Comparative Analysis of Dietary and Physical Activity Behavior between Prediabetic and Normoglycemic Populations

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However, the differences between PA and diet in prediabetic and normoglycemic individuals remain unclear. Methods A cross-sectional study was conducted on Chinese adults without diabetes, who attended the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou from December 2023 to August 2024. Participants were divided into the preDM group (n = 151) or the normoglycemic group (n = 302). We assessed diet using the Planetary Health Diet Score (PHD-S), which was derived from one month of recall data, and PA using one-week recall data. After controlling for confounding factors using the propensity score matching (PSM), we compared dietary and PA differences between the two groups using the independent-samples t-tests, rank-sum tests, chi-square tests, or Fisher’s exact tests. Statistical analysis was performed using the R software. Results The preDM group had higher PHD-S values for saturated oils (p = 0.007) and added sugars (p = 0.011), but lower values for fish (p = 0.021), soy foods (p = 0.002), and nuts (p < 0.001) compared to the normoglycemic group. Regarding PA, the preDM group had significantly higher metabolic equivalent of task (MET) for light PA (p < 0.001) but participated in fewer days of moderate (p < 0.001) and vigorous PA (p = 0.032). Mediation analysis revealed that BMI significantly mediated the relationship between diet and uric acid levels in the preDM group, accounting for 29.3% of the mediation effect. Conclusions Significant differences in diet and PA were observed between the preDM and normoglycemic groups. Although the preDM group were more in line with recommended levels of saturated oil and added sugar intake compared to the normoglycemic group, their other categories of diet still fell short of guideline recommendations. Moreover, the preDM group had higher levels of light PA. Our study demonstrated that more standardized and individualized health interventions are needed to improve the lifestyle behaviors of prediabetic individuals. Prediabetes diet physical activity normoglycemic population Figures Figure 1 1. Introduction Prediabetes (preDM) is a transitional stage before the onset of diabetes, which includes impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or both (IFG + IGT). It is an intermediate state between normoglycemia and diabetes mellitus [ 1 ] . The global prevalence of preDM is increasing with population ageing, changes in lifestyle and environment, including increased sedentary lifestyles, increased availability of high-calorie fast food and changes in the living environment of the population. According to the International Diabetes Federation, by 2045 more than 500 million adults aged 20 ~ 79 years will have preDM [ 2 ] , which has become a major public health problem. Furthermore, preDM is associated with increased risk of cardiovascular disease, microangiopathy, neoplasms, and dementia [ 1 ] . Therefore, early detection and management of preDM is essential to prevent the progression of preDM. Diet has a strong influence on glucolipid metabolism [ 3 – 6 ] . Globally, mortality from type 2 diabetes, cardiovascular diseases, and dysregulated glycolipid metabolism due to unhealthy diets is also on the rise [ 7 ] . As a cornerstone of lifestyle interventions, dietary factors are key modifiable influences. Research has shown that unhealthy dietary patterns, such as high intake of animal meet and fried foods, were associated with an elevated risk of developing preDM after adjusting for confounders [ 8 ] . Furthermore, dietary composition affected the body’s metabolism [ 9 ] , with increased fat intake (≥ 35%) being associated with augmentation in low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and glycosylated hemoglobin (HbA1c). In addition, excessive carbohydrate intake led to elevated TG and HbA1c [ 10 ] . Among dietary components, the intake of dietary fiber has a protective effect against diabetes. Dietary fiber, although not easily hydrolyzed by human digestive enzymes, can be metabolized by specific gut bacteria to produce a range of metabolites through fermentation and other metabolic pathways [ 11 – 13 ] . A recent study has shown that higher dietary fiber intake was associated with a favorable gut microbiota and a circulating metabolite profile in type 2 diabetes [ 14 ] . Therefore, targeted fiber supplementation may be a cost-effective strategy for preDM management. Physical activity (PA) refers to physical movement that requires the expenditure of energy in leisure, transportation, work, or home environments [ 15 ] . PA is categorized by intensity into light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) [ 16 ] . Regular PA enhances insulin sensitivity and glucolipid metabolism, reduces cardiovascular risk factors, and improves glycemic control. It is an effective primary prevention strategy for diabetes in high-risk individuals [ 15 , 17 – 19 ] . However, the Global Status Report on Physical Activity 2022 revealed that approximately 1.8 billion adults worldwide did not meet the World Health Organization (WHO) recommendation of at least 150 minutes of moderate or equivalent PA weekly [ 20 , 21 ] . Since regular PA is beneficial to an individual’s health, it is important to consider not only the intensity but also the frequency and duration of PA during the week. The International Physical Activity Questionnaire (IPAQ) working group categorized individuals’ PA levels into low, medium, and high groups on certain criteria. Individuals in the low-PA level group were at greater risk of preDM [ 22 ] and dyslipidemia compared to the high-PA level group. These findings suggest that a high level of PA plays an important role in improving glucolipid metabolism [ 23 ] . Previous studies have shown that diet and PA affect metabolic indices, and that body mass index (BMI) is a key factor influencing metabolism [ 24 – 27 ] . Although the effects of diet and PA on metabolic health are well established, the role of BMI in these associations remains less clear. It is important to consider whether the relationship between diet, PA, and metabolic indices is independent of or mediated by BMI, as BMI has the potential to modify or modulate these effects. This perspective helps to elucidate the complex interactions between these factors and their joint impact on metabolic health. In conclusion, diet and PA are closely associated with the development of preDM [ 28 , 29 ] . Therefore, effective interventions targeting these factors can prevent or delay the progression of diabetes and its complications [ 30 , 31 ] . However, the dietary and PA patterns in the prediabetic and normoglycemic populations remains uncertain. This study aimed to investigate the dietary and PA status among the preDM and normoglycemic groups and to analyze the differences between them, which may lay the foundation for future effective interventions for prediabetic populations. 2. Materials and methods 2.1 Study population This cross-sectional study was conducted from December 2023 to August 2024. A total of 453 participants were recruited by convenience sampling from patients attending or hospitalized in the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, Guangdong Province, China. All voluntary participants sequentially completed a general information survey; PA and diet surveys; and height, weight, and glucolipid metabolism measurements. The study was reviewed and approved by the Medical Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Ethical Review Approval No. RG2023-262-02), and all participants provided written informed consent. 2.1.1 Inclusion criteria (1) PreDM group: a. Age ≥ 18 years; b. In accordance with the 1999 WHO definition and diagnostic criteria for preDM [1] : patients with 6.1 mmol/L ≤ fasting blood glucose (FBG) < 7.0 mmol/L and 2-h postprandial blood glucose (2hPBG) < 7.8 mmol/L were diagnosed with IFG; those with FBG < 6.1 mmol/L and 7.8 mmol/L ≤ 2hPBG < 11.1 mmol/L and/or 5.7% ≤ HbA1c < 6.5% were diagnosed with IGT; and patients with 6.1 mmol/L ≤ FBG < 7.0 mmol/L and 7.8 mmol/L ≤ 2hPBG < 11.1 mmol/L were diagnosed with IFG + IGT; c. Ability to understand and respond accurately to survey questions; d. Signed informed consent to participate in this survey. (2) Normoglycemic group: a. Age ≥ 18 years; b. FBG was 3.9–6.1 mmol/L; c.Ability to understand and respond accurately to survey questions; d. Signed informed consent to participate in the study. 2.1.2 Exclusion criteria (1) PreDM group: a. Patients were diagnosed with contraindications to PA or dietary restrictions due to medical conditions; b. Secondary obesity is caused by endocrine, genetic, metabolic, or central nervous system diseases, such as hypothalamic obesity; pituitary obesity; hypothyroid obesity; obesity caused by Cushing’s syndrome; and hypogonadal obesity; c. Pregnant, planning to become pregnant, or breastfeeding women. (2) Normoglycemic group: a. The individual was diagnosed with any type of diabetes; b. Individuals were diagnosed with contraindications to PA or dietary restrictions due to medical conditions; c. Secondary obesity is caused by endocrine, genetic, metabolic, or central nervous system diseases, such as hypothalamic obesity; pituitary obesity; hypothyroid obesity; obesity caused by Cushing’s syndrome; and hypogonadal obesity; d. Pregnant, planning to become pregnant, or breastfeeding women. 2.2 General information surveys Participants reported demographic and lifestyle information through a general information questionnaire, including sex, age, education, place of residence, history of previous illnesses, family history of diabetes, and history of smoking and alcohol consumption. Among the study subjects, education level was categorized as: high school or less, three-year college, undergraduate or above. The place of residence was categorized as: city, cities and towns, and countryside. The study population was categorized into smoker and nonsmoker according to the WHO definition of smokers in 1997, which is “a person who has smoked continuously or cumulatively for six months or more during his or her lifetime” [32] . According to the International Guidelines for Monitoring Alcohol Consumption and Related Harm issued by the WHO in 2000 [33] , the study population was categorized into drinkers and non-drinkers based on the definition of “those who consumed alcohol≥1 drink per week in the past 1 year, and whose average daily alcohol intake was≥61g in male drinkers and≥41g in female drinkers”. According to the definition of hypertension in the Chinese Guidelines for the Prevention and Treatment of Hypertension (2024 Revision) [34] , “without the use of antihypertensive medication, clinic blood pressure≥140/90mmHg, or home blood pressure≥135/85mmHg, or 24-h ambulatory blood pressure≥130/80mmHg, with daytime blood pressure≥135/85mmHg, nighttime blood pressure≥120/70mmHg”, which categorized the study subjects into hypertensive and non-hypertensive patients. According to the definition of hyperlipidemia in the Chinese Guidelines for Lipid Management (2023) [35] , “elevated serum total cholesterol (TC) and/or triglyceride (TG) levels”, in which the normal levels of TC are 3~6.18mmol/L and TG are 0.3~1.8mmol/L, the study subjects were categorized into hyperlipidemic patients and non-hyperlipidemic patients. Information on their family history of diabetes was obtained by asking the study subjects, and if any of their first-degree relatives, such as father, mother, siblings, and children, had diabetes, it was determined that the study subjects had a family history of diabetes [36] . 2.3 Dietary survey The dietary survey was conducted through the Food Frequency Questionnaire (FFQ) [37] . Participants recalled and recorded the types, frequency, and portion sizes of foods consumed daily over the past year. The questionnaire categorized foods into 15 groups: cereals, vegetables, fruits, livestock and meat, fish and shrimp, eggs, milk and dairy products, beans and bean products, nuts, fats and oils, condiments, water, beverages, barbecue and frying, and pickles. Food frequency was classified into nine levels: not eaten or less than 1 time/month, 1 time/month, 2~3 times/month, 1 time/week, 2 times/week, 3~4 times/week, 5~6 times/week, 1 time/day, or 2 or more times/day. In order to make the recall of the amount of food consumed by the study participants in the past period of time more accurate, we used the “Retrospective Dietary Survey Auxiliary Reference Food Atlas” developed by Prof. Wang Zhixu of Nanjing Medical University [38] when collecting their dietary data from the study participants. The atlas categorized and edited all the pictures of the food according to 13 categories, which included: cereal yams and miscellaneous beans, vegetables, fruits, livestock, poultry and meat,Fish, shrimp and shellfish, eggs, dairy products, soybeans and soybean products, nuts, cooking oils and fats, confectionery, sweets and condiments, processed food, and tableware and containers, with a total of 659 pictures of 195 food items. With the help of three visual reference systems, namely, the comparison of the food’s own shape or portion size, the background scale coordinates and the familiar objects in daily life, and the corresponding grams of food portions labeled with 4~10 different quantity levels for different foods, the study participants could better recall their daily diets. The mean daily dietary intake data were used to construct 14 food groups on the basis of the Planetary Health Diet Score (PHD-S) [39] . The dietary components included: (1) rice, wheat, corn, and others; (2) tubers (e.g., potatoes, cassava); (3) all vegetables; 4) all fruits; (5) dairy foods; (6) beef, lamb, pork; (7) chicken and poultry; (8) eggs; (9) fish; (10) dry beans, lentils, peas, and soy foods; (11) peanuts or tree nuts; (12) animal oils, such as: palm oil, dairy fats (in milk), lard, or tallow; (13) unsaturated oils; and (14) all sweeteners. The 14 dietary components were categorized based on health effects: Adequacy components are used to indicate healthy foods with higher recommended intakes, such as: vegetables, fruits, nuts, legumes, unsaturated fats, and fish; Optimum components represent nutrient-dense foods but can be harmful if consumed in large amounts daily, such as: potatoes, dairy foods, poultry, eggs; Moderation components are defined as those that could increase the risk of chronic diseases with increased consumption, such as: total grains, red meat, saturated fats, and added sugars. Each dietary component was scored from 0 to 10, with total PHD-S scores ranging from 0 to 140 (We have attached the specific grading criteria in Supplementary file 1.). According to the EAT-Lancet Commission on healthy diets from sustainable food systems [40] the global average per capita energy intake is estimated to be 2370 kcal per day, with adult males consuming about 2800 kcal per day and adult females consuming about 2000~2200 kcal per day. The energy intake is lower in those with a lower body mass index (BMI, measured in kg/m 2 ), and higher in those who are more physically active. Moreover, the PHD-S calculation methodology [39] also stipulates that the daily calorie intake of each study participant be standardized to 2500 kcal/day. Therefore, we used 2500 kcal per day as the basis for different isocaloric dietary regimens (i.e., with similar caloric values). 