Abstract
Objectives:
The aim of this study was to explore the factors influencing type 2 diabetes mellitus (T2DM) among adults in Zhejiang Province.
Methods:
A stratified cluster sampling technique was employed, and adults without known diabetes were included in the analysis. Food consumption was assessed using three consecutive days of 24-h dietary recall. Blood samples were collected to measure fasting blood glucose (FBG), blood lipids [total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), 25-(OH)D vitamin D (VD), and vitamin A (VA). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were also measured. Ordinal regression was used to explore factors influencing T2DM.
Results:
The analysis included a total of 5,804 adults. The prevalence rates of T2DM and prediabetes were 5.5 and 5.8%, respectively. Significant differences were observed in age, sex, nutritional status, hypertension, and blood lipid levels among adults with normal fasting blood glucose, prediabetes, and T2DM (p < 0.05). Adults aged 55 years and above, those who were overweight or obese, those with hypertension, and those with higher TG levels had a greater risk of developing diabetes (p < 0.05).
Conclusion:
This study revealed that adults aged 55 years and above, those who are overweight or obese, those with hypertension, and those with higher TG levels have a greater risk of developing diabetes. These findings underscore the need for targeted interventions to manage these risk factors in the prevention and management of T2DM.
Introduction
Type 2 diabetes mellitus (T2DM) is a major global health priority and is the eighth leading cause of death and disability (1). The 2017 Global Burden of Diseases studies further estimated that high fasting plasma glucose (FPG) was the third most common global risk factor for disability-adjusted life years (2). T2DM is the most prevalent type of diabetes, accounting for more than 90% of diabetes mellitus (DM) cases (3). Diabetes can cause blindness, kidney failure, heart attack, stroke and lower limb amputation. Research has also shown that prediabetes increases the risk of developing T2DM (4). Moreover, prediabetes is associated with an increased risk of cardiovascular disease and all-cause mortality in adults (5). Diabetes has a substantial effect on an individual’s quality of life and places a substantial economic burden on national economies (6). Despite significant progress in the treatment of T2DM, prevention strategies remain crucial given the increasing prevalence of this disease, driven by factors such as population ageing, sedentary lifestyles, and unhealthy dietary habits (7). T2DM is often preventable. Factors that contribute to the development of T2DM include being overweight, insufficient physical activity, and a genetic predisposition.
Given the significant role of nutrition in influencing fasting blood glucose (FBG) levels, prior studies have delved into how various dietary components and patterns impact FBG in adults (8). Some research has indicated that a diet high in refined carbohydrates and saturated fats can lead to elevated FBG levels (9). Conversely, diets rich in fiber, whole grains, and healthy fats have been associated with better glycemic control (10). However, existing findings are inconsistent to some extent and often fail to comprehensively consider the interplay of multiple nutrition factors. Previous Chinese nutrition surveys have provided valuable insights into the general dietary habits and health status of the population but have not fully explored the detailed relationships between specific nutritional factors and T2DM risk in a community-based setting. Our study aims to fill this gap by conducting a more thorough and nuanced analysis of the relationships between nutrition and adult FBG levels. We hypothesize that specific sociodemographic, anthropometric, biochemical, and lifestyle-related factors, particularly nutritional factors, play significant roles in the development of T2DM. This study seeks to explore these factors in depth. The findings of this study can be applied in future public health interventions to reduce the burden and disabilities associated with diabetes.
Methods
Study and participants
This study included adults participating in provincial nutrition surveillance, excluding those with a history of T2DM, gestational diabetes, or other specific types of diabetes. A stratified cluster sampling technique was employed. The sampling design was constructed based on the principles of representativeness across regions, sex, and age. In addition to ensuring scientific rigor, the sampling plan also accounted for the feasibility of economic resources and onsite investigations. Streets/townships were designated as the primary sampling units. Stratified sampling was subsequently conducted to recruit participants from different sex and age groups in accordance with their demographic characteristics. The selection of streets/townships was based on census data and geographic information. Within each selected street/township, households were randomly chosen using a systematic sampling method to ensure equal representation of different demographic groups.
All members of the selected households were subsequently interviewed. The field investigation, which included the collection of demographic information, dietary data, and blood samples, was conducted in 2022.
The present study utilized a structured questionnaire from the Chinese Resident Nutrition and Health Survey (CNHS) (11), which collected detailed information on sociodemographic characteristics, daily dietary intake, and daily life behaviours relevant to this study.
Anthropometric and laboratory assessment
Measurements were conducted by trained investigators using standardized methods in a centralized setting. Height was measured without shoes to the nearest 0.2 cm using a portable stadiometer (TZG, Wuxi weighing instrument factory Co., Ltd., Wuxi, China), and weight was measured without shoes and in light clothing to the nearest 0.1 kg on a calibrated beam scale (G&G tc——200 k, Shanghai Taizhiheng Electronic Weighing Instrument Co., Ltd., Shanghai, China). Body Mass Index (BMI) is the most widely used measure of obesity, and the World Health Organization cut-off is 18.5–23.9 kg/m2 for a normal BMI. BMI was calculated as weight (kg)/height (m)2. A BMI greater than or equal to 24.0 kg/m2 or less than 28.0 kg/m2 is considered overweight, whereas a BMI greater than or equal to 28.0 kg/m2 is considered obese (12). To assess SBP and DBP, participants rested for 15 min before the measurement. An upper-arm electronic blood pressure monitor was used to measure blood pressure. The diagnostic criteria for hypertension were systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg (13).
