Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Population
2.2. Data Collection
2.3. Dietary Assessment
2.4. Statistical Analysis
3. Results
Characteristics | IFG Status | p 1 | |
---|---|---|---|
No (1327) | Yes (132) | ||
Age (year) | 41.0 (32.0–50.0) | 50.0 (41.0–58.0) | 0.000 |
BMI (kg/m2) | 25.1 (23.0–27.1) | 26.3 (24.4–28.5) | 0.000 |
TC (mmol/L) | 4.8 (4.3–5.3) | 5.1 (4.6–5.8) | 0.000 |
TG (mmol/L) | 1.4 (0.9–2.0) | 1.7 (1.2–2.7) | 0.000 |
HDL-C (mmol/L) | 1.1 (1.0–1.3) | 1.1 (1.0–1.3) | 0.546 |
LDL-C(mmol/L) | 3.0 (2.5–3.5) | 3.2 (2.8–3.8) | 0.000 |
FPG(mmol/L) | 5.4 (5.2–5.7) | 6.4 (6.2–6.6) | 0.000 |
Total energy intake (kcal) | 2086.9 (1646.1–2603.7) | 2049.1 (1595.1–2481.7) | 0.827 |
Smoking (%) | 0.067 | ||
Current | 41.5 | 48.5 | |
Past | 12.4 | 15.9 | |
Never | 46.1 | 35.6 | |
Drinking (%) | 0.015 | ||
Everyday | 8.7 | 16.7 | |
Sometimes | 71.4 | 66.7 | |
Past | 8.0 | 9.1 | |
Never | 11.9 | 7.6 | |
Physical activity (%) | |||
Light | 80.8 | 81.0 | 0.970 |
Middle | 51.5 | 60.3 | 0.136 |
Heavy | 35.2 | 38.8 | 0.525 |
Foods/Food Groups | Vegetables-Fruits Pattern | Animal Offal-Dessert Pattern | White Rice-Red Meat Pattern |
---|---|---|---|
White rice | −0.132 | −0.077 | 0.628 |
Vegetables | 0.698 | 0.091 | 0.220 |
Coarse cereals | 0.533 | −0.244 | 0.152 |
Refined wheat | 0.419 | −0.116 | 0.006 |
Tubers | 0.618 | 0.036 | −0.114 |
Fruits | 0.582 | 0.311 | 0.086 |
Seaweeds and mushrooms | 0.575 | 0.250 | 0.198 |
Soybean products | 0.474 | −0.064 | 0.341 |
Red meat | −0.050 | 0.123 | 0.750 |
Poultry | 0.108 | 0.161 | 0.610 |
Peanuts | 0.408 | 0.230 | −0.014 |
Seafood | 0.354 | 0.454 | 0.039 |
Dairy products | 0.272 | 0.115 | −0.018 |
Eggs | 0.206 | −0.038 | 0.576 |
Tea | 0.203 | 0.288 | −0.063 |
Dessert | 0.167 | 0.636 | −0.007 |
Condiments | 0.133 | 0.348 | 0.236 |
Animal offal | 0.090 | 0.648 | 0.115 |
Alcohol beverages | 0.015 | 0.347 | −0.022 |
Fast food | 0.004 | 0.603 | 0.021 |
Beverages | −0.094 | 0.595 | 0.053 |
Variance of explained (%) | 16.8 | 8.7 | 7.8 |
Characteristics | Vegetables-Fruits Pattern | p Trend 2 | ||
---|---|---|---|---|
T1 (n = 486) 1 | T2 (n = 486) | T3 (n = 487) | ||
Age (year) | 38.5 (32.0–44.0) | 41.0 (32.0–51.0) | 45.0 (37.0–54.0) | 0.000 |
BMI (kg/m2) | 25.2 (23.1–27.3) | 25.0 (22.9–27.1) | 25.2 (23.5–27.2) | 0.743 |
Energy (kcal) | 1555.4 (1296.3–1889.5) | 2052.6 (1752.7–2363.4) | 2687.6 (2321.8–3207.0) | 0.000 |
Fat (g) | 50.0 (45.5–54.7) | 48.1 (42.4–54.2) | 46.8 (38.8–52.7) | 0.000 |
Fat (% energy) | 21.0 (18.5–23.9) | 19.8 (17.2–22.4) | 19.3 (16.3–21.3) | 0.000 |
Protein (g) | 89.4 (84.2–94.7) | 89.8 (84.8–95.9) | 89.0 (82.2–97.8) | 0.962 |
Protein (% energy) | 16.9 (15.5–18.3) | 16.6 (15.6–17.7) | 16.1 (15.1–17.4) | 0.000 |
Carbohydrate (g) | 335.0 (312.0–350.5) | 340.6 (317.1–358.8) | 343.3 (315.6–369.1) | 0.000 |
Carbohydrate (% energy) | 59.8 (54.5–64.1) | 62.0 (57.4–65.7) | 63.1 (59.1–66.8) | 0.000 |
Fiber (g) | 19.1 (17.2–20.9) | 20.6 (18.3–22.7) | 22.9 (20.1–26.5) | 0.000 |
Cholesterol (g) | 564.2 (458.3–662.5) | 537.5 (446.6–627.1) | 498.2 (392.4–610.2) | 0.000 |
Total fatty acids (g) | 39.1 (35.1–44.7) | 38.8 (32.7–46.2) | 38.2 (30.8–46.1) | 0.010 |
SFA (g) | 11.7 (10.4–13.9) | 11.4 (9.7–13.9) | 10.9 (8.8–13.1) | 0.000 |
MUFAs (g) | 13.8 (12.3–16.1) | 13.4 (11.3–16.0) | 12.7 (10.2–15.3) | 0.000 |
PUFAs (g) | 10.0 (8.7–11.5) | 9.9 (8.0–12.4) | 10.1 (7.7–13.6) | 0.096 |
Magnesium, Mg (mg) | 444.9 (420.2–465.8) | 466.3 (438.1–490.0) | 497.3 (461.6–531.0) | 0.000 |
Iron, Fe (mg) | 37.8 (35.0–39.8) | 37.0 (33.9–39.2) | 36.8 (33.6–39.6) | 0.001 |
