Healthcare Performance Metrics

Success metrics lie. I watched a founder hit every target on his way to collapse. Jake's Series B startup was a case study in success: ↳ MRR grew 27% quarter over quarter ↳ Team expanded from 15 to 42 people ↳ Net promoter score consistently above 70 Then one Tuesday morning, he didn't show up for our call. He was in the ER with chest pains at 36. The diagnosis wasn't cardiac. It was burnout. His business dashboard showed all green. His body was flashing red. After coaching dozens of founders through similar crises, I've identified the metrics paradox: 1. Misaligned incentives create invisible damage ↳ When investors celebrate growth, you suppress fatigue ↳ When your team needs leadership, you hide exhaustion ↳ When customers demand more, you borrow from your future self 2. The scoreboard only shows what you decide to measure ↳ What if recovery metrics were as visible as revenue? ↳ What if your calendar tracked energy spent, not just time? ↳ What if board decks included founder sustainability scores? 3. Sustainable founders build counter-metrics ↳ For each revenue target: set a 90-minute deep recovery block ↳ For each hiring sprint: schedule a full day of strategic silence ↳ For each funding round: establish concrete delegation thresholds Jake now maintains parallel dashboards. Weekly, he tracks: → Deep sleep hours (target: 6.5+ per night) → Focused work vs. reactive work ratio (target: 2:1) → Personal connection moments outside work (target: 5 per day) His business continues growing. But now, so is he. What invisible metric is your body tracking that your business dashboard ignores? ps: His name is not Jake, but the story is his

I track 15+ biomarkers quarterly. But only 3 predict my cognitive health 20 years from now. After reviewing the longevity research and tracking my own data for 5 years, I've learned most biomarkers are noise. These 3 are signal. Peter Attia is right: VO2 max is the strongest predictor of lifespan. But for brain health specifically, these matter more: 1. ApoB (not LDL) Target: Under 60 mg/dL Why it matters: ↳ Vascular dementia accounts for 40% of all dementia ↳ ApoB measures actual atherosclerotic particles ↳ Predicts cognitive decline 15-20 years before symptoms What most docs miss: You can have "normal" LDL and terrible ApoB. LDL is cholesterol content. ApoB is particle number. Particle number drives plaque formation. Plaque drives vascular dementia. How to improve it: ↳ Prioritize fiber (40g daily minimum) ↳ Limit saturated fat ↳ Consider ezetimibe or PCSK9 inhibitors if lifestyle isn't enough 2. Fasting insulin (not fasting glucose) Target: Under 5 uIU/mL Why it matters: ↳ High insulin precedes high glucose by 10+ years ↳ Insulin resistance doubles Alzheimer's risk ↳ Inflammation from insulin resistance damages neurons What most docs miss: Fasting glucose stays normal until insulin can't compensate anymore. By then, you've had insulin resistance for a decade. How to improve it: ↳ Zone 2 cardio (4-5 hours weekly) ↳ Strength training (maintain muscle glucose disposal) ↳ Time-restricted eating (12-14 hour fast minimum) 3. Sleep efficiency (not sleep duration) Target: Above 85% Why it matters: ↳ Brain clears amyloid and tau during deep sleep ↳ Poor sleep quality doubles dementia risk ↳ Sleep fragmentation prevents memory consolidation What most docs miss: 7 hours of broken sleep is not the same as 7 hours of consolidated sleep. You need time in deep and REM stages. How to improve it: ↳ Same bedtime/wake time (even weekends) ↳ No alcohol (destroys REM sleep) ↳ Screen sleep apnea if efficiency stays low My personal data over 5 years: ApoB dropped from >100 to <60 (added rosuvastatin + ezetimibe) Fasting insulin dropped from 8 to 4 (strength training + walking) Sleep efficiency went from <80% to >90% Cognitive testing: Improved across all domains. What I don't track anymore: Total cholesterol (not actionable) BMI (muscle mass matters more) Generic "inflammation markers" Many supplement levels The longevity doctors are right about one thing: What gets measured gets managed. But measuring everything is paralysis. These 3 biomarkers give you the most actionable data for preventing cognitive decline. Start here. ⁉️ What biomarkers do you track for longevity? ♻️ Repost if you believe in data-driven prevention 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for evidence-based longevity strategies

