Dataset Information

Association between metabolic syndrome and incidence of cholelithiasis in the Korean population.

ABSTRACT:

Background and aim

Cholelithiasis is one of the most common gastrointestinal diseases worldwide. The metabolic syndrome (MetS), a combination of various metabolic abnormalities, is also common with a continually increasing prevalence. These diseases are associated with several risk factors. However, data on the association between MetS components and cholelithiasis are insufficient. This study aimed to analyze the association of MetS and its components with the incidence of cholelithiasis using national data from the Korean population.

Methods

Data were obtained from the National Health Insurance Corporation of Korea, and 207 850 individuals without cholelithiasis in 2009 were enrolled and followed up until 2013. A multivariate Cox proportional hazard model was used to calculate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the incidence of cholelithiasis according to the presence of MetS and the number of MetS components. Furthermore, the risk of cholelithiasis was evaluated in individuals with a single metabolic component.

Results

The multivariate adjusted HRs and 95% CIs for incident cholelithiasis according to 1, 2, 3, and 4-5 MetS components were 1.08 (0.93-1.24), 1.22 (1.06-1.41), 1.35 (1.17-1.57), and 1.35 (1.15-1.57), respectively (P < 0.001). This increasing trend was observed in both sexes. Compared with participants with no metabolic components, those with low high-density lipoprotein (HDL) cholesterol had a significantly increased risk for cholelithiasis (adjusted HR, 1.39 [95% CI, 1.05-1.85]).

Conclusions

Metabolic syndrome is a potential risk factor for cholelithiasis. Low HDL cholesterol level is the most relevant factor among MetS components for incident cholelithiasis.

SUBMITTER: Kim Y

PROVIDER: S-EPMC9291184 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRheumatoid arthritis, metabolic syndrome (MS) and cardiovascular disease (CVD) are mutually connected. We aim to investigate the association between rheumatoid factor (RF) and MS in general population, explore the potential value of RF for assessment of metabolic status, and further provide a reference to the establishment of CVD primary prevention for this population.MethodsWe assessed the health check-up subjects, accordance with the inclusive criteria, from 1 January 2015 to 31 October 2021 in a large refereed general hospital, in this retrospective study. Subjects were categorized into four groups according to their levels of RF. Multivariate logistic regression models along with the Odds ratio (OR) and Confidence interval (CI) values were used to measure the association between RF and MS.ResultsA total of 13,690 subjects were analyzed. Prevalence of MS increased with RF level (P for trend < 0.001). Logistic regression analysis showed that, after adjusting for multiple covariates, RF level was significantly associated with MS prevalence (highest RF quartile: OR, 1.420; 95% CI 1.275,1.581, according to the revised National Cholesterol Education Program Adult Treatment Panel III criteria; OR, 2.355; 95% CI 2.085,2.660, according to the International Diabetes Federation criteria) (both P for trends < 0.001). Among the MS components, there were evidence of increasing trends for overweight/obesity (highest RF quartile: OR, 3.165; 95% CI 2.827,3.543) and hypertension (highest RF quartile: OR, 1.722; 95% CI 1.549,1.914) (both P for trends < 0.001), but decreasing trend for low high-density lipoprotein-cholesterol (highest RF quartile: OR, 0.245; 95% CI 0.214,0.281) (P for trend < 0.001), with increasing RF quartiles.ConclusionsRF level is associated with MS prevalence in general population. RF might be a valuable biomarker for assessment of metabolic status in this population. We should be aware of the cardiovascular risk for the higher-RF subjects.