Mg/Fe ratio | 11.6 (11.1–12.4) | 12.4 (11.6–13.3) | 13.2 (12.4–14.7) | 0.000 |
Zinc, Zn (mg) | 14.4 (13.7–15.3) | 14.3 (13.5–15.4) | 14.4 (13.2–15.6) | 0.053 |
Selenium, Se (mg) | 65.4 (58.7–71.0) | 63.9 (57.30–70.2) | 60.8 (53.6–71.4) | 0.088 |
Animal giblets-dessert pattern | ||||
Age (year) | 46.0 (38.0–55.0) | 41.0 (33.0–50.0) | 37.5 (31.0–44.0) | 0.000 |
BMI (kg/m2) | 25.0 (23.1–27.1) | 25.3 (23.1–27.2) | 25.1 (23.1–27.2) | 0.733 |
Energy (kcal) | 1966.7 (1561.7–2397.1) | 1951.0 (1564.9–2434.7) | 2324.1 (1913.5–3033.2) | 0.000 |
Fat (g) | 44.7 (39.1–50.5) | 48.5 (43.9–53.8) | 51.4 (46.9–58.3) | 0.000 |
Fat (% energy) | 18.2 (15.5–20.9) | 20.0 (17.9–22.5) | 21.2 (19.3–23.7) | 0.000 |
Protein (g) | 89.9 (84.2–95.9) | 90.0 (85.2–95.5) | 88.5 (82.4–96.4) | 0.274 |
Protein (% energy) | 16.7 (15.4–17.9) | 16.7 (15.6–17.9) | 16.1 (15.0–17.5) | 0.000 |
Carbohydrate (g) | 348.0 (324.4–367.2) | 336.3 (314.4–353.3) | 333.9 (308.5–352.0) | 0.000 |
Carbohydrate (% energy) | 63.5 (58.5–67.9) | 60.8 (55.9–64.9) | 61.2 (56.7–64.5) | 0.000 |
Fiber (g) | 21.3 (18.9–23.9) | 20.6 (18.4–23.1) | 20.0 (17.2–22.5) | 0.000 |
Cholesterol (g) | 512.0 (408.8–592.0) | 535.9 (444.9–646.8) | 550.8 (450.1–668.9) | 0.000 |
Total fatty acids (g) | 36.7 (30.9–43.5) | 39.3 (34.6–44.7) | 40.6 (34.2–49.4) | 0.000 |
SFA (g) | 10.8 (9.1–12.8) | 11.6 (9.9–13.4) | 11.8 (9.8–14.5) | 0.000 |
MUFAs (g) | 12.6 (10.5–14.8) | 13.6 (11.6–15.5) | 14.2 (11.8–16.7) | 0.000 |
PUFAs (g) | 9.6 (7.7–12.1) | 10.0 (8.5–11.9) | 10.5 (8.5–12.7) | 0.000 |
Magnesium, Mg (mg) | 481.5 (455.9–508.3) | 462.6 (437.1–495.4) | 451.0 (413.3–478.6) | 0.000 |
Iron, Fe (mg) | 37.3 (34.8–39.5) | 37.5 (34.5–39.9) | 36.7 (33.1–39.3) | 0.002 |
Mg/Fe ratio | 12.7 (11.9–13.7) | 12.2 (11.4–13.2) | 11.8 (10.9–13.0) | 0.000 |
Zinc, Zn (mg) | 14.4 (13.4–15.3) | 14.4 (13.6–15.3) | 14.4 (13.4–15.7) | 0.224 |
Selenium, Se (mg) | 58.7 (53.0–64.9) | 63.8 (58.2–69.0) | 70.1 (61.9–78.7) | 0.000 |
White rice-red meat pattern | ||||
Age (year) | 45.0 (36.8–52.0) | 41.0 (33.0–51.0) | 38.0 (32.0–46.0) | 0.000 |
BMI (kg/m2) | 25.0 (23.1–27.1) | 25.1 (23.2–27.0) | 25.3 (23.0–27.5) | 0.337 |
Energy (kcal) | 1739.0 (1379.5–2209.7) | 1999.7 (1662.4–2485.3) | 2440.6 (2052.9–2958.4) | 0.000 |
Fat (g) | 44.6 (39.5–49.1) | 48.6 (43.3–53.7) | 52.4 (47.0–59.9) | 0.000 |
Fat (% energy) | 18.1 (15.3–20.6) | 20.1 (17.8–22.6) | 21.4 (19.4–24.0) | 0.000 |
Protein (g) | 84.7 (79.9–88.9) | 90.4 (85.1–95.2) | 95.3 (88.6–102.9) | 0.000 |
Protein (% energy) | 15.6 (14.6–16.7) | 16.7 (15.6–17.8) | 17.2 (16.1–18.6) | 0.000 |
Carbohydrate (g) | 350.7 (335.3–370.6) | 337.9 (318.6–358.8) | 321.0 (293.8–345.4) | 0.000 |
Carbohydrate (% energy) | 64.2 (60.3–68.8) | 61.6 (57.6–65.5) | 59.1 (54.5–63.4) | 0.000 |
Fiber (g) | 21.4 (19.3–23.4) | 20.8 (18.5–23.3) | 19.3 (16.7–22.5) | 0.000 |
Cholesterol (g) | 468.0 (373.0–553.4) | 540.6 (453.0–634.3) | 603.2 (502.7–752.3) | 0.000 |
Total fatty acids (g) | 35.3 (30.3–39.8) | 38.9 (33.2–45.1) | 43.3 (37.3–51.3) | 0.000 |
SFA (g) | 9.8 (8.7–11.2) | 11.5(9.9–13.3) | 13.5(11.6–15.9) | 0.000 |
MUFAs (g) | 11.7 (10.0–13.1) | 13.5 (11.6–15.5) | 15.6 (13.6–18.3) | 0.000 |
PUFAs (g) | 9.6 (7.6–11.3) | 10.2 (8.6–12.4) | 10.5 (8.5–13.2) | 0.000 |
Magnesium, Mg (mg) | 475.9 (451.4–505.0) | 466.9 (439.8–495.6) | 450.9 (415.0–482.3) | 0.000 |
Iron, Fe (mg) | 37.2 (34.5–39.4) | 37.4 (34.4–39.7) | 37.0 (34.2–39.5) | 0.871 |
Mg/Fe ratio | 12.7 (11.8–13.7) | 12.4 (11.4–13.4) | 11.9 (11.1–13.0) | 0.000 |
Zinc, Zn (mg) | 13.5 (12.7–14.1) | 14.4 (13.7–15.1) | 15.6 (14.7–16.6) | 0.000 |