Can blood tests predict the transition from subjective cognitive decline (SCD) to true dementia? 🩸 A recently published (https://lnkd.in/g2myuf_M) prospective cohort study (SCIENCe) followed 298 individuals with SCD who were evaluated at a memory clinic. Notably, 73.2% of the participants were Amyloid negative (tested with Amyloid-PET or CSF), representing an early, high-risk group. The cohort (mean age 61.55 years, 41.6% female) was tracked for an average of 4.8 years. Researchers measured the rate of change (slope) in four blood-based Alzheimer disease (AD) biomarkers, specifically focusing on phosphorylated tau 217 (pTau217), which reflects tau pathology, and glial fibrillary acidic protein (GFAP), which reflects reactive astrogliosis. Cox proportional hazards models were used to calculate the risk (hazard ratio, HR) of progressing to mild cognitive impairment (MCI) or dementia, and the model's prognostic accuracy was measured by the concordance index, or C-index. The central finding was that the rate of change in biomarkers provided strong, independent prognostic value beyond just the measurement taken at the start of the study. A steeper pTau217 slope was strongly associated with progression risk (HR, 3.61; P<0.001). Crucially, adding this slope improved the predictive accuracy of the model from a C-index of 0.86 to 0.89. Similarly, the GFAP slope was associated with progression (HR, 1.51; P=0.04) and improved accuracy from a C-index of 0.77 to 0.81. Both steeper pTau217 and GFAP slopes were associated with a decline in cognitive function across all domains tested. The Aβ42/40 ratio slope, in contrast, was not associated with progression risk (P=0.43), indicating that longitudinal measurement of this specific biomarker did not improve prognosis. The optimal prognostic model, combining baseline Aβ42/40 and the baseline and slope values for pTau217 and GFAP, achieved an overall accuracy of 0.90. The study also found that approximately 1 in 5 initially biomarker-negative participants converted to a positive status during follow-up. A limitation of the study is that the number of participants who progressed to MCI or dementia was modest (33 participants, 11.1%). Also, the participants were recruited exclusively from a memory clinic, which may introduce selection bias and limit how broadly the findings can be applied. The results suggest that monitoring the longitudinal changes in plasma pTau217 and GFAP may be a promising, non-invasive strategy for identifying and tracking AD pathology in high-risk SCD patients. These biomarkers could hold potential utility for future participant selection and outcome monitoring in preclinical AD intervention trials, but further longitudinal studies across different settings are needed to confirm their reliability for individual patient prediction. #neurology #dementia #alzheimers #prognosis #BloodTest

