Abstract
Aims
We aimed to investigate the relationship of dietary zinc intake with new-onset diabetes among Chinese adults.
Materials and Methods
A total of 16 257 participants who were free of diabetes at baseline from the China Health and Nutrition Survey were included. Dietary intake was measured by 3 consecutive 24-hour dietary recalls combined with a household food inventory. Participants with self-reported physician-diagnosed diabetes, or fasting glucose ≥ 7.0 mmol/L, or glycated hemoglobin ≥ 6.5% during the follow-up were defined as having new-onset diabetes.
Results
A total of 1097 participants developed new-onset diabetes during a median follow-up duration of 9.0 years. Overall, the association between dietary zinc intake and new-onset diabetes followed a U-shape (P for nonlinearity < 0.001). The risk of new-onset diabetes was significantly lower in participants with zinc intake < 9.1 mg/day (per mg/day: hazard ratio [HR], 0.73; 95% CI, 0.60-0.88), and higher in those with zinc intake ≥ 9.1 mg/day (per mg/day: HR, 1.10; 95% CI, 1.07-1.13). Consistently, when dietary zinc intake was assessed as deciles, compared with those in deciles 2-8 (8.9 -<12.2 mg/day), the risk of new-onset diabetes was higher for decile 1 (<8.9 mg/day: HR, 1.29; 95% CI, 1.04-1.62), and deciles 9 to 10 (≥12.2 mg/day: HR, 1.62; 95% CI, 1.38-1.90). Similar U-shaped relations were found for plant-derived or animal-derived zinc intake with new-onset diabetes (all P for nonlinearity < 0.001).
Conclusions
There was a U-shaped association between dietary zinc intake and new-onset diabetes in general Chinese adults, with an inflection point at about 9.1 mg/day.
Keywords: dietary zinc intake, new-onset diabetes, general population, CHNS
Diabetes is a major public health challenge worldwide and is a key contributor to morbidity and mortality (1). In 2019, International Diabetes Federation estimates that about 463 million adults are suffering from diabetes worldwide and the number is estimated to reach 700 million by 2045 (2). To curb the trajectory of diabetes burden worldwide, concerted efforts for the primary prevention of diabetes are thus important and necessary.
In recent decades, lifestyle interventions, with overall diet modifications, have been suggested to be effective in reducing diabetes risk (3, 4). Although there is a lot of evidence for different dietary patterns and macronutrient compositions for the prevention of diabetes (4), the influence of micronutrient intakes, such as zinc, on the risk of developing diabetes remains to be elucidated. Zinc is an essential trace element that exists in all cells and is required by thousands of proteins for catalytic, structural, or transcriptional functions (5). Especially, zinc affects multiple aspects of insulin homeostasis and the inflammatory response in diabetes mellitus (6). However, the associations between serum or plasma zinc concentration and risk of diabetes are inconsistent (7-9). Besides, although some randomized controlled trials had reported that zinc supplementation may have some beneficial effect on glucose metabolism, these randomized trials (6, 10) mainly examined the effects of relatively high-dose zinc supplementation (median: about 30 mg/day) in patients with diabetes or prediabetes rather than the effects of dietary zinc derived from foods in general population. Actually, the associations between dietary zinc derived from foods and the risk of new-onset diabetes are also mixed. The Nurses’ Health Study (NHS) cohort reported an inverse association between dietary zinc intake and type 2 diabetes mellitus in US women (5). Some subsequent prospective cohort studies, however, reported inconsistent findings, including the negative (11, 12), positive (13), or no significant relation (14-16) between dietary zinc intake and incident diabetes. Of note, few previous studies have comprehensively examined the possible modifiers on the association between dietary zinc intake and new-onset diabetes. Moreover, few previous studies have been conducted using the dietary zinc intake data continuously, which may allow for the possibility of nonlinear relation of zinc intake with the risk of diabetes and provide more detailed information. Therefore, the relation of dietary zinc intake with new-onset diabetes remains uncertain.
To fill this aforementioned gap in knowledge, our current study aimed to investigate the prospective relation of dietary zinc intake with new-onset diabetes, and examine possible effect modifiers on the association, using data from the China Health and Nutrition Survey (CHNS), a national health and nutrition survey in China.
