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Travel Medicine and Tourist Health

• First Online: 30 November 2021

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• Peter A. Leggat   ORCID: orcid.org/0000-0002-8749-014X 5  

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Following a period of steady growth in tourism, COVID-19 has caused a tremendous decrease in tourist numbers travelling around the world. With the advent of COVID-19 vaccine programs we will no doubt see a resurgence in travel, but it may take years to recover to pre-COVID-19 levels. Travel medicine aims to assist travellers remain healthy and safe during their travels and will be very important to the recovery of tourism globally. Indeed, travel medicine will likely play a key role in when we can travel normally again.

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COVID-19, travel restrictions and environmental consequences

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Leggat, P.A. (2021). Travel Medicine and Tourist Health. In: Wilks, J., Pendergast, D., Leggat, P.A., Morgan, D. (eds) Tourist Health, Safety and Wellbeing in the New Normal. Springer, Singapore. https://doi.org/10.1007/978-981-16-5415-2_2

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Travel medicine – A comprehensive guide to safe world travel

Shiv charan.

Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Ajay Prakash

Bikash medhi.

Travel has become a crucial aspect of contemporary life in a connected globe. Millions of individuals cross borders every day, whether for travel, commerce, or humanitarian purposes. Recognizing the significance of travel medicine in preserving our health and advancing global health is crucial as we embrace the excitement of seeing new cultures and environments. This article clarifies the importance of travel medicine and exhorts travelers to put their health and safety first.

The rapid global spread of pathogens such as drug-resistant Mycobacterium tuberculosis , severe acute respiratory syndrome virus, novel influenza virus strains, and others over the past 10 years has presented challenges for the global public health community.[ 1 ] Despite this enormous number, there are 200 in-flight medical emergencies (IFMEs) globally per day, one serious in-flight emergency every 10–40,000 passengers, and around 0.35 fatalities per million incoming passengers yearly.[ 2 ] With aging and the rising geriatric population, preexisting medical problems account for around 67% of IFMEs.[ 3 ] Travelers are good sentinels for the early detection of infectious diseases, yet they also have the potential to spread infectious diseases that first appear in underdeveloped countries. The most effective places to quickly spot new infections and keep tabs on changing patterns in travel-related diseases are specialized travel and tropical medicine clinics.[ 1 , 3 ]

Vaccination or Travel Medicines for Different Continents

On six continents, GeoSentinel locations are specialized travel medicine clinics that gather clinician-based surveillance data on diseases associated with travel. Global health concerns consist of typhoid from South Asia, dengue from the Caribbean, Central America, and SE Asia, and African tick-typhus from Southern Africa, which were identified in a GeoSentinel analysis of nearly 17,000 sickly visitors.[ 4 ]

Yellow Fever

Tropical and subtropical regions of South America and Africa are home to yellow fever, a virus spread by mosquitoes. Aedes and Haemagogus mosquitoes are the main carriers of the disease. The disease is identified based on past travel to an endemic region, contact with infected mosquitoes, history of vaccination, symptoms, and test results. There is no permanent treatment, but fluids and vigorous supportive care are necessary in severe instances. Yellow fever can be prevented with a live-attenuated vaccine that is both secure and very effective, i.e. YF 17D vaccination, attenuated live. For 99% of patients, a single dose is effective within 30 days and offers lifetime immunity.[ 5 ]

Travel Medicines for Healthy Travelers

Before leaving on a trip, healthy travelers might benefit from taking a few measures and getting advice from travel medical experts, especially if they are going to a place where there are various health hazards. Healthy travelers should make sure their usual immunizations are current and should think about getting additional shots depending on where they are going. Hepatitis A and B, typhoid, tetanus, diphtheria, and influenza vaccines are typical travel vaccines. It may also be advised to get shots for rabies, Japanese encephalitis, or yellow fever, depending on where you are going. Even for travelers who are normally in good health, travel medical specialists are well-equipped to offer advice and recommendations to ensure a safe and healthy vacation.[ 6 ]

Travelers who have Chronic Disease

Chronic disease patients are more likely to develop new illnesses or develop complications from current illnesses. While malaria continues to be the most frequent infectious disease, death connected to travel, motor vehicle injuries, and drowning are other significant preventable causes of death in travelers. People are suffering from chronic illnesses for longer periods of time and receiving cutting-edge therapies from numerous health-care professionals, which frequently leads to fragmented health care. Due to the increased accessibility of efficient medications, the recommendation of numerous chronic illnesses’ treatments in guidelines and modifications in patient expectations. Polypharmacy is becoming more and more prevalent. Patients who have several chronic conditions will often have a long number of diagnoses that have been made over many years.[ 7 ]

General Travel Medicines

Many people who travel internationally bring medications with them to manage short-term or long-term health issues. Nevertheless, each nation has its own laws governing medications. There is no specified drug development process for travel medicines. Pharmaceuticals that are often prescribed or supplied over the counter in the United States may be unregistered or seen as restricted pharmaceuticals in other nations. Even though regulations differ by nation, breaking local laws might have significant repercussions. Some of the general travel medicines are listed in Table 1 .

General travel medicines

Regulatory Acceptance Carrying Travel Medicines in Different Continents

It might be difficult for international travelers to carry drugs across borders. An independent treaty agency that operates internationally is the International Narcotics Control Board (INCB). Most nations have legislation that is based on a list of INCB guidelines that specify which pharmaceuticals and in what amounts may be imported. In Table 2 , the availability of official country-specific information for travelers-carrying drugs is shown.[ 8 , 9 ]

Carrying drugs in different nations approved by International Narcotics Control Board

*Narcotics, − Psychotropics, and +New Zealand data is not regularly updated. INCB=International Narcotics Control Board, NA=Unworkable

International Society for Travel Medicine: An Association Across the Globe

To fulfill the educational needs of both the public and specialists, the International Society of Travel Medicine (ISTM) was founded in 1991. >4000 people worldwide are members of ISTM. ISTM is a thriving, diverse, multinational association dedicated to making continuous, sustained contributions to the global progress of travel medical practice and knowledge. The ISTM promotes and facilitates teaching, service, and research initiatives in the field of travel medicine in collaboration with health-care professionals, academic institutions, the travel industry, and the media.[ 10 , 11 ]

Travel Medicine for the Ocean

The health and safety of people who participate in activities that include underwater environments is the focus of the specialized discipline of medicine known as underwater travel medicine, commonly referred to as underwater or hyperbaric medicine. These activities include scuba diving, commercial diving, submarine operations, and even undersea building. It is advised that travelers schedule a pretrip consultation at a hospital whose staff members are appropriately knowledgeable and skilled in hyperbaric, tropical, and travel medicine.[ 12 ] Antimalarial medications are unknown to be safe and effective for travelers in a hyperbaric setting.[ 13 ] This field includes the medical issues related to these activities.

These are some essential components of travel medicine for the ocean as shown in Table 3 .

Essential components of travel medicine for the ocean

DCS=Decompression sickness, HBOT=Hyperbaric oxygen therapy

Travel Medicines for Sports Person

Sportspeople who travel for contests or training need to take special safety measures and pay attention to performance-enhancing factors to keep themselves healthy.[ 14 ] From a medical standpoint, more people attending these kinds of sports events raise the possibility of significant casualties. The medical staff providing coverage for the sporting event has to be equipped to handle any kind of emergency.[ 14 , 15 ] Crucial decisions are important regarding the nation or region you will be visiting [ Table 4 ].

Crucial decisions for athletes to carry medical kit

Travel Medicines for who Travel to High Attitude

A common leisure activity that carries a risk of high-altitude disease is traveling to elevations exceeding 2500 m. Up to 75% of hikers who attempt to ascend Tanzania’s Mount Kilimanjaro (5895 m) are afflicted with acute mountain sickness (AMS). General practitioners ought to be qualified to offer helpful guidance on avoiding high-altitude sickness.[ 16 ] Due to pressed schedules, travelers—especially those traveling in organized groups—might not leave enough time for acclimatization, and in certain situations, chemoprophylaxis—which speeds up the body’s response to hypobaric hypoxia—might be necessary.[ 17 ]

Safety Guidelines for Disease and Vaccination While Traveling

Primary care doctors must evaluate the risk of travel and ensure that potential travelers are properly prepared for their excursions. It is advised to provide the proper vaccinations and to prepare an emergency pack. A posttrip follow-up procedure should be created, and the patient should be informed about safe travel practices.[ 18 ] According to a 2008 study on the prevalence of health issues, while traveling to underdeveloped nations, traveler’s diarrhea affects 20%–60% of all visitors.[ 18 , 19 ] To evaluate and reduce travel-related hazards, consultations with immunocompromised individuals should start many months before departure. Improved pretravel counseling and treatments come from a systematic strategy that takes into consideration each patient’s particular immunocompromised status.[ 20 ]

The General Protocol to Follow While Assessing the Travelers is as Follows

• Examine the health of the traveler
• Identify the illness exposure risk
• Administer vaccinations and pertinent counseling
• Medical care
• Counseling food and water.

Mobile Health Applications for Travel

The regulatory bodies ought to examine the ethical concerns surrounding travel-related mobile health applications, pinpoint significant ethical gaps, and make recommendations for crucial ethical issues that should be addressed in upcoming travel-related apps.[ 20 , 21 ] Utilizing mobile health applications on a smartphone is one strategy that has proven advantage due to improvements in the caliber of mobile health technology and the widespread use of smartphones, which permit real-time data gathering, monitoring traveler health behavior as well as faced dangers, has grown simpler and more reliable. Despite its many benefits, such as having access to real-time data, mobile applications for travel medicine have certain ethical issues, such as worries about security and privacy.[ 22 ] Travel medicine will change with the arrival of mobile health and medical applications.

Future Prospects of Travel Medicine in Terms of the Pandemic

A number of variables, such as improvements in medical research, modifications in travel habits, and the worldwide reaction to infectious diseases, are expected to have an impact on the future of travel medicine in light of the pandemic. Potential trends and developments include the following

• Passports for vaccinations and health certificates
• A more thorough pretrip health screening
• Emphasis on virtual consultations and telemedicine
• Quick diagnostic equipment
• New vaccine development and research
• Early warning and public health surveillance systems.

Travel medicine is a crucial component of modern travel. To be able to explore the world with confidence and come home with priceless memories, it is essential to take a proactive approach to travel medicine, which includes immunization, illness prevention, and managing preexisting medical concerns. Adopting travel medicine helps to protect our health as well as the worldwide effort to promote health and stop the spread of infectious illnesses.

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This article is only available in the PDF format. Download the PDF to view the article, as well as its associated figures and tables.

The health consequences of travel are not a new concern for the bipedal wanderer. The author of The Practical Physician for Travellers (1729) states this age-old problem thus:

The Places we travel through, the Change of Air, the universal Diet we meet with, the various sorts of Liquor, the Seasons of Travelling, our sitting up late, and rising early, the various Fatigues of our mind, and so many accidental and unaccustomed Changes, do Travellers, even if they be Princes, meet with, that it is impossible almost for them to bear them without Injury.