Dietary intake calculation
The dietary assessment included three consecutive days of 24-h dietary recall and a concurrent seasoning weighing survey. Family members reported their dietary intake over the prior 24 h, including foods consumed both at home and outside, over three consecutive days, including two weekdays and one weekend day, to estimate daily food and nutrient intake. The seasoning weighing survey assessed household consumption of edible oils, salt, monosodium glutamate, and other major seasonings using the weighing method during the same period, with diner numbers recorded. Both surveys were conducted simultaneously using the Chinese Resident Nutrition and Health Survey (CNHS) questionnaire to ensure accuracy. Daily nutrient intake was calculated using the China Food Composition Table (14).
Laboratory assessment
Blood samples were collected to measure the concentrations of FBG, blood lipids [including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), vitamin D, and vitamin A]. FBG levels were measured using the hexokinase method. In accordance with the Chinese guidelines for T2DM, FBG ≥ 7.0 mmol/L was used to define diabetes, whereas FBG ≥ 6.1 mmol/L and < 7.0 mmol/L were used to define impaired fasting glucose (IFG) (15). Serum TG levels were measured via a Roche Cobas 8,000 analyser, whereas TC levels were measured via a Hitachi LST008AS analyser. HDL-C and LDL-C levels were measured using the direct method via the Hitachi LST008AS. Abnormal lipid profiles were defined according to the Li et al. (16) as follows:
Dyslipidaemia was defined as the presence of any one of the following four factors: elevated LDL-C levels, low HDL-C levels, elevated TG levels, or elevated TC levels. Critical lipid profiles were defined according to the Li et al. (16) as follows:
Vitamin D levels were measured via high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) (AB SCIEX 5500MPX) as recommended by the Chinese national standard (WS/T677-2020) (17). Vitamin D deficiency and insufficiency were assessed on the basis of the concentration of serum 25-hydroxyvitamin D [25-(OH)D] (WS/T677-2020). Specifically, vitamin D deficiency was defined as a serum 25-(OH)D level less than 12 μg/L. Vitamin D insufficiency was defined as a serum 25-(OH)D level less than 20 μg/L. Vitamin A levels were measured via high-performance liquid chromatography (HPLC) (WS/T553-2017) (18). Vitamin A deficiency and marginal deficiency were assessed on the basis of the concentration of serum retinol, according to the Chinese national standard (WS/T553-2017). Specifically, vitamin A deficiency was defined as a serum retinol concentration less than 0.2 μg/mL. Marginal vitamin A deficiency was defined as serum retinol levels less than 0.3 μg/mL but greater than or equal to 0.2 μg/mL.
Ethical considerations
All procedures performed in this study were in accordance with the ethical standards of the institutional research committee. Ethical approval was obtained from the Ethics Committee of the Zhejiang Provincial Center for Disease Control and Prevention (approval number: 2022–018-01), and informed consent was obtained from all individual participants included in the study.
Statistical analysis
Data processing and statistical analyses were performed using SAS 9.2 software. Missing data were handled using multiple imputation techniques. The distributions of DM (FBG ≥ 7.0 mmol/L), IFG (FBG ≥ 6.1 mmol/L and < 7.0 mmol/L), and normal FBG (FBG < 6.1 mmol/L) were analyzed using the chi-square (χ2) test. Continuous variables with normal distributions are presented as the means ± standard deviations (SDs). Differences in daily food and dietary nutrient intake among adults with or without DM and IFG were assessed using analysis of variance (ANOVA). Influencing factors were evaluated using ordinal regression models. The outcome variable was IFG status,” which was categorized into three levels: normal fasting glucose (FBG < 6.1 mmol/L), impaired fasting glucose (FBG ≥ 6.1 and <7.0 mmol/L), and DM (FBG ≥ 7.0 mmol/L). On the basis of previous research findings, we identified a set of variables that could confound the relationship between the exposure variables and the outcome. To control for these confounders in the analysis, we first included all the identified potential confounding variables in the univariate analysis. Then, variables with p values less than 0.1 in the univariate analysis were included in the ordinal regression model. The initial set of potential confounding variables included age, sex, nutritional status, smoking status, hypertension, and blood lipid levels (TG, TC, HDL-C, LDL-C), vitamin D, and vitamin A. Variables with p-values less than 0.1 in the univariate analysis were then included in the ordinal regression model to assess their independent association with IFG status. To address potential seasonal effects and day-to-day variability in the 3-day dietary recall method, data were collected throughout the year, and the recall days included both weekdays and weekends. This approach helps to capture the variability in dietary intake across different seasons and weekdays, ensuring a more comprehensive and representative assessment of dietary patterns. All tests were two-sided, and the level of significance was set at p < 0.05.
Results
Study participants
A total of 5,804 adults were included in the analysis. Among the 5,940 individuals initially recruited, 136 were excluded due to not meeting the inclusion criteria or having incomplete data for key variables. The final analysis included 5,804 participants with complete data for all variables assessed in the study. The participants had a mean age of 51.4 years (SD, 15.9), with women accounting for 57% of the sample. The characteristics of the participants are presented in Table 1 according to their FBG levels. Of these, 5,149 participants (88.7%) had normal fasting blood glucose (FBG < 6.1 mmol/L), 336 participants (5.8%) had IFG (FBG ≥ 6.1 and <7.0 mmol/L), and 319 participants (5.5%) had diabetes mellitus (FBG ≥ 7.0 mmol/L). Overall, 9.3% of obese adults and 8.2% of adults aged ≥55 years had an FBG ≥ 7.0 mmol/L. As shown in Table 1, significant differences were observed among adults with DM, IFG, and normal FBG when stratified age by (<55 years, ≥55 years) (χ2 = 105.483, p < 0.001), sex (male, female) (χ2 = 14.670, p = 0.001), nutritional status (normal weight, overweight, obesity) (χ2 = 78.720, p < 0.001), smoking status (smokes every day, does not smoke every day, used to smoke, never smoked) (χ2 = 14.982, p = 0.020), hypertension status (yes, no) (χ2 = 48.215, p < 0.001), TG level (<1.0 mmol/L, 1.0–1.5 mmol/L, ≥1.5 mmol/L) (χ2 = 115.547, p < 0.001), TC level (<4.4 mmol/L, 4.4–5.2 mmol/L, ≥5.2 mmol/L) (χ2 = 34.489, p < 0.001), HDL-C level (≥1.2 mmol/L, 1.0–1.2 mmol/L, <1.0 mmol/L) (χ2 = 44.760, p < 0.001), LDL-C level (<2.8 mmol/L, 2.8–3.4 mmol/L, ≥3.4 mmol/L) (χ2 = 19.940, p = 0.001), vitamin D level (≥20 ng/mL, 12–20 ng/mL, <12 ng/mL) (χ2 = 11.061, p = 0.026), vitamin level A (<0.2 μg/mL, 0.2–0.3 μg/mL, ≥0.3 μg/mL) (χ2 = 9.642, p = 0.047), and sitting time (<4 h, ≥4 h) (χ2 = 6.197, p = 0.045).
