Long-term trajectories of sleep duration are associated with incident diabetes in middle-to-older-aged Black and White Americans.

AIMS/HYPOTHESIS: Both short and long sleep durations have been linked to higher diabetes risk. However, sleep duration may vary over time, and there has been limited research focusing on individual sleep trajectories and diabetes risk. There are substantial racial disparities in both sleep health and diabetes risk in the USA. Thus, it is important to understand the role of suboptimal sleep patterns in diabetes risk in different racial groups. METHODS: We assessed long-term trajectories of sleep duration and incident diabetes in 22,285 Black adults (mean age ± SD, 51.1 ± 8.2 years; 64.8% women) and 13,737 White adults (mean age ± SD, 54.4 ± 9.0 years; 63.8% women) enrolled in the Southern Community Cohort Study. Nine sleep trajectories were derived based on self-reported sleep duration at baseline and after a mean of 5 years of follow-up: normal-normal (reference), short-normal, normal-short, short-short, long-normal, normal-long, long-long, long-short and short-long. Diabetes was reported using a validated questionnaire. Multivariable-adjusted logistic regression was used to determine relationships between sleep trajectories and incident diabetes. RESULTS: When compared with the normal-normal trajectory, suboptimal sleep trajectories were associated with higher likelihoods of developing diabetes (OR; 95% CI: short-normal 1.19; 1.09, 1.31; normal-short 1.14; 1.02, 1.27; short-short 1.17; 1.07, 1.28; long-normal 1.13; 0.98, 1.30; normal-long 1.16; 1.00, 1.34; long-long 1.23; 1.02, 1.48; long-short 1.45; 1.19, 1.77; short-long 1.51; 1.28, 1.77). Stratified analyses by race and socioeconomic status (i.e. education and household income) showed that most suboptimal sleep trajectories were consistently associated with incident diabetes in all sociodemographic subgroups. We also noted potential interaction with race and education for several sleep trajectories (i.e. short-long and normal-short with race; long-long and short-short with education). CONCLUSIONS/INTERPRETATION: Adults with suboptimal sleep duration trajectories are more likely to develop incident diabetes. Future research is needed to study how sociodemographic factors modulate this relationship.

Very short sleep duration reveals a proteomic fingerprint that is selectively associated with incident diabetes mellitus but not with incident coronary heart disease: a cohort study.

BACKGROUND: The molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease. METHODS: We assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46-68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates. RESULTS: Sixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06-1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD. CONCLUSIONS: We here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.

COVID-19 infection and incident diabetes in American Indian and Alaska Native people: a retrospective cohort study.

Background: Evidence suggests an increased risk of new-onset diabetes following COVID-19 infection. American Indian/Alaska Native (AI/AN) people were disparately impacted by the COVID-19 pandemic and historically have had higher diabetes incidence than other racial/ethnic groups in the US. We measured the association between COVID-19 infection and incident diabetes in AI/AN people. Methods: We conducted a retrospective cohort study using de-identified patient data from the Indian Health Service's (IHS) National Patient Information Reporting System. We estimated age-adjusted diabetes incidence rates, incidence rate ratios, and adjusted hazard ratios among three cohorts spanning pre-pandemic (1/1/2018-2/28/2020) and pandemic (3/1/2020-12/31/2021) timeframes: 1) pre-pandemic cohort (1,503,085 individuals); 2) no-COVID-19 pandemic cohort (1,344,339 individuals); and 3) COVID-19 cohort (176,483 individuals). Findings: The COVID-19 cohort had an increased hazard of diabetes compared to the no-COVID-19 group (adjusted hazard ratio (aHR) = 1.56; 95% CI: 1.50-1.62) and the pre-pandemic group (aHR = 1.27; 95% CI: 1.22-1.32). The association between COVID-19 infection and new-onset diabetes was stronger in those with severe COVID-19 illness. A sensitivity analysis comparing the COVID-19 cohort to members of other cohorts that had acute upper respiratory infections showed an attenuated but higher risk of new-onset diabetes in those with COVID-19. Interpretation: AI/AN people diagnosed with COVID-19 had an elevated risk of a new diabetes diagnosis when compared to the no-COVID-19 group and the pre-pandemic group. The increased diabetes risk in the COVID-19 group remained in a sensitivity analysis that limited the comparator groups to individuals with an AURI diagnosis. Funding: US National Institute of Diabetes and Digestive and Kidney Diseases.

Association between cardiovascular disease risk and incident type 2 diabetes mellitus in individuals with prediabetes: A retrospective cohort study.

AIMS: In this study, we aimed to evaluate the impact of overall cardiovascular disease (CVD) risk on the development of incident T2DM in patients with prediabetes. METHODS: We retrospectively enrolled 5,908 subjects with prediabetes who underwent health check-ups at the Asan Medical Center. CVD risk was estimated using the Framingham Risk Score (FRS). We compared moderate- to high-risk groups with low-risk controls based on the FRS. Cox proportional hazards regressions were conducted to estimate the time-to-develop incident T2DM. RESULTS: Among the 5908 subjects with prediabetes, 3031 (51.8 %) were identified to have either moderate or high CVD risk scores. During a median follow-up of 5.2 years, 278 (9.2 %) patients from the moderate- to high-risk group and 171 (5.9 %) from the low-risk group were diagnosed with T2DM. The covariate-adjusted hazard ratio for the incident T2DM was 1.30 (95 % CI, 1.06-1.60, p = 0.011) in the moderate- to high-risk group compared to the low-risk controls. CONCLUSION: Among patients with prediabetes, those with high CVD risk were more likely to develop incident T2DM, as determined by the FRS. CVD risk factors should be properly evaluated and managed in individuals with prediabetes to reduce the risk of both incident T2DM and associated cardiovascular complications.

Impact of migration on diabetes burden: audit in the metropolitan area of Bologna, Italy.

PURPOSE: To investigate the impact of diabetes in immigrants on the Italian healthcare system, as well as their compliance with standard protocols of control and treatment. METHODS: The prevalence of immigrants with diabetes living in the metropolitan area of Bologna (about 1 million inhabitants) in 2019 was investigated using a database containing all subjects in active follow-up for diabetes, based on antidiabetic drug use, disease-specific copayment exemption, ICD-9 codes, continuous care in diabetes units. Country of origin was derived from fiscal code. RESULTS: The overall prevalence of diabetes (n = 53,941; 51.8% males, median age 64) was 6.1% in both Italy-born and immigrant cohorts. Immigrant prevalence was 12.4%, moderately higher than that observed in the total population (12.2%). Diabetes risk was increased in the whole immigrant cohort (odds ratio (OR) 1.74; 95% Confidence Interval (CI) 1.69-1.79). Among cases with incident diabetes, the proportion of immigrants (median age, 49 vs. 65 in Italy-born individuals) increased progressively from 11.7% to 26.5% from 2011 to 2019 (males, 8.9-21.0%; females, 14.9-32.8%) in all age groups, particularly in young adults, but also in older subjects. Metabolic control was lower in immigrants, as was adherence to shared diagnostic and therapeutic protocols, without systematic differences in antidiabetic drug use, but much lower use of drugs for comorbid conditions. CONCLUSIONS: The population with diabetes in the metropolitan area of Bologna is rapidly changing. Quality improvement initiatives are needed to reduce the burden for the universalistic Italian health care system generated by the rapidly-growing high-risk immigrant population.

Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence.

PURPOSE OF REVIEW: Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS: Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.

The change of non-alcoholic fatty liver disease is associated with risk of incident diabetes.

