The retinopathy-derived HbA1c threshold of 6.5% for type 2 diabetes also captures the risk of diabetic nephropathy in NHANES

Using nephropathy as an outcome to determine the HbA1c diagnostic threshold for type 2 diabetes

OBJECTIVE

The hemoglobin A1c (HbA1c) diagnostic threshold for type 2 diabetes (T2D) of 6.5 % (48 mmol/mol) was based on the prevalence of retinopathy found in populations not known to have T2D. It is unclear if nephropathy has a similar HbA1c threshold, partly because it is a rarer complication of early diabetes. This cohort study investigated a very high diabetes prevalence population to determine if a better diagnostic HbA1c value can be established for predicting nephropathy rather than retinopathy in subjects without T2D.

METHODS

The urine albumin:creatinine ratios (UACRs) of 2920 healthy individuals from the Qatar Biobank who had an HbA1c ≥ 5.6 %. were studied. Nephropathy was defined as a UACR≥30 mg/g and its prediction by HbA1c was assessed using cut-points ranging from 5.7 to 7.0 % to dichotomize high from low HbA1c.

RESULTS

Although there was a significant trend for an increased prevalence of abnormal UACR as the HbA1c threshold increased (p < 0.01), significance was due mostly to subjects with HbA1c ≥ 7.0 % (53 mmol/mol). The odds ratios for abnormal UACR were similar over the 5.7-6.9 % HbA1c threshold range, with a narrow odds ratio range of 1.2-1.6. Utilizing area-under-receiver-operating characteristic curves, no HbA1c threshold <7.0 % was identified as the best predictor of nephropathy.

CONCLUSION

Even in a population with a high prevalence of known and unknown diabetes, no HbA1c threshold <7.0 % could be found predicting an increased prevalence of nephropathy. This means there is not a requirement to change the existing retinopathy-based HbA1c threshold of 6.5 % to also accommodate diabetes nephropathy risk.

Modifiable lifestyle, mental health status and diabetic retinopathy in U.S. adults aged 18-64 years with diabetes: a population-based cross-sectional study from NHANES 1999-2018

BACKGROUND

The relationship between integrated lifestyles, mental status and their impact on overall well-being has attracted considerable attention. This study aimed to evaluate the association between lifestyle factors, depression and diabetic retinopathy (DR) in adults aged 18-64 years.

METHODS

A cohort of 3482 participants diagnosed with diabetes was drawn from the National Health and Nutrition Examination Survey (NHANES) spanning the years 1999-2018. DR was defined based on self-reported diabetic retinopathy diagnoses by professional physicians, relying on Diabetes Interview Questionnaires. Subgroup analysis was employed to assess lifestyle and psychological factors between participants with DR and those without, both overall and stratified by diabetic duration. Continuous variables were analyzed using the student's t test, while weighted Rao-Scott χ2 test were employed for categorical variables to compare characteristics among the groups.

RESULTS

Of the 3482 participants, 767 were diagnosed with diabetic retinopathy, yielding a weighted DR prevalence of 20.8%. Patients with DR exhibited a higher prevalence of heavy drinking, depression, sleep deprivation, and insufficient physical activity compared to those without DR. Furthermore, multivariable logistic regression analysis revealed that sleeping less than 5 h (OR = 3.18, 95%CI: 2.04-4.95, p < 0.001) and depression (OR = 1.35, 95%CI:1.06-1.64, p = 0.025) were associated with a higher risk of DR, while moderate drinking (OR = 0.49, 95%CI: 0.32-0.75, p = 0.001) and greater physical activity (OR = 0.64, 95%CI: 0.35-0.92, p = 0.044) were identified as protective factors.

CONCLUSIONS

Adults aged 18-64 years with DR exhibited a higher prevalence of lifestyle-related risk factors and poorer mental health. These findings underscore the need for concerted efforts to promote healthy lifestyles and positive emotional well-being in this population.

Semiparametric modelling of diabetic retinopathy among people with type II diabetes mellitus

BACKGROUND

The proportion of patients with diabetic retinopathy (DR) has grown with increasing number of diabetes mellitus patients in the world. It is among the major causes of blindness worldwide. The main objective of this study was to identify contributing risk factors of DR among people with type II diabetes mellitus.

