Category: 8. Health

People who want to feel younger, look fitter, or perhaps slough off a little layer of belly fat have been turning to an increasingly popular kind of treatment — one you can get without a prescription.

They’ve got obscure names like BPC-157, tesamorelin, and cerebrolysin. All it takes is a tiny needle and a little clear vial filled with injectable molecules.

Welcome to the world of peptides.

“Absolutely everybody’s asking for it, the field is popping,” Dr. Florence Comite, a longevity doctor who serves concierge medicine clients in New York City, told Business Insider.

The peptide landscape is so large that it almost defies definition. The prescription drugs Ozempic and Mounjaro, often used for weight loss, are peptides. So is insulin. There are peptides in skin creams, hair products, and high-end serums marketed to women to reduce fine lines and stimulate collagen. The wildly popular fitness supplement creatine? Also a peptide.

Then, there are the gym bro shots, said to boost muscle, burn fat, stimulate testosterone, and aid recovery.

Demand for peptide injections — something that biohackers and longevity-seekers have already been quietly using in the shadows for decades — is booming. Patients in pockets of the country saturated with peptides, like Beverly Hills, San Diego, Silicon Valley, and Manhattan are increasingly asking their doctors: “should I try peptides?”

Many physicians aren’t sure what to say because there isn’t a ton of great evidence around about how much peptides can really do. Plus, the FDA has been cracking down on peptide compounders in recent years. They worry that the hype is outpacing good evidence.

How peptides boost your body

Unlike most pills that doctors prescribe, peptides live in a more slippery area, between drug and bodily substance.

A peptide is a chain of organic compounds — specifically, amino acids — that stimulate natural processes. Depending on which amino acids a peptide is made of, and how it is used, the molecule can have all kinds of impacts on how our hormones operate. Peptides can improve fertility in both men and women, tamp down inflammation, remove dangerous visceral belly fat, or help build muscle. Others are thought to help improve sleep quality, even possibly improve brain health.

“What’s great about peptides is that they mimic the body,” said Comite, who has been working with peptides since she was a research fellow at the National Institutes of Health over 30 years ago.

Since most peptides are too fragile to be formulated as pills, they are often packaged as a clear liquid in a little vial. Users learn to inject their peptides using a very fine, short needle, right at home.

The popularity of peptides has soared on their reputation as ostensibly “natural” products. The idea being that, unlike other drugs or steroids, peptides are a safer choice because they’re just stimulating your body to do its own thing.

Taking growth hormones, for example, comes with a suite of undesirable potential side effects, like an increased risk of cancer and type 2 diabetes. What if you could just take a peptide that would stimulate your own growth hormone to make you stronger, leaner, and more energetic?

“The theory is that even if you use a growth hormone stimulating peptide, your body’s only going to be able to make so much growth hormone,” Dr. Sajad Zalzala, a longevity physician and one of the cofounders of AgelessRx, said. “Kind of like a check valve already in place. Again, that’s the theory.”

The peptides gym bros take to get chiseled muscles

One darling peptide of gym bros and longevity fiends alike is a substance called BPC-157. It’s known as the “Wolverine” shot for its perceived ability to heal you up and regenerate your body real fast like the Marvel character, Logan, after a big fight.

B-P-C stands for “body protection compound.” BPC-157 was first derived from stomach juices. It’s being investigated to treat inflammatory bowel diseases, including Crohn’s and ulcerative colitis. But the reason that athletes like it is because it’s thought to reduce inflammation and improve blood flow — and perhaps do even more.

There are a few other super popular peptides:

Tesamorelin, an injectable peptide, is prescribed to HIV patients to reduce excess belly fat. Sermorelin is supposed to help with sleep and recovery. CJC-1295 binds to growth hormone receptors in the body, and people often take it alongside impamorelin, which stimulates the hypothalamus. The two in tandem are said to deliver better muscle gains.

On Reddit and YouTube people share how they “stack” different peptides like this, taking multiple different kinds with the goal of boosting the effects of each.

One snag: The FDA is making peptides harder to access

Peptide fans get their shots at clinics and med spas — or, for less money, online.

Increasingly, people are ordering peptides that are labeled “for research only,” meaning they are supposed to be used by lab workers for experimentation, and were never meant to be put into human bodies.

That’s partly because the FDA crackdown on peptides has intensified in recent years, just as pharmaceutical compounding (a sort of acceptable way to get knock off medications) has surged in popularity, with people seeking cheaper versions of GLP-1 drugs like Ozempic and Mounjaro. At the beginning of 2022, the FDA had a list of four peptides that they said “may present significant safety risks” and should not be compounded. By the end of 2023, there were 26.

Comite thinks the FDA crackdown is a shame. She is finding it harder and harder to source compounded liquid BPC-157. She often uses a patch form of BPC-157 on herself, placing it over sore areas or injuries. Recently, she tore a calf muscle, so she’s been using it there, but she also just likes how taking a little bit of it keeps her active and moving.

“I use it almost every day,” she said. “It’s amazing for joints and everything — at a very tiny dose.”

Zalzala, who doesn’t usually prescribe peptides, ordered some topical BPC-157 recently when his wife had a knee injury. “My wife says it works,” he said, though he cautioned that it’s hard to really know if that’s true without more proper research.

Bracken Darrell, the CEO of VF Corporation and one of Comite’s patients, is also a BPC-157 convert. A self-proclaimed “basketball nut,” he’s on the court about three days a week. On the days when he doesn’t pick up a ball, he’s out cross-training on a bike or lifting weights.

So when he tore his meniscus about four months ago, he was worried. Under Comite’s supervision, he started taking liquid BPC-157 about three to four times a week.

He told BI it was “weird” at first, learning to inject the needle into an area of skin near his knee. But, pretty soon, it was just part of his routine.

“I believe it helped a lot, but it’s hard to know for sure,” he said. “There are people with a severely torn meniscus who don’t ever play basketball again, and I’m back — I certainly wouldn’t conclude that’s because I’m taking BPC-157, but at a minimum it didn’t hurt. And it sure seems like it helps.”

Proceed with caution, doctors say

Even longevity doctors who prescribe and use peptides regularly agree that some folks are overdoing it, and that could be dangerous.

“Proceed with caution, because you have to know the source and you have to know it’s active,” Comite said. “It’s not like Lowe’s or Home Depot where you can get stuff and you can fix the plumbing.”

In reality, the evidence for peptides is still murky. There are no big, randomized clinical trials like what we have for prescription drugs or vaccines. The current hype is based on anecdotal evidence, a few small human studies from decades ago, and rodent studies.

