Increased risk for diabetes development in subjects with large variation in total cholesterol levels in 2,827,950 Koreans: A nationwide population-based study

Risk of Subsequent Vitiligo in Transplant Recipients With Comorbid Graft-vs-Host Disease

Importance

Vitiligo is a multifactorial, depigmenting skin disorder characterized by selective loss of melanocytes. Large-scale studies are lacking to determine the risk of vitiligo in transplant recipients with graft-vs-host disease (GVHD).

Objective

To investigate the incidence rates and risk of vitiligo in patients who had received solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT) overall and by HSCT graft type and concomitant GVHD.

Design, Setting, and Participants

This population-based cohort study included data from the National Health Insurance Service database of Korea for patients aged 20 years or older who had received a transplant (SOT or HSCT) between January 2010 and December 2017, with follow-up until December 2019. A cohort of age- and sex-matched (1:5) control individuals who did not receive a transplant was included for comparison. Data were analyzed from July 2021 to December 2021.

Exposure

Transplant (SOT or HSCT) and GVHD.

Main Outcomes and Measures

The main outcome was risk of vitiligo, assessed using multivariable Cox proportional hazards regression analyses adjusting for potential confounding factors.

Results

The study included 23 829 patients who had undergone SOT or HSCT (62.78% male; mean [SD] age, 49.58 [11.59] years) and 119 145 age- and sex-matched controls. Patients who had undergone transplant had a significantly higher risk of vitiligo compared with controls (adjusted hazard ratio [AHR], 1.73; 95% CI, 1.35-2.22). Risk of vitiligo was also slightly higher in kidney transplant recipients and liver transplant recipients compared with the controls but was highest in HSCT recipients (AHR, 12.69; 95% CI, 5.11-31.50). Patients who had received allogeneic grafts (AHR, 14.43; 95% CI, 5.61-37.15), those who had received autologous grafts (AHR, 5.71; 95% CI, 1.20-3.18), those with comorbid GVHD (AHR, 24.09; 95% CI, 9.16-63.35), and those without GVHD (AHR, 8.21; 95% CI, 3.08-21.87) had a higher risk of vitiligo compared with controls.

Conclusion and Relevance

In this study, risk of vitiligo was significantly higher in transplant recipients, especially in HSCT recipients and those with allogeneic grafts or comorbid GVHD. These findings provide new insights into the association between the risk of vitiligo and transplant and GVHD. Clinicians should be aware of these risks, implementing a multidisciplinary approach for monitoring.

A nude mutant rat derived from Sprague Dawley-National Institute of Nutrition rat colony with normal thymus: A potential model for noncommunicable diseases

BACKGROUND

A spontaneous mutant rat with a hairless phenotype and an intact thymus was discovered in a long-standing Sprague Dawley-National Institute of Nutrition (SD/NIN) rat colony at a national animal resource facility.

OBJECTIVE

We conducted extensive phenotypic and biochemical analyses on this mutant strain to determine its suitability as a preclinical model for immunocompetent testing in noncommunicable disease research.

MATERIALS AND METHODS

We subjected the mutant rats to strict and frequent phenotypic and genetic surveillance to accomplish this objective. The animals were assessed for food intake, body weight, blood cell profile, clinical chemistry, adipose tissue deposition, and bone mineral density (BMD) using total electrical body conductance (TOBEC) and dual-energy X-ray absorptiometry (DXA) analysis.

RESULTS

Initially, only two hairless mutant rats, a male and a female, were born from a single dam in the SD/NIN rat strain. However, the results indicate that the mutant colony propagated from these unique pups displayed distinct phenotypic features and exhibited differences in feeding behavior, weight gain, and clinical biochemistry. The food conversion rate was significantly higher in nude females (2.8-fold) while 26% lower in nude males. Both sexes of nude rats had significantly higher triglycerides and lower glucose levels in females. However, glucose levels did not change in male nude rats. Furthermore, nude female and male rats had significantly lower fat (TOBEC) and bone mineral content (DXA). Nonetheless, BMD was only slightly lower (7%-8%) compared to the heterozygous groups.

CONCLUSIONS

These findings indicate that the spontaneous mutant rat has the potential to serve as an immunopotent and modulatory testing system in pharmacokinetics/pharmacodynamics and toxicology, which can be further explored for therapeutic drug discovery.

Association of variability in metabolic parameters with the incidence of type 2 diabetes: evidence from a functional community cohort

BACKGROUND

To investigate the association of variability in metabolic parameters such as total cholesterol concentrations (TC), uric acid (UA), body mass index (BMI), visceral adiposity index (VAI) and systolic blood pressure (SBP) with incident type 2 diabetes (T2D) and whether variability in these metabolic parameters has additive effects on the risk of T2D.

METHODS

Based on the Beijing Functional Community Cohort, 4392 participants who underwent three health examinations (2015, 2016, and 2017) were followed up for incident T2D until the end of 2021. Variability in metabolic parameters from three health examinations were assessed using the coefficient of variation, standard deviation, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability index. Participants were grouped according to the number of high-variability metabolic parameters. Cox proportional hazards models were performed to assess the hazard ratio (HR) and 95% confidence interval (CI) for incident T2D.

RESULTS

During a median follow-up of 3.91 years, 249 cases of incident T2D were identified. High variability in TC, BMI, VAI and SBP was significantly associated with higher risks of incident T2D. As for UA, significant multiplicative interaction was found between variability in UA and variability in other four metabolic parameters for incident T2D. The risk of T2D significantly increased with the increasing numbers of high-variability metabolic parameters. Compared with the group with low variability for 5 parameters, the HR (95% CI) for participants with 1-2, 3, 4-5 high-variability metabolic parameters were 1.488 (1.051, 2.107), 2.036 (1.286, 3.222) and 3.017 (1.549, 5.877), respectively. Similar results were obtained in various sensitivity analyses.

