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The associations between dietary minerals, obesity and hypertension in cold region
doi: 10.1515/fzm-2026-0002
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Abstract:
Objective This study aimed to compare dietary patterns and nutrient intakes between cold and non-cold regions of China, and to assess the associations between dietary mineral intake and the risks of overweight, obesity, abdominal obesity and hypertension in residents of cold region. Methods A total of 12, 190 participants from the China Health and Nutrition Survey were included, of whom 1261 were residents of Heilongjiang province. Dietary intake was assessed using three consecutive 24 h individual dietary recalls. General linear models were applied to compare dietary differences between Heilongjiang and other provinces, and Cox proportional hazard models were used to evaluate the associations between mineral intake and the aforementioned health outcomes among Heilongjiang residents. Results Significant differences were observed in the intake of fruits, vegetables, nuts, whole grains, processed meats, vitamin C, calcium, phosphorus, and magnesium between Heilongjiang and other provinces (all P < 0.05). In Heilongjiang residents, higher intakes of phosphorus, iron, and calcium were more strongly associated with lower risks of overweight, obesity, abdominal obesity, and hypertension than potassium or magnesium. The hazard ratios (HRs) and 95% confidence intervals (CIs) across tertiles of calcium, phosphorus and iron intake were as follows: 0.37 (0.28-0.50), 0.37 (0.28-0.49), 0.48 (0.36-0.64) for overweight; 0.53 (0.35-0.79), 0.50 (0.34-0.75), 0.45 (0.30-0.69) for obesity; 0.49 (0.38-0.64), 0.52 (0.40-0.66), 0.59 (0.46-0.77) for abdominal obesity; and 0.42 (0.32-0.54), 0.42 (0.33-0.53), 0.49 (0.38-0.63) for hypertension. Conclusion Distinct dietary patterns exist between cold and other region of China. Adequate intake of phosphorus, iron, calcium, potassium and magnesium consumption may help protect against obesity and hypertension in populations living in cold environments. -
Key words:
- mineral /
- obesity /
- abdominal obesity /
- hypertension
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Table 1. Baseline characteristics of variables in obesity and hypertension patients and controls
