#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Health conditions associated with overweight in climacteric women


Authors: Maria Suzana Marques aff001;  Ronilson Ferreira Freitas aff001;  Daniela Araújo Veloso Popoff aff001;  Fernanda Piana Santos Lima de Oliveira aff002;  Maria Helena Rodrigues Moreira aff003;  Andreia Maria Araújo Drummond aff004;  Dorothéa Schmidt França aff002;  Luís Antônio Nogueira dos Santos aff001;  Marcelo Eustáquio de Siqueira e Rocha aff001;  João Pedro Brant Rocha aff004;  Maria Clara Brant Rocha aff005;  Maria Fernanda Santos Figueiredo Brito aff001;  Antônio Prates Caldeira aff001;  Fabiana Aparecida Maria Borborema aff002;  Viviane Maria Santos aff002;  Josiane Santos Brant Rocha aff001
Authors place of work: State University of Montes Claros, Montes Claros, Minas Gerais, Brazil aff001;  Fipmoc University Center (UNIFIPMoc), Montes Claros, Minas Gerais, Brazil aff002;  University of Trás-dos-Montes and Alto Douro, Department of Sports Science, Exercise and Health, Vila Real, Portugal aff003;  Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil aff004;  Faculty of Medical Sciences of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil aff005
Published in the journal: PLoS ONE 14(12)
Category: Research Article
doi: https://doi.org/10.1371/journal.pone.0218497

Summary

This study aims to investigate the association between health conditions and overweight in climacteric women assisted by primary care professionals. It is a cross-sectional study conducted with 874 women from 40 to 65 years of age, selected by probabilistic sampling between August 2014 and August 2015. In addition to the outcome variable, overweight and obesity, other variables such as sociodemographic, reproductive, clinical, eating and behavioural factors were evaluated. Descriptive analyses of the variables investigated were performed to determine their frequency distributions. Then, bivariate analyses were performed through Poisson regression. For the multivariate analyses, hierarchical Poisson regression was used to identify factors associated with overweight and obesity in the climacteric period. The prevalence of overweight and obesity was 74%. Attending public school (PR: 1.30–95% CI 1.14–1.50), less schooling (PR: 1.11–95% CI 1.01–1.23), gout (PR: 1.18–95% CI 1.16–1.44), kidney disease (PR: 1.18–95% CI 1.05–1.32), metabolic syndrome (MS) (PR: 1.19–95% CI 1.05–1.34) and fat intake (PR: 1.12–95% CI 1.02–1.23) were considered risk factors for overweight. Having the first birth after 18 years of age (PR: 0.89–95% CI 0.82 to 0.97) was shown to be a protective factor for overweight and obesity. The presence of overweight and obesity is associated with sociodemographic, reproductive, clinical and eating habits.

Keywords:

Women's health – Schools – Fats – obesity – Brazil – kidneys – Eating habits – gout

Introduction

Brazil has been experiencing a rapid process of demographic and epidemiological transition, leading to the frequent occurrence of chronic degenerative diseases[1]. The increase in the prevalence of overweight, represented by overweight and obesity, among the elderly female population raises great concern in developed and developing countries. Since overweight and obesity are risk factors for adverse health events[2], such as disturbances in lipid and glucidic metabolism, psychological stress and sleep alterations, there is an increased risk of cardiovascular diseases[3], musculoskeletal disease, acute myocardial infarction[4], cancer[5] and worse quality of life[6] among patients who are overweight and obese in comparison to those who are satisfied with their body weight[7].

Overweight and obesity have become public health problems worldwide. The projection for 2025 is that approximately 2.3 billion adults will be overweight, and more than 700 million will be obese. According to a study conducted in 2016, the rate of overweight among Brazilian women is 50.5%, and this this frequency increases with age up to 64 years[8].

Epidemiological data associating excess weight with behavioural and clinical variables in climacteric women[9], using probabilistic samples[10], are still scarce. Considering that the climacteric period is an important part of the life cycle of women and that this period is related to the potential peak of fat mass and obesity in this group, the current study aimed to investigate the association between health conditions and an excess weight ratio in climacteric women assisted by primary care professionals, since this phase may assume pathological characteristics or be associated with other chronic diseases.

Materials and methods

This is a component study of the project entitled “Health problems of climacteric women: an epidemiological study”, conducted in the city of Montes Claros, Minas Gerais, Brazil, whose central theme is the health of climacteric women. This project was developed by a group of researchers and considers the central theme in the following lines of research: metabolic syndrome, mental health, obesity, quality of life, sleep disorders, health perception, urinary incontinence, perception of climacteric symptoms and levels of physical activity; each of these themes was developed by researchers who make up the research group.

