Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China

Emerging biomarkers in type 2 diabetes mellitus

Diabetes mellitus is a chronic condition caused by high blood glucose resulting from insufficient insulin production or cellular resistance to insulin action or both. It is one of the fastest-growing public health concerns worldwide. Development of long-term nephropathy, retinopathy, neuropathy, and cardiovascular disease are some of the complications commonly associated with poor blood glycemic control. Type 2 diabetes mellitus (T2DM), the most prevalent type of diabetes, accounts for around 95 % of all cases globally. Although middle-aged or older adults are more likely to develop T2DM, its prevalence has grown in children and young people due to increased obesity, sedentary lifestyle and poor nutrition. Furthermore, it is believed that more than 50 % of cases go undiagnosed annually. Routine screening is essential to ensure early detection and reduce risk of life-threatening complications. Herein, we review traditional biomarkers and highlight the ongoing pursuit of novel and efficacious biomarkers driven by the objective of achieving early, precise and prompt diagnoses. It is widely acknowledged that individual biomarkers will inevitably have certain limitations necessitating the need for integrating multiple markers in screening.

Effects of ginseng berry saponins from panax ginseng on glucose metabolism of patients with prediabetes: A randomized, double-blinded, placebo-controlled, crossover trial

BACKGROUND

Prediabetes strongly increases the risk of type 2 diabetes and cardiovascular events. However, lifestyle intervention, the first-line treatment for prediabetes currently, was inconsistently beneficial for glucose metabolism, and the conventional medicines, such as metformin, is controversial for prediabetes due to the possible side effects.

PURPOSE

This study was designed to evaluate the effects of Zhenyuan Capsule, a Chinese patented medicine consisting of ginseng berry saponins extracted from the mature berry of Panax Ginseng, on the glucose metabolism of prediabetic patients as a complementary therapy.

STUDY DESIGN AND METHODS

In this randomized, double-Blinded, placebo-controlled, crossover trial, 195 participants with prediabetes were randomized 1:1 to receive either placebo followed by Zhenyuan Capsule, or vice versa, alongside lifestyle interventions. Each treatment period lasted 4 weeks with a 4-week washout period in between. The primary outcomes were the changes in fasting plasma glucose (FPG) and 2-h postprandial plasma glucose (2-h PG) from baseline. Secondary outcomes includes the changes in fasting and 2-h postprandial insulin and C-peptide, the homeostatic model assessment-insulin resistance (HOMA-IR) index and quantitative insulin sensitivity check index (QUICKI) from baseline. Blood lipids and adverse events were also assessed.

RESULTS

Compared with placebo, Zhenyuan Capsule caused remarkable reduction in 2-h PG (-0.98 mmol/l) after adjusting treatment order. Zhenyuan Capsule also reduced the fasting and 2-h postprandial levels of insulin and C-peptide, lowered HOMA-IR index (-1.26), and raised QUICKI index (+0.012) when compared to placebo. Additionally, a significant increase in high density lipoprotein cholesterol (HDL-C; +0.25 mmol/l) was found in patients with Zhenyuan Capsule. No serious adverse event occurred during the study.

CONCLUSIONS

Among prediabetic patients, Zhenyuan Capsule further reduced 2-h PG level, alleviated insulin resistance and raised HDL-C level on the background of lifestyle interventions. The study protocol is registered with the Chinese Clinical Trial Registry (ChiCTR2000034000).

Elevated triglyceride/high-density lipoprotein-cholesterol ratio as a risk factor for progression to prediabetes: a 5-year retrospective cohort study in Japan

Purpose

The triglyceride-to-high-density lipoprotein-cholesterol (TG/HDL-C) ratio is considered an alternative marker for insulin resistance. This longitudinal retrospective study investigated the relationship between TG/HDL-C ratio and the risk of progression to prediabetes.

Methods

We investigated 24,604 Japanese participants (14,609 men and 9,995 women) who underwent annual medical health checkups in 2017 (baseline) and 2022. All participants had no diabetes and prediabetes at baseline. No lipid-lowering medications were taken during the follow-up period. Participants were divided into four groups according to the quartiles of TG/HDL-C ratio at baseline. Multivariable-adjusted Cox regression analysis was conducted to examine hazard ratios (HRs) of progression to prediabetes. Receiver operating characteristic curves were used to determine the optimal cutoff value of TG/HDL-C ratio for prediction of prediabetes.

Results

Compared with the lowest TG/HDL-C ratio quartile (Q1) group, the adjusted HRs (95% confidence intervals (CI)) of progression to prediabetes in the Q2, Q3, and Q4 groups, respectively, were 1.17 (0.92-1.47), 1.26 (1.01-1.56), and 1.77 (1.41-2.23) for men and 1.07 (0.60-1.11), 1.19 (1.08-1.29), and 1.58 (1.18-2.31) for women. For every 1 unit increase in TG/HDL-C ratio, the adjusted HRs (95% CI) for progression to prediabetes was 1.09 (1.04-1.13) in men and 1.10 (1.04-1.15) in women. The optimal TG/HDL-C ratio cutoffs were 1.71 and 0.97 in men and women, respectively, but the area under the curve was > 0.70 in both sexes.