It is something of a surprise, therefore, that the health of travelers has only recently emerged as a special area of medical expertise. In the past, tropical medicine and the study of infectious diseases subsumed travel medicine. The spectacular advances and expansion of travel over the past century, however, have enlarged the scope of knowledge and experience

Lee RV , Byrd GD. Journal of Travel Medicine. JAMA. 1996;275(17):1366. doi:10.1001/jama.1996.03530410080042

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• Published: 13 May 2024

Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial

• Donna H. Ryan 1 ,
• Ildiko Lingvay   ORCID: orcid.org/0000-0001-7006-7401 2 ,
• John Deanfield 3 ,
• Steven E. Kahn 4 ,
• Eric Barros   ORCID: orcid.org/0000-0001-6613-4181 5 ,
• Bartolome Burguera 6 ,
• Helen M. Colhoun   ORCID: orcid.org/0000-0002-8345-3288 7 ,
• Cintia Cercato   ORCID: orcid.org/0000-0002-6181-4951 8 ,
• Dror Dicker 9 ,
• Deborah B. Horn 10 ,
• G. Kees Hovingh 5 ,
• Ole Kleist Jeppesen 5 ,
• Alexander Kokkinos 11 ,
• A. Michael Lincoff   ORCID: orcid.org/0000-0001-8175-2121 12 ,
• Sebastian M. Meyhöfer 13 ,
• Tugce Kalayci Oral 5 ,
• Jorge Plutzky   ORCID: orcid.org/0000-0002-7194-9876 14 ,
• André P. van Beek   ORCID: orcid.org/0000-0002-0335-8177 15 ,
• John P. H. Wilding   ORCID: orcid.org/0000-0003-2839-8404 16 &
• Robert F. Kushner 17  

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In the SELECT cardiovascular outcomes trial, semaglutide showed a 20% reduction in major adverse cardiovascular events in 17,604 adults with preexisting cardiovascular disease, overweight or obesity, without diabetes. Here in this prespecified analysis, we examined effects of semaglutide on weight and anthropometric outcomes, safety and tolerability by baseline body mass index (BMI). In patients treated with semaglutide, weight loss continued over 65 weeks and was sustained for up to 4 years. At 208 weeks, semaglutide was associated with mean reduction in weight (−10.2%), waist circumference (−7.7 cm) and waist-to-height ratio (−6.9%) versus placebo (−1.5%, −1.3 cm and −1.0%, respectively; P  < 0.0001 for all comparisons versus placebo). Clinically meaningful weight loss occurred in both sexes and all races, body sizes and regions. Semaglutide was associated with fewer serious adverse events. For each BMI category (<30, 30 to <35, 35 to <40 and ≥40 kg m − 2 ) there were lower rates (events per 100 years of observation) of serious adverse events with semaglutide (43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo). Semaglutide was associated with increased rates of trial product discontinuation. Discontinuations increased as BMI class decreased. In SELECT, at 208 weeks, semaglutide produced clinically significant weight loss and improvements in anthropometric measurements versus placebo. Weight loss was sustained over 4 years. ClinicalTrials.gov identifier: NCT03574597 .

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What is the pipeline for future medications for obesity?

Two-year effects of semaglutide in adults with overweight or obesity: the STEP 5 trial

The worldwide obesity prevalence, defined by body mass index (BMI) ≥30 kg m − 2 , has nearly tripled since 1975 (ref. 1 ). BMI is a good surveillance measure for population changes over time, given its strong correlation with body fat amount on a population level, but it may not accurately indicate the amount or location of body fat at the individual level 2 . In fact, the World Health Organization defines clinical obesity as ‘abnormal or excessive fat accumulation that may impair health’ 1 . Excess abnormal body fat, especially visceral adiposity and ectopic fat, is a driver of cardiovascular (CV) disease (CVD) 3 , 4 , 5 , and contributes to the global chronic disease burden of diabetes, chronic kidney disease, cancer and other chronic conditions 6 , 7 .

Remediating the adverse health effects of excess abnormal body fat through weight loss is a priority in addressing the global chronic disease burden. Improvements in CV risk factors, glycemia and quality-of-life measures including personal well-being and physical functioning generally begin with modest weight loss of 5%, whereas greater weight loss is associated with more improvement in these measures 8 , 9 , 10 . Producing and sustaining durable and clinically significant weight loss with lifestyle intervention alone has been challenging 11 . However, weight-management medications that modify appetite can make attaining and sustaining clinically meaningful weight loss of ≥10% more likely 12 . Recently, weight-management medications, particularly those comprising glucagon-like peptide-1 receptor agonists, that help people achieve greater and more sustainable weight loss have been developed 13 . Once-weekly subcutaneous semaglutide 2.4 mg, a glucagon-like peptide-1 receptor agonist, is approved for chronic weight management 14 , 15 , 16 and at doses of up to 2.0 mg is approved for type 2 diabetes treatment 17 , 18 , 19 . In patients with type 2 diabetes and high CV risk, semaglutide at doses of 0.5 mg and 1.0 mg has been shown to significantly lower the risk of CV events 20 . The SELECT trial (Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity) studied patients with established CVD and overweight or obesity but without diabetes. In SELECT, semaglutide was associated with a 20% reduction in major adverse CV events (hazard ratio 0.80, 95% confidence interval (CI) 0.72 to 0.90; P  < 0.001) 21 . Data derived from the SELECT trial offer the opportunity to evaluate the weight loss efficacy, in a geographically and racially diverse population, of semaglutide compared with placebo over 208 weeks when both are given in addition to standard-of-care recommendations for secondary CVD prevention (but without a focus on targeting weight loss). Furthermore, the data allow examination of changes in anthropometric measures such as BMI, waist circumference (WC) and waist-to-height ratio (WHtR) as surrogates for body fat amount and location 22 , 23 . The diverse population can also be evaluated for changes in sex- and race-specific ‘cutoff points’ for BMI and WC, which have been identified as anthropometric measures that predict cardiometabolic risk 8 , 22 , 23 .

This prespecified analysis of the SELECT trial investigated weight loss and changes in anthropometric indices in patients with established CVD and overweight or obesity without diabetes, who met inclusion and exclusion criteria, within a range of baseline categories for glycemia, renal function and body anthropometric measures.

Study population

The SELECT study enrolled 17,604 patients (72.3% male) from 41 countries between October 2018 and March 2021, with a mean (s.d.) age of 61.6 (8.9) years and BMI of 33.3 (5.0) kg m − 2 (ref. 21 ). The baseline characteristics of the population have been reported 24 . Supplementary Table 1 outlines SELECT patients according to baseline BMI categories. Of note, in the lower BMI categories (<30 kg m − 2 (overweight) and 30 to <35 kg m − 2 (class I obesity)), the proportion of Asian individuals was higher (14.5% and 7.4%, respectively) compared with the proportion of Asian individuals in the higher BMI categories (BMI 35 to <40 kg m − 2 (class II obesity; 3.8%) and ≥40 kg m − 2 (class III obesity; 2.2%), respectively). As the BMI categories increased, the proportion of women was higher: in the class III BMI category, 45.5% were female, compared with 20.8%, 25.7% and 33.0% in the overweight, class I and class II categories, respectively. Lower BMI categories were associated with a higher proportion of patients with normoglycemia and glycated hemoglobin <5.7%. Although the proportions of patients with high cholesterol and history of smoking were similar across BMI categories, the proportion of patients with high-sensitivity C-reactive protein ≥2.0 mg dl −1 increased as the BMI category increased. A high-sensitivity C-reactive protein >2.0 mg dl −1 was present in 36.4% of patients in the overweight BMI category, with a progressive increase to 43.3%, 57.3% and 72.0% for patients in the class I, II and III obesity categories, respectively.

Weight and anthropometric outcomes

Percentage weight loss.

The average percentage weight-loss trajectories with semaglutide and placebo over 4 years of observation are shown in Fig. 1a (ref. 21 ). For those in the semaglutide group, the weight-loss trajectory continued to week 65 and then was sustained for the study period through week 208 (−10.2% for the semaglutide group, −1.5% for the placebo group; treatment difference −8.7%; 95% CI −9.42 to −7.88; P  < 0.0001). To estimate the treatment effect while on medication, we performed a first on-treatment analysis (observation period until the first time being off treatment for >35 days). At week 208, mean weight loss in the semaglutide group analyzed as first on-treatment was −11.7% compared with −1.5% for the placebo group (Fig. 1b ; treatment difference −10.2%; 95% CI −11.0 to −9.42; P  < 0.0001).

a , b , Observed data from the in-trial period ( a ) and first on-treatment ( b ). The symbols are the observed means, and error bars are ±s.e.m. Numbers shown below each panel represent the number of patients contributing to the means. Analysis of covariance with treatment and baseline values was used to estimate the treatment difference. Exact P values are 1.323762 × 10 −94 and 9.80035 × 10 −100 for a and b , respectively. P values are two-sided and are not adjusted for multiplicity. ETD, estimated treatment difference; sema, semaglutide.

Categorical weight loss and individual body weight change

Among in-trial (intention-to-treat principle) patients at week 104, weight loss of ≥5%, ≥10%, ≥15%, ≥20% and ≥25% was achieved by 67.8%, 44.2%, 22.9%, 11.0% and 4.9%, respectively, of those treated with semaglutide compared with 21.3%, 6.9%, 1.7%, 0.6% and 0.1% of those receiving placebo (Fig. 2a ). Individual weight changes at 104 weeks for the in-trial populations for semaglutide and placebo are depicted in Fig. 2b and Fig. 2c , respectively. These waterfall plots show the variation in weight-loss response that occurs with semaglutide and placebo and show that weight loss is more prominent with semaglutide than placebo.

a , Categorical weight loss from baseline at week 104 for semaglutide and placebo. Data from the in-trial period. Bars depict the proportion (%) of patients receiving semaglutide or placebo who achieved ≥5%, ≥10%, ≥15%, ≥20% and ≥25% weight loss. b , c , Percentage change in body weight for individual patients from baseline to week 104 for semaglutide ( b ) and placebo ( c ). Each patient’s percentage change in body weight is plotted as a single bar.

Change in WC

WC change from baseline to 104 weeks has been reported previously in the primary outcome paper 21 . The trajectory of WC change mirrored that of the change in body weight. At week 208, average reduction in WC was −7.7 cm with semaglutide versus −1.3 cm with placebo, with a treatment difference of −6.4 cm (95% CI −7.18 to −5.61; P  < 0.0001) 21 .

WC cutoff points

We analyzed achievement of sex- and race-specific cutoff points for WC by BMI <35 kg m − 2 or ≥35 kg m − 2 , because for BMI >35 kg m − 2 , WC is more difficult technically and, thus, less accurate as a risk predictor 4 , 25 , 26 . Within the SELECT population with BMI <35 kg m − 2 at baseline, 15.0% and 14.3% of the semaglutide and placebo groups, respectively, were below the sex- and race-specific WC cutoff points. At week 104, 41.2% fell below the sex- and race-specific cutoff points for the semaglutide group, compared with only 18.0% for the placebo group (Fig. 3 ).

WC cutoff points; Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm, non-Asian men <102 cm.

Waist-to-height ratio

At baseline, mean WHtR was 0.66 for the study population. The lowest tertile of the SELECT population at baseline had a mean WHtR <0.62, which is higher than the cutoff point of 0.5 used to indicate increased cardiometabolic risk 27 , suggesting that the trial population had high WCs. At week 208, in the group randomized to semaglutide, there was a relative reduction of 6.9% in WHtR compared with 1.0% in placebo (treatment difference −5.87% points; 95% CI −6.56 to −5.17; P  < 0.0001).

BMI category change

At week 104, 52.4% of patients treated with semaglutide achieved improvement in BMI category compared with 15.7% of those receiving placebo. Proportions of patients in the BMI categories at baseline and week 104 are shown in Fig. 4 , which depicts in-trial patients receiving semaglutide and placebo. The BMI category change reflects the superior weight loss with semaglutide, which resulted in fewer patients being in the higher BMI categories after 104 weeks. In the semaglutide group, 12.0% of patients achieved a BMI <25 kg m − 2 , which is considered the healthy BMI category, compared with 1.2% for placebo; per study inclusion criteria, no patients were in this category at baseline. The proportion of patients with obesity (BMI ≥30 kg m − 2 ) fell from 71.0% to 43.3% in the semaglutide group versus 71.9% to 67.9% in the placebo group.

In the semaglutide group, 12.0% of patients achieved normal weight status at week 104 (from 0% at baseline), compared with 1.2% (from 0% at baseline) for placebo. BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (BMI ≥40 kg m − 2 ).