The Risk Factors for Dementia We Often Miss We talk a lot about memory decline, but we rarely talk about the upstream systems that drive it decades earlier. Most of us still think dementia is a late-life brain problem. In practice, many risk factors begin silently in midlife across metabolic, vascular, immune and lifestyle pathways. Dementia is not one disease but a convergence of metabolic inflammation, vascular dysfunction, lifestyle habits and genetic susceptibility. The good news is that many pathways are modifiable. 1/ NAFLD (Fatty Liver Disease) ↳ Chronic hepatic inflammation accelerates neuroinflammation ↳ Strong association with insulin resistance and cognitive decline Solutions: ↳ Weight reduction of 5–10% ↳ Mediterranean dietary pattern 2/ Gut Microbiome ↳ Dysbiosis increases peripheral inflammation ↳ Alters microglial activation and blood–brain barrier integrity Solutions: ↳ Prebiotic fibre daily ↳ Fermented foods 3 to 5 times weekly 3/ Lipids and Lipoproteins ↳ Elevated LDL and ApoB increase vascular injury ↳ Dyslipidaemia worsens small-vessel brain disease Solutions: ↳ LDL-lowering to guideline targets ↳ Replace saturated fats with unsaturated fats 4/ Hypertension ↳ Chronic high BP damages cerebral perfusion ↳ Strong determinant of vascular dementia Solutions: ↳ Tight BP control ↳ Daily aerobic activity 5/ Genetics ↳ APOE-ε4 increases amyloid accumulation ↳ Interaction with lifestyle amplifies risk Solutions: ↳ Precision lifestyle approach ↳ Aggressive management of all modifiable risks 6/ Diabetes ↳ Insulin resistance impairs neuronal glucose uptake ↳ Higher risk of both vascular and Alzheimer’s dementia Solutions: ↳ HbA1c optimisation ↳ High-intensity lifestyle change 7/ Diet Quality ↳ Ultra-processed foods increase inflammation ↳ Deficiency in omega-3 and polyphenols reduces neuroprotection Solutions: ↳ Mediterranean or MIND diet ↳ Reduce ultra-processed food burden 8/ Physical Inactivity ↳ Reduces BDNF and neurogenesis ↳ Increases vascular and metabolic risk Solutions: ↳ 150 minutes weekly exercise ↳ Strength training twice weekly 9/ Body Mass Index ↳ Midlife obesity predicts late-life cognitive decline ↳ Visceral fat increases systemic inflammation Solutions: ↳ Target 5–10% weight loss ↳ Strength and aerobic training 10/ Smoking ↳ Accelerates oxidative stress ↳ Worsens cerebrovascular injury Solutions: ↳ Smoking cessation support ↳ Nicotine replacement or pharmacotherapy Most dementia pathways begin decades before symptoms. If we intervene early across metabolic, vascular, and lifestyle domains, we can meaningfully shift long-term cognitive outcomes. Cognitive decline is not inevitable. The earlier we intervene, the more we protect the ageing brain. #LifestyleMedicine #BrainHealth #LongevityMedicine #MetabolicHealth #PreventiveMedicine

Protein-based Diagnosis and Analysis of Co-pathologies Across Neurodegenerative Diseases: Large-Scale AI-Boosted CSF and Plasma Classification This article, available as a Preprint https://lnkd.in/e4gV4WWZ, developed and validated explainable, boosting-based multi-disease AI classifiers utilizing proteomic data from over 21,000 cerebrospinal fluid and plasma samples to address the diagnostic complexities of neurodegenerative diseases, including Alzheimer's, Parkinson's, Frontotemporal dementia, and Dementia with Lewy bodies. The models demonstrated robust performance, achieving weighted AUCs of 0.97 for CSF and 0.88 for plasma in the testing datasets, comparable to those of established biomarkers. Validation against neuropathological and clinical data confirmed the strong generalizability of the model without requiring retraining. The framework also enabled the reclassification of disease subtypes, clarified ambiguous clinical presentations, and identified cognitively normal individuals at risk, providing a computational benchmark for enhanced diagnostic precision by quantifying individual-level disease probabilities and co-pathologies. #genetics #genomics #precisionmedicine #genomicmedicine #brain #neurology #neuroscience #neurodegeneration #neuroinflammation #inflammation #aging #longevity #cognition #dementia #movementdisorders #geroscience #alzheimer #parkinson #ftd #proteomics #omics #ai #biomarkers #therapeutics #biotechnology #innovation #research #clinicalresearch #science #sciencecommunication

👉 Low-density lipoprotein cholesterol levels and risk of incident dementia: a distributed network analysis using common data models WHAT THIS STUDY ADDS ⇒ LDL-C levels below 70 mg/dL (1.8 mmol/L) are associated with a significant reduction in the risk of all-cause dementia (26%) and Alzheimer’s disease-related dementia (28%). ⇒ Statin use contributes additional protection against dementia in individuals with LDL-C levels below 70 mg/dL (1.8 mmol/L), highlighting a synergistic effect. ⇒ Very low LDL-C levels (<30 mg/dL (<0.8 mmol/L)) do not reduce dementia risk further, suggesting a threshold effect for optimal cognitive benefit. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY ⇒ These findings emphasize the importance of targeted LDL-C management as part of dementia prevention strategies, with potential integration into clinical guidelines. ⇒ The results support the use of statin therapy within specific LDL-C ranges for both cardiovascular and cognitive health benefits. ⇒ Future research may focus on refining LDL-C thresholds and exploring mechanisms linking lipid metabolism to cognitive decline. 🔓 Open Access https://lnkd.in/dtFCs2MC European Atherosclerosis Society

