UCLA Health researchers have identified four distinct pathways that lead to Alzheimer's disease by analyzing electronic health records, offering new insights into how the condition develops over time rather than from isolated risk factors.

The study, published in the journal eBioMedicine, examined longitudinal health data from nearly 25,000 patients in the University of California Health Data Warehouse and validated findings in the nationally diverse All of Us Research Program. Unlike previous research that focused on individual risk factors, the UCLA analysis mapped sequential diagnostic patterns that revealed how conditions progress step-by-step toward Alzheimer's disease.

"We found that multi-step trajectories can indicate greater risk factors for Alzheimer's disease than single conditions," said first author Mingzhou Fu, a medical informatics pre-doctoral student at UCLA. "Understanding these pathways could fundamentally change how we approach early detection and prevention."

The research identified four major trajectory clusters:

  • Mental health pathway: Psychiatric conditions leading to cognitive decline
  • Encephalopathy pathway: Brain dysfunction conditions that escalate over time
  • Mild cognitive impairment pathway: Gradual cognitive decline progression
  • Vascular disease pathway: Cardiovascular conditions that contribute to dementia risk

Each pathway showed distinct demographic and clinical characteristics, suggesting that different populations may be vulnerable to different progression routes.

The study found that approximately 26% of diagnostic progressions showed consistent directional ordering. For example, hypertension often preceded depressive episodes, which then increased Alzheimer's risk.

"Recognizing these sequential patterns rather than focusing on diagnoses in isolation may help clinicians improve Alzheimer's disease diagnosis," said lead author Dr. Timothy Chang, assistant professor in Neurology at UCLA Health.

When validated in an independent population, these multi-step trajectories predicted Alzheimer's disease risk more accurately than single diagnoses alone. This finding suggests that healthcare providers could use trajectory patterns for:

  • Enhanced risk stratification: Identifying high-risk patients earlier in disease progression
  • Targeted interventions: Interrupting harmful sequences before they advance
  • Personalized prevention: Tailoring strategies based on individual pathway patterns

The validation in the All of Us Research Program -- a diverse, nationally representative cohort -- confirmed that these trajectory patterns apply across different populations and demographics.

The team analyzed 5,762 patients who contributed 6,794 unique Alzheimer's progression trajectories. Using advanced computational methods including dynamic time warping, machine learning clustering, and network analysis, researchers mapped the temporal relationships between diagnoses leading to Alzheimer's disease.

MF, SS, BP, KV, and TSC were supported by the National Institutes of Health (NIH) National Institute on Aging (NIA) grant R01AG085518-01A1. Additionally, MF and TSC received support from NIH/NIA grant K08AG065519-01A1, while TSC and KV were supported by NIH/NIA grant UH2AG083254. KV received further support from several NIH grants, including R01AG081768A, R01NS033310, R01AG075955, R01AG058820, R01AG068317, U01NS100608, and U24AG056270. TSC and KV were also supported by the California Department of Public Health (CDPH), Chronic Disease Control Branch, Alzheimer's Disease Program under Contracts #22-10079 and #23-10648, with TSC receiving additional support from CDPH under Contract #24-10127. TSC was also supported by the NIH National Institute of Neurological Disorders and Stroke (NINDS) grant U54NS123746. SS received funding from the National Science Foundation (NSF) through CAREER award 1943497 and grant R35GM153406. BP was supported by NIH grants R01HG009120, R01MH115676, and R01HG006399. The authors also acknowledge support from the National Center for Advancing Translational Sciences (NCATS) of the NIH under the UCLA Clinical and Translational Science Institute grant UL1TR001881, as well as analytical and technical support from the UC Health Center for Data-driven Insights and Innovation (CDI2).

Read more …Alzheimer’s doesn’t strike at random: These 4 early-warning patterns tell the story

Most patients undergoing "tummy tuck" surgery (abdominoplasty) to remove excess skin and tissue after weight loss continue to lose weight in the months and years after surgery, suggests a follow-up study in the July issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS). The journal is published in the Lippincott portfolio by Wolters Kluwer.

"We found that patients not only maintained their weight loss after abdominoplasty, but also continued to lose weight over time - up to ten pounds, on average," comments senior author John Y.S. Kim of Northwestern University Feinberg School of Medicine, Chicago. "This postoperative weight loss appears greater, and increases at later follow-up times, in patients with initially higher body mass index [BMI]."

Continued weight loss up to five years after tummy tuck

Abdominoplasty is a cosmetic surgical procedure to improve the appearance of the abdomen. In 2023, ASPS Member Surgeons performed more than 170,000 abdominoplasties, according to ASPS statistics. Many of these procedures are performed in patients with massive weight loss that leaves them with excess, sagging skin.

Plastic surgeons have observed that patients may continue to lose weight after abdominoplasty. However, there is little research evidence on this issue, including whether the abdominoplasty procedure itself contributes to long-term weight loss.

Dr. Kim and colleagues performed a study to assess changes in body weight in 188 patients who underwent abdominoplasty between 2018 and 2022. Ninety-seven percent of patients were women. The average preoperative weight was about 168 pounds with a BMI of 27.7. Most patients underwent liposuction or a further procedure to remove excess fat (lipectomy) at the same time as abdominoplasty. Trends in body weight were assessed through up to five years after surgery.