A cross-sectional and analytical study was carried out in the city of Montes Claros, Minas Gerais, Brazil, from August 2014 to August 2015; the target population consisted of 30,801 climacteric women enrolled in 73 health care units, excluding pregnant, postpartum or bedridden women. This study was carried out in the Family Health Strategy (FHS) system, which represents the primary health care (PHC) mechanism in the public health system in Brazil[11].

Sampling was of the probabilistic type, and the selection of the sample occurred in two stages. Each health care unit team was taken as a conglomerate, from which 20 units were drawn, covering the urban and rural areas for data collection. Following this stage, a proportional number of women were randomly selected according to the climacteric stratification criteria of the Brazilian Society of Climacteric women (SOBRAC), in 2013[12]. For each unit, 48 women were selected; a total of 960 women summoned. To incorporate the structure of the complex sampling plan in the statistical analysis of the data, each interviewee was associated with a weight (w), which corresponded to the inverse of their probability of inclusion in the sample (f)[13]. Women between 40 and 65 years of age who were enrolled in the selected teams and physically able to respond to the questionnaires and be submitted to anthropometric measurements and laboratory tests (12-hour fasting) were considered eligible to participate in the study. The researchers previously trained all data collectors and interviewers and maintained supervision during the data collection stage. After training the interviewers and prior to the actual data collection, a pilot study was conducted in a unit of the FHS, with women belonging to the age group studied and not part of the final sample. The pilot study allowed the questionnaire and the interviewers' performance to be tested in practice. After this phase, the field research was started. Adjustments to the data collection instrument were not required. After selection, the women were invited to arrive for research participation on a previously established date. The final sample consisted of 874 climacteric women who were invited to sign the informed and post-informed consent forms.

Overweight and obesity, which was considered the outcome variable of this work was evaluated by body mass index (BMI). Despite the inclusion of some patients who were over 60 years old, women were categorized into eutrophic (BMI <25 kg/m2) and overweight (IMC ≥ 25 kg/m2), following a categorization model used in other studies with similar population groups[14, 15, 16]. Initially, women were weighed wearing light clothing and without footwear, in an orthostatic position, with their feet together and arms relaxed beside the body, by a mechanical anthropometric medical scale (Balmak 11®) with a capacity of 150 kg and weight increments divided into 100g. The stature was measured by an anthropometer (SECA 206®) that was fixed to a flat wall and was without skirting. In this measurement, the women were instructed to keep their feet together and stand in an upright position, with their head positioned in the Frankfurt plane. For the calculation of BMI, the body weight in kilograms was divided by the squared height, expressed in metres (BMI = P/A2).

The women answered questions related to the independent variables, which were allocated in three blocks: (1) sociodemographic, (2) reproductive, and (3) clinical, eating and behavioural factors.

The block of sociodemographic variables included age (40–45, 46–51, 52–65 years); type of school (public, private); level of schooling (elementary school I, elementary school II, high school or higher education); marital status (married, separated, divorced, widowed); labour occupation (yes, no); monthly income (≥ 01 minimum wage, <01 minimum wage), where the minimum wage was equivalent to US $217,42 at the time of data collection; number of people residing in the same house (up to 2, more than 2); and skin colour (white, not white).

The reproductive variables comprised the age of menarche (≤ 11 years, 12–14 years and ≥ 15 years), first birth weight (<4000 g; ≥ 4000 g), climacteric symptoms assessed by the Kupperman index[17] (absent/mild; moderate/severe) and age at first delivery (≤ 18 years old, > 18 years).

The clinical, eating and behavioural variables included liver disease (absent, present), gout (absent, present), renal disease (absent, present), metabolic syndrome (MS) (absent, present); urinary incontinence (absent, present), cardiovascular disease risk (low risk, intermediate risk, high risk), drinking (yes, no), fat intake (yes, no), smoking (yes, no), symptoms of depression, quality of sleep and physical activity.

Metabolic syndrome (MS) was evaluated using the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP-ATPIII) criteria of the Brazilian Society of Diagnosis and Treatment of MS[18]; urinary incontinence was assessed by the International Consultation on Incontinence Questionnaire-Short Form ICIQ-SF[19]; the risk for cardiovascular diseases was assessed by the Framingham Global Risk Score[20]; the symptoms of depression were evaluated by the Beck Depression Inventory[21]; sleep quality was assessed by the Pittsburgh Sleep Quality Index[22]; and physical activity practice was assessed through the International Physical Activity Questionnaire (IPAQ short version)[23].

The women were submitted to peripheral venous blood collection to analyse the laboratory parameters. Serum triglyceride levels were determined by the colourimetric enzymatic method. The level of high-density lipoprotein (HDL) cholesterol was obtained by selective precipitation of ((low-density lipoprotein (LDL) cholesterol and very low-density lipoprotein (VLDL) cholesterol with dextran sulfate in the presence of magnesium ions, followed by dosing by the enzymatic system cholesterol oxidase/peroxidase with calorimetry and reading, as performed in the total cholesterol dosage, using Labtest® reagents, in a Cobas Mira®[24] apparatus. The lipid profile was analysed according to parameters proposed by the Brazilian Society of Cardiology[25] and fasting glycaemia according to the standards of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus[26].