𝗕𝗲𝘆𝗼𝗻𝗱 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗪𝗵𝘆 𝗬𝗼𝘂𝗿 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲’𝘀 𝗣𝗵𝘆𝘀𝗶𝗼𝗹𝗼𝗴𝘆 𝗛𝗼𝗹𝗱𝘀 𝘁𝗵𝗲 𝗞𝗲𝘆 𝘁𝗼 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 For years, we’ve measured employee experience through surveys, turnover rates, and engagement scores. But here’s the problem: by the time these metrics signal trouble, the damage is already done. Burnout has set in. Talent has disengaged. Customers have noticed. What if there was an earlier, more honest signal, one that doesn’t rely on self-reporting or lagging indicators? That signal exists. It’s not in a spreadsheet. It’s in the human body. Emerging neuroscience reveals that our physiology registers workplace stress, poor management, and toxic dynamics long before we consciously acknowledge them. Heart rate variability drops. Recovery time shrinks. Sleep fragments. These aren’t signs of weakness, they’re objective data points about organisational health. This isn’t theoretical. In my latest LinkedIn newsletter, “Interoceptive Organisational Intelligence”, I explore how forward-thinking organisations are beginning to use ethical, team-level physiological insights not to monitor individuals, but to diagnose systemic issues like:  • Chronic overload from unrealistic workloads,    • The hidden cost of talent hoarding (when managers silo high performers),    • And the true access cost of talent—which includes far more than recruitment fees. I propose a new model: an Interoceptive Feedback Loop that blends anonymised biometric trends (like HRV and recovery scores) with reflective dialogue to help leaders respond before burnout, errors, or attrition occur. Critically, this is not surveillance. Individual data remains private. Only aggregated, team-level patterns are shared so managers can ask: “What’s causing this strain?” rather than “Who’s underperforming?” The financial case is compelling. Early pilots suggest that even modest improvements in physiological recovery can yield significant returns: • 10–15% reduction in rework and service errors,   • 20%+ faster project delivery when talent silos are broken,   • And avoidance of £100K+ per role in replacement costs by preventing burnout-driven exits. This builds directly on the classic Service-Profit Chain, but updates it to “Employee Value Chain” for the realities of modern work. Employee value isn’t just about pay and perks. It’s about physiological safety, interoceptive attunement, and fluid access to capability. I’ve spent years bridging theory and practice in organisational design. This is the most promising integration I’ve seen: where neuroscience meets management, and compassion meets ROI.

Researchers have developed a new prediction tool that estimates a person’s risk of developing memory and thinking problems linked to Alzheimer’s disease long before symptoms appear. The model combines three key factors: a person’s age, their genetic risk associated with the APOE ε4 gene variant, and how much amyloid protein has accumulated in the brain. Amyloid buildup is one of the earliest changes in Alzheimer’s disease and can be measured with PET brain scans. Using these inputs, the tool can calculate both the 10‑year risk and lifetime likelihood of developing mild cognitive impairment or dementia. Age and APOE ε4 status help indicate a baseline genetic and demographic risk profile, while amyloid PET measurements gauge the biological severity of early Alzheimer’s changes even when a person still feels cognitively normal. Higher amyloid levels strongly increase both short‑term and lifetime risk, especially in people carrying the APOE ε4 gene. This kind of risk assessment may allow doctors and patients to consider earlier interventions or lifestyle changes long before noticeable memory loss emerges. It’s important to note that this tool is currently used in research settings and relies on specialized brain imaging that is not yet widely available for routine screening. Future versions may incorporate easier biomarkers, such as blood tests, to make it more accessible for broader clinical use. Research Paper 📄 DOI: 10.1016/S1474-4422(25)00350-3

