Official websites use. Share sensitive information only on official, secure websites. Methods: To determine the specific phenotype of ESKD patients in relation to their vaccine response, a clustering approach was used in a cohort of fully vaccinated patients.
Results: Clustering revealed three distinct clinical phenotypes among hemodialysis patients in terms of immunological response. Two clusters, consisting of either women with a long dialysis history or male subjects with diabetes with a moderate history of dialysis, exhibited low levels of IgG anti-spike antibodies.
The third cluster consisted of non-diabetic middle-aged men with a moderate dialysis vintage and a very good serological response to vaccination. Conclusions: These vaccinal phenotypes of dialysis patients are easily identifiable in current practice, allowing for differential serological follow-up and tailored booster SARS-CoV-2 vaccination. They experienced high mortality rates, notably during the first wave [ 1 ].
This is best appreciated by neutralizing antibodies and, in clinical practice, by anti-spike antibodies, which are considered to be surrogate markers [ 2 , 4 ]. In addition to age and previous COVID infection, multivariable analyses have shown that malnutrition, comorbidities, and previous immunosuppressive treatment can negatively impact the vaccine response in ESKD patients [ 5 , 6 , 7 ].
Of note, evaluating SARS-CoV-2 vaccination in ESKD has many similarities with the evaluation of patients in some other medical conditions, notably inflammatory bowel disease treated by biological or anti-metabolite drugs where patients are also immunocompromised [ 8 , 9 ]. Cluster analysis, also known as clustering or numerical taxonomy, is a statistical method used to group objects or subjects based on their similarities.
