I am trying to estimate the joint interaction for continuous variables with the `emmeans::emtrends()`

function but I am having trouble doing so. Any help would be greatly appreciated it. See example below

```
library("tibble")
n=1e4
simdata <- tibble(
age = rnorm(n, mean=30, sd=2),
bmi = rnorm(n, mean=22, sd=2),
age_c = age - mean(age),
bmi_c = bmi - mean(bmi),
y = rnorm(n, mean=2 + 2*age_c + 3*bmi_c + 4*age_c*bmi_c, sd=2)
)
model_y <- glm(y~age_c*bmi_c, family = gaussian(link = "identity"), data=simdata)
summary(model_y)
#>
#> Call:
#> glm(formula = y ~ age_c * bmi_c, family = gaussian(link = "identity"),
#> data = simdata)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -7.773 -1.382 0.014 1.365 7.950
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.990902 0.020204 98.54 <2e-16 ***
#> age_c 1.999852 0.010100 198.00 <2e-16 ***
#> bmi_c 3.000901 0.009994 300.27 <2e-16 ***
#> age_c:bmi_c 4.003130 0.005000 800.55 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 4.08059)
#>
#> Null deviance: 3217872 on 9999 degrees of freedom
#> Residual deviance: 40790 on 9996 degrees of freedom
#> AIC: 42447
#>
#> Number of Fisher Scoring iterations: 2
library("emmeans")
#mean difference in y for 1 unit increase in age_c across levels of bmi_c
(beta_age_c_bmi_c0 = 2)
#> [1] 2
(beta_age_c_bmi_c1 = 2 + 3.99)
#> [1] 5.99
emmeans::emtrends(model_y,
specs = "bmi_c",
var = "age_c",
at = list(bmi_c= c(0, 1)))
#> bmi_c age_c.trend SE df lower.CL upper.CL
#> 0 2.000 0.01010 9996 1.980 2.020
#> 1 6.003 0.01112 9996 5.981 6.025
#>
#> Confidence level used: 0.95
#mean difference in y for 1 unit increase in bmi_c across levels of age_c
(beta_bmi_c_age_c0 = 3)
#> [1] 3
(beta_bmi_c_age_c1 = 3 + 3.99)
#> [1] 6.99
emmeans::emtrends(model_y,
specs = "age_c",
var = "bmi_c",
at = list(age_c= c(0, 1)))
#> age_c bmi_c.trend SE df lower.CL upper.CL
#> 0 3.001 0.009994 9996 2.981 3.020
#> 1 7.004 0.011227 9996 6.982 7.026
#>
#> Confidence level used: 0.95
```

I want to get the mean difference in y for 1-unit increase in age_c AND 1-unit increase in bmi_c. I would like to obtain it using `emtrends()`

but I am not sure what the command is. I am trying to get the output below

```
(beta_bmi_c1_age_c1 = 3 + 2 + 3.99)
#> [1] 8.99
```