95% confidence interval) respectively: 10.0 (95% CI 3.5, 28.7), 5.3 (95% CI 2.0, 14.1), 2.7 (95% CI 1.1, 6.8), 4.9 (95% CI 2.8–8.8), 2.5 (95% CI 0.31– 20.6). The anemia and small for gestational age were found in underweight group with adjusted odd ratio2.47 (95% CI 1.6– 3.6), 4.6 (95% CI 2.6, 8.1) respectively........

scholarly journals Timed Up and Go (TUG) Test

2016 ◽  
Vol 8 (1) ◽  
pp. 9-13 ◽  
Author(s):  
Breelan M. Kear ◽  
Thomas P. Guck ◽  
Amy L. McGaha
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Primary Care ◽  
Body Mass Index ◽  
Socioeconomic Status ◽  
Health Risk ◽  
Body Mass ◽  
Physical And Mental Health ◽  
Health Risk Factors ◽  
Timed Up And Go

Purpose: The Timed Up and Go (TUG) test is a reliable, cost-effective, safe, and time-efficient way to evaluate overall functional mobility. However, the TUG does not have normative reference values (NRV) for individuals younger than 60 years. The purpose of this study was to establish NRV for the TUG for individuals aged between 20 and 59 years and to examine the relationship between the TUG and demographic, physical, and mental health risk factors. Methods: Two hundred participants, 50 per decade (ages 20-29, 30-39, 40-49, 50-59 years) were selected at their primary care visit, and timed as they performed the TUG by standing up out of a chair, walking 3 m, turning around, walking back to the chair, and sitting down. Information regarding the risk factors socioeconomic status, body mass index, an index of multimorbidities, perceptions of overall physical and mental health was obtained and used as predictors of TUG time independent of age. Results: TUG times were significantly different among the decades ( F = 6.579, P = .001) with slower times occurring with the 50-year-old decade compared with the 20s ( P = .001), 30s ( P = .001), and 40s ( P = .020). Slower TUG times were associated with lower SES, higher body mass index, more medical comorbidities, and worse perceived physical and mental health. Regression results indicated that perceived physical and mental health accounted for unique variance in the prediction of TUG time beyond age, gender, and socioeconomic status. Conclusions: This study provided TUG NRV for adults in their 20s, 30s, 40s, and 50s. The TUG may have utility for primary care providers as they assess and monitor physical activity in younger adults, especially those with physical and mental health risk factors.

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