Weight and anthropometric outcomes by subgroups

The forest plot illustrated in Fig. 5 displays mean body weight percentage change from baseline to week 104 for semaglutide relative to placebo in prespecified subgroups. Similar relationships are depicted for WC changes in prespecified subgroups shown in Extended Data Fig. 1 . The effect of semaglutide (versus placebo) on mean percentage body weight loss as well as reduction in WC was found to be heterogeneous across several population subgroups. Women had a greater difference in mean weight loss with semaglutide versus placebo (−11.1% (95% CI −11.56 to −10.66) versus −7.5% in men (95% CI −7.78 to −7.23); P  < 0.0001). There was a linear relationship between age category and degree of mean weight loss, with younger age being associated with progressively greater mean weight loss, but the actual mean difference by age group is small. Similarly, BMI category had small, although statistically significant, associations. Those with WHtR less than the median experienced slightly lower mean body weight change than those above the median, with estimated treatment differences −8.04% (95% CI −8.37 to −7.70) and −8.99% (95% CI −9.33 to −8.65), respectively ( P  < 0.0001). Patients from Asia and of Asian race experienced slightly lower mean weight loss (estimated treatment difference with semaglutide for Asian race −7.27% (95% CI −8.09 to −6.46; P  = 0.0147) and for Asia −7.30 (95% CI −7.97 to −6.62; P  = 0.0016)). There was no difference in weight loss with semaglutide associated with ethnicity (estimated treatment difference for Hispanic −8.53% (95% CI −9.28 to −7.76) or non-Hispanic −8.52% (95% CI −8.77 to 8.26); P  = 0.9769), glycemic status (estimated treatment difference for prediabetes −8.53% (95% CI −8.83 to −8.24) or normoglycemia −8.48% (95% CI −8.88 to −8.07; P  = 0.8188) or renal function (estimated treatment difference for estimated glomerular filtration rate (eGFR) <60 or ≥60 ml min −1  1.73 m − 2 being −8.50% (95% CI −9.23 to −7.76) and −8.52% (95% CI −8.77 to −8.26), respectively ( P  = 0.9519)).

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and are not adjusted for multiplicity. The dots show estimated treatment differences, and the error bars show 95% CIs. Details of the statistical models are available in Methods . ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Safety and tolerability according to baseline BMI category

We reported in the primary outcome of the SELECT trial that adverse events (AEs) leading to permanent discontinuation of the trial product occurred in 1,461 patients (16.6%) in the semaglutide group and 718 patients (8.2%) in the placebo group ( P  < 0.001) 21 . For this analysis, we evaluated the cumulative incidence of AEs leading to trial product discontinuation by treatment assignment and by BMI category (Fig. 6 ). For this analysis, with death modeled as a competing risk, we tracked the proportion of in-trial patients for whom drug was withdrawn or interrupted for the first time (Fig. 6 , left) or cumulative discontinuations (Fig. 6 , right). Both panels of Fig. 6 depict a graded increase in the proportion discontinuing semaglutide, but not placebo. For lower BMI classes, discontinuation rates are higher in the semaglutide group but not the placebo group.

Data are in-trial from the full analysis set. sema, semaglutide.

We reported in the primary SELECT analysis that serious adverse events (SAEs) were reported by 2,941 patients (33.4%) in the semaglutide arm and by 3,204 patients (36.4%) in the placebo arm ( P  < 0.001) 21 . For this study, we analyzed SAE rates by person-years of treatment exposure for BMI classes (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ) and provide these data in Supplementary Table 2 . We also provide an analysis of the most common categories of SAEs. Semaglutide was associated with lower SAEs, primarily driven by CV event and infections. Within each obesity class (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ), there were fewer SAEs in the group receiving semaglutide compared with placebo. Rates (events per 100 years of observation) of SAEs were 43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo, with no evidence of heterogeneity. There was no detectable difference in hepatobiliary or gastrointestinal SAEs comparing semaglutide with placebo in any of the four BMI classes we evaluated.

The analyses of weight effects of the SELECT study presented here reveal that patients assigned to once-weekly subcutaneous semaglutide 2.4 mg lost significantly more weight than those receiving placebo. The weight-loss trajectory with semaglutide occurred over 65 weeks and was sustained up to 4 years. Likewise, there were similar improvements in the semaglutide group for anthropometrics (WC and WHtR). The weight loss was associated with a greater proportion of patients receiving semaglutide achieving improvement in BMI category, healthy BMI (<25 kg m − 2 ) and falling below the WC cutoff point above which increased cardiometabolic risk for the sex and race is greater 22 , 23 . Furthermore, both sexes, all races, all body sizes and those from all geographic regions were able to achieve clinically meaningful weight loss. There was no evidence of increased SAEs based on BMI categories, although lower BMI category was associated with increased rates of trial product discontinuation, probably reflecting exposure to a higher level of drug in lower BMI categories. These data, representing the longest clinical trial of the effects of semaglutide versus placebo on weight, establish the safety and durability of semaglutide effects on weight loss and maintenance in a geographically and racially diverse population of adult men and women with overweight and obesity but not diabetes. The implications of weight loss of this degree in such a diverse population suggests that it may be possible to impact the public health burden of the multiple morbidities associated with obesity. Although our trial focused on CV events, many chronic diseases would benefit from effective weight management 28 .

There were variations in the weight-loss response. Individual changes in body weight with semaglutide and placebo were striking; still, 67.8% achieved 5% or more weight loss and 44.2% achieved 10% weight loss with semaglutide at 2 years, compared with 21.3% and 6.9%, respectively, for those receiving placebo. Our first on-treatment analysis demonstrated that those on-drug lost more weight than those in-trial, confirming the effect of drug exposure. With semaglutide, lower BMI was associated with less percentage weight loss, and women lost more weight on average than men (−11.1% versus −7.5% treatment difference from placebo); however, in all cases, clinically meaningful mean weight loss was achieved. Although Asian patients lost less weight on average than patients of other races (−7.3% more than placebo), Asian patients were more likely to be in the lowest BMI category (<30 kg m − 2 ), which is known to be associated with less weight loss, as discussed below. Clinically meaningful weight loss was evident in the semaglutide group within a broad range of baseline categories for glycemia and body anthropometrics. Interestingly, at 2 years, a significant proportion of the semaglutide-treated group fell below the sex- and race-specific WC cutoff points, especially in those with BMI <35 kg m − 2 , and a notable proportion (12.0%) fell below the BMI cutoff point of 25 kg m − 2 , which is deemed a healthy BMI in those without unintentional weight loss. As more robust weight loss is possible with newer medications, achieving and maintaining these cutoff point targets may become important benchmarks for tracking responses.

The overall safety profile did not reveal any new signals from prior studies, and there were no BMI category-related associations with AE reporting. The analysis did reveal that tolerability may differ among specific BMI classes, since more discontinuations occurred with semaglutide among lower BMI classes. Potential contributors may include a possibility of higher drug exposure in lower BMI classes, although other explanations, including differences in motivation and cultural mores regarding body size, cannot be excluded.

Is the weight loss in SELECT less than expected based on prior studies with the drug? In STEP 1, a large phase 3 study of once-weekly subcutaneous semaglutide 2.4 mg in individuals without diabetes but with BMI >30 kg m − 2 or 27 kg m − 2 with at least one obesity-related comorbidity, the mean weight loss was −14.9% at week 68, compared with −2.4% with placebo 14 . Several reasons may explain the observation that the mean treatment difference was −12.5% in STEP 1 and −8.7% in SELECT. First, SELECT was designed as a CV outcomes trial and not a weight-loss trial, and weight loss was only a supportive secondary endpoint in the trial design. Patients in STEP 1 were desirous of weight loss as a reason for study participation and received structured lifestyle intervention (which included a −500 kcal per day diet with 150 min per week of physical activity). In the SELECT trial, patients did not enroll for the specific purpose of weight loss and received standard of care covering management of CV risk factors, including medical treatment and healthy lifestyle counseling, but without a specific focus on weight loss. Second, the respective study populations were quite different, with STEP 1 including a younger, healthier population with more women (73.1% of the semaglutide arm in STEP 1 versus 27.7% in SELECT) and higher mean BMI (37.8 kg m − 2 versus 33.3 kg m − 2 , respectively) 14 , 21 . Third, major differences existed between the respective trial protocols. Patients in the semaglutide treatment arm of STEP 1 were more likely to be exposed to the medication at the full dose of 2.4 mg than those in SELECT. In SELECT, investigators were allowed to slow, decrease or pause treatment. By 104 weeks, approximately 77% of SELECT patients on dose were receiving the target semaglutide 2.4 mg weekly dose, which is lower than the corresponding proportion of patients in STEP 1 (89.6% were receiving the target dose at week 68) 14 , 21 . Indeed, in our first on-treatment analysis at week 208, weight loss was greater (−11.7% for semaglutide) compared with the in-trial analysis (−10.2% for semaglutide). Taken together, all these issues make less weight loss an expected finding in SELECT, compared with STEP 1.

The SELECT study has some limitations. First, SELECT was not a primary prevention trial, and the data should not be extrapolated to all individuals with overweight and obesity to prevent major adverse CV events. Although the data set is rich in numbers and diversity, it does not have the numbers of individuals in racial subgroups that may have revealed potential differential effects. SELECT also did not include individuals who have excess abnormal body fat but a BMI <27 kg m − 2 . Not all individuals with increased CV risk have BMI ≥27 kg m − 2 . Thus, the study did not include Asian patients who qualify for treatment with obesity medications at lower BMI and WC cutoff points according to guidelines in their countries 29 . We observed that Asian patients were less likely to be in the higher BMI categories of SELECT and that the population of those with BMI <30 kg m − 2 had a higher percentage of Asian race. Asian individuals would probably benefit from weight loss and medication approaches undertaken at lower BMI levels in the secondary prevention of CVD. Future studies should evaluate CV risk reduction in Asian individuals with high CV risk and BMI <27 kg m − 2 . Another limitation is the lack of information on body composition, beyond the anthropometric measures we used. It would be meaningful to have quantitation of fat mass, lean mass and muscle mass, especially given the wide range of body size in the SELECT population.

An interesting observation from this SELECT weight loss data is that when BMI is ≤30 kg m − 2 , weight loss on a percentage basis is less than that observed across higher classes of BMI severity. Furthermore, as BMI exceeds 30 kg m − 2 , weight loss amounts are more similar for class I, II and III obesity. This was also observed in Look AHEAD, a lifestyle intervention study for weight loss 30 . The proportion (percentage) of weight loss seems to be less, on average, in the BMI <30 kg m − 2 category relative to higher BMI categories, despite their receiving of the same treatment and even potentially higher exposure to the drug for weight loss 30 . Weight loss cannot continue indefinitely. There is a plateau of weight that occurs after weight loss with all treatments for weight management. This plateau has been termed the ‘set point’ or ‘settling point’, a body weight that is in harmony with the genetic and environmental determinants of body weight and adiposity 31 . Perhaps persons with BMI <30 kg m − 2 are closer to their settling point and have less weight to lose to reach it. Furthermore, the cardiometabolic benefits of weight loss are driven by reduction in the abnormal ectopic and visceral depots of fat, not by reduction of subcutaneous fat stores in the hips and thighs. The phenotype of cardiometabolic disease but lower BMI (<30 kg m − 2 ) may be one where reduction of excess abnormal and dysfunctional body fat does not require as much body mass reduction to achieve health improvement. We suspect this may be the case and suggest further studies to explore this aspect of weight-loss physiology.

In conclusion, this analysis of the SELECT study supports the broad use of once-weekly subcutaneous semaglutide 2.4 mg as an aid to CV event reduction in individuals with overweight or obesity without diabetes but with preexisting CVD. Semaglutide 2.4 mg safely and effectively produced clinically significant weight loss in all subgroups based on age, sex, race, glycemia, renal function and anthropometric categories. Furthermore, the weight loss was sustained over 4 years during the trial.

