The differences of serum lipid profiles between primary aldosteronism and essential hypertension: a meta-analysis and systematic review

BACKGROUND

The data on lipid profile differences between primary aldosteronism (PA) and essential hypertension (EH) patients are inconsistent and inconclusive. Most studies reported lower levels of lipid profiles in PA than in EH. This meta-analysis aimed to explore differences in serum lipid profiles including triglyceride (TG), total cholesterol (TC), LDL and HDL levels in PA patients and EH patients.

METHODS

A search of published studies was performed using PubMed, Embase and Scopus databases from their inception through August 2022. Thirty studies involving 11,175 patients were identified. Inclusion criteria included 1) observational studies which contained data on any of the lipid profiles of interest (TG, TC, LDL and HDL) which could be acquired from baseline data or the outcomes, 2) data which should be compared between adult PA and EH patients and 3) the use of appropriate methods to diagnose PA. Standardized mean difference (SMD) with a 95% confidence interval (95% CI) was calculated to assess effect size by using STATA program version 15.0. Risk of bias was assessed by Joanna Briggs Institute (JBI) Critical Appraisal Tools for cross-sectional, cohort and case-control studies.

RESULTS

Levels of the lipid parameters TG (SMD - 0.16 mmol/L; 95%CI (- 0.25, - 0.07)), TC (SMD - 0.30 mmol/L; 95%CI (- 0.41, - 0.19)) and LDL (SMD - 0.17 mmol/L; 95%CI (- 0.27, - 0.08)) were significantly lower in PA than in EH patients. There was no statistically significant difference in HDL between PA and EH patients (SMD - 0.08 mmol/L; 96%CI (- 0.23,0.07)). High levels of heterogeneity for TG, TC, HDL and LDL were observed in all studies. Risk of bias among the studies was low to moderate.

CONCLUSION

Lower levels of TG, TC and LDL were observed in PA than in EH patients. Further study should be conducted to address the underlying mechanisms of lipid alteration in PA.

876. Renal Function, Lipid Profile, and Cardiovascular Events After Switching to Abacavir Containing Regimen in Antiretroviral-Therapy-Experienced People Living with HIV in Northern Thailand

Background

Abacavir (ABC) is commonly used as part of antiretroviral therapy (ART) regimen for people living with HIV (PLWH) with renal dysfunction in resource limiting countries. While the renal function changes and association with cardiovascular (CV) events have been well described in developed countries, these information is limited in Asian population. Herein, this study aims to describe the changes in renal function, lipid profile and CV events after ABC switching in ART-experienced PLWH in Northern Thailand.

Methods

This retrospective chart-review study was conducted among adults ART-experienced PLWH (≥18 years old) who received ABC-containing regimen during January 2016 to December 2018 at Maharaj Nakorn Chiang Mai Hospital. Demographic data, HIV-related treatments, creatinine, lipid profile and CV events were collected. Patients were categorized into early switching group and late switching group (CrCl≥50 ml/min and CrCl< 50 ml/min before switching to ABC). The change of CrCl, urinalysis profiles, lipid profiles, CD4, viral load, and cardiovascular events at 12 months after ABC initiation were assessed.

Results

Total of 115 participants were enrolled with mean age of 55.2±10.7 years and 63.5% were male. Of those, 87.8% of patients had received Tenofovir disoproxil fumarate (TDF) prior to ABC. Mean of CrCl at baseline was 47.6±16.8 ml/min and at 12th month was 49.56±19.42 ml/min with mean difference of 3.7 ml/min (95%CI 1.6-5.8, P< 0.001). The improvement of CrCl at 12 months in early switching group was statistically significant compared to late switching. Other two associated factors with improved CrCl after switching to ABC were duration of TDF exposure during CrCl< 60 ml/min (OR 9.26, P 0.004) and history of protease inhibitors (PIs) exposure (OR 0.06, P 0.03). No significant changed in lipid profile, CD4 and virological outcome overtime. There were only 2 CV events observed (9.3:1000 person-year, 95%CI 2.3-37.1).

Figure. (1) Creatinine clearance (ml/min) during follow up period. (2) Triglyceride (mg/dl) during follow-up period. (3) Total cholesterol (mg/dl) during follow up period. (4) LDL (mg/dl) during follow up period. (5) HDL (mg/dl) during follow up period. (6) CD4 (cells/mm³) during follow up period

Conclusion

ABC used in Thai ART-experienced PLWH appeared to be effective with low CV event in the first year. Despite the statistically significant in the change of CrCl after ABC switching, the change was subtle and need further evaluation.

Disclosures

All Authors: No reported disclosures

A diagnostic predictive model for secondary osteoporosis in patients with fragility fracture: a retrospective cohort study in a tertiary care hospital

Identifying secondary causes among osteoporotic patients is crucial. However, there is no simple tool for screening secondary osteoporosis. A predictive model for screening secondary osteoporosis was constructed using simple clinical and biochemical parameters. This predictive model may provide clinicians with guidance to perform further investigations for specific causes of osteoporosis.

PURPOSE

Establishing whether a fragility fracture is secondary to a specific cause of osteoporosis is crucial for treatment outcomes. Therefore, this study aimed to develop a simple screening tool for secondary osteoporosis in the elderly initially presented with fragility fractures.

METHODS

A retrospective cohort study including 456 patients with fragility hip and vertebral fractures that occurred between January 2017 and July 2022 was conducted. Demographic, clinical, biochemical, and final diagnostic data were retrieved. Potential predictors for secondary osteoporosis were determined by multivariable logistic regression analysis, and a predictive model for secondary osteoporosis was subsequently developed using identified potential predictors.

RESULTS

This study included 343 females and 113 males with a mean age of 76.9 ± 11.0 years. One hundred and twenty-one patients (26.5%) were diagnosed with secondary osteoporosis. Vitamin D deficiency (71.9%) was the most common cause of secondary osteoporosis, followed by glucocorticoid-induced osteoporosis (23.9%) and primary hyperparathyroidism (9.9%). The potential predictors for secondary osteoporosis included in the predictive model were age, body mass index (BMI), corrected calcium, phosphate, thyroid stimulating hormone, and a 10-year probability of hip fractures calculated by BMI-based FRAX®. With a cut-off level of 0.22, the proposed predictive model has an AuROC of 0.75 (95% CI 0.69 to 0.81) with a sensitivity of 77%, a specificity of 66%, and an accuracy of 68.9%.

CONCLUSION

A predictive model for screening secondary osteoporosis was constructed using simple clinical and biochemical parameters. This newly developed predictive model may provide clinicians with guidance to perform further advanced investigations for secondary causes of osteoporosis.