The common blood test that predicts how fast Alzheimer’s hits

Insulin resistance detected by routine triglyceride-glucose (TyG) index can flag people with early Alzheimer's who are four times more likely to present rapid cognitive decline, according to new research presented at the European Academy of Neurology (EAN) Congress 2025.¹

Neurologists at the University of Brescia reviewed records for 315 non-diabetic patients with cognitive deficits, including 200 with biologically confirmed Alzheimer's disease. All subjects underwent an assessment of insulin resistance using the TyG index and a clinical follow-up of 3 years. When patients were divided according to TyG index, those in the highest third of the Mild Cognitive Impairment AD subgroup deteriorated far more quickly than their lower-TyG peers, losing >2.5 points on the Mini Mental State Examination per year (hazard ratio 4.08, 95% CI 1.06-15.73). No such link appeared in the non-AD cohort.

"Once mild cognitive impairment is diagnosed, families always ask how fast it will progress," said lead investigator Dr. Bianca Gumina. "Our data show that a simple metabolic marker available in every hospital laboratory can help identify more vulnerable subjects who may be suitable candidates for targeted therapy or specific intervention strategies."

While insulin resistance has been linked to the onset of Alzheimer's disease, its role in how quickly the condition progresses has received less attention. This study aimed to fill that gap by focusing on its impact during the prodromal mild cognitive impairment (MCI) stage, when patients follow highly variable trajectories. The researchers used the TyG index, which offers a low-cost, routinely available surrogate for insulin resistance, to explore whether metabolic dysfunction could help predict the pace of cognitive decline after diagnosis.

In AD specifically, insulin resistance is believed to impair neuronal glucose uptake, promote amyloid accumulation, disrupt the blood-brain barrier, and fuel inflammation - pathways that are less relevant or differently regulated in other neurodegenerative diseases.

"We were surprised to see the effect only in the Alzheimer's spectrum and not in other neurodegenerative diseases," Dr. Gumina noted. "It suggests a disease-specific vulnerability to metabolic stress during the prodromal window, when interventions may still change the trajectory."

The researchers at University of Brescia, led by Professor Padovani and Professor Pilotto, found that high TyG was also associated with blood-brain barrier disruption and cardiovascular risk factors, yet it showed no interaction with the APOE ε4 genotype, indicating that metabolic and genetic risks may act through distinct pathways.²

Identifying high-TyG patients could refine enrolment for anti-amyloid or anti-tau trials and prompt earlier lifestyle or pharmacological measures to improve insulin sensitivity. The researchers are currently investigating whether TyG levels also track with neuroimaging biomarkers to aid earlier detection and stratification.

"If targeting metabolism can delay progression, we will have a readily modifiable target that works alongside emerging disease-modifying drugs," concluded Dr. Gumina.

References:

1. Gumina B., Galli A., Tolassi C. et al. The Triglyceride-Glucose Index as Predictor of Cognitive Decline in Alzheimer's Spectrum Disorders. Presented at the European Academy of Neurology (EAN) Congress 2025; 23 June 2025; Helsinki, Finland.
2. Padovani A., Galli A., Bazzoli E., et al. (2025). The role of insulin resistance and APOE genotype on blood-brain barrier integrity in Alzheimer's disease. Alzheimer's & Dementia. Advance online publication. https://doi.org/10.1002/alz.14556

Courtesy:

Beyond. "The common blood test that predicts how fast Alzheimer’s hits." ScienceDaily. ScienceDaily, 22 June 2025. <www.sciencedaily.com/releases/2025/06/250622224303.htm>. 

 

 

Artificial intelligence isn’t hurting workers—It might be helping

As artificial intelligence reshapes workplaces worldwide, a new study provides early evidence suggesting AI exposure has not, thus far, caused widespread harm to workers' mental health or job satisfaction. In fact, the data reveals that AI may even be linked to modest improvements in worker physical health, particularly among employees with less than a college degree.

But the authors caution: It is way too soon to draw definitive conclusions.

The paper, "Artificial Intelligence and the Wellbeing of Workers," published June 23 in Nature: Scientific Reports, uses two decades of longitudinal data from the German Socio-Economic Panel. Using that rich data, the researchers -- Osea Giuntella of the University of Pittsburgh and the National Bureau of Economic Research (NBER), Luca Stella of the University of Milan and the Berlin School of Economics, and Johannes King of the German Ministry of Finance -- explored how workers in AI-exposed occupations have fared in contrast to workers in less-exposed roles.

"Public anxiety about AI is real, but the worst-case scenarios are not inevitable," said Professor Stella, who is also affiliated with independent European bodies the Center for Economic Studies (CESifo) and the Institute for Labor Economics (IZA). "So far, we find little evidence that AI adoption has undermined workers' well-being on average. If anything, physical health seems to have slightly improved, likely due to declining job physical intensity and overall job risk in some of the AI-exposed occupations."

Yet the study also highlights reasons for caution.

The analysis relies primarily on a task-based measure of AI exposure -- considered more objective -- but alternative estimates based on self-reported exposure reveal small negative effects on job and life satisfaction. In addition, the sample excludes younger workers and only covers the early phases of AI diffusion in Germany.

"We may simply be too early in the AI adoption curve to observe its full effects," Stella emphasized. "AI's impact could evolve dramatically as technologies advance, penetrate more sectors, and alter work at a deeper level."

Key findings from the study include:

• No significant average effects of AI exposure on job satisfaction, life satisfaction, or mental health.
• Small improvements in self-rated physical health and health satisfaction, especially among lower-educated workers.
• Evidence of reduced physical job intensity, suggesting that AI may alleviate physically demanding tasks.
• A modest decline in weekly working hours, without significant changes in income or employment rates.
• Self-reported AI exposure suggests small but negative effects on subjective well-being, reinforcing the need for more granular future research.

• Due to the data supply, the study focuses on Germany -- a country with strong labor protections and a gradual pace of AI adoption. The co-authors noted that outcomes may differ in more flexible labor markets or among younger cohorts entering increasingly AI-saturated workplaces.

  "This research is an early snapshot, not the final word," said Pitt's Giuntella, who previously conducted significant research into the effect of robotics on households and labor, and on types of workers. "As AI adoption accelerates, continued monitoring of its broader impacts on work and health is essential. Technology alone doesn't determine outcomes -- institutions and policies will decide whether AI enhances or erodes the conditions of work."

   

  Journal Reference:

• Osea Giuntella, Johannes Konig, Luca Stella. Artificial intelligence and the wellbeing of workers. Scientific Reports, 2025; 15 (1) DOI: 10.1038/s41598-025-98241-3 

Courtesy:

University of Pittsburgh. "Artificial intelligence isn’t hurting workers—It might be helping." ScienceDaily. ScienceDaily, 23 June 2025. <www.sciencedaily.com/eleases/2025/06/250623072753.htm>.