Background & aims: The effect of change in non-alcoholic fatty liver disease (NAFLD) status on incident diabetes has not been well studied. We aimed to investigate the association of NAFLD development and remission with the risk of incident diabetes during a median of 3.5-year follow-up. Methods: A total of 2690 participants without diabetes were recruited in 2011-2012 and assessed for incident diabetes in 2014. Abdominal ultrasonography was used to determine the change of NAFLD. 75 g oral glucose tolerance test (OGTT) was performed to determine diabetes. NAFLD severity was assessed using Gholam's model. The odds ratios (ORs) for incident diabetes were estimated by logistic regression models. Results: NAFLD was developed in 580 (33.2%) participants and NAFLD remission occurred in 150 (15.9%) participants during a median of 3.5-year follow-up. A total of 484 participants developed diabetes during follow-up, including 170 (14.6%) in consistent non-NAFLD group, 111 (19.1%) in NAFLD developed group, 19 (12.7%) in NAFLD remission group, and 184 (23.2%) in sustained NAFLD group. The development of NAFLD increased the risk of incident diabetes by 43% (OR, 1.43; 95%CI, 1.10-1.86) after adjustment for multiple confounders. Compared with sustained NAFLD group, remission of NAFLD reduced the risk of incident diabetes by 52% (OR, 0.48; 95%CI, 0.29-0.80). The effect of NAFLD alteration on incident diabetes was not changed after adjustment for body mass index or waist circumference, change of body mass index or waist circumference. In NAFLD remission group, participants with non-alcoholic steatohepatitis (NASH) at baseline were more likely to develop diabetes (OR, 3.03; 95%CI, 1.01-9.12). Conclusions: NAFLD development increases the risk of incident diabetes, whereas NAFLD remission reduces the risk of incident diabetes. Moreover, presence of NASH at baseline could attenuate the protective effect of NAFLD remission on incident diabetes. Our study suggests that early intervention of NAFLD and maintenance of non-NAFLD are important for prevention of diabetes.

The association between fatty liver index and onset of diabetes: secondary analysis of a population-based cohort study.

BACKGROUND: According to research, the fatty liver index (FLI) is associated with diabetes. However, few studies have been conducted to investigate the relationship between FLI and diabetes risk from various perspectives. This study comprehensively investigated the relationship between FLI and incident diabetes in a large Japanese population. METHODS: This retrospective cohort study included 14,280 participants from Murakami Memorial Hospital in Japan from 2004 to 2015. The independent and dependent variables are FLI and risk of type 2 diabetes mellitus (T2DM), respectively. To examine the link between FLI and incident T2DM, Cox proportional-hazards regression was employed. In addition, we performed a number of sensitivity studies to guarantee the validity of the results. Moreover, we conducted subgroup analyses. RESULTS: After adjusting covariates, the results showed that FLI was positively associated with the risk of T2DM (HR = 1.019, 95%CI: 1.012, 1.025). Additionally, the sensitivity analysis showed how reliable the outcomes were. And a stronger association between FLI and incident T2DM was observed in the regular exercisers (HR = 1.036, 95%CI: 1.019-1.053, P < 0.0001) and the population without ethanol consumption (HR = 1.028, 95%CI: 1.017-1.039, P < 0.0001). Besides, receiver operating characteristic (ROC) curve analysis showed that FLI was better than waist circumference, triglycerides, body mass index, and gamma-glutamyl transferase in predicting incident T2DM. CONCLUSION: FLI is positively associated with incident T2DM.

Sex difference in the associations among obesity-related indices with incidence of diabetes mellitus in a large Taiwanese population follow-up study.