“People wanna take the peptides because they’re not from big pharma, they’re not mainstream medicine, they gotta be better than those cockamamie doctors,” Dr. Eric Topol, a cardiologist and longevity expert, said recently on the Dax Shepard podcast. “Where’s the data?”

For people who are using peptides, experts shared two pieces of advice:

Comite urges patients to start slow. A common mistake people who are dosing themselves make is thinking that “if a little bit is good, then a lot must be better,” she said. That’s not the case.

“Taking mega doses of tesmorelin along with testosterone causes your organs to overgrow,” Comite said. Sometimes she’ll see a toned gymgoer with a potbelly, and wonder whether that’s due to an enlarged liver or spleen.

Darrell recommends testing your peptides with an independent lab so you know what you’re getting is both real and uncontaminated.

Zalzala says his company started thinking about offering peptides a few years back, due to consumer demand, but they haven’t yet. There are just so many peptides out there, and it’s hard to tell which might be the very best.

Some of the most research-backed ways to have an impact on your longevity and fitness are still the simplest anyway: eat decent amounts of fiber and protein regularly, work out — at least a couple sessions with weights each week, and cut back (or ideally, eliminate) liquid sugar in your diet like juice and soda.

A total of 3247 critically ill stroke patients were included in this study. The median age of the included patients was 70.167 years (95% CI 69.693–70.641), with 1701 (52.4%) being male and 1709 (52.6%) having ischemic stroke. The in-hospital mortality and ICU mortality rates were 17.2% and 9.7%, respectively. The median TyG index of all participants was 8.787 (95% CI 8.767–8.806) (Table 1); the median AIP was 0.375 (95% CI 0.366–0.385) (Table 2); and the median TC_HDL ratio was 3.68 (95% CI 3.637–3.724) (Table 3).

Baseline characteristics

The baseline characteristics of critically ill stroke patients stratified by quartiles of TyG, AIP, and TC_HDL indices are presented in Tables 1, 2, 3. Enrolled individuals were divided into four groups based on admission levels of TyG, AIP, and TC_HDL. The median TyG indices for each quartile were 8.156 (95% CI 8.137–8.174), 8.581 (95% CI 8.576–8.587), 8.881 (95% CI 8.874–8.888), and 9.532 (95% CI 9.501–9.564), respectively. The median AIP values for each quartile were 0.081 (95% CI 0.071–0.092), 0.314 (95% CI 0.313–0.314), 0.397 (95% CI 0.395–0.400), and 0.720 (95% CI 0.706–0.734), respectively. The median TC_HDL ratios for each quartile were 2.524 (95% CI 2.496–2.552), 3.349 (95% CI 3.346–3.352), 3.611 (95% CI 3.603–3.619), and 5.318 (95% CI 5.217–5.418), respectively.

Compared with patients in the lower TyG quartiles, those in the highest TyG quartile generally exhibited higher BMI, higher admission weight, a higher prevalence of diabetes and prior stroke, lower HDL-C, higher LDL-C, shorter APTT, higher fasting glucose, higher triglycerides and total cholesterol, as well as higher ICU and in-hospital mortality rates. Similarly, patients in the highest AIP quartile, relative to those in lower AIP quartiles, had higher BMI, higher admission weight, longer ICU length of stay, higher APS and APACHE scores, a higher history of coronary heart disease, lower HDL-C, higher LDL-C, longer PT, higher INR, higher fasting glucose, higher triglycerides and total cholesterol, and higher ICU and in-hospital mortality rates. For the TC_HDL ratio, patients in the highest quartile, compared with those in lower quartiles, showed higher BMI, higher admission weight, longer ICU length of stay, higher APACHE scores, a higher prevalence of dementia and hematological malignancy, lower HDL-C, higher LDL-C, longer PT, higher INR, higher platelet count, higher fasting glucose, higher triglycerides and total cholesterol, and higher ICU and in-hospital mortality rates.

Differences in baseline characteristics between in-hospital survivors and non-survivors are shown in Table 4. Non-survivors were more likely to be female, older, with lower BMI, higher APS and APACHE scores, and higher levels of fasting glucose, total cholesterol, and triglycerides. The TyG index (8.96 vs. 8.75, *P* < 0.001), AIP (0.44 vs. 0.36, *P* < 0.001), and TC_HDL ratio (3.94 vs. 3.63, *P* < 0.001) were significantly higher in non-survivors than in survivors.

Primary outcomes

Kaplan–Meier survival curves were used to analyze the incidence of primary outcomes across groups. As shown in Fig. 2 (stratified by TyG quartiles), patients with higher TyG indices had an increased risk of in-hospital and ICU mortality. For AIP quartiles (Fig. 3), higher AIP indices were associated with a higher risk of in-hospital mortality, but no significant difference in ICU mortality was observed. For TC_HDL quartiles (Fig. 4), higher TC_HDL ratios were linked to a trend toward increased ICU and in-hospital mortality, though the difference was not statistically significant.

Kaplan–Meier survival analysis curves for all-cause mortality according to AIP grouping. The Kaplan–Meier curves show the cumulative probability of all-cause mortality during ICU stay (a) and in-hospital stay (b) across different groups

Kaplan–Meier survival analysis curves for all-cause mortality according to TC_HDL grouping. The Kaplan–Meier curves show the cumulative probability of all-cause mortality during ICU stay (a) and in-hospital stay (b) across different groups

Cox proportional hazards analyses were performed to assess the associations between TyG, AIP, TC_HDL, and in-hospital mortality. For TyG: when analyzed as a continuous variable, it was an important risk factor in the unadjusted model [HR 1.53 (95% CI 1.349–1.736), *P* < 0.001], partially adjusted model [HR 1.637 (95% CI 1.436–1.865), *P* < 0.001], and fully adjusted model [HR 1.643 (95% CI 1.370–1.970), *P* < 0.001]. When analyzed as an ordinal variable (quartiles), compared with the lowest quartile (Q1), patients in higher TyG quartiles had a significantly elevated risk of in-hospital mortality across all models: unadjusted [HR 1.389 (95% CI 1.103–1.750), *P* = 0.005], partially adjusted [HR 1.511 (95% CI 1.193–1.913), *P* = 0.001], and fully adjusted [HR 1.434 (95% CI 1.040–1.619), *P* = 0.013], with a trend of increasing risk across quartiles. Similar results were observed for the association between TyG and ICU mortality in multivariate Cox analyses (Table 5).