METHOD

A sample of 191 people with type II diabetes mellitus was selected from the Black Lion Specialized Hospital diabetic unit from 1 March 2018 to 1 April 2018. A multivariate stochastic regression imputation technique was applied to impute the missing values. The response variable, DR is a categorical variable with two outcomes. Based on the relationship derived from the exploratory analysis, the odds of having DR were not necessarily linearly related to the continuous predictors for this sample of patients. Therefore, a semiparametric model was proposed to identify the risk factors of DR.

RESULT

From the sample of 191 people with type II diabetes mellitus, 98 (51.3%) of them had DR. The results of semiparametric regression model revealed that being male, hypertension, insulin treatment, and frequency of clinical visits had a significant linear relationships with the odds of having DR. In addition, the log- odds of having DR has a significant nonlinear relation with the interaction of age by gender (for female patients), duration of diabetes, interaction of cholesterol level by gender (for female patients), haemoglobin A1c, and interaction of haemoglobin A1c by fasting blood glucose with degrees of freedom [Formula: see text], respectively. The interaction of age by gender and cholesterol level by gender appear non significant for male patients. The result from the interaction of haemoglobin A1c (HbA1c) by fasting blood glucose (FBG) showed that the risk of DR is high when the level of HbA1c and FBG were simultaneously high.

CONCLUSION

Clinical variables related to people with type II diabetes mellitus were strong predictive factors of DR. Hence, health professionals should be cautious about the possible nonlinear effects of clinical variables, interaction of clinical variables, and interaction of clinical variables with sociodemographic variables on the log odds of having DR. Furthermore, to improve intervention strategies similar studies should be conducted across the country.

Identifying Distinct Risk Thresholds of Glycated Hemoglobin and Systolic Blood Pressure for Rapid Albuminuria Progression in Type 2 Diabetes From NHANES (1999–2018)

Background

It is widely recognized that glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) are two key risk factors for albuminuria and renal function impairment in patients with type 2 diabetes mellitus (T2DM). Our study aimed to identify the specific numerical relationship of albumin/creatinine ratio (ACR) with HbA1c and SBP among a large population of adults with T2DM.

Method

A total of 8,626 patients with T2DM were included in the data analysis from the National Health and Nutrition Examination Surveys (NHANES) (1999-2018). The multiple linear regressions were used to examine the associations of ACR with HbA1c and SBP. Generalized additive models with smooth functions were performed to identify the non-linear relations between variables and interactions were also tested.

Results

Significantly threshold effects were observed between ACR and HbA1c or SBP after multivariable adjustment, with the risk threshold values HbA1c = 6.4% and SBP = 127 mmHg, respectively. Once above thresholds were exceeded, the lnACR increased dramatically with higher levels of HbA1c (β = 0.23, 95 CI%:0.14, 0.32, P &lt; 0.001) and SBP (β = 0.03, 95 CI%:0.03, 0.04, P &lt; 0.001). Subgroup analysis showed high protein diet was related to higher ACR. In addition, a higher risk of ACR progression was observed in central obesity participants with HbA1C ≥ 6.4% or hyperuricemia participants with SBP ≥ 127 mmHg among patients withT2DM.

Conclusion

We identified thresholds of HbA1c and SBP to stratify patients with T2DM through rapid albuminuria progression. These might provide a clinical reference value for preventing and controlling diabetes kidney disease.

Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study

Purpose

To develop a nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in type 2 diabetes mellitus (T2DM) patients.

Methods

We collect information from electronic medical record systems. The data were split into a training set (n=521) containing 73.8% of patients and a validation set (n=185) holding the remaining 26.2% of patients based on the date of data collection. Stepwise and multivariable logistic regression analyses were used to screen out DN risk factors. A predictive model including selected risk factors was developed by logistic regression analysis. The results of binary logistic regression are presented through forest plots and nomogram. Lastly, the c-index, calibration plots, and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram in internal and external validation. The clinical benefit of the model was evaluated by decision curve analysis.

Results

Predictors included serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and Diabetic peripheral neuropathy (DPN). Harrell’s C-indexes were 0.773 (95% CI:0.726–0.821) and 0.758 (95% CI:0.679–0.837) in the training and validation sets, respectively. Decision curve analysis (DCA) demonstrated that the novel nomogram was clinically valuable.

Conclusion

Our simple nomogram with seven factors may help clinicians predict the risk of DN incidence in patients with T2DM.

