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According to the "Global Cancer Statistics 2020", the incidence of esophageal cancer ranks seventh in the world, and it is the sixth leading cause of cancer death in the world
.
Asia accounts for about 78% of all esophageal cancer cases, while 49% of cases occur in China
According to the "Global Cancer Statistics 2020", the incidence of esophageal cancer ranks seventh in the world, and it is the sixth leading cause of cancer death in the world
The occurrence and prognosis of ESCC are affected by many factors
However, both theoretically and empirically, due to the limitations of a single nutrient or food approach, dietary pattern assessment has emerged as an alternative method for measuring dietary exposure in nutritional epidemiology
A total of 422 patients and 423 controls were recruited
Fatty acid factor load for the four main factors
Fatty acid factor load for the four main factors Fatty acid factor load for the four main factorsThe correlation matrix of the 36 fatty acids is shown in Figure 1
.
Factor analysis, including 36 major fatty acids, identified 4 factors that explained 60.
The correlation matrix of the 36 fatty acids is shown in Figure 1
Correlations between dietary fatty acid scores and food groups
Correlations between dietary fatty acid scores and food groupsMLC-SFA pattern scores were positively correlated with 'animal oil' intake ( r=0.
124, P=0.
002)
.
The EC-UFA pattern represented a diet relatively high in "peanut oil" ( r= 0.
MLC-SFA pattern scores were positively correlated with 'animal oil' intake ( r=0.
According to the AIC evaluation of the model fitting performance, model 3 has the lowest AIC and the best fitting effect
Linear trend between dietary fatty acid score and ESCC incidence
Linear trend between dietary fatty acid score and ESCC incidenceThe dose-response relationship between intake of the four FAPs and ESCC risk is shown in Figure 2
.
There was a non-linear positive correlation between EC-UFA patterns and ESCC risk (non-linear p< 0.
The dose-response relationship between intake of the four FAPs and ESCC risk is shown in Figure 2
Stratified analysis of dietary fatty acid patterns and ESCC risk
Stratified analysis of dietary fatty acid patterns and ESCC riskFigure 3 shows the association between FAP and ESCC risk stratified by life>
.
The association between MLC-SFA patterns and ESCC risk was different when stratified by fried foods ( I 2 = 75.
7%, P for heterogeneity = 0.
043)
.
The association between the EC-UFA pattern and ESCC risk varied by preserved food ( I 2 = 78.
2%, P for heterogeneity = 0.
032)
.
The association between SFA patterns and ESCC risk changed when stratified by smoking ( I 2 = 99.
3%, P for heterogeneity < 0.
001)
.
In addition, in addition, there were differences in alcohol consumption ( I 2 = 91.
4%, P heterogeneity = 0.
001) , pickled foods ( I 2 = 91.
4%, P heterogeneity ) and fried foods ( I 2 = 93.
8%, P for heterogeneity < 0.
001) , differences in the association between n-3 LC-PUFA patterns and ESCC risk were observed
.
.
The association between MLC-SFA patterns and ESCC risk was different when stratified by fried foods ( I 2 = 75.
7%, P for heterogeneity = 0.
043)
.
The association between the EC-UFA pattern and ESCC risk varied by preserved food ( I 2 = 78.
2%, P for heterogeneity = 0.
032)
.
The association between SFA patterns and ESCC risk changed when stratified by smoking ( I 2 = 99.
3%, P for heterogeneity < 0.
001)
.
In addition, I 2 P heterogeneity, I 2 P heterogeneity, and I 2 P heterogeneity also increased in alcohol consumption ( I 2 = 91.
4%, P heterogeneity = 0.
001)I2P heterogeneity , preserved food ( I2 = 91.
4%, P heterogeneity ) I2P heterogeneity and fried food ( I2 = 93.
8 % , P heterogeneity < 0.
001 ) I2P heterogeneity In qualitative populations, differences in the association between n-3 LC-PUFA patterns and ESCC risk were observed .
Association of dietary fatty acid patterns with clinicopathological factors
Association of dietary fatty acid patterns with clinicopathological factorsAmong clinicopathological features, N stage was significantly associated with EC-SFA pattern ( r s = 0.
270, P < 0.
001)
.
Higher intake of the EC-UFA pattern may be associated with a higher risk of advanced disease
.
M stage was negatively correlated with the pattern of n-3 LC-PUFAs ( rs = -0.
175 , P = 0.
003)
.
However, T stage was not significantly associated with FAP
.
270, P < 0.
001)
.
Higher intake of the EC-UFA pattern may be associated with a higher risk of advanced disease
.
M stage was negatively correlated with the pattern of n-3 LC-PUFAs ( rs = -0.
175 , P = 0.
003)
.
However, T stage was not significantly associated with FAP rs s P rs s P .
In this hospital-based case-control study, four major dietary patterns were identified, namely medium and long-chain SFA (MLC-SFA), even-chain unsaturated fatty acids (EC-UFA), SFA and n-3 long Chain polyunsaturated fatty acid (n-3 LC-PUFA) pattern
.
The EC-UFA pattern was associated with an increased risk of ESCC, whereas the n-3 LC-PUFA pattern was associated with a reduced risk
.
However, there was no significant correlation between MLC-SFA or SFA patterns observed in the study subjects
.
.
EC-UFA However, there was no significant correlation between MLC-SFA or SFA patterns observed in the study subjects
.
This is the first study to reveal the relationship between dietary FAP and ESCC risk in a Chinese population
.
In our daily life, the food that people eat is made up of various fatty acids, not just one fatty acid
.
Therefore, FAP analysis can reflect the actual dietary quality and summarize the role of various dietary FAs, which is of great significance
.
In contrast to traditional methods of analyzing single fatty acids, factor analysis can investigate relationships between dietary components
.
.
In our daily life, the food that people eat is made up of various fatty acids, not just one fatty acid
.
Therefore, FAP analysis can reflect the actual dietary quality and summarize the role of various dietary FAs, which is of great significance
.
In contrast to traditional methods of analyzing single fatty acids, factor analysis can investigate relationships between dietary components
.
Original source:
Hu C, Lin Z, Liu Z, et al.
Dietary fatty acid patterns and risk of oesophageal squamous cell carcinoma.
PeerJ.
2022;10:e13036.
Published 2022 Mar 31.
doi:10.
7717/peerj.
13036.