Prevalence of metabolic syndrome in Bangladesh: a systematic review and meta-analysis of the studies.

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obesity 15 endocrinologydiseases
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hyperglycemia 4 endocrinologydiseases
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Insulin 6229 strategy used in different databasesMEDLINEPubMedEMBASE 1. exp Metabolic Syndrome X/ 2. exp. Syndrome/ 3. Insulin resistance syndrome.mp. 4. exp. Hypertension/ 5. high blood pressure.mp. 6. exp. Hyperlipidemias/ 7.
Insulin 6748 16 or 17 or 18 or 19 or 20 24. 21 and 22 and 23Search (((((((((((Metabolic Syndrome) OR Syndrome) OR Insulin resistance syndrome) OR ((((Hypertension) OR high blood pressure)) AND ((Hyperlipidemia) OR lipid disorder)))
Insulin 7396 high blood sugar)))) AND prevalence) AND Bangladesh1. exp Metabolic Syndrome X/2. exp. Syndrome/3. Insulin resistance syndrome.mp.4. exp. Hypertension/5. high blood pressure.mp.6. exp. Hyperlipidemia/7. lipid
Select Disease Character Offset Disease Term Instance
diabetes mellitus 6937 ((Hyperlipidemia) OR lipid disorder))) OR ((((Hypertension) OR high blood pressure)) AND (((hyperglycemia) OR diabetes mellitus ) OR high blood sugar))) OR ((abdominal obesity) AND ((Hypertension) OR high blood pressure))) OR ((((Hyperlipidemia)
diabetes mellitus 7117 ((Hypertension) OR high blood pressure))) OR ((((Hyperlipidemia) OR lipid disorder)) AND (((hyperglycemia) OR diabetes mellitus ) OR high blood sugar))) OR ((abdominal obesity) AND ((Hyperlipidemia) OR lipid disorder))) OR ((abdominal
diabetes mellitus 7275 obesity) AND ((Hyperlipidemia) OR lipid disorder))) OR ((abdominal obesity) AND (((hyperglycemia) OR diabetes mellitus ) OR high blood sugar)))) AND prevalence) AND Bangladesh1. exp Metabolic Syndrome X/2. exp. Syndrome/3.
diabetes mellitus 7548 Hypertension/5. high blood pressure.mp.6. exp. Hyperlipidemia/7. lipid disorder.mp.8. exp. hyperglycemia/9. exp. diabetes mellitus /10. high blood sugar.mp.11. exp. abdominal obesity/12. 4 or 513. 6 or 714. 8 or 9 or 1015. 12 and 1316.
hyperglycemia 6919 pressure)) AND ((Hyperlipidemia) OR lipid disorder))) OR ((((Hypertension) OR high blood pressure)) AND ((( hyperglycemia ) OR diabetes mellitus) OR high blood sugar))) OR ((abdominal obesity) AND ((Hypertension) OR high blood
hyperglycemia 7099 obesity) AND ((Hypertension) OR high blood pressure))) OR ((((Hyperlipidemia) OR lipid disorder)) AND ((( hyperglycemia ) OR diabetes mellitus) OR high blood sugar))) OR ((abdominal obesity) AND ((Hyperlipidemia) OR lipid
hyperglycemia 7257 sugar))) OR ((abdominal obesity) AND ((Hyperlipidemia) OR lipid disorder))) OR ((abdominal obesity) AND ((( hyperglycemia ) OR diabetes mellitus) OR high blood sugar)))) AND prevalence) AND Bangladesh1. exp Metabolic Syndrome
hyperglycemia 7526 syndrome.mp.4. exp. Hypertension/5. high blood pressure.mp.6. exp. Hyperlipidemia/7. lipid disorder.mp.8. exp. hyperglycemia /9. exp. diabetes mellitus/10. high blood sugar.mp.11. exp. abdominal obesity/12. 4 or 513. 6 or 714.
metabolic syndrome 38 Title: BMC Public HealthPrevalence of metabolic syndrome in Bangladesh: a systematic review and meta-analysis of the studiesMohammad Ziaul Islam ChowdhuryAtaul
metabolic syndrome 1006 situation, we conducted a systematic review and meta-analysis with an objective to assess the prevalence of metabolic syndrome among the Bangladeshi population using data already published in the scientific literature.MethodsWe
metabolic syndrome 1809 in rural populations and study participants were mostly females. The weighted pooled prevalence of metabolic syndrome regardless of gender and criteria used to define metabolic syndrome, was 30.0% with high heterogeneity
metabolic syndrome 1877 The weighted pooled prevalence of metabolic syndrome regardless of gender and criteria used to define metabolic syndrome , was 30.0% with high heterogeneity observed. Weighted pooled prevalence of metabolic syndrome is higher
metabolic syndrome 1971 define metabolic syndrome, was 30.0% with high heterogeneity observed. Weighted pooled prevalence of metabolic syndrome is higher in females (32%) compared to males (25%) though not statistically significant (p = 0.434).