Materials and Methods
Population and Study Design
The present study used data from the CHNS, an ongoing multipurpose, prospective open-cohort study initiated in 1989 in China. Details on the study design and major results of the CHNS can be accessed from the official website (http://www.cpc.unc.edu/projects/china) and previous reports (17-21). The CHNS was scheduled for follow-up every 2 to 4 years. The CHNS rounds have been conducted in 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. In each survey round, demographic, socioeconomic, lifestyle, nutritional, and health information were collected. By 2011, the CHNS included 12 provinces/autonomous cities and 288 communities, and the provinces included in the CHNS constituted 47% of China’s population (17).
The current study used 7 rounds of CHNS data from 1997 to 2015. Among the initial participants (94 532 person-waves), those who were pregnant (360 person-waves), aged < 18 years (17 672 person-waves), with missing diabetes diagnosis (1034 person-waves), and who participated with only 1 survey wave (8919 person-waves) were excluded. Then, we generated a cohort based on participants with 2 or more survey waves, which include 16 895 participants of 66 547 person-waves. In this cohort, we further excluded 444 participants with diabetes at baseline, 103 participants with missing dietary zinc data, and 91 participants with implausible dietary energy intake (22) (male: >4200 or < 600 kcal/day; female: >3500 or < 500 kcal/day). Finally, a total of 16 257 participants were included in the final analysis (Supplemental Figure 1) (23).
The institutional review boards of the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety, and the Chinese Center for Disease Control and Prevention, approved the study. Each CHNS participant provided their written informed consent.
Assessments Dietary Nutrient Intakes
Dietary data in the CHNS were collected by trained nutritionists through a face-to-face interview in each survey round. The individual diet was repeatedly assessed by 3 consecutive 24-hour dietary recalls at the individual level in combination with a weighing inventory over the same 3 days at the household level. The 3 consecutive days were randomly allocated from Monday to Sunday and are almost equally balanced across the 7 days of the week for each sampling unit. Nutrient intakes were calculated based on diet data and the compositions in the China Food Composition Tables (FCTs). The accuracy of 24-hour dietary recall designed to assess energy and nutrient intake has been validated (24, 25).
In the analyses, 3-day average intakes of dietary macronutrients and micronutrients in each round were calculated. Cumulative average intake values of each nutrient from baseline to the last visit before the date of new-onset diabetes or the end of follow-up were further calculated to represent long-term dietary intake and minimize within-person variation. In the current study, we evaluated energy-adjusted nutrient intake for dietary zinc using the residual method (26).
Assessments of Covariates
Information on sociodemographic status (age, sex, residence, region, education level, physical activity, and occupation) and lifestyle behaviors (smoking and drinking status) were obtained through the questionnaires at each follow-up survey. Height, weight, and waist were measured following a standard procedure with calibrated equipment. Blood pressure was calculated as the average of 3 independent measurements by trained research staff. Body mass index (BMI) was calculated as weight (kg) by height squared (m2).
Assessments of Outcomes
Diabetes status was identified by the questionnaire-based interview at each follow-up. Answering “yes” to the question “Has a doctor ever told you that you suffer from diabetes?” was defined as having self-reported diagnosed diabetes. In addition, overnight-fasting blood samples were collected and assayed only in 2009. Therefore, an additional criterion (fasting blood glucose ≥ 7.0 mmol/L or glycated hemoglobin [HbA1c] ≥ 6.5%) (27) was added for outcome ascertainment in 2009. Moreover, the reliable concordance between questionnaire-based diagnosis and HbA1c/glycemia-based diagnosis was identified among the 8202 participants in the 2009 wave. Among the 247 participants with self-reported physician-diagnosed diabetes in the 2009 wave, 182 (74%) of those were diagnosed with fasting glucose ≥ 7.0 mmol/L or HbA1c ≥ 6.5%. Of the remaining 65 participants, 58 were under the glucose-lowering treatments, which may maintain fasting glucose and HbA1c at a relatively normal level.
When a participant was first identified with new-onset diabetes in the following survey, the middle date between this and the nearest survey before was used to calculate the follow-up time. For those free of diabetes in all following surveys, the last survey date was used to calculate the follow-up time.
Statistical Analysis
We divided the dietary zinc intake into deciles. Because the deciles 2 through 8 and deciles 9 and 10 had relatively similar diabetes incidence rates, we combined them into 1 category, respectively. Baseline characteristics of the participants were presented as mean (SD) for continuous variables or proportions for categorical variables by zinc categories (decile 1: <8.9 mg/day, deciles 2-8: 9.8-<12.2 mg/day, deciles 9-10: ≥12.2 mg/day). Differences in characteristics were compared using ANOVA tests or χ2 tests accordingly.
Incidence rates of diabetes, expressed as per 1000 person-years, were calculated as the number of new diabetes cases divided by the person-years of follow-up. The relation of dietary zinc intake with new-onset diabetes was estimated using Cox proportional hazards models (hazards ratio [HR] and 95% CI) with and without adjustments for age, sex, BMI, systolic blood pressure (SBP), smoking and drinking status, education, occupation, region, urban or rural residence, as well as energy intake. Threshold analysis in the association of dietary zinc intake with the study outcome was conducted with a 2-piecewise Cox regression model using a smoothing function. The inflection point was determined using the likelihood-ratio test and bootstrap resampling method by the R package segmented. We additionally performed restricted cubic spline Cox regression, with 4 knots (5th, 35th, 65th, and 95th percentiles of dietary zinc intake), to test for linearity and explore the shape of the dose-response relation of zinc intake and new-onset diabetes. As additional exploratory analyses, possible modifications on the association of zinc intake and new-onset diabetes were evaluated by stratified analyses and interaction testing.
A 2-tailed P < 0.05 was considered to be statistically significant in all analyses. R software, version 3.6.1 (http://www.R-project.org), was used for all data analyses.
Results
Study Participants and Baseline Characteristics
As illustrated in the flowchart (23), a total of 16 257 participants were included in the current study. The average age of the study population was 43.1 (SD, 15.3) years. A total of 7949 (48.9%) of the participants were male. The mean zinc intake was 10.9 (SD, 1.9) mg/day.
We divided the dietary zinc intake into deciles and presented the baseline characteristics of study participants by merged categorical of dietary zinc in Table 1 and Supplemental Table 1. Participants with higher dietary zinc intake were more likely to be male, current smokers and drinkers, living in the south region, had higher percentages of urban residence, had higher education levels, had a higher intake of protein, aquatic, nut, red meat, copper, potassium, vitamin A, riboflavin, niacin, vitamin C, magnesium, iron, and less likely to be farmer, had lower BMI, had a lower intake of energy, fat, carbohydrate, whole grain, and sodium (Table 1, Supplemental Table 1) (23).
Table 1.
Population characteristics by categories of dietary zinc intake
| Characteristics | Sample size | Zinc intake, mg/day | P value | ||
|---|---|---|---|---|---|
| Decile 1 | Deciles 2-8 | Deciles 9-10 | |||
| (<8.9) | (8.9-<12.2) | (≥12.2) | |||
| N | 16 257 | 1626 | 11379 | 3252 | |
| Age, y | 16 257 | 42.5 (15.7) | 43.3 (15.4) | 42.8 (15.1) | 0.064 |
| Sex, no. (%) | 16 257 | <0.001 | |||
| Male | 804 (49.4) | 5295 (46.5) | 1850 (56.9) | ||
| Female | 822 (50.6) | 6084 (53.5) | 1402 (43.1) | ||
| BMI, kg/m2 | 14 704 | 23.3 (3.4) | 22.8 (3.3) | 22.9 (3.3) | <0.001 |
| SBP, mmHg | 14 780 | 121.0 (17.7) | 119.5 (17.4) | 119.9 (16.6) | 0.008 |
| DBP, mmHg | 14 779 | 77.9 (11.1) | 77.4 (10.7) | 77.9 (10.5) | 0.01 |
| Smoking, no. (%) | 16 208 | <0.001 | |||
| No | 1114 (68.6) | 7917 (69.8) | 2078 (64.1) | ||
| Yes | 511 (31.4) | 3422 (30.2) | 1166 (35.9) | ||
| Alcohol drinking, no. (%) | 16 073 | <0.001 | |||
| No | 1054 (65.5) | 7448 (66.2) | 1828 (57.0) | ||
| Yes | 556 (34.5) | 3809 (33.8) | 1378 (43.0) | ||
| Residence, no. (%) | 16 257 | <0.001 | |||
| Urban | 534 (32.8) | 4044 (35.5) | 1566 (48.2) | ||
| Rural | 1092 (67.2) | 7335 (64.5) | 1686 (51.8) | ||
| Region, no. (%) | 16 257 | <0.001 | |||
| Central | 1054 (64.8) | 5416 (47.6) | 1349 (41.5) | ||
| North | 330 (20.3) | 2569 (22.6) | 584 (18.0) | ||
| South | 242 (14.9) | 3394 (29.8) | 1319 (40.6) | ||
| Occupation, no. (%) | 16 105 | <0.001 | |||
| Famer | 463 (28.9) | 4147 (36.8) | 715 (22.1) | ||
| Worker | 205 (12.8) | 1238 (11.0) | 453 (14.0) | ||
| Unemployed | 526 (32.9) | 3171 (28.1) | 890 (27.5) | ||
| Other | 407 (25.4) | 2714 (24.1) | 1176 (36.4) | ||
| Education, no. (%) | 15 913 | <0.001 | |||
| Illiteracy | 328 (20.5) | 2443 (22.0) | 465 (14.5) | ||
| Primary school | 295 (18.4) | 2279 (20.5) | 522 (16.3) | ||
| Middle school | 546 (34.1) | 3495 (31.5) | 1053 (32.9) | ||
| High school or above | 434 (27.1) | 2891 (26.0) | 1162 (36.3) | ||
| Dietary intake | |||||
| Energy, Kcal/day | 16 257 | 2235.7 (554.5) | 2114.8 (499.0) | 2155.3 (518.5) | <0.001 |
| Fat, g/day | 16 257 | 86.8 (36.3) | 72.1 (26.7) | 75.8 (26.6) | <0.001 |
| Carbohydrate, g/day | 16 257 | 303.8 (105.6) | 302.8 (93.7) | 291.2 (97.3) | <0.001 |
| Protein, g/day | 16 257 | 59.7 (18.4) | 63.6 (16.3) | 77.0 (21.3) | <0.001 |
| Sodium, g/day | 16 257 | 5.6 (3.0) | 5.1 (2.7) | 4.9 (3.2) | <0.001 |
| Potassium, g/day | 16 257 | 1.5 (0.5) | 1.6 (0.4) | 1.9 (0.8) | <0.001 |
| Copper intake, mg/day | 16 257 | 1.7 (0.7) | 1.9 (0.6) | 2.1 (0.9) | <0.001 |
Variables are presented as mean (SD) or n (%).
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure.
The characteristics of participants who were excluded and had dietary zinc intake data were presented in Supplemental Table 2. Consistently, participants with higher dietary zinc intake were also more likely to be male, older, be current smokers and drinkers, living in the south region, had higher percentages of urban residence, had a higher intake of protein, potassium, copper, and were less likely to be farmer, had lower BMI, had a lower intake of energy, fat, carbohydrate, and sodium (23).
Association Between Dietary Zinc Intake and New-onset Diabetes
During a median follow-up of 9.0 years (interquartile range, 4.1-15.1 years), 1097 participants developed new-onset diabetes.
Overall, the association between dietary zinc intake (Fig. 1A) and the risk of new-onset diabetes followed a U-shape (P for nonlinearity < 0.001). Accordingly, in the threshold effect analysis, the risk of new-onset diabetes was significantly lower with the increment of zinc intake (per mg/day: HR, 0.73; 95% CI, 0.60-0.88) in participants with zinc intake < 9.1 mg/day, and higher with the increment of zinc intake (per mg/day: HR, 1.10; 95% CI, 1.07-1.13) in participants with zinc intake ≥ 9.1 mg/day (Table 2). Moreover, the Wald Likelihood Ratio χ 2 test statistic for the overall decile of zinc intake has 9 degrees of freedom, a χ 2 = 35.30, and a P < 0.001. Therefore, decile of zinc intake overall was associated with new-onset diabetes. When dietary zinc intake was assessed as deciles, compared with decile 1, participants in deciles 2 through 8 have a lower risk of new-onset diabetes, whereas participants in deciles 9 and 10 have a relatively higher risk of new-onset diabetes. Therefore, we combined dietary zinc intake into 3 categories, compared with those in deciles 2 through 8 (8.9-<12.2 mg/day), the risk of new-onset diabetes was higher for decile 1 (<8.9 mg/day: HR,1.29; 95% CI, 1.04-1.62), and deciles 9 and 10 (≥12.2 mg/day: HR, 1.62; 95% CI, 1.38-1.90) (Table 3).