CONCLUSIONS

High variability of TC, BMI, VAI and SBP were independent predictors of incident T2D, respectively. There was a graded association between the number of high-variability metabolic parameters and incident T2D.

Use of Multiple Fluid Biomarkers for Predicting the Co-occurrence of Diabetes and Hypertension Using Machine Learning Approaches

The co-existence of diabetes and hypertension can complicate and affect the management of these diseases. The early detection of these comorbidities can help in developing personalized preventive treatments and thereby, reduce the healthcare burden. The inclusion of readily available fluid biomarkers from different body fluids can be used as diagnostic tools and can facilitate in the designing of treatment strategies. In this work, an attempt has been made using multiple fluid biomarkers to differentiate diabetic from diabetic and hypertensive comorbid (DHC) condition. The fluid biomarkers are obtained from a publicly available dataset for diabetic (N=105) and DHC (N=57) conditions. The features, such as systolic blood pressure, fasting blood glucose, diastolic blood pressure, and total cholesterol are extracted and statistically analyzed. Data balancing technique namely synthetic minority oversampling technique is applied on the minority class to balance the dataset. Machine learning techniques namely, linear discriminant analysis, random forest, K-nearest neighbor, and linear support vector machine are used to perform the classification between the two groups. The results show that systolic blood pressure, diastolic blood pressure, and total cholesterol are elevated in the comorbid condition. These features also exhibit a statistical significance (p<0.001) between the two groups. This study also addresses the data imbalance issue, which is resolved by using an oversampling technique to mitigate the bias resulting from imbalanced data. The LDA classifier achieves a maximum accuracy of 61.2% in distinguishing between the two conditions. Machine learning based approaches may help in the prediction of comorbid conditions. This can act as a guideline for future studies on the progression of diseases and the identification of fluid biomarkers.

Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan

Cardiovascular risk factors aggregate in determined individuals. Patients with Type 2 diabetes mellitus (T2DM) have higher cardiovascular This study aimed to investigate insulinresistance (IR) and β-cell function using the homeostasis model assessment (HOMA) indexes in a general Kazakh population and determine the effect he effect that cardiovascular factors may have on those indexes. We conducted a cross-sectional study among employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) aged between 27 and 69 years. Sociodemographic variables, anthropometric measurements (body mass, height, waist circumference, hip circumference), and blood pressure were obtained. Fasting blood samples were collected to measure insulin, glucose, total cholesterol (TC), triglycerides (TG), and high- (HDL) andlow-density lipoprotein (LDL) levels. Oral glucose tolerance tests were performed. Hierarchical and K-means cluster analyses were obtained. The final sample was composed of 427 participants. Spearmen correlation analysis showed that cardiovascular parameters were statistically associated with HOMA-β (p < 0.001) and not with HOMA IR. Participants were aggregated into the three clusters where the cluster with a higher age and cardiovascular risk revealed deficient β-cell functioning, but not IR (p < 0.000 and p = 0.982). Common and easy to obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors have been demonstrated to be associated with significant deficiency in insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role not just for risk stratification of patients for cardiovascular prevention but also for targeted vigilant glucose monitoring.

Causal Association of Obesity and Dyslipidemia with Type 2 Diabetes: A Two-Sample Mendelian Randomization Study

Recent studies have suggested an association between obesity and dyslipidemia in the development of type 2 diabetes (T2D). The purpose of this study was to explore the causal effects of obesity and dyslipidemia on T2D risk in Asians. Two-sample Mendelian randomization (MR) analyses were performed to assess genetically predicted obesity using body mass index (BMI) and dyslipidemia using high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TCHL), and triglycerides (TG) versus T2D susceptibility using genome-wide association study (GWAS) results derived from the summary statistics of Biobank Japan (n = 179,000) and DIAbetes Meta-ANalysis of Trans-Ethnic association studies (n = 50,533). The MR analysis demonstrated evidence of a causal effect of higher BMI on the risk of T2D (odds ratio (OR) > 1.0, p < 0.05). In addition, TG showed a protective effect on the risk of T2D (ORs 0.68-0.85). However, HDL, LDL, and TCHL showed little genetic evidence supporting a causal association between dyslipidemia and T2D. We found strong genetic evidence supporting a causal association of BMI with T2D. Although HDL, LDL, and TCHL did not show a causal association with T2D, TG had a causal relationship with the decrease of T2D. Although it was predicted that TG would be linked to a higher risk of T2D, it actually exhibited a paradoxical protective effect against T2D, which requires further investigation.

Analysis of blood lipid changes and influencing factors in physical examination population of a city in central China

Purpose

The prevalence of cardiovascular diseases (CVDs) associated with lipid levels is increasing worldwide. Our purpose is to analyze the distribution level and influencing factors of lipid in the whole population and to put forward suggestions for preventing abnormal lipid levels.

Methods

The study was based on a sample of 91,480 Chinese who participated in a nationwide physical examination program in Wuhan, a midland city in China, in 2018. The distribution of blood lipid in the population was observed using average, and the relationship between the influencing factors and blood lipid level was observed by quantile regression (QR).

