Category: 8. Health

Who was the victim?

Luigi Di Sarno, 52, was an artist and musician from Cercola in the province of Naples. He had been returning from a family holiday in Calabria when the tragedy occurred, according to a report in London Evening Standard.

How and when did the death occur?

On Thursday, Di Sarno stopped with his family at a food truck on the seafront in Diamante, Cosenza province, where they ordered broccoli and sausage sandwiches. Shortly after resuming their journey, while driving on the highway near Lagonegro in Potenza, he suddenly fell ill and was forced to pull over in the town of Basilicata. Emergency services rushed to the scene, but Di Sarno died before reaching hospital.

Who else fell ill?

Nine other people, including members of Di Sarno’s family, two teenagers and two women in their 40s, were hospitalised after eating from the same vendor. All were taken to the intensive care unit at Annunziata Hospital in Cosenza, with two patients reported to be in a serious condition.

Is this part of a wider outbreak?

Most likely. The incident comes just weeks after eight people were hospitalised in Sardinia following botulism cases linked to guacamole served at a Mexican food stand during the Fiesta Latin festival in Monserrato between 22 and 25 July. A 38-year-old woman died after eating tacos with guacamole at a Cagliari festival, and an 11-year-old boy was flown to Rome for treatment, according to The Mirror.

What is botulism?

Botulism is caused by toxins produced by Clostridium botulinum bacteria, which can develop in improperly processed foods. It attacks the nervous system, causing breathing difficulties, muscle paralysis, and can be fatal in around 10 per cent of cases.

According to the U.S. Centers for Disease Control and Prevention (CDC), “Foodborne botulism can happen by eating foods that have been contaminated with botulinum toxin. Common sources… are homemade foods that have been improperly canned, preserved or fermented. Though uncommon, store-bought foods also can be contaminated with botulinum toxin.”

Using a representative sample of U.S. adults, our analysis identified a significant positive association between AISI and MASLD prevalence, independent of major demographic and clinical confounders. The association remained statistically robust following adjustments for an extensive set of demographic, metabolic, and liver-related variables. These results align with previous studies that implicate systemic inflammation in the development of hepatic steatosis and metabolic liver conditions [30]. Notably, nonlinearity in the association was observed via restricted cubic spline modeling in the AISI–MASLD association, with an inflection point identified at approximately (log _2) AISI = 8.552. Below this threshold, the likelihood of MASLD rose progressively with increasing AISI levels, whereas at higher values, the association plateaued or modestly declined. Such a threshold effect may suggest an underlying immunometabolic mechanism, consistent with the multiple parallel hits hypothesis in MASLD development [21]. Given that AISI integrates several peripheral immune cell counts, our results support its potential utility as a composite marker of systemic inflammation in liver-related epidemiologic research [31].

A growing body of literature has proposed that systemic inflammation plays a central role in the pathophysiology of MASLD, which is echoed by our findings. Numerous prior studies have demonstrated associations between MASLD risk and peripheral blood-based inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the systemic immune-inflammation index (SII) [25, 28]. For instance, Zhu et al. demonstrated that elevated NLR levels correlated with hepatic steatosis in patients with type 2 diabetes [30], while Kaya et al. reported that both PLR and NLR were associated with the severity of ultrasound-detected MASLD in a Turkish cohort [12]. In the investigation of the associations between MASLD pathogenesis and related indices, BMI represents a critical factor that warrants consideration. Extensive research has established that obesity functions as an independent risk factor for the development of MASLD. The mechanisms underlying this relationship involve the induction of insulin resistance and chronic inflammatory states, both of which contribute to hepatic steatosis and liver injury. Additionally, BMI is inversely correlated with AISI. Metabolic disturbances associated with obesity can interfere with insulin signaling pathways, thereby exacerbating insulin resistance. If not properly adjusted for, obesity can confound the relationship between AISI and MASLD, thereby obscuring their intrinsic association. Therefore, it is essential to account for BMI as an important confounding variable in relevant studies, in order to more accurately elucidate the independent association between AISI and MASLD and to further delineate the pathogenesis of MASLD. In clinical practice, the control of BMI is of critical importance for mitigating the risk of MASLD and improving patient outcomes [4].

In contrast to these more frequently studied indices, the application of AISI in the field of hepatology remains underreported. Most published work to date has focused on its prognostic role in oncology and cardiovascular medicine [32]. Although indicators such as the Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) have been extensively studied, current research on the AISI is predominantly focused on the fields of oncology and cardiovascular diseases, with relatively limited application in liver-related studies.

