Association of fourteen years diet quality trajectories and type 2 diabetes mellitus with related biomarkers

Diet quality distance trajectories

We identified three trajectories with comparable DQD throughout 14 years of diet intake, as shown in Figure 1. The first trajectory labelled class 1 represented 2.14 % of the participants with a moderate to persistent decline of DQD level corresponding to Chinese adults who decreased their DQD level from moderate to low. The second trajectory identified as class 2 represented 6.18% of the participants with a high to persistently moderate DQD level, which corresponds to Chinese adults with a high DQD level at early adulthood whose level persistently declined and remained moderate with age. The third trajectory, named class 3, represented 91.68% of the participants with a persistently high trajectory for those with a high DQD level throughout adulthood. According to the elevated DQD level found in all trajectories, we defined the three classes as lower unhealthier group, moderate unhealthier group, and higher unhealthier group for class 1, class 2, and class 3.

Participants’ characteristics by trajectory class

Participants within the three DQD trajectories class had distinct clinical profiles, as shown in Table 1. Significant differences in sex, age, DBP among the three classes were observed. The proportion of men was the largest in class 2 (68.8%), and women were more represented in class 3 (53.6%). Participants in class 1 were younger (mean (SD) = 38.48 (10.78)), while participants in classes 2 and 3 were relatively older (mean (SD) = 48.02 (8.51) and (mean (SD) = 45.01(11.32)) years respectively. The DBP significantly varied across different trajectories. There was no significant difference in BMI, PAL, SBP, hypertension status, smoking status across DQD trajectories (p-value > 0.05). Significant distinct demographic profiles were also observed across trajectories. The participants in the first class tended to have higher education (10.7%), with a high urbanicity index (56.0%), lived in the northern region (60.9%), all p-value < 0.05. Participants of all classes tended to live in rural areas, with the highest percentage of 67.5% represented in the third class.

Participants’ food components scores frequency

Table 2 shows the scores for the DBI-07 food subgroups and the participants’ percentages with each score. Excessive (DBI-HBS) and insufficient (DBI-LBS) food intake were coexistent. In total, 43.6% and 32.6% of the participants consumed red meat products, poultry, and game in insufficient and excessive amounts, respectively. Cooking oil, alcohol, and salt were consumed in suitable quantities; almost 100% of the participants obtained a score of 0. Overall, 62% of the participants had high cereals intake, and 13.7% had reached the maximum score of +12. Fruits, vegetables, dairy, diet variety, drinking water, soybean, eggs, fish, and shrimp were consumed in small quantities by the majority. Almost 40% had a score between -8 and -7 for water intake, 47.4% of adults had a score between -6 and -5, representing the lower scores for soybean intake, 96.7%, 80.3%, and 75.1% of the participants had the lower score for dairy, fruits, fish, and shrimps’ intake respectively. Only 1.4%, 0.2%, 2%, and 0% had a score of 0 for water, dairy, fruits, and diet variety, respectively.