2.4 PA survey The IPAQ was used to assess the PA of the study participants over the past 7 days, including LPA, MPA, and VPA during work, transportation, housework, and leisure time. The IPAQ demonstrated good reliability and validity in measuring PA in individuals with preDM [41] . To assess strength-related PA participation over the last 7 days, the activity was measured by the metabolic equivalent of energy (MET) value, which was multiplied by the weekly frequency and time of day. The total PA (metabolic equivalent of task [MET]-h/week) was determined by summing the three PA intensities (LPA, MPA, and VPA) [42] . The participants were then categorized into low-, medium-, and high-PA level groups on the basis of their IPAQ-calculated PA levels [43] . For high-PA level, meet any 1 of the 2 criteria listed below: (1) ≥3 days of all types of vigorous physical activity combined and a total physical activity level of ≥1500 MET-min/week. (2) The total number of days of vigorous, moderate, light physical activity is ≥7 days, with a total weekly physical activity level of ≥3000 MET-min/w. For medium-PA level, meet any 1 of the following 3 criteria: (1) meet at least 20 min/day of vigorous physical activity and the total number of high-intensity physical activity days in a week is ≥3 days. (2) meet at least 30 min/day of moderate physical activity and/or walking on a total of ≥5 days per week. (3) physical activity of all 3 intensities combined for ≥5 days and total physical activity level of ≥600 MET-min/week. For low-PA level, meet any 1 of the 2 criteria below: (1)no activity reported. (2) some activity was reported, but the criteria for medium and high groupings above were not yet met. 2.5 Measurement of height and weight Height (m) was measured by a wall-mounted height scale (accurate to two decimal places), and weight (kg) was measured by a body composition analyzer (model: InBody570) (accurate to one decimal place). BMI was calculated as BMI (kg/m 2 ) = weight (kg)/height (m) 2 . According to the “Guiding Principles of Weight Management (2024 Edition)” Chinese adults aged 18 years and above BMI judgment standards, China's healthy adults BMI is divided into: ① BMI <18.5kg/m 2 for underweight, ② the normal range of 18.5 ~ 23.9kg/m 2 , ③ 24.0 ~ 27.9kg/m 2 for overweight, ④ BMI ≥ 28.0kg/m 2 for obese. [44] 2.6 Measurement of metabolic indicators Fasting venous blood samples were drawn from each study participant in the early morning after overnight fasting to determine their FBG and HbA1c, as well as serum total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid (UA) levels. The participants subsequently underwent a standard oral glucose tolerance test, in which 75 g of anhydrous glucose powder dissolved in 250–300 mL of water was taken orally within 5 min, starting from the first sip of the solution. Venous blood was drawn 2 h later to determine the 2hPBG level after sugar loading. 2.7 Statistical analysis The data were analyzed by R language version 4.4.1. The study subjects were divided into prediabetic and normoglycemic groups based on whether they met the diagnostic criteria for prediabetes. Considering the effect of confounders between groups and in order to increase the comparability between groups, we used the PSM method to equalize the bias caused by the unbalance of covariates between groups. The PSM method matches individuals with similar propensity scores (PS) in the prediabetes group as the reference group and in the normoglycemic group and performs a balanced test with standardized mean difference (SMD) values of each covariate, which is used to determine whether the prediabetes group in the study and the normoglycemic group, to determine whether the differences in various socio-demographic data in the study were statistically significant, and to assess the balance between the groups before and after matching, where SMD < 0.1 indicated a better balance and a smaller difference between the two groups. We used the R language Match It package for 1:1 PSM, controlled for confounders after analysis, and constructed a binary logit regression model with the dichotomous variable of “1=pre-diabetes group, 0=normal glycemic group” as the dependent variable, and confounders as the independent variable X. The model was then analyzed by binary logit regression.The model was then used to calculate the propensity score Pscore value, with the chi-square value set at 0.1; the confounding variables included age, gender, education, place of residence, smoking, alcohol consumption, hypertension, hyperlipidemia, and family history of diabetes mellitus. For the data after PSM, the previous analysis was performed. For continuous quantitative data, if they conformed or approximately conformed to a normal distribution, they were described by the mean ± standard deviation, and comparisons between two groups were made by the independent-samples t-test . If the data did not conform to a normal distribution, they were statistically described by the median [ P 25 , P 75 ], and comparisons between two groups were made via the Wilcoxon rank sum test. Count data are described as the number of cases (%), and comparisons between groups were made via the chi-square test or Fisher’s exact probability method. Correlations between diet, PA, BMI, and metabolic indicators were evaluated via through Pearson correlation analysis in the preDM and normoglycemic groups. Mediation analysis via R language was conducted to explore the mediating role of BMI in the correlations among PHD-S, PA, and metabolic indicators. The analysis was conducted for the whole population, the preDM group, and the normoglycemic group. Confidence intervals were estimated via 1,000 bootstrap samples. All tests were two-sided, and P < 0.05 was considered statistically significant. 3. Results Figure 1 . presents the study's recruitment process. Of the 480 participants assessed for eligibility, 160 were in the preDM group and 320 were in the normoglycemic group. The participants in preDM group 9 were excluded, and in normoglycemic group 18 were excluded. Thus, 151 participants in preDM group and 302 participants in normoglycemic group completed the questionnaires and were included in the data analysis. A total of 453 individuals participated in this study, of whom 159 (35.1%) were male and 294 (64.9%) were female, with a median age of 38.00 (31.00, 46.00) years old. Before PSM, there were 151 participants in the preDM group and 302 participants in the normoglycemic group. Statistically significant differences (P < 0.05) in age, BMI, education level, hypertension, hyperlipidemia, and family history of diabetes mellitus were detected between the two groups. After performing PSM, 87 participants were included in the preDM group, and 148 participants were included in the normoglycemic group. Except for BMI, which remained statistically significant (P 0.05), indicating a good matching effect. The details were presented in Table 1 . Table 1 Sociodemographic characteristics of the population in the preDM group and normoglycemic group Characteristic Before PSM After PSM Total (n = 453) PreDM (n = 151) Normoglycemic (n = 302) p -Value Total (n = 235) PreDM(n = 87) Normoglycemic (n = 148) p -Value Age, years; Median (Q1,Q3) 38.0 (31.0, 46.0) 35.0 (26.0, 44.0) 38.0 (34.0, 46.0) < 0.001 37.00 (31.00, 44.00) 37.00 (28.00, 44.00) 37.00 (32.00, 43.25) 0.342 sex, n (%) 0.296 0.428 Male 159 (35.1) 48 (31.8) 111 (36.8) 58 (24.7) 24 (27.6) 34 (23.0) Female 294 (64.9) 103 (68.2) 191 (63.2) 177 (75.3) 63 (72.4) 114 (77.0) BMI, Kg/m 2 ;Median (Q1,Q3) 23.62 (21.30, 27.36) 28.64 (24.09, 32.72) 22.30 (20.72, 24.76) <0.001 23.53 (21.37, 27.45) 28.64 (23.72, 31.78) 22.42 (20.64, 24.53) <0.001 Grouping of BMI, n (%) <0.001 0.0005 underweight 17 (3.8) 3 (2.0) 14 (4.6) 12 (5.1) 2 (2.3) 10 (6.8) normal weight 225 (49.7) 33 (21.9) 192 (63.6) 113 (48.1) 21 (24.1) 92 (62.2) overweight 110 (24.3) 36 (23.8) 74 (24.5) 55 (23.4) 19 (21.8) 36 (24.3) obese 101 (22.3) 79 (52.3) 22 (7.3) 55 (23.4) 45 (51.7) 10 (6.8) Education level, n (%) 0.023 0.977 High school or less 93 (20.5) 40 (26.5) 53 (17.5) 55 (23.4) 21 (24.1) 34 (23.0) three-year college 81 (17.9) 31 (20.5) 50 (16.6) 36 (15.3) 13 (14.9) 23 (15.5) Undergraduate or above 279 (61.6) 80 (53.0) 199 (65.9) 144 (61.3) 53 (60.9) 91 (61.5) Place of residence, n (%) 0.468 0.578 City 364 (80.4) 117 (77.5) 247 (81.8) 186 (79.1) 72 (82.8) 114 (77.0) Cities and towns 49 (10.8) 20 (13.2) 29 (9.6) 29 (12.3) 9 (10.3) 20 (13.5) Countryside 40 (8.8) 14 (9.3) 26 (8.6) 20 (8.5) 6 (6.9) 14 (9.5) Smoking status, n (%) 0.053 0.619 Nonsmoker 391 (86.3) 137 (90.7) 254 (84.1) 211 (89.8) 77 (88.5) 134 (90.5) Current smoker 62 (13.7) 14 (9.3) 48 (15.9) 24 (10.2) 10 (11.5) 14 (9.5) Drinking status, n (%) 0.672 0.947 Non-drinker 403 (89.0) 133 (88.1) 270 (89.4) 213 (90.6) 79 (90.8) 134 (90.5) Current drinker 50 (11.0) 18 (11.9) 32 (10.6) 22 (9.4) 8 (9.2) 14 (9.5) Hypertension, n (%) <0.001 0.864 No 402 (88.7) 116 (76.8) 286 (94.7) 217 (92.3) 80 (92.0) 137 (92.6) Yes 51 (11.3) 35 (23.2) 16 (5.3) 18 (7.7) 7 (8.0) 11 (7.4) Hyperlipidemia, n (%) <0.001 0.279 No 385 (85.0) 102 (67.5) 283 (93.7) 202 (86.0) 72 (82.8) 130 (87.8) Yes 68 (15.0) 49 (32.5) 19 (6.3) 33 (14.0) 15 (17.2) 18 (12.2) Family history of diabetes, n (%) <0.001 0.081 No 321 (70.9) 65 (43.0) 256 (84.8) 162 (68.9) 54 (62.1) 108 (73.0) Yes 132 (29.1) 86 (57.0) 46 (15.2) 73 (31.1) 33 (37.9) 40 (27.0) Table 2 presented the PHD-S for each category in the preDM and normoglycemic groups before and after PSM. After adjusting for confounders such as age, sex, education level, and place of residence, the difference in total PHD-S between the preDM and normoglycemic groups was not statistically significant. However, the intake of animal oils and added sugars was significantly greater in the preDM group, with a statistically significant difference. Conversely, the intake of fish, soy foods, and nuts was significantly greater in the normoglycemic group than in the preDM group, and this difference was also statistically significant. Table 2 Planetary health diet score (PHD-S) for each type of diet in the preDM group and normoglycemic group Variables Before PSM After PSM Total (n = 453) PreDM (n = 151) Normoglycemic (n = 302) p -Value Total (n = 235) PreDM(n = 87) Normoglycemic (n = 148) p -Value PHDs1: total grains, Median (Q1,Q3) 10.00 (6.69, 10.00) 10.00 (6.30, 10.00) 10.00 (7.12, 10.00) 0.069 10.00 (6.76, 10.00) 10.00 (6.47, 10.00) 10.00 (7.04, 10.00) 0.505 PHDs2: tubers of starchy vegetables, Median (Q1,Q3) 5.32 (0.90, 7.66) 5.54 (0.39, 8.29) 5.17 (1.21, 7.56) 0.651 5.61 (0.49, 7.89) 5.61 (0.00, 8.33) 5.61 (1.01, 7.66) 0.719 PHDs3: all vegetables, Median (Q1,Q3) 10.00 (10.00, 10.00) 10.00 (10.00, 10.00) 10.00 (9.01, 10.00) 0.009 10.00 (10.00, 10.00) 10.00 (10.00, 10.00) 10.00 (10.00, 10.00) 0.127 PHDs4: all fruits, Median (Q1,Q3) 5.26 (2.37, 10.00) 5.26 (2.49, 10.00) 5.27 (2.37, 10.00) 0.732 7.49 (2.73, 10.00) 6.19 (2.46, 10.00) 8.44 (3.40, 10.00) 0.205 PHDs5: dairy foods, Median (Q1,Q3) 4.51 (0.69, 7.57) 4.01 (0.69, 7.40) 4.76 (0.69, 7.83) 0.536 4.36 (0.40, 7.77) 3.90 (0.15, 7.40) 4.86 (0.57, 8.21) 0.367 PHDs6: beef, lamb, pork, Median (Q1,Q3) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.005 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.128 PHDs7: chicken and other poultry, Median (Q1,Q3) 0.77 (0.00, 4.98) 0.00 (0.00, 4.60) 0.97 (0.00, 5.09) 0.188 0.93 (0.00, 4.63) 0.52 (0.00, 4.38) 0.93 (0.00, 4.65) 0.562 PHDs8: eggs, Median (Q1,Q3) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.485 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.636 PHDs9: fish, Median (Q1,Q3) 10.00 (10.00, 10.00) 10.00 (8.98, 10.00) 10.00 (10.00, 10.00) < 0.001 10.00 (10.00, 10.00) 10.00 (9.11, 10.00) 10.00 (10.00, 10.00) 0.021 PHDs10: dry beans, lentils, peas, soy foods, Median (Q1,Q3) 4.33 (1.84, 9.05) 3.01 (0.71, 7.53) 5.06 (2.35, 9.31) < 0.001 4.07 (1.61, 8.95) 2.74 (0.67, 7.40) 4.86 (2.44, 9.30) 0.002 PHDs11: peanuts or tree nuts, Median (Q1,Q3) 1.28 (0.21, 4.61) 0.43 (0.00, 2.31) 2.13 (0.46, 5.12) < 0.001 1.09 (0.20, 3.51) 0.43 (0.00, 2.68) 1.53 (0.36, 4.17) < 0.001 PHDs12: animal oils, Median (Q1,Q3) 5.59 (0.62, 10.00) 10.00 (1.14, 10.00) 3.38 (0.47, 10.00) < 0.001 6.40 (0.65, 10.00) 10.00 (1.46, 10.00) 3.42 (0.52, 10.00) 0.007 PHDs13: unsaturated oils, Median (Q1,Q3) 7.50 (4.29, 10.00) 7.58 (3.81, 10.00) 7.43 (4.40, 10.00) 0.585 7.27 (4.31, 10.00) 7.84 (3.78, 10.00) 6.88 (4.39, 10.00) 0.831 PHDs14: added sugars, Median (Q1,Q3) 0.11 (0.00, 10.00) 2.33 (0.00, 10.00) 0.00 (0.00, 9.49) 0.135 0.00 (0.00, 10.00) 6.52 (0.00, 10.00) 0.00 (0.00, 7.46) 0.011 Total PHD-S, Mean ± SD 68.77 ± 12.62 68.13 ± 12.02 69.09 ± 12.92 0.434 68.92 ± 12.27 68.97 ± 11.89 68.89 ± 12.53 0.959 Note: PSM: propensity score matching; preDM: prediabetes. Table 3 presented the PA levels of the preDM and normoglycemic groups before and after PSM. After adjusting for confounders such as age, sex, education level, and place of residence, the total number of days of PA per week and the number of days per week involving vigorous and moderate PA were significantly greater in the normoglycemic group than in the preDM group. These differences were statistically significant. Conversely, the total MET values for overall and light PA were significantly greater in the preDM group than