Skip to main content
Download PDF

Mediating effect of BMI on the relation of dietary patterns and glycemic control inT2DM patients: results from China community-based cross-sectional study

BMC Public Health volume 23, Article number: 468 (2023) Cite this article

Abstract

Objective

To analyze the effects of different dietary types on in type 2 diabetes mellitus (T2DM) and determine the mediating effects of Body Mass Index (BMI) on dietary type with Fasting Plasma Glucose (FPG), Glycosylated Hemoglobin (HbA1c) on the associations in T2DM.

Methods

Data of community-based cross-sectional study with 9602 participants including 3623 men and 5979 women were collected from the project ‘Comprehensive Research in prevention and Control of Diabetes mellitus (CRPCD)’ conducted by Jiangsu Center for Disease Control and Prevention in 2018. The dietary data were collected from a food frequency qualitative questionnaire (FFQ) and dietary patterns were derived through Latent Class Analysis (LCA). Then, Logistics regression analyses were used to evaluate the associations of FPG, HbA1c with different dietary patterns. The BMI (BMI = height/weight2) was used as a moderator to estimate the mediating effect. Mediation analysis was performed using hypothetical variables, the mediation variables, to identify and explain the observed mechanism of association between the independent and dependent variables while the moderation effect was tested with multiple regression analysis with interaction terms.

Results

After completing Latent Class Analysis (LCA), the dietary patterns were divided into three categories: TypeI, TypeII, TypeIII. After adjusting for confounding factors such as gender, age, education level, marital status, family income, smoking, drinking, disease course, HDL-C, LDL-C, TC, TG, oral hypoglycemic drugs, insulin therapy, Hypertension, Coronary heart disease, Stroke, Type III were all significantly associated with HbA1c compared to those with Type I (P < 0.05), and the research showed the patients with Type III had High glycemic control rate. Taking type I as the reference level, the 95% Bootstrap confidence intervals of the relative mediating effect of TypeIII on FPG were (-0.039, -0.005), except 0, indicating that the relative mediating effect was significant (αIII = 0.346*, βIIIFPG = -0.060*). The mediating effect analysis was performed to demonstrate that BMI was used as a moderator to estimate the moderation effect.

Conclusions

Our findings demonstrate that consuming Type III dietary patterns associates with good glycemic control in T2DM and the BMI associations would be playing a two-way effect between diet and FPG in Chinese population with T2DM, indicated that Type III could not only directly affect FPG, but also affect FPG through the mediating effect of BMI.

Peer Review reports

Introduction

Diabetes has become a serious public health problem worldwide, the International Diabetes Federation (IDF) reported about 463 million with diabetesandaccounting for 9.3%in 2019,and the prevalence would from to 578 million in 2030, and will continue to 700 million in 2040, representing 10.9% of the global population [1]. As well as, evidence indicates thatobesity may influence human health through excessive energy intake [2]. Overfeeding, as major an unhealthy lifestyle of person, can have a strong impact on diabetes [3]. A study reported that diabetes treatment such as lifestyle changes and hypoglycemic drugs were associated with glucose decreases into the normal range [4], which these studies were conducted in worldwide and found that nutritional therapy were associated with the treatment of diabetic mellitus(DM) effectively [5]. In currently, vegetarian diet, Mediterranean diet and DASH diet have been advocated to play a role in treating diabetes mellitus [6, 7].

There are few studies on dietary patterns on glycemic of patients with type 2 diabetes [8, 9]. Before, a South China research found that fruits and whole grains were associated with lower risk of T2DM [10]. Previous studies targeted the morbidity of T2DM [11], to our knowledge, no such big-sample T2DM patients between dietary patterns and glycemic have been reported based on Chinese community. In addition, the associations between BMI and dietary type with glycemic remain unclear.