The data were tabulated in the statistical software Statistical Package for Social Science (SPSS, version 21, Chicago, Illinois). Initially, descriptive analyses of all variables were carried out to determine their frequency distributions, and then, bivariate analyses of the outcome variable with each independent variable were performed using the chi-square test. Gross prevalence ratios (PRs) were estimated with their respective 95% confidence intervals. Variables with a descriptive level (p-value) of less than 0.25 were selected for multivariate analysis using the hierarchical Poisson regression model, adapted to the model proposed by other authors[10]. The model was composed of blocks of distal (sociodemographic variables), intermediate (reproductive) and proximal (clinical, eating and behavioural) variables. Adjusted prevalence ratios (PRs) with their respective 95% confidence intervals were estimated, and only those that presented a descriptive level of p<0.05 remained in the model. At each hierarchical level, the stepwise forward procedure was adopted: the statistically significant variables selected in the bivariate analysis started in the model, and then other variables were added (Fig 1).

Model with the statistically significant variable selected in the bivariate analysis, and then adding other variables.
Fig. 1. Model with the statistically significant variable selected in the bivariate analysis, and then adding other variables.

As this study involved humans, it was submitted, evaluated and approved for execution by the Research Ethics Committee of the Faculdades Integradas Pitágoras (Protocol: 817.666).

Results

The sample consisted of 874 women between 40 and 65 years of age, of whom 74.1% were overweight and obese. When categorized by climacteric status, it was observed that postmenopausal women had a higher prevalence of overweight/obesity (54.3%).

The results of the bivariate analysis revealed that the following variables were associated with the overweight and obesity outcome: age between 52 and 65 years (p = 0.184), private school attendance (p = 0.000), less schooling (p = 0.093) (p = 0.0006), liver disease (p = 0.000), gout (p = 0.000), kidney disease (p = 0.106), weight of the 1st child at birth equal to or greater than 4000 g (p = .039), high risk for cardiovascular diseases (p = 0.000), alcohol consumption (p = 0.039) and fat intake (p = 0.065). However, women between 46 and 51 years of age (p = 0.184), who had a late menarche age (p = 0.039) and had children over 18 years old (p = 0.004) experienced a protective effect against overweight and obesity. It should be emphasized that there was a high prevalence of overweight and obesity in all the independent variables presented (Table 1).

Tab. 1. Sample characteristics and gross prevalence ratios (PRs) for overweight and obesity women according to the sociodemographic, reproductive, clinical, behavioural and eating factors of menopausal women.
Sample characteristics and gross prevalence ratios (PRs) for overweight and obesity women according to the sociodemographic, reproductive, clinical, behavioural and eating factors of menopausal women.

Some sociodemographic (marital status, monthly income, number of individuals residing in the same house and colour of skin), clinical and behavioural (smoking, physical activity, depression symptoms, sleep quality) factors did not present significant associations (p <0.250) with overweight and obesity and were not included in the hierarchical model.

The health conditions that were associated with overweight and obesity in the hierarchical model at the distal level were private school attendance (PR = 1.30, p = 0.000) and low level of education (PR = 1.11, p = 0.033). After adjusting for sociodemographic factors, an association at an intermediate level between age at first childbirth above 18 years (PR = 0.90, p = 0.010) was observed, and this variable had a protective effect against the occurrence of overweight and obesity (Table 2). At the proximal level, after adjusting for the potential confounding factors analysed, the presence of gout (RP = 1.18, p = 0.004), MS (PR = 1.29, p = 0.000), kidney disease (PR = 1.19, p = 0.006) and fat intake (PR = 1.12, p = 0.014) were found to be positively associated with overweight and obesity (Table 2).

Tab. 2. Adjusted prevalence ratios for overweight and obesity according to sociodemographic, reproductive, clinical, eating and behavioural factors of climacteric women.
Adjusted prevalence ratios for overweight and obesity according to sociodemographic, reproductive, clinical, eating and behavioural factors of climacteric women.

Discussion

The prevalence of overweight and obesity in the population of the present study was higher than 2/3 of the sample, with a mean BMI of 28.67 ± 6.35 kg/m2 and with a predominance of overweight in postmenopausal women. These findings are in accordance with a study conducted in São Paulo/Brazil, where the mean BMI in postmenopausal women was 29.0 ± 5.6 kg/m2[27].