in the normoglycemic group. The number of active days per week and the duration of a single activity session for VPA and MPA at work, VPA during housework, and MPA, such as lifting/carrying light objects and cleaning the yard, were significantly greater in the normoglycemic group than in the preDM group. For LPA during transportation, such as walking, both the number of active days per week and the duration of a single session were significantly greater in the preDM group than in the normoglycemic group. Walking at work as part of LPA was performed on more active days per week in the normoglycemic group than in the preDM group. MPA during household chores, such as window cleaning and hand washing, and LPA during transportation via motor vehicles, were more common per single session in the preDM group than in the normoglycemic group. All of the above differences were statistically significant. Details of the components of physical activity in the preDM group and normoglycemic group can be found in Supplementary file 2. Table 3 Physical activity in the preDM group and normoglycemic group Variables Before PSM After PSM Total (n = 453) PreDM (n = 151) Normoglycemic (n = 302) p -Value Total (n = 235) PreDM(n = 87) Normoglycemic (n = 148) p -Value VPA_MET, Median (Q1,Q3) 160.00 (0.00, 960.00) 160.00 (0.00, 1280.00) 160.00 (0.00, 870.00) 0.850 96.00 (0.00, 792.00) 0.00 (0.00, 720.00) 136.00 (0.00, 810.00) 0.482 MPA_MET, Median (Q1,Q3) 480.00 (200.00, 1040.00) 480.00 (120.00, 1200.00) 550.00 (240.00, 1012.00) 0.513 480.00 (200.00, 1110.00) 480.00 (170.00, 1090.00) 560.00 (215.00, 1105.00) 0.560 LPA_MET, Median (Q1,Q3) 1386.00 (726.00, 2475.00) 2013.00 (1072.50, 3399.00) 1122.00 (627.00, 1980.00) < 0.001 1452.00 (750.75, 2772.00) 2079.00 (1155.00, 3184.50) 1138.50 (639.38, 2087.25) < 0.001 total_MET, Median (Q1,Q3) 2623.00 (1506.00, 4402.00) 3111.50 (1970.00, 5878.50) 2478.00 (1400.88, 4011.38) < 0.001 2727.00 (1460.75, 4761.00) 3066.00 (1873.50, 5672.00) 2516.25 (1383.88, 4406.62) 0.039 VPA_day_total, Median (Q1,Q3) 1.00 (0.00, 5.00) 1.00 (0.00, 4.00) 1.00 (0.00, 5.00) 0.116 1.00 (0.00, 5.00) 0.00 (0.00, 3.00) 1.00 (0.00, 5.00) 0.032 MPA_day_total, Median (Q1,Q3) 7.00 (2.00, 11.00) 5.00 (1.00, 9.00) 7.00 (3.00, 11.00) < 0.001 7.00 (2.00, 11.00) 4.00 (1.00, 8.50) 7.50 (3.00, 11.25) < 0.001 LPA_day_total, Median (Q1,Q3) 14.00 (9.00, 18.00) 14.00 (10.00, 18.00) 13.50 (9.00, 17.00) 0.614 13.53 ± 6.04 13.50 ± 6.06 13.55 ± 6.04 0.954 MET_day_total, Median (Q1,Q3) 23.00 (16.00, 29.00) 21.00 (14.00, 28.00) 24.00 (17.00, 29.75) 0.037 23.00 (15.00, 29.00) 19.00 (12.50, 28.00) 24.00 (17.00, 29.25) 0.029 Note: LPA: Light physical activity; MPA: Moderate physical activity; VPA: Vigorous physical activity; Because the data after PSM for this part of LPA_day_total conforms to a normal distribution, this part is described in the table using Mean ± SD. The results of the correlation analysis between diet, PA, BMI, and metabolic indicators in the preDM and normoglycemic groups were shown in Table 4 . In the preDM group, diet was negatively correlated with UA, indicating that a higher PHD-S was associated with lower UA levels. PA was negatively correlated with 2hFBG, suggesting that higher total METs of PA were associated with lower 2hFBG levels. BMI was negatively correlated with HDL-C and positively correlated with UA, indicating that a higher BMI was associated with lower HDL-C levels and higher UA levels. In the normoglycemic group, BMI was positively correlated with both TG and UA, indicating that a higher BMI was associated with higher TG and UA levels. Table 4 Correlations of diet, physical activity, BMI, and metabolic indicators in the preDM group with those in the normoglycemic group (r) Variables HbA1c FBG 2hFBG TC TG LDL-C HDL-C UA PreDM (n = 151) PHDscore 0.078 0.143 −0.084 0.027 −0.116 0.034 0.027 −0.229* Physical activity −0.124 −0.134 −0.176* −0.011 −0.050 −0.039 0.129 −0.171 BMI 0.119 −0.022 −0.072 −0.122 0.140 0.034 −0.216* 0.387*** Normoglycemic (n = 302) PHDscore 0.108 0.039 −0.014 0.051 0.009 0.022 0.040 −0.011 Physical activity 0.036 −0.036 0.061 0.058 −0.094 −0.015 −0.057 −0.064 BMI 0.094 0.013 0.112 0.002 0.210*** 0.029 −0.012 0.242*** Note: *P < 0.05,**P < 0.01,***P < 0.001 Mediation analysis revealed that in the preDM group, diet had a significant indirect effect on UA, which was mediated by BMI. In the normoglycemic group, diet had a significant direct effect on HDL-C, whereas PA had a significant direct effect on TG. See Table 5 for details. Table 5 Mediation Analysis of the Effects of PHD-S and Physical Activity on Metabolic Indicators Mediated by BMI Variables UA HDL-C TG Estimate 95%Cl p -Value Estimate 95%Cl p -Value Estimate 95%Cl p -Value PreDM group (n = 151) PHD-S Total effect −1.9524 (-3.6347, − 0.2541) 0.020 Indirect effect (mediation) −0.5720 (− 1.1944, − 0.0932) 0.026 Direct effect −1.3804 (− 2.9557, 0.1803) 0.092 % of total effect mediated 0.2930 (0.0218, 1.1550) 0.046 Normoglycemic group (n = 302) PHD-S Total effect 0.0107 (0.0006, 0.0263) 0.038 Indirect effect (mediation) 0.0000 (− 0.0012, 0.0016) 0.952 Direct effect 0.0107 (0.0003, 0.0261) 0.048 % of total effect mediated 0.0006 (− 0.1520, 0.2155) 0.946 Normoglycemic group (n = 302) physical activity Total effect −0.1209 (− 0.2239, − 0.0129) 0.034 Indirect effect (mediation) −0.0101 (− 0.0330, 0.0137) 0.368 Direct effect −0.1108 (− 0.2140, − 0.0030) 0.048 % of total effect mediated 0.0835 (− 0.2391, 0.4779) 0.390 4. Discussion With the development of the national economy, the improvement of living standards, and changes in lifestyle, the number of chronic diseases associated with these factors is increasing day by day, among which the proportion of patients with prediabetes is also increasing [ 1 ] . Reasonable diet and moderate PA, as core components of lifestyle interventions, are not only the basis for the prevention and treatment of preDM, but also can effectively improve the metabolic level of prediabetic patients. In this study, we investigated the current status of general information, diet, and PA in the preDM and normoglycemic populations. The results of the general data analysis revealed that the preDM group had a higher BMI than normoglycemic group. Similarly, a 30-year study by Schreiner et al. [ 45 ] demonstrated that the mean cumulative BMI was higher in patients with preDM than in those without preDM. Studies have shown that high BMI leads to systemic or localized chronic low-grade inflammation, which in turn, exacerbates systemic insulin resistance [ 46 ] . High BMI is an important risk factor for noncommunicable diseases such as type 2 diabetes and diabetes-related mortality [ 47 , 48 ] , and controlling BMI reduces the risk of progression to type 2 diabetes in individuals with preDM. The American Diabetes Association (ADA) and the Intervention for Adults with Prediabetes: A Chinese Expert Consensus (2023 Edition) recommended that people with preDM consume foods rich in polyunsaturated or monounsaturated fatty acids, limit their intake of saturated fatty acids (e.g., animal oils), choose water over nutritious and nonnutritive sweetened beverages, and consume nonfried lean meats such as fish and chicken as protein sources. In this study, the preDM group had higher PHD-S for animal oil and added sugar intake than normoglycemic group, while the normoglycemic group had higher PHD-S for fish, soy foods, and nut intake than normoglycemic group. Since prediabetic patients often receive dietary guidance from healthcare professionals, their intake of animal oils and added sugars may be intentionally controlled because they are aware of their prediabetic status. The findings of Yin, Siegel et al. [ 49 , 50 ] similarly suggested that adults diagnosed with preDM generally had better macronutrient compositions and overall diet quality. However, while the dietary habits of diagnosed individuals were superior to those of the undiagnosed population, these advantages have decreased over the last 2 years [ 49 ] . Our results showed that, in terms of protein and unsaturated fatty acid intake, the preDM group often neglected the intake of high-quality proteins (e.g., fish and soy foods) and unsaturated fatty acids (e.g., nuts). As nutrition therapy is a critical component of preDM management, the ADA’s Standards of Care in Diabetes 2025 suggest that patients with preDM should ideally follow an individualized medical nutrition therapy (MNT) plan developed by a registered dietitian nutritionist with expertise in preDM care. Healthcare professionals should enhance MNT by providing evidence-based guidance to help patients make informed food choices tailored to their personal and cultural preferences, health literacy, numeracy, access to healthy foods, willingness, and ability to change behaviors, while addressing existing barriers to behavior change [ 51 ] . We also found that the total number of days of PA in a week and the frequency of VPA and MPA were greater in the normoglycemic group than in the preDM group. Conversely, the total METs for PA and LPA were greater in the preDM group. This suggests that while healthcare professionals often provide general counseling on PA, individualized PA plans are frequently overlooked. As a result, individuals in the preDM group may prefer light activities, such as walking, over moderate or VPA, deviating from guideline recommendations [ 1 , 16 , 52 ] . Cheng Jinqun et al. [ 53 ] reported that reallocating sedentary time to LPA or MPA had varying effects on 2hPBG, with no significant change observed when sedentary time was replaced with LPA compared with MPA. Providing patients with systematic cognitive education about PA, including details on frequency, intensity, modality, duration, total exercise volume, and progression, is a prerequisite for improving their current PA status [ 54 ] . Therefore, healthcare professionals should design standardized individualized exercise prescriptions on the basis of patients’ BMI, physical fitness, and underlying health conditions [ 55 ] . Regular assessments and timely adjustments of exercise regimens are necessary to help patients gradually transition from LPA to MPA and VPA, ultimately supporting long-term maintenance of PA behaviors. The underlying biological mechanisms by which diet and PA influence the onset and progression of preDM need to be clarified. Diet and PA have been shown to improve metabolic levels by modulating inflammatory responses and insulin sensitivity, thereby preventing the onset of type 2 diabetes [ 56 , 57 ] . For example, high BMI and unhealthy dietary habits may exacerbate the risk of preDM by promoting a chronic low-grade inflammatory response and increasing insulin resistance. In contrast, moderate PA and a balanced diet can effectively slow the progression of preDM by decreasing levels of inflammatory markers and improving insulin sensitivity. Furthermore, PA level analysis revealed that 44.8% of the individuals in the preDM group and 35.1% of those in the normoglycemic group were still categorized as having low-PA levels. This highlights the importance of addressing PA levels in both groups rather than focusing solely on prediabetic individuals. In this study, correlation analysis revealed associations between BMI and metabolic indicators such as HDL-C and UA. Previous studies have reported significant improvements in BMI and UA levels in individuals adhering to specific dietary patterns while maintaining consistent PA levels [ 58 ] . Additionally, higher BMI is an independent risk factor for hyperuricemia (HUA) [ 59 ] . The results of the mediation analysis in our study also revealed that diet had a significant indirect effect on UA in the preDM group, which was mediated by BMI. Therefore, when developing dietary intervention programs for prediabetic patients, healthcare professionals should prioritize weight loss strategies in addition to dietary modifications, aiming for at least a 3 ~ 7% reduction in baseline body weight [ 60 ] . Such strategies may be more effective in preventing comorbid metabolic diseases in prediabetic patients. In addition to individualized dietary and exercise recommendations, our study carries significant public health implications. First, considering the high prevalence of prediabetes and its risk of progression to type 2 diabetes, our results suggest that prediabetes can be effectively prevented or slowed down through improved weight management, dietary modification, and increased physical activity. Therefore, public health policies should enhance early screening for prediabetes and develop personalized interventions based on individual health status and lifestyle. Governments and health institutions can raise health awareness among the population by providing nutrition education, exercise programs, and lifestyle interventions, and promote community-based health promotion programs. In addition, it is recommended that dietary habits such as maintenance of normal BMI and moderate polyunsaturated fatty acid intake, along with the promotion of moderate-intensity and vigorous-intensity physical activity, should be promoted among different populations as a core component of public health prevention and treatment measures. This study has several limitations. First, as a cross-sectional study, it inherently limited causal inferences; it was unable to clarify causal relationships between variables or accurately capture changes in variable behaviors over time. Future prospective cohort studies with repeated measures are needed to clarify causal relationships about variables as well as time series. Second, self-report-based diet and physical activity data may be subject to recall bias and social desirability bias, which may lead to misreporting or underreporting and may bias the true relationship between health behaviors and outcomes. Future research could utilize smart devices to objectively quantify activity intensity as well as dietary data to realistically respond to outcomes. 