Table 1
| Factors | FPG | ||||
|---|---|---|---|---|---|
| FBG ≥ 7 mmol/L | 6.1 ≤ FBG < 7 mmol/L | FBG < 6.1 mmol/L | χ2 | P | |
| Sex (%) | |||||
| Male (N = 2,495, 43%) | 167 (6.8%) | 152 (6.2%) | 2,176 (87.0%) | 14.670 | 0.001 |
| Female (N = 3,309, 57%) | 152 (4.6%) | 184 (5.6%) | 2,973 (89.8%) | ||
| Nutritional status (%) | |||||
| Normal weight | 133 (3.9%) | 149 (4.4%) | 3,086 (91.6%) | 78.720 | <0.001 |
| Overweight | 134 (7.3%) | 135 (7.4%) | 1,565 (85.3%) | ||
| Obese | 52 (9.3%) | 52 (9.3%) | 458 (81.5%) | ||
| Smoking (%) | |||||
| Smoking everyday | 58 (7.0%) | 48 (5.8%) | 717 (87.1%) | 14.982 | 0.020 |
| Smoking but not every day | 7 (6.9%) | 8 (7.8%) | 87 (85.3%) | ||
| Used to smoke | 28 (8.5%) | 24 (7.3%) | 276 (84.1%) | ||
| Never smoked | 225 (5.0%) | 256 (5.7%) | 4,028 (89.3%) | ||
| Age (year) | |||||
| <55 (N = 3,233, 55.7%) | 108 (3.3%) | 133 (4.1%) | 2,992 (92.5%) | 105.483 | <0.001 |
| ≥55 (N = 2,571, 44.3%) | 211 (8.2%) | 203 (7.9%) | 2,157 (83.9%) | ||
| TG (mmol/L) | |||||
| <1.0 | 39 (2.4%) | 54 (3.3%) | 1,543 (94.3%) | 115.547 | <0.001 |
| 1.0–1.5 | 48 (3.6%) | 77 (5.8%) | 1,195 (90.5%) | ||
| ≥1.5 | 167 (8.2%) | 167 (8.2%) | 1710 (83.7%) | ||
| TC (mmol/L) | |||||
| <4.4 | 72 (3.8%) | 97 (5.1%) | 1732 (91.1%) | 34.489 | <0.001 |
| 4.4–5.2 | 102 (5.3%) | 104 (5.4%) | 1714 (89.3%) | ||
| ≥5.2 | 145 (7.5%) | 135 (7.0%) | 1,651 (85.5%) | ||
| HDL-C (mmol/L) | |||||
| ≥1.2 | 70 (7.8%) | 79 (8.9%) | 743 (83.3%) | 44.760 | <0.001 |
| 1.0–1.2 | 79 (6.2%) | 90 (7.1%) | 1,101 (86.7%) | ||
| <1.0 | 170 (4.7%) | 167 (4.7%) | 3,253 (90.6%) | ||
| LDL-C (mmol/L) | |||||
| <2.8 | 153 (5.0%) | 159 (5.2%) | 2,765 (89.9%) | 19.940 | 0.001 |
| 2.8–3.4 | 88 (5.9%) | 80 (5.4%) | 1,314 (88.7%) | ||
| ≥3.4 | 78 (6.5%) | 97 (8.1%) | 1,018 (85.3%) | ||
| VD (ng/ml) | |||||
| ≥20 | 8 (2.8%) | 12 (4.2%) | 269 (93.1%) | 11.061 | 0.026 |
| ≥12–20 | 63 (4.7%) | 72 (5.4%) | 1,192 (89.8%) | ||
| <12 | 242 (6.2%) | 236 (6%) | 3,452 (87.8%) | ||
| VA (μg/ml) | |||||
| <0.20 | 0 | 1 (33.3%) | 2 (66.7%) | 9.642 | 0.047 |
| ≥0.20–0.30 | 0 | 3 (4.4%) | 65 (95.6%) | ||
| ≥0.30 | 165 (5.0%) | 197 (6.0%) | 2,932 (89.0%) | ||
| Sitting time (hour) | |||||
| <4 | 177 (6.0%) | 190 (6.4%) | 2,579 (87.5%) | 6.197 | 0.045 |
| ≥4 | 141 (5.1%) | 145 (5.3%) | 2,472 (89.6%) | ||
| Hypertension | |||||
| Yes | 66 (8.5%) | 63 (8.2%) | 643 (83.3%) | 48.215 | <0.001 |
| No | 167 (4.4%) | 160 (4.2%) | 3,507 (91.5%) | ||
Participant characteristics according to fasting blood glucose (N = 5,804).
