Lipid profiling can predict risk of diabetes, cardiovascular disease
decades before onset
Early testing may allow targeted interventions before disease develops


Date:
March 3, 2022
Source:
PLOS
Summary:
Simultaneous measurement of dozens of types of fats in the blood
('lipidomics') can predict the risk of developing type 2 diabetes
(T2D) and cardiovascular disease (CVD) years in the future,
according to a new study. Such early prediction through lipidomic
profiling may provide the basis for recommending diet and lifestyle
interventions before disease develops.



FULL STORY ========================================================================== Simultaneous measurement of dozens of types of fats in the blood
("lipidomics") can predict the risk of developing type 2 diabetes (T2D)
and cardiovascular disease (CVD) years in the future, according to a new
study publishing March 3rd in the open-access journal PLOS Biology from
Chris Lauber of Lipotype, Germany, and colleagues. Such early prediction through lipidomic profiling may provide the basis for recommending diet
and lifestyle interventions before disease develops.


========================================================================== Current assessment of risk for T2D and CVD relies largely on patient
history and current risk behaviors, and the levels and ratio of two major
blood lipids, high- and low-density cholesterol. But the blood contains
over one hundred other types of lipids, which are thought to reflect at
least in part aspects of metabolism and homeostasis throughout the body.

To assess whether a more comprehensive measure of blood lipids could
increase the accuracy of risk prediction, the authors drew on data and
blood samples from a longitudinal health study of over 4,000 healthy, middle-aged Swedish residents, first assessed from 1991 to 1994, and
followed until 2015. Using baseline blood samples, the concentrations
of 184 lipids were assessed with high-throughput, quantitative mass spectrometry. During the follow-up period, 13.8% of participants developed
T2D, and 22% developed CVD.

To develop the lipid-based risk profile, the authors performed repeated training/test rounds on the data, using a randomly chosen two-thirds
of lipid data to create a risk model, and then seeing if the model
accurately predicts risk in the remaining third. Once the model was
developed, individuals were clustered into one of six subgroups based
on their lipidomics profile.

Compared to the group averages, the risk for T2D in the highest-risk
group was 37%, an increase in risk of 168%. The risk for CVD in the highest-risk group was 40.5%, an increase in risk of 84%. Significant reductions in risk compared to the averages were also seen in the
lowest-risk groups. The increased risk for either disease was independent
of known genetic risk factors, and independent of the number of years
until disease onset.

There are several potentially important implications of these
findings. On an individual level, it may be possible to define risk
decades before disease onset, possibly in time to take steps to avert
disease. Lipidomics, either in combination with genetics and patient
history or independent of them, may provide new insights into when and why disease begins. In addition, by identifying those lipids that contribute
most to risk, it may be possible to identify new drug candidates.

"The lipidomic risk, which is derived from only one single
mass-spectrometric measurement that is cheap and fast, could extend
traditional risk assessment based on clinical assay," Lauber said. In
addition, individual lipids in blood may be the consequences of or
contribute to a wide variety of metabolic processes, which may be
individually significant as markers of those processes.

If that is true, Lauber said, "the lipidome may provide
insights much beyond diabetes and cardiovascular disease
risk." Lauber adds, "Strengthening disease prevention is a
global joint effort with many facets. We show how lipidomics
can expand our toolkit for early detection of individuals at
high risk of developing diabetes and cardiovascular diseases." ========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Chris Lauber, Mathias J. Gerl, Christian Klose, Filip Ottosson, Olle
Melander, Kai Simons. Lipidomic risk scores are independent of
polygenic risk scores and can predict incidence of diabetes and
cardiovascular disease in a large population cohort. PLOS Biology,
2022; 20 (3): e3001561 DOI: 10.1371/journal.pbio.3001561 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220303141145.htm

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