5. Conclusions In conclusion, by comparing diet and PA in the preDM and normoglycemic groups, we found that although prediabetic patients performed better in terms of animal oil and added sugar intake, and certain categories of LPA, compared with normoglycemics, prediabetic patients had lower PHD-S of fish, soy foods, and nuts, and participated in fewer vigorous and moderate physical activity per week and in less total days of PA per week. These findings suggest that although prediabetic patients have increased awareness of their pre-diabetic status, the implementation of their dietary and PA behaviors continued to deviate from guideline recommendations. Abbreviations preDM Prediabetes IFG Impaired fasting glucose IGT Impaired glucose tolerance FFQ Food Frequency Questionnaire PHD-S Planetary Health Diet Score PA Physical activity IPAQ International Physical Activity Questionnaire LPA Light physical activity MPA Moderate physical activity VPA Vigorous physical activity PSM Propensity score matching LDL-C Low-density lipoprotein cholesterol HDL-C High-density lipoprotein cholesterol TC Cholesterol TG Triacylglycerol HbA1c Glycosylated hemoglobin UA Uric acid FBG Fasting blood glucose 2hPBG 2-h postprandial blood glucose Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Committee of the Third Affiliated Hospital of Sun Yat-sen University (Ethics Approval: No. RG2023-262-02, Approval Date: 22 November 2023). Consent for publication Not applicable. Availability of data and materials The data generated and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the National natural science foundation of China No. 72204277; the Nursing innovation development research project No. YJYZ202304; Guangdong Basic and Applied Basic Research Foundation NO. 2025A1515012706, and the 3rd Affiliated Hospital of Sun Yat-sen University, Clinical Research Program No. YHJH202404. Authors' contributions Formal analysis, Jiarui Lin and Shuhong Liu; funding acquisition, Xiling Hu; investigation, Jiarui Lin, Shuhong Liu and Xiaodi Guo; supervision, Lingling Gao, Xiaodi Guo and Xiling Hu; writing – original draft, Jiarui Lin; writing – review & editing, Jiarui Lin and Xiling Hu. Acknowledgements Not applicable. 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MYHRSTAD M C W, TUNSJØ H, CHARNOCK C, etal. Dietary Fiber, Gut Microbiota, and Metabolic Regulation—Current Status in Human Randomized Trials[J]. Nutrients, 2020, 12(3): 859. DOI:10.3390/nu12030859. WANG Z, PETERS B A, YU B, etal. Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes[J]. Circulation research, 2024, 134(7): 842-854. DOI:10.1161/CIRCRESAHA.123.323634. WORLD HEALTH ORGANIZATION. Physical activity[EB/OL]. (2024-06-26) [2024-07-11]. https://www.who.int/news-room/fact-sheets/detail/physical-activity. COMPOSING AND EDITORIAL BOARD OF PHYSICAL ACTIVITY GUIDELINES FOR CHINESE. Physical Activity Guidelines for Chinese (2021). Chinese Journal of Public Health, 2022, 38(2): 129-130. DOI: 10.11847/zgggws1137503. NATIONAL CENTER OF GERONTOLOGY,CHINESE DIABETES SOCIETY,CHINA SPORT SCIENCE SOCIETY. Guideline for exercise therapy of Type 2 Diabetes Mellitus in China (2024 Edition). Chinese General Practice, 2024, 27(30): 3709-3738. DOI: 10.12114/j.issn.1007-9572.2024.A0019. LEE Y Y, KAMARUDIN K S, WAN MUDA W A M. Associations between self-reported and objectively measured physical activity and overweight/obesity among adults in Kota Bharu and Penang, Malaysia[J]. BMC Public Health, 2019, 19(1): 621. DOI:10.1186/s12889-019-6971-2. MILLER K, MORLEY C, FRASER B J, etal. Types of leisure-time physical activity participation in childhood and adolescence, and physical activity behaviours and health outcomes in adulthood: a systematic review[J]. BMC Public Health, 2024, 24(1): 1789. DOI:10.1186/s12889-024-19050-3. WORLD HEALTH ORGANIZATION. (‎2022)‎. Global status report on physical activity 2022.World Health Organization. 2023. WORLD HEALTH ORGANIZATION. Nearly 1.8 billion adults at risk of disease from not doing enough physical activity[EB/OL]. (2024-06-26) [2024-07-11]. https://www.who.int/zh/news/item/26-06-2024-nearly-1.8-billion-adults-at-risk-of-disease-from-not-doing-enough-physical-activity. 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A prospective study of the association between family history of diabetes mellitus and diabetes mellitus in adults [J]. Chinese Journal of Preventive Medicine, 2020, 54(8) : 828-833. DOI: 10.3760/cma.j.cn112150-20200212-00091. GAO J, FEI J, JIANG L, et al. Assessment of the reproducibility and validity of a simple food-frequency questionnaire used in dietary patterns studies. Acta Nutrimenta Sinica, 2011, 33(5): 452-456. DOI:10.13325/j.cnki.acta.nutr.sin.2011.05.012. WANG Zhixu.Development of an auxiliary reference food atlas for retrospective dietary surveys[C].Proceedings of the Seventh National Conference on Maternal and Child Nutrition of the Chinese Society of Nutrition. 2010:560-564. YE Y X, GENG T T, ZHOU Y F, et al. Adherence to a Planetary Health Diet, Environmental Impacts, and Mortality in Chinese Adults[J]. JAMA network open, 2023, 6(10): e2339468. DOI:10.1001/jamanetworkopen.2023.39468. WILLETT W, ROCKSTRÖM J, LOKEN B, et al. Food in the Anthropocene: the EAT– Lancet Commission on healthy diets from sustainable food systems[J]. The Lancet, 2019, 393(10170): 447-492. DOI:10.1016/S0140-6736(18)31788-4. LI B, JIA M, ZHOU Y, et al. Physical inactivity and sedentary behaviors in relation to prevalence of Dysglycemia. Journal of Wuhan Sports University, 2018, 52(5): 95-100. DOI:10.15930/j.cnki.wtxb.2018.05.015. QU N N, LI K J. Study on the reliability and validity of international physical activity questionnaire (Chinese Vision, IPAQ). Chinese Journal of Epidemiology, 2004(3): 87-90. MENGYU F, JUN L, PINGPING H. Chinese guidelines for data processing and analysis concerning the International Physical Activity Questionnaire[J]. Chinese Journal of Epidemiology, 2014, 35(08): 961-964. DOI:10.3760/cma.j.issn.0254-6450.2014.08.019. General Office of the National Health Commission.Notice of the Guiding Principles of Weight Management (2024 Edition)[EB/OL]. [2025-04-10]. http://www.nhc.gov.cn/ylyjs/s3573d/202412/4cf1905d32304c15ac3bc4446ddb83f1.shtml. SCHREINER P J, BAE S, ALLEN N, et al. Cumulative BMI and incident prediabetes over 30 years of follow-up: The CARDIA study[J]. Obesity, 2023, 31(11): 2845-2852. DOI:10.1002/oby.23866. CHE K, LU M, QIU J. Aerobic exercise to combat obesity-related insulin resistance: targeting inflammation as a perspective. Chinese Journal of Prevention and Control of Chronic Diseases, 2024, 32(10): 790-795. DOI:10.16386/j.cjpccd.issn.1004-6194.2024.10.012. DAI H, ALSALHE T A, CHALGHAF N, et al. The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study[J]. PLoS Medicine, 2020, 17(7): e1003198. DOI:10.1371/journal.pmed.1003198. WANG Y, JIANG J, ZHU Z. Trends in disease burden of type 2 diabetes, stroke, and hypertensive heart disease attributable to high BMI in China: 1990–2019[J]. Open Medicine, 2024, 19(1). DOI:10.1515/med-2024-1087. YIN J, HUANG Y, LIU G, et al. Trends in dietary macronutrient composition and diet quality among US adults with diagnosed and undiagnosed elevated glycemic status: a serial cross-sectional study[J]. The American Journal of Clinical Nutrition, 2022, 115(6): 1602-1611. DOI:10.1093/ajcn/nqac061. SIEGEL K R, PAVKOV M E, BENOIT S R, et al. 1182-P: Is Prediabetes Awareness Associated with Leisure-Time Physical Activity and Dietary Behaviors?[J]. Diabetes, 2022, 71(Supplement_1): 1182-P. DOI:10.2337/db22-1182-P. AMERICAN DIABETES ASSOCIATION PROFESSIONAL PRACTICE COMMITTEE. 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes—2025[J]. Diabetes Care, 2024, 48(Supplement_1): S86-S127. DOI:10.2337/dc25-S005. WORLD HEALTH ORGANIZATION. WHO guidelines on physical activity and sedentary behaviour[EB/OL]. (2020-11-25) [2024-11-28]. https://www.who.int/publications/i/item/9789240015128. CHENG J, HUANG Y, REN Z, et al. Compositional isotemporal substitution analysis of physical activity, sedentary behaviour and cardiometabolic biomarkers in US adults: A nationally representative study[J]. European Journal of Sport Science, 2023, 23(11): 2119-2128. DOI:10.1080/17461391.2023.2177198. KAN Y. Study on the trajectory of physical activity and influencing factors in patients with Type 2 diabetes mellitus[D]. YANGZHOU UNIVERSITY, 2024. DOI:10.27441/d.cnki.gyzdu.2023.001788. EXPERT GROUP ON CHINESE EXPERT CONSENSUS ON EXERCISE PRESCRIPTION (2023). Chinese expert consensus on exercise prescription (2023). Chinese Journal of Sports Medicine, 2023, 42(1): 3-13. DOI:10.16038/j.1000-6710.2023.01.012. ESSER N, LEGRAND-POELS S, PIETTE J, et al. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes[J]. Diabetes Research and Clinical Practice, 2014, 105(2): 141-150. DOI:10.1016/j.diabres.2014.04.006. HAYASHINO Y, JACKSON J L, HIRATA T, et al. Effects of exercise on C-reactive protein, inflammatory cytokine and adipokine in patients with type 2 diabetes: A meta-analysis of randomized controlled trials[J]. Metabolism, 2014, 63(3): 431-440. DOI:10.1016/j.metabol.2013.08.018. SOROŃ-LISIK M, WIĘCH P, DĄBROWSKI M. Beneficial Effect of Dietary Approaches to Stop Hypertension Diet Combined with Regular Physical Activity on Fat Mass and Anthropometric and Metabolic Parameters in People with Overweight and Obesity[J]. Nutrients, 2024, 16(18): 3187. DOI:10.3390/nu16183187. KUWABARA M, KUWABARA R, NIWA K, et al. Different Risk for Hypertension, Diabetes, Dyslipidemia, and Hyperuricemia According to Level of Body Mass Index in Japanese and American Subjects[J]. Nutrients, 2018, 10(8): 1011. DOI:10.3390/nu10081011. AMERICAN DIABETES ASSOCIATION PROFESSIONAL PRACTICE COMMITTEE. 8. Obesity and Weight Management for the Prevention and Treatment of Type 2 Diabetes: Standards of Care in Diabetes–2025[J]. Diabetes Care, 2024, 48(Supplement_1): S167-S180. DOI:10.2337/dc25-S008. Additional Declarations No competing interests reported. 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study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5796923/v1/3ad3ecfef9c03859b414cc97.png"},{"id":93420498,"identity":"3321ee79-b22a-4713-90e8-9fb1f5f530c6","added_by":"auto","created_at":"2025-10-13 16:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5796923/v1/472b22f8-39d4-495d-8036-ef95399b417a.pdf"},{"id":82046957,"identity":"949dc3fa-7d69-4d9f-b5e6-29a153d71faf","added_by":"auto","created_at":"2025-05-06 09:40:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18365,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1..docx","url":"https://assets-eu.researchsquare.com/files/rs-5796923/v1/aec7e913b62d92637e13cd60.docx"},{"id":82046960,"identity":"7f64f3ba-3063-40b1-8fc7-dc8926468ade","added_by":"auto","created_at":"2025-05-06 09:40:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30810,"visible":true,"origin":"","legend":"","description":"","filename":"Supplententaryfile2..docx","url":"https://assets-eu.researchsquare.com/files/rs-5796923/v1/4d3e1212fb5faf3928bc94a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Dietary and Physical Activity Behavior between Prediabetic and Normoglycemic Populations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePrediabetes (preDM) is a transitional stage before the onset of diabetes, which includes impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or both (IFG\u0026thinsp;+\u0026thinsp;IGT). It is an intermediate state between normoglycemia and diabetes mellitus\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The global prevalence of preDM is increasing with population ageing, changes in lifestyle and environment, including increased sedentary lifestyles, increased availability of high-calorie fast food and changes in the living environment of the population. According to the International Diabetes Federation, by 2045 more than 500\u0026nbsp;million adults aged 20\u0026thinsp;~\u0026thinsp;79 years will have preDM\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, which has become a major public health problem. Furthermore, preDM is associated with increased risk of cardiovascular disease, microangiopathy, neoplasms, and dementia\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Therefore, early detection and management of preDM is essential to prevent the progression of preDM.\u003c/p\u003e \u003cp\u003eDiet has a strong influence on glucolipid metabolism\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Globally, mortality from type 2 diabetes, cardiovascular diseases, and dysregulated glycolipid metabolism due to unhealthy diets is also on the rise\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. As a cornerstone of lifestyle interventions, dietary factors are key modifiable influences. Research has shown that unhealthy dietary patterns, such as high intake of animal meet and fried foods, were associated with an elevated risk of developing preDM after adjusting for confounders\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Furthermore, dietary composition affected the body\u0026rsquo;s metabolism\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, with increased fat intake (\u0026ge;\u0026thinsp;35%) being associated with augmentation in low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and glycosylated hemoglobin (HbA1c). In addition, excessive carbohydrate intake led to elevated TG and HbA1c\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Among dietary components, the intake of dietary fiber has a protective effect against diabetes. Dietary fiber, although not easily hydrolyzed by human digestive enzymes, can be metabolized by specific gut bacteria to produce a range of metabolites through fermentation and other metabolic pathways\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A recent study has shown that higher dietary fiber intake was associated with a favorable gut microbiota and a circulating metabolite profile in type 2 diabetes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Therefore, targeted fiber supplementation may be a cost-effective strategy for preDM management.