HDL, high-density lipoprotein; TC, total cholesterol; TG, triglycerides; LDL, low-density lipoprotein; VD,: vitamin D; VA, vitamin A; FBG, fasting blood glucose.
A total of 319 patients were included in the analysis. Among them, the age ranged from 18 to 95 years. The gender distribution was as follows: 167 males (52.3%) and 152 females (47.6%). In terms of body weight classification, 133 patients (41.7%) were of normal weight, 134 patients (42.0%) were overweight, and 52 patients (16.3%) were obese. Regarding lipid parameters, 167 patients (52.3%) had TG levels ≥1.5 mmol/L. For cholesterol indicators, 145 patients (52.4%) presented TC levels ≥5.2 mmol/L, 78 patients (24.5%) had LDL - C levels ≥3.4 mmol/L, and 170 patients (53.3%) showed HDL - C levels ≤1.0 mmol/L.
Comparison of daily nutrient and food intake among adults with DM, IFG, and normal FBG
Table 2 presents the daily and nutrient food intake according to FBG levels. Significant differences were observed in the intake of dietary fibre among adults with DM, IFG, and normal FBG (F = 3.554, p = 0.029). Specifically, adults with an FBG ≥ 7.0 mmol/L had a lower intake of dietary fibre. Additionally, adults with FBG ≥ 7.0 mmol/L had lower intakes of vitamin B1 and vegetables, although these differences were not statistically significant (F = 2.835, 2.359; p = 0.059, 0.095).
Table 2
| FBG | F | P | |||
|---|---|---|---|---|---|
| Dietary intake | FBG < 6.1 mmol/L | 6.1 ≤ FBG < 7 mmol/L | FBG ≥ 7 mmol/L | ||
| Protein(g) | 64.48 ± 29.89 | 63.80 ± 27.20 | 66.09 ± 31.59 | 0.390 | 0.677 |
| Fat (g) | 71.15 ± 34.49 | 70.68 ± 35.93 | 73 ± 37.97 | 0.329 | 0.719 |
| Carbohydrate (g) | 162.68 ± 83.82 | 163.91 ± 82.80 | 167.12 ± 95.16 | 0.306 | 0.736 |
| Dietary fibre (g) | 7.30 ± 6.80 | 6.20 ± 4.36 | 6.90 ± 7.62 | 3.554 | 0.029 |
| Zn (mg) | 8.67 ± 5.22 | 8.66 ± 4.33 | 8.62 ± 4.07 | 0.011 | 0.989 |
| Fe (mg) | 16.39 ± 10.77 | 16.65 ± 10.82 | 16.50 ± 8.99 | 0.077 | 0.926 |
| Ca (mg) | 417.49 ± 330.40 | 409.55 ± 435.02 | 415.82 ± 373.91 | 0.068 | 0.935 |
| Vitamin C (mg) | 68.55 ± 111.128 | 64.97 ± 47.47 | 64.59 ± 52.19 | 0.268 | 0.765 |
| Vitamin E (mg) | 8.55 ± 5.76 | 8.71 ± 6.11 | 8.53 ± 5.56 | 0.094 | 0.910 |
| Vitamin B1 (mg) | 0.77 ± 0.38 | 0.79 ± 0.44 | 0.83 ± 0.44 | 2.835 | 0.059 |
| Vitamin B2 (mg) | 0.69 ± 0.40 | 0.72 ± 0.86 | 0.68 ± 0.49 | 0.583 | 0.558 |
| Vitamin A (μg RE) | 327.54 ± 428.67 | 341.58 ± 368.89 | 324.52 ± 328.51 | 0.145 | 0.865 |
| Na (mg) | 4863.46 ± 6091.712 | 5312.69 ± 4906.71 | 4934.78 ± 2917.13 | 0.710 | 0.492 |
| K (mg) | 1524.64 ± 765.09 | 1496.13 ± 872.07 | 1479.33 ± 731.49 | 0.501 | 0.606 |
| Mg (mg) | 238.63 ± 112.47 | 237.30 ± 126.87 | 241.93 ± 104.38 | 0.111 | 0.894 |
| Cereals and starchy vegetables(g) | 244.82 ± 126.38 | 241.11 ± 109.02 | 248.86 ± 138.32 | 0.227 | 0.797 |
| Vegetables(g) | 242.74 ± 148.54 | 225.92 ± 121.32 | 229.29 ± 133.57 | 2.359 | 0.095 |
| Fruits(g) | 115.06 ± 130.69 | 106.64 ± 113.72 | 97.75 ± 88.32 | 0.650 | 0.522 |
| Livestock and poultry meat(g) | 117.13 ± 80.13 | 118.15 ± 80.43 | 124.03 ± 68.88 | 0.787 | 0.455 |
| Fish and shrimp(g) | 79.69 ± 61.72 | 78.56 ± 59.56 | 82.12 ± 63.40 | 0.178 | 0.837 |
| Eggs(g) | 41.28 ± 46.65 | 40.67 ± 27.24 | 40.34 ± 28.67 | 0.054 | 0.947 |
| Milk and dairy products(g) | 130.6 ± 89.03 | 123.27 ± 98.20 | 118.99 ± 82.42 | 0.506 | 0.603 |
| Soy products and nuts(g) | 22.27 ± 25.29 | 24.61 ± 41.00 | 20.54 ± 22.10 | 1.058 | 0.347 |
| Fats and oils(g) | 32.89 ± 28.71 | 33.19 ± 32.34 | 33.87 ± 34.38 | 0.121 | 0.886 |
| Salt(g) | 7.81 ± 13.04 | 8.43 ± 7.98 | 8.17 ± 6.02 | 0.353 | 0.702 |
Daily nutrient and food intake according to fasting blood glucose.