Baseline variable Obesity Hypertension No (N = 1008) Yes (N = 165) No (N = 564) Yes (N = 483) Age (years) 39.8 ± 13.7 38.2 ± 12.0 33.5 ± 11.9 39.7 ± 12.6 Female 549 (51.5) 82 (49.7) 333 (59.0) 224 (46.4) BMI (kg/m2) 22.3 ± 2.4 24.7 ± 2.2 22.1 ± 2.6 23.1 ± 2.8 Waist (cm) 77.8 ± 8.0) 84.1 ± 8.6 76.6 ± 7.8 79.7 ± 8.7 WHR 0.83 ± 0.07 0.86 ± 0.06 0.83 ± 0.07 0.84 ± 0.07 PAL (MET-h /week) 274.4 ± 189.6 277.9 ± 150.9 302.0 ± 309.9 256.4 ± 164.4 Systolic pressure (mmHg) 121.2 ± 16.0 121.1 ± 14.2 114.0 ± 9.5 117.2 ± 8.7 Diastolic pressure (mmHg) 80.4 ± 10.8 81.5 ± 11.5 75.6 ± 6.9 77.1 ± 6.8 Income (yuan) 7329.3 ± 10214.3 6969.2 ± 6998.9 7939.4 ± 11977.7 6909.6 ± 9803.4 Energy intake (kcal/day) 2187.6 ± 720.5 2362.4 ± 810.7 2267.4 ± 738.1 2182.5 ± 734.4 Calcium intake (mg/day) 628.7 ± 232.9 624.1 ± 280.9 641.3 ± 257.4 560.3 ± 229.1 Phosphorus intake (mg/day) 1147.1 ± 319.3 1152.5 ± 341.6 1159.2 ± 341.6 1038.5 ± 309.5 Potassium intake (mg/day) 2088.5 ± 592.2 2151.6 ± 739.0 2113.9 ± 626.5 1964.9 ± 630.5 Sodium intake (mg/day) 805.8 ± 601.4 808.2 ± 481.4 799.1 ± 387.7 714.5 ± 424.4 Magnesium intake (mg/day) 390.1 ± 109.0 393.9 ± 115.7 390.8 ± 118.5 361.1 ± 106.0 Iron intake (mg/day) 33.2 ± 10.2 33.6 ± 12.9 33.2 ± 10.9 29.7 ± 9.9 Zinc intake (mg/day) 14.5 ± 4.2 15.6 ± 5.3 14.7 ± 4.4 14.2 ± 4.7 Selenium intake (μg/day) 63.2 ± 21.8 70.0 ± 26.4 64.9 ± 23.6 65.9 ± 24.0 Copper intake (mg/day) 3.4 ± 1.2 3.6 ± 1.4 3.5 ± 1.4 3.2 ± 1.1 Manganese intake (mg/day) 8.9 ± 2.4 9.2 ± 2.8 8.9 ± 2.4 8.4 ± 2.4 Living in city 333 (33.3) 60 (36.4) 200 (35.5) 146 (30.2) Urban index 52.7 ± 22.9 55.0 ± 23.5 55.1 ± 22.6 50.8 ± 20.1 Smoking 358 (35.5) 56 (33.9) 145 (25.7) 190 (39.3) Drinking 408 (40.5) 67 (40.6) 180 (31.9) 209 (43.3) High school education 265 (26.2) 45 (27.3) 180 (32.0) 111 (23.1) BMI, body mass index; WHR, waist-hip ratio; PAL, physical activity level. Continuous variables were presented as mean ± standard deviation, category variables are presented as N (%). 
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Table 2. Food and nutrients intake of the participants in Heilongjiang and other provinces of China
Food and nutrients Heilongjiang (N = 1261) Others (N = 10929) P value Egg (g/day) 30.4 ± 21.2 22.1 ± 21.8 0.035 Rice (g/day) 204.8 ± 82.1 204.0 ± 135.4 < 0.001 Fish (g/day) 21.8 ± 26.0 32.0 ± 44.6 < 0.001 Potato (g/day) 79.2 ± 51.8 24.1 ± 34.7 < 0.001 Bean (g/day) 63.7 ± 41.2 24.2 ± 31.7 < 0.001 Fruit (g/day) 80.3 ± 88.5 32.6 ± 56.7 < 0.001 Vegetable (g/day) 237.7 ± 174.5 342.8 ± 193.8 < 0.001 Nut (g/day) 65.9 ± 41.2 27.3 ± 33.3 < 0.001 Whole grain (g/day) 25.5 ± 37.9 14.5 ± 31.1 < 0.001 Red meat (g/day) 49.4 ± 30.5 70.4 ± 65.5 < 0.001 White meat (g/day) 52.8 ± 24.0 60.0 ± 37.4 < 0.001 Poultry (g/day) 5.01 ± 11.9 10.9 ± 19.3 < 0.001 Processed meat (g/day) 4.6 ± 12.9 4.4 ± 11.5 < 0.001 Energy (kcal/day) 2310.0 ± 495.5 2251.4 ± 501.6 0.860 Protein (g/day) 83.2 ± 19.8 87.3 ± 22.3 < 0.001 Fat (g/day) 40.8 ± 17.5 47.6 ± 20.4 < 0.001 Carbohydrate (g/day) 406.5 ± 94.4 369.7 ± 90.5 < 0.001 Dietary fiber (g/day) 20.9 ± 8.5 19.5 ± 8.7 0.012 Saturated fatty acid (g/day) 13.3 ± 7.3 14.8 ± 8.0 < 0.001 Monounsaturated fatty acid (g/day) 16.1 ± 7.0 20.0 ± 8.5 < 0.001 Polyunsaturated fatty acids (g/day) 11.8 ± 4.4 12.1 ± 5.2 < 0.001 Cholesterol (mg/day) 430.9 ± 245.5 387.8 ± 309.6 < 0.001 Vitamin A (μg/day) 972.3 ± 318.2 934.1 ± 389.2 < 0.001 Vitamin C (mg/day) 137.2 ± 58.8 95.6 ± 48.9 < 0.001 Vitamin D (mg/day) 41.9 ± 109.5 38.8 ± 119.9 0.340 Vitamin E (mg/day) 16.4 ± 7.5 18.8 ± 9.6 < 0.001 Calcium (mg/day) 646.3 ± 186.4 636.9 ± 230.9 < 0.001 Phosphorus (mg/day) 1177.9 ± 261.6 1160.7 ± 293.4 < 0.001 Potassium (mg/day) 2140.4 ± 498.3 2061.3 ± 542.7 0.020 Sodium (mg/day) 811.9 ± 409.9 1052.0 ± 563.2 < 0.001 Magnesium (mg/day) 399.3 ± 88.2 378.1 ± 99.6 < 0.001 Iron (mg/day) 34.5 ± 8.6 35.6 ± 10.3 < 0.001 Zinc (mg/day) 14.6 ± 3.4 15.5 ± 4.1 < 0.001 Selenium (μg/day) 62.2 ± 17.0 61.4 ± 22.5 < 0.001 Copper (mg/day) 3.5 ± 0.9 3.2 ± 0.9 0.970 Manganese (mg/day) 9.1 ± 2.0 9.4 ± 4.4 < 0.001 Variables were presented as mean ± standard deviation (SD). General linear model was adjusted for age, sex, WHR, smoking status, drinking status, physical activity, individual income, urban or rural residence, education level, urbanization index and energy intake.
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Table 3. Hazard ratio (95% CIs) for overweight risk by tertiles of mineral intake in participants from Heilongjiang province
Mineral Tertiles of mineral consumption P for trend T1 T2 T3 Calcium Model 1 1 0.49 (0.38-0.64) 0.57 (0.44-0.73) < 0.001 Model 2 1 0.49 (0.38-0.64) 0.57 (0.44-0.73) < 0.001 Model 3 1 0.45 (0.35-0.59) 0.48 (0.36-0.64) < 0.001 Phosphorus Model 1 1 0.48 (0.38-0.62) 0.48 (0.37-0.62) < 0.001 Model 2 1 0.50 (0.38-0.64) 0.44 (0.34-0.58) < 0.001 Model 3 1 0.47 (0.36-0.60) 0.37 (0.28-0.50) < 0.001 Potassium Model 1 1 0.55 (0.42-0.71) 0.71 (0.56-0.91) 0.012 Model 2 1 0.55 (0.42-0.72) 0.72 (0.56-0.92) 0.016 Model 3 1 0.54 (0.41-0.71) 0.71 (0.55-0.92) 0.016 Sodium Model 1 1 0.79 (0.62-1.02) 0.86 (0.67-1.11) 0.250 Model 2 1 0.70 (0.54-0.90) 0.78 (0.60-1.00) 0.050 Model 3 1 0.68 (0.52-0.89) 0.79 (0.62-1.03) 0.050 Magnesium Model 1 1 0.65 (0.50-0.83) 0.55 (0.42-0.71) < 0.001 Model 2 1 0.68 (0.53-0.87) 0.58 (0.44-0.75) < 0.001 Model 3 1 0.66 (0.51-0.86) 0.57 (0.43-0.75) < 0.001 Iron Model 1 1 0.45 (0.35-0.58) 0.42 (0.32-0.54) < 0.001 Model 2 1 0.44 (0.34-0.56) 0.40 (0.31-0.52) < 0.001 Model 3 1 0.40 (0.30-0.51) 0.37 (0.28-0.49) < 0.001 Zinc Model 1 1 0.76 (0.59-0.99) 1.17 (0.91-1.50) 0.180 Model 2 1 0.73 (0.56-0.96) 1.06 (0.83-1.37) 0.550 Model 3 1 0.72 (0.55-0.94) 1.04 (0.79-1.36) 0.760 Selenium Model 1 1 0.82 (0.63-1.08) 1.48 (1.05-2.03) 0.008 Model 2 1 0.82 (0.62-1.08) 1.68 (1.00-1.87) 0.011 Model 3 1 0.83 (0.63-1.10) 1.53 (0.98-2.20) 0.021 Copper Model 1 