Selenium, Se (mg) | 64.3 (57.4–70.9) | 64.5 (57.2–70.8) | 62.0 (54.9–70.5) | 0.835 |
Dietary Pattern | No. of IFG | Crude Model | Multivatiate Model 1 1 | Multivatiate Model 2 2 | |
---|---|---|---|---|---|
Vegetables-fruits pattern | T1 3 | 45 | 1.00 | 1.00 | 1.00 |
T2 | 36 | 0.78 (0.50–1.24) | 0.63 (0.39–1.02) | 0.57 (0.31–1.09) | |
T3 | 51 | 1.15 (0.75–1.75) | 0.73 (0.46–1.15) | 0.57 (0.34–0.95) | |
p trend 4 | 0.151 | 0.700 | 0.013 | ||
Animal offal-dessert pattern | T1 | 37 | 1.00 | 1.00 | 1.00 |
T2 | 45 | 1.24 (0.78–1.95) | 1.84 (1.13–2.99) | 1.86 (1.14–3.02) | |
T3 | 50 | 1.39 (0.89–2.17) | 2.89 (1.76–4.75) | 3.15 (1.87–5.30) | |
p trend | 0.370 | 0.001 | 0.035 | ||
White rice-red meat pattern | T1 | 49 | 1.00 | 1.00 | 1.00 |
T2 | 48 | 0.98 (0.64–1.49) | 1.14 (0.74–1.76) | 1.13 (0.73–1.76) | |
T3 | 35 | 0.69 (0.44–1.10) | 0.91 (0.59–1.53) | 0.92 (0.55–1.52) | |
p trend | 0.095 | 0.942 | 0.557 |
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix
Dietary Items | All subjects | Vegetables-Fruits Pattern | Animal offal-Dessert Pattern | White Rice-Red Meat Pattern | ||||||
---|---|---|---|---|---|---|---|---|---|---|
T1 1 | T2 | T3 | T1 | T2 | T3 | T1 | T2 | T3 | ||
White rice (g) | 131.7 (128.7–184.4) | 131.7 (128.7–360.4) | 141.8 (128.7–184.4) | 131.7 (65.9–184.4) | 180.2 (128.7–198.5) | 131.7 (128.7–184.4) | 131.7 (65.9–184.4) | 70.9 (64.4–131.7) | 131.7 (128.7–184.4) | 198.5 (180.2–368.8) |
Vegetables (g) | 309.7 (217.6–436.5) | 209.4 (155.6–282.4) | 304.5 (236.4–382.6) | 458.0 (361.3–612.6) | 308.6 (216.5–441.7) | 291.9 (210.8–410.8) | 337.8 (230.6–457.5) | 278.9 (195.8–384.3) | 304.2 (215.4–420.3) | 358.7 (262.2–488.9) |
Coarse cereals (g) | 82.4 (47.3–130.6) | 46.7 (18.7–82.4) | 82.4 (54.7–129.0) | 129.0 (82.4–188.7) | 116.7 (60.9–166.4) | 76.8 (46.7–129.0) | 74.6 (32.9–116.7) | 76.8 (32.9–116.7) | 82.4 (53.8–129.0) | 107.6 (53.8–164.7) |
Refined wheat (g) | 140.8 (94.5–196.4) | 95.4 (67.9–135.8) | 147.8 (107.0–194.4) | 185.0 (135.5–272.6) | 147.0 (96.3–194.6) | 137.5 (93.0–188.2) | 140.2 (94.5–206.3) | 147.0 (95.4–204.8) | 143.1 (95.5–191.2) | 136.3 (92.2–194.4) |
Tubers (g) | 52.9 (28.3–94.0) | 28.3 (17.9–45.1) | 52.9 (35.6–64.9) | 100.5 (52.9–141.6) | 52.0 (28.2–93.8) | 45.1 (27.3–82.8) | 52.9 (28.3–100.5) | 45.1 (27.8–100.5) | 52.9 (28.3–82.8) | 51.1 (28.3–82.8) |
Fruits (g) | 228.2 (134.5–344.2) | 136.5 (89.8–210.7) | 230.6 (164.5–311.1) | 341.1 (241.3–473.2) | 198.3 (110.8–296.0) | 203.7 (120.3–308.8) | 281.9 (182.2–418.2) | 201.0 (121.6–336.5) | 236.1 (126.7–339.2) | 242.9 (147.5–355.2) |
Seaweeds and mushrooms (g) | 15.7 (8.6–27.3) | 8.6 (6.1–13.2) | 15.7 (8.6–25.0) | 28.6 (15.7–39.3) | 12.1 (7.9–25.0) | 13.2 (7.9–25.0) | 21.4 (12.1–30.3) | 12.1 (7.9–25.0) | 13.2 (8.6–28.6) | 17.5 (10.3–28.6) |
Soybean products (g) | 78.8 (46.1–127.7) | 46.4 (27.1–77.6) | 78.8 (52.4–120.3) | 103.1 (78.8–152.2) | 81.2 (46.4–139.3) | 78.8 (46.4–126.0) | 77.5 (45.1–104.0) | 64.5 (36.1–82.3) | 78.8 (46.4–116.5) | 93.7 (68.8–150.7) |
Red meat (g) | 49.3 (31.9–81.6) | 41.0 (33.4–74.8) | 41.0 (33.4–85.3) | 51.7 (31.9–81.6) | 37.4 (19.9–71.3) | 41.0 (33.8–74.8) | 67.4 (37.4–85.6) | 31.9 (13.9–37.4) | 51.7 (34.0–71.3) | 85.6 (67.7–107.2) |
Poultry (g) | 25.1 (10.1–50.3) | 25.1 (10.1–30.2) | 25.1 (10.1–30.2) | 25.1 (12.1–50.3) | 25.1 (10.1–30.2) | 25.1 (10.1–30.2) | 25.1 (12.1–50.3) | 12.1 (6.0–25.1) | 25.1 (11.4–30.2) | 50.3 (25.1–70.4) |
Peanuts (g) | 11.1 (6.9–23.6) | 6.9 (0.0–11.1) | 11.1 (6.9–23.6) | 23.6 (8.3–38.7) | 6.9 (0.0–20.9) | 8.3 (6.9–23.6) | 13.9 (6.9–34.7) | 11.1 (6.6–23.6) | 11.1 (6.9–26.4) | 8.3 (4.2–23.6) |