For AIP: when treated as a continuous variable, it was a significant risk factor in the unadjusted model [HR 2.198 (95% CI 1.653–2.923), *P* < 0.001], partially adjusted model [HR 2.430 (95% CI 1.811–3.261), *P* < 0.001], and fully adjusted model [HR 3.944 (95% CI 2.258–6.890), *P* < 0.001]. When analyzed as an ordinal variable, compared with Q1, patients in higher AIP quartiles had a significantly higher risk of in-hospital mortality across models: unadjusted [HR 1.507 (95% CI 1.191–1.907), *P* = 0.001], partially adjusted [HR 1.629 (95% CI 1.279–2.074), *P* < 0.001], and fully adjusted [HR 1.631 (95% CI 1.156–2.301), *P* = 0.005], with a trend of increasing risk across quartiles. Similar results were observed for the association between AIP and ICU mortality (Table 6).

For the TC_HDL ratio: when treated as a continuous variable, it was a significant risk factor in the unadjusted model [HR 1.121 (95% CI 1.064–1.182), *P* < 0.001] and partially adjusted model [HR 1.126 (95% CI 1.066–1.190), *P* < 0.001], but the association was not statistically significant in the fully adjusted model [HR 1.066 (95% CI 0.921–1.234), *P* = 0.39]. When analyzed as an ordinal variable, no consistent significant association was observed between higher TC_HDL quartiles and in-hospital mortality across models: unadjusted [HR 1.253 (95% CI 0.988–1.588), *P* = 0.063], partially adjusted [HR 1.305 (95% CI 1.022–1.666), *P* = 0.033], and fully adjusted [HR 0.834 (95% CI 0.545–1.278), *P* = 0.405]. Similar results were observed for the association between the TC_HDL ratio and ICU mortality (Table 7). To validate that TyG has higher predictive accuracy than the other two indices, we generated receiver operating characteristic (ROC) curves (Supplementary Figure 1), which showed that TyG exhibited higher predictive accuracy for both ICU and in-hospital ACM.

Additionally, restricted cubic spline regression models revealed that the risks of in-hospital death and ICU mortality increased nonlinearly with the TyG index (P for non-linearity < 0.001 for both) (Fig. 5) and the AIP index (P for non-linearity < 0.001 for both) (Fig. 6). In contrast to TyG and AIP, ICU mortality increased nonlinearly with the TC_HDL ratio (P for non-linearity = 0.029), while the risk of in-hospital death increased linearly with the TC_HDL ratio (P for non-linearity = 0.067) (Fig. 7). Furthermore, TyG and AIP exhibited distinct threshold effects.

Restricted cubic spline curves for hazard ratios of the TyG index. The thick central line represents the estimated adjusted hazard ratio, and the shaded area represents the 95% confidence interval. a Restricted cubic spline curve for ICU mortality. b Restricted cubic spline curve for in-hospital mortality

Restricted cubic spline curves for hazard ratios of the AIP index. The thick central line represents the estimated adjusted hazard ratio, and the shaded area represents the 95% confidence interval. a Restricted cubic spline curve for ICU mortality. b Restricted cubic spline curve for in-hospital mortality

Restricted cubic spline curves for hazard ratios of the TC_HDL index. The thick central line represents the estimated adjusted hazard ratio, and the shaded area represents the 95% confidence interval. a Restricted cubic spline curve for ICU mortality. b Restricted cubic spline curve for in-hospital mortality

Subgroup analysis

The risk stratification value of the TyG index for primary endpoints was further analyzed across multiple subgroups of enrolled patients, including gender, age, stroke type, BMI, history of coronary heart disease, hypertension, atrial fibrillation, diabetes, and prior stroke. The TyG index was significantly associated with higher in-hospital mortality in the following subgroups of critically ill stroke patients: females [HR 1.61 (95% CI 1.36–1.91)], age < 60 years [HR 2.06 (95% CI 1.61–2.63)], ischemic stroke [HR 1.76 (95% CI 1.49–2.08)], BMI ≥ 30 [HR 1.82 (95% CI 1.49–2.21)], no history of coronary heart disease [HR 1.54 (95% CI 1.34–1.76)], history of hypertension [HR 1.58 (95% CI 1.37–1.82)], no atrial fibrillation [HR 1.60 (95% CI 1.39–1.83)], and history of diabetes [HR 1.56 (95% CI 1.10–2.22)] (Fig. 8).

Forest plots of hazard ratios for the hospital mortality in different subgroup. HR: hazard ratio; CI: confidence interval; CHD: coronary heart disease; AF: atrial fibrillation; BMI: body mass index

Similarly, in the stratified analysis of ICU mortality, the TyG index was significantly associated with higher ICU mortality in the following subgroups: females [HR 1.67 (95% CI 1.32–2.12)], age < 60 years [HR 2.07 (95% CI 1.55–2.78)], ischemic stroke [HR 1.79 (95% CI 1.42–2.27)], BMI ≥ 30 [HR 1.75 (95% CI 1.35–2.27)], no history of coronary heart disease [HR 1.56 (95% CI 1.31–1.86)], history of hypertension [HR 1.64 (95% CI 1.37–1.98)], no history of atrial fibrillation [HR 1.60 (95% CI 1.32–1.92)], history of diabetes [HR 1.78 (95% CI 1.39–2.64)], and no history of prior stroke [HR 1.52 (95% CI 1.25–1.85)] (Fig. 9).

Forest plots of hazard ratios for the ICU mortality in different subgroup. HR: hazard ratio; CI: confidence interval; CHD: coronary heart disease; AF: atrial fibrillation; BMI: body mass index

Additionally, Kaplan–Meier curves demonstrated that individuals with an elevated TyG index had higher ICU and in-hospital mortality, regardless of whether they had ischemic stroke (Fig. 10) or hemorrhagic stroke (Fig. 11), which is consistent with previous literature.

Kaplan–Meier survival analysis curves for all-cause mortality in patients with ischemic stroke stratified by TyG values. The Kaplan–Meier curves show the cumulative probability of all-cause mortality during intensive care unit (ICU) stay (a) and in-hospital stay (b) across different groups

Kaplan–Meier survival analysis curves for all-cause mortality in patients with hemorrhagic stroke stratified by TyG values. The Kaplan–Meier curves show the cumulative probability of all-cause mortality during intensive care unit (ICU) stay (a) and in-hospital stay (b) across different groups

WHO has released new sexually transmitted infections (STIs) guidance and policy implementation data, ahead of the STI & HIV 2025 World Congress in Montreal, 26–30 July 2025.

The 2 new components of the upcoming consolidated guidelines on STI prevention and care include the Guidelines for the management of asymptomatic STIs, and the Recommendations on the delivery of health services for STI prevention and care.

These evidence-based guidelines aim to strengthen STI prevention, screening, diagnosis and treatment, especially in high-burden, resource-limited settings. They complement existing guidance covering syndromic management, management of specific STIs: such as gonorrhoea, chlamydia, syphilis, trichomoniasis, genital candidiasis, Mycoplasma genitalium, human papillomavirus (anogenital warts) and bacterial vaginosis, syphilis testing, partner services. 