1 0.77 (0.59-0.99) 0.84 (0.65-1.08) 0.190 Model 2 1 0.81 (0.62-1.04) 0.82 (0.63-1.06) 0.140 Model 3 1 0.80 (0.62-1.04) 0.80 (0.61-1.03) 0.090 Manganese Model 1 1 0.82 (0.64-1.05) 0.75 (0.58-0.97) 0.030 Model 2 1 0.85 (0.66-1.10) 0.78 (0.60-1.01) 0.060 Model 3 1 0.88 (0.68-1.15) 0.77 (0.58-1.02) 0.070 CI, confidence interval. Model 1 was adjusted for age. Model 2 was further adjusted for sex, waist-hip ratio, smoking status, drinking status, physical activity. Model 3 was additionally adjusted for individual income, urban or rural residence, education level, urbanization index and energy intake.
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Table 4. Hazard ratio (95% CIs) for obesity risk by tertiles of mineral intake in participants from Heilongjiang province
Mineral Tertiles of mineral consumption P for trend T1 T2 T3 Calcium Model 1 1 0.38 (0.26-0.57) 0.57 (0.40-0.83) < 0.001 Model 2 1 0.36 (0.24-0.53) 0.50 (0.34-0.73) < 0.001 Model 3 1 0.35 (0.24-0.53) 0.45 (0.30-0.69) < 0.001 Phosphorus Model 1 1 0.38 (0.25-0.57) 0.72 (0.50-1.03) 0.040 Model 2 1 0.33 (0.22-0.49) 0.55 (0.38-0.80) 0.001 Model 3 1 0.30 (0.20-0.46) 0.53 (0.35-0.79) 0.001 Potassium Model 1 1 0.46 (0.31-0.68) 0.61 (0.42-0.87) 0.007 Model 2 1 0.47 (0.32-0.70) 0.59 (0.41-0.85) 0.005 Model 3 1 0.49 (0.33-0.72) 0.61 (0.42-0.90) 0.011 Sodium Model 1 1 0.68 (0.47-0.98) 0.84 (0.58-1.22) 0.320 Model 2 1 0.56 (0.38-0.82) 0.71 (0.49-1.04) 0.070 Model 3 1 0.55 (0.37-0.81) 0.67 (0.46-1.02) 0.060 Magnesium Model 1 1 0.52 (0.35-0.75) 0.59 (0.40-0.85) 0.040 Model 2 1 0.49 (0.33-0.71) 0.50 (0.34-0.74) < 0.001 Model 3 1 0.50 (0.34-0.74) 0.54 (0.36-0.81) 0.003 Iron Model 1 1 0.47 (0.32-0.70) 0.59 (0.41-0.86) 0.005 Model 2 1 0.45 (0.30-0.66) 0.49 (0.34-0.72) < 0.001 Model 3 1 0.44 (0.30-0.66) 0.50 (0.34-0.75) 0.001 Zinc Model 1 1 0.95 (0.65-1.40) 1.27 (0.87-1.85) 0.190 Model 2 1 0.84 (0.57-1.24) 1.13 (0.77-1.66) 0.480 Model 3 1 0.81 (0.54-1.21) 1.06 (0.70-1.61) 0.730 Selenium Model 1 1 0.83 (0.56-1.24) 1.46 (1.01-2.11) 0.035 Model 2 1 0.82 (0.55-1.23) 1.43 (0.99-2.08) 0.043 Model 3 1 0.87 (0.57-1.31) 1.40 (0.95-2.06) 0.070 Copper Model 1 1 0.60 (0.41-0.89) 0.87 (0.60-1.25) 0.480 Model 2 1 0.55 (0.37-0.81) 0.74 (0.51-1.07) 0.130 Model 3 1 0.56 (0.38-0.83) 0.71 (0.48-1.04) 0.100 Manganese Model 1 1 0.96 (0.67-1.39) 0.84 (0.57-1.24) 0.380 Model 2 1 0.92 (0.63-1.33) 0.75 (0.51-1.12) 0.170 Model 3 1 0.97 (0.66-1.42) 0.82 (0.54-1.26) 0.380 CI, confidence interval. Model 1 was adjusted for age. Model 2 was further adjusted for sex, waist-hip ratio, smoking status, drinking status, physical activity. Model 3 was additionally adjusted for individual income, urban or rural residence, education level, urbanization index and energy intake.
