Total energy intake may be more associated with glycemic control compared to each proportion of macronutrients in the korean diabetic population

The association between multiple trajectories of macronutrient intake and the risk of new-onset diabetes in Chinese adults

BACKGROUND

The association between macronutrient intake and diabetes is unclear. We used data from the China Health and Nutrition Survey to explore the association between macronutrient intake trajectories and diabetes risk in this study.

METHODS

We included 6755 participants who did not have diabetes at baseline and participated in at least three surveys. The energy supply ratio of carbohydrate, protein, and fat was further calculated from dietary data; different macronutrient trajectories were determined using multitrajectory models; and multiple Cox regression models were used to evaluate the association between these trajectories and diabetes.

RESULTS

We found three multitrajectories: decreased low carbohydrate-increased moderate protein-increased high fat (DLC-IMP-IHF), decreased high carbohydrate-moderate protein-increased low fat (DHC-MP-ILF), and balanced-macronutrients (BM). Compared to the BM trajectory, DHC-MP-ILF trajectories were significantly associated with increased risk of diabetes (hazard ratio [HR]: 3.228, 95% confidence interval [CI]: 1.571-6.632), whereas no association between DLC-IMP-IHF trajectories and diabetes was found in our study (HR: 0.699, 95% CI: 0.351-1.392).

CONCLUSIONS

The downward trend of high carbohydrate and the increasing trend of low fat increased the risk of diabetes in Chinese adults.

Prevalence and the association between clinical factors and Diabetes-Related Distress (DRD) with poor glycemic control in patients with type 2 diabetes: A Northern Thai cross-sectional study

BACKGROUND

Glycemic control is important to prevent diabetic complications. However, evidence linking factors such as diabetes-related distress (DRD) to poor glycemic outcomes is lacking in Thailand. Therefore, this study aimed to investigate the prevalence and associated factors of poor glycemic control type 2 diabetes.

METHODS

A cross-sectional study was conducted on 127 type 2 diabetic patients between December 2021 and March 2022 at Maharaj Nakorn Chiang Mai Hospital, Thailand. Data collection included demographic data, clinical data (duration of being type 2 diabetes, diabetic treatment modalities, weight, height, blood pressure, FBS, and HbA1c), behavioral data (self-care behavior, physical activity, dietary assessment, smoking, alcohol consumption, and sleep quality), and psycho-social data (depression and DRD). Poor glycemic control was defined as not achieving the target HbA1c based on the 2021 American Diabetes Association (ADA) Guideline. Multivariable logistic regression was used to explore the associations between potential factors including DRD, and poor glycemic control.

RESULTS

The prevalence of poor glycemic control in patients with type 2 diabetes was 29.1%. Our analysis revealed that age under 65 years old (OR 6.40, 95% CI 2.07-19.77, p = 0.001), obesity (BMI ≥ 25 kg/m2) (OR 2.96, 95% CI 1.05-8.39, p = 0.041), and DRD (OR 14.20, 95% CI 3.76-53.64, p<0.001) were significantly associated with poor glycemic control. Three dimensions of DRD were associated with poor glycemic control, including emotional distress (OR 4.23, 95% CI 1.51-11.85, p = 0.006), regimen-related distress (OR 6.00, 95% CI 1.88-19.18, p = 0.003), and interpersonal distress (OR 5.25, 95% CI 1.39-20.02, p = 0.015).

CONCLUSION AND RECOMMENDATION

Age, obesity, and DRD are associated with poor glycemic control. A holistic approach that includes addressing DRD is crucial for improving glycemic outcomes in patients with type 2 diabetes. Further studies in broader populations using a cohort design are recommended.