Key new recommendations include targeted screening for gonorrhoea and chlamydia in high-prevalence settings with available resources, focusing on pregnant women, adolescents and young people aged 10–24, sex workers and men who have sex with men (MSM). 

Screening should align with individual risk and resource considerations, with at least annual or 6-monthly screening recommended for sex workers and MSM. 

Service delivery improvements include decentralizing and integrating STI services, task sharing with trained providers and community health workers, and leveraging digital tools to complement in-person care.

Together with existing guidance, these new components will form part of the forthcoming WHO consolidated guidelines on STI prevention and care. 

“These new recommendations aim to close persistent policy and service gaps, especially for asymptomatic STIs, and help countries move faster toward the 2030 goals,” said Dr Meg Doherty, Director of WHO’s Global HIV, Hepatitis and STIs Programmes.

WHO reports mixed progress on adopting and implementing WHO policy in countries

As part of its continued efforts to strengthen global STI responses, WHO has released new data highlighting both progress and persistent challenges in national policy implementation. 

Among countries reporting to Global AIDS Monitoring in 2024–2025, 89% have a national STI strategy or action plan in place – yet only 43% have updated it since 2023. Similarly, while 97% of countries report having national case management guidelines, only half of them have revised them since 2020. 

Gonococcal resistance monitoring remains limited, with just 37% of countries conducting routine surveillance. Encouragingly, 95 countries have integrated dual HIV/syphilis rapid tests into their national policy – nearly half of these countries adopted them for both pregnant women and key populations. 

National plans to eliminate mother-to-child transmission of HIV and syphilis are in place in 72% of reporting countries. As of 1 July 2025, 147 Member States (76%) have included human papillomaviruses (HPV) vaccine – critical to preventing cervical cancer – in their national immunization schedule, and 2 countries have reported partial introduction of the vaccine. 

These findings underscore the urgent need to accelerate updates to national policies, expand surveillance, and close implementation gaps. STIs remain prevalent and continue to present a major burden of morbidity and mortality. To reduce STIs and prevent complications, the provision of quality STI prevention and care services is essential. 

Participant characteristics

The group comprised six older adults and two researchers (RT and SAH/SK). A total of 23 older adults met the eligibility criteria. The most frequently cited reasons for refusing to participate included a lack of interest (n = 9), other commitments (n = 5), and transport difficulties (n = 4). The demographic characteristics of the recruited participants are provided in Table 1. Participants had an average age of 83, five members were male (80%), and participants’ sedentary time ranged from 4 to 13 h per day.

Overview of findings

This analysis, informed by the ecological model of sedentary behaviour and the COM-B model of behaviour, identified the types of sedentary activities participants engaged in, and their perceived barriers and facilitators to reducing sedentary time. These frameworks aligned to the Behaviour Change Wheel used to guide the intervention development process [17], and provided insights into how sedentary behaviour in this population is shaped by individual, social and environmental influences. Several analytical themes emerged from this process, including shifting perceptions of sedentary behaviour throughout older adulthood, the impact of daytime sleeping on energy levels, and how social influences can promote or discourage sedentary lifestyles. These themes highlight the complex interrelationship between personal, social, and contextual factors that influence sedentary behaviour in older adults.

Descriptive theme: activities performed in sitting

Sedentary activities, mostly leisurely and home-based, were mapped to the ecological model of sedentary behaviour [31]. The reported sedentary and non-sedentary activities are provided in Table 2.

Descriptive theme: barriers and facilitators to reducing sedentary behaviour

The barriers and facilitators to reducing sedentary behaviour were charted to the COM-B Model of behaviour change (Table 3) and are described narratively below.

Physical capability

Pain, fatigue and physical health problems contributed to participants’ sedentary behaviour. Participants perceived this decline as age-related and that their sedentary behaviour had increased throughout older adulthood. Members still performed non-sedentary activities but would perform them for shorter durations or replace them with less taxing activities. Some members recognised that prolonged sitting contributed to their pain and stiffness, and reducing sedentary behaviour may improve this.

Psychological capability

Members reported that their mental health, particularly feelings of depression and anxiety, increased their sedentary behaviour. When present, members described having little motivation to engage in physical activities, instead opting for sedentary endeavours. Members also described older adults that they knew who found it difficult to leave their homes for fears of falling or following the bereavement of a spouse. Conversely, members described how reducing their sedentary time, particularly through social activities, improved their overall mood.

Physical opportunity

Participants identified barriers to reducing sedentary behaviour at home and in the external environment. Home-related barriers included lack of space, (single-storey dwellings) and the implications of downsizing the home in later life. External barriers explored how public transport, neighbourhoods, poor weather, and financial constraints contributed to increased sedentary behaviour. Home-related facilitators included the presence of stairs, larger living areas and garden access can reduce sedentary behaviour. External facilitators which promoted the reduction of their sedentary time included employment or volunteering, affordable public transport, enticing and affordable local facilities, and outdoor seating in public areas.

Social opportunity

Participants described the impact of retirement on sedentary behaviour. For some members, their work was active, and upon retiring, they continued performing non-sedentary activities which they enjoyed that kept them active. Others described using volunteering to mitigate the loss of role following retirement, as it would provide the necessary structure and organisation to their day whilst discouraging sedentary behaviour. Although not applicable to the members, social isolation was described as promoting sedentary behaviour in older adults, with participants discussing older adults they knew who were socially isolated and would use sedentary activities to pass their day. Additionally, the impact of bereavement on social support networks and social opportunities was discussed. Social support from family and friends was an important facilitator in reducing their sedentary time.

Reflective motivation

Participants described how they did not consider their sedentary time, were largely unaware and unconvinced of the negative health consequences of their sedentary behaviour, nor the benefits of reducing their sedentary time. Some members were aware of the consequences of prolonged sedentary time and could reason that reducing their sedentary time may improve their health. Additionally, upon being presented with the evidence, participants were amenable to reducing their sedentary behaviour if it would improve their well-being.

Automatic motivation

Members expressed how established routines contribute to prolonged uninterrupted sitting, particularly in the evenings. These habits were firmly ingrained in their daily lives and oftentimes included sedentary activities which they enjoyed and had little interest in changing. Conversely, some members described being habitually active following employment in non-sedentary jobs. Necessary activities of daily living such as preparing food, drinking, and getting medication were motivators to interrupting their sedentary behaviour.

