diff --git a/HISTORY.md b/HISTORY.md
index 52616315d..80f301582 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,5 +1,9 @@
 # DynamicPPL Changelog
 
+## 0.35.9
+
+Fixed the `isnan` check introduced in 0.35.7 for distributions which returned NamedTuple.
+
 ## 0.35.8
 
 Added the `DynamicPPL.TestUtils.AD.run_ad` function to test the correctness and/or benchmark the performance of an automatic differentiation backend on DynamicPPL models.
diff --git a/Project.toml b/Project.toml
index 6c72e55d9..1cb375890 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.35.8"
+version = "0.35.9"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
diff --git a/src/debug_utils.jl b/src/debug_utils.jl
index 76c097e94..ef661fc3a 100644
--- a/src/debug_utils.jl
+++ b/src/debug_utils.jl
@@ -242,6 +242,10 @@ function _has_missings(x::AbstractArray)
     return false
 end
 
+_has_nans(x::NamedTuple) = any(_has_nans, x)
+_has_nans(x::AbstractArray) = any(_has_nans, x)
+_has_nans(x) = isnan(x)
+
 # assume
 function record_pre_tilde_assume!(context::DebugContext, vn, dist, varinfo)
     record_varname!(context, vn, dist)
@@ -291,7 +295,7 @@ function record_pre_tilde_observe!(context::DebugContext, left, dist, varinfo)
         )
     end
     # Check for NaN's as well
-    if any(isnan, left)
+    if _has_nans(left)
         error(
             "Encountered a NaN value on the left-hand side of an" *
             " observe statement; this may indicate that your data" *
diff --git a/test/debug_utils.jl b/test/debug_utils.jl
index b79ff1fbc..2becb690c 100644
--- a/test/debug_utils.jl
+++ b/test/debug_utils.jl
@@ -130,8 +130,15 @@
                 x[i] ~ Normal(a)
             end
         end
-        model = demo_nan_in_data([1.0, NaN])
-        @test_throws ErrorException check_model(model; error_on_failure=true)
+        m = demo_nan_in_data([1.0, NaN])
+        @test_throws ErrorException check_model(m; error_on_failure=true)
+        # Test NamedTuples with nested arrays, see #898
+        @model function demo_nan_complicated(nt)
+            nt ~ product_distribution((x=Normal(), y=Dirichlet([2, 4])))
+            return x ~ Normal()
+        end
+        m = demo_nan_complicated((x=1.0, y=[NaN, 0.5]))
+        @test_throws ErrorException check_model(m; error_on_failure=true)
     end
 
     @testset "incorrect use of condition" begin