Different Interactive Effects of Metformin and Acarbose With Dietary Macronutrient Intakes on Patients With Type 2 Diabetes Mellitus: Novel Findings From the MARCH Randomized Trial in China

Antidiabetic oral agents and nutrition management are frequently used together as first-line therapies for type 2 diabetes mellitus (T2DM). However, less is known about their interaction. The interactive effect of two classic antidiabetic medications, namely, acarbose and metformin, with dietary intakes of macronutrients on glycemic control and cardiometabolic risk factors was investigated in the metformin and acarbose in Chinese as the initial hypoglycemic treatment (MARCH) randomized clinical trial. The patients with newly diagnosed T2DM from China were included in the trial. Participants were randomized to receive either metformin or acarbose monotherapy as the initial treatment, followed by a 24-week treatment phase, during which add-on therapy was used if necessary. Dietary intakes of carbohydrate, protein, fat, and total energy were calculated by a 24-h food diary recall method. Linear mixed-effect models combined with a subgroup analysis were used to investigate independent and interactive effects of drugs and diet on clinical outcomes. A data analysis was performed on 551 of the 788 patients randomly assigned to treatment groups. Metformin therapy was independently associated with higher triglycerides (TGs, β = 0.471, P = 0.003), 2 h postprandial plasma glucose (2hPPG, β = 0.381, P = 0.046) but lower low-density lipoprotein cholesterol (LDL-C, β = −0.149, P = 0.013) compared with acarbose therapy. Higher carbohydrates and lower fat intakes were independently associated with poorer glycemic control, less weight loss, and greater insulin secretion. Higher total energy intake was also independently associated with higher fasting (β = 0.0002, P = 0.001) and postprandial blood glucose (β = 0.0004, P = 0.001). Interaction and subgroup analyses demonstrated that glucagon-like peptide-1 (GLP-1) was positively related to total energy (β = 0.268, P = 0.033), carbohydrates intake, and insulin secretion (β = 2,045.2, P = 0.003) only in the acarbose group, while systolic blood pressure (SBP) was negatively related to protein intake in the metformin group (β = 23.21, P = 0.014). The results of this study showed that metformin and acarbose mainly exerted different interactive effects with dietary energy, carbohydrate, and protein intakes on GLP-1 secretion, insulin release, and SBP. The interaction between drug therapy and nutrition intervention in glycemia highlights the complexity of combination therapy.

Habitual Miso (Fermented Soybean Paste) Consumption Is Associated with Glycemic Variability in Patients with Type 2 Diabetes: A Cross-Sectional Study

Glycemic control, including glycemic variability, is important for the prevention of diabetic vascular complications in patients with type 2 diabetes mellitus (T2DM). There was an association between miso soup intake and insulin resistance. However, the relationship between habitual miso consumption and glycemic control, including glycemic variability, in patients with T2DM remains unknown. We defined people without habitual miso consumption if they did not consume miso soup at all in a day. The average, standard deviation (SD), and coefficient of variation (CV), calculated as CV = (SD/average HbA1c) × 100 (%), of hemoglobin (Hb) A1c levels were evaluated. The proportions of habitual miso consumption of male and female were 88.1% and 82.3%, respectively. The average (7.0 [6.4-7.5] vs. 7.3 [6.8-8.4] %, p = 0.009), SD (0.21 [0.12-0.32] vs. 0.37 [0.20-0.72], p = 0.004), and CV (0.03 [0.02-0.04] vs. 0.05 [0.03-0.09], p = 0.005) of HbA1c levels in female with habitual miso consumption were lower than those of female without. Moreover, habitual miso consumption correlated with average (β = -0.251, p = 0.009), SD (β = -0.175, p = 0.016), and CV (β = -0.185, p = 0.022) of HbA1c levels after adjusting for covariates. However, no association between habitual miso consumption and any glycemic parameters was shown among male. This study clarified the association between habitual miso consumption and good glycemic control, including glycemic variability, in female, but not in male.

The Effects of Dietary Nutrition Intake on Glycemic Variability in Type 1 Diabetes Mellitus Adults

INTRODUCTION

Type 1 diabetes mellitus (T1DM) is characterized by an absolute deficiency of insulin and dependence on insulin therapy. Therefore, glycemic control and management are important for T1DM patients, particularly glycemic variability, which is associated with the development of diabetic complications. However, insufficient attention has been paid to the glycemic variability in T1DM patients so far. Our objective was to identify the effects of food intake on glycemic variability in T1DM patients.

