Uniform Lp bound implies finite Lp norm with random index? Suppose that $\{X_N:N\in\mathbb N\}$ is a sequence of random variables uniformly bounded in $L^p$ norm for all $p>1$:
$$\sup_N\|X_N\|_{L^p}<C(p).$$
Let $\mathcal N$ be some random variable taking values in $\mathbb N$ that is independent of the sequence $\{X_N\}$,
and define $X_{\mathcal N}$ as the random variable
$$\omega\to X_{\mathcal N(\omega)}(\omega).$$
Is it then possible to prove that
$\|X_{\mathcal N}\|_{L^{p}}<\infty$?
It is easy to see that
$$\|X_{\mathcal N}\|_{L^{p}}=\left\|\sum_{N}X_N\cdot1_{\{N=\mathcal N\}}\right\|_{L^{p}},$$
and thus we get the result by Minkowski's and Hölder's inequalities
if
$$\sum_N\|1_{\{N=\mathcal N\}}\|_{L^{2p}}<\infty.$$
However,
it is possible to prove this with weaker (or ideally no) conditions on the distribution of $\mathcal N$?

Notice that for all $\omega$
$$
X_{\mathcal N}\left(\omega\right)=\sum_{n=1}^{+\infty}X_{n}(\omega)\mathbf 1\left\{\mathcal N(\omega)=n\right\}
$$
and using pairwise disjointness of the events $\left\{\omega\mid \mathcal N(\omega)=n\right\}$, we get 
$$
\left\lvert X_{\mathcal N}\left(\omega\right)\right\rvert^p=\sum_{n=1}^{+\infty}\left\lvert X_{n}(\omega)\right\rvert^p\mathbf 1\left\{\mathcal N(\omega)=n\right\}.
$$
Integrating and using independence between $\mathcal N$ and the sequence $\left(X_n\right)_{n\geqslant 1}$, we get 
$$
\mathbb E\left\lvert X_{\mathcal N}\left(\omega\right)\right\rvert^p=\sum_{n=1}^{+\infty}\mathbb E\left\lvert X_{n}(\omega)\right\rvert^p\mathbb P\left\{\mathcal N(\omega)=n\right\}\leqslant c(p)\sum_{n=1}^{+\infty} \mathbb P\left\{\mathcal N(\omega)=n\right\}=c(p).
$$