At present, this study was based on a large-scale community-based research, of which the participants were enrolled in basic public health service management in Huai'an city, Jiangsu Province in 2018. We conducted a survey based on the dietary habits of 9602 T2DM patients in the past year. We aimed to examine: (1)Explore the dietary status of T2DM population in Chinese community. Dietary pattern is the summary of people's food intake in a period of time, which is a relatively rigorous dietary measurement standard at present. (2) Whether such associations that between diet and glycemic are affected by the regulation of BMI from the T2DM of Chinese. We hypothesized that different dietary patterns would be associated with Fasting Plasma Glucose(FPG), Glycosylated Hemoglobin (HbA1c)control in T2DM, and such association would be have a mediating affections by BMI.

Methods

Study object and data source

The project “Comprehensive Research in Prevention and Control of Diabetes Mellitus (CRPCD)” was a community-based, large, ongoing study aiming to exploring an applicable technology for comprehensive intervention in people with T2DM, which was included at baseline investigation of T2DM patients in community chronic disease health management from in Huai'an city, Jiangsu Province. The CRPCD baseline data was collected in December 2013 to January 2014 and demographic characteristics, physical examination (height, weight, waist circumference, blood pressure), daily dietary lifestyle and disease history were self-reported by T2DM patients by investigated under the guidance of professionals. After excluding those who were in poor physical condition and refused to participate in the survey, unqualified questionnaires, some food groups and glycemic deficiency values, a total of 9602 study subjects were enrolled in the data analyses. And extracted fasting venous blood from the patients, what is it used for examining fasting plasma glucose (FPG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG) and etc. This study was reviewed by the ethics Committee of Jiangsu Center for Disease Control and Prevention (No.2013026), and all subjects signed informed consent. The specific measurement methods are shown in the published papers of our research group [12].

Description of variables

FPG and HbA1c cutoff points according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2013 Edition) [13], abnormal glycemic: FPG ≥ 7.0 mmol/L; HbA1c ≥ 6.5% mmol/L. Daily dietary intake was obtained using a semi-FFQ (food frequency questionnaire) by trained workers, which presents the consumption of each food item among T2DM, who were asked to rate whether they eating or not of rice, grains, fried foods, livestock and poultry aquatic products, egg, dairy products, fresh fruits, vegetable, Bean Products, nuts and cakes. Cumulative smoking of more than 100 cigarettes was defined as smoking, drinking was defined by whether or not they were currently drinking alcohol.

Statistical analysis

All analyses were carried out using the IBM SPSS Statistics version 20 with the exception of the mediation analyses that used the SPSS PROCESS V3.4, the dietary pattern analysis that performed using Mplus Version 7 [14].

Latent Class Analysis (LCA) was conducted to identify the dietary of113 categories of food that reflect underlying optimal number of dietary types including category probability and latent probability [15, 16], which hypothesizes the existence of dietary patterns with food groups, indicated a general dietary pattern of theT2DM patients.

Demographic characteristics were reported across the latent cluster-3, and Mean ± SD as used to describe the continuous variables with the normal distribution, inter-quartile rangewas used to represent the non-normal distribution data and the frequency (composition ratio) was used to describe the characteristics. Chi-square test for categorical variables and ANOVA for continuous variables were carried out to analyze differences between groups, statistically significance was set at p < 0.05.

Result

Dietary pattern assessment

We used Latent Category Analysis(LCA) to assigned food groups into the class corresponding to the maximum posterior probability of cluster membership. It can be seen from the Table 1 that AIC, BIC and adjusted BIC continued to decline as the number of categories increased. However, the likelihood-ratio difference test finds that the Cluster—4 is greater than 0.05 and is not meaningful, and thinks that the Cluster—3 model is due to the fourth type. Therefore, considering comprehensively, the 3-cluster is the ideal model. Finally, concluded that when food groups were divided into three modes, the model had the most moderate AIC and BIC, and the BLRT test P < 0.001, which was statistically significant, manifesting that our class was successful.

Table 1 Fitting indexes of models of different potential categories
Full size table

Based on the best models identified for the 3 potential categories, we obtained conditional probability and category probability of 13 explicit variables related to dietary patterns (Fig. 1). It is not difficult to find that in the three categories, rice, miscellaneous grains, vegetables and so on are the necessities of daily diet intake.The Cluste-3 of scores generated through the LCA were labeled “poor diet”, “moderate diet” and “balanced diet”, and the number of people corresponding to the three dietary types was,1409, 5479 and 2714 respectively. (Table 1).