The biological plausibility of our findings is underpinned by established immunopathological mechanisms in MASLD. Chronic low-grade inflammation has been recognized as a central driver of hepatic steatosis, insulin resistance, and progressive liver injury, consistent with the multiple parallel hits hypothesis [21]. Neutrophils and monocytes play a pivotal role in hepatic immune responses by producing proinflammatory mediators such as tumor necrosis factor-alpha (TNF-(alpha )), interleukin-6 (IL-6), and reactive oxygen species (ROS). These factors synergistically contribute to hepatocyte damage and oxidative stress, thereby exacerbating liver injury [11].

Platelets actively regulate hepatic microvascular integrity and facilitate leukocyte adhesion and transendothelial migration, thereby amplifying intrahepatic inflammatory cascades [17]. In contrast, lymphopenia—as represented in the denominator of the AISI formula—indicate compromised immune surveillance or immune exhaustion, reflecting an inadequate host response to persistent inflammatory stimuli [18, 22]. Thus, the AISI captures a balance between pro-inflammatory drive and immunoregulatory control. Our study demonstrates that even modest elevations in AISI are linked to higher MASLD prevalence, particularly in individuals without overt comorbidities, supporting its potential role as an early indicator of inflammation-related hepatic dysfunction.

Subgroup analyses revealed that the association between the Aggregate Index of Systemic Inflammation (AISI) and MASLD prevalence was more pronounced among adults aged 20-60 years and individuals without hypertension. This pattern suggests that systemic inflammation may exert a stronger pathogenic role in relatively metabolically healthy populations, wherein traditional cardiometabolic risk factors are less predominant. Conversely, in individuals with established metabolic syndrome or vascular comorbidities, the contribution of systemic inflammation may be attenuated or obscured by multifactorial pathophysiological interactions [19]. From a public health perspective, these results suggest that AISI may be particularly useful for identifying individuals with early-stage or subclinical MASLD who may otherwise go unnoticed in standard metabolic screening algorithms.

The observed nonlinear association between AISI and MASLD prevalence merits further exploration. Restricted cubic spline modeling identified a saturation threshold at (log _2) AISI (approx ) 8.552, beyond which the prevalence of MASLD plateaued and subsequently declined. This attenuation may be attributable to confounding factors [5]. First, elevated AISI levels may be indicative of acute inflammatory conditions, systemic infections, or cancer-associated immune activation. These conditions could introduce confounding effects, thereby obscuring the predictive utility of AISI in the context of chronic hepatic steatosis [32]. Second, the observed attenuation may reflect compensatory immunoregulatory mechanisms. Specifically, the activation of regulatory T cells (Tregs) and the enhanced secretion of anti-inflammatory cytokines, such as interleukin-10 (IL-10), could modulate systemic inflammatory cascades in a threshold-dependent manner. This regulatory response may serve to mitigate hepatic injury, thereby contributing to the observed plateau and subsequent decline in MASLD prevalence [23, 29]. These findings underscore the importance of considering potential nonlinearities and biological ceilings when interpreting inflammatory biomarkers. Future research should aim to further investigate these mechanisms to enhance our understanding of the complex relationship between AISI and MASLD.

In this study, a one-unit increase in (log _2) AISI (i.e., a doubling of AISI) is associated with a 17% increased odds of MASLD (OR = 1.17, 95% CI: 1.08-1.27). To better interpret this finding, we will compare it with effect sizes of other inflammatory markers (e.g., NLR, PLR) and established MASLD risk factors (e.g., BMI, diabetes). For instance, existing research suggests that a one-unit increase in BMI may be linked to a 30% higher risk of MASLD. Compared to such well-known risk factors, the effect size of AISI is relatively small, indicating its predictive capacity for MASLD might be limited. Moreover, we will explore the potential synergistic effects between AISI and other risk factors, and evaluate the added value of AISI in comprehensive risk assessment for MASLD.

Taken collectively, these results suggest that AISI could serve as a practical, low-cost, and scalable indicator for MASLD risk stratification in routine healthcare settings. As it is derived from standard complete blood count (CBC) parameters, AISI can be easily integrated into everyday clinical workflows, particularly in primary care and community health environments. From a nursing perspective, the use of AISI may enhance early detection efforts, enabling frontline providers to identify individuals at elevated risk and facilitate timely referral for imaging or lifestyle counseling. In contexts where diagnostic infrastructure is limited, AISI may provide an accessible option for population-level liver health screening. However, its modest effect sizes, nonlinear associations, and potential nonspecificity highlight the need for further validation in longitudinal studies before clinical adoption. Nonetheless, this study has several methodological limitations. The cross-sectional design of NHANES inherently limits the ability to infer causality or assess temporal dynamics between inflammation and MASLD onset. Although 43% of the initial sample were excluded due to missing data or predefined criteria, a comparison of baseline characteristics between included and excluded participants (see Appendix Table 7) showed no meaningful differences in age, sex, race/ethnicity, or key metabolic variables, suggesting that selection bias is unlikely to have materially affected our findings. While we controlled for a broad spectrum of demographic and clinical confounders, the possibility of residual confounding from unmeasured lifestyle variables—such as nutrition, physical activity, or pharmacologic exposures—remains.