Seafood (g) | 25.7 (17.6–43.0) | 22.2 (11.8–32.5) | 25.1 (19.7–43.0) | 38.2 (22.2–69.3) | 21.5 (10.9–34.2) | 23.7 (18.6–40.8) | 38.9 (22.2–69.3) | 25.7 (17.1–43.0) | 24.2 (18.6–44.3) | 27.0 (18.6–44.3) |
Dairy products (g) | 78.3 (17.9–178.6) | 48.7 (10.5–95.5) | 90.7 (21.0–178.6) | 114.7 (46.2–199.6) | 46.2 (17.9–146.9) | 70.3 (19.1–148.0) | 95.5 (48.7–191.1) | 77.2 (17.9–178.6) | 86.6 (19.1–178.6) | 78.3 (17.9–147.0) |
Eggs (g) | 62.5 (36.6–77.1) | 56.3 (28.4–73.7) | 66.4 (49.3–77.1) | 69.0 (50.3–78.0) | 69.0 (49.3–73.7) | 66.4 (49.3–77.1) | 57.2 (31.2–78.0) | 47.4 (25.9–61.2) | 61.5 (49.3–74.3) | 77.1 (69.0–94.2) |
Tea (mL) | 192.9 (21.4–364.3) | 107.1 (0.0–300.0) | 171.4 (37.5–342.9) | 300.0 (42.9–600.0) | 96.4 (0.0–321.4) | 171.4 (42.9–342.9) | 257.1 (85.7–600.0) | 182.1 (42.9–428.6) | 214.3 (42.9–385.7) | 150.0 (0.0–342.9) |
Dessert (g) | 14.4 (3.1–29.4) | 13.2 (3.1–23.2) | 13.9 (3.1–25.4) | 17.5 (3.1–43.2) | 3.1 (0.0–11.0) | 14.4 (4.3–22.8) | 32.3 (17.5–57.0) | 13.2 (0.0–27.7) | 15.2 (3.1–26.5) | 15.3 (3.1–30.6) |
Condiments (g) | 14.3 (3.5–29.6) | 12.9 (2.5–29.4) | 14.5 (2.9–29.3) | 16.4 (5.1–31.9) | 6.8 (1.4–17.8) | 14.3 (3.9–29.3) | 25.1 (13.2–32.5) | 8.4 (2.5–17.8) | 15.7 (4.2–29.6) | 20.0 (5.1–32.5) |
Animal offal (g) | 6.4 (0.0–15.3) | 6.4 (0.0–15.3) | 6.4 (0.0–15.3) | 6.4 (0.0–21.6) | 0.0 (0.0–6.4) | 6.4 (0.0–13.5) | 20.6 (7.9–40.7) | 6.4 (0.0–15.3) | 6.4 (0.0–15.3) | 8.1 (0.0–19.9) |
Alcohol beverages (mL) | 71.4 (0.0–233.9) | 65.9 (0.0–205.4) | 61.8 (0.0–222.6) | 83.2 (0.0–273.6) | 29.6 (0.0–107.7) | 79.6 (14.3–250.0) | 148.2 (29.6–367.9) | 79.1 (0.0–250.0) | 65.4 (0.0–219.7) | 65.4 (0.0–219.6) |
Fast food (g) | 18.6 (5.3–42.6) | 17.5 (5.3–37.3) | 18.6 (5.3–38.4) | 19.4 (5.3–47.9) | 5.3 (0.0–14.9) | 18.6 (8.9–32.3) | 45.7 (24.6–74.6) | 17.0 (0.0–37.6) | 19.3 (5.3–42.2) | 19.3 (5.3–45.7) |
Beverages (mL) | 27.7 (0.0–84.0) | 41.1 (0.0–84.0) | 26.8 (0.0–83.1) | 26.8 (0.0–82.1) | 0.0 (0.0–26.8) | 27.7 (0.0–68.8) | 84.0 (29.5–206.3) | 26.8 (0.0–82.1) | 26.8 (0.0–81.2) | 31.4 (0.0–133.9) |
References
- American Diabetes Association. Standards of medical care in diabetes-2012. Diabetes Care 2012, 35, S11–S63. [Google Scholar]
- Schmidt, M.I.; Duncan, B.B.; Bang, H.; Pankow, J.S.; Ballantyne, C.M.; Golden, S.H.; Folsom, A.R.; Chambless, L.E. Identifying individuals at high risk for diabetes—The Atherosclerosis Risk in Communities study. Diabetes Care 2005, 28, 2013–2018. [Google Scholar] [CrossRef] [PubMed]
- Levitzky, Y.S.; Pencina, M.J.; D’Agostino, R.B.; Meigs, J.B.; Murabito, J.M.; Vasan, R.S.; Fox, C.S. Impact of impaired fasting glucose on cardiovascular disease. J. Am. Coll. Cardiol. 2008, 51, 264–270. [Google Scholar] [CrossRef] [PubMed]
- Ford, E.S.; Zhao, G.X.; Li, C.Y. Pre-Diabetes and the risk for cardiovascular disease asystematic review of the evidence. J. Am. Coll. Cardiol. 2010, 55, 1310–1317. [Google Scholar] [CrossRef] [PubMed]
- Xu, Y.; Wang, L.M.; He, J.; Bi, Y.F.; Li, M.; Wang, T.G.; Wang, L.H.; Jiang, Y.; Dai, M.; Lu, J.L.; et al. Prevalence and Control of Diabetes in Chinese Adults. J. Am. Med. Assoc. 2013, 310, 948–958. [Google Scholar] [CrossRef] [PubMed]
- Tabak, A.G.; Herder, C.; Rathmann, W.; Brunner, E.J.; Kivimaki, M. Prediabetes: A high-risk state for diabetes development. Lancet 2012, 379, 2279–2290. [Google Scholar] [CrossRef]
- Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 2002, 346, 393–403. [Google Scholar]
