Diabetes
The most important finding of this study is the tendency towards increased prevalence of the cardiovascular comorbidities, especially diabetes, in the fNPH patients compared to their non-iNPH relatives. Although the age difference between the two groups was nearly 7 years, diabetes remained independently significant in the multivariate model when adjusted to age, whereas arterial hypertension or cardiac insufficiency did not. Previous studies have compared the differences in the prevalence of diabetes between the iNPH patients and the healthy controls with comparable age distribution [
7‐
14]. Using a table from the review by Hudson et al. [
38] that summarized the results of seven of these studies, we can calculate the pooled prevalence of diabetes among the iNPH patients and the controls. In these seven studies the pooled diabetes rates in iNPH were 24% compared to 10% in the controls, prevalence ratio 2.4:1, p < 0.001 (χ2-test) (only 70–90-year-olds included from the Eide and Pripp’s study [
11]). Our results with the novel study design closely agree with these previous results when it comes to iNPH (32% vs. 14%; prevalence ratio 2.3:1). Additionally, in our previous study [
25], no significant differences were found in the prevalence of diabetes between the sporadic iNPH and the fNPH patients.
Other cardiovascular risk factors, including arterial hypertension, dyslipidemia, obesity and physical inactivity have been also found to be overrepresented in the iNPH patients [
7,
9‐
13,
15], suggesting that they could be possible risk factors for the development of iNPH. This is also backed by the finding of cerebral microbleeds being detected more often in the iNPH patients in magnetic resonance imaging (MRI), and thus a vascular component could possibly affect the pathophysiology of iNPH [
39]. A recent study that compared four different types of adult hydrocephalus (transitional, unrecognized congenital, acquired and iNPH) found out that the prevalence of cardiovascular comorbidities in iNPH was significantly higher compared to the other types [
40]. This finding together with the later onset age of iNPH indicates that the cardiovascular comorbidities could have a chronic effect on its development.
There is evidence that the glymphatic system dysfunction could affect the development of iNPH [
41‐
43]. It has been suggested that in iNPH the glymphatic system is possibly impaired through neuroinflammation, reactive astrogliosis, depolarization and reduced density of aquaporin-4 (AQP4) and sleep disturbances, which could reduce the normal clearance of CSF [
43‐
45]. Interestingly in rat models, diabetes has been found to cause glymphatic system dysfunction, reduction in AQP4 density, neuroinflammation, microvascular damage, blood–brain barrier damage and cognitive decline that could be associated with glymphatic system dysfunction [
46‐
49]. It seems that diabetes could also cause astrogliosis and dysregulated metabolism in astrocytes in mouse and rat models [
49]. By affecting the astrocytes diabetes has also been found to reduce the glutamate uptake in brain in rat models [
50,
51]. Interestingly, iNPH patients have been found to suffer from corticospinal hyperexcitability and it has been hypothesized to possibly derive from increased activity of glutamatergic system [
52,
53].
Some studies have also found the iNPH patients to suffer from a decreased cerebral metabolic rate of glucose [
54], reduced thalamic N-acetylaspartate and total N-acetylaspartate, an important metabolite in the central nervous system [
55], and the down-regulation of the adenosine receptors that together with adenosine are important for the vascular protection and the modulation of inflammation [
56]. This together with the high prevalence of cardiovascular comorbidities shows that metabolic dysfunction seems to be present in iNPH and potentially also in fNPH. On the other hand, it has also been suggested that diabetes in iNPH could be a consequence of ventriculomegaly and compression damage to the hypothalamic pituitary axis causing hormonal imbalances [
38].
The questionnaire did not classify the type of diabetes the participants had. We can assume that nearly all of the cases were type 2 diabetes (T2DM) since the overall prevalence of T2DM among the elderly is remarkably higher than type 1 diabetes (T1DM) [
57]. We would expect the rationale about iNPH/fNPH, cardiovascular risk factors and diabetes to hold true at least in T2DM, T1DM and latent autoimmune diabetes in adults (LADA) but there seems to be only very few studies concerning NPH and the different types of diabetes other than T2DM. One reason could be that the life expectancy of a patient with T1DM used to be quite poor in the past compared to the average onset age of iNPH [
58]. In one study, a possible presence of NPH was found in 6 insulin-dependent diabetic patients with recurrent hypoglycemic coma (mean age 62, mean diabetes duration 25 years) [
59]. Their diabetes types were not precisely classified in the study but most likely either T1DM, LADA or progressed T2DM.
These findings support the idea that diabetes could impact the development of iNPH and fNPH and even its phenotype. However, it is unclear how significant this impact is as the majority of iNPH patients do not seem to have diabetes although it being clearly overrepresented in iNPH compared to the general population. It is also unclear whether the treatment or the prevention of certain metabolic dysfunctions or the cardiovascular comorbidities would effectively prevent the development of iNPH/fNPH or if there were other factors affecting it. Especially the potential genetic aspect of diabetes in iNPH/fNPH is intriguing and warrants further research.