Fig. 1
figure 1

Conditional probabilities of food groups on three potential categories

Full size image

(1) Type I: it showed the mainly characterized by a high consumption of rice, grains and vegetables and accounting for 14.7%.

(2) Type II: this group accounts for 57.1% and their dietary intake is mainly poultry, livestock, aquatic products, eggs and soy products on the basis of necessities such as rice, cereals and vegetables, and the number of the moderate diet category remained the most through all dietary patterns.

(3) Type III: it is mainly consumed poultry meat, livestock meat, aquatic products, eggs, dairy products, fruits and soy products on the basis of necessities such as rice, cereals and vegetables, which accounted for 28.3%.

Baseline characteristics

The baseline characteristics of the three potential category groups are shown in Table 2, 9602 type 2 diabetic population including 3623 males and 5979 females. The mean age for study subjects were (61.58 ± 10.09) years, and for the Type I was the highest (63.59 ± 9.69) years. Significant differences were found in gender, age, FPG,HbA1c, educational level, marital status, annual family income, smoking, alcohol consumption, disease course, hypertension and stroke among different dietary patterns (P < 0.05).

Table 2 Baseline characteristics of different dietary patterns in T2DM
Full size table

Effect of dietary patterns on glycemic control and Glycemic outcome

Different indexes of glycemic control in women with study subjects for different dietary patterns. Participants with the Type I had the highest value of HbA1c and the FPG. Data analysis showed that the control rate of FPG andHbA1c was different among three dietary patterns. There were421 (29.9%), 1798 (32.8%) and 948 (34.9%) FPG controls in the Type I, Type II, Type III, respectively. Moreover,531 (37.7%), 2073 (37.8%) and 1143 (42.1%) had HbA1c control, respectively.

The results of the effect of dietary patterns on different indexes of glycemic control are presented in Table 3 and Fig. 2. After adjusting gender, age, education level, marital status, family income, smoking, drinking, disease course, Hypertension, Coronary heart disease, Stroke. Logistic regression analysis showed that compared to patients with Type I, Type III had a higher control rate of FPG andHbA1c. The OR was 0.75(0.64,0.87),0.76(0.65,0.89), respectively.

Table 3 Effects of different dietary types on glycemic control
Full size table
Fig. 2
figure 2

Logistic regression analysis of dietary pattern and FPG and HbA1c. Note:*:p < 0.05.VS:Type I.**: p < 0.001.VS:Type I.OR0: Unadjusted for confounders. OR1: Adjusted for confounding factors such as gender,age,BMI, education level, marital status, family income, smoking, drinking,disease course,HDL-C,LDL-C,TC,TG,oral hypoglycemic drugs, insulin therapy, Hypertension, Coronary heart disease, Stroke

Full size image

Mediating effect analysis

As shown in Table 4 and Fig. 3, when Type I was used as a reference, the mediating effect analysis showed that dietary Type III had a statistically total effect on FPG (γ*II = 0.442, P < 0.001). After the addition of BMI, the mediating effect of BMI on FPG in group Type III was -0.021, and the 95% Bootstrap confidence interval was (-0.039,-0.005), which did not include 0, indicating significant mediating effect. The direct effect of Type II on FPG was 0.422, and the 95% Bootstrap confidence interval was (-0.687,-0.156), indicating that the direct effect was also significant. These results indicated that Type III could not only directly affect FPG, but also affect FPG through the mediating effect of BMI. However, the 95% Bootstrap confidence interval of the Type II group was (-0.028,0.002), indicating that the mediating effect was not significant in the group.

Table 4 Analysis of the mediating effect of body mass index on diet type and glycemiccontrol
Full size table
Fig. 3
figure 3

The mediating effect model of BMI on dietary type and FPG and HbA1c

Full size image

The total effect between BMI and Type III and HbA1c was -0.248 (-0.378, -0.118), p < 0.001, the difference was significant. The direct effect of BMI was 0.245(-0.375,-0.114) and the mediating effect of BMI was -0.003(-0.005,0.000) in TypeIII and HbA1c. The difference has some significance, but the significance is very weak. No mediating effect of BMI was found in TypeII.