Participants' food subgroups intake by DQD trajectory class

We observed significant differences in most food subgroups intake across all trajectories, as shown in Table 3. Participants of all trajectories had a significantly different intake of almost all food subgroups. Diet patterns across all classes tended to be low in alcohol, salt, cooking oil, fruits, dairy, diet variety, water, with sufficient vegetable intake. Cereals were consumed in appropriate amounts in class 3 (mean (SD) = 433.35g (290.30)), while class 1 and class 2 had higher cereals’ intake. Participants across class 3 had the lowest intake of meat products (mean (SD) = 38.19g (66.01)), alcohol (mean (SD) = 0.6g (2.8)), fish (mean (SD) = 23.11g (48.04)), fruits (mean (SD) =29.97g (96.22)), eggs(mean (SD) = 20.65g(32.46)), dairy (mean (SD) = 5.01g(30.49)), diet variety(mean (SD) = 59.08g (36.73)) and water(mean (SD) = 539.42 L (215.71)). Although they were all consumed in insufficient amounts, class 1 had higher amount of fruits (mean (SD) = 133.86g (173.08)), dairy(mean (SD) = 60.83g (104.03)), fish and shrimp(mean (SD) = 58.89g (76.92)), diet variety(mean (SD) = 92.79g (41.66)), and water(mean (SD) = 873.86L (592.12)). Red meat products, poultry and game intake were high in class 1(mean (SD) = 154.46g (126.38)). Participants in the second trajectory had an adequate intake of red meat products, poultry and game (mean (SD) = 71.19g (86.01)). Vegetables were consumed in adequate amounts in all trajectories. In terms of scores, the diet patterns mentioned above were somewhat confirmed in Supplementary Table 1, considering the participant’s distribution by food components score across all trajectories. Class 3 had the higher proportion of participants with a smaller negative score of (-6) to (-5) for fruits (81.1%), dairy (97.8%), soybean (48.3%), and the score of (-4) to (-3) for fish, shrimps (75.8%) and eggs (45.5%). Diet variety and water intake were inadequate for most participants across all trajectories. More than 40% of the participants had a low score of (-8) to (-7) for water intake and 30.6% for diet variety intake across class 3. Participants across class 3 had adequate intake for cereals’; 17.2% had reached the minimum score of 0. Class 1 had a higher percentage (44.1%) of the participants with a score of 0 for vegetable intake. Participants across class 1 had the smallest percentage of adults with the lower score of (-6) to (-5) for fruits (42.7%), dairy (73.1%), soybean (21.2%), and the score of (-4) to (-3) for fish and shrimps (30.6%), and eggs (6.3%). The same trajectory had a higher proportion of individuals with high meat products intake for an overall 56.3%, with almost 30% of the participants having a higher score of 4 for red meat products, poultry, and game intake. Throughout the third trajectory, only 7.5% of the participants had a high score of 4 for meat products intake.

Diet quality distance trajectories and the risk of T2DM.

The association between diet quality distance trajectories and T2DM is represented in Table 4. During the follow-up period, 332 participants were identified as having T2DM. Compared to class 1, the multivariate-adjusted models showed an increased risk of T2DM in all classes for all models. In class 2 (HR = 3.81, 95% CI: 1.69-8.58); (HR = 4.14, 95% CI: 1.85-9.25) and (HR = 4.40 95%CI: 2.02-9.59) for models 1, 2, and 3 respectively. Class 3 (HR = 3.04, 95%CI: 1.77-5.23); (HR = 3.47, 95% CI: 1.96-6.12); and (HR = 3.68; (95% CI: 2.11-6.43) for models 1, 2, and 3 respectively. Using class 3 as the reference since it had the lower intake of meat products, after adjusting for all covariates, we still found an increased risk of T2DM in class 1 (HR = 3.28, 95% CI: 1.91-5.63, (HR = 2.88, 95%CI: 1.63-5.08) and (HR = 2.71, (95% CI: 1.55-4.73) for model 1,2, and 3 respectively. There was no significant association for class 2 considering class 3 as reference group.

Diet quality distance trajectories and T2DM biomarkers

Table 5 shows the T2DM biomarkers' differences across all trajectories. Fasting Plasma Glucose (FPG), HbA1c (hemoglobin), HDL-C, Uric acid, and triglyceride concentrations in class 2 and class 3 were higher than class 1 (all p for trend < 0.05). High-sensitivity C-reactive protein (hs-CRP) and HOMA-IR in all trajectories showed non-significant higher trends (p >0.05).

Association between diet quality distance trajectories and T2DM related markers.

We implemented a random-effects model to describe the change in biomarkers among the three classes (see Table 6). After adjusting for all potential covariates, no associations were observed across all trajectories with all identified biomarkers (p values > 0.05) (Table 6).

Ethics statement

The China Health and Nutrition Survey (CHNS) used for this study was approved by the Institutional Review Committees of the University of North Carolina at Chapel Hill; the China-Japan Friendship Hospital, the Chinese Ministry of Health Institute of Nutrition and Food Safety, and China Centers for Disease Control. The subjects gave informed consent for participation.

Study design and population

This study used the data collected by the CHNS, which is an open, prospective survey designed to examine the association between economic and social changes and a range of health behaviours across space and time [43]. The CHNS collected health data in 228 communities throughout China from 1989 to 2011 in eight survey rounds. A multistage, random cluster sampling method was used to select the samples from nine provinces (Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou) in China. Questionnaires were used to collect the following data: social and economic factors, health factors, nutrition factors, and population factors of sociology. Blood samples were collected only for the first time in 2009. More details about the CHNS data have been described elsewhere [44].