Analytical themes

Theme 1: perspectives on sedentary behaviour throughout older adulthood

Participants’ perspectives on sedentary behaviour evolved throughout the discussions, which highlighted the complexity of its meaning and perceived implications across older adulthood. Initially, sedentary behaviour was often described in simple, or negative terms such as “sitting doing nothing” (P5-84 M-MoF). However, other participants introduced nuance by distinguishing between passive and mentally engaging sedentary activities, such as reading, sewing and knitting: “To be sitting occupied. Yeah, reading something… I wouldn’t want to sit a long time. Just sitting. If I was sitting sewing, I’d have accomplished something” (P1-83 F-MF).

Technology was a common point of reference in some participants’ conceptualisation of sedentary behaviour, being described as a contributor to prolonged sedentary behaviour: “I have often heard it linked to modern technology and worries about by individuals who seem to experience it directly, as for being glued to the computer but perhaps it’s inevitable.” (P6-83 M-MF). Technology was helpful in making group members more aware of their sedentary time, with two having experience with activity monitoring devices, however, the majority lacked awareness of their sedentary time: “I didn’t really think about the amount of time I spent sitting until was asked about it” (P5-84 M-MoF).

There was tension between participant’s awareness of their sedentary time, and their belief that being physically active during the day would counteract their sedentary behaviour: “I used to think if I’m active during the day, which I am that I’m doing well. I’m now reconsidering that. Only because I’ve read about this. Sitting and it’s not good for you.” (P1-83 F-MF). This view was predominantly held by group members who were more physically active, who weren’t convinced that sitting could be detrimental if otherwise active: “So I’d like to know how they come to the solution that is detrimental to anybody to sit?” (P4-82 M-SF).

Similarly, they questioned the health relevance of reducing sedentary time: “If you could prove to me that it is beneficial? And in what way would that be beneficial?” (P4-82 M-SF). Some members were aware of the health benefits of reducing their sitting time: “That must indicate that the longer you sit down that the less beneficial it is for your body?” (P3-82 M-Mod); whereas others were more amenable to change if supported by sources they deemed credible: “I would certainly if my doctor said I need to. Definitely. That’s for my health and well-being” (P1-83 F-MF). In the accompanying intervention development work, participants chose educational approaches delivered by credible sources (e.g. healthcare professionals) that addressed the distinction between physical inactivity, the health risks of prolonged sedentary behaviour and the benefits of reducing sedentary time.

Participants also believed that sedentary behaviour was a necessary component of their day during older adulthood: “I think as you get older, it’d be difficult. What could you do to keep moving? Sensibly like, what would they want us to do? Besides just sitting?” (P2-84 M-SF). They attributed this increase in sedentary time was a natural response to physical decline: “My Dad used to say the hills are getting steeper. And as I get older, I realise he wasn’t daft at all. I was the daft one.” (P6-83 M-MF). This decline resulted in them reducing the physical activities they enjoyed: “Twice a week we go out walking. And then I started to have problems with one hip, which led to another hip…over two years, we relinquished our membership of the walking club, we never really got back to long distance walking”. (P4-82 M-SF).

These physical limitations were compounded by age-related societal expectations: “She also comes by public transport as though we both come in by helicopters. Does nobody travel by bus? As though it was something quite, you know, extraordinary” (P1-83 F-MF). This quote illustrates the subtle social messaging that older adults should limit mobility or that remaining active is exceptional rather than normative. Additional perspectives on social influences on sedentary behaviour are provided later. This theme highlights how older adults’ perceptions of sedentary behaviour are influenced by their understanding, and are shaped by physical limitations, social norms and varying levels of understanding of the health consequences of sitting throughout older adulthood.

Theme 2: sleep and the energy balance

Although sedentary behaviour considers waking behaviours, participants described sleep and fatigue as key determinants of their movement and resting patterns. Fatigue, and disrupted sleep were described as normal features of ageing and framed as adaptive responses to changing energy demands and influenced their daily activity.

All participants reported experiencing daytime napping but had mixed perceptions of the activity. For some, it was something that was shameful or a source of embarrassment: “I suppose this is a confession. I hope I’m not being too awful. I tend to have a nap in the afternoon… you lot don’t” (P6-83 M-MF). Others viewed napping as an inevitable aspect of daily life: “I don’t avoid them, I can’t avoid them necessarily, it happens!” (P5-84 M-MoF). One participant reframed this rest positively by adopting terms such as “power nap,” often validated by advice from peers or health professionals: “I sometimes feel guilty over an afternoon nap. But a nurse friend of mine said don’t think that sleeping call it a power nap…” (P6-83 M-MF).

Sleep-related fatigue was associated with a number of causes, including poor nighttime sleeping: “I like to have a good night’s sleep I’m a really bad sleeper. It’d be nice if I woke up and felt refreshed” (P6-83 M-MF); medication side effects: “I have my breakfast, and I have my tablets and those tablets that I’ve got make you drowsy and I find I go off to sleep again having only got out of bed in an hour or so beforehand” (P5-84 M-MoF); or evening tiredness: “If I haven’t slept, I can sit down probably about half past five. And just nod off, you know, just go for about half an hour.” (P4-82 M-SF). This tiredness oftentimes affected participants’ activity planning: “I’ll sleep for half an hour or something like that. And then I feel awful because I wanted to get on or I wanted to go out” (P1-83 F-MF).

Post-nap experiences also varied considerably among participants. Some described napping as akin to pacing strategies, and was used to revitalise them physically: “When you’ve got a nap like that, even if it’s only for five minutes you find that that has revived you mentally physically for more than two-three hours” (P4-82 M-SF), whereas others felt disorientated and sluggish: “For a good 10 minutes, I feel dreadful and don’t know where I am when I wake up.” (P5-84 M-MoF). These accounts highlight how daytime napping can serve as a self-regulation strategy but may not always have positive outcomes, illustrating the importance of individual contexts in how sleep and fatigue influence sedentary behaviour. This suggests that both energy management and activity promotion should be considered when attempting to reduce sedentary behaviour in older adults.

Theme 3: sedentary behaviour and social connectedness

Throughout the series of focus groups, social connectedness emerged as a key influence on sedentary behaviour, potentially due to the group members being socially active. To participants, movement was seen as a means to facilitate social interaction, not just to meet a physical goal. Socially interacting with others can provide both motivation and opportunities for movement: “We’re social animals. We need each other. And to be with other people you have to make the effort to move, which means moving and getting up and getting out of the chair at home” (P6-83 M-MF).