Trial design and participants

The current work complies with all relevant ethical regulations and reports a prespecified analysis of the randomized, double-blind, placebo-controlled SELECT trial ( NCT03574597 ), details of which have been reported in papers describing study design and rationale 32 , baseline characteristics 24 and the primary outcome 21 . SELECT evaluated once-weekly subcutaneous semaglutide 2.4 mg versus placebo to reduce the risk of major adverse cardiac events (a composite endpoint comprising CV death, nonfatal myocardial infarction or nonfatal stroke) in individuals with established CVD and overweight or obesity, without diabetes. The protocol for SELECT was approved by national and institutional regulatory and ethical authorities in each participating country. All patients provided written informed consent before beginning any trial-specific activity. Eligible patients were aged ≥45 years, with a BMI of ≥27 kg m − 2 and established CVD defined as at least one of the following: prior myocardial infarction, prior ischemic or hemorrhagic stroke, or symptomatic peripheral artery disease. Additional inclusion and exclusion criteria can be found elsewhere 32 .

Human participants research

The trial protocol was designed by the trial sponsor, Novo Nordisk, and the academic Steering Committee. A global expert panel of physician leaders in participating countries advised on regional operational issues. National and institutional regulatory and ethical authorities approved the protocol, and all patients provided written informed consent.

Study intervention and patient management

Patients were randomly assigned in a double-blind manner and 1:1 ratio to receive once-weekly subcutaneous semaglutide 2.4 mg or placebo. The starting dose was 0.24 mg once weekly, with dose increases every 4 weeks (to doses of 0.5, 1.0, 1.7 and 2.4 mg per week) until the target dose of 2.4 mg was reached after 16 weeks. Patients who were unable to tolerate dose escalation due to AEs could be managed by extension of dose-escalation intervals, treatment pauses or maintenance at doses below the 2.4 mg per week target dose. Investigators were allowed to reduce the dose of study product if tolerability issues arose. Investigators were provided with guidelines for, and encouraged to follow, evidence-based recommendations for medical treatment and lifestyle counseling to optimize management of underlying CVD as part of the standard of care. The lifestyle counseling was not targeted at weight loss. Additional intervention descriptions are available 32 .

Sex, race, body weight, height and WC measurements

Sex and race were self-reported. Body weight was measured without shoes and only wearing light clothing; it was measured on a digital scale and recorded in kilograms or pounds (one decimal with a precision of 0.1 kg or lb), with preference for using the same scale throughout the trial. The scale was calibrated yearly as a minimum unless the manufacturer certified that calibration of the weight scales was valid for the lifetime of the scale. Height was measured without shoes in centimeters or inches (one decimal with a precision of 0.1 cm or inches). At screening, BMI was calculated by the electronic case report form. WC was defined as the abdominal circumference located midway between the lower rib margin and the iliac crest. Measures were obtained in a standing position with a nonstretchable measuring tape and to the nearest centimeter or inch. The patient was asked to breathe normally. The tape touched the skin but did not compress soft tissue, and twists in the tape were avoided.

The following endpoints relevant to this paper were assessed at randomization (week 0) to years 2, 3 and 4: change in body weight (%); proportion achieving weight loss ≥5%, ≥10%, ≥15% and ≥20%; change in WC (cm); and percentage change in WHtR (cm cm −1 ). Improvement in BMI category (defined as being in a lower BMI class) was assessed at week 104 compared with baseline according to BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (≥40 kg m − 2 ). The proportions of individuals with BMI <35 or ≥35 kg m − 2 who achieved sex- and race-specific cutoff points for WC (indicating increased metabolic risk) were evaluated at week 104. The WC cutoff points were as follows: Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm and non-Asian men <102 cm.

Overall, 97.1% of the semaglutide group and 96.8% of the placebo group completed the trial. During the study, 30.6% of those assigned to semaglutide did not complete drug treatment, compared with 27.0% for placebo.

Statistical analysis

The statistical analyses for the in-trial period were based on the intention-to-treat principle and included all randomized patients irrespective of adherence to semaglutide or placebo or changes to background medications. Continuous endpoints were analyzed using an analysis of covariance model with treatment as a fixed factor and baseline value of the endpoint as a covariate. Missing data at the landmark visit, for example, week 104, were imputed using a multiple imputation model and done separately for each treatment arm and included baseline value as a covariate and fit to patients having an observed data point (irrespective of adherence to randomized treatment) at week 104. The fit model is used to impute values for all patients with missing data at week 104 to create 500 complete data sets. Rubin’s rules were used to combine the results. Estimated means are provided with s.e.m., and estimated treatment differences are provided with 95% CI. Binary endpoints were analyzed using logistic regression with treatment and baseline value as a covariate, where missing data were imputed by first using multiple imputation as described above and then categorizing the imputed data according to the endpoint, for example, body weight percentage change at week 104 of <0%. Subgroup analyses for continuous and binary endpoints also included the subgroup and interaction between treatment and subgroup as fixed factors. Because some patients in both arms continued to be followed but were off treatment, we also analyzed weight loss by first on-treatment group (observation period until first time being off treatment for >35 days) to assess a more realistic picture of weight loss in those adhering to treatment. CIs were not adjusted for multiplicity and should therefore not be used to infer definitive treatment effects. All statistical analyses were performed with SAS software, version 9.4 TS1M5 (SAS Institute).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data will be shared with bona fide researchers who submit a research proposal approved by the independent review board. Individual patient data will be shared in data sets in a deidentified and anonymized format. Information about data access request proposals can be found at https://www.novonordisk-trials.com/ .

Change history

28 may 2024.

In the version of the article initially published, in Fig. 2b the data was inadvertently shifted upwards and has now been corrected in the HTML and PDF versions of the article.

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Acknowledgements

Editorial support was provided by Richard Ogilvy-Stewart of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines ( www.ismpp.org/gpp-2022 ).

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Donna H. Ryan

Department of Internal Medicine/Endocrinology and Peter O’ Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

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Contributions

D.H.R., I.L. and S.E.K. contributed to the study design. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. were study investigators. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. enrolled patients. D.H.R. was responsible for data analysis and manuscript preparation. All authors contributed to data interpretation, review, revisions and final approval of the manuscript.

Corresponding author

Correspondence to Donna H. Ryan .

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Competing interests.

D.H.R. declares having received consulting honoraria from Altimmune, Amgen, Biohaven, Boehringer Ingelheim, Calibrate, Carmot Therapeutics, CinRx, Eli Lilly, Epitomee, Gila Therapeutics, IFA Celtics, Novo Nordisk, Pfizer, Rhythm, Scientific Intake, Wondr Health and Zealand Pharma; she declares she received stock options from Calibrate, Epitomee, Scientific Intake and Xeno Bioscience. I.L. declares having received research funding (paid to institution) from Novo Nordisk, Sanofi, Mylan and Boehringer Ingelheim. I.L. received advisory/consulting fees and/or other support from Altimmune, AstraZeneca, Bayer, Biomea, Boehringer Ingelheim, Carmot Therapeutics, Cytoki Pharma, Eli Lilly, Intercept, Janssen/Johnson & Johnson, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Regeneron, Sanofi, Shionogi, Structure Therapeutics, Target RWE, Terns Pharmaceuticals, The Comm Group, Valeritas, WebMD and Zealand Pharma. J.D. declares having received consulting honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk and Bayer, and research grants from British Heart Foundation, MRC (UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate and Roche. S.E.K. declares having received consulting honoraria from ANI Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk and Oramed, and stock options from AltPep. B.B. declares having received honoraria related to participation on this trial and has no financial conflicts related to this publication. H.M.C. declares being a stockholder and serving on an advisory panel for Bayer; receiving research grants from Chief Scientist Office, Diabetes UK, European Commission, IQVIA, Juvenile Diabetes Research Foundation and Medical Research Council; serving on an advisory board and speaker’s bureau for Novo Nordisk; and holding stock in Roche Pharmaceuticals. C.C. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Merck, Brace Pharma and Eurofarma. D.D. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and AstraZeneca, and received research grants through his affiliation from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and Rhythm. D.B.H. declares having received research grants through her academic affiliation from Novo Nordisk and Eli Lilly, and advisory/consulting honoraria from Novo Nordisk, Eli Lilly and Gelesis. A.K. declares having received research grants through his affiliation from Novo Nordisk and Pharmaserve Lilly, and consulting honoraria from Pharmaserve Lilly, Sanofi-Aventis, Novo Nordisk, MSD, AstraZeneca, ELPEN Pharma, Boehringer Ingelheim, Galenica Pharma, Epsilon Health and WinMedica. A.M.L. declares having received honoraria from Novo Nordisk, Eli Lilly, Akebia Therapeutics, Ardelyx, Becton Dickinson, Endologix, FibroGen, GSK, Medtronic, Neovasc, Provention Bio, ReCor, BrainStorm Cell Therapeutics, Alnylam and Intarcia for consulting activities, and research funding to his institution from AbbVie, Esperion, AstraZeneca, CSL Behring, Novartis and Eli Lilly. S.M.M. declares having received consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Daichii-Sankyo, esanum, Gilead, Ipsen, Eli Lilly, Novartis, Novo Nordisk, Sandoz and Sanofi; he declares he received research grants from AstraZeneca, Eli Lilly and Novo Nordisk. J.P. declares having received consulting honoraria from Altimmune, Amgen, Esperion, Merck, MJH Life Sciences, Novartis and Novo Nordisk; he has received a grant, paid to his institution, from Boehringer Ingelheim and holds the position of Director, Preventive Cardiology, at Brigham and Women’s Hospital. A.P.v.B. is contracted via the University of Groningen (no personal payment) to undertake consultancy for Novo Nordisk, Eli Lilly and Boehringer Ingelheim. J.P.H.W. is contracted via the University of Liverpool (no personal payment) to undertake consultancy for Altimmune, AstraZeneca, Boehringer Ingelheim, Cytoki, Eli Lilly, Napp, Novo Nordisk, Menarini, Pfizer, Rhythm Pharmaceuticals, Sanofi, Saniona, Tern Pharmaceuticals, Shionogi and Ysopia. J.P.H.W. also declares personal honoraria/lecture fees from AstraZeneca, Boehringer Ingelheim, Medscape, Napp, Menarini, Novo Nordisk and Rhythm. R.F.K. declares having received consulting honoraria from Novo Nordisk, Weight Watchers, Eli Lilly, Boehringer Ingelheim, Pfizer, Structure and Altimmune. E.B., G.K.H., O.K.J. and T.K.O. are employees of Novo Nordisk A/S.

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Extended data

Extended data fig. 1 effect of semaglutide treatment or placebo on waist circumference from baseline to week 104 by subgroups..

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and not adjusted for multiplicity. The dots show estimated treatment differences and the error bars show 95% confidence intervals. Details of the statistical models are available in Methods . BMI, body mass index; CI, confidence interval; CV, cardiovascular; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Supplementary information

Reporting summary, supplementary tables 1 and 2.

Supplementary Table 1. Baseline characteristics by BMI class. Data are represented as number and percentage of patients. Renal function categories were based on the eGFR as per Chronic Kidney Disease Epidemiology Collaboration. Albuminuria categories were based on UACR. Smoking was defined as smoking at least one cigarette or equivalent daily. The category ‘Other’ for CV inclusion criteria includes patients where it is unknown if the patient fulfilled only one or several criteria and patients who were randomized in error and did not fulfill any criteria. Supplementary Table 2. SAEs according to baseline BMI category. P value: two-sided P value from Fisher’s exact test for test of no difference.

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Ryan, D.H., Lingvay, I., Deanfield, J. et al. Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial. Nat Med (2024). https://doi.org/10.1038/s41591-024-02996-7

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• Pedestrian safety on the road to net zero: cross-sectional study of collisions with electric and hybrid-electric cars in Great Britain
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• http://orcid.org/0000-0003-4431-8822 Phil J Edwards ,
• Siobhan Moore ,
• Craig Higgins
• London School of Hygiene & Tropical Medicine , London , UK
• Correspondence to Dr Phil J Edwards, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK; phil.edwards{at}LSHTM.ac.uk

Background Plans to phase out fossil fuel-powered internal combustion engine (ICE) vehicles and to replace these with electric and hybrid-electric (E-HE) vehicles represent a historic step to reduce air pollution and address the climate emergency. However, there are concerns that E-HE cars are more hazardous to pedestrians, due to being quieter. We investigated and compared injury risks to pedestrians from E-HE and ICE cars in urban and rural environments.