Discussion

We classified T2DM patients into three dietary patterns classes based on their self-reported diet, and indicate. It is found that Type, Type II and Type III of the classes, our results indicate that rice, grains and vegetables were the main food components of each dietary. The trait should be a primary focus in Type I, which we initially thought seemed that related to the economic level, it is more in line with the current low-income population in China, and these rice, grains and vegetables seem to be just the necessary food sources only to maintain the human body. Based on the TypeI, TypeII people will take poultry, livestock, aquatic products, eggs and soy products as the sources of daily dietary intake. Different from the first two groups, in TypeII, people have consumed rice, grains, vegetables, poultry, livestock, aquatic products, eggs and soy products, dairy products, fruits, and so on (Fig. 1).

The Chinese Community research demonstrated that a correlation between different dietary patterns and glycemic, which the balanced diet was a consistent protective factor of FPG and HbA1camong 9602 T2DM patients enrolled in this study,3747 (39.0%) of FPG and 3167 (33.0%) of HbA1c were within the control range. Latent Class Analysis need to be taken into account in the research, in order to accurately explore which diet was more beneficial to control the glycemic in the study subjects. American Diabetes Association with higher adherence to reduce the intake of high-energy and high-fat foods about in 2013 [17]. It is well known that nutritional balance is an important factor in maintaining human health and well-being throughout the lifecycle [18]. Ensuring a balanced diet means having food diversity, which is the reason of food contains a variety of nutrients, and a variety of nutrients in different foods [19].

Our study was able to identify three dietary patterns associated with glycemic control in T2DM patients. Among the three dietary types, TypeIII had a higher glycemic control rate and had a greater reduction in glycemic risk than TypeI. TypeIII significantly reduced FPG (0.75(0.64,0.87)) and HbA1c (0.75(0.65,0.89), which the main feature of dietary intake is greater diversity, suggesting that study subjects should maintain a diversified diet as much as possible in order to better improve the glycemic control rate of T2DM patients.

The relationship between Type III and glycemic may have the following explanations: First, Dietary diversity is considered by nutritionists to be an important factor in improving dietary quality, which can improve the nutritional status of the human body. The richer the diet type, the more balanced the nutrition [20, 21]. Secondly, only when the dietary intake is more abundant, the interaction of various nutrients in the body can maintain balance [22]. For example, dairy products were the main source of calcium and vitamin D, which could help the body better and protect its function [23]. And that fresh fruits are important sources of antioxidants, which can be consumed in moderation to prevent functional damage by reducing inflammation and oxidative stress [24]. Therefore, this suggests that study subjects should be encouraged to intake more and pay attention to their daily diet and eat a variety of foods within the economic range to ensure a balanced diet.

BMI is widely used to measure general obesity and is currently recognized as the best indicator to measure systemic obesity [25]. Our study showed that using TypeI as a control, FPG and HbA1c were associated with TypeIII through BMI, with a mediating effect of -0.021(-0.039,-0.005). A possible explanation is that there may be close relationship between BMI and diet. Besides, there is previous evidence for the associations between each of these factors (dietary-glycemic、dietary-BMI, BMI-glycemic) [26,27,28,29,30,31]. And in this study, we find that TypeIII can significantly reduce glycemic.

Long-term development of type 2 diabetes is associated with inadequate glycemic control [32]. Previous studies have confirmed that the effects of FPG and HbA1c are different, and study subjects who vegetarian dietary pattern can effectively reduce HbA1c, but has little effect on FPG [33]. Before adjusting confounding factors, TypeII could reduce HbA1c in this study (P = 0,04), but had little effect on FPG, compared with TypeI as a control. At the same time, the results of mediation analysis showed that BMI accounted for about 4.98% of the influence between TypeIII and FPG. However, the effect of BMI between TypeIII and HbA1c was small, accounting for about 1.22%, and our mediation analysis found that the mediation effect was also close to the critical value, which may be limited by our sample size and study population.