FBG, fasting blood glucose.
F (ANOVA) refers to the analysis of variance statistic.
Ordinal regression analysis
We identified potential covariates that may influence the study outcomes. These include age, sex, nutritional status, smoking status, hypertension, and TG, TC, HDL-C, LDL-C, vitamin D, and vitamin A levels. Age was categorized using 55 years as the cut-off, as it reflects the distinct differences in diabetes-related factors observed in our data between younger and older individuals. We constructed several ordinal regression models with different combinations of covariates. After comparing the model fit and parameter estimates, we finalized a model that best represented the data (Table 3).
Table 3
| Variable | Category | Reference category | β | Wald χ2 | OR | p |
|---|---|---|---|---|---|---|
| Sex | Male | Female | 0.017 | 0.018 | 1.02 | 0.895 |
| Age (years) | <55 | ≥55 | 0.773 | 43.856 | 2.17 | <0.001 |
| Nutritional status | Normal weight | Obese | −0.538 | 10.661 | 0.58 | 0.001 |
| Overweight | Obese | −0.285 | 3.000 | 0.75 | 0.083 | |
| Smoking | Smoking every day | never smoked | 0.202 | 1.626 | 1.22 | 0.202 |
| Smoking but not every day | never smoked | −0.247 | 0.339 | 0.78 | 0.560 | |
| Used to smoke | never smoked | 0.275 | 1.969 | 1.32 | 0.161 | |
| Hypertension | Yes | No | 0.491 | 18.155 | 1.63 | <0.001 |
| TG (mmol/L) | <1.0 | ≥1.5 | −0.839 | 30.282 | 0.43 | <0.001 |
| 1.0 ~ 1.5 | ≥1.5 | −0.474 | 12.664 | 0.62 | <0.001 | |
| TC (mmol/L) | <4.4, | ≥5.2 | −0.15 | 0.554 | 0.86 | 0.457 |
| 4.4 ~ 5.2 | ≥5.2 | −0.177 | 1.185 | 0.84 | 0.276 | |
| HDL (mmol/L) | ≥1.2 | <1.0 | 0.129 | 0.764 | 1.14 | 0.382 |
| 1.0 ~ 1.2 | <1.0 | −0.07 | 0.273 | 0.93 | 0.602 | |
| LDL (mmol/L) | <2.8 | ≥3.4 | −0.147 | 0.547 | 0.86 | 0.460 |
| 2.8 ~ 3.4 | ≥3.4 | −0.208 | 1.495 | 0.81 | 0.221 | |
| VD (ng/ml) | ≥20 | <12 | −0.585 | 3.48 | 0.56 | 0.062 |
| ≥12 ~ 20 | <12 | 0.061 | 0.248 | 1.06 | 0.619 | |
| VA (μg/ml) | <0.20 | ≥0.30 | 0.384 | 0.107 | 1.47 | 0.744 |
| 0.20 ~ 0.30 | ≥0.30 | −0.513 | 0.699 | 0.60 | 0.403 | |
| Sitting time | <4 h | ≥4 h | 0.179 | 3.023 | 1.20 | 0.082 |
Ordinal regression of factors influencing IFG*.
HDL, high-density lipoprotein; TC, total cholesterol; TG, triglycerides; LDL, low-density lipoprotein; VD, vitamin D; VA, vitamin A; IFG, impaired fasting glucose.
*IFG status was categorized into three levels: normal fasting glucose (FBG <6.1 mmol/L), impaired fasting glucose (FBG ≥6.1 and <7.0 mmol/L), and diabetes mellitus (FBG ≥7.0 mmol/L).
Adults aged 55 years and older had a 2.17 times higher risk of having IFG/T2DM compared to those younger than 55 years (OR = 2.17, Wald χ2 = 43.856, p < 0.001). Normal weight adults had a 0.58 times lower risk of IFG/T2DM compared to obese individuals (OR = 0.58, Wald χ2 = 10.661, p = 0.001). Adults with hypertension had a 1.63 times higher risk of IFG/T2DM compared to those without hypertension (OR = 1.63, Wald χ2 = 18.155, p < 0.001). This indicates that hypertension is a significant risk factor for developing IFG/T2DM. Adults with TG levels below 1.0 mmol/L had a 0.43 times lower risk of IFG/T2DM compared to those with levels of 1.5 mmol/L or higher (OR = 0.43, Wald χ2 = 30.282, p < 0.001). Adults with TG levels between 1.0 and 1.5 mmol/L had a 0.62 times lower risk of IFG/ T2DM compared to those with levels of 1.5 mmol/L or higher (OR = 0.62, Wald χ2 = 12.664, p < 0.001).
Discussion
The results of our study revealed several significant factors influencing the presence of T2DM/IFG among adults in Zhejiang Province. These factors include age, nutritional status, hypertension, and TG levels. Specifically, adults aged 55 years and older, those who are overweight or obese, those with hypertension, and those with higher TG levels have a higher risk of developing IFG/T2DM.
Age and T2DM/IFG
The prevalence of T2DM and prediabetes increases with age. Our study found that adults aged 55 years and older had a 2.17 times higher risk of developing IFG/T2DM compared to younger adults. This finding underscores the need for targeted interventions in older adults to manage risk factors and prevent the progression to T2DM.
The increased risk of developing IFG/T2DM can be attributed to age-related physiological changes, such as a reduction in muscle mass, an increase in visceral fat, and a decline in β-cell function and insulin sensitivity (19–22). As individuals age, they typically experience a reduction in muscle mass and changes in visceral fat distribution, both of which contribute to systemic insulin resistance.