Figure 1.
Relation of (A) total, (B) plant-derived, and (C) animal-derived dietary zinc intake with risk of new-onset diabetes*. *Adjusted for age, sex, body mass index (BMI), systolic blood pressure (SBP), smoking and drinking status, education, occupation, region, urban or rural residence, as well as energy intake.
Table 2.
Threshold effect analyses of dietary zinc intake on the risk of new-onset diabetes using 2-piecewise regression models
| Zinc intake, mg/day | Crude model | Adjusted modela | ||
|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | |
| <9.1 | 0.66 (0.56-0.77) | <0.001 | 0.73 (0.60-0.88) | 0.001 |
| ≥9.1 | 1.09 (1.06-1.13) | <0.001 | 1.10 (1.07-1.13) | <0.001 |
Abbreviation: HR, hazard ratio.
a Adjusted for age, sex, body mass index, systolic blood pressure, smoking and drinking status, education, occupation, region, urban or rural residence, as well as energy intake.
Table 3.
The association between dietary zinc intake and the risk of new-onset diabetes
| Zinc intake, mg/d | N | Cases (incidence ratea) | Crude models | Adjusted modelsb | ||
|---|---|---|---|---|---|---|
| HR (95%CI) | P value | HR (95%CI) | P value | |||
| Deciles | ||||||
| <8.9 | 1626 | 111 (8.8) | Ref | Ref | ||
| 8.9-<9.5 | 1626 | 95 (5.7) | 0.62 (0.47-0.81) | <0.001 | 0.67 (0.50-0.91) | 0.009 |
| 9.5-<9.9 | 1625 | 109 (6.3) | 0.68 (0.52-0.88) | 0.004 | 0.74 (0.56-1.00) | 0.046 |
| 9.9-<10.3 | 1626 | 106 (6.2) | 0.67 (0.51-0.87) | 0.003 | 0.74 (0.55-0.99) | 0.042 |
| 10.3-<10.7 | 1625 | 126 (7.6) | 0.82 (0.64-1.06) | 0.139 | 0.94 (0.71-1.25) | 0.677 |
| 10.7-<11.1 | 1626 | 105 (6.2) | 0.66 (0.51-0.86) | 0.002 | 0.76 (0.57-1.02) | 0.069 |
| 11.1-<11.6 | 1626 | 91 (5.6) | 0.61 (0.46-0.81) | <0.001 | 0.74 (0.54-0.99) | 0.046 |
| 11.6-<12.2 | 1625 | 108 (6.4) | 0.70 (0.53-0.91) | 0.008 | 0.79 (0.59-1.06) | 0.122 |
| 12.2-<13.1 | 1626 | 131 (8.4) | 0.92 (0.72-1.19) | 0.525 | 1.15 (0.87-1.53) | 0.320 |
| ≥13.1 | 1626 | 115 (9.0) | 1.03 (0.79-1.33) | 0.836 | 1.36 (1.02-1.81) | 0.037 |
| Categories | ||||||
| Decile 1 (<8.9) | 1626 | 111 (8.8) | 1.47 (1.20-1.80) | <0.001 | 1.29 (1.04-1.62) | 0.024 |
| Deciles 2-8 (8.9-<12.2) | 11379 | 740 (6.3) | Ref | Ref | ||
| Deciles 9-10 (≥12.2) | 3252 | 246 (8.7) | 1.42 (1.23-1.65) | <0.001 | 1.62 (1.38-1.90) | <0.001 |
a Incident rate is presented as per 1000 person-years of follow-up.
b Adjusted for age, sex, body mass index, systolic blood pressure, smoking and drinking status, education, occupation, region, urban or rural residence, as well as energy intake.