Results

A total of 91,480 people were evaluated in this study, among which 59,165 (64.68%) were female with a mean age of 51.71 ± 10.82 years. QR results showed that different physical examination indexes had different effects on lipid levels. Fasting plasma glucose (FBG) has the largest QR coefficient and BMI had positive effects on total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C). In males, age has a positive influence on TC, LDL-C, and high-density lipoprotein cholesterol (HDL-C), while in females, age has a positive influence on all four indexes.

Conclusion

We found that the TC and LDL-C levels of females were more susceptible to age than males, and the lipid levels of older females were higher than males. BMI has a greater effect on lipid levels in males than in females. Regardless of gender should pay attention to dyslipidemia caused by diabetes and abnormal liver function.

Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS

Metabolic syndrome (MetS) is a disorder characterized by a group of factors that can increase the risk of chronic diseases, including cardiovascular diseases and type 2 diabetes mellitus (T2D). Metabolomics has provided new insight into disease diagnosis and biomarker identification. This cross-sectional investigation used an untargeted metabolomics-based technique to uncover metabolomic alterations and their relationship to pathways in normoglycemic and prediabetic MetS participants to improve disease diagnosis. Plasma samples were collected from drug-naive prediabetic MetS patients (n = 26), normoglycemic MetS patients (n = 30), and healthy (normoglycemic lean) subjects (n = 30) who met the inclusion criteria for the study. The plasma samples were analyzed using highly sensitive ultra-high-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). One-way ANOVA analysis revealed that 59 metabolites differed significantly among the three groups (p < 0.05). Glutamine, 5-hydroxy-L-tryptophan, L-sorbose, and hippurate were highly associated with MetS. However, 9-methyluric acid, sphinganine, and threonic acid were highly associated with prediabetes/MetS. Metabolic pathway analysis showed that arginine biosynthesis and glutathione metabolism were associated with MetS/prediabetes, while phenylalanine, D-glutamine and D-glutamate, and lysine degradation were highly impacted in MetS. The current study sheds light on the potential diagnostic value of some metabolites in metabolic syndrome and the role of their alteration on some of the metabolic pathways. More studies are needed in larger cohorts in order to verify the implication of the above metabolites on MetS and their diagnostic value.

Prevalence and characteristics associated with diabetes mellitus and impaired fasting glucose among people aged 15 to 64 years in rural and urban Rwanda: secondary data analysis of World Health Organization surveillance data

Introduction

diabetes mellitus is an increasing public health burden in developing countries. The magnitude of diabetes association with traditional risk factors for diabetes have been given less attention in rural population. This study aims to determine the prevalence of diabetes and impaired fasting glucose and to assess associated characteristics to hyperglycemia in rural and urban Rwanda.

Methods

this is a secondary analysis of data from a population-based cross-sectional study of 7240 people describing risk factors for non-communicable diseases using the WHO stepwise methods (STEPS). Relative frequencies of variables of interest were compared in rural and urban residence using Pearson chi-square tests. Diabetes and impaired fasting glucose were combined in a single hyperglycemia variable and odds ratios with 95% confidence intervals were used to explore associations between hyperglycemia, socio-demographic and health factors in urban and rural populations.

Results

the prevalence in rural and urban areas was 7.5% and 9.7% (p.005) for diabetes and 5.0% and 6.2% for impaired fasting glucose (p.079) respectively. Obesity (AOR 2.57: CI: 0.86-7.9), high total cholesterol (AOR 3.83: CI: 2.03-7.208), hypertension (AOR 1.18: CI: 0.69-2.00), increasing age were associated with hyperglycemia in urban participants but only high total cholesterol and low high density lipoproteins (HDL) cholesterol were risk factors for hyperglycemia in rural participants.

Conclusion

approximately one in six people in Rwanda have hyperglycemia. The magnitude of the association with traditional risk factors for diabetes differ in rural and urban settings. Different approaches to primary and secondary prevention of diabetes may be needed in rural populations.

Variability, Mean, and Baseline Values of Metabolic Parameters in Predicting Risk of Type 2 Diabetes

CONTEXT

The effect of baseline (B) and alteration of metabolic parameters (MPs), including plasma glucose (PG) testing, insulin resistance surrogates, and lipid profile and their mutual interactions on the development of type 2 diabetes mellitus (T2DM), has not been investigated systematically.

OBJECTIVE

To access the association of the past variability (V), past mean (M), and B values of various MPs and their mutual interaction with the risk of T2DM.

METHODS

A community-based, longitudinal analysis was conducted using the Korean Genome and Epidemiology Study comprising 3829 nondiabetic participants with completed MPs measurements during 3 biannually visits who were followed over the next 10 years. Outcomes included the incidence of T2DM during follow-up.

RESULTS

Among predictors, PG concentrations measured during the oral glucose tolerance test were the most prominent T2DM determinants, in which the M of the average value of fasting PG (FPG), 1-hour, and 2-hour PGs had the strongest discriminative power (hazard ratios and 95% CI for an increment of SD: 3.00 (2.5-3.26), AUC: 0.82). The M values of MPs were superior to their B and V values in predicting T2DM, especially among postload PGs. Various mutual interactions between indices and among MPs were found. The most consistent interactants were the M values of high-density lipoprotein cholesterol and the M and V values of FPG. The findings were similar in normal glucose tolerance participants and were confirmed by sensitivity analyses.

CONCLUSION

Postload PG, past alteration of measurements, and mutual interactions among indices of MPs are important risk factors for T2DM development.

Temporal sequence of blood lipids and insulin resistance in perimenopausal women: the study of women's health across the nation

INTRODUCTION

To explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women.

RESEARCH DESIGN AND METHODS

The longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women's Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR).