Additionally, MASLD was identified using transient elastography-derived CAP scores, which, despite their validation, do not offer histopathologic confirmation or fibrosis staging. In this study, we employed CAP (ge )274 dB/m as the diagnostic criterion for MASLD. While CAP is a validated non-invasive method for assessing hepatic steatosis, it has limitations in distinguishing between simple steatosis and NASH or evaluating fibrosis stage, which are critical for MASLD severity assessment. Given that systemic inflammation may be more strongly associated with advanced forms of MASLD such as NASH and significant fibrosis, our inability to stratify by disease severity might have resulted in an underestimation of the association between AISI and severe MASLD [19]. Moreover, the 5% liver fat threshold corresponding to CAP (ge )274 dB/m may include individuals with minimal steatosis who might not have clinically significant MASLD, potentially attenuating the observed association. Future studies should incorporate liver stiffness measurements to assess fibrosis and explore higher CAP thresholds in sensitivity analyses. This would enhance the understanding of the relationship between systemic inflammation and MASLD across different disease stages [6]. Our findings provide a foundation for further research into AISI as a potential screening tool for MASLD risk in general populations and underscore the need for additional studies to clarify its clinical utility.A further limitation is that AISI may be influenced by transient inflammatory responses unrelated to liver disease, potentially diminishing its specificity. Furthermore, the calculation of AISI involves multiple laboratory measurements, each with inherent measurement error. The propagation of these errors through the AISI formula could introduce substantial variability that is not accounted for in the analysis. Future analyses should consider these limitations and conduct sensitivity analyses excluding participants with evidence of acute inflammation, such as elevated C-reactive protein or white blood cell count.

Future studies employing longitudinal designs and interventional methodologies are needed to clarify the temporal role of AISI in MASLD development and to assess its predictive value in tracking disease progression or response to targeted interventions.

Despite covering some ultra-processed foods, the UK’s HFSS rules leave millions of daily calories unregulated, researchers say it’s time to rethink policy and tackle additives head-on.

Study: Overlap between ultra-processed food and food that is high in fat, salt or sugar: analysis of 11 annual waves of the UK National Diet and Nutrition Survey 2008/2009–2018/2019. Image Credit: Rimma Bondarenko / Shutterstock

In a recent study published in the journal BMJ Nutrition, Prevention & Health, researchers in the United Kingdom analyzed more than a decade’s national dietary data to quantify the dietary overlap between explicitly regulated HFSSs and UPFs that are indirectly covered under HFSS rules in the United Kingdom (UK).

While the UK’s food policies are designed to curb the consumption of HFSS, the health risks of UPFs are a growing concern.

Study findings revealed a surprisingly large gap between what is classified as HFSS and what is considered ultra-processed. While more than half of UPF products were also classified as HFSS, overlap is partial; many UPFs are regulated via HFSS rules, but ~40–45% fall outside the HFSS net.

A substantial portion comprising notable inclusions like low-calorie soft drinks and white bread was not. This suggests that the UK’s current nutrient profiling model captures, at best, just over half of consumed UPFs, highlighting a significant gap in public health policy.

Results were broadly similar using the 2018 NPM, though overlap was slightly smaller; current UK policy still uses the 2004/05 NPM.

Background

In the United Kingdom, two concepts dominate the discussion surrounding unhealthy diets:

1. The older ‘foods high in fat, salt, or sugar (HFSS)’
2. The more recent ‘ultra-processed foods (UPFs)’

The UK’s current public health policy is designed and built around its HFSS classification, the output of a custom Nutrient Profiling Model (NPM). This model highlights foods considered unhealthy (HFSS), enabling subsequent policy implementations to curb the marketing of such products alongside other public health initiatives.

Unfortunately, a growing body of evidence explicitly investigates the physiological outcomes of UPF consumption. UPFs are industrial formulations comprising predominantly food derivatives or synthetic lab products, and have been associated in cohort studies with adverse cardiometabolic and cancer outcomes.

Recent reports suggest that HFSS foods and UPFs make up over half of the calories consumed in the UK, highlighting a need to evaluate the classification overlap between the two to guide future public health policy. Specifically, if HFSS and UPFs demonstrate a high degree of overlap, then the present UK NPM-based policy might be sufficient to curb the spread of both food categories, and vice versa.