Pregnancy Status Is Associated with Lower Hemoglobin A1c among Nondiabetes Women in the United States from NHANES 2005-2016

BACKGROUND

It has been verified that the incidence rate of diabetes mellitus (DM) is sharply increased in pregnant female adults. However, the relationship between pregnant status and hemoglobin A1c (HbA1c) in nondiabetes women remains unclear.

METHODS

We conducted a cross-sectional study of 7762 participants in the National Health and Nutrition Examination Survey (NHANES) 2005-2016. Multivariable linear regression models were performed to evaluate the associations between pregnant status with HbA1c and serum glucose in nondiabetes women.

RESULTS

HbA1c was significantly lower in the pregnant group than in the nonpregnant group. There was a negative association between urine pregnancy test and HbA1c in all three models (model 1: β = -0.23, 95% CI: (-0.18 to -0.27); model 2: β = -0.20, 95% CI: (-0.15 to -0.24); model 3: β = -0.24, 95% CI: (-0.20 to -0.29)). In the subgroup analysis stratified by age, this negative association existed in all age subgroups (age <20: β = -0.20, 95% CI: (-0.04 to -0.27); age ≥20, <35: β = -0.24, 95% CI: (-0.20 to -0.29); age ≥35: β = -0.28, 95% CI: (-0.17, -0.39)). In the subgroup analysis stratified by race, the negative associations steadily existed in different subgroups (Mexican American:β = -0.20, 95% CI:(-0.11 to -0.29); Other Hispanic:β = -0.31, 95% CI: (-0.16 to -0.46); Non-Hispanic White: β = -0.24, 95% CI: (-0.17 to -0.31); Non-Hispanic Black: β = -0.21, 95% CI: (-0.12 to -0.31); Other races:β = -0.22, 95% CI: (-0.08 to -0.35)). On the other hand, a negative association between self-reported pregnant status and HbA1c was also found (model 1: β = -0.22, 95% CI: (-0.18 to -0.27); model 2: β = -0.19, 95% CI: (-0.15 to -0.2); model 3: β = -0.23, 95% CI: (-0.19 to -0.28)). In the subgroup analysis stratified by age, this negative association also existed in all age subgroups.

CONCLUSIONS

The study indicated that nondiabetes women with pregnant status had significantly lower HbA1c compared with those nonpregnant. Moreover, the negative associations between pregnant status and HbA1c steadily existed in subgroups stratified by age and gender.

Increased Glycemic Variability Evaluated by Continuous Glucose Monitoring is Associated with Osteoporosis in Type 2 Diabetic Patients

BACKGROUND

Subjects with type 2 diabetes mellitus (T2DM) are susceptible to osteoporosis. This study was conducted to evaluate the association between glycemic variability evaluated by continuous glucose monitoring (CGM) and osteoporosis in type 2 diabetic patient.

METHODS

A total of 362 type 2 diabetic subjects who underwent bone mineral density (BMD) measurement and were monitored by a CGM system from Jan 2019 to May 2020 were enrolled in this cross-sectional study. Glycemic variability was calculated with the Easy GV software, including 24-hour mean blood glucose (24-h MBG), the standard deviation of 24-h MBG (SDBG), coefficient of variation (CV), mean amplitude of glycemic excursions (MAGE), and time in range between 3.9 and 10.0 mmol/L (TIR). Other potential influence factors for osteoporosis were also examined.

RESULTS

Based on the T-scores of BMD measurement, there were 190 patients with normal bone mass, 132 patients with osteopenia and 40 patients with osteoporosis. T2DM patients with osteoporosis showed a higher 24-h MBG, SDBG, CV, and MAGE, but a lower TIR (all p < 0.05). Multivariate logistic regression analysis revealed that age, female gender, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA) and MAGE independently contribute to osteoporosis, and corresponding odds ratio [95% confidence interval (CI)] was 1.129 (1.072-1.190), 4.215 (1.613-11.012), 0.801 (0.712-0.901), 2.743 (1.385-5.431), 0.993 (0.988-0.999), and 1.380 (1.026-1.857), respectively. Further receiver operating characteristic analysis with Youden index indicated that the area under the curve and its 95% CI were 0.673 and 0.604-0.742, with the optimal cut-off value of MAGE predicting osteoporosis being 4.31 mmol/L.

CONCLUSION

In addition to conventional influence factors including age, female gender, BMI, LDL-C and SUA, increased glycemic variability assessed by MAGE is associated with osteoporosis in type 2 diabetic patients.