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Average non-HDL cholesterol levels

What you should know about this indicator

How is this data described by its producer?

Definition

Mean total cholesterol, mean high-density lipoprotein (HDL) and mean non-HDL cholesterol of defined population in mmol/l. Desirable individual levels are: Total cholesterol <5.0 mmol/L

Method of estimation

1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older were used to estimate mean total trends of HDL and non-HDL cholesterol from 1980 to 2018. Most studies in the analysis measured total cholesterol and HDL cholesterol, from which non-HDL cholesterol can be calculated through subtraction. non-HDL cholesterol predicts IHD risk as well as low density lipoprotein (LDL) cholesterol, and can be measured at a lower cost. More information on input and data methods is available at: NCD Risk Factor Collaboration (NCD-RisC). Repositioning of the global epicentre of non-optimal cholesterol. Nature 582, 73–77 (2020).

Average non-HDL cholesterol levels
Mean total cholesterol, mean high-density lipoprotein (HDL) and mean non-HDL cholesterol of defined population in mmol/l. Desirable individual levels are: Total cholesterol <5.0 mmol/L
Source
World Health Organization - Global Health Observatory (2025) – processed by Our World in Data
Last updated
May 19, 2025
Next expected update
May 2026
Date range
1980–2018

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Sources and processing

This data is based on the following sources

World Health Organization – Global Health Observatory

The GHO data repository is WHO's gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Retrieved on
May 19, 2025
Retrieved from
https://www.who.int/data/gho
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
World Health Organization. 2025. Global Health Observatory data repository. http://www.who.int/gho/en/.

The GHO data repository is WHO's gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Retrieved on
May 19, 2025
Retrieved from
https://www.who.int/data/gho
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
World Health Organization. 2025. Global Health Observatory data repository. http://www.who.int/gho/en/.

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All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Average non-HDL cholesterol levels”. Our World in Data (2026). Data adapted from World Health Organization. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/average-non-hdl-cholesterol-levels.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

World Health Organization - Global Health Observatory (2025) – processed by Our World in Data

Full citation

World Health Organization - Global Health Observatory (2025) – processed by Our World in Data. “Average non-HDL cholesterol levels” [dataset]. World Health Organization, “Global Health Observatory” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/average-non-hdl-cholesterol-levels.html (archived on March 4, 2026).

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Data API

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Data URL (CSV format)
https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/average-non-hdl-cholesterol-levels.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear