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For: Luo X, Li Y, Zhou Y, Zhang C, Li L, Luo Y, Wang J, Duan Y, Xie J. Association of Non-alcoholic Fatty Liver Disease With Salt Intake and Dietary Diversity in Chinese Medical Examination Adults Aged 18-59 Years: A Cross-Sectional Study. Front Nutr 2022;9:930316. [PMID: 35903450 PMCID: PMC9315371 DOI: 10.3389/fnut.2022.930316] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/10/2022] [Indexed: 12/04/2022] Open

For:	Luo X, Li Y, Zhou Y, Zhang C, Li L, Luo Y, Wang J, Duan Y, Xie J. Association of Non-alcoholic Fatty Liver Disease With Salt Intake and Dietary Diversity in Chinese Medical Examination Adults Aged 18-59 Years: A Cross-Sectional Study. Front Nutr 2022;9:930316. [PMID: 35903450 PMCID: PMC9315371 DOI: 10.3389/fnut.2022.930316] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/10/2022] [Indexed: 12/04/2022] Open

Number

Cited by Other Article(s)

Li J, Wan S, Dai X, Cui Y, Lu Z. The relationship between dietary sodium intake and all-cause mortality in patients with non-alcoholic fatty liver disease: a cohort study from NHANES 2003-2018. Front Nutr 2025;12:1530025. [PMID: 40336965 PMCID: PMC12055778 DOI: 10.3389/fnut.2025.1530025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Accepted: 04/04/2025] [Indexed: 05/09/2025] Open

Abstract

Background

The relationship between sodium intake and the incidence and mortality of non-alcoholic fatty liver disease (NAFLD) is underexplored in nutritional epidemiology, highlighting the need for further research.

Methods

This longitudinal cohort study analyzed data from 13,853 Participants aged 20 and older from the National Health and Nutrition Examination Survey (NHANES) (2003-2018), including 4,465 participants with NAFLD. We collected comprehensive data on mortality, dietary sodium intake, and relevant covariates. Logistic regression assessed the relationship between sodium consumption and NAFLD incidence, while Cox regression and smooth curve fitting explored sodium intake's link to all-cause mortality among Participants with NAFLD.

Results

After adjusting for confounders, logistic regression revealed a positive association between higher sodium intake and NAFLD incidence (OR = 1.16, 95% CI = 1.11, 1.21). Adjusted odds ratios for the second (Q2), third (Q3), and fourth (Q4) quartiles of sodium intake were 0.91, 1.23, and 1.52, respectively. Smooth curve fitting and threshold analysis revealed a non-linear association between sodium intake and NAFLD risk, with an inflection point at 2.49 g/d, above which NAFLD risk significantly increased. In Cox regression, sodium intake was inversely correlated with all-cause mortality in Participants with NAFLD (HR = 0.87, 95% CI = 0.80, 0.96), with adjusted hazard ratios for Q2, Q3, and Q4 being 0.79, 0.66, and 0.63, respectively. A nonlinear model indicated a threshold effect, revealing a correlation between dietary sodium intake and mortality risk (p = 0.001). We identified a threshold intake of 3.5 grams per day (equivalent to 8.9 grams of sodium chloride): below this, each unit increase in sodium intake was associated with a 16% reduction in mortality risk (HR = 0.84, 95% CI = 0.80, 0.90). For intakes above this threshold, no significant relationship with mortality risk was observed (HR = 0.99, 95% CI = 0.90, 1.08).

Conclusion

This study suggests that higher sodium intake in individuals with NAFLD is associated with increased disease incidence but decreased all-cause mortality. The dose-response relationship between sodium intake and mortality risk exhibited a nonlinear pattern, with a critical inflection point around 3.5 grams per day.

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Jahromi MK, Daftari G, Tehrani AN, Amirshekari G, Farhadnejad H, Teymoori F, Salehi-Sahlabadi A, Mirmiran P. The association of the healthy food diversity index with the risk of non-alcoholic fatty liver disease among the adult population. Clin Nutr ESPEN 2024;59:404-411. [PMID: 38220403 DOI: 10.1016/j.clnesp.2023.12.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 08/13/2023] [Accepted: 12/20/2023] [Indexed: 01/16/2024]

Abstract

BACKGROUND AND AIM

Dietary diversity index is a useful evaluation index for examining the role of dietary pattern in predicting chronic diseases risk, including non-alcoholic fatty liver disease(NAFLD). In the present study, we aimed to examine the possible association of dietary diversity using US Healthy Food Diversity(US HFD) index and the NAFLD risk in Iranian adults.

METHODS

A total of 675 individuals (225 patients with NAFLD and 450 controls) aged 20-60 years were recruited for the current case-control study. Data on dietary intakes were determined using a validated food frequency questionnaire, and dietary diversity was calculated using the US HFD index. In patients with NAFLD, an ultrasound scan of the liver was used to detect NAFLD. Logistic regression models were used to estimate the odds ratios(ORs) and 95 % confidence interval(CI) of NAFLD across tertiles of the US HFD index.