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Table 5. Hazard ratio (95% CIs) for abdominal obesity risk by tertiles of mineral intake in participants from Heilongjiang province
Mineral Tertiles of mineral consumption P for trend T1 T2 T3 Calcium Model 1 1 0.64 (0.51-0.81) 0.74 (0.58-0.94) 0.013 Model 2 1 0.61 (0.48-0.78) 0.69 (0.54-0.87) 0.002 Model 3 1 0.57 (0.45-0.73) 0.59 (0.46-0.77) < 0.001 Phosphorus Model 1 1 0.54 (0.43-0.69) 0.61 (0.48-0.78) < 0.001 Model 2 1 0.55 (0.43-0.70) 0.57 (0.44-0.72) < 0.001 Model 3 1 0.52 (0.40-0.66) 0.49 (0.38-0.64) < 0.001 Potassium Model 1 1 0.66 (0.52-0.83) 0.64 (0.51-0.82) < 0.001 Model 2 1 0.65 (0.51-0.82) 0.61 (0.48-0.77) < 0.001 Model 3 1 0.65 (0.51-0.83) 0.61 (0.48-0.78) < 0.001 Sodium Model 1 1 0.94 (0.74-1.19) 0.98 (0.77-1.25) 0.900 Model 2 1 0.87 (0.68-1.10) 0.86 (0.67-1.10) 0.200 Model 3 1 0.77 (0.60-0.99) 0.67 (0.51-0.88) 0.005 Magnesium Model 1 1 0.59 (0.47-0.75) 0.55 (0.43-0.70) < 0.001 Model 2 1 0.60 (0.48-0.77) 0.55 (0.43-0.70) < 0.001 Model 3 1 0.63 (0.49-0.81) 0.60 (0.46-0.77) < 0.001 Iron Model 1 1 0.56 (0.44-0.71) 0.57 (0.45-0.72) < 0.001 Model 2 1 0.53 (0.42-0.68) 0.54 (0.42-0.68) < 0.001 Model 3 1 0.51 (0.40-0.66) 0.52 (0.40-0.66) < 0.001 Zinc Model 1 1 0.84 (0.66-1.07) 1.18 (0.93-1.49) 0.180 Model 2 1 0.80 (0.63-1.02) 1.05 (0.82-1.33) 0.680 Model 3 1 0.71 (0.55-0.92) 0.89 (0.69-1.16) 0.440 Selenium Model 1 1 0.83 (0.65-1.07) 1.55 (1.22-1.96) < 0.001 Model 2 1 0.82 (0.64-1.06) 1.41 (1.11-1.79) 0.005 Model 3 1 0.80 (0.62-1.04) 1.28 (0.93-1.68) 0.060 Copper Model 1 1 0.85 (0.67-1.08) 0.73 (0.57-0.92) 0.009 Model 2 1 0.83 (0.66-1.06) 0.69 (0.55-0.88) 0.003 Model 3 1 0.83 (0.65-1.05) 0.67 (0.52-0.85) 0.001 Manganese Model 1 1 0.70 (0.55-0.89) 0.70 (0.55-0.89) 0.004 Model 2 1 0.71 (0.56-0.90) 0.70 (0.55-0.89) 0.004 Model 3 1 0.80 (0.62-1.04) 0.82 (0.63-1.06) 0.150 CI, confidence interval. Model 1 was adjusted for age. Model 2 was further adjusted for sex, waist-hip ratio, smoking status, drinking status, physical activity. Model 3 was additionally adjusted for individual income, urban or rural residence, education level, urbanization index and energy intake.