The results showed continued weight loss after abdominoplasty. At three to six months, average weight loss was between five and six pounds, with about a three percent decrease in BMI. From one to four years, weight loss was about five pounds, for a BMI reduction of about two percent. By five years (in a limited number of patients), average weight loss was nearly ten pounds, with more than a five percent decrease in BMI.

'Near-constant negative change in body weight' after abdominoplasty

Overall, about 60% of patients lost weight during follow-up. Further analysis showed a "near constant negative change in body weight that did not significantly change over time," the researchers write.

After adjustment for other factors, continued weight loss was more likely for older patients, for those who underwent liposuction/lipectomy, and those who had never smoked. Weight loss was greater for patients who had higher body weight and BMI before surgery, and for a small number of patients who used the newer weight loss medication semaglutide.

The study adds new evidence that "post-abdominoplasty weight reduction is a quantifiable phenomenon and that patients undergoing abdominoplasty continue to lose a significant amount of weight for up to five years after surgery," the researchers write. They note some key limitations of their study, including varying follow-up times and potential confounding factors.

The study cannot definitively explain why patients continue to lose weight after surgery. However, Dr. Kim and coauthors write, "We have found that patients who were able to achieve weight loss after their abdominoplasty succeeded in developing healthy habits that centered around nutrition and exercise." They highlight the need for an "evidence-based platform" to assess weight changes after abdominoplasty and to identify factors associated with long-term weight loss.

Read more …Study finds tummy-tuck patients still shedding pounds five years later

  • Two areas of the brain may work in combination to tell the brain when it's "feeling" tired.
  • People with depression and post-traumatic stress disorder (PTSD) often experience cognitive fatigue.
  • Results of the study may provide a way for physicians to better evaluate and treat people who experience such fatigue.

In experiments with healthy volunteers undergoing functional MRI imaging, scientists have found increased activity in two areas of the brain that work together to react to, and possibly regulate, the brain when it's "feeling" tired and either quits or continues exerting mental effort.

The experiments, designed to help detect various aspects of brain fatigue, may provide a way for physicians to better evaluate and treat people who experience overwhelming mental exhaustion, including those with depression and post-traumatic stress disorder (PTSD), the scientists say.

A report on the NIH-funded study was published online June 11 in the Journal of Neuroscience, detailing results on 18 female and 10 male healthy adult volunteers given tasks to exercise their memory.

"Our lab focuses on how [our minds] generate value for effort," says Vikram Chib, Ph.D., associate professor of biomedical engineering at the Johns Hopkins University School of Medicine and a research scientist at Kennedy Krieger Institute. "We understand less about the biology of cognitive tasks, including memory and recall, than we do about physical tasks, even though both involve a lot of effort." Anecdotally, Chib says, scientists know cognitive tasks are tiring, and relatively less about why and how such fatigue develops and plays out in the brain.

The 28 study participants, who ranged in age from 21 to 29, were paid $50 to participate in the study, and were told they could receive additional payments based on their performance and choices. All participants received a baseline MRI scan before the experiments began.

The tests of their working memory, which took place while undergoing subsequent MRI scans of their brains, included looking at a series of letters, in sequence, on a screen and recalling the position of certain letters. The farther back a letter was in the series of letters, the harder it was to recall its position, increasing the cognitive effort expended. The participants were given feedback on their performance after each test and opportunities to receive increasing payments ($1-$8) with more difficult recall exercises. The participants also were asked before and after each test to self-rate their level of cognitive fatigue.

Overall, the test results found increased activity and connectivity in two brain areas when participants reported cognitive fatigue: the right insula, an area deep in the brain that has been associated with feelings of fatigue, and the dorsal lateral prefrontal cortex, areas on both sides of the brain that control working memory. For each participant, activity in both brain locations during cognitive fatigue increased by more than twice the level of baseline measurements taken before starting the tests.

"Our study was designed to induce cognitive fatigue and see how people's choices to exert effort change when they feel fatigue, as well as identify locations in the brain where these decisions are made," says Chib.

Notably, Chib and his research team members Grace Steward and Vivian Looi found that the financial incentives need to be high in order for participants to exert increased cognitive effort, suggesting that external incentives prompt such effort.

"That outcome wasn't entirely surprising, given our previous work finding the same need for incentives in spurring physical effort," says Chib.

"The two areas of the brain may be working together to decide to avoid more cognitive effort unless there are more incentives offered. However, there may be a discrepancy between perceptions in cognitive fatigue and what the human brain is actually capable of doing," says Chib.

Fatigue is linked with many neurological conditions, including PTSD and depression, says Chib. "Now that we've likely identified some of the neural circuits for cognitive effort in healthy people, we need to look at how fatigue manifests in the brains of people with these conditions," he adds.

Chib says it may be possible to use medication or cognitive behavior therapy to combat cognitive fatigue, and the current study using decision tasks and functional MRI could be a framework for objectively classifying cognitive fatigue.

Functional MRI uses blood flow to measure broad areas of activity in the brain; however, it does not directly measure neuron activation, nor more subtle nuances in brain activity.

"This study was performed in an MRI scanner and with very specific cognitive tasks. It will be important to see how these results generalize to other cognitive effort and real-world tasks," says Chib.

Funding for the research was provided by the National Institutes of Health (R01HD097619, R01MH119086).

Read more …Feeling mental exhaustion? These two areas of the brain may control whether people give up or...

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