Nutritional status and T2DM/IFG
Our study also identified nutritional status as a significant risk factor. Specifically, normal weight adults had a 0.6 times lower risk of IFG/T2DM compared to those with obesity, while overweight adults had a 0.8 times lower risk. These findings are supported by previous research indicating that obesity is the most significant modifiable risk factor for developing T2DM (23). For instance, a study by Xiong H highlighted the strong correlation between obesity and diabetes, suggesting that excess body fat contributes to insulin resistance and chronic inflammation, which can exacerbate metabolic dysfunction (24). And obesity is more sensitive than BMI as an early indicator of chronic disease (25).
Hypertension and T2DM/IFG
Hypertension was another significant risk factor identified in our study. Adults with hypertension had a 1.6 times higher risk of IFG/T2DM compared to those without hypertension. This finding aligns with numerous studies that have established the comorbidity of hypertension and diabetes. For example, a study by Hu X found that a large proportion of individuals with T2DM also develop hypertension (26). The shared pathophysiological mechanisms include insulin resistance and the effects of elevated blood pressure on pancreatic islet function (27). Weight-management-based strategies should be promoted for the prevention of diabetes mellitus. A previous study, which was conducted as part of the China Patient-Centred Evaluative Assessment of Cardiac Events Million Persons Project and included 898,929 young and middle-aged participants, reported a prevalence of hypertension of 26.15% (28). Appropriate attention should be given, and effective policies should be implemented to mitigate the increasing trends of diabetes, prediabetes, and hypertension among adults.
Dietary factors and T2DM/IFG
The associations between dietary nutrients and blood glucose levels provide valuable insights into the potential dietary factors influencing glycaemic control. The significant difference in dietary fibre intake among adults with DM, IFG, and normal FBG highlights the importance of fibre in blood glucose management. Dietary fibre is known to slow carbohydrate absorption and improve insulin sensitivity (29), which may explain the observed pattern of lower fibre intake in individuals with higher FBG levels. The trend of lower vitamin B1 and vegetable intake in adults with FBG ≥ 7.0 mmol/L, although not statistically significant, aligns with previous research suggesting that these nutrients play a role in glucose metabolism. These findings underscore the need for further investigation into how specific dietary components can be optimized to support better glycaemic outcomes. Consistent with our findings, a study by Danielle et al. (30) demonstrated that higher dietary fibre intake was associated with improved glycaemic control. Tang et al. reported that increased vegetable consumption was linked to a reduced risk of developing T2DM (31). These studies reinforce the importance of dietary factors in diabetes prevention and management.
A study revealed that uncontrolled T2DM is associated with high levels of LDL, TG, and TC, regardless of differences in food habits and lifestyles (32). In this study, elevated TG levels were also found to be associated with an increased risk of IFG/T2DM. Adults with TG levels below 1.0 mmol/L had a 0.4 times lower risk of IFG/T2DM compared to those with TG levels of 1.5 mmol/L or higher, while adults with TG levels between 1.0 and 1.5 mmol/L had a 0.6 times lower risk. This finding is supported by previous research that has highlighted the role of dyslipidemia in the development of insulin resistance and metabolic syndrome (33). Another study revealed a nonlinear association between the TG/HDL-C ratio and the likelihood of diabetes in nonobese individuals from East Asia (34). Dehzad reported elevated TG levels in individuals with diabetes (35). This consistency across studies underscores the significant role of lipid metabolism in diabetes pathogenesis. When comparing the determinants of elevated FBG in non-diabetic adults with established risk factors for T2DM, several biological and behavioral distinctions emerge. Biologically, elevated FBG in non-diabetic adults may reflect early stages of β-cell dysfunction and insulin resistance, which are precursors to T2DM. However in non-diabetic individuals, these metabolic alterations may not yet have reached the threshold for a diabetes diagnosis. Behaviorally, non-diabetic adults with elevated FBG may exhibit dietary patterns and lifestyle habits that resemble those of individuals at risk for T2DM, such as high intake of refined carbohydrates and low levels of physical activity. However, the extent and duration of these behavioral risk factors may differ between the two groups.
Public health implications
The findings of this study have several implications for public health and community settings in Zhejiang Province. Targeted prevention strategies should focus on older adults, those who are overweight or obese, and individuals with hypertension. Specific recommendations include:
Promoting healthy eating habits: Encourage the consumption of high-fiber foods, whole grains, and vegetables.
Encouraging physical activity: Promote regular exercise to manage weight and improve overall health.
Regular health check-ups: Implement routine screening for T2DM/IFG, especially in high-risk groups.
Integrated management: Ensure that primary care settings provide comprehensive management for hypertension and diabetes.
Our study has several strengths. First, this is the first study to examine the factors influencing prediabetes and diabetes through community nutrition surveillance in Zhejiang Province. The sample used in this study was obtained using a stratified cluster sampling technique, which ensures that it is representative of the population in the region. Nonetheless, our study also has several limitations that should be acknowledged. The cross-sectional design limits our ability to establish causality between these factors and IFG/prediabetes. Additionally, we did not perform an oral glucose tolerance test (OGTT), which is considered the gold standard for diagnosing T2DM and IFG. The absence of OGTT results may have led to some misclassification of diabetes and IFG cases, potentially underestimating the true prevalence and associated risk factors. Future research should incorporate OGTTs to improve diagnostic accuracy. We acknowledge that our study’s focus on non-diabetic individuals and single - measurement FBG levels limits causal inference. FBG variability and longitudinal trends were not addressed in this study. Future research should incorporate multiple FBG measurements and longitudinal data to better understand the causal relationships and progression of diabetes. Dietary intake data were obtained through participant recall, which may introduce recall bias and inaccurate reporting of dietary habits. Participants may forget certain foods or misreport portion sizes, leading to discrepancies between reported and actual intake. This recall bias could influence the observed associations between dietary components and glycaemic outcomes. Furthermore, while we identified several influencing factors, there may be other unmeasured variables that also play a role in the development of IFG and prediabetes. However, the characteristics of age, nutritional status, hypertension, and triglyceride levels observed in our study are common factors influencing diabetes and prediabetes globally. Therefore, these findings may be generalizable to other populations beyond Zhejiang Province. However, it should be noted that differences in dietary habits, genetic factors, and the availability of health care resources across different regions may affect the generalizability of these findings. Future studies are encouraged to explore the generalizability of these findings through multicentre studies.