metabolic syndrome 2530 sample size criteria and evidence of small study effect was also detected.ConclusionsThe prevalence of metabolic syndrome is high and rising in Bangladesh. Strategies aimed at primary prevention are required to mitigate a
metabolic syndrome 2753 further increase in the prevalence and for the reduction of the morbidity and mortality associated with metabolic syndrome .Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5209-z) contains
metabolic syndrome 3146 pressure, high triglycerides, elevated blood sugar and low HDL cholesterol. The underlying causes of metabolic syndrome include overweight and obesity, insulin resistance, an unhealthy dietary pattern, physical inactivity,
metabolic syndrome 3329 dietary pattern, physical inactivity, genetic factors and aging [[1]]. The worldwide prevalence of metabolic syndrome in the adult population is on the rise with an estimated prevalence of 20–25% [[1]]. Adults with metabolic
metabolic syndrome 3447 in the adult population is on the rise with an estimated prevalence of 20–25% [[1]]. Adults with metabolic syndrome are twice as likely to die from and are three times as likely to have a heart attack or stroke compared
metabolic syndrome 3594 from and are three times as likely to have a heart attack or stroke compared with people without the metabolic syndrome [[1]–[5]]. In addition, they have a five-fold greater risk of developing type 2 diabetes [[6]]. In
metabolic syndrome 3729 addition, they have a five-fold greater risk of developing type 2 diabetes [[6]]. In this backdrop, metabolic syndrome is being considered as public health issue globally [[7], [8]].Prevalence of non-communicable chronic
metabolic syndrome 4444 irregular meal times, less physical activity, etc. [[12]].Though a handful of studies were conducted about metabolic syndrome in Bangladeshi population [[13], [14]], few studies have described the prevalence of metabolic syndrome
metabolic syndrome 4548 metabolic syndrome in Bangladeshi population [[13], [14]], few studies have described the prevalence of metabolic syndrome and its related factors, hence restricting the quality of information available on the magnitude of
metabolic syndrome 4967 central administrative health data’s to obtain accurate information’s on prevalence of disease like metabolic syndrome . Having comprehensive information about the prevalence of metabolic syndrome can be very effective for
metabolic syndrome 5044 prevalence of disease like metabolic syndrome. Having comprehensive information about the prevalence of metabolic syndrome can be very effective for planning and executing preventive strategies for such diseases. To provide
metabolic syndrome 5303 situation, we conducted a systematic review and meta-analysis with an objective to assess the prevalence of metabolic syndrome among the Bangladeshi population using data already published in the scientific literature. The performance
metabolic syndrome 5507 literature. The performance of meta-analysis will help to combine the existing data on the prevalence of metabolic syndrome and explore possible heterogeneity between studies.MethodsData sources and searchesWe systematically
metabolic syndrome 5730 searched MEDLINE, EMBASE and PubMed (from inception to February 10, 2017) for studies on prevalence of metabolic syndrome among Bangladeshi population. We also searched the reference lists of all identified relevant publications
metabolic syndrome 6008 limited inclusion to studies published in English. The search strategy focused on three key elements: metabolic syndrome , prevalence and Bangladesh. The search strategy is provided in detail in Table 1.Table 1Search strategy
metabolic syndrome 8133 reported data from an original study (review articles were excluded) and reported on the prevalence of metabolic syndrome in Bangladesh. We used broad inclusion criteria to provide a comprehensive systematic review of the
metabolic syndrome 8439 study), geographic region (e.g., urban, rural) or age ranges. Studies that reported the prevalence of metabolic syndrome in the general population were included. We excluded those studies where prevalence of metabolic syndrome
metabolic syndrome 8545 metabolic syndrome in the general population were included. We excluded those studies where prevalence of metabolic syndrome was measured only in specific clinical population (e.g. individuals with hypertension or diabetes) regardless
metabolic syndrome 8846 exclude studies where hypertension or diabetes in the general population was used as a criteria to define metabolic syndrome in order to measure its prevalence. We excluded the animal and biomedical studies that did not report
metabolic syndrome 8981 measure its prevalence. We excluded the animal and biomedical studies that did not report prevalence of metabolic syndrome , non-human studies, and studies that only focused on the prevalence of a component of metabolic syndrome
metabolic syndrome 9086 metabolic syndrome, non-human studies, and studies that only focused on the prevalence of a component of metabolic syndrome (e.g. prevalence of hypertension). Articles were retained when anyone of the reviewers believed that
metabolic syndrome 9537 articles were subsequently screened on the basis of a full-text review. We considered any definition of metabolic syndrome .Data extractionFrom the finally selected articles, the following information’s were extracted: author
metabolic syndrome 9874 which the study was carried out, sample selection procedure, study design, criteria for diagnosis of metabolic syndrome , and the prevalence of metabolic syndrome and its components. Two reviewers independently extracted
metabolic syndrome 9916 selection procedure, study design, criteria for diagnosis of metabolic syndrome, and the prevalence of metabolic syndrome and its components. Two reviewers independently extracted data using a standardized form. Study quality
metabolic syndrome 10836 and accounted for.Data analysisWe grouped studies on the basis of the criteria used to diagnosis of metabolic syndrome , whether National Cholesterol Education Program Adult Treatment Panel III criteria (NCEP ATP III), modified
metabolic syndrome 11200 effects meta-analysis to obtain the weighted average prevalence with 95% CIs for studies. While defining metabolic syndrome , studies were not all consistent with the way they define different individual components of metabolic
metabolic syndrome 11312 syndrome, studies were not all consistent with the way they define different individual components of metabolic syndrome . For example, some studies used the term elevated blood pressure while other used the term hypertension.
