A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study

Baseline and change in serum lipid and uric acid level over time and incident of nonalcoholic fatty liver disease (NAFLD) in Chinese adults

Observational studies have shown that non-alcoholic fatty liver disease (NAFLD) is strongly associated with metabolic dysfunction. However, there is a paucity of research on whether changes in indicators of serum metabolism contribute to the development of NAFLD. This study was conducted with 4084 participants who underwent healthy physical examinations at Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China, in 2022 and 2023. Baseline and follow-up measurements, including anthropometric data, abdominal ultrasound and blood samples were collected. The diagnosis of NAFLD was based on the 2010 Chinese Guidelines on Diagnosis and Treatment of NAFLD. Multiple logistic regression was utilized to analyze the odds ratios (ORs) for the 1-year risk of NAFLD in connection with both baseline metabolic indicators and changes in metabolic indicators observed over the course of 1 year. A total of 3425 study participants who were free of NAFLD at baseline, including 1146 men and 2279 women, were included in the final analysis. The mean age was 34.43 ± 7.20 years. Participants who developed NAFLD were older, male and had higher levels of body mass index (BMI), blood pressure, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), free triiodothyronine (fT3), uric acid (UA), alanine aminotransferase (ALT) and aspartate aminotransferase (AST); and lower levels of high-density lipoprotein cholesterol (HDL-C) and free thyroxine (fT4) (all P values < 0.05). The multivariable model showed that baseline BMI, diastolic blood pressure (DBP), TG, TC, HDL-C, LDL-C, UA, fT4, fT3, ALT and changes in TG, HDL-C, and UA were associated with the 1-year risk of developing NAFLD. The risk of NAFLD increased by 56% [OR 1.56, 95% Confidence Interval (CI) 1.32-1.87] and 40% (OR 1.40, 95% CI 1.19-1.64) for each standard deviation (SD) increase in altered TG values (1.01 mmol/L) and altered UA values (55 µmol/L) respectively. Conversely, for each SD (0.27 mmol/L) increase in HDL-C change, the 1-year risk of incident NAFLD was reduced by 50% (OR 0.50, 95% CI 0.40-0.62). The present study suggested that increases in TG and UA, and decreases in HDL-C, significantly increase the risk of developing NAFLD. Therefore, more attention should be paid to these factors in the management and prevention of NAFLD.

Association of white blood cell count to mean platelet volume ratio with type 2 diabetic peripheral neuropathy in a Chinese population: a cross-sectional study

BACKGROUND

The white blood cell count to mean platelet volume ratio (WMR) is considered a promising inflammatory marker, and its recognition is increasing. Inflammation is closely related to metabolic diseases such as diabetes and its complications. However, there are currently no reports on the correlation between WMR and type 2 diabetic peripheral neuropathy (DPN). This study aims to explore the correlation between WMR and DPN in type 2 diabetes patients. By understanding this association, we hope to provide a theoretical basis for preventing DPN through the improvement of inflammatory responses.

METHODS

This was a cross-sectional study involving 2515 patients with T2DM. Logistic regression analysis was conducted to assess the associations between WMR and DPN. Finally, the receiver operating characteristic curve (ROC curve) was employed to evaluate the predictive efficacy of WMR for DPN.

RESULTS

Patients in higher WMR quartiles exhibited increased presence of DPN. Additionally, WMR remained significantly associated with a higher odds ratio (OR) of DPN (OR 4.777, 95% confidence interval [CI] 1.296-17.610, P < 0.05) after multivariate adjustment. Moreover, receiver operating characteristic curve analysis indicated that the optimal cutoff value for WMR in predicting DPN presence was 0.5395 (sensitivity: 65.40%; specificity: 41.80%; and area under the curve [AUC]: 0.540).

CONCLUSIONS

In patients with T2DM, WMR was significantly increased in DPN and independently associated with an increased risk of DPN presence in Chinese patients. This suggests that WMR may serve as a useful and reliable biomarker of DPN, highlighting the importance of paying more attention to T2DM patients with high WMR to further prevent and reduce the development of DPN and related unfavorable health outcomes.

Development and application of a novel model to predict the risk of non-alcoholic fatty liver disease among lean pre-diabetics with normal blood lipid levels

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) has been associated with type 2 diabetes, but its relationship with pre-diabetes is still unknown. This study aims to determine whether pre-diabetes is associated with NAFLD, followed by establishing a NAFLD predictive nomogram for lean Chinese pre-diabetics with normal blood lipids.

METHODS

Datasets from 3 previous studies, 1 (2774 pre-diabetics with normal blood lipids for training, 925 for validation), 2 (546 for longitudinal internal validation, post-5-year follow-up), and 3 (501 from another institution for external validation), were used. Kaplan-Meier determined cumulative NAFLD hazard, and least absolute shrinkage and selection operator regression analysis uncovered its risk factors. Multivariate logistic regression analysis constructed the nomogram, followed by validation with receiver operating characteristic curve, calibration plot, and decision curve analyses.