Background: Obesity is a major risk factor for diabetes mellitus (DM), which is in turn a major risk factor for cardiovascular diseases such as coronary artery disease and stroke. As few studies have investigated sex differences in the association between obesity and incidence of DM, the aim of this longitudinal study was to explore this issue in a large group of Taiwanese participants. Methods: A total of 24,346 participants were enrolled in this study, of whom 8,334 (mean age, 50.6 ± 11.0 years) were male and 16,012 (mean age, 50.5 ± 10.1 years) were female. The following obesity-related indices were studied: body mass index, waist-to-height ratio, waist-to-hip ratio (WHR), body roundness index, conicity index (CI), body adiposity index, abdominal volume index, lipid accumulation product (LAP), and visceral adiposity index (VAI). Results: The analysis showed significant associations between all of these indices with incidence of DM (all p < 0.001). In the male participants, the strongest predictors for incidence of DM were LAP (AUC = 0.692), WHtR (AUC = 0.684), and WHR (AUC = 0.683). In the female participants, the strongest predictors were LAP (AUC = 0.744), WHtR (AUC = 0.710) and VAI (AUC = 0.710), followed by BRI (AUC = 0.708). Conclusion: Strong associations were found between the studied obesity-related indices and incidence of DM, and sex differences were found. Hence, to better control DM, reducing body weight may be beneficial in addition to lifestyle modifications, diet control, and pharmacological interventions.

Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model.

AIM: To develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes. METHODS: There were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance. RESULTS: We developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively. CONCLUSIONS: We developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Prognostic value of exercise capacity in incident diabetes: a country with high prevalence of diabetes.

BACKGROUND: Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population. METHODOLOGY: A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell's c-index, net reclassification index (NRI), and integrated discrimination index (IDI). RESULTS: A total of 8,722 participants (mean age 46 ± 12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17-8.78) years, there were 2,280 (≈ 26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each METs achieved (HR, 0.9; 95% CI, 0.88-0.92; P < 0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC = 0.62 to 0.66 and c-index = 0.62 to 0.68). CONCLUSION: Exercise capacity improved the overall predictability of diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.

Perfluorinated compounds in adults and their association with fasting glucose and incident diabetes: a prospective cohort study.

BACKGROUND: The endocrine disruption of perfluorinated compounds is an emerging issue. We aimed to examine the association of serum perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) levels with incident diabetes and fasting serum glucose concentration. METHODS: This prospective cohort study was based on an urban-based cohort subpopulation from the Korean Genome and Epidemiology Study. Serum samples (600 µL) were received from 100 participants in the normoglycemic baseline survey (2004-2013), and concentrations of PFOA and PFOS were measured using mass spectrometry. The incidence of diabetes was tracked in the follow-up survey (2012-2016). RESULTS: The mean age was 56.4 years (men, 59%). The median serum PFOA and PFOS concentrations were 4.29 ng/mL and 9.44 ng/mL, respectively. PFOA and PFOS concentrations differed according to age, sex, and residential area. After 60 months, 23 patients had diabetes. Log-transformed PFOA (lnPFOA) and log-transformed PFOS (lnPFOS) were significantly higher in those who transitioned to diabetes than in those who did not (both p < 0.05). After multivariate adjustment, lnPFOA (coefficient = 6.98, 95% CI -0.04-14, p = 0.054) and lnPFOS (coefficient = 7.06, 95% CI -0.96-15.08, p = 0.088) predicted increased fasting glucose without statistical significance. In addition, lnPFOA, but not lnPFOS, significantly predicted incident diabetes (HR = 3.98, 95% CI 1.42-11.1, p < 0.01). CONCLUSION: Exposure to PFOA and PFOS may have a potential dysglycemic effect. In particular, exposure to PFOA increased the risk of diabetes. Further research with larger sample size is warranted.

Erratum: Dyslipidemia and 10-year diabetes incidence in Japanese people: Population-based Panasonic cohort study 9.

[This corrects the article DOI: 10.3389/fendo.2022.957728.].

Dyslipidemia and 10-year diabetes incidence in Japanese people: Population-based Panasonic cohort study 9.

