Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health.

AIM: The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels. METHODS: We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors. RESULTS: The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits. CONCLUSIONS: Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

Urinary interleukin-6 excretion reflects mean 24-hour systolic blood pressure in type 2 diabetes.

AIMS: Research into new risk markers for diabetic kidney disease is required. We aimed to study 24-hour urinary interleukin- 6 excretion (uIL-6) in type 2 diabetic patients in relation to organ damage induced by increased blood pressure. METHODS: 24-hour uIL-6 and albumin excretion and 24-hour blood pressure recording were evaluated in 49 patients with type 2 diabetes and normal renal function. Patients with optimized mean 24-hour systolic blood pressure (SBP), defined as SBP ≤ 130 mmHg, and those with uncontrolled SBP (SBP > 130 mmHg) were compared. Multiple linear regression analysis was performed to study significant contributors to variance in the 24-hour uIL-6 excretion rate. RESULTS: Albumin excretion rate (AER) and uIL-6 were significantly correlated (r=0.63; p<0.0001). Patients with mean 24-hour SBP above 130 mmHg (n=27) had significantly higher mean uIL-6 excretion than those with a mean 24-hour SBP equal to or below 130 mmHg (n=22) (p=0.009). The strength of the association of uIL-6 with diurnal and mean diastolic blood pressure (DBP) was significantly greater than that with AER. Mean SBP (p<0.0001) contributed to 25% of AER variance after body mass index, age, sex, mean SBP, mean DBP, HbA1c and smoking status were accounted for. Mean 24-hour SBP (p=0.005) and smoking (p=0.03) contributed to 15% and 9%, respectively, of uIL-6 variance. CONCLUSIONS: Increased uIL-6, perhaps by reflecting significant tissue damage and remodelling, could be a marker for increased mean SBP in type 2 diabetes.

Citalopram- and escitalopram-induced symptomatic hyponatremia.

We report the cases of 2 patients who developed syndrome of inappropriate antidiuretic hormone secretion (SIADH) after initiating therapy with the selective serotonin reuptake inhibitors, citalopram and escitalopram. Both the patients were women, aged 65 and 81 years old. The first consulted for headache, nausea and vomiting and the second for asthenia and malaise. Both were under treatment for depression with citalopram (20 mg/day) and escitalopram (10 mg/day), respectively. We review the literature on SIADH induced by citalopram and escitalopram and the pathophysiological mechanisms of this syndrome.