Conclusion
The results of this study demonstrate that adults aged 55 years and above, those who are overweight or obese, those with hypertension, and those with higher TG levels have a greater risk of IFG.
Given that T2DM is a major global health priority, interest in the prevention of T2DM and prediabetes has increased. As suggested by our study findings, controlling BMI and preventing hypertension and elevated TG levels are vital for the prevention of IFG.
Statements
Data availability statement
The ethics committee confirmed that the raw data cannot be shared with third parties, meaning we are unable to provide the raw data directly. Requests for further information should be directed to RZ, yingyang901@yeah.net.
Ethics statement
Ethical approval was obtained from the Ethics Committee of the Zhejiang Provincial Center for Disease Control and Prevention (approval number: 2022–008). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
YZ: Resources, Formal analysis, Writing – review & editing, Supervision, Funding acquisition, Writing – original draft, Project administration, Software, Methodology, Conceptualization, Investigation. LH: Investigation, Software, Formal analysis, Writing – original draft. MH: Validation, Writing – original draft, Investigation. DH: Validation, Writing – original draft, Investigation. DS: Supervision, Investigation, Writing – original draft. PX: Project administration, Writing – original draft, Data curation. RZ: Supervision, Writing – original draft, Project administration.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Research Project on Youth and Youth Work in Zhejiang Province (ZQ202284) and the Basic Public Welfare Research Plan of Zhejiang Province (LGF19H260002).
Acknowledgments
The authors would like to thank all the staff as well as those who participated in the present study.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
OngKLStaffordLKMcLaughlinSABoykoEJVollsetSESmithAEet al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet. (2023) 402:203–34. doi: 10.1016/S0140-6736(23)01301-6
2.
GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. (2018) 392:1923–94. doi: 10.1016/S0140-6736(18)32225-6
3.
International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels, Belgium: (2021).
4.
Diabetes Prevention Program Research GroupKnowlerWCFowlerSEHammanRFChristophiCAHoffmanHJet al. 10-year follow-up of diabetes incidence and weight loss in the diabetes prevention program outcomes study. Lancet. (2009) 374:1677–86. doi: 10.1016/S0140-6736(09)61457-4
5.
HuangYCaiXMaiWLiMHuY. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. (2016) 355:i5953. doi: 10.1136/bmj.i5953
6.
BommerCSagalovaVHeesemannEManne-GoehlerJAtunRBärnighausenTet al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. (2018) 41:963–70. doi: 10.2337/dc17-1962
7.
Diabetes. Available online at: https://www.who.int/news-room/fact-sheets/detail/diabetes
8.
RahimFFAbdulrahmanSAKader MaideenSFRashidA. Prevalence and factors associated with prediabetes and diabetes in fishing communities in Penang, Malaysia: a cross-sectional study. PLoS One. (2020) 15:e0228570. doi: 10.1371/journal.pone.0228570
9.
MinariTPde RossoVVManzanoCFHumsiMJYugarLBTSedenho-PradoLGet al. Correlation of the Mediterranean-dietary approaches to stop hypertension diet vs. ultra-processed diet with health outcomes in type 2 diabetes and hypertension: new insights from a 12-month interventional study. J Hypertens. (2025) 43:1622–42. doi: 10.1097/HJH.0000000000004061
10.
MuszalskaAWiecanowskaJMichałowskaJPastusiak-ZgolińskaKPolokIŁompieśKet al. The role of the planetary diet in managing metabolic syndrome and cardiovascular disease: a narrative review. Nutrients. (2025) 17:862. doi: 10.3390/nu17050862
11.
LiSBoYRenHZhouCLaoXZhaoLet al. Regional differences in the prevalence of anaemia and associated risk factors among infants aged 0-23 months in China: China nutrition and health surveillance. Nutrients. (2021) 13:1293. doi: 10.3390/nu13041293
12.
ZhouBF. Cooperative Meta-analysis Group of the Working Group on obesity in China predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. (2002) 15:83–96.
13.
LouYMaWJWangZJYangNSunYJLiuYLet al. (Writing protocols for the Chinese clinical practice guidelines of hypertension). Zhonghua Xin Xue Guan Bing Za Zhi (2022)50:671–675. Chinese. doi: 10.3760/cma.j.cn112148-20211126-01021
14.
YangY. China food composition. 6th ed. Beijing, Beijing, China: Peking University Press (2018).
15.
Chinese Diabetes SocietyDalongZ. Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group; Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society; Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society; Geriatric Professional Committee of Beijing Medical Award Foundation; National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). [Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)]. Zhonghua Nei Ke Za Zhi. (2022) 61:12–50. doi: 10.3760/cma.j.cn112138-20211027-00751
16.
LiJJZhaoSPZhaoDLuGPPengDQLiuJet al. 2023 Chinese guideline for lipid management. Front Pharmacol. (2023) 14:1190934. doi: 10.3389/fphar.2023.1190934
17.
WS/T677-2020. Method for vitamin D deficiency screening. Chinese National Health and family planning commission. Beijing: Standard Press of China. (2022)
18.