Methods We conducted a cross-sectional study of pedestrians injured by cars or taxis in Great Britain. We estimated casualty rates per 100 million miles of travel by E-HE and ICE vehicles. Numerators (pedestrians) were extracted from STATS19 datasets. Denominators (car travel) were estimated by multiplying average annual mileage (using National Travel Survey datasets) by numbers of vehicles. We used Poisson regression to investigate modifying effects of environments where collisions occurred.

Results During 2013–2017, casualty rates per 100 million miles were 5.16 (95% CI 4.92 to 5.42) for E-HE vehicles and 2.40 (95%CI 2.38 to 2.41) for ICE vehicles, indicating that collisions were twice as likely (RR 2.15; 95% CI 2.05 to 2.26) with E-HE vehicles. Poisson regression found no evidence that E-HE vehicles were more dangerous in rural environments (RR 0.91; 95% CI 0.74 to 1.11); but strong evidence that E-HE vehicles were three times more dangerous than ICE vehicles in urban environments (RR 2.97; 95% CI 2.41 to 3.7). Sensitivity analyses of missing data support main findings.

Conclusion E-HE cars pose greater risk to pedestrians than ICE cars in urban environments. This risk must be mitigated as governments phase out petrol and diesel cars.

• WOUNDS AND INJURIES
• CLIMATE CHANGE

Data availability statement

Data are available in a public, open-access repository. Numerator data (numbers of pedestrians injured in collisions) are publicly available from the Road Safety Data (STATS19) datasets ( https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data ). Denominator data (100 million miles of car travel per year) may be estimated by multiplying average annual mileage by numbers of vehicle registrations (publicly available from Department for Transport, https://www.gov.uk/government/statistical-data-sets/veh02-licensed-cars ). Average annual mileage for E-HE and ICE vehicles may be estimated separately for urban and rural environments using data that may obtained under special licence from the National Travel Survey datasets ( http://doi.org/10.5255/UKDA-Series-2000037 ).

https://doi.org/10.1136/jech-2024-221902

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Electric cars are quieter than cars with petrol or diesel engines and may pose a greater risk to pedestrians.

The US National Highway Transportation Safety Agency found that during 2000–2007 the odds of an electric or hybrid-electric car causing a pedestrian injury were 35% greater than a car with a petrol or diesel engine.

The UK Transport Research Laboratory found the pedestrian casualty rate per 10 000 registered electric or hybrid-electric vehicles during 2005–2007 in Great Britain was lower than the rate for petrol or diesel vehicles.

WHAT THIS STUDY ADDS

In Great Britain during 2013–2017, pedestrians were twice as likely to be hit by an electric or hybrid-electric car than by a petrol or diesel car; the risks were higher in urban areas.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

The greater risk to pedestrian safety posed by electric or hybrid-electric cars needs to be mitigated as governments proceed to phase out petrol and diesel cars.

Drivers of electric or hybrid-electric cars must be cautious of pedestrians who may not hear them approaching and may step into the road thinking it is safe to do so, particularly in towns and cities.

Introduction

Many governments have set targets to reach net-zero emissions to help mitigate the harms of climate change. Short-term health benefits of reduced emissions are expected from better air quality with longer-term benefits from reduced global temperatures. 1

Transition to electric and hybrid-electric (E-HE) cars

One such target is to phase out sales of new fossil fuel-powered internal combustion engine (ICE) vehicles and replace these with E-HE vehicles. 2 3

Pedestrian safety

Road traffic injuries are the leading cause of death for children and young adults. 4 A quarter of all road traffic deaths are of pedestrians. 5 Concerns have been raised that E-HE cars may be more hazardous to pedestrians than ICE cars, due to being quieter. 6 7 It has been hypothesised that E-HE cars pose a greater risk of injury to pedestrians in urban areas where background ambient noise levels are higher. 8 However, there has been relatively little empirical research on possible impacts of E-HE cars on pedestrian road safety. A study commissioned for the US National Highway Transportation Safety Agency based on data from 16 States found that the odds of an E-HE vehicle causing a pedestrian injury were 35% greater than an ICE vehicle. 9 In contrast, a study commissioned by the UK Department for Transport found pedestrian casualty rates from collisions with E-HE vehicles during 2005–2007 were lower than for ICE vehicles. 10 Possible reasons for these conflicting results are that the two studies used different designs and estimated different measures of relative risk—the first used a case–control design and estimated an OR, whereas the second used a cross-sectional study and estimated a rate ratio. ORs will often differ from rate ratios. 11 Other reasons include differences between the USA and the UK in the amount and quality of walking infrastructure. 12

Aim and objectives

We aimed to add to the evidence base on whether E-HE cars pose a greater injury risk to pedestrians than ICE cars by analysing road traffic injury data and travel survey data in Great Britain.

We sought to improve on the previous UK study by using distance travelled instead of number of registered vehicles as the measure of exposure in estimation of collision rates.

The objectives of this study were:

To estimate pedestrian casualty rates for E-HE and ICE vehicles and to compare these by calculating a rate ratio;

To assess whether or not the evidence supports the hypothesis that casualty rate ratios vary according to urban or rural environments. 8

Study design

This study was an analysis of differences in casualty rates of pedestrians per 100 million miles of E-HE car travel and rates per 100 million miles of ICE car travel.

This study was set in Great Britain between 2013 and 2017.

Participants

The study participants were all pedestrians reported to have been injured in a collision with a car or a taxi.

The exposure was the type of propulsion of the colliding vehicle, E-HE or ICE. E-HE vehicles were treated as a single powertrain type, regardless of the mode of operation that a hybrid vehicle was in at the time of collision (hybrid vehicles typically start in electric mode and change from battery to combustion engine at higher speeds). 13

The outcome of interest was a pedestrian casualty.

Effect modification by road environment

We used the urban–rural classification 14 of the roads on which the collisions occurred to investigate whether casualty rate ratios comparing E-HE with ICE vehicles differed between rural and urban environments.

Data sources/measurement

Numerator data (numbers of pedestrians injured in collisions) were extracted from the Road Safety Data (STATS19) datasets. 15

Denominator data (100 million miles of car travel per year) were estimated by multiplying average annual mileage by numbers of vehicle registrations. 16 Average annual mileage for E-HE and ICE vehicles was estimated separately for urban and rural environments using data obtained under special licence from the National Travel Survey (NTS) datasets. 17 We estimated average annual mileage for the years 2013–2017 because the NTS variable for the vehicle fuel type did not include ‘hybrid’ prior to 2013 and data from 2018 had not been uploaded to the UK data service due to problems with the archiving process (Andrew Kelly, Database Manager, NTS, Department for Transport, 23 March 2020, personal communication). Denominators were thus available for the years 2013–2017.

Data preparation

The datasets for collisions, casualties and vehicles from the STATS19 database were merged using a unique identification number for each collision.

Statistical methods

We calculated annual casualty rates for E-HE and ICE vehicles separately and we compared these by calculating a rate ratio. We used Poisson regression models to estimate rate ratios with 95% CIs and to investigate any modifying effects of the road environment in which the collisions occurred. For this analysis, our regression model included explanatory terms for the main effects of the road environment, plus terms for the interaction between type of propulsion and the road environment. The assumptions for Poisson regression were met in our study: we modelled count data (counts of pedestrians injured), traffic collisions were independent of each other, occurring in different places over time, and never occurring simultaneously. Data preparation, management and analyses were carried out using Microsoft Access 2019 and Stata V.16. 18

Sensitivity analysis

We conducted an extreme case analysis where all missing propulsion codes were assumed to be ICE vehicles (there were over a 100 times more ICE vehicles than E-HE vehicles on the roads in Great Britain during our study period, 16 so missing propulsion is more likely to have been ICE).

The sample size for this study included all available recorded road traffic collisions in Great Britain during the study period. We estimated that for our study to have 80% power at the 5% significance level to show a difference in casualty rates of 2 per 100 miles versus 5.5 per 100 miles, we would require 481 million miles of vehicle travel in each group (E-HE and ICE); whereas to have 90% power at the 1% significance level to show this difference, 911 million miles of vehicle travel would be required in each group. Our study includes 32 000 million miles of E-HE vehicle travel and 3 000 000 million miles of ICE vehicle travel and therefore our study was sufficiently powered to detect differences in casualty rates of these magnitudes.

Between 2013 and 2017, there were 916 713 casualties from reported road traffic collisions in Great Britain. 120 197 casualties were pedestrians. Of these pedestrians, 96 285 had been hit by a car or taxi. Most pedestrians—71 666 (74%) were hit by an ICE car or taxi. 1652 (2%) casualties were hit by an E-HE car or taxi. For 22 829 (24%) casualties, the vehicle propulsion code was missing. Most collisions occurred in urban environments and a greater proportion of the collisions with E-HE vehicles occurred in an urban environment (94%) than did collisions with ICE vehicles (88%) ( figure 1 ).

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Flow chart of pedestrian casualties in collisions with E-HE or ICE cars or taxis from reported road traffic collisions in Great Britain 2013–2017. E-HE, electric and hybrid-electric; ICE, internal combustion engine.

Main results

During the period 2013 to 2017, the average annual casualty rates of pedestrians per 100 million miles were 5.16 (95% CI 4.92 to 5.42) for E-HE vehicles and 2.40 (95% CI 2.38 to 2.41) for ICE vehicles, which indicates that collisions with pedestrians were on average twice as likely (RR 2.15 (95% CI 2.05 to 2.26), p<0.001) with E-HE vehicles as with ICE vehicles ( table 1 ).

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Pedestrian casualties due to collisions with cars or taxis from reported road traffic collisions in Great Britain 2013–2017—by vehicle propulsion type

In our extreme case analysis, the 22 829 pedestrian casualties where vehicle propulsion was missing were all assumed to have been struck by ICE vehicles. In this case, average casualty rates of pedestrians per 100 million miles were 3.16 (95% CI 3.14 to 3.18) for ICE vehicles, which would indicate that collisions with pedestrians were on average 63% more likely (RR 1.63 (95% CI 1.56 to 1.71), p<0.001) with E-HE vehicles than with ICE vehicles ( table 2 ).

Extreme case sensitivity analysis—pedestrian casualties due to collisions with cars or taxis from reported road traffic collisions in Great Britain 2013–2017 by vehicle propulsion type where 22 829 missing vehicle propulsion codes are assumed to be ICE vehicles

Relative risks according to road environment

Casualty rates were higher in urban than rural environments ( tables 3 and 4 ).

Pedestrian casualties due to collisions with cars or taxis from reported road traffic collisions in Great Britain 2013–2017—by vehicle propulsion type in urban road environments

Pedestrian casualties due to collisions with cars or taxis from reported road traffic collisions in Great Britain 2013–2017—by vehicle propulsion type in rural road environments

Urban environments

Collisions with pedestrians in urban environments were on average over two and a half times as likely (RR 2.69 (95% CI 2.56 to 2.83, p<0.001) with E-HE vehicles as with ICE vehicles ( table 3 ).

The extreme case sensitivity analysis showed collisions with pedestrians in urban environments were more likely with E-HE vehicles (RR 2.05; 95% CI 1.95 to 2.15).

Rural environments

Collisions with pedestrians in rural environments were equally likely (RR 0.91; 95% CI 0.74 to 1.11) with E-HE vehicles as with ICE vehicles ( table 4 ).

The extreme case sensitivity analysis found evidence that collisions with pedestrians in rural environments were less likely with E-HE vehicles (RR 0.68; 95% CI 0.55 to 0.83).

