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ATN (β-amyloid [Aβ], tau, neurodegeneration) system categorizes individuals based on their core Alzheimer disease (AD) biomarkers. An important potential future use for ATN is therapeutic decision-making in clinical practice once disease-modifying treatments (e.g., anti-amyloid), become widely available. In this cross-sectional study, we applied ATN and estimated potential eligibility for anti-amyloid treatment in a real-life memory clinic with biomarker assessments integrated into the routine diagnostic procedure and all specialized resources available for the implementation of novel treatments.We included all consecutive patients at the Karolinska University Hospital Memory clinic in Solna, Stockholm, Sweden, who had their first diagnostic visit in April 2018-February 2021, informed consent for the clinic research database, and available clinical and biomarker (CSF and imaging) data. ATN classification was based on CSF Aβ42 (or Aβ42/40; A), CSF phosphorylated tau (T), and medial temporal lobe atrophy (N). For CSF markers, we applied laboratory cutoffs and data-driven cutoffs for comparison (determined with Gaussian mixture modeling). Eligibility for anti-amyloid treatment was assessed following the published recommendations for aducanumab (AD dementia or mild cognitive impairment [MCI] with no evidence of non-AD etiology, appropriate level of cognition, and AD-consistent CSF profile).The study population consisted of 410 patients (52% subjective cognitive impairment, 23% MCI, and 25% any dementia; age 59 ± 7 years, 56% women). Regardless of biomarker cutoffs, most patients were A-T-N- (54%-57%). A+ prevalence was 17%-30% (higher with data-driven cutoffs). Up to 13% of all patients (27% of those with MCI and 28% of those with dementia) were potentially eligible for anti-amyloid treatment when AD-consistent CSF was defined as any A+ profile. When A+T+ profile was required, treatment was targeted more to the dementia than MCI stage (eligibility up to 14% in MCI and 22% in dementia). The opposite applied to earlier-stage intervention (A+T-N-; eligibility up to 12% in MCI and 2% in dementia).In a memory clinic setting with all necessary infrastructure and national guidelines in place for dementia diagnostic examination (best-case scenario), most of the patients did not meet the eligibility criteria for anti-amyloid treatment. Continuing the development of disease-modifying treatments with different mechanisms of action is a priority.

Decreased levels of serum high-density lipoprotein (HDL) cholesterol have previously been linked to systemic inflammation and neurodegenerative diseases, such as Alzheimer’s disease. Here, we aimed to analyze the lipoprotein profile and inflammatory indicators, the high-sensitivity C-reactive peptide (hs-CRP) and glycoprotein acetyls (GlycA), in sporadic and C9orf72 repeat expansion-associated frontotemporal lobar degeneration (FTLD) patients. The C9orf72 hexanucleotide repeat expansion is the most frequent genetic etiology underlying FTLD. The concentrations of different lipid measures in the sera of 67 FTLD patients (15 C9orf72 repeat expansion carriers), including GlycA, were analyzed by nuclear magnetic resonance spectroscopy. To verify the state of systemic inflammation, hs-CRP was also quantified from patient sera. We found that the total serum HDL concentration was decreased in C9orf72 repeat expansion carriers when compared to non-carriers. Moreover, decreased concentrations of HDL particles of different sizes and subclass were consistently observed. No differences were detected in the very low- and low-density lipoprotein subclasses between the C9orf72 repeat expansion carriers and non-carriers. Furthermore, hs-CRP and GlycA levels did not differ between the C9orf72 repeat expansion carriers and non-carriers. In conclusion, the HDL-related changes were linked with C9orf72 repeat expansion associated FTLD but were not seen to associate with systemic inflammation. The underlying reason for the HDL changes remains unclear.

Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

OBJECTIVES We examined longitudinal associations between late-life personality traits and cognitive impairment, dementia, and mortality in the population-based Cardiovascular Risk Factors, Aging and Dementia (CAIDE) Study. METHODS Anger expression and trait anger (State-Trait Anger Expression Inventory), anxiety (State-Trait Anxiety Inventory), and sense of coherence (Sense of Coherence Scale) were assessed at the 1998 CAIDE visit (1266 cognitively normal individuals, mean age 71.0 years). Totally, 582 participants had complete re-examination in 2005-2008 (105 mild cognitive impairment, MCI; and 29 dementia). National registers data until 2008 were also used for both participants and nonparticipants to ascertain incident dementia (96 cases) and mortality (227 died). Analyses were adjusted for age, sex, education, follow-up time, cardiovascular and lifestyle factors, and depressive symptoms. RESULTS Higher anxiety was associated with higher risk of MCI/dementia (OR 1.68, 95% CI 1.07-2.63) and death (HR 1.46, 95% CI 1.08-1.98). High sense of coherence was associated with lower mortality (HR 0.65, 95% CI 0.45-0.93). These associations were attenuated after accounting for depressive symptoms (OR 1.57, 95% CI 0.96-2.58 for anxiety-MCI/dementia; HR 1.35, 95% CI 0.97-1.86 for anxiety-mortality; and HR 0.68, 95% CI 0.45-1.04 for sense of coherence-mortality). Trait anger was associated with higher dementia risk even after adjustments (HR 1.90, 95% CI 1.14-3.18). CONCLUSIONS Anxiety was linked to worse cognitive outcome and mortality and sense of coherence to lower mortality. Depressive symptoms attenuated the associations. As a novel finding, trait anger was connected to dementia risk. These findings emphasize the importance of personality-related risk factors for dementia and mortality. J Am Geriatr Soc 68:-, 2020.

Introduction: Depression in patients with mild cognitive impairment (MCI) and dementia of the Alzheimer’s type (AD) is associated with worse prognosis. Indeed, depressed MCI patients have worse cognitive performance and greater loss of gray-matter volume in several brain areas. To date, knowledge of the factors that can mitigate this detrimental effect is still limited. The aim of the present study was to understand in what way cognitive reserve/brain reserve and depression interact and are linked to regional atrophy in early stage AD.\ud \ud Methods: Depression was evaluated with the Patient Health Questionnaire-9 in 90 patients with early AD, and a cutoff of ≥ 5 was used to separate depressed (n = 44) from non-depressed (n = 46) patients. Each group was further stratified into high/low cognitive reserve/brain reserve. Cognitive reserve was calculated using years of education as proxy, while normalized parenchymal volumes were used to estimate brain reserve. Voxel-based morphometry was carried out to extract and analyze gray-matter maps. 2 × 2 ANCOVAs were run to test the effect of the reserve-by-depression interaction on gray matter. Age and hippocampal ratio were used as covariates. Composite indices of major cognitive domains were also analyzed with comparable models.\ud \ud Results: No reserve-by-depression interaction was found in the analytical models of gray matter. Depression was associated with less gray matter volume in the cerebellum and parahippocampal gyrus. The brain reserve-by-depression interaction was a significant predictor of executive functioning. Among those with high brain reserve, depressed patients had poorer executive skills. No significant results were found in association with cognitive reserve.\ud \ud Conclusion: These findings suggest that brain reserve may modulate the association between neurodegeneration and depression in patients with MCI and dementia of the AD type, influencing in particular executive functioning.

Background: Idiopathic normal pressure hydrocephalus (iNPH) patients often develop Alzheimer’s disease (AD) related brain pathology. Disease State Index (DSI) is a method to combine data from various sources for differential diagnosis and progression of neurodegenerative disorders. Objective: To apply DSI to predict clinical AD in shunted iNPH-patients in a defined population. Methods: 335 shunted iNPH-patients (median 74 years) were followed until death (n = 185) or 6/2015 (n = 150). DSI model (including symptom profile, onset age of NPH symptoms, atrophy of medial temporal lobe in CT/MRI, cortical brain biopsy finding, and APOE genotype) was applied. Performance of DSI model was evaluated with receiver operating characteristic (ROC) curve analysis. Results: A total of 70 (21%) patients developed clinical AD during median follow-up of 5.3 years. DSI-model predicted clinical AD with moderate effectiveness (AUC = 0.75). Significant factors were cortical biopsy (0.69), clinical symptoms (0.66), and medial temporal lobe atrophy (0.66). Conclusion: We found increased occurrence of clinical AD in previously shunted iNPH patients as compared with general population. DSI supported the prediction of AD. Cortical biopsy during shunt insertion seems indicated for earlier diagnosis of comorbid AD.

Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

We evaluated the performance of the Disease State Index (DSI) method when predicting progression to Alzheimer's disease (AD) in patients with subjective cognitive impairment (SCI), amnestic or non-amnestic mild cognitive impairment (aMCI, naMCI). The DSI model measures patients' similarity to diagnosed cases based on available data, such as cognitive tests, the APOE genotype, CSF biomarkers and MRI. We applied the DSI model to data from the DE-SCRIPA cohort, where non-demented patients (N=775) with different subtypes of cognitive impairment were followed for 1 to 5 years. Classification accuracies for the subgroups were calculated with the DSI using leave-one-out cross-validation. The DSI's classification accuracy in predicting progression to AD was 0.75 (AUC=0.83) in the total population, 0.70 (AUC=0.77) for aMCI and 0.71 (AUC=0.76) for naMCI. For a subset of approximately half of the patients with high or low DSI values, accuracy reached 0.86 (all), 0.78 (aMCI), and 0.85 (naMCI). For patients with MRI or CSF biomarker data available, they were 0.78 (all), 0.76 (aMCI) and 0.76 (naMCI), while for clear cases the accuracies rose to 0.90 (all), 0.83 (aMCI) and 0.91 (naMCI). The results show that the DSI model can distinguish between clear and ambiguous cases, assess the severity of the disease and also provide information on the effectiveness of different biomarkers. While a specific test or biomarker may confound analysis for an individual patient, combining several different types of tests and biomarkers could be able to reveal the trajectory of the disease and improve the prediction of AD progression.

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BACKGROUND Several risk loci for Alzheimer's disease (AD) have been identified during recent years in large-scale genome-wide association studies. However, little is known about the mechanisms by which these loci influence AD pathogenesis. OBJECTIVE To investigate the individual and combined risk effects of the newly identified AD loci. METHODS Association of 12 AD risk loci with AD and AD-related cerebrospinal fluid (CSF) biomarkers was assessed. Furthermore, a polygenic risk score combining the effect sizes of the top 22 risk loci in AD was calculated for each individual among the clinical and neuropathological cohorts. Effects of individual risk loci and polygenic risk scores were assessed in relation to CSF biomarker levels as well as neurofibrillary pathology and different biochemical measures related to AD pathogenesis obtained from the temporal cortex. RESULTS Polygenic risk scores associated with CSF amyloid-β42 (Aβ42) levels in the clinical cohort, and with soluble Aβ42 levels and γ-secretase activity in the neuropathological cohort. The γ-secretase effect was independent of APOE. APOE-e4 associated with CSF Aβ42 (p < 0.001) levels. For the other risk loci, no significant associations with AD risk or CSF biomarkers were detected after multiple testing correction. CONCLUSIONS AD risk loci polygenically contribute to Aβ pathology in the CSF and temporal cortex, and this effect is potentially associated with increased γ-secretase activity.

Background: Poly-T repeat lengths of rs10524523 in TOMM40 together with APOE polymorphism have been reported to affect the risk of late-onset Alzheimer's disease (LOAD) and the age of onset (AOO). Objective: To explore whether the AOO and cerebrospinal fluid biomarkers Aβ42, total tau and phosphorylated tau are associated with different repeat lengths. Methods: We conducted both the fragment and sequencing analysis of rs10524523 in 336 LOAD patients with a known APOE genotype. Results: AOO and Aβ42 levels associated significantly with certain poly-T repeat lengths of rs10524523 in LOAD patients encompassing APOE 34/44 genotype. Conclusion: We conclude that the poly-T repeat associations of rs10524523 in TOMM40 reflect the APOE ε4-dependent association in LOAD.

While behavioral symptoms are both early and prevalent features of behavioral variant frontotemporal dementia (bvFTD), they can be present in other types of dementia as well, including Alzheimer’s disease (AD) and even mild cognitive impairment (MCI). The Frontal Behavioral Inventory (FBI) was specifically developed to capture the behavioral and personality changes in bvFTD; it has also been modified into a self-administered caregiver questionnaire (FBI-mod). We examined the utility of the FBI-mod in differentiating bvFTD (n= 26), primary progressive aphasia (PPA) (n= 7), AD(n=53),and MCI (n= 50) patients, and investigated how the FBI-mod may be associated with neuropsychological measures.The bvFTD patients scored significantly higher as compared to all other patient groups on the FBI-mod Total (p< 0.005),Negative (p< 0.005), and Positive (p< 0.01) scores. The cut-off point for the FBI-mod Total score that best discriminated thebvFTD and AD patients in our sample was 16, thus substantially lower than reported for the original FBI. For the bvFTD group, only mild correlations emerged between the FBI-mod and the cognitive measures. However, significant correlations between the FBI-mod and depressive symptoms as measured by the BDI-II were found for bvFTD. This suggests thatwhile behavioral symptoms appear independent from cognitive deficits in bvFTD, they may nevertheless be interrelated with depressive symptoms. We conclude that the FBI-mod is an easily administered behavioral scale that can aid in differential diagnosis of bvFTD and should be used in clinical practice. The FBI-mod may further be considered as an outcome measure in clinical trials., final draft, peerReviewed