- Perreault, L.; Pan, Q.; Mather, K.J.; Watson, K.E.; Hamman, R.F.; Kahn, S.E.; Diabetes Prevention Program Research Group. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: Results from the Diabetes Prevention Program Outcomes Study. Lancet 2012, 379, 2243–2251. [Google Scholar] [CrossRef]
- Williams, D.E.; Prevost, A.T.; Whichelow, M.J.; Cox, B.D.; Day, N.E.; Wareham, N.J. A cross-sectional study of dietary patterns with glucose intolerance and other features of the metabolic syndrome. Br. J. Nutr. 2000, 83, 257–266. [Google Scholar] [CrossRef] [PubMed]
- Liu, E.; McKeown, N.M.; Newby, P.K.; Meigs, J.B.; Vasan, R.S.; Quatromoni, P.A.; D’ Agostino, R.B.; Jacques, P.F. Cross-sectional association of dietary patterns with insulin-resistant phenotype among adults without diabetes in the Framinham Offspring study. Br. J. Nutr. 2009, 102, 576–583. [Google Scholar] [CrossRef] [PubMed]
- Mizoue, T.; Yamaji, T.; Tabata, S.; Yamaguchi, K.; Ogawa, S.; Mineshita, M.; Kono, S. Dietary patterns and glucose tolerance abnormalities in Japanese men. J. Nutr. 2006, 136, 1352–1358. [Google Scholar] [PubMed]
- He, Y.N.; Hu, Y.; Ma, G.; Feskens, E.J.; Zhai, F.; Yang, X.; Li, Y. Dietary Patterns and Glucose Tolerance Abnormalities in Chinese Adults. Diabetes Care 2009, 32, 1972–1976. [Google Scholar] [PubMed]
- Jia, Q.; Xia, Y.; Zhang, Q.; Wu, H.; Du, H.; Liu, L.; Wang, C.; Shi, H.; Guo, X.; Liu, X.; et al. Dietary patterns are associated with prevalence of fatty liver disease in adults. Eur. J. Clin. Nutr. 2015, 69, 914–921. [Google Scholar] [CrossRef] [PubMed]
- Montonen, J.; Knekt, P.; Harkanen, T.; Jarvinen, R.; Heliovaara, M.; Aromaa, A.; Reunanen, A. Dietary patterns and the incidence of type 2 diabetes. Am. J. Epidemiol. 2005, 161, 219–227. [Google Scholar] [CrossRef] [PubMed]
- Li, M.; Fan, Y.; Zhang, X.; Hou, W.; Tang, Z. Fruit and vegetable intake and risk of type 2 diabetes mellitus: Meta-analysis of prospective cohort studies. BMJ Open 2014, 4, e005497. [Google Scholar] [CrossRef] [PubMed]
- Pan, X.R.; Li, G.W.; Hu, Y.H.; Wang, J.X.; Yang, W.Y.; An, Z.X.; Hu, Z.X.; Lin, J.; Xiao, J.Z.; Cao, H.B.; et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: The Da Qing IGT and diabetes study. Diabetes Care 1997, 20, 537–544. [Google Scholar] [CrossRef] [PubMed]
- Hamer, M.; Chida, Y. Intake of fruit, vegetables, and antioxidants and risk of type 2 diabetes: Systematic review and meta-analysis. J. Hypertens. 2007, 25, 2361–2369. [Google Scholar] [CrossRef] [PubMed]
- Feskens, E.J.; Virtanen, S.M.; Räsänen, L.; Tuomilehto, J.; Stengård, J.; Pekkanen, J.; Nissinen, A.; Kromhout, D. Dietary factors determining diabetes and impaired glucose tolerance: A 20-year follow-up of the Finnish and Dutch cohorts of the Seven Countries Study. Diabetes Care 1995, 18, 1104–1112. [Google Scholar] [CrossRef] [PubMed]
- Salonen, J.T.; Nyyssönen, K.; Tuomainen, T.P.; Mäenpää, P.H.; Korpela, H.; Kaplan, G.A.; Lynch, J.; Helmrich, S.P.; Salonen, R. Increased risk of non-insulin dependent diabetes mellitus at low plasma vitamin E concentrations: A four year follow up study in men. BMJ 1995, 311, 1124–1127. [Google Scholar] [CrossRef] [PubMed]
- Reunanen, A.; Knekt, P.; Aaran, R.K.; Aromaa, A. Serum antioxidants and risk of non-insulin dependent diabetes mellitus. Eur. J. Clin. Nutr. 1998, 52, 89–93. [Google Scholar] [CrossRef] [PubMed]
- Barbagallo, M.; Dominguez, L.J.; Galioto, A.; Ferlisi, A.; Cani, C.; Malfa, L.; Pineo, A.; Busardo, A.; Paolisso, G. Role of magnesium in insulin action, diabetes and cardio-metabolic syndrome X. Mol. Aspects. Med. 2003, 24, 39–52. [Google Scholar] [CrossRef]
