Association between Metabolic Syndrome and Job Rank

Mehrdad, Ramin; Pouryaghoub, Gholamreza; Moradi, Mahboubeh

doi:10.15171/ijoem.2018.1197

Cited by 15 publications

(12 citation statements)

References 28 publications

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“…However, the association of MetS risk with not only occupation type but also SES must be determined. 22 32 34 35 SES is a concept encompassing salary, social status and education and can be indicated by an individual’s occupation. 36 Al-Thani et al 34 and Mehrdad et al 22 reported that occupation type and seniority in a company, respectively, are not significantly associated with MetS risk.…”

Section: Introductionmentioning

confidence: 99%

“… 22 32 34 35 SES is a concept encompassing salary, social status and education and can be indicated by an individual’s occupation. 36 Al-Thani et al 34 and Mehrdad et al 22 reported that occupation type and seniority in a company, respectively, are not significantly associated with MetS risk. Therefore, this study conducted in Taiwan focused on the relationship of type of occupation with MetS prevalence as well as with the biochemical indexes of related chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

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Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status

2021

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ObjectivesTo determine whether occupation type, distinguished by socioeconomic status (SES) and sedentary status, is associated with metabolic syndrome (MetS) risk.MethodsWe analysed two data sets covering 73 506 individuals. MetS was identified according to the criteria of the modified Adult Treatment Panel III. Eight occupational categories were considered: professionals, technical workers, managers, salespeople, service staff, administrative staff, manual labourers and taxi drivers; occupations were grouped into non-sedentary; sedentary, high-SES; and sedentary, non-high-SES occupations. A multiple logistic regression was used to determine significant risk factors for MetS in three age-stratified subgroups. R software for Windows (V.3.5.1) was used for all statistical analyses.ResultsMetS prevalence increased with age. Among participants aged ≤40 years, where MetS prevalence was low at 6.23%, having a non-sedentary occupation reduced MetS risk (OR=0.88, p<0.0295). Among participants aged >60 years, having a sedentary, high-SES occupation significantly increased (OR=1.39, p<0.0247) MetS risk.ConclusionsThe influence of occupation type on MetS risk differs among age groups. Non-sedentary occupations and sedentary, high-SES occupations decrease and increase MetS risk, respectively, among younger and older adults, respectively. Authorities should focus on individuals in sedentary, high-SES occupations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status

2021

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“…The occurrence of MetS had significantly increased amongst female blue-collar workers, those with lower education level and household income 16 . Part-time or temporary workers of either gender showed higher MetS prevalence than full-time workers 16 , but job rank was not associated with MetS in Mehrdad et al study 18 . The prevalence of MetS significantly augmented with being married 16 , and smoking status 16,19 .…”

Section: Demographic and Social Risk Factors Related To Metabolic Synmentioning

confidence: 75%

Determinant components of newly onset versus improved metabolic syndrome in a population of Iran

Lankarani

Honarvar

Keshani

et al. 2020

Sci Rep

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This study aimed to determine the risk factors related to regression and progression of metabolic syndrome, in a 4-year cohort study. A total of 540 individuals (≥ 18 years old) participated in both phase of the study. Participants were categorized into 3 categories of regressed, progressed and unchanged metabolic syndrome (MetS). Demographic, anthropometric and biochemical parameters were assessed for each individual in both phase. Variables differences (delta: Δ) between the two phase of study were calculated. Unchanged group was considered as baseline category. Based on IDF, MetS had been regressed and progressed in 42 participants (7.7%) and 112 (20.7%) participants respectively, in the second phase. More than 47% of people, whose MetS regressed, experienced also NAFLD regression. Results of multiple variable analysis revealed that increased age, positive Δ-TG, and Δ-FBS, significantly increased the odds of MetS progression based on IDF and ATP III definitions, while negative Δ-HDL and Δ-neutrophil to lymph ration increased the odds of progression. On the other hand, negative Δ-TG and positive Δ-HDL significantly increased the odds of Mets regression based of both IDF and ATP III. Management of hypertriglyceridemia, hyperglycemia, and HDL is a critical, non-invasive and accessible approach to change the trend of MetS.

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“…The estimated prevalence of metabolic syndrome (MetS) is 9.5% in machine operators and 7.5–10.6% in Dutch trade workers [ 4 ]. In Taiwan, 7.7% of labourers [ 5 ], 8.2% of high-tech labour [ 1 ], and 10.3% of hospital employees [ 6 ] meet the criteria for metabolic syndrome. According to the Health Promotion Hospital (HPH) trend, the health of hospital employees is a matter of great concern for hospital management.…”

Section: Introductionmentioning

confidence: 99%

Metabolic Syndrome and Its Related Factors among Hospital Employees: A Population-Based Cohort Study

Tzeng

Chu

et al. 2021

IJERPH

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Several studies have reported on metabolic syndrome (MetS) based on cross-sectional designs, which cannot show a long-term result. Information is lacking on MetS and related factors based on a longitudinal cohort. This study aimed to examine the relationship between MetS and related factors for a total of six years among hospital employees. A population-based study was conducted, including 746 staff. A total of 680 staff without MetS in 2012 were enrolled in the analysis for repeated measurement of six years of the longitudinal cohort. Data were retrieved from the hospital’s Health Management Information System. Analyses were performed using Student’s t-test, chi-square test, logistic regression, and generalised estimating equations. Statistical significance was defined as p < 0.05. Hospital employees aged between 31 and 40 (odds ratio (OR) = 4.596, p = 0.009), aged between 41 and 50 (OR = 7.866, p = 0.001), aged greater than 50 (OR = 10.312, p < 0.001), with a body mass index (BMI) of 25.0~29.9 kg/m2 (OR = 3.934, p < 0.001), a BMI ≥ 30 kg/m2 (OR = 13.197, p < 0.001), higher level of white blood counts (β = 0.177, p = 0.001), alanine aminotransferase (β = 0.013, p = 0.002), and uric acid (β = 0.223, p = 0.005) were at risk of being diagnosed with MetS. The identification of at-risk hospital employees and disease management programs addressing MetS-related factors are of great importance in hospital-based interventions.

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Association between Metabolic Syndrome and Job Rank

Abstract: Background: The occupation of the people can influence the development of metabolic

Cited by 15 publications

References 28 publications

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status

Determinant components of newly onset versus improved metabolic syndrome in a population of Iran

Metabolic Syndrome and Its Related Factors among Hospital Employees: A Population-Based Cohort Study

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