Weight gain in climacteric women is due to the ageing process and oestrogenic depletion, with a centralized distribution of fat mass related to ovarian failure[28], which leads to a change in the hormonal environment previously dominated by oestrogen to an environment where there is a predominance of testosterone, favouring androgenicity[29]. Additionally, inadequate lifestyle habits, such as a sedentary lifestyle and the consumption of fats and sugars, can lead to physiological and metabolic alterations[30]. The limited perception of body weight and the importance of its control[31] and the use of medications such as antidepressants, analgesics, and anxiolytics[32] also compete for a role in this condition.

Obesity is associated with insulin resistance and chronic inflammation predisposing obese individuals to various diseases, including breast cancer, whose pathogenesis has been linked to increased oestrogen levels[33].

In addition, excessive body weight also contributes to the occurrence of systemic arterial hypertension (SAH), depression and worsening of climacteric symptoms[34]. Together with other comorbidities, excessive body weight impairs the quality of life of women and impacts their functionality[6,35,36].

According to the findings of this study, having attended private school seems to be associated with overweight in the climacteric women. This may be due to an increased accessibility of high caloric foods in childhood and adolescence or maternal obesity during pregnancy36 that leads to weight excess, which could be perpetuated in adult life. However, the literature cannot explain these findings consistently, presenting evidence of a higher prevalence of weight excess among students of private schools in other age groups[37,38].

Nevertheless, some studies have shown an association between less schooling and high BMI[39], in agreement with the present findings, suggesting that a higher level of education may favour healthier living habits, such as the intake of vegetables and fruits[40] and the regular practice of physical activity[41]. Physical activity, including strength and endurance training, has a significant effect on aspects related to women's health in menopause, including favourable aspects of mineral metabolism, such as iron[42], which may also be influenced by probiotic supplementation, which improves the quality of the impaired intestinal microbiota in obese patients[43].

Regarding the gynaecological aspects, having a first delivery that occurred after the age of 18 was shown to be a protective factor for overweight and obesity. Other studies have also shown an association between overweight and obesity, early parturition and parity[44,45]. Findings suggest that younger maternal age at first delivery is independently associated with a higher risk of central obesity and MS in climacteric women[46]. One explanation would be the possibility of a higher number of pregnancies among women with early parturition and lifestyle changes, although the pathophysiology of this association is still unclear and deserves additional study[47]. Multiparity is associated with an increase in the prevalence of MS since it favours abdominal obesity[48] and insulin resistance in climacteric women[49].

The diagnosis of gout is also associated with overweight and obesity in climacteric women. This finding becomes relevant since hyperuricaemia is correlated with insulin resistance, hypertension, obstructive sleep apnoea, chronic renal disease (CKD), MS and elevated cardiovascular risk[50,51]. According to this context, hyperuricaemia may be related to an increase in the prevalence of coronary artery disease (CAD) and to the incidence of major cardiovascular events in climacteric women as an independent risk factor[52]. Chromosomal abnormalities are associated with elevated serum levels of uric acid and gout in postmenopausal women, demonstrating a possible role of sex hormones in the regulation of the urate transporter in gout[53].

An association between kidney disease and overweight and obesity was found in the present study. These data are consistent with the Brazilian Society of Nephrology's Dialysis Survey in 2014, which showed that 37% of dialysis patients were overweight or obese and that overweight and obesity was as a risk factor for CKD[54]. In addition, obesity was associated with MS, which is also a risk factor for the development of CKD[55].

Overweight is related to compensatory hyperfiltration, which occurs to meet the metabolic demands increased by body weight, with possible damage to the kidneys and increased risk of long-term glomerulopathy, in addition to being a risk factor for nephrolithiasis and kidney cancer. The obese patient also has a higher relative risk for developing albuminuria and a decrease in the glomerular filtration rate, even without CKD[56].

In climacteric women, with increased risk for obesity, MS becomes more prevalent, increasing the incidence of cardiovascular disease and the risk of acute myocardial infarction (AMI)[57], a vulnerability attributed to the decrease of oestrogen and insulin resistance[58]. The association between overweight and obesity and MS was observed in the present study with a consequent risk elevation for cardiovascular diseases. Another study corroborated these findings and demonstrated that the prevalence of MS was also higher in postmenopausal women[59]. Obesity presents as a possible primary factor for the occurrence of MS and the risk of cardiovascular diseases, since an overweight patient may also have visceral adiposity, which is one of the diagnostic criteria of MS.

Among the overweight and obese women in this study, a diet characterized by fat intake was associated with overweight. A document published by the Health Surveillance Agency points out that excessive consumption of saturated fat, as well as sugars, is related to the development of chronic noncommunicable diseases, including obesity[60]. A balance in fat consumption is a viable strategy for a possible reduction of cardiovascular risk in this population[61], since inadequate diet is the leading cause of cardiovascular mortality[25].