RESULTS

Three latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, -0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (-0.091, P < 0.001) and HOMA-IR→RCT (-0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (-0.028, p=0.113) were not. The sensitivity analyses showed consistent results.

CONCLUSIONS

These findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.

The Influence of Obesity and Metabolic Health on Vascular Health

The prevalence of obesity is rapidly increasing worldwide. Obesity should not be understood only as the accumulation of fat in the body, but instead as a phenomenon that exerts different effects on our health according to the place of fat deposition and its stability. Obesity is the starting point of most metabolic diseases, such as diabetes, hypertension, metabolic syndrome, sleep apnea, and eventually cardiovascular disease. There are different kinds of obesity, ranging from simple obesity to sarcopenic obesity. The main purpose of intervening to address obesity is to decrease the ultimate consequence of obesity-namely, cardiovascular disease. The main mechanism through which obesity, especially abdominal obesity, increases cardiovascular risk is the obesity-induced derangement of metabolic health, leading to the development of metabolic diseases such as diabetes, non-alcoholic fatty liver disease, and metabolic syndrome, which are the main initiators of vascular damage. In this review, I discuss the influence of various types of obesity on the risk of metabolic diseases, and how these diseases increase cardiovascular disease risk.

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Background

Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD.

Methods

We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics.

Results

Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%.

Conclusions

Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.

Metabolome-wide association study of serum exogenous chemical residues in a cohort with 5 major chronic diseases

BACKGROUND

Chronic diseases have become main killers affecting the health of human, and environmental pollution is a major health risk factor that cannot be ignored. It has been reported that exogenous chemical residues including pesticides, herbicides, fungicides, veterinary drugs and persistent organic pollutants are associated with chronic diseases. However, the evidence for their relationship is equivocal and the underlying mechanisms are unclear.

OBJECTIVES

We aim to investigate the linkages between serum exogenous chemical residues and 5 main chronic diseases including obesity, hyperuricemia, hypertension, diabetes and dyslipidemia, and further reveal the metabolic perturbations of chronic diseases related to exogenous chemical residue exposure, then gain potential mechanism insight at the metabolic level.

METHODS

LC-MS-based targeted and nontargeted methods were respectively performed to quantify exogenous chemical residues and acquire metabolic profiling of 496 serum samples from chronic disease patients. Non-parametric test, correlation and regression analyses were carried out to investigate the association between exogenous chemical residues and chronic diseases. Metabolome-wide association study combined with the meeting-in-the-middle strategy and mediation analysis was performed to reveal and explain exposure-related metabolic disturbances and their risk to chronic diseases.

RESULTS

In the association analysis of 106 serum exogenous chemical residues and 5 chronic diseases, positive associations of serum perfluoroalkyl substances (PFASs) with hyperuricemia were discovered while other associations were not significant. 240 exposure markers of PFASs and 84 disease markers of hyperuricemia were found, and 47 of them were overlapped and considered as putative effective markers. Serum uric acid, amino acids, cholesterol, carnitines, fatty acids, glycerides, glycerophospholipids, ceramides, and a part of sphingolipids were positively correlated with PFASs and associated with increased risk for hyperuricemia. Creatine, creatinine, glyceryl monooleate, phosphatidylcholine 36:6, phosphatidylethanolamine 40:6, cholesterol and sphingolipid 36:1;2O were significant markers which mediated the associations of the residues with hyperuricemia.

CONCLUSIONS

Our study demonstrated a significantly positive association between PFASs exposure and hyperuricemia. The most significant metabolic abnormality was lipid metabolism which not only was positively associated with PFASs, but also increased the risk of hyperuricemia.

Increased Visit-to-Visit Liver Enzyme Variability Is Associated with Incident Diabetes: A Community-Based 12-Year Prospective Cohort Study

BACKGROUND

Fatty liver and/or increased liver enzyme values have been reported to be associated with incident diabetes. We sought to determine whether increased visit-to-visit liver enzyme variability is associated with incident diabetes.

METHODS

Study participants were recruited from the Korean Genome and Epidemiologic Study (KoGES). A total of 4,151 people aged 40 to 69 years was recruited and tested every 2 years for up to 12 years. Visit-to-visit aspartate aminotransferase (AST) and alanine aminotransferase (ALT) variability was evaluated in first the 6-year period through the use of various variability measurements: standard deviation (SD), average successive variability, coefficient of variation (CV), and variation independent of mean (VIM). Oral glucose tolerance test was performed at every visit.

RESULTS

During the 6-year follow-up appointments, 13.0% (538/4,151) of people developed incident diabetes. Visit-to-visit AST variability was associated with an increased risk of diabetes independent of conventional risk factors for diabetes (hazard ratio per 1-SD increment [95% confidence interval]: 1.06 [1.00 to 1.11], 1.12 [1.04 to 1.21], and 1.13 [1.04 to 1.22] for SD, CV, and VIM, respectively; all P<0.05); however, no such associations were observed in the visit-to-visit ALT variability. According to alcohol consumption status, both AST and ALT variability were independent predictors for incident diabetes in subjects with heavy alcohol consumption; however, neither AST nor ALT variability was associated with diabetes risk in subjects who did not drink alcohol heavily.

CONCLUSION

Visit-to-visit liver enzyme variability is an independent predictor of incident diabetes. Such association was more evident in those who consumed significant amounts of alcohol.

Triglyceride to high-density lipoprotein cholesterol ratio variability and incident diabetes: A 7-year prospective study in a Chinese population

AIMS/INTRODUCTION

The correlation between triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio variability and incident diabetes has not been fully elucidated. We aimed to characterize the relationship between TG/HDL-C ratio variability and new-onset diabetes in Chinese adults.