Results of Poisson regression analysis

Our Poisson regression model results ( table 5 ) showed that pedestrian injury rates were on average 9.28 (95% CI 9.07 to 9.49) times greater in urban than in rural environments. There was no evidence that E-HE vehicles were more dangerous than ICE vehicles in rural environments (RR 0.91; 95% CI 0.74 to 1.11), consistent with our finding in table 4 . There was strong evidence that E-HE vehicles were on average three times more dangerous than ICE vehicles in urban environments (RR 2.97; 95% CI 2.41 to 3.67).

Results of Poisson regression analysis of annual casualty rates of pedestrians per 100 million miles by road environment and the interaction between vehicle propulsion type and environment

Statement of principal findings

This study found that in Great Britain between 2013 and 2017, casualty rates of pedestrians due to collisions with E-HE cars and taxis were higher than those due to collisions with ICE cars and taxis. Our best estimate is that such collisions are on average twice as likely, and in urban areas E-HE vehicles are on average three times more dangerous than ICE vehicles, consistent with the theory that E-HE vehicles are less audible to pedestrians in urban areas where background ambient noise levels are higher.

Strengths and weaknesses of the study

There are several limitations to this study which are discussed below.

The data used were not very recent. However, ours is the most current analysis of E-HE vehicle collisions using the STATS19 dataset.

Before we can infer that E-HE vehicles pose a greater risk to pedestrians than ICE vehicles, we must consider whether our study is free from confounding and selection bias. Confounding occurs when the exposure and outcome share a common cause. 19 Confounders in this study would be factors that may both cause a traffic collision and also cause the exposure (use of an E-HE car). Younger, less experienced drivers (ie, ages 16–24) are more likely to be involved in a road traffic collision 20 and are also more likely to own an electric car. 21 Some of the observed increased risk of electric cars may therefore be due to younger drivers preferring electric cars. This would cause positive confounding, meaning that the true relative risk of electric cars is less than we have estimated in our study. Regarding selection bias, it is known that the STATS19 dataset does not include every road traffic casualty in Great Britain, as some non-fatal casualties are not reported to the police. 22 If casualties from collisions are reported to the police differentially according to the type of vehicle propulsion, this may have biased our results; however, there is no reason to suspect that a pedestrian struck by a petrol or diesel car is any more or less likely to report the collision to the police than one struck by an electric car.

We must also address two additional concerns as ours is a cross-sectional study: The accuracy of exposure assignment (including the potential for recall bias) and the adequacy of prevalence as a proxy for incidence. 23 First, the accuracy of exposure assignment and the potential for recall bias are not issues for this study, as the exposure (type of propulsion of the colliding vehicle, E-HE or ICE), is assigned independently of the casualties by the UK Department for Transport who link the vehicle registration number (VRN) of each colliding vehicle to vehicle data held by the UK Driver Vehicle and Licensing Agency (DVLA). 10 Second, we have not used prevalence as a proxy for incidence but have estimated incidence using total distance travelled by cars as the measure of exposure.

We may therefore reasonably infer from our study results that E-HE vehicles pose a greater risk to pedestrians than ICE vehicles in urban environments, and that part of the risk may be due to younger people’s preference for E-HE cars.

A major limitation of the STATS19 road safety dataset used in this study was that it did not contain a vehicle propulsion code for all vehicles in collisions with pedestrians. We excluded these vehicles from our primary analysis (a complete case analysis) and we also conducted an extreme case sensitivity analysis. We will now argue why imputation of missing vehicle propulsion codes would not have added value to this study. Vehicle propulsion data are obtained for the STATS19 dataset by the UK Department for Transport who link the VRN of each colliding vehicle recorded in STATS19 to vehicles data held by the UK DVLA. The STATS19 data on reported collisions and casualties are collected by a Police Officer when an injury road accident is reported to them; Most police officers write details of the casualties and the vehicles involved in their notebooks for transcription onto the STATS19 form later at the Police station. 24 The VRN is one of 18 items recorded on each vehicle involved in a collision. Items may occasionally be missed due to human error during this process. Where a VRN is missing, vehicle propulsion will be missing in the STATS19 dataset. The chance that any vehicle-related item is missing will be independent of any characteristics of the casualties involved and so the vehicle propulsion codes are missing completely at random (MCAR). As the missing propulsion data are very likely MCAR, the set of pedestrians with no missing data is a random sample from the source population and hence our complete case analysis for handling the missing data gives unbiased results. The extreme case sensitivity analysis we performed shows a possible result that could occur, and it demonstrates our conclusions in urban environments are robust to the missing data. Lastly, to impute the missing data would require additional variables which are related to the likelihood of a VRN being missing. Such variables were not available and therefore we do not believe a useful multiple imputation analysis could have been performed.

Strengths and weaknesses in relation to other studies

Our study uses hundreds of millions of miles of car travel as the denominators in our estimates of annual pedestrian casualty rates which is a more accurate measure of exposure to road hazards than the number of registered vehicles, which was used as the denominator in a previous study in the UK. 10 Our results differ to this previous study which found that pedestrian casualty rates from collisions with E-HE vehicles during 2005–2007 were lower than those from ICE vehicles. Our study has updated this previous analysis and shows that casualty rates due to E-HE vehicle collisions exceed those due to ICE vehicle collisions. Similarly, our study uses a more robust measure of risk (casualty rates per miles of car travel) than that used in a US study. 9 Our study results are consistent with this US study that found that the odds of an E-HE vehicle causing a pedestrian injury were 35% greater than an ICE vehicle. Brand et al 8 hypothesised, without any supporting data, that “hybrid and electric low-noise cars cause an increase in traffic collisions involving vulnerable road users in urban areas” and recommended that “further investigations have to be done with the increase of low-noise cars to prove our hypothesis right.” 8 We believe that our study is the first to provide empirical evidence in support of this hypothesis.

Meaning of the study: possible explanations and implications for clinicians and policymakers

More pedestrians are injured in Great Britain by petrol and diesel cars than by electric cars, but compared with petrol and diesel cars, electric cars pose a greater risk to pedestrians and the risk is greater in urban environments. One plausible explanation for our results is that background ambient noise levels differ between urban and rural areas, causing electric vehicles to be less audible to pedestrians in urban areas. Such differences may impact on safety because pedestrians usually hear traffic approaching and take care to avoid any collision, which is more difficult if they do not hear electric vehicles. This is consistent with audio-testing evidence in a small study of vision-impaired participants. 10 From a Public Health perspective, our results should not discourage active forms of transport beneficial to health, such as walking and cycling, rather they can be used to ensure that any potential increased traffic injury risks are understood and safeguarded against. A better transport policy response to the climate emergency might be the provision of safe, affordable, accessible and integrated public transport systems for all. 25

Unanswered questions and future research

It will be of interest to investigate the extent to which younger drivers are involved in collisions of E-HE cars with pedestrians.

If the braking distance of electric cars is longer, 26 and electric cars are heavier than their petrol and diesel counterparts, 27 these factors may increase the risks and the severity of injuries sustained by pedestrians and require investigation.

As car manufacturers continue to develop and equip new electric cars with Collision Avoidance Systems and Autonomous Emergency Braking to ensure automatic braking in cases where pedestrians or cyclists move into the path of an oncoming car, future research can repeat the analyses presented in this study to evaluate whether the risks of E-HE cars to pedestrians in urban areas have been sufficiently mitigated.

Conclusions

E-HE vehicles pose a greater risk to pedestrians than petrol and diesel powered vehicles in urban environments. This risk needs to be mitigated as governments proceed to phase out petrol and diesel cars.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the LSHTM MSc Research Ethics Committee (reference #16400). The study uses the anonymised records of people injured in road traffic collisions, data which are routinely collected by UK police forces. The participants are unknown to the investigators and could not be contacted.

Acknowledgments

We thank Rebecca Steinbach for her advice on analysis of National Travel Survey data, Jonathan Bartlett for his advice on missing data, and Ben Armstrong for his advice on Poisson regression. We are grateful to the reviewers and to Dr C Mary Schooling, Associate Editor, whose comments helped us improve the manuscript. We are grateful to Jim Edwards and Graham Try for their comments on earlier versions of this manuscript.

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• ↵ WHO factsheet on road traffic injuries . Available : https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries#:~:text=Approximately%201.19%20million%20people%20die,adults%20aged%205%E2%80%9329%20years [Accessed 14 Apr 2024 ].
• ↵ Reported road casualties great Britain, annual report . 2022 . Available : https://www.gov.uk/government/statistics/reported-road-casualties-great-britain-annual-report-2022 [Accessed 14 Apr 2024 ].
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Contributors CH and PJE developed the idea for this study and supervised SM in performing the literature search, downloading, managing and analysing the data. SM wrote the first draft of the manuscript, which was the dissertation for her MSc in Public Health. PJE prepared the first draft of the manuscript for the journal. All authors assisted in editing and refining the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. PJE (guarantor) accepts full responsibility for the work and the conduct of the study, had access to the data and controlled the decision to publish.

Funding This study was conducted in part fulfilment of the Masters degree in Public Health at the London School of Hygiene & Tropical Medicine. The second author was self-funded for her studies for this degree.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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• Chuangshi Wang 4 ,
• Gregory YH Lip 5 , 6 and
• http://orcid.org/0000-0002-3643-9408 Hualiang Lin 1
• 1 Department of Epidemiology , Sun Yat-Sen University , Guangzhou , China
• 2 Department of Epidemiology and Biostatistics, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
• 3 School of Social Work, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
• 4 Medical Research and Biometrics Centre , Fuwai Hospital, National Centre for Cardiovascular Diseases, Peking Union Medical College , Beijing , China
• 5 Liverpool Centre for Cardiovascular Science , University of Liverpool and Liverpool Heart and Chest Hospital , Liverpool , UK
• 6 Department of Clinical Medicine , Aalborg University , Aalborg , Denmark
• Correspondence to Dr Hualiang Lin, Department of Epidemiology, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China; linhualiang{at}mail.sysu.edu.cn

Objective To examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death.

Design Prospective cohort study.

Setting UK Biobank study, 1 January 2006 to 31 December 2010, with follow-up to 31 March 2021 (median follow-up 11.9 years).

Participants 415 737 participants, aged 40-69 years, enrolled in the UK Biobank study.

Main outcome measures Incident cases of atrial fibrillation, major adverse cardiovascular events, and death, identified by linkage to hospital inpatient records and death registries. Role of fish oil supplements in different progressive stages of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage).

Results Among 415 737 participants free of cardiovascular diseases, 18 367 patients with incident atrial fibrillation, 22 636 with major adverse cardiovascular events, and 22 140 deaths during follow-up were identified. Regular use of fish oil supplements had different roles in the transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For people without cardiovascular disease, hazard ratios were 1.13 (95% confidence interval 1.10 to 1.17) for the transition from healthy status to atrial fibrillation and 1.05 (1.00 to 1.11) from healthy status to stroke. For participants with a diagnosis of a known cardiovascular disease, regular use of fish oil supplements was beneficial for transitions from atrial fibrillation to major adverse cardiovascular events (hazard ratio 0.92, 0.87 to 0.98), atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and heart failure to death (0.91, 0.84 to 0.99).

Conclusions Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for progression of cardiovascular disease from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements.