Conclusion

High TG/HDL-C ratio is a risk factor for progression to prediabetes in Japanese men and women, but it had low discriminative ability in predicting prediabetes risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01329-8.

Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators

Purpose

The purpose of this study is to explore the independent-influencing factors from normal people to prediabetes and from prediabetes to diabetes and use different prediction models to build diabetes prediction models.

Methods

The original data in this retrospective study are collected from the participants who took physical examinations in the Health Management Center of Peking University Shenzhen Hospital. Regression analysis is individually applied between the populations of normal and prediabetes, as well as the populations of prediabetes and diabetes, for feature selection. Afterward,the independent influencing factors mentioned above are used as predictive factors to construct a prediction model.

Results

Selecting physical examination indicators for training different ML models through univariate and multivariate logistic regression, the study finds Age, PRO, TP, and ALT are four independent risk factors for normal people to develop prediabetes, and GLB and HDL.C are two independent protective factors, while logistic regression performs best on the testing set (Acc: 0.76, F-measure: 0.74, AUC: 0.78). We also find Age, Gender, BMI, SBP, U.GLU, PRO, ALT, and TG are independent risk factors for prediabetes people to diabetes, and AST is an independent protective factor, while logistic regression performs best on the testing set (Acc: 0.86, F-measure: 0.84, AUC: 0.74).

Conclusion

The discussion of the clinical relationships between these indicators and diabetes supports the interpretability of our feature selection. Among four prediction models, the logistic regression model achieved the best performance on the testing set.

Triglycerides/HDL cholesterol ratio and type 2 diabetes incidence: Panasonic Cohort Study 10

BACKGROUND

Previous studies have investigated the association between the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) and the incidence of diabetes in adults and discovered that a high TG/HDL-C ratio was linked to an elevated risk of new-onset diabetes. However, the comparison of predicting diabetes development among lipid profiles including the TG/HDL-C ratio, and the ratio of TG/HDL-C cut-off value has received limited attention. We examined the relationship between diabetes onset and the TG/HDL-C ratio in addition to the applicable cut-off value for predicting diabetes onset.

METHODS

This study included 120,613 participants from the health examination database at Panasonic Corporation from 2008 to 2017. Cox regression analysis employing multivariable models was used to investigate the association between lipid profiles, particularly the ratio of TG/HDL-C and the development of type 2 diabetes (T2D). The multivariable model was adjusted for age, sex, BMI, systolic blood pressure, plasma glucose levels after fasting, smoking status, and exercise habits. Areas under time-dependent receiver operating characteristic (ROC) curves (AUCs) were employed to assess the prediction performance and cut-off values of each indicator. A fasting plasma glucose level of 126 mg/dL, a self-reported history of diabetes, or usage of antidiabetic medicines were used to identify T2D.

RESULTS

During the course of the study, 6,080 people developed T2D. The median follow-up duration was 6.0 (3-10) years. Multivariable analysis revealed that the ratio of TG/HDL-C (per unit, HR; 1.03 [95% CI 1.02-1.03]) was substantially linked to the risk of incident T2D. AUC and cut-off points for the ratio of TG/HDL-C for T2D development after 10 years were 0.679 and 2.1, respectively. Furthermore, the AUC of the ratio of TG/HDL-C was considerably larger compared to that of LDL-C, HDL-C, and TG alone (all P < 0.001). We discovered an interaction effect between sex, BMI, and lipid profiles in subgroup analysis. Females and participants having a BMI of < 25 kg/m2 showed a higher correlation between lipid profile levels and T2D onset.

CONCLUSIONS

The ratio of TG/HDL-C was found to be a stronger predictor of T2D development within 10 years than LDL-C, HDL-C, or TG, indicating that it may be useful in future medical treatment support.

From normal population to prediabetes and diabetes: study of influencing factors and prediction models

OBJECTIVE

The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes.

MATERIALS AND METHODS

The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman's correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model).

RESULTS

This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set.

CONCLUSION

Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.

Increased TG Levels and HOMA-IR Score Are Associated With a High Risk of Prediabetes: A Prospective Study

This study aimed to determine the association of blood lipid and insulin levels with the development of prediabetes. In this prospective cohort study, we collected and analyzed data related to demographic characteristics, lipid profiles, and insulin parameters at baseline (2008-2012) and at follow-up (2019-2020). A total of 1205 participants were included. The study found that maintained or elevated Homeostatic Model Assessment for Insulin Resistance (HOMO-IR) score and elevated triglyceride (TG) levels from baseline to follow-up were associated with an increased risk of prediabetes. However, the interaction between blood lipids and insulin had no significant effect on the risk of prediabetes. Our findings indicate that elevated TG or HOMA-IR levels are associated with an increased risk of prediabetes. These findings emphasize the need to formulate initiatives that can help reduce dyslipidemia to prevent the onset of prediabetes and diabetes.

