Serum ferritin and obstructive sleep apnea-epidemiological study

Ferritin is a potential marker of cardiometabolic risk in adolescents and young adults with sleep-disordered breathing

Objective

To explore markers that reflect sleep-disordered breathing (SDB) severity and investigate their associations with cardiometabolic risk factors in adolescents and young adults.

Methods

Participants were recruited from our SDB epidemiological cohort. They underwent overnight polysomnography and ambulatory blood pressure (BP) monitoring. Complete blood count, ferritin, high-sensitivity C-reactive protein (hs-CRP), fasting blood glucose, and lipid profile were measured. Multiple linear regression was used to examine the association between red cell indices (RCIs), ferritin, and obstructive apnea-hypopnea index (OAHI). Subgroup analyses on participants with SDB were performed for the association of RCIs and ferritin with lipid profile, hs-CRP, and BP.

Results

There were 88 participants with SDB and 155 healthy controls aged 16-25 years. Hemoglobin (Hb; p < .001), hematocrit (HCT; p < .001), and ferritin (p < .001) were elevated with increasing SDB severity and were independently associated with OAHI (β=1.06, p < .001; β=40.2, p < .001; β=4.89 × 10-3, p = .024, respectively). In participants with SDB, after adjusting for age, sex, and BMI, significant associations were found between ferritin with low-density lipoprotein (LDL; β=0.936 × 10-3, p = .008) and triglyceride (TG; β =1.08 × 10-3, p < .001), as well as between Hb (β=1.40, p = .007), HCT (β=51.5, p = .010) and mean arterial pressure (MAP). Ferritin (β=0.091, p = .002), Hb (β=0.975, p = .005), and HCT (β=38.8, p = .004) were associated with hs-CRP independent of age, sex, BMI, plasma LDL, and MAP. OAHI was not associated with LDL and TG in the multivariable models.

Conclusions

Serum ferritin, but not OAHI, was associated with LDL and TG in participants with SDB, suggesting it is a potential marker of cardiometabolic risk in patients with SDB.

Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020

Background

Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.

Methods

We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.

Results

The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.

Conclusion

The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

Serum Ferritin Levels in Severe Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) has been associated with various acute and chronic inflammatory diseases, as has serum ferritin, an intracellular iron storage protein. Little is known about the relationship between severity of OSA and serum ferritin levels in otherwise healthy subjects. In this study, all polysomnographic recordings, serum levels of ferritin, C-reactive protein (CRP), and hemoglobin, as well as patient files from 90 consecutive, otherwise healthy individuals with suspected OSA who presented to a tertiary sleep medical center were retrospectively analyzed. For comparison, three groups were formed based on apnea-hypopnea index (AHI; none or mild OSA: <15/h vs. moderate OSA: 15-30/h vs. severe OSA: >30/h). Serum ferritin levels were significantly positively correlated with AHI (r = 0.3240, p = 0.0020). A clear trend of higher serum ferritin levels was found when patients with severe OSA were compared to those without or with mild OSA. Serum CRP and serum hemoglobin levels did not differ significantly among OSA severity groups. Age and body-mass index (BMI) tended to be higher with increasing OSA severity. The BMI was significant higher in patients with severe OSA compared to those without or with mild (p < 0.001). Therefore, serum ferritin levels may provide a biochemical surrogate marker for OSA severity.

Transferrin saturation is independently associated with the severity of obstructive sleep apnea syndrome and hypoxia among obese subjects

INTRODUCTION & AIMS

Obstructive sleep apnea syndrome (OSAS) is a frequent complication of obesity. Intermittent chronic hypoxia which frequently results from OSAS could modulate the systemic control of iron metabolism and alter serum iron parameters, especially among obese patients.

AIMS

to evaluate whether serum parameters of iron bioavailability and storage (primary), as well as age, waist circumference, arterial hypertension and tobacco use (secondary) are associated with OSAS severity and/or hypoxia.

METHODS

design: a single-center retrospective study with prospective data collection; inclusion criteria: consecutive patients referred for initial assessment for obesity underwent nocturnal respiratory polygraphy and iron status serum assessment within a 3-month period. The adjusted analyzes were performed using ANOVA and reported as adjusted means and 95% confidence interval (95% CI).

RESULTS

13 men and 56 women were included. OSAS prevalence: 72% (n = 50). Ferritin (mean ± SD, 260 ± 276 vs. 111 ± 89 μg/l, p = 0.01) and transferrin saturation (31 ± 10 vs. 24 ± 9%, p = 0.002) were significantly higher in case of moderate/severe OSAS than in absent/mild OSAS, independently from gender and tobacco use. Serum iron (19.4 μg/l [CI95%, 16.5-22.3] vs. 16.2 μg/l ([14.1-18.2], p = 0.056) and transferrin saturation (31.5% [26.3-36.7]) vs. 25.3% [21.6-29.1], p = 0.043) were higher when time under oxygen saturation <90% was >15%. Age (mean ± SD, 51 ± 11 vs. 41 ± 12 yr, p = 0.001), waist circumference (136 ± 18 vs. 123 ± 12 cm, p = 0.003), arterial hypertension (59% (n = 13/22) vs. 23% (n = 11/47), p = 0.004) and tobacco use (64% (n = 14/22) vs. 32% (n = 15/47), p = 0.01) were significantly greater in moderate/severe OSAS than in absent/mild OSAS.

CONCLUSIONS

Transferrin saturation was associated with OSAS severity and time under hypoxia. This suggests a relationship between OSAS-induced hypoxia and iron metabolism among obese patients.