Low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and LDL/HDL ratio have been associated with new-onset diabetes; however, their cut-off levels have not been determined. We clarified the association between dyslipidemia and the incidence of diabetes. People who underwent a health checkup under a program conducted by Panasonic Corporation from 2008 to 2018 were included. In total, 87,570 participants were included, of whom 5,110 developed type 2 diabetes. Cox regression analyses and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the association between LDL cholesterol, HDL cholesterol, or LDL/HDL ratio and incident diabetes and to identify the cut-off values for incident diabetes. Multivariate analysis showed that LDL cholesterol, HDL cholesterol, and LDL/HDL ratio were significantly associated with the risk of incident type 2 diabetes. Further, the area under the ROC curve and optimized cut-off values for LDL cholesterol, HDL cholesterol, and LDL/HDL ratio for incident type 2 diabetes at 10 years were 0.613 and 124 mg/dl, 0.640 and 54 mg/dl, and 0.662 and 2.4 mg/dl, respectively. The LDL/HDL ratio with a cut-off value of 2.4 was a better predictor of incident diabetes within 10 years than LDL and HDL cholesterol.

Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults.

Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field.

The Long-Term Effect of COVID-19 Disease Severity on Risk of Diabetes Incidence and the Near 1-Year Follow-Up Outcomes among Postdischarge Patients in Wuhan.

We assessed the nearly 1-year health consequences following discharge and related risk factors of COVID-19 infection and further explored the long-term effect of COVID-19 disease severity on the risk of diabetes incidence. This prospective study included 248 COVID-19 patients discharged from Wuhan Hospital of Traditional Chinese Medicine who were followed up between 1 March and 10 June 2021. Logistic regression models were used to evaluate risk factors. The top ten symptoms were shortness of breath (30.3%), sore or dry throat (25.7%), cough (23.2%), expectoration (23.2%), body pain (22.3%), chest tightness (20.8%), palpitations (17.8%), sleep difficulties (17.0%), fatigue (16.6%), and anxiety (15.3%). Hypertension was associated with fatigue (OR = 2.51, 95% CI: 1.08, 5.80), shortness of breath (OR = 2.34, 95% CI: 1.16, 4.69), palpitations (OR = 2.82, 95% CI: 1.26, 6.31), expectoration (OR = 2.08, 95% CI: 1.01, 4.30), and sore or dry throat (OR = 2.71, 95% CI: 1.30, 5.65). Diabetes was associated with palpitations (OR = 3.22, 95% CI: 1.18, 8.81). Critical illness was associated with an increased risk of diabetes incidence after discharge (OR = 2.90, 95% CI: 1.07, 7.88), which seemed more evident in males. Long COVID-19 symptoms were common at 1-year postdischarge; hypertension and diabetes could be projected as potential risk factors. We are among the first researchers to find that critical illness is associated with incident diabetes after discharge.

Non-linear relationship between high-density lipoprotein cholesterol and incident diabetes mellitus: a secondary retrospective analysis based on a Japanese cohort study.

BACKGROUND AND OBJECTIVE: High-density lipoprotein cholesterol (HDL-C) may be directly involved in glucose metabolism by enhancing insulin sensitivity and insulin secretion. This current study aimed to explore the association between HDL-C and the risk of diabetes mellitus (DM) in Japanese population. METHODS: This retrospective cohort study was based on a publicly available DRYAD dataset. We enrolled 15,388 Japanese participants who received medical examinations from 2004 to 2015 at Murakami Memorial Hospital. Our study selected HDL-C at baseline and incident DM during follow-up as the target independent variable and the dependent variable, respectively. Cox proportional-hazards regression was used to investigate the association between HDL-C and DM, generalized additive models to identify non-linear relationships. RESULTS: After adjusting for the demographic and clinical covariates, the result showed low HDL-C levels were associated with increased risk for diabetes (HR = 0.54, 95%CI (0.35, 0.82)). The results remained robust in a series of sensitive analysis. A non-linear relationship was detected between HDL-C and incident DM with an inflection point of HDL-C at 1.72 mmol/L (Log-likelihood ratio test P = 0.005). Subgroup analysis showed that a stronger association could be found in ex-smokers and current-smokers. The same trend was also seen in the community with hypertension (P for interaction = 0.010, HR = 1.324). CONCLUSION: This study demonstrates a negative and non-linear relationship between HDL-C and diabetes in the Japanese population. There is a threshold effect between HDL-C and diabetes. When HDL-C is lower than 1.72 mmol/L, the decreased HDL-C levels were associated with an increased risk for diabetes.

Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study.

AIMS: To develop a simple multivariate predictor model of incident type 2 diabetes in general population. METHODS: Participants were recruited from the Spanish Di@bet.es cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model. RESULTS: 156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92-106 mg/dL (OR = 3.76, 95%CI = 2.36-6.00), > 106 mg/dL (OR = 13.21; 8.26-21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89-43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs  = 97-180 mg/dL, OR = 3.12; 1.05-9.24). This model correctly classified 93.5% of individuals. CONCLUSIONS: The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.

Associations of HIV and Depression with Incident Diabetes Mellitus: Veterans Aging Cohort Study.

BACKGROUND: Persons with HIV (PWH) are at increasingly higher risk for metabolic complications, including diabetes mellitus (DM). Additionally, depression is highly prevalent among PWH and has been associated with increased risk for DM in the general population. However, the association of HIV and depression with incident DM has not been well established. METHODS: Using the Veterans Aging Cohort Study (VACS), we selected adults with and without HIV who did not have DM at baseline. Prevalent depression was defined as having a Patient Health Questionnaire-9 (PHQ-9) score of ≥10. Incident DM was identified using validated Kelly's criteria. Basic clinical and demographic characteristics were collected, and cox proportional hazards regression models were run to test the association between depression and incident DM stratified by HIV serostatus. RESULTS: A total of 5,722 participants were analyzed, 2,886 (53%) had HIV and 1,124 (20%) had depression at baseline. 1,235 (22%) participants developed incident DM during follow-up, with 26% of HIV-negative participants developing DM compared to 17% of participants with HIV. Depression was significantly associated with increased risk of incident DM among HIV-negative participants (adjusted HR [aHR] = 1.31; p-value 0.003), but not among participants with HIV (aHR 1.09; p-value 0.44). However, among participants with HIV with baseline viral load < 500 copies/mL, we noted a stronger association between depression and incident DM. CONCLUSIONS: Incident DM in the VACS cohort is significantly higher for HIV-negative participants compared to veterans with HIV. A significant association between depression and incident DM was noted among HIV-negative participants but not among those with HIV.

Sex difference in the associations among liver function parameters with incident diabetes mellitus in a large Taiwanese population follow-up study.

Background: The prevalence of diabetes mellitus (DM) in Taiwan between 2017 and 2020 was 11.05%, which is higher than the global prevalence (10.5%). Previous studies have shown that patients with DM have higher liver enzyme levels than those without DM. However, it is unclear whether there are sex differences in the association between incident DM and liver function. Therefore, the aim of this longitudinal study was to investigate this issue in a large Taiwanese cohort. Methods: We identified 27,026 participants from the Taiwan Biobank, and excluded those with baseline DM (n = 2,637), and those without follow-up data on DM, serum fasting glucose or glycosylated hemoglobin A1c (n = 43). The remaining 24,346 participants (male: 8,334; female: 16,012; mean age 50.5 ± 10.4 years) were enrolled and followed for a median of 4 years. Results: Of the enrolled participants, 1,109 (4.6%) had incident DM and 23,237 (95.4%) did not. Multivariable analysis showed that high levels of glutamic-oxaloacetic transaminase (AST) (p < 0.001), glutamic-pyruvic transaminase (ALT) (p < 0.001), albumin (p = 0.003), α-fetoprotein (p = 0.019), and gamma-glutamyl transpeptidase (GGT) (p = 0.001) were significantly associated with incident DM in the male participants. In comparison, high levels of AST (p = 0.010), ALT (p < 0.001), albumin (p = 0.001) and GGT (p < 0.001), and low total bilirubin (p = 0.001) were significantly associated with incident DM in the female participants. There were significant interactions between total bilirubin and sex (p = 0.031), and GGT and sex (p = 0.011) on incident DM. Conclusion: In conclusion, liver function parameters were significantly associated with incident DM. Further, there were differences in the associations between the male and female participants.