Selection criteria

Exclusion criteria

A total of 25611 adults (men and women) provided a 3-day 24h dietary recall from 1997-2011.

Were excluded from this study subjects,

• less than 18 years old (n = 10415);

• more than 65 years old (n = 891);

• with extreme values of daily energy intake less than 800 and more than 4200 for men (n =225), energy intake less than 500 and more than 3500 for women (n = 281),

• with prior diagnosis of obesity (n = 542), hypertension (n = 2822), diabetes (n = 189), other cardiovascular diseases (n=14) at wave of entry;

• pregnant women (n= 181);

• with only one-year survey (n=2104)

Inclusion criteria

A total of 8088 participants aged from 18-65 years old, without underlying conditions, unpregnant, within acceptable daily energy intake limits, with more than one-year survey, were included in this study. The number of visits ranged from two to six surveys (two visits, n = 2,528; three visits, n = 1,552; four visits, n =1,499; five visits, n = 1,318; six visits, n = 1,191; median = four visits; across 29,444 observations). Figure 2 is a flow chart showing participants' selection.

Dietary assessment and food grouping

The food intake was measured over three consecutive days (including two weekdays and one weekend day) with 24 h recalls and a household food inventory. Every household member aged two and above was asked to report any food consumed, whether at home or away from home. Trained interviewers recorded the types and amounts of food consumed at each meal via a face-to-face interview with food image and model aids. During the past 12 months, participants were asked about the drinking frequency and the number of alcoholic beverages (such as high alcohol liquor, low alcohol liquor, wine, beer, etc.). Alcohol was converted to DBI-07 criteria in each alcoholic beverage [45] and then summed up to calculate average alcohol consumption per day. After carefully reviewing the dietary data, food group classifications were summed up corresponding to the Chinese Table of Food Composition [46].

Dietary balance index-07

The revised Chinese DBI-07 (revised from the DBI-2002) aims to assess the Chinese population's overall dietary quality. The DBI-07 is based on the core items of the Chinese dietary guide [23] formulated by the Chinese society of nutrition. The core items are composed of seven components, including (1) cereals; (2) vegetables and fruits; (3) milk and dairy products, soybean and soybean products; (4) Animal food; (5) condiments and alcoholic beverage; (6) Diet variety; (7) drinking water [45]. For each component of DBI-07, a score of 0 indicates that the recommended intake amounts are met. In DBI-07, positive scores (range= 0–12) are used to assess the excessive intake levels of alcoholic beverages and condiments, which are recommended to be consumed in a "reduced" or "limited" amount according to the guidelines. The DBI-07 gives negative scores (range = -12–0) assessing inadequate intake of vegetables and fruits; dairy products, soybean and soybean products; dietary variety and drinking water recommended by nutritional guidelines to be consumed 'plenty' or 'sufficient' amounts. Furthermore, DBI-07 also used both negative and positive scores for cereals (range = -12–12) and animal food (range = -12–8), which the dietary guidelines recommended consuming in 'appropriate amount'. The scoring of the 7 components is based on seven levels of energy intake when considering the difference in nutrient requirements in energy consumption. To evaluate the diet variety in DBI-07,12 identified food subgroups are used: 1) rice and rice products; 2) wheat and wheat products; 3) corn, coarse grains, and products, starchy roots and products; 4) dark-colored vegetables; 5) light-colored vegetables; 6) fruits;7) soybean and soybean products; 8) milk and dairy products; 9) red meat and meat products; 10) poultry and games; 11) eggs; 12) fish and shellfish. A food subgroup was assigned a score of 0 if the intake reached or exceeded the lowest recommended intake else; a score of −1 was assigned. For soybean and products, the lowest recommended intake is 5 g, and for the other 11 food subgroups, 25 g. Diet variety component scores vary between −12 to 0. There are diet quality indicators calculated based on the scores for each DBI-07 component. Higher bound score (HBS), the excessive food intake indicator, is calculated by adding all the positive scores. Lower bound score (LBS), the indicator of inadequate food intake, is calculated by adding all the negative scores. Diet quality distance (DQD) assesses imbalanced food intake by adding absolute values of both positive and negative scores. If the overall score is negative, it shows that the average intake of food is insufficient; if the overall score is positive, it shows that the average intake of food is excessive. [45, 47, 48] More details on DBI-07 can be found in “The Chinese Diet Balance Index revised” [45].