MATERIALS AND METHODS

A total of 45,911 patients with three TG and HDL measurements between 2006 and 2011 were enrolled. Average real variability (ARV) were used to evaluate variability, and participants were grouped according to tertiles of TG/HDL-ARV.

RESULTS

There were 3,724 cases of incident diabetes mellitus during the observation period (6.24 ± 1.2 years). The 7-year cumulative incidences of diabetes mellitus in tertiles 1, 2 and 3 were 6.13%, 8.09% and 11.77%, respectively. New-onset diabetes increased with the tertiles of TG/HDL-ARV. This association was further confirmed after adjustment for mean TG/HDL-C ratio, TG/HDL-C ratio change slope, fasting plasma glucose variability (ARV) and other traditional risk factors for diabetes, the hazard ratio value for incident diabetes was 1.38 (1.25-1.50) for the highest tertile, and risk of diabetes increases by 4% with a one standard deviation increase in TG/HDL-C ratio variability. Restricted cubic splines showed a dose-response relationship between TG/HDL-C ratio variability and incident diabetes. Similar results were obtained in various subgroup and sensitivity analyses.

CONCLUSIONS

High TG/HDL-C variability was associated with a higher risk of diabetes in Chinese adults, independent of the direction of TG/HDL-C variability.

The mortality rate of Parkinson's disease and related comorbidities: a nationwide population-based matched cohort study in Korea

BACKGROUND

previous studies on mortality of Parkinson's disease (PD) enrolled a relatively small number of participants and were conducted in western countries. The objective of this study was to evaluate mortality rate of PD using a large nationwide cohort in Korea and to evaluate effects comorbidities have on mortality in PD.

METHODS

the nationwide population-based cohort study was conducted using the Korean National Health Insurance Service-National Sample Cohort data. Patients with a primary diagnosis of PD were selected from the database. A matched cohort without PD was enrolled through randomly matching patients by sex, age, year of diagnosis, residential area and income level to the PD group with a ratio of 1:9. The Cox proportional hazard model was used to assess mortality risk between the two cohorts. A logistic regression analysis was used to identify mortality risk factors in PD cohort.

RESULTS

in total, 25,620 patients were enrolled. The Cox proportional regression model had an adjusted hazard ratio of 2.479 [95% confidence interval (CI), 2.272-2.704] for mortality in PD cohort. Comorbidities, such as ischaemic stroke [odds ratios (OR) = 2.314, 95% CI, 1.895-2.824], haemorrhagic stroke (OR = 2.281, 95% CI, 1.466-3.550) and chronic obstructive pulmonary disease (OR = 1.307, 95% CI, 1.048-1.630) were associated with increased mortality, whereas dyslipidemia (OR = 0.285, 95% CI, 0.227-0.358) was negatively correlated with mortality.

CONCLUSION

over the 10 year follow-up period, the PD cohort's mortality rate was 2.5 times higher than the comparison cohort. Understanding the effects that comorbidities have on morality in PD would be useful for predicting mortality in patients with PD.

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

Innumerable studies have suggested "the lower, the better" for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.

Variability in estimated glomerular filtration rate and the incidence of type 2 diabetes: a nationwide population-based study

OBJECTIVE

Variability in estimated glomerular filtration rate (eGFR) has been associated with adverse outcomes in patients with diabetes or chronic kidney disease (CKD). However, no previous study has explored the relationship between eGFR variability and type 2 diabetes incidence.

RESEARCH DESIGN AND METHODS

In this nationwide, longitudinal, cohort study, we investigated the association between eGFR variability and type 2 diabetes risk using the Korean National Health Insurance Service datasets from 2002 to 2017. eGFR variability was calculated using the variability independent of the mean (eGFR-VIM), coefficient of variation (eGFR-CV), standard deviation (eGFR-SD) and average real variability (eGFR-ARV).

RESULTS

Within 7 673 905.58 person-years of follow-up (mean follow-up: 3.19 years; n=2 402 668), 11 981 cases of incident type 2 diabetes were reported. The HRs and 95% CIs for incident type 2 diabetes increased according to advance in quartiles of eGFR-VIM (HR (95% CI): Q2, 1.068 (1.009 to 1.130); Q3, 1.077 (1.018 to 1.138); Q4, 1.203 (1.139 to 1.270)) even after adjusting for confounding factors including mean eGFR and mean fasting plasma glucose levels. The subgroup analyses according to risk factors as well as analyses using eGFR-CV, eGFR-SD and eGFR-ARV showed consistent results. The association between increased eGFR variability and type 2 diabetes risk was more prominent in men, individuals with dyslipidemia and those with CKD as shown in the subgroup analysis (p for interaction <0.001).

CONCLUSIONS

Increased eGFR variability may be an independent predictor of type 2 diabetes and might be useful for risk stratification of individuals without diabetes.

A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population

PURPOSE

To develop a nomogram for predicting 5-year incidence of type 2 diabetes (T2D) in Chinese adults.

METHODS

This is a retrospective cohort study from a prospectively collected database. We included a total 32,766 adults free of T2D at baseline with a median follow-up of 3 years. Univariate and multivariate Cox regression analyses were applied to identify independent predictors. A nomogram was constructed to predict 5-year incident rate of T2D based on the multivariate analysis results. Harrell's C-indexes and calibration plots were used to evaluate the accuracy of the nomogram in both internal and external validations.