METHODS

This was a single-center study that took place in the outpatient clinics of Peking Union Medical College Hospital. A total of 68 Chinese T1DM patients between June 2018 and June 2019 were enrolled. After the baseline demographic and clinical characteristics were evaluated, each participant underwent 14-day flash glucose monitoring (FGM). They recorded caloric intake of breakfast, lunch, and dinner at least 3 days/week using a "Menthol Health" app. After 2 weeks, we obtained the FGM data and did further data analysis. Baseline characteristics and glycemic variability index generated by FGM were compared among groups. A general linear model was used to compare data among groups after adjusting for potential confounding factors. The quantitative relationship between two continuous variables was explored by constructing a linear regression equation.

RESULTS

The results showed that the C-peptide level was independently correlated with the mean of daily differences (MODD) after adjusting for the possible confounders (β =  - 0.239, p = 0.046). The dietary nutrition intake had no effect on glycemic variability. However, the nutritional composition of carbohydrate, fat, and protein was an independent risk factor for time spent in hypoglycemia (TBR) post adjustment (β =  - 0.213, p = 0.054). However, there was no impact of daily total energy intake on glycemic variability index.

CONCLUSION

In our study, dietary nutrition intake had no effect on glycemic variability, but residual β-cell function was identified as an influencing factor for glycemic variability in T1DM adults. However, nutritional macronutrient composition played some roles in the occurrence of hypoglycemia. This might provide new evidence for the clinical glycemic control and management of T1DM in the Chinese population.

Dietary Macronutrient Intakes and Mortality among Patients with Type 2 Diabetes

The best macronutrient percentages of dietary intake supporting longevity remains unclear. The strength of association between dietary intake and mortality in patients with type 2 diabetes (T2DM) should be quantified as a basis for dietary recommendations. Our study cohort consisted of 15,289 type 2 diabetic patients aged 30 years and older in Taiwan during 2001-2014 and was followed up through 2016. Percentages of macronutrient intakes were calculated as dietary energy intake contributed by carbohydrate, protein, and fat, divided by the total energy intake using a 24 h food diary recall approach. Cox proportional hazard models were applied to examine the temporal relation of macronutrient intakes with all-cause and cause-specific mortality. The average follow-up time was 7.4 years, during which 2,784 adults with T2DM died. After multivariable adjustment, people with fourth and fifth quintiles of total energy, second and third quintiles of carbohydrate, and fourth quintiles of protein intakes were likely to have lower risks of all-cause and expanded cardiovascular disease (CVD) mortality. People with fifth quintiles of total energy intake were likely to have decreased non-expanded CVD mortality. We found a significant interaction between gender and fat intake on all-cause and expanded CVD mortality. Fat intake was associated with all-cause, expanded and non-expanded CVD mortality among males with T2DM. Total energy, carbohydrate, and protein intakes were associated with lower risks of all-cause and expanded CVD mortality, with minimal risks observed at ≥1673 Kcal total energy, 43-52% carbohydrate intake, and 15-16% protein intake among people with T2DM.

The impact of carbohydrate intake and its sources on hemoglobin A1c levels in Japanese patients with type 2 diabetes not taking anti-diabetic medication

BACKGROUND

Although postprandial glucose levels largely depend on carbohydrate intake, the impact of carbohydrate and its sources on hemoglobin A1c (HbA1c) levels has not been demonstrated in patients with type 2 diabetes (T2DM) probably because, in previous studies, more than 50% of patients were taking anti-diabetic medication, and the researchers used energy percent of carbohydrate as an indicator of carbohydrate intake.

PATIENTS AND METHODS

We recruited 125 Japanese men (mean age 58±12 years) and 104 women (mean age 62±10 years) with T2DM who were not taking anti-diabetic medication and dietary therapy. We used 3-day dietary records to assess total carbohydrate intake and its sources, computed Spearman's correlation coefficients, and conducted multiple regression analyses for associations of carbohydrate sources with HbA1c by sex.