About the study

The present study aims to address this knowledge gap by providing the first detailed analysis of the overlap between HFSS foods and UPFs in the UK diet. The study leveraged a massive dataset comprising 11 annual waves of nationwide nutrition data from the UK National Diet and Nutrition Survey (NDNS; 2008-2019).

Data of interest includes nutrient intake, overall food consumption, and nutritional status. The present study used data from 15,655 individuals, with participant- or parent/guardian-reported food items classified into:

1. HFSS (using the UK’s official 2004/2005 NPM)
2. UPF (classified using the NOVA classification system)

Notably, NPM-based HFSS classification involves scoring foods based on their energy, saturated fat, sugar, and sodium content, balanced against beneficial components like fruits, vegetables, fiber, and protein. In contrast, NOVA classifies foods by degree of industrial processing (not by a nutrient score).

Comparative analyses between HFSSs and UPFs were conducted using three independent metrics:

1. Proportion of food items (food-level analysis)
2. Percentage of total energy intake (in kilocalories)
3. Percentage of total food weight (in grams)

Study findings

The present study reveals a substantial but far from complete overlap between NPM-based HFSS and NOVA-based UPF classifications. When comparing participants’ total energy consumption, statistical analyses found that UPFs comprised 59.8% while HFSSs comprised only 47.4%. Under this metric, UPF and HFSS classification overlapped 58.7%, highlighting that the UK’s current HFSS-based policies do not capture over 40% of UPF-derived calories.

By weight, the overlap was even smaller, with only 38.3% of the grams of UPFs also classified as HFSS, reflecting the exclusion of many high-volume but low-calorie products such as artificially sweetened beverages. Person-level estimates show UPF energy shares highest in ages 11–18 (~65%) and modest male > female differences.

Study findings were even more bleak across other evaluated metrics – Under the lens of proportion of food items consumed, 44.4% of UPF products were not categorized or regulated under the UK’s HFSS policy, with low-calorie soft drinks and white bread notably excluded from regulation.

Other prominent excluded items included brown and wholemeal bread and high-fiber breakfast cereals, highlighting a key limitation of the current NPM – it fails to account for industrial additives like non-nutritive sweeteners and emulsifiers. Ironically, the study found that many foods that were classified as HFSS (and hence regulated) but not UPF were traditional, less-processed products high in fat or sugar, such as cheese, butter, whole milk, and sugars/preserves.

Conclusions

The present study is the first to investigate whether the UK’s HFSS-based policies can account for the recent rise of UPFs. It demonstrates that while there is considerable overlap between HFSS foods and UPFs (~50-60%), the UK’s current nutrient profiling model fails to identify and regulate a large and vital segment of the ultra-processed foods that dominate its national diet.

Relying solely on an HFSS-based approach means that policies aimed at improving public health are missing a substantial (>40%) portion of the suboptimal nutrition problem. The authors emphasise that causality for UPF harms is not established and call for an environmental impact assessment.

They also suggest that future strategies could include “deformulation”, removing non-nutritive additives such as sweeteners and emulsifiers, and note potential environmental trade-offs for certain plant-based UPFs.

This research provides critical evidence for a more nuanced and practical approach to public health nutrition policy in the UK.

A man who was diagnosed with bowel cancer at the age of 32 is urging other people to get themselves checked.

James Carroll spoke to BBC Radio Bristol as part of Joe Sims’ Wake Up Call series of broadcasts highlighting the impact of various serious health conditions including heart disease, breast cancer and prostate cancer.

Mr Carroll, who is originally from Headley Park, Bristol, was diagnosed after having a blood test as part of fertility treatment and so credits his now four-year-old daughter with saving his life.

Earlier this year the NHS expanded its bowel cancer screening programme to those aged 50 and above.

When Mr Carroll was diagnosed in 2018, he had no idea he was suffering with a serious illness.

He said: “Looking back now I did have symptoms.

“I had an ache … but at the time I just didn’t clock it.”

Mr Carroll added it was not the first time he had faced sickness problems, which meant he dismissed his current symptoms at first.

“As a kid they thought I had asthma,” he said.

“I used to play a lot of football as a kid and I used to struggle breathing.

“I think, with the tumour it’s quite slow growing in the bowel, so I just put it down to having asthma.”

As of January 2025, anyone between the ages of 50 and 74 is automatically sent a home testing kit for bowel cancer every two years.

The faecal immunochemical test, also known as the poo test, checks for blood in a small stool sample which is a common sign of cancer.

“You’ve got to do it [the poo test],” Mr Carroll said.

“If there’s any issues at all just go to your doctor, just get it checked out.

“Just do it. Don’t delay it.”

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