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Table 6. Hazard ratio (95% CIs) for hypertension risk by tertiles of mineral intake in participants from Heilongjiang province
Mineral Tertiles of mineral consumption P for trend T1 T2 T3 Calcium Model 1 1 0.54 (0.43-0.68) 0.51 (0.40-0.66) < 0.001 Model 2 1 0.58 (0.46-0.73) 0.50 (0.39-0.64) < 0.001 Model 3 1 0.57 (0.45-0.72) 0.49 (0.38-0.63) < 0.001 Phosphorus Model 1 1 0.60 (0.48-0.75) 0.46 (0.36-0.59) < 0.001 Model 2 1 0.58 (0.46-0.72) 0.45 (0.35-0.57) < 0.001 Model 3 1 0.56 (0.44-0.70) 0.42 (0.32-0.54) < 0.001 Potassium Model 1 1 0.69 (0.55-0.87) 0.54 (0.43-0.69) < 0.001 Model 2 1 0.68 (0.54-0.86) 0.51 (0.40-0.66) < 0.001 Model 3 1 0.67 (0.53-0.84) 0.51 (0.40-0.65) < 0.001 Sodium Model 1 1 1.01 (0.80-1.29) 1.33 (1.05-1.68) 0.012 Model 2 1 1.05 (0.83-1.34) 1.41 (1.11-1.80) 0.003 Model 3 1 1.15 (0.89-1.49) 1.54 (1.17-2.03) 0.002 Magnesium Model 1 1 0.58 (0.46-0.72) 0.52 (0.41-0.66) < 0.001 Model 2 1 0.56 (0.44-0.70) 0.51 (0.40-0.65) < 0.001 Model 3 1 0.52 (0.41-0.66) 0.49 (0.38-0.63) < 0.001 Iron Model 1 1 0.48 (0.38-0.60) 0.43 (0.34-0.54) < 0.001 Model 2 1 0.49 (0.38-0.61) 0.42 (0.33-0.53) < 0.001 Model 3 1 0.47 (0.37-0.60) 0.42 (0.33-0.53) < 0.001 Zinc Model 1 1 0.71 (0.56-0.90) 1.02 (0.81-1.27) 0.820 Model 2 1 0.68 (0.53-0.86) 0.94 (0.74-1.18) 0.620 Model 3 1 0.70 (0.55-0.89) 0.95 (0.74-1.22) 0.710 Selenium Model 1 1 0.82 (0.64-1.05) 1.42 (1.07-1.86) < 0.001 Model 2 1 0.79 (0.61-1.01) 1.25 (0.93-1.66) 0.480 Model 3 1 0.78 (0.61-1.00) 1.21 (0.89-1.61) 0.600 Copper Model 1 1 0.69 (0.55-0.88) 0.74 (0.58-0.93) 0.012 Model 2 1 0.67 (0.53-0.85) 0.70 (0.55-0.89) 0.004 Model 3 1 0.67 (0.53-0.85) 0.72 (0.57-0.92) 0.010 Manganese Model 1 1 0.81 (0.64-1.02) 0.87 (0.69-1.09) 0.270 Model 2 1 0.79 (0.62-1.00) 0.84 (0.67-1.06) 0.190 Model 3 1 0.74 (0.57-0.94) 0.79 (0.62-1.02) 0.110 CI, confidence interval. Model 1 was adjusted for age. Model 2 was further adjusted for sex, waist-hip ratio, smoking status, drinking status, physical activity. Model 3 was additionally adjusted for individual income, urban or rural residence, education level, urbanization index and energy intake.
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