\u003c/p\u003e \u003cp\u003ePhysical activity (PA) refers to physical movement that requires the expenditure of energy in leisure, transportation, work, or home environments\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. PA is categorized by intensity into light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Regular PA enhances insulin sensitivity and glucolipid metabolism, reduces cardiovascular risk factors, and improves glycemic control. It is an effective primary prevention strategy for diabetes in high-risk individuals\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, the Global Status Report on Physical Activity 2022 revealed that approximately 1.8\u0026nbsp;billion adults worldwide did not meet the World Health Organization (WHO) recommendation of at least 150 minutes of moderate or equivalent PA weekly\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Since regular PA is beneficial to an individual\u0026rsquo;s health, it is important to consider not only the intensity but also the frequency and duration of PA during the week. The International Physical Activity Questionnaire (IPAQ) working group categorized individuals\u0026rsquo; PA levels into low, medium, and high groups on certain criteria. Individuals in the low-PA level group were at greater risk of preDM\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e and dyslipidemia compared to the high-PA level group. These findings suggest that a high level of PA plays an important role in improving glucolipid metabolism\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that diet and PA affect metabolic indices, and that body mass index (BMI) is a key factor influencing metabolism\u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Although the effects of diet and PA on metabolic health are well established, the role of BMI in these associations remains less clear. It is important to consider whether the relationship between diet, PA, and metabolic indices is independent of or mediated by BMI, as BMI has the potential to modify or modulate these effects. This perspective helps to elucidate the complex interactions between these factors and their joint impact on metabolic health.\u003c/p\u003e \u003cp\u003eIn conclusion, diet and PA are closely associated with the development of preDM\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Therefore, effective interventions targeting these factors can prevent or delay the progression of diabetes and its complications\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, the dietary and PA patterns in the prediabetic and normoglycemic populations remains uncertain. This study aimed to investigate the dietary and PA status among the preDM and normoglycemic groups and to analyze the differences between them, which may lay the foundation for future effective interventions for prediabetic populations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003ch3\u003e2.1 Study population\u003c/h3\u003e\n\u003cp\u003eThis cross-sectional study was conducted from December 2023 to August 2024. A total of 453 participants were recruited by convenience sampling from patients attending or hospitalized in the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, Guangdong Province, China. All voluntary participants sequentially completed a general information survey; PA and diet surveys; and height, weight, and glucolipid metabolism measurements. The study was reviewed and approved by the Medical Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Ethical Review Approval No. RG2023-262-02), and all participants provided written informed consent.\u003c/p\u003e\n\u003ch4\u003e2.1.1 Inclusion criteria\u003c/h4\u003e\n\u003cp\u003e(1) PreDM group: a. Age \u0026ge; 18 years; b. In accordance with the 1999 WHO definition and diagnostic criteria for preDM\u003csup\u003e[1]\u003c/sup\u003e: patients with 6.1 mmol/L\u0026nbsp;\u0026le;\u0026nbsp;fasting blood glucose (FBG) \u0026lt; 7.0 mmol/L and \u0026nbsp;2-h postprandial blood glucose (2hPBG) \u0026lt; 7.8 mmol/L were diagnosed with IFG; those with FBG \u0026lt; 6.1 mmol/L and 7.8 mmol/L\u0026nbsp;\u0026le;\u0026nbsp;2hPBG \u0026lt; 11.1 mmol/L and/or 5.7%\u0026nbsp;\u0026le;\u0026nbsp;HbA1c \u0026lt; 6.5% were diagnosed with IGT; and patients with 6.1 mmol/L\u0026nbsp;\u0026le;\u0026nbsp;FBG \u0026lt; 7.0 mmol/L and 7.8 mmol/L\u0026nbsp;\u0026le;\u0026nbsp;2hPBG \u0026lt; 11.1 mmol/L were diagnosed with IFG + IGT; c. Ability to understand and respond accurately to survey questions; d. Signed informed consent to participate in this survey.\u003c/p\u003e\n\u003cp\u003e(2) Normoglycemic group: a. Age \u0026ge; 18 years; b. FBG was 3.9\u0026ndash;6.1 mmol/L; c.Ability to understand and respond accurately to survey questions; d. Signed informed consent to participate in the study.\u003c/p\u003e\n\u003ch4\u003e2.1.2 Exclusion criteria\u003c/h4\u003e\n\u003cp\u003e(1) PreDM group: a. Patients were diagnosed with contraindications to PA or dietary restrictions due to medical conditions; b. Secondary obesity is caused by endocrine, genetic, metabolic, or central nervous system diseases, such as hypothalamic obesity; pituitary obesity; hypothyroid obesity; obesity caused by Cushing\u0026rsquo;s syndrome; and hypogonadal obesity; c. Pregnant, planning to become pregnant, or breastfeeding women.\u003c/p\u003e\n\u003cp\u003e(2) Normoglycemic group: a. The individual was diagnosed with any type of diabetes; b. Individuals were diagnosed with contraindications to PA or dietary restrictions due to medical conditions; c. Secondary obesity is caused by endocrine, genetic, metabolic, or central nervous system diseases, such as hypothalamic obesity; pituitary obesity; hypothyroid obesity; obesity caused by Cushing\u0026rsquo;s syndrome; and hypogonadal obesity; d. Pregnant, planning to become pregnant, or breastfeeding women.\u003c/p\u003e\n\u003ch3\u003e2.2 General information surveys\u003c/h3\u003e\n\u003cp\u003eParticipants reported demographic and lifestyle information through a general information questionnaire, including sex, age, education, place of residence, history of previous illnesses, family history of diabetes, and history of smoking and alcohol consumption.\u003c/p\u003e\n\u003cp\u003eAmong the study subjects, education level was categorized as: high school or less, three-year college, undergraduate or above. The place of residence was categorized as: city, cities and towns, and countryside. The study population was categorized into smoker and nonsmoker according to the WHO definition of smokers in 1997, which is \u0026ldquo;a person who has smoked continuously or cumulatively for six months or more during his or her lifetime\u0026rdquo;\u003csup\u003e[32]\u003c/sup\u003e.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eAccording to the International Guidelines for Monitoring Alcohol Consumption and Related Harm issued by the WHO in 2000\u003csup\u003e[33]\u003c/sup\u003e, the study population was categorized into drinkers and non-drinkers based on the definition of \u0026ldquo;those who consumed alcohol\u0026ge;1 drink per week in the past 1 year, and whose average daily alcohol intake was\u0026ge;61g in male drinkers and\u0026ge;41g in female drinkers\u0026rdquo;. According to the definition of hypertension in the Chinese Guidelines for the Prevention and Treatment of Hypertension (2024 Revision)\u003csup\u003e[34]\u003c/sup\u003e, \u0026ldquo;without the use of antihypertensive medication, clinic blood pressure\u0026ge;140/90mmHg, or home blood pressure\u0026ge;135/85mmHg, or 24-h ambulatory blood pressure\u0026ge;130/80mmHg, with daytime blood pressure\u0026ge;135/85mmHg, nighttime blood pressure\u0026ge;120/70mmHg\u0026rdquo;, which categorized the study subjects into hypertensive and non-hypertensive patients. According to the definition of hyperlipidemia in the Chinese Guidelines for Lipid Management (2023)\u003csup\u003e[35]\u003c/sup\u003e, \u0026ldquo;elevated serum total cholesterol (TC) and/or triglyceride (TG) levels\u0026rdquo;, in which the normal levels of TC are 3~6.18mmol/L and TG are 0.3~1.8mmol/L, the study subjects were categorized into hyperlipidemic patients and non-hyperlipidemic patients. Information on their family history of diabetes was obtained by asking the study subjects, and if any of their first-degree relatives, such as father, mother, siblings, and children, had diabetes, it was determined that the study subjects had a family history of diabetes\u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2.3 Dietary survey\u003c/h3\u003e\n\u003cp\u003eThe dietary survey was conducted through the Food Frequency Questionnaire (FFQ)\u003csup\u003e[37]\u003c/sup\u003e. Participants recalled and recorded the types, frequency, and portion sizes of foods consumed daily over the past year. The questionnaire categorized foods into 15 groups: cereals, vegetables, fruits, livestock and meat, fish and shrimp, eggs, milk and dairy products, beans and bean products, nuts, fats and oils, condiments, water, beverages, barbecue and frying, and pickles. Food frequency was classified into nine levels: not eaten or less than 1 time/month, 1 time/month, 2~3 times/month, 1 time/week, 2 times/week, 3~4 times/week, 5~6 times/week, 1 time/day, or 2 or more times/day. In order to make the recall of the amount of food consumed by the study participants in the past period of time more accurate, we used the \u0026ldquo;Retrospective Dietary Survey Auxiliary Reference Food Atlas\u0026rdquo; developed by Prof. Wang Zhixu of Nanjing Medical University\u003csup\u003e[38]\u003c/sup\u003e when collecting their dietary data from the study participants. The atlas categorized and edited all the pictures of the food according to 13 categories, which included: cereal yams and miscellaneous beans, vegetables, fruits, livestock, poultry and meat,Fish, shrimp and shellfish, eggs, dairy products, soybeans and soybean products, nuts, cooking oils and fats, confectionery, sweets and condiments, processed food, and tableware and containers, with a total of 659 pictures of 195 food items. With the help of three visual reference systems, namely, the comparison of the food\u0026rsquo;s own shape or portion size, the background scale coordinates and the familiar objects in daily life, and the corresponding grams of food portions labeled with 4~10 different quantity levels for different foods, the study participants could better recall their daily diets.\u003c/p\u003e\n\u003cp\u003eThe mean daily dietary intake data were used to construct 14 food groups on the basis of the Planetary Health Diet Score (PHD-S)\u003csup\u003e[39]\u003c/sup\u003e. The dietary components included: (1) rice, wheat, corn, and others; (2) tubers (e.g., potatoes, cassava); (3) all vegetables; 4) all fruits; (5) dairy foods; (6) beef, lamb, pork; (7) chicken and poultry; (8) eggs; (9) fish; (10) dry beans, lentils, peas, and soy foods; (11) peanuts or tree nuts; (12) animal oils, such as: palm oil, dairy fats (in milk), lard, or tallow; (13) unsaturated oils; and (14) all sweeteners. The 14 dietary components were categorized based on health effects: Adequacy components are used to indicate healthy foods with higher recommended intakes, such as: vegetables, fruits, nuts, legumes, unsaturated fats, and fish; Optimum components represent nutrient-dense foods but can be harmful if consumed in large amounts daily, such as: potatoes, dairy foods, poultry, eggs; Moderation components are defined as those that could increase the risk of chronic diseases with increased consumption, such as: total grains, red meat, saturated fats, and added sugars. Each dietary component was scored from 0 to 10, with total PHD-S scores ranging from 0 to 140 (We have attached the specific grading criteria in Supplementary file 1.). According to the EAT-Lancet Commission on healthy diets from sustainable food systems\u003csup\u003e[40]\u003c/sup\u003e the global average per capita energy intake is estimated to be 2370 kcal per day, with adult males consuming about 2800 kcal per day and adult females consuming about 2000~2200 kcal per day. The energy intake is lower in those with a lower body mass index (BMI, measured in kg/m\u003csup\u003e2\u003c/sup\u003e ), and higher in those who are more physically active. Moreover, the PHD-S calculation methodology\u003csup\u003e[39]\u003c/sup\u003e also stipulates that the daily calorie intake of each study participant be\u0026nbsp;standardized to 2500 kcal/day. Therefore, we used 2500 kcal per day as the basis for different isocaloric dietary regimens (i.e., with similar caloric values).\u003c/p\u003e\n\u003ch3\u003e2.4 PA survey\u003c/h3\u003e\n\u003cp\u003eThe IPAQ was used to assess the PA of the study participants over the past 7 days, including LPA, MPA, and VPA during work, transportation, housework, and leisure time. The IPAQ demonstrated good reliability and validity in measuring PA in individuals with preDM\u003csup\u003e[41]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo assess strength-related PA participation over the last 7 days, the activity was measured by the metabolic equivalent of energy (MET) value, which was multiplied by the weekly frequency and time of day. The total PA (metabolic equivalent of task [MET]-h/week) was determined by summing the three PA intensities (LPA, MPA, and VPA)\u003csup\u003e[42]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe participants were then categorized into low-, medium-, and high-PA level groups on the basis of their IPAQ-calculated PA levels\u003csup\u003e[43]\u003c/sup\u003e. For high-PA level,\u0026nbsp;meet any 1 of the 2 criteria listed below: (1) \u0026ge;3 days of all types of vigorous physical activity combined and a total physical activity level of \u0026ge;1500 MET-min/week. (2) The total number of days of vigorous, moderate, light physical activity is \u0026ge;7 days, with a total weekly physical activity level of \u0026ge;3000 MET-min/w. For\u0026nbsp;medium-PA level, meet any 1 of the following 3 criteria: (1) meet at least 20 min/day of vigorous physical activity and the total number of high-intensity physical activity days in a week is\u0026nbsp;\u0026ge;3 days. (2) meet at least 30 min/day of moderate physical activity and/or walking on a total of\u0026nbsp;\u0026ge;5 days per week. (3) physical activity of all 3 intensities combined for\u0026nbsp;\u0026ge;5 days and total physical activity level of\u0026nbsp;\u0026ge;600 MET-min/week. For\u0026nbsp;low-PA level, meet any 1 of the 2 criteria below: (1)no activity reported. (2) some activity was reported, but the criteria for medium and high groupings above were not yet met.\u003c/p\u003e\n\u003ch3\u003e2.5 Measurement of height and weight\u003c/h3\u003e\n\u003cp\u003eHeight (m) was measured by a wall-mounted height scale (accurate to two decimal places), and weight (kg) was measured by a body composition analyzer (model: InBody570) (accurate to one decimal place).