The perceived benefits of social activity for healthy ageing were highlighted by participants: “I think being in the company of other people, whether old or young, preferably, the whole mix, I think it keeps you young” (P1-83 F-MF). Social interaction was described as a facilitator to reducing sedentary time and commonly arose through interactions with loved ones: “I have two sons that are married. With kids. It’s like almost having three houses now. Because I do the decorating, and all three, painting and everything. So you have the opportunity.” (P4-82 M-SF). Even if social activities were sedentary, participants could recognise how this reduced their sedentary behaviour: “I’ll do a sewing group. But you see, we’re sitting down with that. But even with that, commuting across and getting there” (P1-83 F-MF).

Conversely, the absence of social support was seen as a barrier to reducing sedentary, especially among those who lived alone: “Yeah. Difficult. Because there’s nobody there to disturb you.” (P1-83 F-MF), one participant replied when asked about the impact of living alone. Other barriers included reduced social support due to bereavements in their social network, which was reported by all participants and contributed to declining physical and social activity: “I think one of the worst things is that when you reach our age. You lose some of your close friends.” (P4-82 M-SF). Participants noted that loneliness was increasingly common following the COVID-19 pandemic: “What struck me was the number of people that said they felt so lonely” (P6-83 M-MF). In the absence of this social interaction, older adults can readily find themselves becoming socially isolated: “I do know people that do just sit and sort of look out the window because they don’t know what to do, they have no hobbies” (P1-83 F-MF).

Although discussions were initially framed around sitting, by the end of the focus group series, participants began to recognise the broader psychosocial factors that influence sedentary behaviour: “Although this is on the surface, about physical acts, sitting down, actually it’s about the way we think and act, isn’t it?” (P6-83 M-MF). Participants did not view excessive sedentary behaviour as a purely physical issue, but instead, as an interaction between physical, social and mental well-being: “We could separate social care, from physical and mental challenges, but we shouldn’t really separate them, because it’s all a part of us, in our minds and our bodies and our circumstances.” (P3-82 M-MoF). Social contact, even when sedentary, provided both meaning and satisfaction to participants’ lives: “I’m always pleased when I get home that I’ve been in the company of people” (P1-83 F-MF). Social interaction was also described as beneficial for wellbeing, and a lens through which activities were experienced: “I think it’s, it’s quite apparent that being in other people’s company is really, really beneficial” (P6-83 M-MF). These findings highlight the interactions between social connectedness and sedentary behaviour in community-dwelling older adults, suggesting that promoting social engagement may be important for reducing sedentary behaviour in this population.

Study design and participants

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey employing multistage stratified sampling. The initial national baseline survey (Wave 1) was conducted in 2011, enrolling 17,708 middle-aged and older adults across 450 villages from 150 districts in 28 Chinese provinces. Demographic and clinical information was collected via standardized questionnaires. Follow-up surveys were conducted biennially to triennially thereafter to monitor health outcomes. No additional interviews were conducted for this analysis. The study incorporated five waves of existing CHARLS data (2011, 2013, 2015, 2018, and 2020). The detailed methodologies and data collection procedures for CHARLS have been previously described [22]. Ethical approval was granted by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015), and all participants provided written informed consent.

Fasting status, blood glucose, and triglyceride levels were obtained from blood samples collected during Waves 1 and 3. The exclusion criteria were as follows: (1) age ≤ 45 years; (2) prior CVD event or missing CVD data before Wave 3 (2015); (3) nonfasting status; (4) missing data for age, sex, C-reactive protein (CRP), fasting blood glucose (FBG), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), or total cholesterol (TC); and (5) missing or abnormal waist circumference measurements (defined as values exceeding three standard deviations from the mean). The detailed inclusion and exclusion process is outlined in Fig. 1. The final analysis cohort comprised 4157 participants.

Assessment of CTI changes

The TyG index, CTI, and cumulative CTI (cuCTI) were calculated via the following formulas: TyG index = ln [triglycerides (mg/dL) × glucose (mg/dL)/2]; CTI = 0.412 × ln(CRP [mg/L]) + ln(TG [mg/dL] × FPG [mg/dL])/2 [15].

The cumulative CTI (cuCTI) was calculated via a linear model as the product of the mean CTI values from 2012 and 2015 and the time interval, following the formula: cuCTI = (CTI₂₀₁₂ + CTI₂₀₁₅)/2 × time interval (2015 − 2012). CTI₂₀₁₂ and CTI₂₀₁₅ denote CTI measurements from 2012 (Wave 2) and 2015 (Wave 3), with a 3-year interval between measurements. The mean of these two time points was computed to reflect the average exposure level, then multiplied by the time duration to calculate long-term cumulative exposure. This approach adheres to the methodology for cumulative metabolic indices in the CHARLS cohorts, which is consistent with the approach described by Zou [23] and Lu [24].

Assessment of incident CVD

The presence of heart disease was determined by asking, “Have you been doctor-diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems? Or have you been doctor-diagnosed with stroke?” CVD was defined as self-reported heart disease and stroke. CVD determination was consistent with prior studies using CHARLS data [20]. Time to CVD onset was determined as the period between the last study interview and the first documented CVD event.

Covariates

Covariates included sociodemographic characteristics (age, sex, marital status, education level, smoking status, alcohol consumption), health indicators (systolic blood pressure [SBP], diastolic blood pressure [DBP], hypertension, diabetes, dyslipidemia), and laboratory parameters (C-reactive protein [CRP], fasting blood glucose [FBG], total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], glycated hemoglobin [HbA1c]. Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or self-reported hypertension. Diabetes was defined as FBG ≥ 7.0 mmol/L, HbA1c ≥ 6.5% or self-reported diabetes diagnosis. Dyslipidemia was defined as TC ≥ 240 mg/dL, TG ≥ 150 mg/dL, LDL-C ≥ 160 mg/dL, or self-reported dyslipidemia.

Covariates included sociodemographic characteristics (age, sex, marital status, education, smoking, and drinking status), health status (systolic blood pressure (SBP), diastolic blood pressure (DBP), hypertension, diabetes, and dyslipidemia), and laboratory measurements (CRP, FBG, TG, TC, HDL-C, LDL-C).