• Health policy
• Nutritional sciences
• Public health

Data availability statement

Data are available upon reasonable request. UK Biobank is an open access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/ .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjmed-2022-000451

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Findings of the effects of omega 3 fatty acids or fish oil on the risk of cardiovascular disease are controversial

Most previous studies focused on one health outcome and did not characterise specific cardiovascular disease outcomes (eg, atrial fibrillation, myocardial infarction, stroke, heart failure, and major adverse cardiovascular events)

Whether fish oil could differentially affect the dynamic course of cardiovascular diseases, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death, is unclear

WHAT THIS STUDY ADDS

In people with no known cardiovascular disease, regular use of fish oil supplements was associated with an increased relative risk of atrial fibrillation and stroke

In people with known cardiovascular disease, the beneficial effects of fish oil supplements were seen on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to myocardial infarction, and heart failure to death

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY

Regular use of fish oil supplements might have different roles in the progression of cardiovascular disease

Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements

Introduction

Cardiovascular disease is the leading cause of death worldwide, accounting for about one sixth of overall mortality in the UK. 1 2 Fish oil, a rich source of omega 3 fatty acids, containing eicosapentaenoic acid and docosahexaenoic acid, has been recommended as a dietary measure to prevent cardiovascular disease. 3 The UK National Institute for Health and Care Excellence recommends that people with or at high risk of cardiovascular disease consume at least one portion of oily fish a week, and the use of fish oil supplements has become popular in the UK and other western countries in recent years. 4 5

Although some epidemiological and clinical studies have assessed the effect of omega 3 fatty acids or fish oil on cardiovascular disease and its risk factors, the findings are controversial. The Agency for Healthcare Research and Quality systematically reviewed 37 observational studies and 61 randomised controlled trials, and found evidence indicating the beneficial effects of higher consumption of fish oil supplements on ischaemic stroke, whereas no beneficial effect was found for atrial fibrillation, major adverse cardiovascular events, myocardial infarction, total stroke, or all cause death. 6 In contrast, the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) reported a decreased risk of major adverse cardiovascular events with icosapent ethyl in patients with raised levels of triglycerides, regardless of the use of statins. 7 Most of these findings, however, tended to assess the role of fish oil at a certain stage of cardiovascular disease. For example, some studies restricted the study population to people with a specific cardiovascular disease or at a high risk of cardiovascular disease, 8 9 whereas others evaluated databases of generally healthy populations. 10 All of these factors might preclude direct comparison of the effects of omega 3 fatty acids on atrial fibrillation events or on further deterioration of cardiovascular disease. Few studies have fully characterised specific cardiovascular disease outcomes or accounted for differential effects based on the complex disease characteristics of participants. Hence, in this study, we hypothesised that fish oil supplements might have harmful, beneficial, or no effect on different cardiovascular disease events in patients with varying health conditions.

Most previous studies on the association between fish oil and cardiovascular diseases generally focused on one health outcome. Also, no study highlighted the dynamic progressive course of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). Clarifying this complex pathway in relation to the detailed progression of cardiovascular diseases would provide substantial insights into the prevention or treatment of future disease at critical stages. Whether fish oil could differentially affect the dynamic course of cardiovascular disease (ie, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death) is unclear.

To deal with this evidence gap, we conducted a longitudinal cohort study to estimate the associations between fish oil supplements and specific clinical cardiovascular disease outcomes, including atrial fibrillation, major adverse cardiovascular events, and all cause death in people with no known cardiovascular disease or at high risk of cardiovascular disease for the purpose of primary prevention. We also assessed the modifying effects of fish oil supplements on the disease process, from atrial fibrillation to other outcomes, in people with known cardiovascular disease for the purpose of secondary prevention.

The UK Biobank is a community based cohort study with more than half a million UK inhabitants aged 40-69 years at recruitment. 11–13 Participants were invited to participate in this study if they were registered with the NHS and lived within 35 km of one of 22 Biobank assessment centres. Between 1 March 2006 and 31 July 2010, a baseline survey was conducted, based on a touch screen questionnaire and face-to-face interviews, to collect detailed personal, socioeconomic, and lifestyle characteristics, and information on diseases. 11–13

We excluded patients who had a diagnosis of atrial fibrillation (n=8326), heart failure (n=2748), myocardial infarction (n=11 949), stroke (n=7943), or cancer (n=48 624) at baseline; who withdrew from the study during follow-up (n=1299); or who had incomplete or outlier data for the main information (n=11 748). Because we focused only on a specific sequence of progression of cardiovascular disease (ie, from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death), we excluded 1983 participants with other transition patterns. The remaining 415 737 participants were included in this analysis ( figure 1 ).

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Flowchart of selection of participants in study. The count of diagnosed diseases does not equate to the total number of individuals, because each person could have multiple diagnoses

Determining use of fish oil supplements

Information on regular use of fish oil supplements was collected from a self-reported touchscreen questionnaire during the baseline survey. 14 15 Each participant was asked whether they regularly used any fish oil supplement. Trained staff conducted a verbal interview with participants, asking if they were currently receiving treatments or taking any medicines, including omega 3 or fish oil supplements. Based on this information, we classified participants as regular users of fish oil supplements and non-users.

Follow-up and outcomes

Participants were followed up from the time of recruitment to death, loss to follow-up, or the end date of follow-up (31 March 2021), whichever came first. Incident cases of interest, including atrial fibrillation, heart failure, stroke, and myocardial infarction, were identified by linkage to death registries, primary care records, and hospital inpatient records. 11 Information on deaths was obtained from death registries of the NHS Information Centre, for participants in England and Wales, and from the NHS Central Register Scotland, for participants in Scotland. 11 Outcomes were defined by a three character ICD-10 (international classification of diseases, 10th revision) code. In this study, atrial fibrillation was defined by ICD-10 code I48, and major adverse cardiovascular events was determined by a combination of heart failure (I50, I11.0, I13.0, and I13.2), stroke (I60-I64), and myocardial infarction (I21, I22, I23, I24.1, and I25.2) codes.

We collected baseline data on age (<65 years and ≥65 years), sex (men and women), ethnic group (white and non-white), Townsend deprivation index (with a higher score indicating higher levels of deprivation), smoking status (never, previous, and current smokers), and alcohol consumption (never, previous, and current drinkers). Data for sex were taken from information in UK Biobank rather than from patient reported gender. Baseline dietary data were obtained from a dietary questionnaire completed by the patient or by an interviewer. The questionnaire was established for each nation (ie, England, Scotland, and Wales) to assess an individual's usual food intake (oily fish, non-oily fish, vegetables, fruit, and red meat). Diabetes mellitus was defined by ICD-10 codes E10-E14, self-reported physician's diagnosis, self-reported use of antidiabetic drugs, or haemoglobin A1c level ≥6.5% at baseline. Hypertension was defined by ICD-10 code I10 or I15, self-reported physician's diagnosis, self-reported use of antihypertensive drugs, or measured systolic and diastolic blood pressure ≥130/85 mm Hg at baseline. Information on other comorbidities (obesity (ICD-10 code E66), chronic obstructive pulmonary disease (J44), and chronic renal failure (N18)) was extracted from the first occurrence (UKB category ID 1712). Information on the use of drugs, including antihypertensive drugs, antidiabetic drug, and statins, was extracted from treatment and drug use records. Biochemistry markers were measured immediately at the central laboratory from serum samples collected at baseline. Binge drinking was defined as consumption of ≥6 standard drinks/day for women or ≥8 standard drinks/day for men. Detailed information on alcohol consumption and binge drinking in the UK Biobank was reported previously. 16

Statistical analysis

Characteristics of participants are summarised as number (percentages) for categorical variables and mean (standard deviation (SD)) for continuous variables. Comparisons between regular users of fish oil supplements and non-users were made with the χ 2 test or Student's t test.

We used a multi-state regression model to assess the role of regular use of fish oil supplements in the temporal disease progression from healthy status to atrial fibrillation, to major adverse cardiovascular events, and subsequently to death. The multi-state model is an extension of competing risks survival analysis. 17–19 The model allows simultaneous estimation of the role of risk factors in transitions from a healthy state to atrial fibrillation (transition A), healthy state to major adverse cardiovascular events (transition B), healthy state to death (transition C), atrial fibrillation to major adverse cardiovascular events (transition D), atrial fibrillation to death (transition E), and major adverse cardiovascular events to death (transition F) (transition pattern I, figure 2 ). The focus on these six transitions rather than on all possible health state transitions was preplanned and evidence based. If participants entered different states on the same date, we used the date of the theoretically previous state as the entry date of the latter state minus 0.5 days.

Numbers of participants in transition pattern I, from baseline to atrial fibrillation, major adverse cardiovascular events, and death

We further examined the effects of regular use of fish oil supplements on other pathways. For example, we divided major adverse cardiovascular events into three individual diseases (heart failure, stroke, and myocardial infarction), resulting in three independent pathways (transition patterns II, III, and IV, online supplemental figures S1–S3 ). All models were adjusted for age, sex, ethnic group, Townsend deprivation index, consumption of oily fish, consumption of non-oily fish, smoking status, alcohol consumption, obesity, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, chronic renal failure, and use of statins, antidiabetic drugs, and antihypertensive drugs.

Supplemental material

We conducted several sensitivity analyses for the multi-state analyses of transition pattern A: additionally adjusting for setting (urban and rural), body mass index (underweight, normal, overweight, and obese), and physical activity (low, moderate, and high) in the model; adjusting for binge drinking rather than alcohol consumption; additionally adjusting for other variables of dietary intake (consumption of vegetables, fruit, and red meat); calculating participants' entry date into the previous state with different time intervals (0.5 years, one year, and two years); excluding participants who entered different states on the same date; excluding events occurring in the first two years of follow-up; restricting the follow-up date to 31 March 2020 to evaluate the influence of the covid-19 pandemic; and the use of the inverse probability weighted method to deal with biases between the regular users and non-users of fish oil supplements. Also, we conducted grouped analyses for sex, age group, ethnic group, smoking status, consumption of oily fish, consumption of non-oily fish, hypertension, and drug use, to examine effect modification. The interactions were tested with the likelihood ratio test. All analyses were carried out with R software (version 4.0.3), and the multi-model analysis was performed with the mstate package. A two tailed P value <0.05 was considered significant.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Participants were involved in developing the ethics and governance framework for UK Biobank and have been engaged in the progress of UK Biobank through follow-up questionnaires and additional assessment visits. UK Biobank keeps participants informed of all research output through the study website ( https://www.ukbiobank.ac.uk/explore-your-participation ), participant events, and newsletters.

A total of 415 737 participants (mean age 55.9 (SD 8.1) years; 55% women), aged 40-69 years, were analysed, and 31.4% (n=1 30 365) of participants reported regular use of fish oil supplements at baseline ( figure 1 ). Table 1 shows the characteristics of regular users (n=130 365) and non-users (n=285 372) of fish oil supplements. In the group of regular users of fish oil supplements, we found higher proportions of elderly people (22.6% v 13.9%), white people (95.1% v 94.2%), and women (57.6% v 53.9%), and higher consumption of alcohol (93.1% v 92.0%), oily fish (22.1% v 15.4%), and non-oily fish (18.0% v 15.4%) than non-users. The Townsend deprivation index (mean −1.5 (SD 3.0) v −1.3 (3.0)) and the proportion of current smokers (8.1% v 11.4%) were lower in regular users of fish oil supplements. Online supplemental table S1 provides more details on patient characteristics and online supplemental table S2 compares the basic characteristics of included and excluded people.

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Baseline characteristics of study participants grouped by use of fish oil supplements

Over a median follow-up time of of 11.9 years, 18 367 participants had atrial fibrillation (transition A) and 17 826 participants had major adverse cardiovascular events (transition B); 14 902 participants died without having atrial fibrillation or major adverse cardiovascular events (transition C). Among patients with incident atrial fibrillation, 4810 developed major adverse cardiovascular events (transition D) and 1653 died (transition E). Among patients with incident major adverse cardiovascular events, 5585 died during follow-up (transition F, figure 2 ). In separate analyses for individual diseases (transition patterns II, III, and IV, online supplemental figures S1–S3 ), in patients with atrial fibrillation, 3085 developed heart failure, 1180 had a stroke, and 1415 had a myocardial infarction. During follow-up, 2436, 2088, and 2098 deaths occurred in patients with heart failure, stroke, and myocardial infarction, respectively.