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI was calculated as BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) = weight (kg)/height (m)\u003csup\u003e2\u003c/sup\u003e. According to the \u0026ldquo;Guiding Principles of Weight Management (2024 Edition)\u0026rdquo; Chinese adults aged 18 years and above BMI judgment standards, China\u0026apos;s healthy adults BMI is divided into: ① BMI \u0026lt;18.5kg/m\u003csup\u003e2\u003c/sup\u003e for underweight, ② the normal range of 18.5 ~ 23.9kg/m\u003csup\u003e2\u003c/sup\u003e, ③ 24.0 ~ 27.9kg/m\u003csup\u003e2\u003c/sup\u003e for overweight, ④ BMI \u0026ge; 28.0kg/m\u003csup\u003e2\u003c/sup\u003e for obese.\u003csup\u003e[44]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.6 Measurement of metabolic indicators\u003c/h3\u003e\n\u003cp\u003eFasting venous blood samples were drawn from each study participant in the early morning after overnight fasting to determine their FBG and HbA1c, as well as serum total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid (UA) levels. The participants subsequently underwent a standard oral glucose tolerance test, in which 75 g of anhydrous glucose powder dissolved in 250\u0026ndash;300 mL of water was taken orally within 5 min, starting from the first sip of the solution. Venous blood was drawn 2 h later to determine the 2hPBG level after sugar loading.\u003c/p\u003e\n\u003ch3\u003e2.7 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eThe data were analyzed by R language version 4.4.1. The study subjects were divided into prediabetic and normoglycemic groups based on whether they met the diagnostic criteria for prediabetes. Considering the effect of confounders between groups and in order to increase the comparability between groups, we used the PSM method to equalize the bias caused by the unbalance of covariates between groups. The PSM method matches individuals with similar propensity scores (PS) in the prediabetes group as the reference group and in the normoglycemic group and performs a balanced test with standardized mean difference (SMD) values of each covariate, which is used to determine whether the prediabetes group in the study and the normoglycemic group, to determine whether the differences in various socio-demographic data in the study were statistically significant, and to assess the balance between the groups before and after matching, where SMD \u0026lt; 0.1 indicated a better balance and a smaller difference between the two groups. We used the R language Match It package for 1:1 PSM, controlled for confounders after analysis, and constructed a binary logit regression model with the dichotomous variable of \u0026ldquo;1=pre-diabetes group, 0=normal glycemic group\u0026rdquo; as the dependent variable, and confounders as the independent variable X. The model was then analyzed by binary logit regression.The model was then used to calculate the propensity score Pscore value, with the chi-square value set at 0.1; the confounding variables included age, gender, education, place of residence, smoking, alcohol consumption, hypertension, hyperlipidemia, and family history of diabetes mellitus. For the data after PSM, the previous analysis was performed. For continuous quantitative data, if they conformed or approximately conformed to a normal distribution, they were described by the mean \u0026plusmn; standard deviation, and comparisons between two groups were made by the independent-samples \u003cem\u003et-test\u003c/em\u003e. If the data did not conform to a normal distribution, they were statistically described by the median [\u003cem\u003eP\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e\u003c/em\u003e], and comparisons between two groups were made via the Wilcoxon rank sum test. Count data are described as the number of cases (%), and comparisons between groups were made via the chi-square test or Fisher\u0026rsquo;s exact probability method.\u003c/p\u003e\n\u003cp\u003eCorrelations between diet, PA, BMI, and metabolic indicators were evaluated \u003cs\u003evia\u0026nbsp;\u003c/s\u003ethrough Pearson correlation analysis in the preDM and normoglycemic groups. Mediation analysis via R language was conducted to explore the mediating role of BMI in the correlations among PHD-S, PA, and metabolic indicators. The analysis was conducted for the whole population, the preDM group, and the normoglycemic group. Confidence intervals were estimated via 1,000 bootstrap samples. All tests were two-sided, and P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the study's recruitment process. Of the 480 participants assessed for eligibility, 160 were in the preDM group and 320 were in the normoglycemic group. The participants in preDM group 9 were excluded, and in normoglycemic group 18 were excluded. Thus, 151 participants in preDM group and 302 participants in normoglycemic group completed the questionnaires and were included in the data analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 453 individuals participated in this study, of whom 159 (35.1%) were male and 294 (64.9%) were female, with a median age of 38.00 (31.00, 46.00) years old. Before PSM, there were 151 participants in the preDM group and 302 participants in the normoglycemic group. Statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in age, BMI, education level, hypertension, hyperlipidemia, and family history of diabetes mellitus were detected between the two groups. After performing PSM, 87 participants were included in the preDM group, and 148 participants were included in the normoglycemic group. Except for BMI, which remained statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the differences in all the other variables were no longer statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating a good matching effect. The details were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the population in the preDM group and normoglycemic group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;453)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePreDM (n\u0026thinsp;=\u0026thinsp;151)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;302)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;235)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePreDM(n\u0026thinsp;=\u0026thinsp;87)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;148)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years; Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.0 (31.0, 46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.0 (26.0, 44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.0 (34.0, 46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.00 (31.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.00 (28.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.00 (32.00, 43.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e191 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e177 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Kg/m\u003csup\u003e2\u003c/sup\u003e;Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.62 (21.30, 27.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.64 (24.09, 32.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.30 (20.72, 24.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.53 (21.37, 27.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.64 (23.72, 31.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.42 (20.64, 24.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrouping of BMI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e225 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e113 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ethree-year college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e144 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364 (80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e247 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e186 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72 (82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCities and towns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountryside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonsmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e211 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e134 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e213 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e134 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e402 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e286 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e217 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e137 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e385 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e283 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72 (82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e130 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e256 (84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e162 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108 (73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the PHD-S for each category in the preDM and normoglycemic groups before and after PSM.\u003c/p\u003e \u003cp\u003eAfter adjusting for confounders such as age, sex, education level, and place of residence, the difference in total PHD-S between the preDM and normoglycemic groups was not statistically significant. However, the intake of animal oils and added sugars was significantly greater in the preDM group, with a statistically significant difference. Conversely, the intake of fish, soy foods, and nuts was significantly greater in the normoglycemic group than in the preDM group, and this difference was also statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePlanetary health diet score (PHD-S) for each type of diet in the preDM group and normoglycemic group\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;453)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePreDM (n\u0026thinsp;=\u0026thinsp;151)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;302)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;235)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePreDM(n\u0026thinsp;=\u0026thinsp;87)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;148)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs1: total grains, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 (6.69, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (6.30, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 (7.12, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.00 (6.76, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.00 (6.47, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.00 (7.04, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs2: tubers of starchy vegetables, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.32 (0.90, 7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.54 (0.39, 8.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.17 (1.21, 7.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.61 (0.49, 7.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.61 (0.00, 8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.61 (1.01, 7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs3: all vegetables, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 (9.01, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs4: all fruits, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.26 (2.37, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.26 (2.49, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.27 (2.37, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.49 (2.73, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.19 (2.46, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.44 (3.40, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs5: dairy foods, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.51 (0.69, 7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01 (0.69, 7.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.76 (0.69, 7.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.36 (0.40, 7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.90 (0.15, 7.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.86 (0.57, 8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs6: beef, lamb, pork, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs7: chicken and other poultry, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.00, 4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, 4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.00, 5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93 (0.00, 4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52 (0.00, 4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.93 (0.00, 4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs8: eggs, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs9: fish, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (8.98, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.00 (9.11, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.00 (10.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs10: dry beans, lentils, peas, soy foods, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.33 (1.84, 9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.01 (0.71, 7.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.06 (2.35, 9.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.07 (1.61, 8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.74 (0.67, 7.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.86 (2.44, 9.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs11: peanuts or tree nuts,\u003c/p\u003e \u003cp\u003eMedian (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.21, 4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.00, 2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13 (0.46, 5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09 (0.20, 3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.43 (0.00, 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.53 (0.36, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs12: animal oils, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.59 (0.62, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (1.14, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.38 (0.47, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.40 (0.65, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.00 (1.46, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.42 (0.52, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs13: unsaturated oils, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.50 (4.29, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.58 (3.81, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.43 (4.40, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.27 (4.31, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.84 (3.78, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.88 (4.39, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHDs14: added sugars, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33 (0.