The present study presents as limiting factors the use of BMI as the sole diagnostic criterion for overweight and obesity, as opposed to using other gold standard techniques of body analysis, such as Dual X-ray Densitometry (DEXA). The liver diseases, kidney disease and gout variables were measured by self-report, and it was not possible to establish with precision the different aetiologies of these diseases; however, being able to establish their association in a generic way provoked the need for further studies using more accurate diagnostic tools, such as imaging or laboratory tests. Moreover, this was a cross-sectional study and, therefore, it was unable to establish causality among the studied variables. Despite the presented limitations, the study was carried out with methodological rigor, and the obtained results provide relevant information on the subject in addition to listing variables to be studied in future studies. It should be emphasized that the sample used in the study was representative of the population and was obtained in a probabilistic way, strengthening the results and associations obtained.

In addition, from a socioeconomic point of view, the population studied resides in a region that represents the Brazilian reality with confidence; it is located in a transition zone between what is considered rich Brazil (represented by the southern and southeastern states) and regions of Brazil with characteristics of poverty (represented by the northern and northeastern states). Therefore, the present study reports associations relevant to the health of climacteric women in an emblematic and representative segment of the Brazilian population. These results can be used to implement public policies to assist climacteric women in preventing the occurrence of overweight and its consequences.

Conclusion

The presence of overweight and obesity was associated with climacteric women who had attended private schools, who had low schooling, gout, metabolic syndrome, and kidney disease, who had high cardiovascular risk and who ingested fats in their diet. In turn, having a first delivery after 18 years of age was presented as a protective factor for women not becoming overweight and obese. Monitoring of these modifiable factors is suggested since they were associated with overweight in climacteric women assisted by primary health care services.

Supporting information

S1 File [sav]
DATABASE.


Zdroje

1. Cunha ACNP, Cunha NNP, Barbosa MT. Geriatric teaching in Brazilian medical schools in 2013 and considerations regarding adjustment to demographic and epidemiological transition. Rev Ass Med Bras. 2016; 62(2): 179–183. doi: 10.1590/1806-9282.62.02.179 27167549

2. NCD Risk Factors Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. The Lancet. 2016; 387(10026):1377–96. doi: 10.1016/S0140-6736(16)30054-X

3. Nogueira IAL, da Cruz EJSN, Fontenele AMM, Figueiredo Neto JA. Alterations in postmenopausal plasmatic lipidome. PLoS ONE. 2018; 13 (9): e0203027. doi: 10.1371/journal.pone.0203027 30180197

4. Silveira EA, Vieira LL, Jardim TV, Souza JD. Obesity and its Association with Food Consumption, Diabetes Mellitus, and Acute Myocardial Infarction in the Elderly Arq. Bras. Cardiol. 2016; 107(6): 509–517. doi: 10.5935/abc.20160182 28558083

5. Moley KH, Colditz GA. Effects of obesity on hormonally driven cancer in women. Sci Transl Med. 2016; 8(323): 323ps3. doi: 10.1126/scitranslmed.aad8842 26819193

6. Souza Guerra GE, Junior, Prates Caldeira A, Piana Santos Lima de Oliveira F, Santos Figueiredo Brito MF, de Oliveira Silva Gerra KD, Mendes D’Angelis CE, et al. Quality of life in climacteric women assisted by primary health care. PLoS ONE. 2019;14(2):1–13. doi: 10.1371/journal.pone.0211617 30811409

7. Medeiros de Morais MS, Andrade do Nascimento R, Vieira MCA, Moreira MA, Camara SMAD, Campos Cavalcanti Maciel A et al. Does body image perception relate to quality of life in middle-aged women? PLoS ONE. 2017; 12(9): e0184031. doi: 10.1371/journal.pone.0184031 28926575

8. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde. Vigitel Brasil 2016: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2016 / Ministério da Saúde, Secretaria de Vigilância em Saúde, Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde.–Brasília: Ministério da Saúde, 2017. 160p.: il. ISBN 978-85-334-2479-1. Available at: http://portalarquivos2.saude.gov.br/images/pdf/2018/marco/02/vigitel-brasil-2016.pdf.

9. Lui Filho JF, Baccaro LF, Fernandes T, Conde DM, Costa-Paiva L, Pinto Neto AM. Factors associated with menopausal symptoms in women from a metropolitan region in Southeastern Brazil: a population-based household survey. Rev Bras Ginecol Obstet. 2015; 37(4):152–8. doi: 10.1590/SO100-720320150005282 25992497

10. Gonçalves JTT, Silveira MF, Campos MCC, Costa LHR. Overweight and obesity and factors associated with menopause. Ciência & Saúde Coletiva. 2015; 21(4): 1145–56. doi: 10.1590/1413-81232015214.16552015 27076013

11. Andrade MV, Coelho AQ, Xavier Neto M, Carvalho LR, Atun R, Castro MC. Transition to universal primary health care coverage in Brazil: Analysis of uptake and expansion patterns of Brazil's Family Health Strategy (1998–2012). PLoS ONE. 2018; 13(8): e0201723. doi: 10.1371/journal.pone.0201723 30096201

