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).

"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.