RESULTS

The overall prevalence of T2D was 2.1%. Participants were randomly divided into a training set (n = 21,844) and a validation set (n = 10,922). After multivariate analysis in the training set, age, sex, BMI, hypertension, dyslipidemia, smoking status, and family history were found as risk predictors and integrated into the nomogram. Harrell's C-indexes were 0.815 (95% CI: 0.797-0.834) and 0.779 (95% CI: 0.747-0.811) in the training and validation sets, respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation.

CONCLUSION

Our nomogram could be a simple and reliable tool for predicting 5-year risk of developing T2D in high-risk Chinese. Through the model, early identifying high-risk individuals is helpful for timely intervention to reduce the incidence of T2D.

A CROSS-SECTIONAL STUDY OF THE ASSOCIATION BETWEEN THE 1-HOUR ORAL GLUCOSE TOLERANCE TEST AND THE METABOLIC SYNDROME IN A HIGH-RISK SAMPLE WITH IMPAIRED FASTING GLUCOSE

Objective: The aim of this study was to evaluate the association between the 1-hour oral glucose tolerance test (OGTT) (≥155 mg/dL) and metabolic syndrome (MS) in a sample with previous impaired fasting glucose (IFG). Methods: Three hundred and twenty four Peruvian subjects with a history of IFG ≥100 mg/dL were selected for a cross-sectional study. They underwent a 75 g OGTT and were assigned to different groups according to the result. We evaluated the association between 1-hour OGTT and MS. Results: The mean age was 56.5 ± 12.6 years and 191 (61.5%) were female. During the OGTT, we found 28 (8.6%) subjects with diabetes, 74 (22.8%) with IGT, and 222 (68.5%) with a normal glucose tolerance test with a 2-hour glucose <140 mg/dL (NGT). In the NGT group, 124 (38.3%) had 1-hour glucose levels <155 mg/dL, while 98 (30.2%) had 1-hour glucose levels ≥155 mg/dL. Evaluating the association between the 1-hour value in the OGTT and MS, we found that subjects with a 1-hour glucose ≥155 mg/dL were more than twice as likely to have MS as those with a 1-hour glucose <155 mg/dL (odds ratio = 2.64, 95% confidence interval: 1.52 to 4.57). In addition, body mass index, fasting glycemia, triglycerides, and waist circumferences were significantly higher in subjects with 1-hour glucose levels ≥155 mg/dL compared to those with 1-hour glucose levels <155 mg/dL (P<.05). Conclusion: Among subjects with IFG, performing an OGTT was helpful to identify subjects with 1-hour glucose levels ≥155 mg/dL and NGT who were significantly more likely to have MS and a worse cardiometabolic risk profile. Abbreviations: AST = aspartate aminotransferase; BMI = body mass index; CI = confidence interval; IFG = impaired fasting glucose; IGT = impaired glucose tolerance; LDL = low-density lipoprotein; MS = metabolic syndrome; NGT = normal glucose tolerance; OGTT = oral glucose tolerance test; OR = odds ratio; T2DM = type 2 diabetes; TG = triglycerides.

Development and Validation of a Nomogram to Predict Type 2 Diabetes Mellitus in Overweight and Obese Adults: A Prospective Cohort Study from 82938 Adults in China

BACKGROUND

The twin epidemic of overweight/obesity and type 2 diabetes mellitus (T2DM) is a major public health problem globally, especially in China. Overweight/obese adults commonly coexist with T2DM, which is closely related to adverse health outcomes. Therefore, this study aimed to develop risk nomogram of T2DM in Chinese adults with overweight/obesity.

METHODS

We used prospective cohort study data for 82938 individuals aged ≥20 years free of T2DM collected between 2010 and 2016 and divided them into a training (n = 58056) and a validation set (n = 24882). Using the least absolute shrinkage and selection operator (LASSO) regression model in training set, we identified optimized risk factors of T2DM, followed by the establishment of T2DM prediction nomogram. The discriminative ability, calibration, and clinical usefulness of nomogram were assessed. The results were assessed by internal validation in validation set.

RESULTS

Six independent risk factors of T2DM were identified and entered into the nomogram including age, body mass index, fasting plasma glucose, total cholesterol, triglycerides, and family history. The nomogram incorporating these six risk factors showed good discrimination regarding the training set, with a Harrell's concordance index (C-index) of 0.859 [95% confidence interval (CI): 0.850-0.868] and an area under the receiver operating characteristic curve of 0.862 (95% CI: 0.853-0.871). The calibration curves indicated well agreement between the probability as predicted by the nomogram and the actual probability. Decision curve analysis demonstrated that the prediction nomogram was clinically useful. The consistent of findings was confirmed using the validation set.

CONCLUSIONS

The nomogram showed accurate prediction for T2DM among Chinese population with overweight and obese and might aid in assessment risk of T2DM.

Hypocaloric, plant-based oatmeal interventions in the treatment of poorly-controlled type 2 diabetes: A review

BACKGROUND

Lifestyle interventions, including dietary modifications, play a key role in the treatment of type 2 diabetes. By the second half of the last century, dietary oatmeal interventions had frequently been used in patients with diabetes; however, with the widespread introduction of insulin, this practice gradually fell into disuse. Within the last decades, the original oatmeal intervention, first described in 1903, has been modified towards a hypocaloric, low-fat, and plant-based intervention.

AIM

The aim of this review was to investigate the current role of these adapted short-term dietary oatmeal interventions in the treatment of patients suffering from poorly-controlled type 2 diabetes. A special focus was put on opportunities for and barriers to its clinical implementation and its potential mechanisms of action.

METHODS

The electronic databases of PubMed and Google Scholar were searched using the keywords "oat," "oats," "oatmeal," and "diabetes."