Outcome ascertainment and anthropometric variables assessment T2DM ascertainment

T2DM was identified by self-reports of a history of T2DM diagnosis and/or fasting blood glucose ≥7.0 mmol/l and/or HbA1c ≥40 mmol/mol (6.5%), and/or receiving diabetes treatment. Detailed information on the treatment for T2DM included six sections: (1) special diet; (2) weight control; (3) oral medication; (4) insulin injection; (5) Chinese traditional medicine; (6) home remedies. Data for the incidence of self-reported T2DM were first collected in 1997, and subsequently in 2000, 2004, 2006, 2009 and 2011. Fasting blood glucose and HbA1c were also used to identify incident diabetes cases in the 2009 survey. The total incident T2DM included was 332.

Body mass index

Weight and height were measured in light clothing and without shoes. Weight was measured to the nearest 0.1 kg using a calibrated scale; and height to the nearest 0.2 cm using a portable stadiometer. BMI was calculated by dividing body weight by height in meters squared.

Hypertension

Elevated blood pressure was defined as an average of systolic blood pressure ≥140mmHg, and /or an average of diastolic blood pressure ≥90 mmHg. Participants were classified as having hypertension if their systolic blood pressure and/or diastolic blood pressure or both had exceeded the values above if they were taking hypertension drugs, and/or if they were diagnosed with high blood pressure.

Other variables assessment

Demographic characteristics were collected by trained interviewers using standard questionnaires for each individual at-home visits, including age, gender, area of residence, household income, highest education level. Based on population density, the area of residence was categorized as urban or rural. Physical activity level (PAL) was measured using questionnaires that collected the time spent in all activities in a typical week, including work, leisure, travel, and household chores. A summary measure expressed as total metabolic equivalents (METs) was created by multiplying the time spent in each activity with the METs of each activity. The time spent in each activity was converted into metabolic equivalent of task (MET) hours per week based on the compilation of physical activities [49]. The Urbanicity index was defined using 12 multidimensional components reflecting the heterogeneity in economic, social, demographic, and infrastructural changes at the community level. Further, we also considered geographical factors as northern, East Coast, and central provinces. Smoking status and total energy intake were also considered as potential confounders.

Statistical analysis

LCMM was used to derive 14 years of DBI-DQD trajectories using the revised DBI-07. DBI-DQD is an indicator of the Diet Balance Index for Chinese derived from adding the absolute value of both DBI-LBS and DBI-HBS as described above. DBI-DQD informs us about the level of imbalance of one's diet.

LCMM clustered Chinese adults according to the similarities in DBI-DQD. The membership to class trajectories was treated as the exposure to evaluate the association with T2DM. We identified 3 classes of subjects with comparable DQD throughout 14 years of diet intake. The survey age was used as a timescale for the trajectories. We modelled the DBI-DQD trajectories among participants who were recruited in all wave. To determine the best fit of our models, we used rigorous criteria such as the lowest Bayesian information criterion, including at least 2% of the sample population within each trajectory class. Once DBI-DQD trajectories were determined and all individuals assigned to corresponding trajectories, a chi-square test was used to assess the differences for categorical variables and ANOVA to assess the differences for continuous variables. Food components were also compared among the trajectories to determine their differences in food intake.

Furthermore, we conducted a multivariate mixed-effect cox regression model clustered by ID (identifier) with Breslow method to account for time-varying covariates. Hazard ratio (HR) was used to estimate the association between three DBI-DQD trajectories and T2DM incidence. A random effect linear regression was also used to examine the changes in T2DM biomarkers. A two-sided p values < 0.05 was considered statistically significant. DBI-DQD and T2DM biomarkers were log-transformed to improve the normality of the distribution. All statistical analyses were performed using R 3.5.3 (https://www.r-project.org/).

Data availability

The Data from China Health and Nutrition Survey used in this study can be downloaded at https://www.cpc.unc.edu/projects/china.