Advantages of the research

First, the sample size was large (n = 9602 T2DM patients) and the results were reliable, for which dietary data collected over the past year better reflect participants' daily dietary intake status than the three-day weighting method [34]. To our knowledge, this is the first study of analyzing the role of BMI in dietary patterns and glycemic associated with type 2 diabetes in Chinese which has a very novel characteristics.

Limitations of the research

The current study has several limitations. First, this study is a cross-sectional study, and it is difficult to infer causality. Frequency of self-reported dietary intake by the food frequency questionnaire was prone to recall bias. Second, Analysis of dietary patterns is subject to geographic, cultural, ethnic and economic differences, which is subjective. It is also important to note the limitations of statistical methods. The assignment of food groups to latent classes is based on their highest estimated probability to the identified pattern. Therefore, these underlying patterns should not be considered as actual dietary patterns, but as approximations of more complex patterns. Therefore, prospective studies are needed to further explore the potential mechanism between study subjects diet and glycemic.

Conclusion

TypeIII contains poultry meat, livestock meat, aquatic products, eggs, dairy products, fruits, soy products, rice, cereals and vegetables, are a relatively diverse diet structure. This study found that dietary diversity can significantly reduce FPG and HbA1c in T2DM patients, which firstly identified and BMI significantly mediated the association between diet and fasting glucose. The findings provided new insights in understanding the possible mechanisms involved in the association between dietary and BMI.

Availability of data and materials

The data was obtained from Huai 'an Center for Disease Control and Prevention, Jiangsu Province. The data is confidential and cannot be disclosed. If anyone needs data from this study, they should contact corresponding author Enchun Pan.

References

  1. Aschner P, Karuranga S, James S, Simmons D, Basit A, Shaw JE, Wild SH, Ogurtsova K, Saeedi P. International diabetes federation’s diabetes epidemiological guide writing G: the international diabetes federation’s guide for diabetes epidemiological studies. Diabetes Res Clin Pract. 2021;172: 108630.

    Article  PubMed  Google Scholar 

  2. Oussaada SM, van Galen KA, Cooiman MI, Kleinendorst L, Hazebroek EJ, van Haelst MM, Ter Horst KW, Serlie MJ: The pathogenesis of obesity. Metabolism: clinical and experimental 2019, 92:26–36.

  3. Mozaffarian D, Wilson PW, Kannel WB. Beyond established and novel risk factors: lifestyle risk factors for cardiovascular disease. Circulation. 2008;117(23):3031–8.

    Article  PubMed  Google Scholar 

  4. American Diabetes A: 6. Glycemic Targets: Standards of Medical Care in Diabetes-2019. Diabetes care 2019, 42(Suppl 1):S61-S70.

  5. Franz MJ, MacLeod J, Evert A, Brown C, Gradwell E, Handu D, Reppert A, Robinson M. Academy of nutrition and dietetics nutrition practice guideline for Type 1 and Type 2 diabetes in adults: systematic review of evidence for medical nutrition therapy effectiveness and recommendations for integration into the nutrition care process. J Acad Nutr Diet. 2017;117(10):1659–79.

    Article  PubMed  Google Scholar 

  6. Esposito K, Maiorino MI, Bellastella G, Chiodini P, Panagiotakos D, Giugliano D. A journey into a Mediterranean diet and type 2 diabetes: a systematic review with meta-analyses. BMJ Open. 2015;5(8): e008222.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pawlak R. Vegetarian diets in the prevention and management of diabetes and its complications. Diabetes Spectr. 2017;30(2):82–8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lee YM, Kim SA, Lee IK, Kim JG, Park KG, Jeong JY, Jeon JH, Shin JY, Lee DH. Effect of a brown rice based vegan diet and conventional diabetic diet on glycemic control of patients with type 2 diabetes: A 12-Week randomized clinical trial. PLoS ONE. 2016;11(6): e0155918.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hou Q, Li Y, Li L, Cheng G, Sun X, Li S, Tian H. The Metabolic effects of oats intake in patients with type 2 diabetes: a systematic review and meta-analysis. Nutrients. 2015;7(12):10369–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ye Y, Zhuo S, Lu W, He K, Sui Y, Li Y, Chen Y, Wu S, Chen P, Fang S: A diet rich in fruit and whole grains is associated with a low risk of type 2 diabetes mellitus: findings from a case-control study in South China. Public Health Nutr 2020:1–12.