Project description:Past studies have shown an association between metabolic syndrome and polyneuropathy, but the precise components that drive this association remain unclear.To determine the prevalence of polyneuropathy stratified by glycemic status in well-characterized obese and lean participants and investigate the association of specific components of metabolic syndrome with polyneuropathy.We performed a cross-sectional, observational study from November 1, 2010, to December 31, 2014, in obese participants (body mass index [calculated as weight in kilograms divided by height in meters squared] of 35 or more with no comorbid conditions or 32 or more with at least 1 comorbid condition) from a weight management program and lean controls from a research website. The prevalence of neuropathy, stratified by glycemic status, was determined, and a Mantel-Haenszel ?2 test was used to investigate for a trend. Logistic regression was used to model the primary outcome of polyneuropathy as a function of the components of metabolic syndrome after adjusting for demographic factors. Participants also completed quantitative sudomotor axon reflex testing, quantitative sensory testing, the neuropathy-specific Quality of Life in Neurological Disorders instrument, and the short-form McGill Pain Questionnaire.Components of metabolic syndrome (as defined by the National Cholesterol Education Program Adult Treatment Panel III), including glycemic status (as defined by the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus).Toronto consensus definition of probable polyneuropathy. Secondary outcomes included intraepidermal nerve fiber density and nerve conduction study parameters.We enrolled 102 obese participants (mean [SD] age, 52.9 [10.2] years; 48 men and 54 women; 45 with normoglycemia [44.1%], 31 with prediabetes [30.4%], and 26 with type 2 diabetes [25.5%]) and 53 lean controls (mean [SD] age, 48.5 [9.9] years; 16 men and 37 women). The prevalence of polyneuropathy was 3.8% in lean controls (n?=?2), 11.1% in the obese participants with normoglycemia (n?=?5), 29% in the obese participants with prediabetes (n?=?9), and 34.6% in the obese participants with diabetes (n?=?9) (P?<?.01 for trend). Age (odds ratio, 1.09; 95% CI, 1.02-1.16), diabetes (odds ratio, 4.90; 95% CI, 1.06-22.63), and waist circumference (odds ratio, 1.24; 95% CI, 1.00-1.55) were significantly associated with neuropathy in multivariable models. Prediabetes (odds ratio, 3.82; 95% CI, 0.95-15.41) was not significantly associated with neuropathy.The prevalence of polyneuropathy is high in obese individuals, even those with normoglycemia. Diabetes, prediabetes, and obesity are the likely metabolic drivers of this neuropathy.clinicaltrials.gov Identifier: NCT02689661.

Project description:Background & aimsSensorineural hearing loss (HL) is one of the most common public health problems, and its prevalence increases with increasing life expectancy. An association between HL and metabolic syndrome (MetS) is suspected. Although previous epidemiological studies have investigated the association between the two variables, there have been conflicting conclusions. Therefore, we aimed to evaluate the association between the presence of MetS-and individual components of MetS-and HL, using a longitudinal design and a large-scale population.MethodsA total of 17,513,555 individuals who underwent national health screening between January 2009 and December 2010 were identified. Subject data from the Korean Health Insurance Review and Assessment Service were reviewed. A total of 11,457,931 subjects were ultimately included in the analysis. Baseline comorbidities were defined according to the ICD-10 code from the Korean Health Insurance Review and Assessment Service data. If the participants had an ICD-10 code for HL during the follow-up, they were defined as having incident HL. Criteria for MetS adhered to the revised National Cholesterol Education Program Adult Treatment Panel III.ResultsThere were 7,574,432 subjects without MetS and 3,883,499 with MetS. The incidence of HL in subjects without MetS and with MetS was 1.3% and 1.8% at 1 year, 4.1% and 5.2% at 3 years, and 6.8% and 8.6% at 5 years, respectively (P < 0.001). However, multivariate analyses revealed a negative association. Analyses according to the components of MetS demonstrated a positive association for those associated with dyslipidemia; however, the others exhibited an inverse association with HL. We also performed analyses using 4 groups according to the presence of MetS and the components of dyslipidemia. Univariate analysis revealed a positive association between the presence of MetS and HL; however, multivariate analysis revealed a positive association between the presence of dyslipidemia components and HL, regardless of the presence of MetS.ConclusionAmong the components of MetS, the association between low HDL or high TG levels and HL was most apparent. It is useful to evaluate each MetS component in isolation, such as the presence of low HDL or high TG levels, rather than the presence of MetS as a cluster of components.