To test the robustness of the association, we further performed a series of sensitivity analysis (23). First, further adjustments for the intake of vitamin A, riboflavin, niacin, vitamin C, copper, magnesium, and iron did not substantially change the results (Supplemental Table 3). Second, further adjustments for the intake of aquatic, nut, red meat, and whole grain also did not materially alter the findings (Supplemental Table 3). Third, further adjustments for the intake of fat, carbohydrate and sodium did not substantially change the results (Supplemental Table 4). Fourth, similar U-shaped relations were also found for plant-derived (Fig. 1B) or animal-derived zinc intake (Fig. 1C) with new-onset diabetes. Fifth, similar trends were observed when the follow-up person-time for new-onset diabetes was calculated as the date of the examination at which diabetes was first ascertained (Supplemental Table 5). Sixth, analyses using inverse-probability weighting to account for missing data led to results that were consistent with those in the main analysis. Participants in decile 1 (<8.9 mg/day: HR, 1.34; 95% CI, 1.22-1.47) or deciles 9 and 10 (≥12.2 mg/day: HR, 1.67; 95% CI, 1.53-1.82) of dietary zinc intake showed higher risk of new-onset diabetes, compared with those of deciles 2-8 (8.9-<12.2 mg/day) (Supplemental Table 6). Seventh, participants in deciles 2-8 (8.9-<12.2 mg/day) have more regular physical activity. However, we further adjusted physical activity and found a similar relationship between dietary zinc intake and new-onset diabetes (Supplemental Table 4). Eighth, we generated an E-value to assess the potential effect of unmeasured confounding. The E-values of the point estimate were 1.90 and 2.62 for dietary zinc intake and new-onset diabetes association in participants with dietary zinc intake of decile 1 (<8.9 mg/day) and deciles 9 and 10 (≥12.2 mg/day) compared with those in deciles 2 through 8 (8.9-<12.2 mg/day), respectively.
Moreover, among the 3678 participants younger than 40 years old, we also found a U-shaped association between dietary zinc intake and new-onset diabetes (Supplemental Figure 2) (23). Consistently, the risk of new-onset diabetes was significantly lower with the increment of zinc intake in participants with zinc intake < 9.1 mg/day (per mg/day: HR, 0.46; 95% CI, 0.29-0.73), and higher with the increment of zinc intake in those with zinc intake ≥ 9.1 mg/day (per mg/day: HR, 1.13; 95% CI, 1.06-1.20) (Supplemental Table 7) (23).
Stratified Analyses by Potential Effect Modifiers
We further performed exploratory subgroup analyses to assess the association between dietary zinc intake and new-onset diabetes in 2 groups of participants separated by the inflection point of zinc intake (9.1 mg/day).
None of the variables, including age, sex, BMI, smoking and drinking status, education levels, occupation, SBP, fat, protein, carbohydrate, energy, copper, sodium, potassium, and riboflavin intake, significantly modified the association between dietary zinc and new-onset diabetes (Fig. 2, Supplemental Figure 3) (23). Although the P values for interactions for niacin (<15.6 mg/day; HR, 1.17; 95% CI, 1.11-1.24; vs ≥15.6 mg/day; HR, 1.08; 95% CI, 1.05-1.13; P for interaction = 0.026) and carbohydrate (median, <295.2 g/day; HR, 1.06; 95% CI,1.01-1.11; vs ≥295.2 g/day; HR, 1.15; 95% CI, 1.11-1.20; P for interaction = 0.006) intake among participants with zinc intake ≥ 9.1 mg/day, and for magnesium (median, <273.4 mg/day; HR, 0.59; 95% CI, 0.46-0.77; vs ≥273.4 mg/day; HR, 0.85; 95% CI, 0.65-1.10; P for interaction = 0.041) and protein (median, <58.6 g/day; HR, 0.56; 95% CI, 0.43-0.73; vs ≥58.6 g/day; HR, 0.83; 95% CI, 0.65-1.07; P for interaction = 0.026) intake among participants with zinc intake < 9.1 mg/day were lower than 0.05 because of chance given multiple testing and similar directionality of most of the associations, these results may not have significant clinical implications.
Figure 2.
Stratified analysis by potential effect modifiers for the association between dietary zinc intake and new-onset diabetes in various subgroups divided by 9.1 mg/day. (A) Dietary zinc intake < 9.1 mg/day. (B) Dietary zinc intake ≥ 9.1 mg/day. Incident rate is presented as per 1000 person-years of follow-up. Adjusted, if not stratified, for age, sex, body mass index (BMI), systolic blood pressure (SBP), smoking and drinking status, education, occupation, region, urban or rural residence, as well as energy intake.
Discussion
In this relatively large-scale, nationally prospective cohort among the general population, we found that there was a nonlinear, U-shaped association between dietary zinc intake and new-onset diabetes, with an inflection point at about 9.1 mg/day and minimal risk at 8.9 to 12.2 mg/day of dietary zinc intake.