metabolic syndrome 11920 circumference. Despite some minor differences exists in defining and using thresholds in different components of metabolic syndrome , we ignored such differences while conducting meta-analysis on the prevalence of metabolic syndrome
metabolic syndrome 12020 metabolic syndrome, we ignored such differences while conducting meta-analysis on the prevalence of metabolic syndrome assuming that such minor difference won’t impact the overall result. We also grouped studies on the
metabolic syndrome 12227 studies on the basis of the gender and age of the study participants and assessed temporal change on metabolic syndrome prevalence.Heterogeneity was assessed using the Cochran Q and the I2 statistic and was explored using
metabolic syndrome 12469 stratified analyses according to the gender of the study participants and criteria used to diagnosis metabolic syndrome . Small study effects were examined using funnel plot and Egger’s test. Inter rater reliability was
metabolic syndrome 12892 retrieved 488 and grey literature search retrieved 3 potentially relevant papers on the prevalence of metabolic syndrome in Bangladesh. After removing duplicates, reviewing titles and abstracts, 38 articles remained for full
metabolic syndrome 13714 shown in Fig. 1.Fig. 1PRISMA diagram for systematic review of studies that evaluated the prevalence of metabolic syndrome (MS) in the Bangladeshi populationA summary describing the characteristic of the selected studies on
metabolic syndrome 13852 Bangladeshi populationA summary describing the characteristic of the selected studies on the prevalence of metabolic syndrome in Bangladesh is presented in Table 2. Most of the studies were conducted in rural populations (8 out
metabolic syndrome 14422 Barisal and Rajshahi region (Additional file 1: Figure S1). Various criteria had been used to diagnose metabolic syndrome . Four studies used the criteria for diagnosing metabolic syndrome proposed by the National Cholesterol
metabolic syndrome 14488 criteria had been used to diagnose metabolic syndrome. Four studies used the criteria for diagnosing metabolic syndrome proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) [[18]–[21]];
metabolic syndrome 15341 sampling procedure by 4 studies.Table 2Characteristics of studies that evaluated the prevalence of metabolic syndrome (MS) in the Bangladeshi populationStudyYear PublishedAge RangeGenderSample Size and TypeStudy Area (Urban/Rural)Sampling
metabolic syndrome 19003 above in 2 studies, 30 years and above in 2 studies and 60 years and above in 1 study. Prevalence of metabolic syndrome is reported by all the studies ranged from 8.6% to 72.1%. One study [[19]] did not report the prevalence
metabolic syndrome 19212 the prevalence and only described the association between parity or gravidity and the prevalence of metabolic syndrome . All the studies where study participants was both male and female, reported prevalence data not only
metabolic syndrome 19424 not only for all but also for males and females separately. One study [[18]] reported prevalence of metabolic syndrome by age at menarche. Only one study [[23]] reported age adjusted prevalence of metabolic syndrome, other
metabolic syndrome 19521 of metabolic syndrome by age at menarche. Only one study [[23]] reported age adjusted prevalence of metabolic syndrome , other nine studies reported overall prevalence, gender specific prevalence as well as prevalence in
metabolic syndrome 20027 did not identify any significant difference (p = 0.656) in overall and age adjusted prevalence of metabolic syndrome , leads us to pool all the prevalences in our meta-analysis.The weighted pooled prevalence of metabolic
metabolic syndrome 20139 syndrome, leads us to pool all the prevalences in our meta-analysis.The weighted pooled prevalence of metabolic syndrome regardless of gender, age, and criteria used to define metabolic syndrome, was 30.0% [95% CI: 25% -
metabolic syndrome 20213 weighted pooled prevalence of metabolic syndrome regardless of gender, age, and criteria used to define metabolic syndrome , was 30.0% [95% CI: 25% - 35%]. There was a large amount of heterogeneity in the prevalence of metabolic
metabolic syndrome 20327 syndrome, was 30.0% [95% CI: 25% - 35%]. There was a large amount of heterogeneity in the prevalence of metabolic syndrome (I2 = 98.81%; Cochran Q-statistic p < 0.001; Fig. 2). Meta regression did not identify gender
metabolic syndrome 20549 (male vs female, p = 0.434) as a potential source of heterogeneity in the overall prevalence of metabolic syndrome but did identify area (urban vs rural, p < 0.001) as a source of heterogeneity.Fig. 2Forest plot
metabolic syndrome 20723 as a source of heterogeneity.Fig. 2Forest plot of prevalence, with 95% confidence intervals (CIs) of metabolic syndrome in Bangladeshi population. Sample sizes for the studies are given in parenthesesSubgroup analysis based
metabolic syndrome 20932 analysis based on the gender of study participantsAlmost all studies reported a higher prevalence of metabolic syndrome in females. So we performed a subgroup analysis based on the gender of the study participants (Fig. 3).