12. The North American Menopause Society (NAMS). Menopause Guidebook. Available at: www.menopause.org. Traduzido pela SOBRAC–Associação Brasileira de Climatério. Guia da Menopausa. Ajudando a mulher climatérica a tomar decisões informadas sobre a sua saúde. 7 ed. São Paulo: 2013. ISBN 978-0-9701251-4-9. Available at: http://sobrac.org.br/media/files/publicacoes/00001261_a12361_leigos_rev2mcowfinal.pdf

13. Szwarcwald CL, Damacena GN. Complex Sampling Design in Population Surveys: Planning and effects on statistical data analysis. Rev. bras. epidemiol. [Internet]. 2008; 11(Suppl 1):38–45. doi: 10.1590/S1415-790X2008000500004

14. Mason C, Xiao L, Imayama I, Duggan C, Wang CY, Korde L, et al. Vitamin D3 supplementation during weight loss: a double-blind randomized controlled trial. Am J Clin Nutr. 2014. doi: 10.3945/ajcn.113.073734 24622804

15. Yan LL, Daviglus ML, Liu K, Pirzada A, Garside DB, Schiffer L, Dyer AR, Greenland P. BMI and health-related quality of life in adults 65 years and older. Obes Res. 2004; 12(1): 69–76. doi: 10.1038/oby.2004.10 14742844

16. Fanelli F, Mezzullo M, Belluomo I, Di Lallo VD, Baccini M, Ibarra Gasparini D et al. Plasma 2-arachidonoylglycerol is a biomarker of age and menopause related insulin resistance and dyslipidemia in lean but not in obese men and women. Mol Metab. 2017; 6(5): 406–415. doi: 10.1016/j.molmet.2017.03.005 28462075

17. Kupperman HS, Blatt MHG. Menopausal indice. J Clin Endocrinol. 1953; 13(1): 688–694.

18. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama 2001 May 16; 285(19): 2486–97. doi: 10.1001/jama.285.19.2486 11368702

19. Tamanini JTN, Dambros M, D'Ancona CAL, Palma PCR, Netto NR Jr. Validation of the “International Consultation on Incontinence Questionnaire—Short Form” (ICIQ-SF) for Portuguese. Rev Saúde Pública. 2004;38(3)438–44. doi: 10.1590/s0034-89102004000300015 15243675

20. D'Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53. doi: 10.1161/CIRCULATIONAHA.107.699579 18212285

21. Gorenstein C, Andrade L. Inventário de depressão de Beck: propriedades psicométricas da versão em português. Rev Psiq Clin.1998; 25:245–250.

22. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. doi: 10.1016/0165-1781(89)90047-4 2748771

23. Benedetti TRB, Mazo GZ, Barros MV. Aplicação do Questionário Internacional de Atividade Física para avaliação do nível de atividades físicas de mulheres idosas: validade concorrente e reprodutibilidade teste/reteste. Rev Bras Ciên e Mov. 2004; 12(1):25–33.

24. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. 4337382

25. Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune Neto A et al. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose– 2017. Arq Bras Cardiol 2017; 109(2Supl.1):1–76. doi: 10.5935/abc.20170121 Available at: http://publicacoes.cardiol.br/2014/diretrizes/2017/02_DIRETRIZ_DE_DISLIPIDEMIAS.pdf

26. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 2003;26(1):S5–20. doi: 10.2337/diacare.26.2007.S5

27. Steiner ML, Azevedo LH, Bonacordi CL, Barros AZ, Strufaldi R, Fernandes CE. Avaliação de consumo alimentar, medidas antropométricas e tempo de menopausa de mulheres na pós-menopausa. Rev Bras Ginecol Obstet, 2015; 37(1):16–23. doi: 10.1590/SO100-720320140005138 25607125

28. Karvonen-Gutierrez C, Kim C. Association of Mid-Life Changes in Body Size, Body Composition and Obesity Status with the Menopausal Transition. Healthcare (Basel), 2016; 13;4(3). doi: 10.3390/healthcare4030042 27417630

29. Janssen I, Powell LH, Jasielec MS, Kazlauskaite R. Covariation of change in bioavailable testosterone and adiposity in midlife women. Obesity (Silver Spring), 2015; 23(2): 488–494. doi: 10.1002/oby.20974 25557490

30. Barbalho SM, Bechara MD, Quesada K, Gabaldo MR, Goulart RA, Tofano RJ et al. Metabolic syndrome, atherosclerosis and inflammation: an inseparable triad? J Vasc Bras. 2015; 14(4):319–327. doi: 10.1590/1677-5449.04315

31. Muennig P, Jia H, Lee R, Lubetkin E. I Think Therefore I Am: Perceived Ideal Weight as a Determinant of Health. Am J Public Health 2008; 98(3): 501–506. doi: 10.2105/AJPH.2007.114769 18235062