RESULTS

While there are a limited number of clinical studies including hypocaloric short-term dietary oatmeal interventions, there is evidence that these interventions may lead to a significant decrease in mean blood glucose levels and a significant reduction of insulin dosage in patients suffering from poorly-controlled type 2 diabetes.

CONCLUSION

Modified short-term dietary oatmeal interventions are an effective and economical tool in the treatment of patients suffering from poorly-controlled type 2 diabetes.

HDL-Cholesterol, Its Variability, and the Risk of Diabetes: A Nationwide Population-Based Study

CONTEXT

The bidirectional relationship between low high-density lipoprotein cholesterol (HDL-C) and glucose intolerance is well established. Recent studies suggested an association of lipid variability with various health outcomes.

OBJECTIVE

To investigate the combined effect of HDL-C levels and their variability on the risk of diabetes.

DESIGN

A population-based cohort study.

SETTING AND PARTICIPANTS

In all, 5,114,735 adults without known diabetes in the Korean National Health Insurance System cohort who underwent three or more health examinations from 2009 to 2013 were included. Visit-to-visit HDL-C variability was calculated using variability independent of the mean (VIM) and the coefficient of variation (CV). Low mean and high variability groups were defined as the lowest and highest quartiles of HDL-C mean and variability, respectively.

MAIN OUTCOME MEASURES

Newly developed diabetes.

RESULTS

There were 122,192 cases (2.4%) of incident diabetes during the median follow-up of 5.1 years. Lower mean or higher variability of HDL-C was associated with higher risk of diabetes in a stepwise manner, and an additive effect of the two measures was noted. In the multivariable-adjusted model, the hazard ratios and 95% CIs for incident diabetes were 1.20 (1.18 to 1.22) in the high mean/high VIM group, 1.35 (1.33 to 1.37) in the low mean/low VIM group, and 1.40 (1.38 to 1.42) in the low mean/high VIM group compared with the high mean/low VIM group. Similar results were observed when modeling the variability using CV and in various subgroup analyses.

CONCLUSIONS

Low mean and high variability in HDL-C were independent predictors of diabetes with an additive effect. Both elevating and stabilizing HDL-C may be important goals for reducing diabetes risk.

Improving Prediction of High-Cost Health Care Users with Medical Check-Up Data

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

Quantification of Risk Factors for Cervical Ossification of the Posterior Longitudinal Ligament in Korean Populations: A Nationwide Population-based Case-control Study

STUDY DESIGN

Case-control study.

OBJECTIVE

To quantify risk factors for cervical ossification of the posterior longitudinal ligament (OPLL) using a large nationwide cohort in Korea, a country with a high prevalence of OPLL.

SUMMARY OF BACKGROUND DATA

OPLL is a pathological calcification of the posterior longitudinal ligament of the spine. OPLL progression can cause spinal cord injury that results in disability. Considering neurologic deficits and disability caused by OPLL, identifying OPLL risk factors for early prediction have important health benefits.

METHODS

The nationwide population-based matched cohort study was conducted using the Korean National Health Insurance Service cohort data. We selected patients with a primary diagnosis of OPLL involving cervical lesion (International Classification of Diseases-10 code: M48.82, M48.83). A matched cohort without cervical OPLL was enrolled by randomly matching patients by sex, age, year of diagnosis, and residential area to the OPLL group with a ratio of 1:9. Logistic regression analyses were performed to identify risk associated with OPLL development using odds ratios (OR) and 95% confidence intervals (CI).

RESULTS

Comorbidities, such as hypertension (OR = 1.283, 95% CI 1.071-1.538), ischemic stroke (OR = 1.386, 95% CI 1.017-1.889), diabetes mellitus (OR = 1.331, 95% CI 1.098-1.615), hypothyroidism (OR = 1.562, 95% CI 1.165-2.094), and osteoporosis (OR = 1.456, 95% CI 1.151-1.842), were significantly associated with the prospective development of OPLL, with low predictive value.

CONCLUSION

OPLL was significantly associated with comorbidities such as hypertension, ischemic stroke diabetes mellitus, hypothyroidism, and osteoporosis. Our findings can provide helpful information for OPLL prediction and offer important health benefits.

LEVEL OF EVIDENCE

3.

Increased risk of psoriasis in subjects with abdominal obesity: A nationwide population-based study

Psoriasis is a chronic inflammatory skin disease known to be associated with a variety of systemic comorbidities, such as metabolic syndrome and obesity. Obesity represents a major comorbidity and has been suggested to be related to psoriasis. This nationwide population-based prospective cohort study was performed to investigate the impacts of body mass index (BMI) and waist circumference (WC) on psoriasis. We used the health check-up database and the study population consisted of subjects who had undergone health screening between January 2009 and December 2012. This study investigated patients newly diagnosed with psoriasis (International Classification of Disease, Tenth Revision, code L40) by dermatologists during the follow-up period (5.32 years), based on claims data. The total population consisted of 22 633 536 subjects, among whom 399 461 had newly developed psoriasis. Subjects with BMI of more than 30 had a higher risk of psoriasis (hazards ratio [HR], 1.118; 95% confidence interval [CI], 1.100-1.137) compared with the BMI 18.5-23 group. WC showed a dose-dependent association with psoriatic risk. Subjects with WC over 105 cm showed the highest risk of psoriasis (HR, 1.305; 95% CI, 1.261-1.349) compared with subjects with WC lower than 80/75 after adjusting for confounding factors, including BMI. The risk of psoriasis was highest in males with normal BMI and abdominal obesity (HR, 1.175; 95% CI, 1.150-1.200). Our study indicates that WC is a specific factor affecting psoriatic risk and highlights the association between abdominal obesity and psoriasis, thus increasing awareness of the role of abdominal obesity in the pathogenesis and comorbidities of psoriasis.

Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes

Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.

Triglycerides and total cholesterol concentrations in association with IFG/IGT in Chinese adults in Qingdao, China

Background

To investigative the association of triglycerides (TG) and total cholesterol (TC) concentrations with impaired fasting glucose/ impaired glucose tolerance (IFG/IGT) in Chinese adults.

Methods

The population-based cross-sectional diabetes survey was conducted in 2006 and 2009 in Qingdao, separately. 4400 participants (1 793 men and 2607 women) were include in current analysis. IFG/IGT was defined according to fasting plasma glucose (FPG) and/or 2 h post-load plasma glucose (2 h PG). Logistic regression models and areas under receiver operating characteristic curves (AUROC) were performed to estimate the associations between TG, TC levels and IFG/IGT.

Results

Spearman analysis showed that serum TG and TC was independently and positively associated with FPG and 2 h PG. As compared with normoglycaemia, the odds ratio[(95% confidence intervals), OR(95%CI)] for IFG/IGT corresponding to hypertriglyceridemia (HTG) were 1.61 (1. 17, 2. 22) in men and 1.57(1.15, 2.14) in women for TG and accompany with Hypercholesterolemia (HTC) 1.56 (1.15, 2.13) and 1. 20 (0.93, 1.54) for TC, when adjusting for confounding factor. The AUROCs of TG, TC for IFG/IGT were relatively smaller (0.50 < AUROC< 0. 7) in both gender. The optimal cut-offs for TG and TC was 1.61, 4.91 in men and 1. 24, 5. 32 in women, respectively.

Conclusions

Evaluated TG in both gender and TC in men were independently associated with the present of the IFG/IGT, yet, could not be an authentic predictors of IFG/IGT in both men and women in current Chinese population.

The Association between Persistent Hypertriglyceridemia and the Risk of Diabetes Development: The Kangbuk Samsung Health Study

BACKGROUND

Hypertriglyceridemia is known to have an association with increased risks of insulin resistance and diabetes. The aim of this study was to investigate the risk of diabetes mellitus, according to changes in the concentrations of triglycerides, over time.

METHODS

A total of 15,932 non-diabetic participants (mean age 43.2 years, 68% men) who attended five consecutive annual health check-ups at Kangbuk Samsung Hospital, between January 2010 and December 2014, were recruited. Participants were classified according to their triglyceride concentrations; normal (<150 mg/dL) and abnormal (≥150 mg/dL). According to the triglyceride levels in 2010 and 2012, subjects were divided into four groups: normal-normal, normal-abnormal, abnormal-normal, and abnormal-abnormal. The risk for incident diabetes was assessed in 2014.

RESULTS

Among the total subjects, 67.5% belonged to the normal-normal group, 8.6% to the normal-abnormal group, 9.4% to the abnormal-normal group, and 14.5% to the abnormal-abnormal group. A total of 234 subjects (1.5%) were newly diagnosed with diabetes, between 2010 and 2014. Over 4 years, 1%, 1.5%, 2.1%, and 3.0% of the subjects developed diabetes in the normal-normal, normal-abnormal, abnormal-normal, and abnormal-abnormal groups, respectively. When the risk for incident diabetes was analyzed in the groups, after adjusting the confounding variables, a 1.58-fold increase in the risk of diabetes (95% confidence interval [CI], 1.10 to 2.26) was observed in the participants with persistent hypertriglyceridemia (abnormal-abnormal group). This was attenuated by further adjustments for body mass index (BMI) (hazard ratio, 1.25; 95% CI, 0.86 to 1.80).

CONCLUSION

In this large study population, persistent hypertriglyceridemia, over a period of 2 years, was significantly associated with the risk of incident diabetes, which was attenuated after adjustment for BMI.

The association of triglycerides and total cholesterol concentrations with newly diagnosed diabetes in adults in China

Background

It has already been suggested that high abnormal blood lipid concentration is associated with hyperglycaemia. However, no data is available about the roles of triglycerides (TG) and total cholesterol (TC) levels in diabetes. Here, for the first time we investigated the roles of TG and TC levels, gender and abdominal fat in the development of newly diagnosed diabetes in China.

Materials and Methods

Two population-based cross-sectional surveys were conducted from 2006 to 2009 in Qingdao, China. Newly diagnosed diabetes was defined according to FPG and/or 2 h PG criteria. The associations between diabetes and TG, and TC levels were assessed by multi-variable logistic regression models.

Results

As compared with non-diabetes, the odds ratio[(95% confidence intervals), OR(95% CI)] for diabetes corresponding to hypertriglyceridemia (HTG) were 1.54 (1.01, 2.35) in men and 2.02 (1.49, 3.10) in women for TG and accompany with Hypercholesterolemia (HTC) 2.93 (1.97, 4.37) and 2.13 (1.49, 3.05) for TC, when both were fitted simultaneously in the model adjusting for age, geographic division, marital status, school years, family history of diabetes, monthly income, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist circumference (WC), high density lipoprotein cholesterol (HDL-C), alanine amino transferase (ALT) and gamma-glutamyltransferase (GGT).

Conclusions

HTG in both gender, borderline high TC and HTC in men were an independent risk factor for diabetes in this Chinese population, however, HTC was mediated through abdominal fat for diabetes in women. Our findings may help to enhance the current knowledge of diabetes patho-physiology, and the associations between TG, TC level and diabetes is also clinically informative.