metabolic syndrome 21086 subgroup analysis based on the gender of the study participants (Fig. 3). Weighted pooled prevalence of metabolic syndrome is higher in females, 32% [95% CI: 27% - 38%] compared to males, 25% [95% CI: 16% - 35%]. However meta-regression
metabolic syndrome 21262 males, 25% [95% CI: 16% - 35%]. However meta-regression did not identify this higher prevalence of metabolic syndrome in females statistically significant (p = 0.434). There was significant between study heterogeneity
metabolic syndrome 21467 study heterogeneity (I2 = 98.61%; Cochran Q-statistic p < 0.001; Fig. 3) in the prevalence of metabolic syndrome when study participants was female. Meta-regression shows area (urban vs rural, p < 0.001) is the
metabolic syndrome 21750 study heterogeneity (I2 = 98.66%; Cochran Q-statistic p < 0.001; Fig. 3) in the prevalence of metabolic syndrome when study participants was male. Meta-regression again shows area (urban vs rural, p = 0.007) is
metabolic syndrome 21971 the source of heterogeneity.Fig. 3Forest plot of prevalence, with 95% confidence intervals (CIs) of metabolic syndrome in Bangladeshi population, stratified according to the gender of study participants. Sample sizes for
metabolic syndrome 22179 Sample sizes for the studies are given in parenthesesSubgroup analysis based on criteria used to define metabolic syndrome There are a number of alternative definitions available for metabolic syndrome and these definitions
metabolic syndrome 22257 criteria used to define metabolic syndromeThere are a number of alternative definitions available for metabolic syndrome and these definitions differed in the proposed components as well as in the cut-off points used for
metabolic syndrome 22463 points used for each component, leading to substantial confusion. Recognizing that such differences in metabolic syndrome definition will have an impact on its prevalence, will increase heterogeneity and will raise concern
metabolic syndrome 22608 impact on its prevalence, will increase heterogeneity and will raise concern in meaningful pooling of metabolic syndrome prevalence, a subgroup analysis was performed by dividing the studies based on the criteria they used
metabolic syndrome 22741 subgroup analysis was performed by dividing the studies based on the criteria they used to diagnose metabolic syndrome (Fig. 4). The subgroup analysis will help to explore heterogeneity in the metabolic syndrome prevalence
metabolic syndrome 22835 diagnose metabolic syndrome (Fig. 4). The subgroup analysis will help to explore heterogeneity in the metabolic syndrome prevalence further.Fig. 4Forest plot of prevalence, with 95% confidence intervals (CIs) of metabolic
metabolic syndrome 22945 syndrome prevalence further.Fig. 4Forest plot of prevalence, with 95% confidence intervals (CIs) of metabolic syndrome in Bangladeshi population, stratified according to the criteria used to diagnosis of metabolic syndrome.