  11. Jannasch F, Kroger J, Schulze MB. Dietary patterns and type 2 diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr. 2017;147(6):1174–82.

    Article  CAS  PubMed  Google Scholar 

  12. Jian Su, Qin Yu, Chong S, Yan G, Pan Enchun Du, Wen-Cong Z-y, Zhang Yong-qing Wu, Ming,. Comprehensive control of patients with type 2 diabetes mellitus under community management in Jiangsu province. Chin J Endocrinol Metabolism. 2018;34(2):9.

    Google Scholar 

  13. Weng Jianping: Chinese guidelines for the prevention and treatment of type 2 diabetes mellitus (2013 edition). Chin J Endocrinol Metabolism 2014, 30(10):893–942.

  14. Muthén L, Muthén B, Muthén LK, Muthén BO, Muthén B, Muthén LK, Muthén BO: Mplus user's guide. 1998.

  15. Keshteli AH, Feizi A, Esmaillzadeh A, Zaribaf F, Feinle-Bisset C, Talley NJ, Adibi P. Patterns of dietary behaviours identified by latent class analysis are associated with chronic uninvestigated dyspepsia. Br J Nutr. 2015;113(5):803–12.

    Article  CAS  PubMed  Google Scholar 

  16. Mattsson M, Murray DM, Kiely M, McCarthy FP, McCarthy E, Biesma R, Boland F. Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis. BMC Pediatr. 2021;21(1):180.

    Article  PubMed  PubMed Central  Google Scholar 

  17. American Diabetes A. Standards of medical care in diabetes–2013. Diabetes Care. 2013;36(Suppl 1):S11-66.

    Article  Google Scholar 

  18. Black M, Bowman M. Nutrition and healthy aging. Clin Geriatr Med. 2020;36(4):655–69.

    Article  PubMed  Google Scholar 

  19. Xin S. The dietary guidelines for Chinese residents (2016) was released. Chinese Maternal and Child Health Research. 2016;5:1.

    CAS  Google Scholar 

  20. Zhang J, Zhao A, Wu W, Yang C, Ren Z, Wang M, Wang P, Zhang Y. Dietary diversity is associated with memory status in Chinese adults: a prospective study. Frontiers in aging neuroscience. 2020;12: 580760.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ganpule-Rao AV, Bhat D, Yajnik CS, Rush E. Dietary diversity scores, nutrient intakes and biomarkers vitamin B12, folate and Hb in rural youth from the Pune Maternal Nutrition Study. Br J Nutr. 2021;126(2):236–43.

    Article  CAS  PubMed  Google Scholar 

  22. Mirmiran P, Azadbakht L, Esmaillzadeh A, Azizi F. Dietary diversity score in adolescents - a good indicator of the nutritional adequacy of diets: Tehran lipid and glucose study. Asia Pac J Clin Nutr. 2004;13(1):56–60.

    CAS  PubMed  Google Scholar 

  23. Houston DK, Stevens J, Cai J, Haines PS. Dairy, fruit, and vegetable intakes and functional limitations and disability in a biracial cohort: the Atherosclerosis Risk in Communities Study. Am J Clin Nutr. 2005;81(2):515–22.

    Article  CAS  PubMed  Google Scholar 

  24. Welch AA. Nutritional influences on age-related skeletal muscle loss. Proc Nutr Soc. 2014;73(1):16–33.

    Article  CAS  PubMed  Google Scholar 

  25. Zhang X, Shu XO, Li H, Yang G, Xiang YB, Cai Q, Ji BT, Gao YT, Zheng W. Visceral adiposity and risk of coronary heart disease in relatively lean Chinese adults. Int J Cardiol. 2013;168(3):2141–5.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chan RS, Woo J. Prevention of overweight and obesity: how effective is the current public health approach. Int J Environ Res Public Health. 2010;7(3):765–83.