- Suarez, A.; Pulido, N.; Casla, A.; Casanova, B.; Arrieta, F.J.; Rovira, A. Impaired tyrosine-kinase activity of muscle insulin receptors from hypomagnesaemic rats. Diabetologia 1995, 38, 1262–1270. [Google Scholar] [PubMed]
- Balon, T.W.; Gu, J.L.; Tokuyama, Y.; Jasman, A.P.; Nadler, J.L. Magnesium supplementation reduces development of diabetes in a rat model of spontaneous NIDDM. Am. J. Physiol. 1995, 269, E745–E752. [Google Scholar] [PubMed]
- Lopez-Ridaura, R.; Willett, W.C.; Rimm, E.B.; Liu, S.; Stampfer, M.J.; Manson, J.E.; Hu, F.B. Magnesium intake and risk of type 2 diabetes in men and women. Diabetes Care 2004, 27, 134–140. [Google Scholar] [CrossRef] [PubMed]
- Shi, Z.; Hu, X.; Yuan, B.; Pan, X.; Meyer, H.E.; Holmboe-Ottesen, G. Association between serum ferritin, hemoglobin, iron intake, and diabetes in adults in Jiangsu, China. Diabetes Care 2006, 29, 1878–1883. [Google Scholar] [CrossRef] [PubMed]
- Fernandez-Real, J.M.; Lopez-Bermejo, A.; Ricart, W. Cross-talk between iron metabolism and diabetes. Diabetes 2002, 51, 2348–2354. [Google Scholar] [CrossRef] [PubMed]
- Swaminathan, S.; Fonseca, V.A.; Alam, M.G.; Shah, S.V. The role of iron in diabetes and its complications. Diabetes Care 2007, 30, 1926–1933. [Google Scholar] [CrossRef] [PubMed]
- Shi, Z.M.; Hu, X.S.; Yuan, B.J.; Gibson, R.; Dai, Y.; Garg, M. Association between magnesium: Iron intake ratio and diabetes in Chinese adults in Jiangsu Province. Diabet. Med. 2008, 25, 1164–1170. [Google Scholar] [CrossRef] [PubMed]
- Frank, L.K.; Jannasch, F.; Kröger, J.; Bedu-Addo, G.; Mockenhaupt, F.P.; Schulze, M.B.; Danquah, I. A dietary pattern derived by reduced rank regression is associated with type 2 diabetes in an urban Ghanaian population. Nutrients 2015, 7, 5497–5514. [Google Scholar] [CrossRef] [PubMed]
- Rosa, M.L.; Falcão, P.M.; Yokoo, E.M.; da Cruz Filho, R.A.; Alcoforado, V.M.; de Souza Bda, S.; Pinto, F.N.; Nery, A.B. Brazil’s staple food and incidents diabetes. Nutrition 2014, 30, 365–368. [Google Scholar] [CrossRef] [PubMed]
- Frank, L.K.; Kroger, J.; Schulze, M.B.; Bedu-Addo, G.; Mockenhaupt, F.P.; Danquah, I. Dietary patterns in urban Ghana and risk of type 2 diabetes. Br. J. Nutr. 2014, 112, 89–98. [Google Scholar] [CrossRef] [PubMed]
- Kant, A.K.; Graubard, B.I. A comparison of three dietary pattern indexes for predicting biomarkers of diet and disease. J. Am. Coll. Nutr. 2005, 24, 294–303. [Google Scholar] [CrossRef] [PubMed]
- Schulze, M.B.; Hoffmann, K.; Manson, J.E.; Willett, W.C.; Meigs, J.B.; Weikert, C.; Heidemann, C.; Colditz, G.A.; Hu, F.B. Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am. J. Clin. Nutr. 2005, 82, 675–684. [Google Scholar] [PubMed]
- Fung, T.T.; Schulze, M.; Manson, J.E.; Willett, W.C.; Hu, F.B. Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch. Intern. Med. 2004, 164, 2235–2240. [Google Scholar] [CrossRef] [PubMed]
- Van Dam, R.M.; Rimm, E.B.; Willett, W.C.; Stampfer, M.J.; Hu, F.B. Dietary patterns and risk for type 2 diabetes mellitus in U.S. men. Ann. Intern. Med. 2002, 136, 201–209. [Google Scholar] [CrossRef] [PubMed]
- Wei, J.; Zeng, C.; Gong, Q.Y.; Yang, H.B.; Li, X.X.; Lei, G.H.; Yang, T.B. The association between dietary selenium intake and diabetes: A cross-sectional study among middle-aged and older adults. Nutr. J. 2015, 14, 18. [Google Scholar] [CrossRef] [PubMed]
- Stranges, S.; Sieri, S.; Vinceti, M.; Grioni, S.; Guallar, E.; Laclaustra, M.; Muti, P.; Berrino, F.; Krogh, V. A prospective study of dietary selenium intake and risk of type 2 diabetes. BMC Public Health. 2010, 10, 564. [Google Scholar] [CrossRef] [PubMed]