metabolic syndrome 23049 metabolic syndrome in Bangladeshi population, stratified according to the criteria used to diagnosis of metabolic syndrome . Sample sizes for the studies are given in parenthesesWHO definition emphasized insulin resistance as
metabolic syndrome 23354 obesity, hypertension, high triglycerides, reduced HDL-C level, or microalbuminuria for diagnosis of metabolic syndrome . The NCEP ATP III criteria required the presence of any three of the following five factors as the basis
metabolic syndrome 23512 presence of any three of the following five factors as the basis for establishing the diagnosis of metabolic syndrome : abdominal obesity, elevated triglycerides, reduced HDL-C, elevated blood pressure, and elevated fasting
metabolic syndrome 23731 fasting glucose. The IDF definition makes the presence of abdominal obesity necessary for diagnosis of metabolic syndrome plus any two additional factors listed in the NCEP ATP III definition. The Modified NCEP ATP III definition
metabolic syndrome 24017 minor modifications.Abdominal obesity measured by waist circumference (WC) is a major component of any metabolic syndrome definition. Cut-off point for WC differs among the metabolic syndrome definitions. The IDF definition
metabolic syndrome 24087 (WC) is a major component of any metabolic syndrome definition. Cut-off point for WC differs among the metabolic syndrome definitions. The IDF definition uses ethnic-specific WC cut-off points and recommends cut-off levels
metabolic syndrome 24853 ≥88 cm in women or ≥102 cm in men.Among the studies that used NCEP ATP III criteria to diagnose metabolic syndrome , weighted pooled prevalence of metabolic syndrome was 24% [95% CI: 23% - 26%] with non-significant little
metabolic syndrome 24903 studies that used NCEP ATP III criteria to diagnose metabolic syndrome, weighted pooled prevalence of metabolic syndrome was 24% [95% CI: 23% - 26%] with non-significant little heterogeneity between studies (I2 = 44.64%;
metabolic syndrome 25153 p = 0.09; Fig. 4). The studies that used Modified NCEP ATP III criteria, weighted pooled prevalence of metabolic syndrome was 37% [95% CI: 29% - 46%] with high heterogeneity between studies (I2 = 98.46%; Cochran Q-statistic
metabolic syndrome 25599 identified as a source of heterogeneity. The studies that used IDF criteria, weighted pooled prevalence of metabolic syndrome was 32% [95% CI: 22% - 44%] with high heterogeneity between studies (I2 = 99.11%; Cochran Q-statistic
metabolic syndrome 26006 p = 0.385) was not identified as a source of heterogeneity. Only two studies used WHO criteria to diagnose metabolic syndrome and the weighted pooled prevalence of metabolic syndrome was 20% [95% CI: 12% - 31%] with high heterogeneity
metabolic syndrome 26063 two studies used WHO criteria to diagnose metabolic syndrome and the weighted pooled prevalence of metabolic syndrome was 20% [95% CI: 12% - 31%] with high heterogeneity between studies (I2 = 97.66%; Cochran Q-statistic
metabolic syndrome 26530 obvious consequence of difference in cut-off points in WC is reflected in weighted pooled prevalence of metabolic syndrome . A much higher pooled prevalence (37% Modified NCEP ATP III, 32% IDF) was observed when WC cut-off of
metabolic syndrome 26770 and ≥80 cm in women was used compared to other cut-off point (24% NCEP ATP III). The prevalence of metabolic syndrome varies considerably according to the criteria used to diagnosis of metabolic syndrome. So a subgroup
metabolic syndrome 26856 prevalence of metabolic syndrome varies considerably according to the criteria used to diagnosis of metabolic syndrome . So a subgroup analysis for the prevalence of metabolic syndrome according to the criteria used to diagnosis
metabolic syndrome 26921 the criteria used to diagnosis of metabolic syndrome. So a subgroup analysis for the prevalence of metabolic syndrome according to the criteria used to diagnosis of metabolic syndrome is probably a better option rather
metabolic syndrome 26987 subgroup analysis for the prevalence of metabolic syndrome according to the criteria used to diagnosis of metabolic syndrome is probably a better option rather than pooling all the prevalence of metabolic syndrome from different
metabolic syndrome 27076 diagnosis of metabolic syndrome is probably a better option rather than pooling all the prevalence of metabolic syndrome from different studies.Subgroup analysis based on the age of the study participants and assessment of
metabolic syndrome 27216 studies.Subgroup analysis based on the age of the study participants and assessment of temporal change in metabolic syndrome prevalenceThe prevalence of disease is often strongly age-dependent, so an age effect on the prevalence
metabolic syndrome 27346 prevalenceThe prevalence of disease is often strongly age-dependent, so an age effect on the prevalence of the metabolic syndrome is evident and need to be explored. Beside the overall prevalence, most of the studies (8 out of 10)
metabolic syndrome 27504 Beside the overall prevalence, most of the studies (8 out of 10) provided age stratified prevalence of metabolic syndrome . However the age grouping in different studies were different which restricts us providing weighted
metabolic syndrome 27644 grouping in different studies were different which restricts us providing weighted pooled prevalence of metabolic syndrome in Bangladesh among the different age groups. Instead, we divide the studies based on the mean age of
metabolic syndrome 28254 (p = 0.006, mean age cut-off ≤40 years) higher weighted pooled prevalence (36% versus 14%) of metabolic syndrome in study participants with mean age greater than 40 years. Overall prevalence of metabolic syndrome
metabolic syndrome 28355 metabolic syndrome in study participants with mean age greater than 40 years. Overall prevalence of metabolic syndrome increases 0.4% (p = 0.38; Fig. 5) for every one year increase in mean age of the study participants.
metabolic syndrome 28497 (p = 0.38; Fig. 5) for every one year increase in mean age of the study participants. Such increase in metabolic syndrome prevalence over the age of the participants is quite similar in male and female (Fig. 5a), however,
metabolic syndrome 28652 participants is quite similar in male and female (Fig. 5a), however, little different when definition of metabolic syndrome varies (Fig. 5b). A decrease in metabolic syndrome prevalence over the age of the study participants
metabolic syndrome 28703 5a), however, little different when definition of metabolic syndrome varies (Fig. 5b). A decrease in metabolic syndrome prevalence over the age of the study participants was observed for NCEP ATP III definition (Fig. 5b).Fig.
