Using trend variable for time series linear variable

Question

Is it okay if I use trend variable for my time series linear regression?

    tslm.LE <- tslm(Life_expectancy~Age+Gender+Race+trend, data=ts_df)
    summary(tslm.LE)

If I do not use trend variable then, I get the same output for both linear model regression and time series linear regression.

    Call:
    tslm(formula = Life_expectancy ~ Age + Gender + Race + trend, 
    data = ts_df)

    Residuals:
    Min      1Q  Median      3Q     Max 
    -41.186  -6.064   0.394   6.561  15.729 

    Coefficients:
          Estimate Std. Error t value Pr(>|t|)    
    (Intercept) -13.962344   2.802002  -4.983 7.78e-07 ***
     Age          25.901145   0.361652  71.619  < 2e-16 ***
     Gender       -4.440598   0.588072  -7.551 1.25e-13 ***
     Race          0.816503   1.081518   0.755    0.451    
     trend         0.001875   0.004051   0.463    0.644    
     ---
     Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Residual standard error: 8.022 on 751 degrees of freedom
     Multiple R-squared:  0.8739,   Adjusted R-squared:  0.8732 
     F-statistic:  1301 on 4 and 751 DF,  p-value: < 2.2e-16

What conclusion can be drawn from this? Because the trend variable is insignificant here. My purpose is to determine the model that is suitable for time series data by using linear model and time series linear model.