- Stranges, S.; Marshall, J.R.; Natarajan, R.; Donahue, R.P.; Trevisan, M.; Combs, G.F.; Cappuccio, F.P.; Ceriello, A.; Reid, M.E. Effects of long-term selenium supplementation on the incidence of type 2 diabetes: A randomized trial. Ann. Intern. Med. 2007, 147, 217–223. [Google Scholar] [CrossRef] [PubMed]
- Bleys, J.; Navas-Acien, A.; Guallar, E. Serum selenium and diabetes in U.S. adults. Diabetes Care 2007, 30, 829–834. [Google Scholar] [CrossRef] [PubMed]
- Laclaustra, M.; Navas-Acien, A.; Stranges, S.; Ordovas, J.M.; Guallar, E. Serum selenium concentrations and diabetes in U.S. adults: National Health and Nutrition Examination Survey (NHANES) 2003–2004. Environ. Health. Perspect. 2009, 117, 1409–1413. [Google Scholar] [CrossRef] [PubMed]
- Satyanarayana, S.; Sekhar, J.R.; Kumar, K.E.; Shannika, L.B.; Rajanna, B.; Rajanna, S. Influence of selenium (antioxidant) on gliclazide induced hypoglycaemia/anti hyperglycaemia in normal/alloxan-induced diabetic rats. Mol. Cell. Biochem. 2006, 283, 123–127. [Google Scholar] [CrossRef] [PubMed]
- Goldstein, B.J.; Mahadev, K.; Wu, X. Redox paradox: Insulin action is facilitated by insulin-stimulated reactive oxygen species with multiple potential signaling targets. Diabetes 2005, 54, 311–321. [Google Scholar] [CrossRef] [PubMed]
- Villegas, R.; Liu, S.; Gao, Y.T.; Yang, G.; Li, H.; Zheng, W.; Shu, X.O. Prospective study of dietary carbohydrates, glycemic index, glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women. Arch. Intern. Med. 2007, 167, 2310–2316. [Google Scholar] [CrossRef] [PubMed]
- Shi, Z.; Taylor, A.W.; Hu, G.; Gill, T.; Wittert, G.A. Rice intake, weight change and risk of the metabolic syndrome development among Chinese adults: The Jiangsu Nutrition Study (JIN). Asia Pac. J. Clin. Nutr. 2012, 21, 35–43. [Google Scholar] [PubMed]
- Yu, R.; Woo, J.; Chan, R.; Sham, A.; Ho, S.; Tso, A.; Cheung, B.; Lam, T.H.; Lam, K. Relationship between dietary intake and the development of type 2 diabetes in a Chinese population: The Hong Kong Dietary Survey. Public Health Nutr. 2011, 14, 1133–1141. [Google Scholar] [CrossRef] [PubMed]
- Kaur, K.D.; Jha, A.; Sabikhi, L.; Singh, A.K. Significance of coarse cereals in health and nutrition: A review. J. Food Sci. Technol. 2014, 51, 1429–1441. [Google Scholar] [CrossRef] [PubMed]
- Sartorelli, D.S.; Franco, L.J.; Damião, R.; Gimeno, S.; Cardoso, M.A.; Ferreira, S.R. Dietary glycemic load, glycemic index, and refined grains intake are associated with reduced beta-cell function in prediabetic Japanese migrants. Arq. Bras. Endocrinol. Metabol. 2009, 53, 429–434. [Google Scholar] [CrossRef] [PubMed]
- Bhupathiraju, S.N.; Tobias, D.K.; Malik, V.S.; Pan, A.; Hruby, A.; Manson, J.E.; Willett, W.C.; Hu, F.B. Glycemic index, glycemic load, and risk of type 2 diabetes: Results from 3 large US cohorts and an updated meta-analysis. Am. J. Clin. Nutr. 2014, 100, 218–232. [Google Scholar] [CrossRef] [PubMed]
- Aune, D.; Ursin, G.; Veierod, M.B. Meat consumption and the risk of type 2 diabetes: A systematic review and meta-analysis of cohort studies. Diabetologia 2009, 52, 2277–2287. [Google Scholar] [CrossRef] [PubMed]
- Pan, A.; Sun, Q.; Bernstein, A.M.; Schulze, M.B.; Manson, J.E.; Willett, W.C.; Hu, F.B. Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am. J. Clin. Nutr. 2011, 94, 1088–1096. [Google Scholar] [CrossRef] [PubMed]