metabolic syndrome 28848 the study participants was observed for NCEP ATP III definition (Fig. 5b).Fig. 5Meta-regression of metabolic syndrome prevalence in Bangladesh on age of the study participants. a Prevalence of metabolic syndrome in Bangladesh
metabolic syndrome 28942 5Meta-regression of metabolic syndrome prevalence in Bangladesh on age of the study participants. a Prevalence of metabolic syndrome in Bangladesh over the age of the study participants (stratified by the gender of the study participants).
metabolic syndrome 29084 age of the study participants (stratified by the gender of the study participants). b Prevalence of metabolic syndrome in Bangladesh over the age of the study participants (stratified by the definition of metabolic syndrome)As
metabolic syndrome 29189 metabolic syndrome in Bangladesh over the age of the study participants (stratified by the definition of metabolic syndrome )As time of the study can affect the prevalence of metabolic syndrome, we explored such temporal change
metabolic syndrome 29258 (stratified by the definition of metabolic syndrome)As time of the study can affect the prevalence of metabolic syndrome , we explored such temporal change in metabolic syndrome prevalence. Studies included in present review
metabolic syndrome 29314 time of the study can affect the prevalence of metabolic syndrome, we explored such temporal change in metabolic syndrome prevalence. Studies included in present review has a time span of eight years from 2007 to 2015. We
metabolic syndrome 29684 Meta-regression shows, a significant (p = 0.002) higher weighted pooled prevalence (37% versus 14%) of metabolic syndrome in studies conducted in the year 2012 and afterwards. Overall prevalence of metabolic syndrome increases
metabolic syndrome 29779 14%) of metabolic syndrome in studies conducted in the year 2012 and afterwards. Overall prevalence of metabolic syndrome increases 3.68% (p = 0.006; Fig. 6) for every one year increase in time of the study conducted.
metabolic syndrome 29916 (p = 0.006; Fig. 6) for every one year increase in time of the study conducted. Such increase in metabolic syndrome prevalence over the time of the study conducted is quite similar in male and female (Fig. 6a) and also
metabolic syndrome 30057 time of the study conducted is quite similar in male and female (Fig. 6a) and also when definition of metabolic syndrome varies (Fig. 6b). An increasing trend in pooled prevalence of metabolic syndrome is observed during
metabolic syndrome 30138 when definition of metabolic syndrome varies (Fig. 6b). An increasing trend in pooled prevalence of metabolic syndrome is observed during 2007–2011 time period while a slightly decreasing trend is observed during 2012–2015
metabolic syndrome 30312 slightly decreasing trend is observed during 2012–2015 time period (Fig. 6c).Fig. 6Meta-regression of metabolic syndrome prevalence in Bangladesh on the study year. a Prevalence of metabolic syndrome in Bangladesh over the
metabolic syndrome 30391 6Meta-regression of metabolic syndrome prevalence in Bangladesh on the study year. a Prevalence of metabolic syndrome in Bangladesh over the study year (stratified by the gender of the study participants).b Prevalence
metabolic syndrome 30513 Bangladesh over the study year (stratified by the gender of the study participants).b Prevalence of metabolic syndrome in Bangladesh over the study year (stratified by the definition of metabolic syndrome). c Prevalence
metabolic syndrome 30599 Prevalence of metabolic syndrome in Bangladesh over the study year (stratified by the definition of metabolic syndrome ). c Prevalence of metabolic syndrome in Bangladesh over the different time spanPrevalence of individual
metabolic syndrome 30636 Bangladesh over the study year (stratified by the definition of metabolic syndrome). c Prevalence of metabolic syndrome in Bangladesh over the different time spanPrevalence of individual components of metabolic syndromePrevalence
metabolic syndrome 30736 metabolic syndrome in Bangladesh over the different time spanPrevalence of individual components of metabolic syndrome Prevalence of individual components of metabolic syndrome is reported in 6 studies, of which one study
metabolic syndrome 30793 spanPrevalence of individual components of metabolic syndromePrevalence of individual components of metabolic syndrome is reported in 6 studies, of which one study [[17]] presented prevalence of individual components of
metabolic syndrome 30913 is reported in 6 studies, of which one study [[17]] presented prevalence of individual components of metabolic syndrome graphically for males and females but did not present its numerical values. One study [[23]] reported
metabolic syndrome 33180 confounding factors and subgroups.Table 3Study quality assessment of studies that evaluated the prevalence of metabolic syndrome (MS) in the Bangladeshi populationStudyWas the sample representative of the target population?Were study
metabolic syndrome 34546 presence of small study effects, in which studies of smaller cohorts reporting higher prevalence of metabolic syndrome .Fig. 7Funnel plot for the publication bias of the studies that evaluated the prevalence of metabolic