Untreated sleep apnea may be risk factor for vascular dementia Link: https://lnkd.in/erJmEUUK People who have obstructive sleep apnea may have an increased risk of dementia if left untreated, according to a new study of UK electronic health care records. The study, published in BMJ Thorax, reviewed 2.3 million health care records across 12 years in the UK, and found that among the 193,000 individuals with obstructive sleep apnea syndrome (OSAS), there was a 12% higher risk of developing all-cause dementia and a specific 29% increased risk of developing vascular dementia, although the risk of developing Alzheimer's Disease was unchanged. However, patients who received a treatment called Continuous Positive Airway Pressure (#CPAP) to help regulate breathing during sleep did not see a raised risk compared to the general public. Furthermore, female patients with sleep apnea also didn't have a higher risk. Dr. Jingwa Wang from the University of Birmingham and corresponding author of the study said, "Our findings build on existing evidence that sleep apnea is associated with issues of cognitive decline, and we can see that, when left untreated, OSAS can increase the risk of developing vascular dementia in particular. "Using a large electronic health care dataset also meant we are able to see that women aren't at risk in the same way that men are, which also corresponds with other studies that there are different factors that affect men and women developing dementia." The study used a data tool called DExTER to find relevant health care records of patients with sleep apnea for use in the study, as well as up to four suitable individuals to act as a control group. The resulting dataset of 193,600 patients with OSAS was compared to more than half a million patients without OSAS, and an average of a four-year period was reviewed to find patients who had been diagnosed with a type of dementia. Analysis of the dataset found that OSAS patients, regardless of age and body mass index, had an increased risk of developing all-cause and particularly vascular dementia. Dr. Shamil Haroon from the University of Birmingham and the senior author of the study said, "This study provides further evidence that some conditions such as sleep apnea could contribute to an additional risk of developing diseases such as dementia. "It also backs up other findings that may suggest that the periods of hypoxia, where there are lower levels of oxygen in the body, may be contributing to vascular dementia risk. Clinicians should consider these risks when supporting their patients with obstructive sleep apnea." #sleep #sleepapnea #sleep2025 #sleephealth #health #healthcare #osa #sleepresearch #dementia #hme

Pace of Aging Predicts Risk of Disease, Disability, and Death A major new study "Pace of Aging analysis of healthspan and lifespan in older adults in the US and UK" https://lnkd.in/eVsWuW7z introduces a dynamic way to track how fast someone is aging biologically and shows it’s a powerful predictor of what comes next. 📊 What was studied: Researchers from Columbia University followed over 19,000 older adults from two national cohorts (US HRS https://lnkd.in/eFUb9DjJ and UK ELSA https://lnkd.in/e8-g37F7 ) for up to 15 years. They used repeated measures of inflammation (CRP, cystatin-C), glucose regulation (HbA1c), blood pressure, lung function, gait speed, balance, grip strength, and waist circumference to calculate a “Pace of Aging” score. This score reflects how quickly a person’s body is declining across multiple systems per year of chronological time. 🧠 What they found: Participants with a faster Pace of Aging were significantly more likely to: • Develop chronic diseases • Experience cognitive impairment or dementia • Lose independence in daily functioning • Die earlier These associations held even after adjusting for smoking, obesity, and education. ⚙️ Unlike DNA methylation clocks, this method uses non-invasive, easy-to-collect clinical data. Yet it predicted outcomes as well or better than top epigenetic models like DunedinPACE and GrimAge. 📉 Aging isn’t equal: • Men aged faster than women • Biological aging accelerated with advancing age • Black and Hispanic participants showed faster aging than white peers—underscoring health disparities that persist into later life 💡 Why this matters: Pace of Aging measures the rate of decline, not just current health or accumulated damage. That makes it ideal for: • Evaluating healthy-aging interventions • Identifying high-risk populations • Quantifying social determinants of health • Tracking resilience or deterioration after life events (e.g., illness, surgery, caregiving) This study moves us closer to a practical, population-level tool to track aging as it happens and intervene before irreversible decline. 📂 Full methodology is open source: 🔗 https://lnkd.in/eN5-KSnq 🧭 Open question: How will the Pace of Aging metric integrate with emerging digital biomarkers and continuous monitoring? As #wearables and real-time health data continue to evolve, this kind of dynamic measurement may soon inform not just clinical care but also drive innovation in the wellness and #longevity industries. #HealthyAging #BiologicalAge #Healthspan #AgingScience #LifespanResearch #LongevityScience #WellnessInnovation #WearableTech #FutureOfHealth #AgingMetrics #PrecisionHealth #PopulationHealth