    Article  PubMed  PubMed Central  Google Scholar 

  27. D’Innocenzo S, Biagi C, Lanari M. Obesity and the Mediterranean diet: a review of evidence of the role and sustainability of the Mediterranean diet. Nutrients. 2019;11(6):1306.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sipe AT, Neuhouser ML, Breymeyer KL, Utzschneider KM. Effect of dietary Glycemic index on beta-cell function in prediabetes: a randomized controlled feeding study. Nutrients. 2022;14(4):887.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Alami F, Mohseni GK, Ahmadzadeh M, Vahid F, Gholamalizadeh M, Masoumvand M, Shekari S, Alizadeh A, Shafaei H, Doaei S. The association between fasting blood sugar and index of nutritional quality in adult women. Front Nutr. 2022;9: 883672.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Al-Adwi ME, Al-Haswsa ZM, Alhmmadi KM, Eissa YA, Hamdan A, Bawadi H, Tayyem RF: Effects of different diets on glycemic control among patients with type 2 diabetes: A literature review. Nutrition and health 2022:2601060221112805.

  31. Nogueira-de-Almeida CA, Ued FDV, Contini AA, Martinez EZ, Del Ciampo LA, Nogueira-de-Almeida ME, Ferraz IS, Silva RFB, de Mello ED, Fisberg M. Anthropometric indicators of body composition associated with lipid and glycemic profiles in overweight Brazilian Children and adolescents from 2008 to 2020. Front Nutr. 2022;9: 908562.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wacher NH, Silva M, Valdez L, Cruz M, Gomez-Diaz RA. Poor metabolic control in primary care. Gac Med Mex. 2016;152(3):350–6.

    PubMed  Google Scholar 

  33. Yokoyama Y, Barnard ND, Levin SM, Watanabe M. Vegetarian diets and glycemic control in diabetes: a systematic review and meta-analysis. Cardiovascular diagnosis and therapy. 2014;4(5):373–82.

    PubMed  PubMed Central  Google Scholar 

  34. Yu FL, Ye Y, Yan YS: [Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian]. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi 2017, 38(5):602–610.

Download references

Acknowledgements

The data used in this study were from the survey organized by Huai 'an Center for Disease Control and Prevention, and the data collection service provided by the Qinghe District Center for Disease Control and Prevention in Huai'an city.

Funding

This work was supported by the National Natural Science Foundation of China (71974101) and Jiangsu Provincial Fourth ‘333 Project’ (BRA2013107).

Author information

Authors and Affiliations

  1. Huai ’an Center for Disease Control and Prevention, Huaian, 223001, China

    Saiyi Wang, Jinbo Wen, Dandan Miao, Zhongming Sun & Enchun Pan

  2. School of public health, Xuzhou Medical University, Xuzhou, 221004, China

    Saiyi Wang & Enchun Pan

  3. Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, Nanjing, 211166, China

    Dianjiang Li

Authors
  1. Saiyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinbo Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dandan Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhongming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dianjiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Enchun Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EnchunPan and SaiyiWang designed the study and oversaw the entire process. EnchunPan conducted interviews, SaiyiWang, ZhongmingSun, JinboWen, DianjiangLi collected data and samples, DandanMiao and Siyi Wang analyzed the data and drafted the paper. All authors have read and approved the final paper.

Corresponding author

Correspondence to Enchun Pan.

Ethics declarations

Ethics approval and consent to participate

This study was reviewed by the ethics Committee of Jiangsu Center for Disease Control and Prevention (No.2013026), and all subjects signed informed consent. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declared no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saiyi Wang, Wen, J., Miao, D. et al. Mediating effect of BMI on the relation of dietary patterns and glycemic control inT2DM patients: results from China community-based cross-sectional study. BMC Public Health 23, 468 (2023). https://doi.org/10.1186/s12889-022-14856-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-14856-5

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us