Multi-state regression results

Table 2 shows the different roles of regular use of fish oil supplements in transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For individuals in the primary stage (healthy status), we found that the use of fish oil supplements had a harmful effect on the transition from health to atrial fibrillation, with an adjusted hazard ratio of 1.13 (95% CI 1.10 to 1.17, transition A). The hazard ratio for transition B (from health to major adverse cardiovascular events) was 1.00 (95% CI 0.97 to 1.04) and for transition C (from health to death) was 0.98 (0.95 to 1.02).

Hazard ratios (95% confidence intervals) for each transition, for different transition patterns for progressive cardiovascular disease by regular use of fish oil supplements

For individuals in the secondary stage (atrial fibrillation) at the beginning of the study, regular use of fish oil supplements decreased the risk of major adverse cardiovascular events (transition D, hazard ratio 0.92, 95% CI 0.87 to 0.98), and had a borderline protective effect on the transition from atrial fibrillation to death (transition E, 0.91, 0.82 to 1.01). For transition F, from major adverse cardiovascular events to death, after adjusting for covariates, the hazard ratio was 0.99 (0.94 to 1.06, transition pattern I, table 2 ).

We divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction) and found that regular use of fish oil supplements was marginally associated with an increased risk of stroke in people with a healthy cardiovascular state (hazard ratio 1.05, 95% CI 1.00 to 1.11), whereas a protective effect was found in transitions from healthy cardiovascular states to heart failure (0.92, 0.86 to 0.98). For patients with atrial fibrillation, we found that the beneficial effects of regular use of fish oil supplements were for transitions from atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and from atrial fibrillation to death (0.88, 0.81 to 0.95) for transition pattern IV. For patients with heart failure, we found a protective effect of regular use of fish oil supplements on the risk of mortality (0.91, 0.84 to 0.99) (transition patterns II, III, and IV, table 2 ).

Stratified and sensitivity analyses

We found that age, sex, smoking, consumption of non-oily fish, prevalent hypertension, and use of statins and antihypertensive drugs modified the associations between regular use of fish oil supplements and the transition from healthy states to atrial fibrillation ( online supplemental figure S4 ). We found that the association between regular use of fish oil supplements and risk of transition from healthy states to major adverse cardiovascular events was greater in women (hazard ratio 1.06, 95% CI 1.00 to 1.11, P value for interaction=0.005) and non-smoking participants (1.06, 1.06 to 1.11, P value for interaction=0.001) ( online supplemental figure S4 ). The protective effect of regular use of fish oil supplements on the transition from healthy states to death was greater in men (hazard ratio 0.93, 95% CI 0.89 to 0.98, P value for interaction=0.003) and older participants (0.91, 0.86 to o 0.96, P value for interaction=0.002) ( online supplemental figures S5 and S6 ). The results were not substantially changed in the sensitivity analyses ( online supplemental table S3 ).

Principal findings

Our study characterised the regular use of fish oil supplements on the progressive course of cardiovascular disease, from a healthy state (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). In this prospective analysis of more than 400 000 UK adults, we found that regular use of fish oil supplements could have a differential role in the progression of cardiovascular disease. For people with a healthy cardiovascular profile, regular use of fish oil supplements, a choice of primary prevention, was associated with an increased risk of atrial fibrillation. For participants with a diagnosis of atrial fibrillation, however, regular use of fish oil supplements, as secondary prevention, had a protective effect or no effect on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to death, and major adverse cardiovascular events to death. When we divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction), we found associations that could suggest a mildly harmful effect between regular use of fish oil supplements and transitions from a healthy cardiovascular state to stroke, whereas potential beneficial associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to myocardial infarction, atrial fibrillation to death, and heart failure to death.

Comparison with other studies

Primary prevention.

The cardiovascular benefits of regular use of fish oil supplements have been examined in numerous studies but the results are controversial. Extending previous reports, our study estimated the associations between regular use of fish oil supplements and specific clinical cardiovascular disease outcomes in people with no known cardiovascular disease. Our findings are in agreement with the results of several previous randomised controlled trials and meta-analyses. The Long-Term Outcomes Study to Assess Statin Residual Risk with Epanova in High Cardiovascular Risk Patients with Hypertriglyceridaemia (STRENGTH) reported that consumption of 4 g/day of marine omega 3 fatty acids was associated with a 69% higher risk of new onset atrial fibrillation in people at high risk of cardiovascular disease. 20 A meta-analysis of seven randomised controlled trials showed that users of marine omega 3 fatty acids supplements had a higher risk of atrial fibrillation events, with a hazard ratio of 1.25 (95% CI 1.07 to 1.46, P=0.013). 21 The Vitamin D and Omega-3 Trial (VITAL Rhythm study), a large trial of omega 3 fatty acids for the primary prevention of cardiovascular disease in adults aged ≥50 years, however, found no effects on incident atrial fibrillation, major adverse cardiovascular events, or cardiovascular disease mortality among those treated with 840 mg/day of marine omega 3 fatty acids compared with placebo. 10 22

One possible explanation for the inconsistent results in these studies is that adverse effects might be related to dose and composition. Higher doses of omega 3 fatty acids used in previous studies might have had an important role in causing an adverse effect on atrial fibrillation. 21 One study found that high concentrations of fish oil altered cell membrane properties and inhibited Na-K-ATPase pump activity, whereas a low concentration of fish oil minimised peroxidation potential and optimised activity. 23 In another study, individuals with atrial fibrillation or flutter had higher percentages of total polyunsaturated fatty acids, and n-3 and n-6 polyunsaturated fatty acids, on red blood cell membranes than healthy controls. 24

In terms of composition of omega 3 fatty acids, a recent meta-analysis showed that eicosapentaenoic acid alone can be more effective at reducing the risk of cardiovascular disease than the combined effect of eicosapentaenoic acid and docosahexaenoic acid. 25 Similar outcomes were reported in the INSPIRE study, which showed that higher levels of docosahexaenoic acid reduced the cardiovascular benefits of eicosapentaenoic acid when given as a combination. 26 Another possible explanation is that age, sex, ethnic group, smoking status, dietary patterns, and use of statins and antidiabetic drugs by participants might modify the effects of regular use of fish oil supplements on cardiovascular disease events. Despite these differences in risk estimates, our findings do not support the use of fish oil or omega 3 fatty acid supplements for the primary prevention of incident atrial fibrillation or other specific clinical cardiovascular disease events in generally healthy individuals. Caution might be warranted when fish oil supplements are used for primary prevention because of the uncertain cardiovascular benefits.

Secondary prevention

Our large scale cohort study assessed the role of regular use of fish oil supplements on the disease process, from atrial fibrillation to more serious cardiovascular disease stages, to death, in people with known cardiovascular disease. Contrary to the observations for primary prevention, we found associations that could suggest beneficial effects between regular use of fish oil supplements and most cardiovascular disease transitions. No associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to death, or from major adverse cardiovascular events to death.

Consistent with our hypothesis, the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico (GISSI) Prevenzione study reported an association between administration of low dose prescriptions of n-3 polyunsaturated fatty acids and reduced cardiovascular events in patients with recent myocardial infarction. 27 A meta-analysis of 16 randomised controlled trials also reported a tendency towards a greater beneficial effect for secondary prevention in patients with cardiovascular disease. 28 Why patients with previous atrial fibrillation benefit is unclear. These findings indicate that triglyceride independent effects of omega 3 fatty acids might in part be responsible for the benefits in cardiovascular disease seen in previous trials. 29–31 No proven biological mechanism for this explanation exists, however, and the dose and formulation of omega 3 fatty acids used in clinical practice are not known.

For the disease process, from cardiovascular disease to death, our findings are consistent with the results of secondary prevention trials of omega 3 fatty acids, which have mostly shown a weak or neutral preventive effect in all cause mortality with oil fish supplements. The GISSI heart failure trial (GISSI-HF), conducted in 6975 patients with chronic heart failure, reported that supplemental omega 3 fatty acids reduced the risk of all cause mortality by 9% (hazard ratio 0.91, 95% CI 0.833 to 0.998, P=0.041). 32 Zelniker et al showed that omega 3 fatty acids were inversely associated with a lower incidence of sudden cardiac death in patients with non-ST segment elevation acute coronary syndrome. 33 A meta-analysis found that use of omega 3 supplements of ≤1 capsule/day was not associated with all cause mortality, but among participants with a risk of cardiovascular disease, taking a higher dose was associated with a reduction in cardiac death and sudden death. 28 Individuals who might benefit the most from fish oil or omega 3 fatty acid supplements are possibly more vulnerable individuals, such as those with previous cardiovascular diseases and those who can no longer live in the community. How fish oil supplements stop further deterioration of cardiovascular disease is unclear, but the theory that supplemental omega 3 fatty acids might protect the coronary artery is biologically plausible, suggesting that omega 3 fatty acids have anti-inflammatory and anti-hypertriglyceridaemia effects, contributing to a reduction in thrombosis and improvement in endothelial function. 34–41 Nevertheless, the effects of omega 3 fatty acids vary according to an individual's previous use of statins, which might partly explain the different effects of fish oil supplements in people with and without cardiovascular disease.

Many studies of omega 3 fatty acids, including large scale clinical trials and meta-analyses, have not produced entirely consistent results. 21 25 42 Our study mainly explored the varied potential effects of regular use of fish oil supplements on progression of cardiovascular disease, offering an initial overview of this ongoing discussion. Our findings suggest caution in the use of fish oil supplements for primary prevention because of the uncertain cardiovascular benefits and adverse effects. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms related to the development and prognosis of cardiovascular disease events.

Strengths and limitations of this study

The strengths of our study were the large sample size, long follow-up period, which allowed us to analyse clinically diagnosed incident diseases, and complete data on health outcomes. Another strength was our analytical strategy. The multi-state model gives less biased estimates than the conventional Cox model, and distinguished the effect of regular use of fish oil supplements on each transition in the course of cardiovascular disease.

Our study had some limitations. Firstly, as an observational study, no causal relations can be drawn from our findings. Secondly, although we adjusted for multiple covariates, residual confounding could still exist. Thirdly, information on dose and formulation of the fish oil supplements was not available in this study, so we could not evaluate potential dose dependent effects or differentiate between the effects of different fish oil formulations. Fourthly, the use of hospital inpatient data for determining atrial fibrillation events could have excluded some events triggered by acute episodes, such as surgery, trauma, and similar conditions, resulting in underestimation of the true risk because undiagnosed atrial fibrillation is a common occurrence. 43 Fifthly, most of the participants in this study were from the white ethnic group and whether the findings can be generalised to other ethnic groups is not known. Finally, our study did not consider behavioural changes in populations with different cardiovascular profiles because of limited information, and variations in outcomes for different cardiovascular states merits further exploration.

Conclusions

This large scale prospective study of a UK cohort suggested that regular use of fish oil supplements might have differential roles in the course of cardiovascular diseases. Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for disease progression, from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms for the development and prognosis of cardiovascular disease events.

Ethics statements

Patient consent for publication.

Consent obtained directly from patients.

Ethics approval

The UK Biobank study obtained ethical approval from the North West Multicentre Research ethics committee, Information Advisory Group, and the Community Health Index Advisory Group (REC reference for UK Biobank 11/NW/0382). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This study was conducted with UK Biobank Resource (application No: 69550). We appreciate all participants and professionals contributing to UK Biobank.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

GYL and HL are joint senior authors.

Contributors HL supervised the whole project and designed the work. GC and HL directly accessed and verified the underlying data reported in the manuscript. GC contributed to data interpretation and writing of the report. ZQ, SZ, JZ, ZZ, MGV, HEA, CW, and GYHL contributed to the discussion and data interpretation, and revised the manuscript. All authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. HL is the guarantor. Transparency: The lead author (guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Funding This work was supported by the Bill and Melinda Gates Foundation (grant No INV-016826). Under the grant conditions of the foundation, a creative commons attribution 4.0 generic license has already been assigned to the author accepted manuscript version that might arise from this submission. The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Bill and Melinda Gates Foundation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

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