The different associations between platelet distribution width and hypertension subtypes in males and females

Relationship between platelet distribution width and non-dipping pattern in children with essential hypertension

BACKGROUND

Hypertension has shown a trend of prevalence at younger ages, and the non-dipping pattern is associated with target organ damage in hypertension. However, few studies have yet investigated the clinical characteristics and risk factors of non-dipper status in essential hypertension children. This study aimed to explore the clinical characteristics and possible indicators associated with non-dipper status in children with essential hypertension.

METHODS

A total of 125 children (99 boys, 26 girls) with untreated essential hypertension were retrospectively included in this study. Non-dipping was defined as a nocturnal drop in systolic or diastolic BP (SBP, DBP) < 10%. Clinical data, ambulatory blood pressure monitoring (ABPM), laboratory and echocardiography parameters were recorded from the hospital database.

RESULTS

Non-dipping pattern was found in 74 (59.2%) children and the dipping pattern in 51(40.8%) children, and the nocturnal SBP drop was 8.43 ± 0.71 (%), and the DBP drop was 14.44 ± 0.86 (%). The proportion of children with left ventricular hypertrophy was higher in the non-dipping group than in the dipping group. The platelet distribution width, high-sensitivity C-reactive protein (hs-CRP) and triglycerides (TG) levels were higher in the non-dipping group compared with the dipping group. In multivariate logistic regression analysis, PDW, TG and hs-CRP were found to be associated with the non-dipping pattern.

CONCLUSION

Non-dipping pattern in children hypertension is common, and the proportion of left ventricular hypertrophy is higher in non-dipping hypertension children. Moreover, higher PDW, hs-CRP and TG levels are the risk factors for non-dipping status in essential hypertension children.

A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients

Purpose

Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes.

Patients and Methods

In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records. They were categorized into success (n=479) and failure (n=391) groups based on average blood pressure control efficacy. Predictive models were constructed using six machine learning algorithms. Evaluation metrics encompassed the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis assessed feature contributions to efficacy.

Results

All six machine learning models demonstrated superior predictive performance. Following variable reduction, the model predicting amlodipine efficacy was reconstructed using these algorithms, with the light gradient boosting machine (LightGBM) model achieving the highest overall performance (AUC = 0.803). Notably, amlodipine showed enhanced efficacy in patients with low platelet distribution width (PDW) values, as well as high hematocrit (HCT) and thrombin time (TT) values.

Conclusion

This study utilized machine learning to predict amlodipine's effectiveness in hypertension treatment, pinpointing key factors: HCT, PDW, and TT levels. Lower PDW, along with higher HCT and TT, correlated with enhanced treatment outcomes. This facilitates personalized treatment, particularly for hospitalized hypertensive patients undergoing amlodipine monotherapy.

Association of platelet distribution width with all-cause and cause-specific mortality in US adults

BACKGROUND

Platelet distribution width (PDW) indicates heterogeneity in circulating platelet sizes. Studies reporting PDW association with mortality were limited by small sample sizes. Therefore, we examined the relationship between PDW and all-cause and cause-specific mortality in a large representative cohort.

METHODS

The NHANES III data were linked to mortality files to examine the association between PDW and mortality. We excluded participants <18 years old and had a history of myocardial infarction. Since the hazards violated the proportionality assumption, we used piece-wise spline with 5-year time intervals in Cox models without and with adjustment for age, gender, race, smoking history, diabetes mellitus, hypertension, eGFR and total cholesterol.

RESULTS

Of 15,688 participants, 53.2% were females, 36.2% had a history of hypertension, and 6368(40.6%) died during follow-up (range 0 to 31 years). The mean (SD) age of the participants was 47(20) years, platelet count was 275.0(71.7) 109/L, and PDW 16.5(0.5). In multivariable analyses, PDW was associated with all-cause mortality at 0-5 years (HR = 1.44; 95%CI = 1.21, 1.72; P < 0.001) and at 5-10 years (HR = 1.23; 95%CI =1.03, 1.46; P = 0.02). Similarly, PDW association was significant for the first 0-5 years in cardiovascular mortality (HR = 1.58, 95%CI = 1.10, 2.25; P = 0.013) and for cancer mortality (HR = 1.48 (1.15, 95%CI = 1.15, 1.91, P = 0.003). For other-cause mortality, PDW remained significantly associated for 0-5 years (HR = 1.35, 95%CI =1.05, 1.74; P = 0.02) and for 5-10 years (HR = 1.38, 95%CI = 1.05, 1.83; P = 0.023).

CONCLUSIONS

PDW is an independent, but time-dependent, predictor of all-cause, cardiovascular, cancer and other-cause mortality up to 5 years. The mechanisms underlying this association need further study.

Platelet Indices and Hypertension: Results from Shahedieh Cohort Study, Yazd, Iran

Introduction

Hypertension is one of the most important diseases worldwide. In this study, we aim to demonstrate the relationship between platelet indices and hypertension.

Materials and Methods

We studied 9448 people in the age range of 30 to 70 years. We assessed their hypertension status, platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), smoking, cardiovascular disease history, diabetes status, body mass index, and creatinine levels. Hypertension status was assessed qualitatively. All platelet indices were categorized by quartiles. We then used logistic regression to predict the relationship between these indices and hypertension.