metabolic syndrome 34656 syndrome.Fig. 7Funnel plot for the publication bias of the studies that evaluated the prevalence of metabolic syndrome in ; Bangladeshi populationDiscussionThe present systematic review provides summary estimates for the
metabolic syndrome 34791 populationDiscussionThe present systematic review provides summary estimates for the prevalence of metabolic syndrome in the Bangladeshi population. The findings of this review suggests that, the weighted pooled prevalence
metabolic syndrome 34918 Bangladeshi population. The findings of this review suggests that, the weighted pooled prevalence of metabolic syndrome are between 20.0 and 37.0% depending on the criteria used to define metabolic syndrome. Prevalence was
metabolic syndrome 35005 prevalence of metabolic syndrome are between 20.0 and 37.0% depending on the criteria used to define metabolic syndrome . Prevalence was highest (37%) when Modified NCEP ATP III criteria was used, while it was lowest (20%)
metabolic syndrome 35196 used, while it was lowest (20%) when WHO criteria was used. The observed overall pooled prevalence of metabolic syndrome (30%) in Bangladesh was slightly higher than the prevalence estimated around the world (between 20%
metabolic syndrome 35593 (ATP III)] [[25]]. A combined prospective cohort studies of Europe reported the overall prevalence of metabolic syndrome 15% (WHO) in nondiabetic adults [[26]]. The prevalence of metabolic syndrome among adults according
metabolic syndrome 35670 overall prevalence of metabolic syndrome 15% (WHO) in nondiabetic adults [[26]]. The prevalence of metabolic syndrome among adults according to a national survey of US is 35% (NCEP ATP III) and 39% (IDF) [[27]]. In a systematic
metabolic syndrome 35862 and 39% (IDF) [[27]]. In a systematic review conducted on gulf countries, the reported prevalence of metabolic syndrome with ATP III definition ranged from 19.5–37.2% (men) and from 13.5–42.7% (women), while with the
metabolic syndrome 36189 prevalence in a sub-Saharan African setting varies from 0 to 7.3% according to different definition of metabolic syndrome [[29]]. A systematic review on prevalence of metabolic syndrome in Asia-pacific region reported a prevalence
metabolic syndrome 36253 according to different definition of metabolic syndrome [[29]]. A systematic review on prevalence of metabolic syndrome in Asia-pacific region reported a prevalence of 11.9% (NCEP ATP III) to 49.0% (modified NCEP ATP III)
metabolic syndrome 36414 prevalence of 11.9% (NCEP ATP III) to 49.0% (modified NCEP ATP III) [[30]]. The weighted mean prevalence of metabolic syndrome was 14.0% (WHO), 26.1% (ATPIII), 29.8% (IDF) and 32.5% (modified ATPIII) in South Asia according to
metabolic syndrome 36649 review [[31]].This study demonstrated low HDL cholesterol as the most frequent individual component of metabolic syndrome with weighted pooled prevalence of 89%. High blood pressure was shown the second most prominent metabolic
metabolic syndrome 36764 syndrome with weighted pooled prevalence of 89%. High blood pressure was shown the second most prominent metabolic syndrome component with weighted pooled prevalence of 30%. High fasting glucose was the third most prevalent
metabolic syndrome 36883 component with weighted pooled prevalence of 30%. High fasting glucose was the third most prevalent metabolic syndrome component in our study (28%). The underlying factors behind the increased prevalence of low HDL, high
metabolic syndrome 37498 as physical inactivity, changes in diet and stress and is closely linked with higher prevalence of metabolic syndrome and is evidenced by the higher weighted pooled prevalence of metabolic syndrome observed among urban
metabolic syndrome 37578 higher prevalence of metabolic syndrome and is evidenced by the higher weighted pooled prevalence of metabolic syndrome observed among urban population (56% compared to 21% in rural population).We observed high between-study-heterogeneity
metabolic syndrome 37840 possible source of heterogeneity is the area (urban/rural) where study was conducted. Prevalence of metabolic syndrome also differed in males and females and it is more prevalent in females as identified by subgroup analysis.
metabolic syndrome 38064 analysis. Within the male and female subgroups, there was high between-study-heterogeneity on prevalence of metabolic syndrome . As different studies used different criteria to diagnosis of metabolic syndrome, a subgroup analysis
metabolic syndrome 38145 between-study-heterogeneity on prevalence of metabolic syndrome. As different studies used different criteria to diagnosis of metabolic syndrome , a subgroup analysis based on the criteria used to define metabolic syndrome was performed to assess
metabolic syndrome 38222 criteria to diagnosis of metabolic syndrome, a subgroup analysis based on the criteria used to define metabolic syndrome was performed to assess the heterogeneity on the prevalence of metabolic syndrome among the studies.
