| Literature DB >> 36249217 |
Huanyu Wu1, Jianing Wang2, Hongyan Jiang1, Xin Liu1, Xinyi Sun1, Yunyan Chen1, Cong Hu1, Zheng Wang1, Tianshu Han1, Changhao Sun1, Wei Wei1, Wenbo Jiang1.
Abstract
Background: Current studies on the protective effects of dietary spermidine (SPD) on cardiovascular disease (CVD) are mainly limited to animal studies, and the relationship between dietary SPD and CVD mortality remains inconclusive. Objective: This study aims to evaluate the association between dietary SPD intake and CVD and all-cause mortality.Entities:
Keywords: CVD mortality; NHANES; SPD; all-cause mortality; autophagy
Mesh:
Substances:
Year: 2022 PMID: 36249217 PMCID: PMC9554131 DOI: 10.3389/fpubh.2022.949170
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Baseline characteristics of variables in survived people, CVD mortality, and all-cause mortality status.
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| Age (years) | 49.0[36.0–63.0] | 76.0[66.0–80.0] | <0.001 | 75.0 [64.0–80.0] | <0.001 |
| Male, | 10,026.0 (46.6%) | 434.0 (59.0%) | <0.001 | 1,350.0 (57.1%) | <0.001 |
| Non–Hispanic white, | 10,188.0 (47.3%) | 467.0 (63.5%) | <0.001 | 1,484.0 (62.7%) | <0.001 |
| College graduate or above, | 5,437.0 (25.3%) | 99.0 (13.5%) | <0.001 | 315.0 (13.3%) | <0.001 |
| >$100,000 annual household income, | 2,924.0 (13.6%) | 16.0 (2.2%) | <0.001 | 64.0 (2.7%) | <0.001 |
| BMI (kg/m2) | 28.2 [24.5–32.7] | 27.9[24.5–31.7] | 0.083 | 27.4 [24.1–31.7] | <0.001 |
| Total energy intake | 1,912.5 [1,469.0–2,473.0] | 1,577.8[1,235.6–1,990.2] | <0.001 | 1,633.0 [1,274.0–2,058.0] | <0.001 |
| Regular exercise, | 5,143.0 (23.9%) | 153.0 (20.8%) | 0.2 | 479.0 (20.3%) | <0.001 |
| Current drinking, | 14,506.0 (67.4%) | 447.0 (60.7%) | 0.004 | 1,437.0 (60.8%) | <0.001 |
| Current smoking, | 4,659.0 (21.6%) | 126.0 (17.1%) | 0.023 | 468.0 (19.8%) | 0.06 |
| Hypertension, | 8,055.0 (37.4%) | 510.0 (69.3%) | <0.001 | 1,511.0 (63.9%) | <0.001 |
| Hyperlipidemia, | 8,239.0 (38.3%) | 393.0 (53.4%) | <0.001 | 1,198.0 (50.7%) | <0.001 |
| Diabetes, | 2,622.0 (12.2%) | 216.0 (29.3%) | <0.001 | 626.0 (26.5%) | <0.001 |
| Total SPD (μm/d) | 378.6 [271.2–512,5] | 304.6 [228.0.5–401.1] | <0.001 | 316.5 [235.2–419.7] | <0.001 |
| Fruit SPD (μm/d) | 4.2[0.7–8.7] | 4.9[1.4–8.7] | 0.056 | 4.6[1.4–8.8] | 0.007 |
| Vegetable SPD (μm/d) | 20.1[12.0–31.1] | 16.9 [9.5–26.0] | <0.001 | 17.8[9.9–27.6] | <0.001 |
| Cereals SPD (μm/d) | 309.2[212.2–427.5] | 254.6[189.6–348.4] | <0.001 | 264.9[189.9–358.4] | <0.001 |
| Legumes SPD (μm/d) | 8.2[0,6–18.7] | 2.9[0.0–10.2] | <0.001 | 3.3[0.0–11.0] | <0.001 |
| Fresh meat SPD (μm/d) | 3.2[1.6–5.5] | 2.6[1.2–4.2] | <0.001 | 2.6[1.1–4.3] | <0.001 |
| Cooked meat SPD (μm/d) | 5.6[3.4–8.5] | 4.5[2.7–6.8] | <0.001 | 4.5[2.7–7.1] | <0.001 |
| Nuts SPD (μm/d) | 0.0 [0.0–3.5] | 0.0[0.0–1.9] | <0.001 | 0.0 [0.0–2.0] | <0.001 |
| Egg SPD (μm/d) | 0.07[0.01–0.3] | 0.08[0.01–0.3] | 0.9 | 0.07[0.01–0.3] | 0.4 |
| Seafood SPD (μm/d) | 0.0[0.0–1.7] | 0.0[0.0–0.5] | 0.002 | 0.0[0.0–0.8] | <0.001 |
| Milk&Yogurt SPD (μm/d) | 0.2[0.1–0.5] | 0.3[0.1–0.6] | <0.001 | 0.3[0.1–0.6] | <0.001 |
| Cheese SPD (μm/d) | 9.0[0.7–20.8] | 3.3[0.0–11.4] | <0.001 | 3.6[0.0–12.2] | <0.001 |
Continuous variables are presented as medians (IQRs). Categorical variables are presented as a percentage.
Figure 1Multivariate adjusted hazard ratios (HRs) of the dietary total SPD, fruit-derived SPD, vegetable-derived SPD, cereal-derived SPD, legume-derived SPD, and nut-derived SPD with CVD and all-cause mortality. A logarithmic transformation was performed for non-normal continuous variables. Adjusting factors included age, gender, race, income, education level, regular exercise, smoking, alcohol consumption, BMI, body mass index; total energy intake, AHEI, Alternative Healthy Eating Index; diabetes, hypertension, and hyperlipidemia.
Figure 2Multivariate adjusted hazard ratios (HRs) of the dietary total SPD, fresh meat-derived SPD, cooked meat-derived SPD, egg-derived SPD, seafood-derived SPD, milk & yogurt-derived SPD, and cheese-derived SPD with CVD and all-cause mortality. A logarithmic transformation was performed for non-normal continuous variables. Adjusting factors included age, gender, race, income, education level, regular exercise, smoking, alcohol consumption, BMI, body mass index; total energy intake, AHEI, Alternative Healthy Eating Index; diabetes, hypertension, and hyperlipidemia.