Statistical analysis

Data analysis was performed via R Studio. Two-tailed P values < 0.05 were considered statistically significant. Continuous variables with a normal or approximately normal distribution are presented as the means ± standard deviations (SDs), whereas nonnormally distributed variables are reported as medians (interquartile ranges). Categorical variables are presented as frequencies and percentages. Baseline characteristics were compared across groups via analysis of variance (ANOVA) or the Kruskal‒Wallis test for continuous variables and the chi‒square test for categorical variables. Three multivariable models were constructed to evaluate associations between cuCTI and incident CVD: Model 1: unadjusted; Model 2: adjusted for sex, age, marital status, and education level; Model 3: further adjusted for BMI, drinking status, smoking status, depression, hypertension, dyslipidemia, diabetes, antihypertensive medication, antidiabetic medication, and lipid-lowering medication. The Benjamini‒Hochberg method was used to correct for multiple comparisons, controlling the false discovery rate (FDR) to reduce false-positive results. Kaplan‒Meier survival analysis was used to estimate cumulative CVD risk by cuCTI categories. Mediation analysis was performed to explore interactions between the CRP and TyG components of the CTI. A four-knot restricted cubic spline (RCS) model was used to assess nonlinear dose‒response relationships between cuCTI and CVD risk. Subgroup analyses were conducted stratified by age, sex, marital status, education level, BMI, hypertension, dyslipidemia, and diabetes via Cox proportional hazards regression. Sensitivity analyses were performed to validate the robustness of the findings: logistic regression models were used to evaluate associations between cuCTI and CVD incidence. Participants were clustered into subgroups on the basis of CTI trajectory patterns via K-means clustering, followed by logistic regression analysis. Analyses were repeated after excluding deceased participants. Participants in the 2020 follow-up were excluded from analysis to prevent potential COVID-19 pandemic effects on data reliability and endpoint event reporting.

This study investigated the prevalence of stroke and predictive factors among adults of different genders in Shanxi Province, China. The stroke prevalence in Shanxi Province was 3.3%, higher than the national average [27]. The stroke prevalence was 4.46% in males and 2.46% in females. Traditional logistic regression analysis revealed that males had a higher risk of stroke, with factors such as abnormal lipid levels, hypertension, diabetes, family history of stroke, coronary heart disease, secondhand smoke exposure, and snoring being identified as risk factors. Urban residents also had a higher risk of stroke, and the risk increased with age.

Additional risk factors for males included respiratory pauses and smoking, while females did not share these factors. Furthermore, the impact of similar risk factors differed between genders. For example, females with a family history of stroke had a 5.56-fold increased risk, whereas males had a 4.22-fold increased risk. Using the MMHC algorithm, Bayesian Networks (BNs) in this study indicated that in males, abnormal lipid levels, hypertension, age were direct risk factors for stroke, while snoring, education level, and respiratory pauses were indirect risk factors. In females, age, hypertension, and secondhand smoke exposure were identified as direct risk factors, while snoring was an indirect risk factor for stroke.

In the study of stroke risk factors, traditional logistic regression methods are typically constructed under the assumption that variables are independent, failing to fully leverage data information and accurately reflect the impact of feature variables on stroke [28]. Traditional logistic regression methods use probabilities to reflect the strength of associations, lacking the ability to comprehensively explain the complex relationships between risk factors [21], and are unable to detect direct or indirect risk factors. Therefore, logistic regression models are not flexible enough in capturing patterns and relationships between data.In contrast, Bayesian Networks (BNs) demonstrate more advantages in building risk factor models compared to logistic regression [29]. Firstly, Bayesian Networks do not require any prior assumptions and have the ability to integrate different variables and analyze their relative importance [28]. Therefore, in recent years, many clinical researchers prefer using Bayesian Networks for quantifying the identification of risk factors in specific pathological diagnoses, prognosis, and supporting medical decision-making in diseases [26, 30].We applied Bayesian Networks (BNs) to the study of stroke risk factors by gender. This not only reveals the risk factors for stroke but also determines their direct and indirect impacts on stroke, providing in-depth insights into the complex network relationships among them. It is noteworthy that when constructing Bayesian Networks, the network becomes more complex with the increase of feature variables, so the construction of Bayesian Networks should be based on the selection of different feature variables. In this study, single-factor chi-square and multi-factor logistic analysis were used to screen variables.

Based on our understanding, our study is the first to apply a Bayesian network to analyze risk factors for stroke based on gender. Compared to traditional logistic regression models, Bayesian networks using the MMHC algorithm have significant advantages in analyzing stroke risk factors. First, the Bayesian network with the MMHC algorithm is a data-driven model constructed on the knowledge base associated with the disease [31], without strict requirements on data distribution. This enables it to better discover potential, less obvious but important data information. This data-driven approach provides a more scientific and comprehensive foundation for the assessment, prediction, and prevention of stroke. Therefore, the application of Bayesian networks allows for a deeper understanding of stroke risk factors, providing more accurate guidance for personalized prevention and control strategies.The second advantage involves the interactions between variables. Logistic regression [31] can only provide risk indications for stroke risk factors. However, when analyzing interactions, logistic regression needs to introduce them into the model through addition or multiplication, which adds complexity and may introduce potential biases. Moreover, logistic regression struggles to clearly illustrate the interactions between variables and is limited in exploring the complex relationships of multiple variables. In contrast, Bayesian networks with the MMHC algorithm allow for an intuitive description of the interconnections between these risk factors through graphical methods and can comprehensively explore their direct and indirect interactions [19].

Stroke is relatively common in the elderly population, and previous research reports indicate that age is an unmodifiable risk factor for stroke, applicable to both men and women [32]. Similar to these study findings, our research reveals a higher incidence of stroke in both men and women in the age range of 60–75 years. The potential mechanisms through which age influences stroke include the natural narrowing and hardening of arteries as individuals age. This change is attributed to alterations resulting from endothelial dysfunction and impaired autoregulation of the brain [33].Additionally, the elderly population often experiences a concomitant state of multiple chronic diseases, such as diabetes, hypertension, atrial fibrillation, as well as coronary artery and peripheral artery diseases. The prevalence of these conditions also increases gradually with age [34].

In this study, hypertension, diabetes, dyslipidemia, coronary heart disease, family history of stroke, secondhand smoke exposure, and snoring were identified as common risk factors for stroke in both men and women. These findings are consistent with previous research results [28, 35]. Hypertension has adverse effects on arteries, leading to atherosclerosis and narrowing, which can result in thrombosis or embolism, triggering a stroke [36]. Elevated blood sugar levels can damage endothelial cells, leading to atherosclerosis and narrowing of arteries, causing vascular damage. Impaired kidney function can increase blood volume, reducing the elasticity of blood vessels [37]. Higher cholesterol levels may increase the inflammation and apoptosis of plaques, making them more prone to rupture. After plaque rupture, platelets and coagulation proteins in the blood aggregate at the site of rupture, forming a clot, ultimately leading to a stroke [36]. Family history may be due to shared lifestyles and habits or genetic factors, such as the association of white matter lesions with vertebrobasilar artery atherosclerotic brain disease, which is an autosomal dominant cerebrovascular disease [38] . Harmful substances in secondhand smoke may have a direct toxic effect on the nervous system, increasing the risk of stroke [39]. Snoring is often accompanied by sleep apnea, where breathing briefly stops during sleep. This can lead to a decrease in blood oxygen levels, increasing the risk of stroke. Snoring may also lead to an increase in inflammation in the body, and inflammation is associated with cardiovascular disease and stroke [40, 41].