The identified relatives
When it comes to the excluded iNPH patients with symptomatic relatives that had no brain-imaging available to confirm the relative’s iNPH, the family history is usually based on either symptomatic mother, father or sibling that has already died. It is plausible that some of these potential fNPH cases are actually sporadic. After all, it would be interesting to study both symptomatic and asymptomatic relatives of iNPH patients regardless of the prior family history on potential NPH-related symptoms, although the probability of finding genetic risk factors could be notably higher in those with clear family history. A consensus on determining the diagnosis of fNPH is needed considering that full consensus of definite iNPH diagnosis is actually also lacking.
The pedigrees offer a novel opportunity to study the genetics and the pathophysiology of iNPH/fNPH. In addition to this, when we learn more about the development of iNPH, it allows us to possibly detect the relatives who are at a greater risk of developing iNPH and to potentially achieve a preclinical diagnosis of iNPH, as iNPH seems to show signs of asymptomatic ventriculomegaly (AVIM) in the neuroradiological imaging years before the clinical symptoms appear [
64,
65]. This could be important since delayed shunting seems to hamper the clinical outcome of iNPH [
66]. INPH is quite an unknown disorder among the general population but the knowledge of a possible familial aggregation of iNPH (fNPH) might allow the relatives to detect the symptoms of NPH more easily and to potentially seek treatment before the symptoms progress severely.
Alcohol, sleep apnea, SFMBT1 and APOE ε4
Alcohol consumption was recently suggested to be a potential risk factor for iNPH in two studies [
14,
67]. Our results don’t back up this finding, but it must be noted that our questionnaire represents only the time close to the diagnosis and not their alcohol consumption earlier in life. Also, a frequent association between iNPH and obstructive sleep apnea has been found [
45]. Our analysis with the probable fNPH families showed no differences in the prevalence of sleep apnea between the groups (Table
3).
An interesting finding in the study was the similar prevalence of the CN loss in intron 2 of the
SFMBT1 gene between the fNPH patients and their non-iNPH relatives (9% vs. 9%), despite the allelic variation in
SFMBT1 being discovered to be overrepresented in the iNPH patients in a Japanese study cohort [
27] and also in Finnish and Norwegian cohorts [
28]. This is the first time the
SFMBT1 CN loss has been directly compared between the fNPH patients and their relatives. Korhonen et al. [
28] found the
SFMBT1 CN loss to be present in 5% of the general Finnish population, which is less than it was in the non-iNPH relatives of these probable fNPH patients. We can speculate whether the
SFMBT1 CN loss accumulates in these families exposing them to a greater risk of developing iNPH. The
SFMBT1 CN loss might require some other unknown external factor to trigger the development of iNPH, and interestingly in this study, diabetes was present in 3 out of the 4 probable fNPH patients that had CN loss in the
SFMBT1 gene compared to none out of 2 of the non-iNPH relatives. This indicates that diabetes might be one potential trigger that is needed for the CN loss in intron 2 of
SFMBT1 to cause iNPH and raises a question for further study on the potential gene-environmental interactions. The brain MRIs of these elderly relatives with the CN loss in
SFMBT1 in this study would be beneficial to exclude the possibility of asymptomatic ventriculomegaly [
64,
65]. It must be noted that the number of the
SFMBT1 genotyped relatives in this study is small, so pure coincidence could have possibly affected the results. More studies concerning the mechanism between
SFMBT1 and iNPH are needed.
APOE ε4 did not show any association with fNPH when compared to the relatives (22% vs. 32%). This is in-line with the previous findings [
31,
32] and strengthens the assumption that fNPH has a genetic background independent from AD. From the probable fNPH patients suffering from comorbid AD, 50% were carriers of
APOE ε4 in this study group.
Strengths and limitations
The main strengths of this study are that only the probable fNPH families with multiple confirmed cases were included in the analysis and the families came from a fairly homogeneous population. The questionnaires were well-filled as the modified CCI scores were measurable from 93% of the probable fNPH patients and 98% of the non-iNPH relatives included in the final analysis. The questionnaires sent to the patients and to their relatives were also identical and therefore the results were closely comparable.
There are limitations and potential sources of error. The data used was based on the questionnaires that were filled by the participants themselves or by their next of kin, which might create a potential source of error. The phone-interview-based data recruitment of the relatives to the study is not the most effective and reliable method. Due to the nature of iNPH, dementia was excluded from the modified CCI as it would probably have caused bias. The variables in the study were mainly dichotomous, and therefore assumptions concerning diabetes and cancer in the CCI measurements were made as we have little information about the severity of the comorbidities, which requires further study.