Dyslipidemia Among Diabetes Mellitus Patients: A Case-Control Study From a Tertiary Care Hospital in South India

Background Diabetes mellitus (DM) is a chronic endocrine disease characterized by impaired glucose metabolism. Type 2 DM (T2DM) is an age-related disease that usually affects middle and older-aged people who suffer from increased blood glucose activities. Several complications are associated with uncontrolled diabetes that include abnormal lipid levels/dyslipidemia. This may predispose T2DM patients to life-threatening cardiovascular diseases. Therefore, it is essential to evaluate the activities of lipids among T2DM patients. Methodology A case-control study involving 300 participants was conducted in the outpatient department of medicine attached to Mahavir Institute of Medical Sciences, Vikarabad, Telangana, India. The study included 150 T2DM patients and the same number of age-matched controls. In this study, 5 mL of fasting blood sugar (FBS) was collected from each participant for the estimation of lipids (total cholesterol (TC), triacylglyceride (TAG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), and very low-density lipoprotein-cholesterol (VLDL-C)) and glucose. Results The FBS levels among T2DM patients (211.6 ± 60.97 mg/dL) and non-diabetic individuals (87.34 ± 13.06 mg/dL) were significantly (p < 0001) different. Analysis of lipid chemistry that included TC (174.8 ± 38.28 mg/dL vs. 157.22 ± 30.34 mg/dL), TAG (173.14 ± 83.48 mg/dL vs. 133.94 ± 39.69 mg/dL), HDL-C (37.28 ± 7.84 mg/dL vs. 43.4 ± 10.82 mg/dL), LDL-C (113.44 ± 28.79 mg/dL vs. 96.72 ± 21.53 mg/dL), and VLDL-C (34.58 ± 19.02 mg/dL vs. 26.7 ± 8.61 mg/dL) revealed significant variations among T2DM and non-diabetic individuals. There was a 14.10% decrease in the activities of HDL-C among T2DM patients along with an increase in the activities of TC (11.18%), TAG (29.27%), LDL-C (17.29%), and VLDL-C (30%). Conclusions T2DM patients have demonstrated abnormal lipid activities/dyslipidemia compared to non-diabetic patients. Patients with dyslipidemia may be predisposed to cardiovascular diseases. Therefore, regular monitoring of such patients for dyslipidemia is extremely vital to minimize the long-term complications associated with T2DM.

Evaluation of the value of conventional and unconventional lipid parameters for predicting the risk of diabetes in a non-diabetic population

BACKGROUND

Conventional and unconventional lipid parameters are associated with diabetes risk, the comparative studies on lipid parameters for predicting future diabetes risk, however, are still extremely limited, and the value of conventional and unconventional lipid parameters in predicting future diabetes has not been evaluated. This study was designed to determine the predictive value of conventional and unconventional lipid parameters for the future development of diabetes.

METHODS

The study was a longitudinal follow-up study of 15,464 participants with baseline normoglycemia. At baseline, conventional lipid parameters such as low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) were measured/calculated, and unconventional lipid parameters such as non-HDL-C, remnant cholesterol (RC), LDL/HDL-C ratio, TG/HDL-C ratio, non-HDL/HDL-C ratio, TC/HDL-C ratio and RC/HDL-C ratio were calculated. Hazard ratio (HR) and 95% confidence interval (CI) were estimated by Cox proportional hazard regression adjusting for demographic and diabetes-related risk factors. The predictive value and threshold fluctuation intervals of baseline conventional and unconventional lipid parameters for future diabetes were evaluated by the time-dependent receiver operator characteristics (ROC) curve.

RESULTS

The incidence rate of diabetes was 3.93 per 1000 person-years during an average follow-up period of 6.13 years. In the baseline non-diabetic population, only TG and HDL-C among the conventional lipid parameters were associated with future diabetes risk, while all the unconventional lipid parameters except non-HDL-C were significantly associated with future diabetes risk. In contrast, unconventional lipid parameters reflected diabetes risk better than conventional lipid parameters, and RC/HDL-C ratio was the best lipid parameter to reflect the risk of diabetes (HR: 6.75, 95% CI 2.40-18.98). Sensitivity analysis further verified the robustness of this result. Also, time-dependent ROC curve analysis showed that RC, non-HDL/HDL-C ratio, and TC/HDL-C ratio were the best lipid parameters for predicting the risk of medium-and long-term diabetes.

CONCLUSIONS

Unconventional lipid parameters generally outperform conventional lipid parameters in assessing and predicting future diabetes risk. It is suggested that unconventional lipid parameters should also be routinely evaluated in clinical practice.