Related Experiment Video

Updated: Jul 29, 2025

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

16.7K

Association of a Polygenic Risk Score With Osteoporosis in People Living With HIV: The Swiss HIV Cohort Study

Johannes M Schwenke1, Christian W Thorball2,3, Isabella C Schoepf1,4

  • 1University Department of Medicine and Infectious Diseases Service, Kantonsspital Baselland, University of Basel, Bruderholz.

The Journal of Infectious Diseases
|May 24, 2023
PubMed

Insights

A polygenic risk score (PRS) is linked to osteoporosis in people with HIV (PLWH). This genetic risk factor is associated with low bone mineral density (BMD) independent of traditional risk factors.

Area of Science:

  • Genetics
  • Bone Metabolism
  • Infectious Diseases

Background:

  • Bone mineral density (BMD) loss is a concern for people with HIV (PLWH).
  • The association between polygenic risk scores (PRS) and low BMD in PLWH remains uninvestigated.

Purpose of the Study:

  • To determine if a PRS is associated with low BMD in PLWH.
  • To assess the PRS's independent contribution to osteoporosis risk in PLWH.

Main Methods:

  • Utilized data from the Swiss HIV Cohort Study (2011-2020).
  • Included 438 participants of European descent with multiple dual x-ray absorptiometry (DXA) measurements.
  • Calculated a genome-wide PRS from 9413 single-nucleotide polymorphisms associated with low BMD.

Main Results:

  • Participants with an unfavorable osteoporosis PRS showed significantly higher odds of osteoporosis.
  • Osteoporosis odds ratios (ORs) for the top vs. bottom PRS quintile were 4.76 (univariable) and 4.13 (multivariable).
  • PRS association remained significant after adjusting for traditional and HIV-related factors, including tenofovir disoproxil fumarate exposure.

Conclusions:

  • A BMD-associated PRS is independently associated with osteoporosis in PLWH.
  • Genetic predisposition, assessed by PRS, plays a role in osteoporosis development in this population.
  • Findings highlight the importance of genetic factors in managing bone health in people with HIV.
Abstract

Keywords:
HIV infectionaginglow bone mineral densityosteoporosispolygenic risk score

More Related Videos

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes07:22

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

322
Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty07:27

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

10.3K

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.7K
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
66.1K
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.1K
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
251
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
402
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
391