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00, 9.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00 (0.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.52 (0.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00 (0.00, 7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PHD-S, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.09\u0026thinsp;\u0026plusmn;\u0026thinsp;12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.97\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e68.89\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: PSM: propensity score matching; preDM: prediabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presented the PA levels of the preDM and normoglycemic groups before and after PSM. After adjusting for confounders such as age, sex, education level, and place of residence, the total number of days of PA per week and the number of days per week involving vigorous and moderate PA were significantly greater in the normoglycemic group than in the preDM group. These differences were statistically significant. Conversely, the total MET values for overall and light PA were significantly greater in the preDM group than in the normoglycemic group.\u003c/p\u003e \u003cp\u003eThe number of active days per week and the duration of a single activity session for VPA and MPA at work, VPA during housework, and MPA, such as lifting/carrying light objects and cleaning the yard, were significantly greater in the normoglycemic group than in the preDM group. For LPA during transportation, such as walking, both the number of active days per week and the duration of a single session were significantly greater in the preDM group than in the normoglycemic group. Walking at work as part of LPA was performed on more active days per week in the normoglycemic group than in the preDM group. MPA during household chores, such as window cleaning and hand washing, and LPA during transportation via motor vehicles, were more common per single session in the preDM group than in the normoglycemic group. All of the above differences were statistically significant. Details of the components of physical activity in the preDM group and normoglycemic group can be found in Supplementary file 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical activity in the preDM group and normoglycemic group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;453)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePreDM (n\u0026thinsp;=\u0026thinsp;151)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;302)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;235)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePreDM(n\u0026thinsp;=\u0026thinsp;87)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;148)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA_MET, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160.00 (0.00, 960.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160.00 (0.00, 1280.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160.00 (0.00, 870.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.00 (0.00, 792.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00 (0.00, 720.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e136.00 (0.00, 810.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA_MET, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e480.00 (200.00, 1040.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480.00 (120.00, 1200.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e550.00 (240.00, 1012.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e480.00 (200.00, 1110.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e480.00 (170.00, 1090.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e560.00 (215.00, 1105.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA_MET, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1386.00 (726.00, 2475.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2013.00 (1072.50, 3399.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1122.00 (627.00, 1980.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1452.00 (750.75, 2772.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2079.00 (1155.00, 3184.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1138.50 (639.38, 2087.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal_MET, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2623.00 (1506.00, 4402.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3111.50 (1970.00, 5878.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2478.00 (1400.88, 4011.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2727.00 (1460.75, 4761.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3066.00 (1873.50, 5672.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2516.25 (1383.88, 4406.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA_day_total, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00 (0.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00 (0.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00 (0.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA_day_total, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00 (2.00, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00 (1.00, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00 (3.00, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.00 (2.00, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.00 (1.00, 8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.50 (3.00, 11.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA_day_total, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.00 (9.00, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.00 (10.00, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.50 (9.00, 17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMET_day_total, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.00 (16.00, 29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.00 (14.00, 28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.00 (17.00, 29.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.00 (15.00, 29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.00 (12.50, 28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.00 (17.00, 29.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: LPA: Light physical activity; MPA: Moderate physical activity; VPA: Vigorous physical activity; Because the data after PSM for this part of LPA_day_total conforms to a normal distribution, this part is described in the table using Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the correlation analysis between diet, PA, BMI, and metabolic indicators in the preDM and normoglycemic groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the preDM group, diet was negatively correlated with UA, indicating that a higher PHD-S was associated with lower UA levels. PA was negatively correlated with 2hFBG, suggesting that higher total METs of PA were associated with lower 2hFBG levels. BMI was negatively correlated with HDL-C and positively correlated with UA, indicating that a higher BMI was associated with lower HDL-C levels and higher UA levels. In the normoglycemic group, BMI was positively correlated with both TG and UA, indicating that a higher BMI was associated with higher TG and UA levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations of diet, physical activity, BMI, and metabolic indicators in the preDM group with those in the normoglycemic group (r)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2hFBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePreDM (n\u0026thinsp;=\u0026thinsp;151)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHDscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u0026minus;0.229*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.176*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u0026minus;0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;0.216*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.387***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic (n\u0026thinsp;=\u0026thinsp;302)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHDscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u0026minus;0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u0026minus;0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.210***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.242***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05,**P\u0026thinsp;\u0026lt;\u0026thinsp;0.01,***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMediation analysis revealed that in the preDM group, diet had a significant indirect effect on UA, which was mediated by BMI. In the normoglycemic group, diet had a significant direct effect on HDL-C, whereas PA had a significant direct effect on TG. See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis of the Effects of PHD-S and Physical Activity on Metabolic Indicators Mediated by BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreDM group (n\u0026thinsp;=\u0026thinsp;151) PHD-S\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.9524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.6347, \u0026minus;\u0026thinsp;0.2541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect (mediation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.5720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;1.1944, \u0026minus;\u0026thinsp;0.0932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.3804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;2.9557, 0.1803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of total effect mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0218, 1.1550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic group (n\u0026thinsp;=\u0026thinsp;302) PHD-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0006, 0.0263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect (mediation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.0012, 0.0016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0003, 0.0261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of total effect mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.1520, 0.2155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormoglycemic group (n\u0026thinsp;=\u0026thinsp;302) physical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.1209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.2239, \u0026minus;\u0026thinsp;0.0129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect (mediation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.0330, 0.0137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.1108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.2140, \u0026minus;\u0026thinsp;0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of total effect mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;0.2391, 0.4779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the development of the national economy, the improvement of living standards, and changes in lifestyle, the number of chronic diseases associated with these factors is increasing day by day, among which the proportion of patients with prediabetes is also increasing\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Reasonable diet and moderate PA, as core components of lifestyle interventions, are not only the basis for the prevention and treatment of preDM, but also can effectively improve the metabolic level of prediabetic patients. In this study, we investigated the current status of general information, diet, and PA in the preDM and normoglycemic populations.\u003c/p\u003e \u003cp\u003eThe results of the general data analysis revealed that the preDM group had a higher BMI than normoglycemic group. Similarly, a 30-year study by Schreiner et al.\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e demonstrated that the mean cumulative BMI was higher in patients with preDM than in those without preDM. Studies have shown that high BMI leads to systemic or localized chronic low-grade inflammation, which in turn, exacerbates systemic insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. High BMI is an important risk factor for noncommunicable diseases such as type 2 diabetes and diabetes-related mortality\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, and controlling BMI reduces the risk of progression to type 2 diabetes in individuals with preDM.\u003c/p\u003e \u003cp\u003eThe American Diabetes Association (ADA) and the Intervention for Adults with Prediabetes: A Chinese Expert Consensus (2023 Edition) recommended that people with preDM consume foods rich in polyunsaturated or monounsaturated fatty acids, limit their intake of saturated fatty acids (e.g., animal oils), choose water over nutritious and nonnutritive sweetened beverages, and consume nonfried lean meats such as fish and chicken as protein sources. In this study, the preDM group had higher PHD-S for animal oil and added sugar intake than normoglycemic group, while the normoglycemic group had higher PHD-S for fish, soy foods, and nut intake than normoglycemic group. Since prediabetic patients often receive dietary guidance from healthcare professionals, their intake of animal oils and added sugars may be intentionally controlled because they are aware of their prediabetic status. The findings of Yin, Siegel et al.