The role of dietary zinc intake in the risk of diabetes has been reported in recent decades, and the results were inconsistent (5, 11-16). The NHS cohort (5) and Australian Longitudinal Study (12) reported inverse association between dietary zinc and diabetes among women. Compared with the lowest quintiles (<6.0 mg/day), women with the highest quintiles (≥10.6 mg/day) of dietary zinc intake had a lower risk of type 2 diabetes (relative risk: 0.92; 95% CI, 0.84-1.00, P for trend < 0.009) in the NHS cohort. Similarly, Eshak et al (11) found that dietary intake of zinc was associated with a reduced risk of type 2 diabetes in healthy Japanese adults. Of note, this study reported a relatively lower intake level of dietary zinc (mean: 6.5 ± 1.5 mg/day). Inconsistently, a high dietary zinc intake was associated with higher risk of type 2 diabetes mellitus in a Sweden population-based prospective cohort (13) (quintile 5 [mean: 13.8 mg/day] vs quintile 1 [mean: 8.7 mg/day]: HR, 1.27; 95% CI, 1.06-1.51). However, no associations were found in 2 studies conducted in the US general population (14, 15). Notably, a prior study conducted in China (16) reported that compared with participants in quartile 1 (≤9.8 mg/day), the adjusted ORs (95% CI) of hyperglycemia in quartile 2 (9.8-11.8 mg/day), quartile 3 (11.8-14.1 mg/day), and quartile 4 (>14.1 mg/day) were 0.70 (0.36-1.37), 0.87 (0.41-1.86), and 0.93 (0.35-2.46), respectively. Although the comparisons were not significant, this result also suggests a potential U-shaped relation between dietary zinc intake and hyperglycemia. Overall, these studies suggested that the associations between dietary zinc intake and new-onset diabetes remain uncertain. These inconsistent findings might be due to differences in the target population, dietary zinc intake levels, and sample size. Moreover, few of these studies were conducted, using the dietary zinc intake data continuously, which may allow for the assessment of a dose-response relation of zinc intake with new-onset diabetes. Our current study provides an opportunity to examine the continuous association between dietary zinc intake and new-onset diabetes, with the adjustments for a number of potential cofounders, and a series of subgroup analyses.
Our study provides some new insights in this field. First, among participants with dietary zinc intake < 9.1 mg/day, the risk of new-onset diabetes was significantly lower with the increment of dietary zinc intake. The plausible mechanism may include: first, zinc is abundant in pancreatic islets, and plays an important role in the crystallization and secretion as well as the action of insulin and the translocation of insulin into the cells (28). Second, zinc may participate in the suppression of proinflammatory cytokines, such as TNF-α and IL-1β, avoiding β cell death and protecting insulin (29). Moreover, oxidative stress plays an important role in the pathogenesis of diabetes. Zinc is a structural part of key antioxidant enzymes such as superoxide dismutase, and zinc deficiency may impair their synthesis, leading to increased oxidative stress (30, 31). Further, Miao et al (32) reported that zinc treatment significantly up-regulated the expression and function of nuclear factor (erythroid-derived 2)-like 2, a pivotal regulator of antioxidative mechanism, and the expression of metallothionein, a potent antioxidant. However, the detailed mechanisms still need to be confirmed in more studies.
Second, the risk of new-onset diabetes was significantly higher with the increment of dietary zinc intake in participants with dietary zinc intake ≥ 9.1 mg/day. The underlying mechanisms whereby higher dietary zinc intake could be related to the development of diabetes have yet to be elucidated. A high level of zinc may manipulate hormonal homeostasis such as leptin in the body, resulting in insulin resistance (33). Moreover, excess zinc may cause hyperactivity of β cells and insulin production and this may cause insulin resistance through exhaustion of insulin receptors or overstimulation by long-term elevated zinc may be harmful to the β cell (9). However, future studies are warranted to further examine the optimal intake level and the underlying mechanisms linking zinc and diabetes.
The relation of dietary zinc intake with new-onset diabetes might be ascribed to other nutrients or some unknown components of the main dietary sources of zinc. However, our study showed that adjustments for other major nutrients or the major food groups, including aquatic, nut, red meat, and whole grain, did not materially alter the findings. More importantly, we also found the similar associations for plant-derived or animal-derived zinc intake with new-onset diabetes. These results indicated that the association of zinc intake and new-onset diabetes may be not substantially affected by these factors.