The study has several limitations. Firstly, in Bayesian Networks (BNs), directed edges cannot accurately represent causal relationships between nodes and can only express probabilistic dependencies. Secondly, due to the face-to-face survey method used, participants may rely on memory to answer questions, introducing potential reporting or recall biases in estimating the prevalence of various diseases. Additionally, the survey did not collect some important information, including: (a) variables related to women’s characteristics such as menstrual history and reproductive history, making the analysis of risk factors in women potentially incomplete; (b) data on some inflammatory factors, electrocardiogram data, carotid ultrasound, and coronary artery ultrasound; (c) effectiveness data related to dietary factors. Therefore, we were unable to assess the impact of these factors on the risk of stroke. Furthermore, as the study focused on Bayesian Networks using the MMHC algorithm, it did not compare with other hybrid algorithms. Stroke was not differentiated into ischemic and hemorrhagic, and the study did not analyze the differential effects of biochemical indicators such as blood glucose, blood pressure, and cholesterol on stroke in the absence of medication factors. This will be a focus of our future work. Despite these limitations, the findings of this study provide valuable information for the development of health planning and programs aimed at reducing the burden of stroke in Shanxi Province, China.

A Surrey health trust is investing £1.8m in the latest X-ray and imaging equipment.

Frimley Health NHS Foundation Trust is replacing equipment that has been in place at Frimley Park Hospital for 14 years.

The new equipment can carry out more procedures and poses less risk to patients, according to the trust.

It produces lower doses of radiation, provides better pictures on larger screens and can even produce 3-D images, the trust added.

It will also feature a flexible arm, one of only two in use in the country, which reduces the need to move the patient to capture images from different angles.

It is hoped the enhanced images will lead to better diagnoses.

Consultant radiologist Dr Jeremy Taylor said: “We are so pleased to be able to offer our patients the best possible care, with some of the latest available technology and minimally invasion treatments.

“It will ensure that interventional radiology remains at the forefront of medical advances at Frimley Heath.”

Photo: VCG

 
Chinese health experts reassured the public that there is no need to panic and that effective precautions can be taken to control mosquitoes and protect oneself during daily activities, as the chikungunya outbreak in southern China has drawn public attention in recent days, with total reported cases surpassing 4,000 in Guangdong’s Foshan.

Thepaper.cn on Saturday reported that as of Thursday, all five districts of Foshan in South China’s Guangdong Province had reported cases of chikungunya, with total cases exceeding 4,000. In Shunde district – the epicenter of the outbreak – 3,627 cases have been confirmed, cases have also been reported in other Guangdong cities, including Guangzhou, Yangjiang, and Zhanjiang. 

Macao’s Health Bureau reported that the city confirmed one imported case of chikungunya on July 18, the first such case this year. The patient had visited Foshan’s Shunde district from July 8 to 17.

In response to the outbreak, the Guangdong provincial health authorities issued an open letter on Thursday via their official WeChat account, calling on all residents to join a province-wide campaign to eliminate stagnant water and control mosquito breeding. The campaign aims to curb the spread of both dengue fever and chikungunya fever.

On Friday, the Standing Committee of the CPC Guangdong Provincial Committee convened a meeting to hear updates on chikungunya prevention and control. The meeting noted that epidemic control efforts are at a critical stage, and while initial containment measures have shown results, challenges still remain. In addition, the summer typhoon season and heavy rainfall are complicating prevention efforts. Authorities stressed that there must be no complacency in combating the outbreak, Nanfang Plus reported.

Several cities in other parts of China have issued public health reminders. On Saturday, the Beijing Center for Disease Prevention and Control (CDC) released a Q&A guide on chikungunya, stating that the virus is primarily transmitted through bites from Aedes mosquitoes. It is not spread through coughing, sneezing, talking, or other casual contact. Most infections are mild, with symptoms including sudden fever and joint pain, which typically resolve within a week. 

The Beijing CDC reassured residents that the risk of local transmission in the city remains extremely low and that there is no need for panic. 

In another advisory issued on Tuesday, the Beijing CDC noted that although only sporadic imported cases have been found in the city so far, the growing volume of international travel means the risk of imported cases remains. 

On Tuesday, the World Health Organization (WHO) warned that a major chikungunya virus epidemic could sweep across the globe, calling for urgent action to prevent it. The WHO said it was observing the same early warning signs as in a major outbreak two decades ago and wanted to prevent a repeat, AFP reported.

“Chikungunya is not a disease that is widely known, but it has been detected and transmitted in 119 countries globally, putting 5.6 billion people at risk,” said the WHO’s Diana Rojas Alvarez, per the AFP.

Zhuang Shilihe, a Guangzhou-based medical expert who closely follows public health issues, told the Global Times on Saturday that for residents in Guangdong, prevention remains the most crucial aspect. Zhuang noted that this disease is not transmitted directly from person to person but through mosquitoes as vectors, making mosquito control measures particularly important.

“At the community level, areas with high mosquito density or standing water should be promptly cleared. On an individual level, people are advised to use mosquito repellent, wear long sleeves and pants, and protect exposed areas such as the neck,” Zhuang said.

While the outbreak has shown signs of spreading beyond Foshan to other cities in Guangdong Province, the overall risk remains controllable, as the disease is spread by mosquitoes, northern regions – where mosquitoes are less prevalent – face relatively lower transmission risks, Zhuang noted.

The expert added that Guangdong Province has in recent days launched a province-wide mosquito eradication campaign. Given the disease’s incubation period of around 2 to 9 days, the impact of the control measures will take some time to become apparent.

The transmission of the chikungunya virus typically peaks during the summer season. However, due to increasingly hot weather in recent years, the transmission period may be extended to September, Zhuang said.

China recorded its first imported chikungunya case in 2008. Since then, small-scale outbreaks caused by imported cases occurred in 2010 and 2019, but none reached the scale seen this year.

Why, then, is this year’s outbreak larger than previous ones? Liu Qiyong, chief vector-borne disease expert at the Chinese CDC, explained that the current outbreak is largely driven by the virus’s high global prevalence.

On July 22, WHO experts issued an alert stating that chikungunya transmission has been reported in 119 countries and regions. After the virus entered China, the presence of Aedes mosquitoes, the primary vector, enabled local transmission and small-scale outbreaks, Liu said. 

Favorable climate conditions and the specific virus strain have also contributed to the unusually large scale of this year’s outbreak, Liu noted, as warm, humid weather has increased mosquito density. The imported strain – an Indian Ocean lineage – is also particularly efficient at spreading via Aedes mosquitoes.