BACKGROUND: Lifestyle factors have been associated with the risk of dementia, but the association with Alzheimer's disease (AD) remains unclear. OBJECTIVE: To examine the association between later life lifestyle factors and AD biomarkers (i.e., amyloid-β 1-42 (Aβ42) and tau in cerebrospinal fluid (CSF), and hippocampal volume) in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In addition, to examine the effect of later life lifestyle factors on developing AD-type dementia in individuals with MCI. METHODS: We selected individuals with SCD (n = 111) and MCI (n = 353) from the DESCRIPA and Kuopio Longitudinal MCI studies. CSF Aβ42 and tau concentrations were assessed with ELISA assay and hippocampal volume with multi-atlas segmentation. Lifestyle was assessed by clinical interview at baseline for: social activity, physical activity, cognitive activity, smoking, alcohol consumption, and sleep. We performed logistic and Cox regression analyses adjusted for study site, age, gender, education, and diagnosis. Prediction for AD-type dementia was performed in individuals with MCI only. RESULTS: Later life lifestyle factors were not associated with AD biomarkers or with conversion to AD-type dementia. AD biomarkers were strongly associated with conversion to AD-type dementia, but these relations were not modulated by lifestyle factors. Apolipoprotein E (APOE) genotype did not influence the results. CONCLUSIONS: Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI., Funding Agency:European Commission as part of the 5th Framework Program QLK-6-CT-2002-02455

Objectives: Optimal selection of idiopathic normal pressure hydrocephalus (iNPH) patients for shunt surgery is challenging. Disease State Index (DSI) is a statistical method that merges multimodal data to assist clinical decision-making. It has previously been shown to be useful in predicting progression in mild cognitive impairment and differentiating Alzheimer's disease (AD) and frontotemporal dementia. In this study, we use the DSI method to predict shunt surgery response for patients with iNPH. Methods: In this retrospective cohort study, a total of 284 patients (230 shunt responders and 54 non-responders) from the Kuopio NPH registry were analyzed with the DSI. Analysis included data from patients' memory disorder assessments, age, clinical symptoms, comorbidities, medications, frontal cortical biopsy, CT/MRI imaging (visual scoring of disproportion between Sylvian and suprasylvian subarachnoid spaces, atrophy of medial temporal lobe, superior medial subarachnoid spaces), APOE genotyping, CSF AD biomarkers, and intracranial pressure. Results: Our analysis showed that shunt responders cannot be differentiated from non-responders reliably even with the large dataset available (AUC?=?0.58). Conclusions: Prediction of the treatment response in iNPH is challenging even with our extensive dataset and refined analysis. Further research of biomarkers and indicators predicting shunt responsiveness is still needed.

Even in small amounts, glycolipids are an integral part of lipid rafts and their cellular functions. Here we employ atomistic molecular dynamics simulations to consider galactosylceramide (GalCer), one of the common glycosphingolipids, and investigate its interactions with other raft components (cholesterol, POPC, sphingomyelin) as well as the role and the effects of GalCer on the physical properties of raft-like membranes. Our results for 5 mol % GalCer indicate that whereas the thickness of raft membranes is clearly increased by the addition of GalCer, the average area per lipid and lipid conformational order remain virtually unchanged. Notable changes are observed in lateral diffusion of the raft lipids. This is found to be associated with the interdigitation of GalCer. With cholesterol, GalCer is observed to interact specifically by shielding it from the water phase.

Background: The Disease State Index (DSI) is a method which interprets data originating from multiple different sources, assisting the clinician in the diagnosis and follow-up of dementia diseases. Objective: We compared the differences in accuracy in differentiating stable mild cognitive impairment (S-MCI) and progressive MCI (P-MCI) obtained from different data combinations using the DSI. Methods: We investigated 212 cases with S-MCI and 165 cases with P-MCI from the Alzheimer's Disease Neuroimaging Initiative cohort. Data from neuropsychological tests, cerebrospinal fluid, apolipoprotein E (APOE) genotype, magnetic resonance imaging (MRI) and positron emission tomography (PET) were included. Results: The combination of all parameters gave the highest accuracy (accuracy 0.70, sensitivity 0.71, specificity 0.68). In the different categories, neuropsychological tests (0.65, 0.65, 0.65) and hippocampal volumetry (0.66, 0.66, 0.66) achieved the highest accuracy. Conclusion: In addition to neuropsychological testing, MRI is recommended to be included for differentiating S-MCI from P-MCI. APOE genotype, CSF and PET may provide some additional information.