RESULTS

NAFLD incidence increased with diabetes progression, and pre-diabetics had higher cumulative risk versus non-diabetics, even for lean individuals with normal blood lipids. Six risk factors were identified: body mass index, total cholesterol, alanine aminotransferase:aspartate aminotransferase, triglyceride:high density lipoprotein cholesterol, fasting blood glucose and γ-glutamyl-transferase. The nomogram yielded areas under the curve of 0.808, 0.785, 0.796 and 0.832, for respectively, training, validation, longitudinal internal validation, and external validation, which, along with calibration curve values of p = 0.794, 0.875, 0.854 and 0.810 for those 4 datasets and decision curve analyses, validated its clinical utility.

CONCLUSIONS

Lean pre-diabetic Chinese with normal blood lipids have higher NAFLD risk versus non-diabetics. The nomogram is able to predict NAFLD among such individuals, with high discrimination, enabling its use for early detection and intervention.

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

Background

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

Methods

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test).

Results

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

Conclusions

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01622-5.

Establishment and validation of a nomogram that predicts the risk of type 2 diabetes in obese patients with non-alcoholic fatty liver disease: a longitudinal observational study

OBJECTIVE

This study aimed to establish and validate a nomogram for better assessment of the risk of type 2 diabetes (T2D) in obese patients with non-alcoholic fatty liver disease (NAFLD) based on independent predictors.

METHODS

Of 1820 eligible participants from the NAGALA cohort enrolled in the study. Multivariate Cox regression was employed to construct the nomogram. The performance was assessed by area under the receiver operating characteristic curve (AUC), C-index, calibration curve, decision curve analysis, and Kaplan-Meier analysis.

RESULTS

Five predictors were selected from 17 variables. The AUC values at different time points all indicated that the model constructed with these five predictors had good predictive power. Decision curves indicated that the model could be applied to clinical applications.

CONCLUSIONS

We established and validated a reasonable, economical nomogram for predicting the risk of T2D in obese NAFLD patients. This simple clinical tool can help with risk stratification and thus contribute to the development of effective prevention programs against T2D in obese patients with NAFLD.

A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population

BACKGROUND

This study aimed to distinguish the risk factors for type 2 diabetes mellitus (T2DM) and construct a predictive model of T2DM in Japanese adults with abdominal obesity.

METHODS

This study was a post hoc analysis. A total of 2012 individuals with abdominal obesity were included and randomly divided into training and validation groups at 70% (n = 1518) and 30% (n = 494), respectively. The LASSO method was used to screen for risk variables for T2DM, and to construct a nomogram incorporating the selected risk factors in the training group. We used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration and clinical significance of the nomogram.

RESULTS

In the training cohort, the C-index and receiver operating characteristic were 0.819 and the 95% CI was 0.776-0.858, with a specificity and sensitivity of 77% and 74.68%, respectively. In the validation cohort, the C-index was 0.853; sensitivity and specificity were 77.6% and 88.1%, respectively. The decision curve analysis showed that the model's prediction was effective and cumulative hazard analysis demonstrated that the high-risk score group was more likely to develop T2DM than the low-risk score group.

CONCLUSION

This nomogram may help clinicians screen abdominal obesity at a high risk for T2DM.

Associations Between the Metabolic Score for Insulin Resistance Index and the Risk of Type 2 Diabetes Mellitus Among Non-Obese Adults: Insights from a Population-Based Cohort Study

Purpose

The metabolic score for insulin resistance (METS-IR) index is an emerging surrogate predictor of type 2 diabetes mellitus (T2DM). This study aimed to evaluate the association between the METS-IR index and the risk of T2DM in non-obese Japanese adults.

Methods

A total of 12,290 non-obese participants were selected from the NAGALA prospective cohort study conducted from 2004 to 2015. Cox proportional hazards models were used to assess the association between the baseline METS-IR index and risk of T2DM. Generalized additive models were used to identify nonlinear relationships. In addition, we performed subgroup analyses and interaction tests. Results were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

During a median follow-up of 2050 days, 176 (1.43%) incident T2DM occurred. The fully adjusted HR (95% CI) for the incidence of T2DM in non-obese adults was 1.17 (HR=1.17, 95% CI: 1.09–1.27, P<0.001) for every 1-unit increase in the METS-IR index. The risk of developing T2DM increased with the quartile of change in the METS-IR index, after adjustment for multiple potential confounding, the HRs for the Q4 group versus the Q1 group was 4.01 (95% CI, 1.39–11.57). Generalized additive models also showed a cumulative increase in the risk of T2DM with increasing the METS-IR index. Time-dependent receiver operating curve suggested helpful discriminative power of the METS-IR index for T2DM. The C-statistics by the clinical risk factors significantly improve with the addition of the METS-IR index (from 0.862 to 0.875, P = 0.035); the discriminatory power and risk reclassification also appeared to be substantially better, with the category-free NRI of 0.216, and the IDI of 0.011.

Conclusion

The METS-IR index was a significant and independent predictor for future T2DM development in non-obese adults. The METS-IR index may have clinical significance in identifying groups at high risk of T2DM.