32. Castelo-Branco C. Clinical aspects and relationships between weight gain, obesity and menopause. Maturitas. 2009; 63 (Suppl 1): S19. doi: 10.1016/S0378-5122(09)70075-1

33. Gershuni V, Li YR, Williams AD, So A, Steel L, Carrigan E, et al. Breast cancer subtype distribution is different in normal weight, overweight, and obese women. Breast Cancer Res Treat. 2017;163(2):375–381. doi: 10.1007/s10549-017-4192-x 28293912

34. Blümel JE, Chedraui P, Aedo S, Fica J, Mezones-Holguín E, Barón G, et al. Obesity and its relation to depressive symptoms and sedentary lifestyle in middle-aged women. Maturitas. 2015; 80(1):100–5. doi: 10.1016/j.maturitas.2014.10.007 25459364

35. Sorpreso ICE, Soares Júnior JM, Fonseca AM, Baracat EC. Female aging. Rev Assoc Med Bras 2015; 61(6):553–556. doi: 10.1590/1806-9282.61.06.553 26841166

36. Skrypnik D, Bogdański P, Zawiejska A, Wender-Ożegowska E. Role of gestational weight gain, gestational diabetes, breastfeeding, and hypertension in mother-to-child obesity transmission. Pol Arch Intern Med. 2019; 129: 267–275. doi: 10.20452/pamw.4426 30688285

37. Rosaneli CF, Auler F, Manfrinato CB, Rosaneli CF, Sganzerla C, Bonatto MG et al. Evaluation of the prevalence and nutritional and social determinants of overweight in a population of schoolchildren: a cross-sectional analysis of 5,037 children. Rev. Assoc. Med. Bras. 2012;58 (4): 472–476. doi: 10.1590/S0104-42302012000400019 22930027

38. Simmonds M, Burch J, Llewellyn A, Griffiths C, Yang H, Owen C. The use of measures of obesity in childhood for predicting obesity and the development of obesity-related diseases in adulthood: a systematic review and meta-analysis. Health Technol Assess. 2015 Jun;19(43):1–336. doi: 10.3310/hta19430 26108433

39. Alves RFS, Faerstein E. Educational inequality in the occurrence of abdominal obesity: Pró-Saúde Study. Rev Saude Publica. 2015;49: 65. doi: 10.1590/S0034-8910.2015049005786 26465669

40. Lins APM, Sichieri R, Coutinho WF, Ramos EG, Peixoto MVM, Fonseca VM. Healthy eating, schooling and being overweight among low-income women. Ciência & Saúde Coletiva. 2013;18(2):357–66. doi: 10.1590/S1413-81232013000200007 23358761

41. Freire RS, Lélis FLO, Fonseca Filho JA, Nepomuceno MO, Silveira MF. Regular physical activity: a population-based study in North Minas Gerais, Brazil. Rev Bras Med Esporte. 2014; 20(5):345–349. doi: 10.1590/1517-86922014200502062

42. Skrypnik D, Bogdański P, Skrypnik K, Mądry E, Karolkiewicz J, Szulińska M, Suliburska J, Walkowiak J. Influence of endurance and endurance-strength training on mineral status in women with abdominal obesity: a randomized trial. Medicine (Baltimore). 2019;98(12):e14909. doi: 10.1097/MD.0000000000014909 30896645

43. Skrypnik K, Bogdariski P, Sobieska M, Suliburska J. The effect of multistrain probiotic supplementation in two doses on iron metabolism in obese postmenopausal women: a randomized trial. Food & Function. 2019; 10:5228–5238. doi: 10.1039/C9FO01006H 31384878

44. Kac G, Velásquez-Meléndez G, Valente JG. Menarche, early pregnancy, and obesity in selected Brazilian women from a health care center in Belo Horizonte, Minas Gerais, Brazil. Cad Saúde Pública. 2003; 19(Suppl. 1):111–8. doi: 10.1590/S0102-311X2003000700012 12886441

45. Bobrow KL, Quigley MA, Green J, Reeves GK, Beral V; Million Women Study Collaborators. Persistent effects of women's parity and breastfeeding patterns on their body mass index: results from the Million Women Study. Int J Obes (Lond). 2013; 37(5):712–7. doi: 10.1038/ijo.2012.76 22777544

46. Sim JH, Chung D, Lim JS, Lee MY, Chung CH, Shin JY, et al. Maternal Age at First Delivery Is Associated with the Risk of Metabolic Syndrome in Postmenopausal Women: From 2008–2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2015; 10(5): e0127860. doi: 10.1371/journal.pone.0127860 26010910

47. We JS, Han K, Kwon HS, Kil K. Effect of Maternal Age at Childbirth on Obesity in Postmenopausal Women: A Nationwide Population-Based Study in Korea. Medicine (Baltimore). 2016; 95(19):e3584. doi: 10.1097/MD.0000000000003584 27175656