Background: The Disease State Index (DSI) prediction model measures the similarity of patient data to diagnosed stable and progressive mild cognitive impairment (MCI) cases to identify patients who are progressing to Alzheimer's disease. Objectives: We evaluated how well the DSI generalizes across four different cohorts: DESCRIPA, ADNI, AddNeuroMed, and the Kuopio MCI study. Methods: The accuracy of the DSI in predicting progression was examined for each cohort separately using 10 x 10-fold cross-validation and for inter-cohort validation using each cohort as a test set for the model built from the other independent cohorts using bootstrapping with 10 repetitions. Altogether 875 subjects were included in the analysis. The analyzed data included a comprehensive set of age and gender corrected magnetic resonance imaging (MRI) features from hippocampal volumetry, multi-template tensor-based morphometry, and voxel-based morphometry as well as Mini-Mental State Examination (MMSE), APOE genotype, and additional cohort specific data from neuropsychological tests and cerebrospinal fluid measurements (CSF). Results: The DSI model was used to classify the patients into stable and progressive MCI cases. AddNeuroMed had the highest classification results of the cohorts, while ADNI and Kuopio MCI exhibited the lowest values. The MRI features alone achieved a good classification performance for all cohorts. For ADNI and DESCRIPA, adding MMSE, APOE genotype, CSF, and neuropsychological data improved the results. Conclusions: The results reveal that the prediction performance of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, if they are sufficiently similar.

1⁄40.001) and body/occipital horn ventriculomegaly (left p corrected1⁄40.003, right p corrected1⁄40.002). Trend significant effects of MS4A6Awere found for the temporal horns (right p corrected1⁄40.053, left p corrected1⁄40.07) and BIN1 for the right temporal (p corrected1⁄40.06) and left frontal (p corrected 1⁄40.09) horns. Conclusions:MCI subjects predestined to develop dementia showed significantly larger ventricles relative to both stable MCI and NC at baseline. The differences between stable MCI and those who developed dementia at follow-up localized to the frontal and body/occipital horn. The groups had comparable temporal horns’ radial distance suggesting a similar degree of neurodegenerative changes (hippocampal and temporal lobe atrophy) between the two groups.

Poly-T repeat lengths of rs10524523 in TOMM40 together with APOE polymorphism have been reported to affect the risk of late-onset Alzheimer's disease (LOAD) and the age of onset (AOO).To explore whether the AOO and cerebrospinal fluid biomarkers Aβ42, total tau and phosphorylated tau are associated with different repeat lengths.We conducted both the fragment and sequencing analysis of rs10524523 in 336 LOAD patients with a known APOE genotype.AOO and Aβ42 levels associated significantly with certain poly-T repeat lengths of rs10524523 in LOAD patients encompassing APOE 34/44 genotype.We conclude that the poly-T repeat associations of rs10524523 in TOMM40 reflect the APOE ε4-dependent association in LOAD.

Background: Disease State Index and Disease State Fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer’s disease (AD). Objective: We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects. Methods: Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The Disease State Index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype. Results: We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data. Conclusion: The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.

We use atom-scale molecular dynamics simulations to clarify the role of glycosphingolipids in the dynamics of cholesterol-rich lipid rafts. To this end, we consider lipid membranes that contain varying. amounts of galactosylceramide (GalCer), sphingomyelin, cholesterol, and phosphatidylcholine. The results indicate that increasing the portion of GalCer molecules greatly slows down the lateral diffusion, Only 5-10 mol % of GalCer causes a decrease of almost an order of magnitude compared to corresponding membranes without GalCer. The slowing down is not related to interdigitation, which becomes weaker with increasing GalCer concentration. Instead, the decrease in diffusion is found to correlate with the increasing number of hydrogen bonds formed between GalCer and the phospholipid molecules, which is also observed to have other effects, such as to increase the friction between the membrane leaflets.

Triglycerides are a major component of many important biological entities such as lipoproteins and lipid droplets. This work focuses on two common triglycerides, tripalmitin and triolein, which have been simulated through atomistic molecular dynamics at temperatures of 310 and 350 K for 300-700 ns. In these systems, both structural and dynamical properties have been characterized, paying particular attention to understanding the packing of triglyceride molecules and their molecular conformations. Additionally, we study the liquid-to-crystalline phase transition of tripalmitin through a temperature quench from the high-temperature isotropic liquid phase to 310 K, corresponding to a polymorphic, crystalline-like phase. The transition is characterized in detail through density, average molecular shape, and, in particular, the relevant order parameter describing the transition.