\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e similarly suggested that adults diagnosed with preDM generally had better macronutrient compositions and overall diet quality. However, while the dietary habits of diagnosed individuals were superior to those of the undiagnosed population, these advantages have decreased over the last 2 years\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Our results showed that, in terms of protein and unsaturated fatty acid intake, the preDM group often neglected the intake of high-quality proteins (e.g., fish and soy foods) and unsaturated fatty acids (e.g., nuts). As nutrition therapy is a critical component of preDM management, the ADA\u0026rsquo;s Standards of Care in Diabetes 2025 suggest that patients with preDM should ideally follow an individualized medical nutrition therapy (MNT) plan developed by a registered dietitian nutritionist with expertise in preDM care. Healthcare professionals should enhance MNT by providing evidence-based guidance to help patients make informed food choices tailored to their personal and cultural preferences, health literacy, numeracy, access to healthy foods, willingness, and ability to change behaviors, while addressing existing barriers to behavior change\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also found that the total number of days of PA in a week and the frequency of VPA and MPA were greater in the normoglycemic group than in the preDM group. Conversely, the total METs for PA and LPA were greater in the preDM group. This suggests that while healthcare professionals often provide general counseling on PA, individualized PA plans are frequently overlooked. As a result, individuals in the preDM group may prefer light activities, such as walking, over moderate or VPA, deviating from guideline recommendations\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Cheng Jinqun et al.\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e reported that reallocating sedentary time to LPA or MPA had varying effects on 2hPBG, with no significant change observed when sedentary time was replaced with LPA compared with MPA. Providing patients with systematic cognitive education about PA, including details on frequency, intensity, modality, duration, total exercise volume, and progression, is a prerequisite for improving their current PA status\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Therefore, healthcare professionals should design standardized individualized exercise prescriptions on the basis of patients\u0026rsquo; BMI, physical fitness, and underlying health conditions\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Regular assessments and timely adjustments of exercise regimens are necessary to help patients gradually transition from LPA to MPA and VPA, ultimately supporting long-term maintenance of PA behaviors.\u003c/p\u003e \u003cp\u003eThe underlying biological mechanisms by which diet and PA influence the onset and progression of preDM need to be clarified. Diet and PA have been shown to improve metabolic levels by modulating inflammatory responses and insulin sensitivity, thereby preventing the onset of type 2 diabetes \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. For example, high BMI and unhealthy dietary habits may exacerbate the risk of preDM by promoting a chronic low-grade inflammatory response and increasing insulin resistance. In contrast, moderate PA and a balanced diet can effectively slow the progression of preDM by decreasing levels of inflammatory markers and improving insulin sensitivity.\u003c/p\u003e \u003cp\u003eFurthermore, PA level analysis revealed that 44.8% of the individuals in the preDM group and 35.1% of those in the normoglycemic group were still categorized as having low-PA levels. This highlights the importance of addressing PA levels in both groups rather than focusing solely on prediabetic individuals.\u003c/p\u003e \u003cp\u003eIn this study, correlation analysis revealed associations between BMI and metabolic indicators such as HDL-C and UA. Previous studies have reported significant improvements in BMI and UA levels in individuals adhering to specific dietary patterns while maintaining consistent PA levels\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Additionally, higher BMI is an independent risk factor for hyperuricemia (HUA)\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. The results of the mediation analysis in our study also revealed that diet had a significant indirect effect on UA in the preDM group, which was mediated by BMI. Therefore, when developing dietary intervention programs for prediabetic patients, healthcare professionals should prioritize weight loss strategies in addition to dietary modifications, aiming for at least a 3\u0026thinsp;~\u0026thinsp;7% reduction in baseline body weight\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. Such strategies may be more effective in preventing comorbid metabolic diseases in prediabetic patients.\u003c/p\u003e \u003cp\u003eIn addition to individualized dietary and exercise recommendations, our study carries significant public health implications. First, considering the high prevalence of prediabetes and its risk of progression to type 2 diabetes, our results suggest that prediabetes can be effectively prevented or slowed down through improved weight management, dietary modification, and increased physical activity. Therefore, public health policies should enhance early screening for prediabetes and develop personalized interventions based on individual health status and lifestyle. Governments and health institutions can raise health awareness among the population by providing nutrition education, exercise programs, and lifestyle interventions, and promote community-based health promotion programs. In addition, it is recommended that dietary habits such as maintenance of normal BMI and moderate polyunsaturated fatty acid intake, along with the promotion of moderate-intensity and vigorous-intensity physical activity, should be promoted among different populations as a core component of public health prevention and treatment measures.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a cross-sectional study, it inherently limited causal inferences; it was unable to clarify causal relationships between variables or accurately capture changes in variable behaviors over time. Future prospective cohort studies with repeated measures are needed to clarify causal relationships about variables as well as time series. Second, self-report-based diet and physical activity data may be subject to recall bias and social desirability bias, which may lead to misreporting or underreporting and may bias the true relationship between health behaviors and outcomes. Future research could utilize smart devices to objectively quantify activity intensity as well as dietary data to realistically respond to outcomes.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, by comparing diet and PA in the preDM and normoglycemic groups, we found that although prediabetic patients performed better in terms of animal oil and added sugar intake, and certain categories of LPA, compared with normoglycemics, prediabetic patients had lower PHD-S of fish, soy foods, and nuts, and participated in fewer vigorous and moderate physical activity per week and in less total days of PA per week. These findings suggest that although prediabetic patients have increased awareness of their pre-diabetic status, the implementation of their dietary and PA behaviors continued to deviate from guideline recommendations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epreDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrediabetes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImpaired fasting glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImpaired glucose tolerance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFFQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood Frequency Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHD-S\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlanetary Health Diet Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPAQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Physical Activity Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLight physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModerate physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVigorous physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity score matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriacylglycerol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycosylated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2hPBG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-h postprandial blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Committee of the Third Affiliated Hospital of Sun Yat-sen University (Ethics Approval: No. RG2023-262-02, Approval Date: 22 November 2023).\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe data generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was funded by the National natural science foundation of China No. 72204277; the Nursing innovation development research project No. YJYZ202304; Guangdong Basic\u0026nbsp;and\u0026nbsp;Applied Basic Research Foundation NO. 2025A1515012706, and the 3rd Affiliated Hospital of Sun Yat-sen University, Clinical Research Program No. YHJH202404.\u003c/p\u003e\n\u003ch3\u003eAuthors' contributions\u003c/h3\u003e\n\u003cp\u003eFormal analysis, Jiarui Lin and Shuhong Liu; funding acquisition, Xiling Hu; investigation, Jiarui Lin, Shuhong Liu and Xiaodi Guo; supervision, Lingling Gao, Xiaodi Guo and Xiling Hu; writing – original draft, Jiarui Lin; writing – review \u0026amp; editing, Jiarui Lin and Xiling Hu.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCHINESE SOCIETY OF ENDOCRINOLOGY, CHINESE DIABETES SOCIETY, CHINESE MEDICAL DOCTOR ASSOCIATION OF ENDOCRINOLOGY AND METABOLISM. Intervention for adults with pre‑diabetes: a Chinese expert consensus (2023 edition). Chinese Journal of Diabetes, 2023, 15(06): 484-494. DOI:10.3760/cma.j.cn115791-20230509-00188.\u003c/li\u003e\n \u003cli\u003eMAGLIANO D J, BOYKO E J, IDF diabetes atlas 10th edition scientific committee. IDF diabetes atlas. 10th ed. Brussels: International Diabetes Federation, 2021.\u003c/li\u003e\n \u003cli\u003eZHENG C, DING R, WANG Q, et al. Study on the relationship between dietary patterns and dyslipidemia among residents aged \u0026ge;35 years: based on the Gannan chronic disease cohort survey data. Chinese General Practice, 2024, 27(30): 3739-3745. DOI: 10.12114/j.issn.1007-9572.2024.0048.\u003c/li\u003e\n \u003cli\u003eZHANG J, ZHOU J, YU L, et al. Dietary patterns and cardiovascular disease among adults in Guizhou Province: a cohort study. Chinese Journal of Disease Control \u0026amp; Prevention, 2024, 28(2): 161-170. 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DOI:10.2337/dc25-S008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prediabetes, diet, physical activity, normoglycemic population","lastPublishedDoi":"10.21203/rs.3.rs-5796923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5796923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe prevalence of prediabetes (preDM) is rising among adults, and lifestyle factors such as physical activity (PA) and diet play a critical role in preventing or delaying the progression to diabetes. However, the differences between PA and diet in prediabetic and normoglycemic individuals remain unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA cross-sectional study was conducted on Chinese adults without diabetes, who attended the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou from December 2023 to August 2024. Participants were divided into the preDM group (n\u0026thinsp;=\u0026thinsp;151) or the normoglycemic group (n\u0026thinsp;=\u0026thinsp;302). We assessed diet using the Planetary Health Diet Score (PHD-S), which was derived from one month of recall data, and PA using one-week recall data. After controlling for confounding factors using the propensity score matching (PSM), we compared dietary and PA differences between the two groups using the independent-samples t-tests, rank-sum tests, chi-square tests, or Fisher\u0026rsquo;s exact tests. Statistical analysis was performed using the R software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe preDM group had higher PHD-S values for saturated oils (p\u0026thinsp;=\u0026thinsp;0.007) and added sugars (p\u0026thinsp;=\u0026thinsp;0.011), but lower values for fish (p\u0026thinsp;=\u0026thinsp;0.021), soy foods (p\u0026thinsp;=\u0026thinsp;0.002), and nuts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the normoglycemic group. Regarding PA, the preDM group had significantly higher metabolic equivalent of task (MET) for light PA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but participated in fewer days of moderate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and vigorous PA (p\u0026thinsp;=\u0026thinsp;0.032). Mediation analysis revealed that BMI significantly mediated the relationship between diet and uric acid levels in the preDM group, accounting for 29.3% of the mediation effect.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSignificant differences in diet and PA were observed between the preDM and normoglycemic groups. Although the preDM group were more in line with recommended levels of saturated oil and added sugar intake compared to the normoglycemic group, their other categories of diet still fell short of guideline recommendations. Moreover, the preDM group had higher levels of light PA. Our study demonstrated that more standardized and individualized health interventions are needed to improve the lifestyle behaviors of prediabetic individuals.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Dietary and Physical Activity Behavior between Prediabetic and Normoglycemic Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 09:32:08","doi":"10.21203/rs.3.rs-5796923/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-13T08:38:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-08T08:53:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273776202263343568124208344966179097366","date":"2025-04-29T05:25:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T23:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211976098859982701113702860529261748899","date":"2025-04-28T16:07:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T15:09:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T00:38:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-14T13:59:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6292879a-5275-46d1-95f9-f680c8cc7d71","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:08:30+00:00","versionOfRecord":{"articleIdentity":"rs-5796923","link":"https://doi.org/10.1186/s12889-025-24790-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-10-08 15:58:00","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-05-06 09:32:08","video":"","vorDoi":"10.1186/s12889-025-24790-x","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24790-x","workflowStages":[]},"version":"v1","identity":"rs-5796923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5796923","identity":"rs-5796923","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}