- Nakamura, Y.; Iso, H.; Kita, Y.; Ueshima, H.; Okada, K.; Konishi, M.; Inoue, M.; Tsugane, S. Egg consumption, serum total cholesterol concentrations and coronary heart disease incidence: Japan Public Health Center-based prospective study. Br. J. Nutr. 2006, 96, 921–928. [Google Scholar] [CrossRef] [PubMed]
- Howell, W.H.; McNamara, D.J.; Tosca, M.A.; Smith, B.T.; Gaines, J.A. Plasma lipid and lipoprotein responses to dietary fat and cholesterol: A meta-analysis. Am. J. Clin. Nutr. 1997, 65, 1747–1764. [Google Scholar] [PubMed]
- McNamara, D.J. Eggs and heart disease risk: Perpetuating the misperception. Am. J. Clin. Nutr. 2002, 5, 333–335. [Google Scholar]
- Howard, B.V.; Knowler, W.C.; Vasquez, B.; Kennedy, A.L.; Pettitt, D.J.; Bennett, P.H. Plasma and lipoprotein cholesterol and triglyceride in the Pima Indian population: Comparison of diabetics and nondiabetics. Arteriosclerosis 1984, 4, 462–471. [Google Scholar] [CrossRef] [PubMed]
- Pelletier, X.; Thouvenot, P.; Belbraouet, S.; Chayvialle, J.A.; Hanesse, B.; Mayeux, D.; Debry, G. Effect of egg consumption in healthy volunteers: Influence of yolk, white or whole-egg on gastric emptying and on glycemic and hormonal responses. Ann. Nutr. Metab. 1996, 40, 109–115. [Google Scholar] [CrossRef]
- Shi, Z.; Yuan, B.; Zhang, C.; Zhou, M.; Holmboe-Ottesen, G. Egg consumption and the risk of diabetes in adults, Jiangsu, China. Nutrition 2011, 27, 194–198. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.G.; Wang, Z.H.; Wang, H.J.; Du, W.W.; Su, C.; Zhang, J.; Jiang, H.R.; Zhai, F.Y.; Zhang, B. Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women. Eur. J. Clin. Nutr. 2015, 69, 1009–1014. [Google Scholar] [CrossRef] [PubMed]
- Shi, Z.; Yuan, B.; Hu, G.; Dai, Y.; Zuo, H.; Holmboe-Ottesen, G. Dietary pattern and weight change in a 5 year follow up among Chinese adults: Results from Jiangsu nutrition and health study. Br. J. Nutr. 2011, 105, 1047–1054. [Google Scholar] [CrossRef] [PubMed]
- He, Y.; Li, Y.; Lai, J.; Wang, D.; Zhang, J.; Fu, P.; Yang, X.; Qi, L. Dietary patterns as compared with physical activity in relation to metabolic syndrome among Chinese adults. Nutr. Metab. Cardiovasc. Dis. 2013, 23, 920–928. [Google Scholar] [CrossRef] [PubMed]
- Brown, K.H.; Rivera, J.A.; Bhutta, Z.; Gibson, R.S.; King, J.C.; Lonnerdal, B.; Ruel, M.T.; Sandtrom, B.; Wasantwisut, E.; et al. International Zinc Nutrition Consultative Group (IZiNCG) technical document #1. Assessment of the risk of zinc deficiency in populations and options for its control. Food Nutr. Bull. 2004, 25, S99–S203. [Google Scholar] [PubMed]
- Vashum, K.P.; McEvoy, M.; Shi, Z.; Milton, A.H.; Islam, M.R.; Sibbritt, D.; Patterson, A.; Byles, J.; Loxton, D.; Attia, J. Is dietary zinc protective for type 2 diabetes? Results from the Australian longitudinal study on women’s health. BMC Endocr. Disord. 2013, 13, 40. [Google Scholar] [CrossRef] [PubMed]
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Zhang, M.; Zhu, Y.; Li, P.; Chang, H.; Wang, X.; Liu, W.; Zhang, Y.; Huang, G. Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study. Nutrients 2015, 7, 8072-8089. https://doi.org/10.3390/nu7095382
Zhang M, Zhu Y, Li P, Chang H, Wang X, Liu W, Zhang Y, Huang G. Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study. Nutrients. 2015; 7(9):8072-8089. https://doi.org/10.3390/nu7095382
Chicago/Turabian StyleZhang, Meilin, Yufeng Zhu, Ping Li, Hong Chang, Xuan Wang, Weiqiao Liu, Yuwen Zhang, and Guowei Huang. 2015. "Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study" Nutrients 7, no. 9: 8072-8089. https://doi.org/10.3390/nu7095382