Results

PDW index and hypertension had a statistically significant relationship in the second quartile (16.2 fL < PDW ≤ 16.7 fL) in 30 to 40 years old (AOR: 0.225, 95% CI: 0.063-0.806), in the fourth quartile in 50 to 60 years old (AOR = 1.532, 95% CI: 1.048-2.238), and in all the quartiles of the age range of over 60 years. PLT index had a positive relationship (AOR = 3.147, 0.95% CI: 1.163-8.516) in 30 to 40 years old in the fourth quartile vs. the first quartile. A positive relationship was obtained in the third and fourth quartiles of PLT and the age range of 40 to 50 years, respectively (AOR = 2.063, 0.95% CI: 1.162-3.662) and (AOR = 2.204, 0.95% CI: 1.220-3.981).

Conclusion

According to the results of this study, some platelet indices could be correlated with hypertension, so we may be able to reduce the burden of this disease.

White blood cell and platelet distribution widths are associated with hypertension: data mining approaches

In this paper, we are going to investigate the association between Hypertension (HTN) and routine hematologic indices in a cohort of Iranian adults. The data were obtained from a total population of 9704 who were aged 35-65 years, a prospective study was designed. The association between hematologic factors and HTN was assessed using logistic regression (LR) analysis and a decision tree (DT) algorithm. A total of 9704 complete datasets were analyzed in this cohort study (N = 3070 with HTN [female 62.47% and male 37.52%], N = 6634 without HTN [female 58.90% and male 41.09%]). Several variables were significantly different between the two groups, including age, smoking status, BMI, diabetes millitus, high sensitivity C-reactive protein (hs-CRP), uric acid, FBS, total cholesterol, HGB, LYM, WBC, PDW, RDW, RBC, sex, PLT, MCV, SBP, DBP, BUN, and HCT (P < 0.05). For unit odds ratio (OR) interpretation, females are more likely to have HTN (OR = 1.837, 95% CI = (1.620, 2.081)). Among the analyzed variables, age and WBC had the most significant associations with HTN OR = 1.087, 95% CI = (1.081, 1.094) and OR = 1.096, 95% CI = (1.061, 1.133), respectively (P-value < 0.05). In the DT model, age, followed by WBC, sex, and PDW, has the most significant impact on the HTN risk. Ninety-eight percent of patients had HTN in the subgroup with older age (≥58), high PDW (≥17.3), and low RDW (<46). Finally, we found that elevated WBC and PDW are the most associated factor with the severity of HTN in the Mashhad general population as well as female gender and older age.

Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators

Pulmonary Hypertension (PH) is a global health problem that affects about 1% of the global population. Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease. The present study proposes a Kernel Extreme Learning Machine (KELM) model based on an improved Whale Optimization Algorithm (WOA) for predicting PH mouse models. The experimental results showed that the selected blood indicators, including Haemoglobin (HGB), Hematocrit (HCT), Mean, Platelet Volume (MPV), Platelet distribution width (PDW), and Platelet-Large Cell Ratio (P-LCR), were essential for identifying PH mouse models using the feature selection method proposed in this paper. Remarkably, the method achieved 100.0% accuracy and 100.0% specificity in classification, demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.

Preoperative platelet distribution width-to-platelet ratio combined with serum thyroglobulin may be objective and popularizable indicators in predicting papillary thyroid carcinoma

Objectives

The incidence of papillary thyroid carcinoma (PTC) has increased more rapidly than that of any other cancer type in China. Early indicators with high sensitivity and specificity during diagnosis are required. To date, there has been a paucity of studies investigating the relationship between preoperative platelet distribution width‐to‐platelet count ratio (PPR) and PTC. This study thus aimed to assess the diagnostic value of PPR combined with serum thyroglobulin (Tg) in patients with PTC.

Methods

A total of 1001 participants were included in our study. 876 patients who underwent surgery for nodular goiter were divided into the PTC group or benign thyroid nodule (BTN) group according to pathology reports, and 125 healthy controls (HCs) were included. Preoperative hemogram parameters and serum Tg levels were compared among three groups. Receiver operating characteristic (ROC) curve was used to evaluate the value of PPR combined with serum Tg for diagnosing PTC.

Results

Platelet distribution width (PDW) and PPR levels were higher in the PTC group than in the BTN and HC groups (both p < 0.05) but did not significantly differ between the BTN and HC groups. PDW and PPR levels significantly differed in the presence/absence of lymph node metastasis, the presence/absence of capsule invasion (p = 0.005), and TNM stages (p < 0.001). Multivariable analyses indicated that high serum Tg levels [adjusted odds ratio (OR), 1.007; 95% confidence interval (CI), 1.004–1.009; p < 0.001], high neutrophil‐to‐lymphocyte ratio (NLR,adjusted OR, 1.928; 95% CI, 1.619–2.295; p < 0.001), and high PPR (adjusted OR, 1.378; 95% CI, 1.268–1.497; p < 0.001) were independent risk factors for PTC. In ROC analysis, the areas under the curves (AUCs) of serum Tg, PDW, PPR, and NLR for predicting PTC were 0.603, 0.610, 0.706, and 0.685, respectively. PPR combined with serum Tg (PPR + Tg) had a higher diagnostic value (AUC, 0.738; sensitivity, 60%; specificity, 74.7%) compared with PDW + Tg (AUC, 0.656; sensitivity, 64.4%; specificity, 59.9%) and NLR + Tg (AUC, 0.714; sensitivity, 61.6%; specificity, 71.1%).

Conclusions

Preoperative PPR combined with serum Tg may be objective and popularizable indicators for effective predicting PTC.