RESULTS

Mean HbA1c and total carbohydrate intake were 8.2%±1.9% and 272.0±84.6 g/day in men and 7.6%±1.3% and 226.7±61.5 g/day in women, respectively. We observed positive correlation of total carbohydrate intake (g/day) with HbA1c in men (r_s=0.384) and women (r_s=0.251), but no correlation for % carbohydrate in either sex. Regarding carbohydrate sources, we found positive correlations of carbohydrate from noodles (r_s=0.231) and drinks (r_s=0.325), but not from rice, with HbA1c in men. In women, carbohydrate from rice had a positive correlation (r_s=0.317), but there were no correlations for carbohydrate from noodles and drinks. The association of total carbohydrate intake (g/day) and carbohydrate from soft drinks with HbA1c in men remained significant even after adjustment for total energy by multiple regression analyses.

CONCLUSION

Our findings warrant interventional studies for moderate low-carbohydrate diets that focus on carbohydrate sources and sex differences in order to efficiently decrease HbA1c in patients with T2DM.

Comparison of dietary energy and macronutrient intake at different levels of glucose metabolism

The aim of this study was to evaluate energy and glycolipid metabolism by determining the intake of energy and macronutrients in persons with differing glucose metabolisms. In total, 147 patients who were newly diagnosed with pre-diabetes, 177 patients with diabetes, 139 patients who were previously diagnosed with diabetes, and 140 patients with normal blood sugar were selected from the 103rd Regiment of Xinjiang. All patients had Han nationality and were over 30 years old. Their energy and macronutrient intakes were analyzed from data obtained from a 3-day food weighing household investigation. Compared to the normal group, the patients in the previously and newly diagnosed diabetic groups were older, less educated, and had a greater prevalence of hypertension (P<0.05). Compared to the normal group, patients with abnormal glucose metabolism had larger waist circumferences; higher systolic and diastolic blood pressure; higher postprandial glucose; higher total cholesterol; lower high-density lipoprotein cholesterol (HDL-C; P<0.05); higher intakes of energy, carbohydrates, and fat; and lower intakes of protein and fiber. In addition, the newly and previously diagnosed patients had higher fasting glucose levels (P<0.05). Compared to the normal group, patients with abnormal glucose metabolism in each sex subgroup also had larger waist circumferences, and more men had abdominal obesity (P<0.05). Diabetes or pre-diabetes patients had a higher intake of energy, carbohydrates, and fat, but a lower intake of proteins and fiber. They had severe abdominal obesity, a greater prevalence of hypertension, higher total cholesterol levels, lower HDL-C, and poor blood glucose and glycosylated hemoglobin levels, especially postprandial plasma glucose levels.

Impact of ENPP1 K121Q on change of insulin resistance after web-based intervention in Korean men with diabetes and impaired fasting glucose

Ectoenzyme nucleotide pyrophosphate phosphodiesterase 1 (ENPP1) gene has been studied in relation to type 2 diabetes mellitus (T2DM) and insulin resistance (IR). We hypothesized that the difference in genotype may be one of the factors that affect the outcome of intervention. We genotyped 448 men with fasting glucose≥5.6 mM/L, including 371 in subjects with K allele (KK) (69 control group [CG]; and 302 intervention group [IG]) and 77 in subjects with Q allele (KQ+QQ) (13 CG and 64 IG). The web-based intervention based on a lifestyle modification was delivered by e-mail once a month for 10 months. In the KK, IG demonstrated significantly decreased levels of fasting serum insulin (FSI) as compared to CG and homeostasis model of assessment of insulin resistance (HOMA-IR). In the KQ+QQ IG group, hemoglobin A1c (HbA1c), FSI and HOMA-IR were significantly decreased, and showed further reduction in the HOMA-IR than KQ+QQ CG. After analysis of covariance, K121Q did significantly influence the change of HbA1c in CG after appropriate adjustment. In a multivariate model, BMI change predicted HOMA-IR change (adjusted β=0.801; P=0.022) in KK IG subjects with T2DM. ENPP1 K121Q did not influence the change in IR. However, individuals with T2DM carrying the K121 variant are very responsive to the effect of BMI reduction on HOMA-IR.