WS/T553-2017. Method for vitamin a deficiency screening. Chinese National Health and family planning commission. Beijing: Standard Press of China. (2017)
19.
TuduríESorianoSAlmagroLMontanyaEAlonso-MagdalenaPNadalÁet al. The pancreatic β-cell in ageing: implications in age-related diabetes. Ageing Res Rev. (2022) 80:101674. doi: 10.1016/j.arr.2022.101674
20.
ChaudharyRKhannaJRohillaMGuptaSBansalS. Investigation of pancreatic-beta cells role in the biological process of ageing. Endocr Metab Immune Disord Drug Targets. (2024) 24:348–62. doi: 10.2174/1871530323666230822095932
21.
ChenKTangLWangXLiYZhangXCuiSet al. Prevalence and risk factors for type 2 diabetes mellitus in women with gestational diabetes mellitus: a systematic review and meta-analysis. Front Endocrinol. (2024) 15:1486861. doi: 10.3389/fendo.2024.1486861
22.
SaponaroCSabatiniSGagginiMCarliFRossoCPositanoVet al. Adipose tissue dysfunction and visceral fat are associated with hepatic insulin resistance and severity of NASH even in lean individuals. Liver Int. (2022) 42:2418–27. doi: 10.1111/liv.15377
23.
ShiMZhangXWangH. The prevalence of diabetes, prediabetes and associated risk factors in Hangzhou, Zhejiang Province: a community-based cross-sectional study. Diabetes Metab Syndr Obes. (2022) 15:713–21. doi: 10.2147/DMSO.S351218
24.
XiongHZhaoPMaFTangDLiuD. The effects of interaction between smoking and abdominal obesity on pre-diabetes mellitus. Sci Rep. (2025) 15:1332. doi: 10.1038/s41598-025-85367-7
25.
RossRNeelandIJYamashitaSShaiISeidellJMagniPet al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR working group on visceral obesity. Nat Rev Endocrinol. (2020) 16:177–89. doi: 10.1038/s41574-019-0310-7
26.
HuXHanPLiuY. Metabolic status and hypertension: the impact of insulin resistance-related indices on blood pressure regulation and hypertension risk. J Am Nutr Assoc. (2025) 44:487–97. doi: 10.1080/27697061.2025.2450711
27.
SarzaniRSalviFDessì-FulgheriPRappelliA. Renin-angiotensin system, natriuretic peptides, obesity, metabolic syndrome, and hypertension: an integrated view in humans. J Hypertens. (2008) 26:831–43. doi: 10.1097/HJH.0b013e3282f624a0
28.
MahajanSFengFHuSLuYGuptaAMurugiahKet al. Assessment of prevalence, awareness, and characteristics of isolated systolic hypertension among younger and middle-aged adults in China. JAMA NetwOpen. (2020) 3:e209743. doi: 10.1001/jamanetworkopen.2020.9743
29.
DayibMLarsonJSlavinJ. Dietary fibers reduce obesity-related disorders: mechanisms of action. Curr Opin Clin Nutr Metab Care. (2020) 23:445–50. doi: 10.1097/MCO.0000000000000696
30.
JonesDKyriakidouACooperLAttaNTobolskaPSmithSet al. The effect of high-fibre diets on glycaemic control in women with diabetes in pregnancy: a systematic review and meta-analysis. Diabet Med. (2025) 42:e15435. doi: 10.1111/dme.15435
31.
TangYChenYZhouYWuBZhangSGongYet al. Global burden of type 2 diabetes mellitus attributable to dietary risks in elderly adults: insights from the global burden of disease study 2021. Front Nutr. (2025) 12:1557923. doi: 10.3389/fnut.2025.1557923
32.
DebbarmaSSDasguptaAGhoshAChoudhuryKKolePPaulSet al. Study of pattern of lipid profile amongst type II diabetic patients of two major tribal populations of Tripura. J Family Med Prim Care. (2024) 13:5476–83. doi: 10.4103/jfmpc.jfmpc_1625_23
33.
HiranoT. Clinical significance of small dense low-density lipoprotein cholesterol measurement in type 2 diabetes. J Diabetes Investig. (2025) 16:370–83. doi: 10.1111/jdi.14398
34.
HuangZZhangXSunDYuK. Triglyceride to high-density lipoprotein cholesterol ratio is associated with diabetes incidence in non-obese individuals with normoglycemia: a retrospective cohort study based on individuals from East Asia. Front Endocrinol. (2024) 15:1442731. doi: 10.3389/fendo.2024.1442731
35.
DehzadMJRajaAMoghdaniZSohrabiZFararooeiMFamouriMet al. Effects of yogurt enriched with konjac glucomannan and inulin on insulin sensitivity, Glycemic control, lipid profiles, anthropometric measures and oxidative stress in type 2 diabetes mellitus: a randomized controlled trial. Prev Nutr Food Sci. (2025) 30:120–31. doi: 10.3746/pnf.2025.30.2.120
Summary
Keywords
diabetes mellitus, nutritional status, adults, China, obesity
Citation
Zou Y, Huang L, He M, Han D, Su D, Xu P and Zhang R (2025) Factors influencing type 2 diabetes in adults: a cross-sectional study. Front. Public Health 13:1662519. doi: 10.3389/fpubh.2025.1662519
Received
09 July 2025
Accepted
22 September 2025
Published
07 October 2025
Volume
13 - 2025
Edited by
Ghada A. Soliman, City University of New York, United States
Reviewed by
Roghayeh Molani-Gol, Tabriz University of Medical Sciences, Iran
Yousif AbdulRaheem, University of Baghdad, Iraq
Updates
Copyright
© 2025 Zou, Huang, He, Han, Su, Xu and Zhang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rong-hua Zhang, yingyang901@yeah.net
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.