metabolic syndrome 38304 criteria used to define metabolic syndrome was performed to assess the heterogeneity on the prevalence of metabolic syndrome among the studies. However, heterogeneity was still observed within the subgroups, and study area and
metabolic syndrome 38615 small study effect was detected (p < 0.001), in which smaller studies reported higher prevalence of metabolic syndrome . Publication bias was also evident from the asymmetry on the funnel plot. Study quality assessment shows
metabolic syndrome 39517 through the major academic databases. Also a point needs to be noted that there is no uniformity of metabolic syndrome definitions, age groups, waist circumference cut-offs, and study settings in the studies included in
metabolic syndrome 39840 first comprehensive report to systematically evaluate the scientific literature on the prevalence of metabolic syndrome in Bangladesh. Despite differences in diagnostic criteria, gender, age and geographic area of subjects
metabolic syndrome 39989 differences in diagnostic criteria, gender, age and geographic area of subjects studied, the prevalence of metabolic syndrome is high and rising in Bangladesh according to this systematic review and recommends an urgent attention
metabolic syndrome 40340 further increase in the prevalence and for the reduction of the morbidity and mortality associated with metabolic syndrome . It is also extremely important to explore possible risk factors, especially those related to lifestyle,
metabolic syndrome 40559 lifestyle, which need to be addressed. Knowledge of these factors may be informative in the monitoring of metabolic syndrome and could contribute to planning and prevention strategies to tackle this problem.Additional fileAdditional
metabolic syndrome 40752 problem.Additional fileAdditional file 1:Figure S1. The regions where studies conducted to identify metabolic syndrome prevalence in Bangladesh. (DOCX 83 kb
obesity 3188 blood sugar and low HDL cholesterol. The underlying causes of metabolic syndrome include overweight and obesity , insulin resistance, an unhealthy dietary pattern, physical inactivity, genetic factors and aging [[1]].
obesity 6994 blood pressure)) AND (((hyperglycemia) OR diabetes mellitus) OR high blood sugar))) OR ((abdominal obesity ) AND ((Hypertension) OR high blood pressure))) OR ((((Hyperlipidemia) OR lipid disorder)) AND (((hyperglycemia)
obesity 7174 lipid disorder)) AND (((hyperglycemia) OR diabetes mellitus) OR high blood sugar))) OR ((abdominal obesity ) AND ((Hyperlipidemia) OR lipid disorder))) OR ((abdominal obesity) AND (((hyperglycemia) OR diabetes
obesity 7241 high blood sugar))) OR ((abdominal obesity) AND ((Hyperlipidemia) OR lipid disorder))) OR ((abdominal obesity ) AND (((hyperglycemia) OR diabetes mellitus) OR high blood sugar)))) AND prevalence) AND Bangladesh1.
obesity 7609 disorder.mp.8. exp. hyperglycemia/9. exp. diabetes mellitus/10. high blood sugar.mp.11. exp. abdominal obesity /12. 4 or 513. 6 or 714. 8 or 9 or 1015. 12 and 1316. 12 and 1417. 11 and 1218. 13 and 1419. 11 and 1320.
obesity 11655 fasting blood glucose or diabetes also. Similarly, the terms elevated waist circumference, central obesity or obesity waist was used interchangeably with no unique cut-off to measure abdominal obesity. Even
obesity 11666 blood glucose or diabetes also. Similarly, the terms elevated waist circumference, central obesity or obesity waist was used interchangeably with no unique cut-off to measure abdominal obesity. Even in one study,
obesity 11749 central obesity or obesity waist was used interchangeably with no unique cut-off to measure abdominal obesity . Even in one study, BMI was substituted for waist circumference. Despite some minor differences exists
obesity 23253 insulin resistance as the major underlying risk factor plus presence of two additional risk factors from obesity , hypertension, high triglycerides, reduced HDL-C level, or microalbuminuria for diagnosis of metabolic
obesity 23542 following five factors as the basis for establishing the diagnosis of metabolic syndrome: abdominal obesity , elevated triglycerides, reduced HDL-C, elevated blood pressure, and elevated fasting glucose. The IDF
obesity 23696 elevated blood pressure, and elevated fasting glucose. The IDF definition makes the presence of abdominal obesity necessary for diagnosis of metabolic syndrome plus any two additional factors listed in the NCEP ATP
obesity 23944 III definition maintains the original NCEP ATP III criteria except for minor modifications.Abdominal obesity measured by waist circumference (WC) is a major component of any metabolic syndrome definition. Cut-off
obesity 31371 not report the prevalence of individual components. Five studies reported the prevalence of abdominal obesity . In these five studies, the weighted pooled prevalence of abdominal obesity was 18.0% [95% CI: 11.0%
obesity 31447 prevalence of abdominal obesity. In these five studies, the weighted pooled prevalence of abdominal obesity was 18.0% [95% CI: 11.0% - 27.0%]. Though a markedly different pooled prevalence of abdominal obesity
obesity 31549 obesity was 18.0% [95% CI: 11.0% - 27.0%]. Though a markedly different pooled prevalence of abdominal obesity (8% versus 27%) was observed when WC cut-off values shifted from ≥88 cm in women or ≥102 cm in

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