RESULTS

Mean ± SD age of the study population were 38.13 ± 8.85 years. The median (interquartile) score of the US HFD index in patients with NAFLD and healthy subjects was 0.08(0.07-0.09) and 0.09(0.08-0.10), respectively. In the age and sex-adjusted model, the odds of NAFLD were considerably reduced across tertiles of the US HFD index (OR:0.48; 95%CI:0.32-0.72, Ptrend<0.001). Also, in the final model, after adjusting for age, sex, waist-to-hip ratio, smoking, physical activity, marital status, socioeconomic status, and energy intake, the odds of NAFLD were significantly reduced across tertiles US HFD index (OR:0.55; 95%CI:0.31-0.97, Ptrend<0.001). Furthermore, for each SD increase in the US HFD index, the odds of NAFLD are reduced by 23 % (OR:0.77;95%CI:0.60-0.97,P-Value<0.001).

CONCLUSIONS

Our findings revealed that greater adherence to dietary pattern with a high US HFD score, defined by higher intakes of fruits, vegetables, whole grains, legumes, nuts, low-fat dairy, seeds, soya products, and plant oils may be related to reducing the odds of NAFLD.

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Zhu X, Qucuo N, Zhang N, Tang D, Hu Y, Xie X, Zhao X, Meng Q, Chen L, Jiang X, Zhuoma D, Zeng Q, Xiao X. Dietary patterns and metabolic dysfunction-associated fatty liver disease in China's multi-ethnic regions. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2023;42:141. [PMID: 38093350 PMCID: PMC10717100 DOI: 10.1186/s41043-023-00485-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/02/2023] [Indexed: 12/17/2023]

Jing H, Teng Y, Chacha S, Wang Z, Shi G, Mi B, Zhang B, Cai J, Liu Y, Li Q, Shen Y, Yang J, Kang Y, Li S, Liu D, Wang D, Yan H, Dang S. Is Increasing Diet Diversity of Animal-Source Foods Related to Better Health-Related Quality of Life among Chinese Men and Women? Nutrients 2023;15:4183. [PMID: 37836467 PMCID: PMC10574670 DOI: 10.3390/nu15194183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open

Abstract

Diet plays a crucial role in regulating individuals' lifestyles and is closely related to health. The intake of animal-sourced foods (ASF) provides the human body with high-quality protein and various micronutrients. This study aimed to investigate whether the diversity of animal foods has a positive impact on the health-related quality of life (HRQoL) among residents. The data came from the Shaanxi baseline survey of the Northwest Chinese Regional Ethnic Cohort Study, which recruited more than 100 thousand participants aged 35 to 74 from five provinces between June 2018 and May 2019. A total of 39,997 participants in Shaanxi (mean age: 50 years; 64% women) were finally included in this current study. The animal source food diet diversity score (ASFDDS) was established based on the frequency of consuming pork, mutton, beef, poultry, seafood, eggs, pure milk, and yogurt. The physical component score (PCS) and mental component score (MCS), ranging from 0 to 100 on the 12-Item Short Form Survey (SF-12), were used to assess participants' HRQoL. Better PCS/MCS was defined as scores higher than the 90th percentile. The results showed that men had a higher intake of ASF and ASFDDS than women. After adjusting for potential confounders, compared with those who never or rarely consumed animal foods, the likelihood of having better PCS and MCS increased by 16% (OR = 1.16, 95%CI: 1.01-1.34) and 24% (OR = 1.24, 95%CI: 1.03-1.448), respectively, in men with an ASFDDS ≥ 2. In women, a 34% increase (OR = l.34, 95%CI: 116-l.54) likelihood for better PCS was observed for an ASFDDS ≥ 2, but no association was observed for MCS. Increasing each specific animal source's food intake was associated with better PCS after adjusting for all covariates. However, for MCS, positive associations were only observed in seafood consumption among men and eggs among women. Restricted cubic splines showed a substantial dose-response association between intake frequency of animal-source foods and PCS, both in men and women. The study suggests that a diverse intake of animal-sourced foods can potentially improve the HRQoL of Chinese adults.

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Affiliation(s)

Hui Jing Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Yuxin Teng Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Samuel Chacha Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Ziping Wang Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Guoshuai Shi Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Baibing Mi Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Binyan Zhang Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Jiaxin Cai Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Yezhou Liu Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Qiang Li Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Yuan Shen Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Jiaomei Yang Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Yijun Kang Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Shanshan Li Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Danmeng Liu Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Duolao Wang Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool L7 8XZ, UK;
Hong Yan Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
Shaonong Dang Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)

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Xu J, Mao F, Lu Y, Liu T, Li X, Li Y. Hepatic Transcriptomics Reveals Reduced Lipogenesis in High-Salt Diet Mice. Genes (Basel) 2023;14:genes14050966. [PMID: 37239325 DOI: 10.3390/genes14050966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open

da Silva Ferreira G, Catanozi S, Passarelli M. Dietary Sodium and Nonalcoholic Fatty Liver Disease: A Systematic Review. Antioxidants (Basel) 2023;12:antiox12030599. [PMID: 36978847 PMCID: PMC10045331 DOI: 10.3390/antiox12030599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 03/03/2023] Open