48. Vladutiu CJ, Siega-Riz AM, Sotres-Alvarez D, Stuebe AM, Ni A, Tabb KM, et al. Parity and Components of the Metabolic Syndrome Among US Hispanic/Latina Women: Results From the Hispanic Community Health Study/Study of Latinos. Circ Cardiovasc Qual Outcomes. 2016; 9 (2 Suppl 1): S62–9. doi: 10.1161/CIRCOUTCOMES.115.002464 26908862

49. Kim JH, Lee SJ. Parity and increased risk of insulin resistance in postmenopausal women: the 2010 Korean National Health and Nutrition Examination Survey. Menopause. 2017;13. doi: 10.1097/GME.0000000000000846 28291026

50. Jung JH, Song GG, Ji JD, Lee YH, Kim JH, Seo YH, et al. Metabolic syndrome: prevalence and risk factors in Korean gout patients. Korean J Intern Med. 2018. doi: 10.3904/kjim.2016.062 27729624

51. Lopes HF, Corrêa-Giannella ML, Consolim-Colombo FM, Egan BM. Visceral adiposity syndrome. Diabetol Metab Syndr. 2016; 8:40. doi: 10.1186/s13098-016-0156-2 27437032

52. Zhang JW, He LJ, Cao SJ, Yang Q, Yang SW, Zhou YJ. Association of serum uric acid and coronary artery disease in premenopausal women. PLoS One. 2014; 9(9):e106130. doi: 10.1371/journal.pone.0106130 25184207

53. Zhang L, Spencer KL, Voruganti VS, Jorgensen NW, Fornage M, Best LG et al. Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: the PAGE Study. Am J Epidemiol. 2013; 177(9):923–32. doi: 10.1093/aje/kws330 23552988

54. Sesso RC, Lopes AA, Thomé FS, Lugon JR, Martins CT. Brazilian Chronic Dialysis Census 2014. J Bras Nefrol. 2016; 38(1): 54–61. doi: 10.5935/0101-2800.20160009 27049365

55. Stefanska A, Bergmann K, Sypniewska G. Metabolic Syndrome and Menopause: Pathophysiology, Clinical and Diagnostic Significance. Adv Clin Chem. 2015;72:1–75. doi: 10.1016/bs.acc.2015.07.001 26471080

56. Garofalo C, Borrelli S, Minutolo R, Chiodini P, De Nicola L, Conte G. A systematic review and meta-analysis suggests obesity predicts onset of chronic kidney disease in the general population. Kidney Int. 2017; 91(5):1224–1235. doi: 10.1016/j.kint.2016.12.013 28187985

57. Sharma S, Aggarwal N, Joshi B. Suri V, Badada S. Prevalence of metabolic syndrome in pre- and post-menopausal women: A prospective study from apex institute of North India. J Midlife Health. 2016 Oct-Dec; 7(4): 169–174. doi: 10.4103/0976-7800.195695 28096640

58. Meirelles RMR. Menopause and metabolic syndrome. Arq Bras Endocrinol Metab. 2014;58(2):91–6. doi: 10.1590/0004-2730000002909 24830585

59. Figueiredo Neto JA, Figuerêdo ED, Barbosa JB, Barbosa FF, Costa GRCC, Nina VJS et al. Metabolic Syndrome and Menopause: Cross-Sectional Study in Gynecology Clinic. Arq Bras Cardiol 2010; 95(3): 339–45. doi: 10.1590/s0066-782x2010005000094 20658092

60. Agência Nacional de Saúde Suplementar (Brasil). Diretoria de Normas e Habilitação dos Produtos. Gerência-Geral de Regulação Assistencial. Gerência de Monitoramento Assistencial. Coordenadoria de Informações Assistenciais. Manual de diretrizes para o enfretamento da obesidade na saúde suplementar brasileira [recurso eletrônico]/Agência Nacional de Saúde Suplementar. Diretoria de Normas e Habilitação dos Produtos. Gerência-Geral de Regulação Assistencial. Gerência de Monitoramento Assistencial. Coordenadoria de Informações Assistenciais.–Rio de Janeiro: ANS, 2017. 6.5 MB; ePUB. ISBN 978-85-63059-36-9. Available at: http://www.ans.gov.br/images/Manual_de_Diretrizes_para_o_Enfrentamento_da_Obesidade_na_Sa%C3%BAde_Suplementar_Brasileira.pdf

61. Silver HJ, Kang H, Keil CD, Muldowney JA, Kocalis H, Fazio S, et al. Consuming a balanced high fat diet for 16 weeks improves body composition, inflammation and vascular function parameters in obese premenopausal women. Metabolic. 2014; 63(4):562–73. doi: 10.1016/j.metabol.2014.01.004 24559846


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#