Limitations of the present study should also be noted. First, although we controlled for a number of dietary and nondietary covariates to reduce the confounding effects, unmeasured and residual confounding remains possible. However, we used the E-value (34) sensitivity analysis to quantify the potential implications of unmeasured confounders and found that an unmeasured confounder was unlikely to explain the entirety of the dietary zinc intake and new-onset diabetes association. Second, we did not have detailed information on dietary supplement use. However, data from the 2010 to 2012 China Nutrition and Health Surveillance (CNHS) (35), a nationally representative cross-sectional study covering all 31 provinces, autonomous regions, and municipalities in China, showed that only 0.71%, 0.03%, and 0.21% of the Chinese population reported using nutrient supplements, multimineral, and zinc supplements, respectively. Because of the low supplement proportion of nutrients, especially zinc, we speculate that our results may not be materially changed by the dietary supplement use. Third, although the information about new-onset diabetes was documented based on the physicians’ diagnoses at each follow-up survey, fasting blood glucose and HbA1c were assessed in the 2009 survey round only. This might cause potential under- or overevaluation with subjects confusing prediabetes and diabetes. More frequent measurements of fasting blood glucose and HbA1c levels would have allowed a more accurate assessment of new-onset diabetes. Finally, our study was conducted in Chinese living in China; whether the observed findings can be extrapolated to other populations needs further investigation. Therefore, our results should be regarded as hypothesis-generating. Further confirmation of our findings in more studies is essential.
Conclusions
In summary, we first observed a U-shaped association between dietary zinc intake and new-onset diabetes in general Chinese adults, with an inflection point at about 9.1 mg/day and minimal risk at 8.9 to 12.2 mg/day of dietary zinc intake. If further confirmed, our finding stressed the importance of maintaining an optimal zinc intake levels for the primary prevention of diabetes in the general population.
Acknowledgments
This research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924 and T32 HD007168), the University of North Carolina at Chapel Hill, the National Institutes of Health (NIH; R01-HD30880, DK056350, R24 HD050924, and R01-HD38700), and the NIH Fogarty International Center (D43 TW009077 and D43 TW007709) for financial support for the CHNS data collection and analysis files from 1989 to 2015 and future surveys, and the China-Japan Friendship Hospital, Ministry of Health, for support for CHNS 2009, Chinese National Human Genome Center at Shanghai since 2009, and Beijing Municipal Center for Disease Prevention and Control since 2011.
Funding : The study was supported by the National Natural Science Foundation of China (81973133, 81730019).
Author Contributions: X.Q. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. P.H., H.L., C.L., and X.Q. designed the research; P.H., H.L., M.L., C.Z., Z.Z., Y.Z., Q.L., and X.Q. conducted the research; P.H. and C.L. performed the data management and statistical analyses; P.H. and X.Q. wrote the manuscript; all authors reviewed/edited the manuscript for important intellectual content. All authors read and approved the final manuscript.
Glossary
Abbreviations
- BMI
body mass index
- CHNS
China Health and Nutrition Survey
- HbA1c
glycated hemoglobin
- HR
hazard ratio
- NHS
Nurses’ Health Study
- SBP
systolic blood pressure
Contributor Information
Panpan He, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Huan Li, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Mengyi Liu, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Zhuxian Zhang, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Yuanyuan Zhang, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Chun Zhou, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China.
Qinqin Li, Institute of Biomedicine, Anhui Medical University, Hefei 230032, China.
Chengzhang Liu, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China; Institute of Biomedicine, Anhui Medical University, Hefei 230032, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China.
Xianhui Qin, Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou 510515, China; Institute of Biomedicine, Anhui Medical University, Hefei 230032, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China.
Additional Information
Disclosures : The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. No disclosures were reported.
Data Availability
Detailed survey operation manuals, consent documents, and brochures of each period are available on the China Health and Nutrition Survey website (https://www.cpc.unc.edu/projects/china), and the analytic methods and study materials that support the findings of this study will be available from the corresponding authors on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Detailed survey operation manuals, consent documents, and brochures of each period are available on the China Health and Nutrition Survey website (https://www.cpc.unc.edu/projects/china), and the analytic methods and study materials that support the findings of this study will be available from the corresponding authors on request.